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arxiv-662501
|
2409.18203
|
AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking
|
<|reference_start|>AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking: Whether a large language model policy is an explicit constitution or an implicit reward model, it is challenging to assess coverage over the unbounded set of real-world situations that a policy must contend with. We introduce an AI policy design process inspired by mapmaking, which has developed tactics for visualizing and iterating on maps even when full coverage is not possible. With Policy Projector, policy designers can survey the landscape of model input-output pairs, define custom regions (e.g., "violence"), and navigate these regions with rules that can be applied to LLM outputs (e.g., if output contains "violence" and "graphic details," then rewrite without "graphic details"). Policy Projector supports interactive policy authoring using LLM classification and steering and a map visualization reflecting the policy designer's work. In an evaluation with 12 AI safety experts, our system helps policy designers to address problematic model behaviors extending beyond an existing, comprehensive harm taxonomy.<|reference_end|>
|
arxiv
|
@article{lam2024ai,
title={AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking},
author={Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham,
Kenneth Holstein, Mary Beth Kery},
journal={arXiv preprint arXiv:2409.18203},
year={2024},
archivePrefix={arXiv},
eprint={2409.18203},
primaryClass={cs.HC cs.AI cs.CL cs.LG}
}
|
lam2024ai
|
arxiv-662502
|
2409.18204
|
Toward Efficient Deep Blind RAW Image Restoration
|
<|reference_start|>Toward Efficient Deep Blind RAW Image Restoration: Multiple low-vision tasks such as denoising, deblurring and super-resolution depart from RGB images and further reduce the degradations, improving the quality. However, modeling the degradations in the sRGB domain is complicated because of the Image Signal Processor (ISP) transformations. Despite of this known issue, very few methods in the literature work directly with sensor RAW images. In this work we tackle image restoration directly in the RAW domain. We design a new realistic degradation pipeline for training deep blind RAW restoration models. Our pipeline considers realistic sensor noise, motion blur, camera shake, and other common degradations. The models trained with our pipeline and data from multiple sensors, can successfully reduce noise and blur, and recover details in RAW images captured from different cameras. To the best of our knowledge, this is the most exhaustive analysis on RAW image restoration. Code available at https://github.com/mv-lab/AISP<|reference_end|>
|
arxiv
|
@article{conde2024toward,
title={Toward Efficient Deep Blind RAW Image Restoration},
author={Marcos V. Conde, Florin Vasluianu, Radu Timofte},
journal={arXiv preprint arXiv:2409.18204},
year={2024},
archivePrefix={arXiv},
eprint={2409.18204},
primaryClass={eess.IV cs.CV}
}
|
conde2024toward
|
arxiv-662503
|
2409.18205
|
Bridging OOD Detection and Generalization: A Graph-Theoretic View
|
<|reference_start|>Bridging OOD Detection and Generalization: A Graph-Theoretic View: In the context of modern machine learning, models deployed in real-world scenarios often encounter diverse data shifts like covariate and semantic shifts, leading to challenges in both out-of-distribution (OOD) generalization and detection. Despite considerable attention to these issues separately, a unified framework for theoretical understanding and practical usage is lacking. To bridge the gap, we introduce a graph-theoretic framework to jointly tackle both OOD generalization and detection problems. By leveraging the graph formulation, data representations are obtained through the factorization of the graph's adjacency matrix, enabling us to derive provable error quantifying OOD generalization and detection performance. Empirical results showcase competitive performance in comparison to existing methods, thereby validating our theoretical underpinnings. Code is publicly available at https://github.com/deeplearning-wisc/graph-spectral-ood.<|reference_end|>
|
arxiv
|
@article{wang2024bridging,
title={Bridging OOD Detection and Generalization: A Graph-Theoretic View},
author={Han Wang, Yixuan Li},
journal={arXiv preprint arXiv:2409.18205},
year={2024},
archivePrefix={arXiv},
eprint={2409.18205},
primaryClass={cs.LG stat.ML}
}
|
wang2024bridging
|
arxiv-662504
|
2409.18209
|
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
|
<|reference_start|>A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation: This paper studies a family of estimators based on noise-contrastive estimation (NCE) for learning unnormalized distributions. The main contribution of this work is to provide a unified perspective on various methods for learning unnormalized distributions, which have been independently proposed and studied in separate research communities, through the lens of NCE. This unified view offers new insights into existing estimators. Specifically, for exponential families, we establish the finite-sample convergence rates of the proposed estimators under a set of regularity assumptions, most of which are new.<|reference_end|>
|
arxiv
|
@article{ryu2024a,
title={A Unified View on Learning Unnormalized Distributions via
Noise-Contrastive Estimation},
author={J. Jon Ryu, Abhin Shah, Gregory W. Wornell},
journal={arXiv preprint arXiv:2409.18209},
year={2024},
archivePrefix={arXiv},
eprint={2409.18209},
primaryClass={stat.ML cs.LG math.ST stat.TH}
}
|
ryu2024a
|
arxiv-662505
|
2409.18211
|
Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks
|
<|reference_start|>Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks: The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification. Digital watermarking serves as a crucial approach to address these challenges. Its evolution spans three generations: handcrafted, autoencoder-based, and foundation model based methods. While the robustness of these systems is well-documented, the security against adversarial attacks remains underexplored. This paper evaluates the security of foundation models' latent space digital watermarking systems that utilize adversarial embedding techniques. A series of experiments investigate the security dimensions under copy and removal attacks, providing empirical insights into these systems' vulnerabilities. All experimental codes and results are available at https://github.com/vkinakh/ssl-watermarking-attacks .<|reference_end|>
|
arxiv
|
@article{kinakh2024evaluation,
title={Evaluation of Security of ML-based Watermarking: Copy and Removal
Attacks},
author={Vitaliy Kinakh, Brian Pulfer, Yury Belousov, Pierre Fernandez, Teddy
Furon, Slava Voloshynovskiy},
journal={arXiv preprint arXiv:2409.18211},
year={2024},
archivePrefix={arXiv},
eprint={2409.18211},
primaryClass={cs.CV}
}
|
kinakh2024evaluation
|
arxiv-662506
|
2409.18213
|
A Fly on the Wall -- Exploiting Acoustic Side-Channels in Differential Pressure Sensors
|
<|reference_start|>A Fly on the Wall -- Exploiting Acoustic Side-Channels in Differential Pressure Sensors: Differential Pressure Sensors are widely deployed to monitor critical environments. However, our research unveils a previously overlooked vulnerability: their high sensitivity to pressure variations makes them susceptible to acoustic side-channel attacks. We demonstrate that the pressure-sensing diaphragms in DPS can inadvertently capture subtle air vibrations caused by speech, which propagate through the sensor's components and affect the pressure readings. Exploiting this discovery, we introduce BaroVox, a novel attack that reconstructs speech from DPS readings, effectively turning DPS into a "fly on the wall." We model the effect of sound on DPS, exploring the limits and challenges of acoustic leakage. To overcome these challenges, we propose two solutions: a signal-processing approach using a unique spectral subtraction method and a deep learning-based approach for keyword classification. Evaluations under various conditions demonstrate BaroVox's effectiveness, achieving a word error rate of 0.29 for manual recognition and 90.51% accuracy for automatic recognition. Our findings highlight the significant privacy implications of this vulnerability. We also discuss potential defense strategies to mitigate the risks posed by BaroVox.<|reference_end|>
|
arxiv
|
@article{achamyeleh2024a,
title={A Fly on the Wall -- Exploiting Acoustic Side-Channels in Differential
Pressure Sensors},
author={Yonatan Gizachew Achamyeleh, Mohamad Habib Fakih, Gabriel Garcia,
Anomadarshi Barua, Mohammad Al Faruque},
journal={arXiv preprint arXiv:2409.18213},
year={2024},
archivePrefix={arXiv},
eprint={2409.18213},
primaryClass={cs.SD eess.AS}
}
|
achamyeleh2024a
|
arxiv-662507
|
2409.18214
|
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
|
<|reference_start|>Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey: Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of trustworthiness, such as robustness, fairness, security, privacy, factuality, and explainability, similar to those in traditional deep learning (DL) tasks. Conventional approaches for studying trustworthiness in DL tasks often fall short due to the unique characteristics of T2I DMs, e.g., the multi-modal nature. Given the challenge, recent efforts have been made to develop new methods for investigating trustworthiness in T2I DMs via various means, including falsification, enhancement, verification \& validation and assessment. However, there is a notable lack of in-depth analysis concerning those non-functional properties and means. In this survey, we provide a timely and focused review of the literature on trustworthy T2I DMs, covering a concise-structured taxonomy from the perspectives of property, means, benchmarks and applications. Our review begins with an introduction to essential preliminaries of T2I DMs, and then we summarise key definitions/metrics specific to T2I tasks and analyses the means proposed in recent literature based on these definitions/metrics. Additionally, we review benchmarks and domain applications of T2I DMs. Finally, we highlight the gaps in current research, discuss the limitations of existing methods, and propose future research directions to advance the development of trustworthy T2I DMs. Furthermore, we keep up-to-date updates in this field to track the latest developments and maintain our GitHub repository at: https://github.com/wellzline/Trustworthy_T2I_DMs<|reference_end|>
|
arxiv
|
@article{zhang2024trustworthy,
title={Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey},
author={Yi Zhang, Zhen Chen, Chih-Hong Cheng, Wenjie Ruan, Xiaowei Huang,
Dezong Zhao, David Flynn, Siddartha Khastgir, Xingyu Zhao},
journal={arXiv preprint arXiv:2409.18214},
year={2024},
archivePrefix={arXiv},
eprint={2409.18214},
primaryClass={cs.LG}
}
|
zhang2024trustworthy
|
arxiv-662508
|
2409.18216
|
MMMT-IF: A Challenging Multimodal Multi-Turn Instruction Following Benchmark
|
<|reference_start|>MMMT-IF: A Challenging Multimodal Multi-Turn Instruction Following Benchmark: Evaluating instruction following capabilities for multimodal, multi-turn dialogue is challenging. With potentially multiple instructions in the input model context, the task is time-consuming for human raters and we show LLM based judges are biased towards answers from the same model. We propose MMMT-IF, an image based multi-turn Q$\&$A evaluation set with added global instructions between questions, constraining the answer format. This challenges models to retrieve instructions dispersed across long dialogues and reason under instruction constraints. All instructions are objectively verifiable through code execution. We introduce the Programmatic Instruction Following ($\operatorname{PIF}$) metric to measure the fraction of the instructions that are correctly followed while performing a reasoning task. The $\operatorname{PIF-N-K}$ set of metrics further evaluates robustness by measuring the fraction of samples in a corpus where, for each sample, at least K out of N generated model responses achieve a $\operatorname{PIF}$ score of one. The $\operatorname{PIF}$ metric aligns with human instruction following ratings, showing 60 percent correlation. Experiments show Gemini 1.5 Pro, GPT-4o, and Claude 3.5 Sonnet, have a $\operatorname{PIF}$ metric that drops from 0.81 on average at turn 1 across the models, to 0.64 at turn 20. Across all turns, when each response is repeated 4 times ($\operatorname{PIF-4-4}$), GPT-4o and Gemini successfully follow all instructions only $11\%$ of the time. When all the instructions are also appended to the end of the model input context, the $\operatorname{PIF}$ metric improves by 22.3 points on average, showing that the challenge with the task lies not only in following the instructions, but also in retrieving the instructions spread out in the model context. We plan to open source the MMMT-IF dataset and metric computation code.<|reference_end|>
|
arxiv
|
@article{epstein2024mmmt-if:,
title={MMMT-IF: A Challenging Multimodal Multi-Turn Instruction Following
Benchmark},
author={Elliot L. Epstein and Kaisheng Yao and Jing Li and Xinyi Bai and Hamid
Palangi},
journal={arXiv preprint arXiv:2409.18216},
year={2024},
archivePrefix={arXiv},
eprint={2409.18216},
primaryClass={cs.AI cs.CL cs.LG}
}
|
epstein2024mmmt-if:
|
arxiv-662509
|
2409.18218
|
Learning to Drive via Asymmetric Self-Play
|
<|reference_start|>Learning to Drive via Asymmetric Self-Play: Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of driving data is uninteresting, and deliberately collecting new long-tail scenarios is expensive and unsafe. We propose asymmetric self-play to scale beyond real data with additional challenging, solvable, and realistic synthetic scenarios. Our approach pairs a teacher that learns to generate scenarios it can solve but the student cannot, with a student that learns to solve them. When applied to traffic simulation, we learn realistic policies with significantly fewer collisions in both nominal and long-tail scenarios. Our policies further zero-shot transfer to generate training data for end-to-end autonomy, significantly outperforming state-of-the-art adversarial approaches, or using real data alone. For more information, visit https://waabi.ai/selfplay .<|reference_end|>
|
arxiv
|
@article{zhang2024learning,
title={Learning to Drive via Asymmetric Self-Play},
author={Chris Zhang, Sourav Biswas, Kelvin Wong, Kion Fallah, Lunjun Zhang,
Dian Chen, Sergio Casas, Raquel Urtasun},
journal={arXiv preprint arXiv:2409.18218},
year={2024},
archivePrefix={arXiv},
eprint={2409.18218},
primaryClass={cs.RO cs.CV cs.LG}
}
|
zhang2024learning
|
arxiv-662510
|
2409.18219
|
Revolutionizing Payload Inspection: A Self-Supervised Journey to Precision with Few Shots
|
<|reference_start|>Revolutionizing Payload Inspection: A Self-Supervised Journey to Precision with Few Shots: As networks continue to expand and become more interconnected, the need for novel malware detection methods becomes more pronounced. Traditional security measures are increasingly inadequate against the sophistication of modern cyber attacks. Deep Packet Inspection (DPI) has been pivotal in enhancing network security, offering an in-depth analysis of network traffic that surpasses conventional monitoring techniques. DPI not only examines the metadata of network packets, but also dives into the actual content being carried within the packet payloads, providing a comprehensive view of the data flowing through networks. The integration of advanced deep learning techniques with DPI has introduced modern methodologies into malware detection. However, the challenge with the state-of-the-art supervised learning approaches is that they prevent the generalization to unseen attacks embedded in the payloads, prohibiting them from accurately detecting new attacks and transferring knowledge learned from previous attacks to the new attacks with small labeled sample sizes. This paper leverages the recent advancements in self-supervised learning and few-shot learning. Our proposed self-supervised approach trains a transformer to learn the embedding of the payloads from a vast amount of unlabeled datasets by masking portions of payloads, leading to a learnt representation that well generalizes to various downstream tasks. Once the representation is extracted from payloads, they are used to train a malware detection algorithm. The representation obtained from the transformer is then used to adapt the malware detector to novel types of attacks using few-shot learning approaches. Our experimental results across several datasets show the great success and generalization of the proposed approach to novel scenarios.<|reference_end|>
|
arxiv
|
@article{stein2024revolutionizing,
title={Revolutionizing Payload Inspection: A Self-Supervised Journey to
Precision with Few Shots},
author={Kyle Stein, Arash Mahyari, Guillermo Francia III, Eman El-Sheikh},
journal={arXiv preprint arXiv:2409.18219},
year={2024},
archivePrefix={arXiv},
eprint={2409.18219},
primaryClass={cs.CR cs.LG}
}
|
stein2024revolutionizing
|
arxiv-662511
|
2409.18222
|
Trustworthy AI: Securing Sensitive Data in Large Language Models
|
<|reference_start|>Trustworthy AI: Securing Sensitive Data in Large Language Models: Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user's trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency.<|reference_end|>
|
arxiv
|
@article{feretzakis2024trustworthy,
title={Trustworthy AI: Securing Sensitive Data in Large Language Models},
author={Georgios Feretzakis and Vassilios S. Verykios},
journal={arXiv preprint arXiv:2409.18222},
year={2024},
archivePrefix={arXiv},
eprint={2409.18222},
primaryClass={cs.AI}
}
|
feretzakis2024trustworthy
|
arxiv-662512
|
2409.18223
|
PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction
|
<|reference_start|>PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction: Light field microscopy (LFM) has been widely utilized in various fields for its capability to efficiently capture high-resolution 3D scenes. Despite the rapid advancements in neural representations, there are few methods specifically tailored for microscopic scenes. Existing approaches often do not adequately address issues such as the loss of high-frequency information due to defocus and sample aberration, resulting in suboptimal performance. In addition, existing methods, including RLD, INR, and supervised U-Net, face challenges such as sensitivity to initial estimates, reliance on extensive labeled data, and low computational efficiency, all of which significantly diminish the practicality in complex biological scenarios. This paper introduces PNR (Physics-informed Neural Representation), a method for high-resolution LFM reconstruction that significantly enhances performance. Our method incorporates an unsupervised and explicit feature representation approach, resulting in a 6.1 dB improvement in PSNR than RLD. Additionally, our method employs a frequency-based training loss, enabling better recovery of high-frequency details, which leads to a reduction in LPIPS by at least half compared to SOTA methods (1.762 V.S. 3.646 of DINER). Moreover, PNR integrates a physics-informed aberration correction strategy that optimizes Zernike polynomial parameters during optimization, thereby reducing the information loss caused by aberrations and improving spatial resolution. These advancements make PNR a promising solution for long-term high-resolution biological imaging applications. Our code and dataset will be made publicly available.<|reference_end|>
|
arxiv
|
@article{zhao2024pnr:,
title={PNR: Physics-informed Neural Representation for high-resolution LFM
reconstruction},
author={Jiayin Zhao, Zhifeng Zhao, Jiamin Wu, Tao Yu and Hui Qiao},
journal={arXiv preprint arXiv:2409.18223},
year={2024},
archivePrefix={arXiv},
eprint={2409.18223},
primaryClass={eess.IV cs.CV}
}
|
zhao2024pnr:
|
arxiv-662513
|
2409.18228
|
Analysis of Spatial augmentation in Self-supervised models in the purview of training and test distributions
|
<|reference_start|>Analysis of Spatial augmentation in Self-supervised models in the purview of training and test distributions: In this paper, we present an empirical study of typical spatial augmentation techniques used in self-supervised representation learning methods (both contrastive and non-contrastive), namely random crop and cutout. Our contributions are: (a) we dissociate random cropping into two separate augmentations, overlap and patch, and provide a detailed analysis on the effect of area of overlap and patch size to the accuracy on down stream tasks. (b) We offer an insight into why cutout augmentation does not learn good representation, as reported in earlier literature. Finally, based on these analysis, (c) we propose a distance-based margin to the invariance loss for learning scene-centric representations for the downstream task on object-centric distribution, showing that as simple as a margin proportional to the pixel distance between the two spatial views in the scence-centric images can improve the learned representation. Our study furthers the understanding of the spatial augmentations, and the effect of the domain-gap between the training augmentations and the test distribution.<|reference_end|>
|
arxiv
|
@article{jha2024analysis,
title={Analysis of Spatial augmentation in Self-supervised models in the
purview of training and test distributions},
author={Abhishek Jha, Tinne Tuytelaars},
journal={arXiv preprint arXiv:2409.18228},
year={2024},
archivePrefix={arXiv},
eprint={2409.18228},
primaryClass={cs.CV}
}
|
jha2024analysis
|
arxiv-662514
|
2409.18231
|
ReloPush: Multi-object Rearrangement in Confined Spaces with a Nonholonomic Mobile Robot Pusher
|
<|reference_start|>ReloPush: Multi-object Rearrangement in Confined Spaces with a Nonholonomic Mobile Robot Pusher: We focus on the problem of rearranging a set of objects within a confined space with a nonholonomically constrained mobile robot pusher. This problem is relevant to many real-world domains, including warehouse automation and construction. These domains give rise to instances involving a combination of geometric, kinematic, and physics constraints, which make planning particularly challenging. Prior work often makes simplifying assumptions like the use of holonomic mobile robots or dexterous manipulators capable of unconstrained overhand reaching. Our key insight is we can empower even a constrained mobile pusher to tackle complex rearrangement tasks by enabling it to modify the environment to its favor in a constraint-aware fashion. To this end, we describe a Push-Traversability graph, whose vertices represent poses that the pusher can push objects from and edges represent optimal, kinematically feasible, and stable push-rearrangements of objects. Based on this graph, we develop ReloPush, a planning framework that leverages Dubins curves and standard graph search techniques to generate an efficient sequence of object rearrangements to be executed by the pusher. We evaluate ReloPush across a series of challenging scenarios, involving the rearrangement of densely cluttered workspaces with up to eight objects by a 1tenth mobile robot pusher. ReloPush exhibits orders of magnitude faster runtimes and significantly more robust execution in the real world, evidenced in lower execution times and fewer losses of object contact, compared to two baselines lacking our proposed graph structure.<|reference_end|>
|
arxiv
|
@article{ahn2024relopush:,
title={ReloPush: Multi-object Rearrangement in Confined Spaces with a
Nonholonomic Mobile Robot Pusher},
author={Jeeho Ahn and Christoforos Mavrogiannis},
journal={arXiv preprint arXiv:2409.18231},
year={2024},
archivePrefix={arXiv},
eprint={2409.18231},
primaryClass={cs.RO}
}
|
ahn2024relopush:
|
arxiv-662515
|
2409.18235
|
Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks
|
<|reference_start|>Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks: Deep neural networks (DNNs), while increasingly deployed in many applications, struggle with robustness against anomalous and out-of-distribution (OOD) data. Current OOD benchmarks often oversimplify, focusing on single-object tasks and not fully representing complex real-world anomalies. This paper introduces a new, straightforward method employing graph structures and topological features to effectively detect both far-OOD and near-OOD data. We convert images into networks of interconnected human understandable features or visual concepts. Through extensive testing on two novel tasks, including ablation studies with large vocabularies and diverse tasks, we demonstrate the method's effectiveness. This approach enhances DNN resilience to OOD data and promises improved performance in various applications.<|reference_end|>
|
arxiv
|
@article{ganguly2024visual,
title={Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous
Data in Deep Neural Networks},
author={Debargha Ganguly, Debayan Gupta and Vipin Chaudhary},
journal={arXiv preprint arXiv:2409.18235},
year={2024},
archivePrefix={arXiv},
eprint={2409.18235},
primaryClass={cs.CV cs.LG}
}
|
ganguly2024visual
|
arxiv-662516
|
2409.18236
|
Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View Prediction in Adaptive Point Cloud Video Streaming
|
<|reference_start|>Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View Prediction in Adaptive Point Cloud Video Streaming: Field-of-View (FoV) adaptive streaming significantly reduces bandwidth requirement of immersive point cloud video (PCV) by only transmitting visible points in a viewer's FoV. The traditional approaches often focus on trajectory-based 6 degree-of-freedom (6DoF) FoV predictions. The predicted FoV is then used to calculate point visibility. Such approaches do not explicitly consider video content's impact on viewer attention, and the conversion from FoV to point visibility is often error-prone and time-consuming. We reformulate the PCV FoV prediction problem from the cell visibility perspective, allowing for precise decision-making regarding the transmission of 3D data at the cell level based on the predicted visibility distribution. We develop a novel spatial visibility and object-aware graph model that leverages the historical 3D visibility data and incorporates spatial perception, neighboring cell correlation, and occlusion information to predict the cell visibility in the future. Our model significantly improves the long-term cell visibility prediction, reducing the prediction MSE loss by up to 50% compared to the state-of-the-art models while maintaining real-time performance (more than 30fps) for point cloud videos with over 1 million points.<|reference_end|>
|
arxiv
|
@article{li2024spatial,
title={Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View
Prediction in Adaptive Point Cloud Video Streaming},
author={Chen Li, Tongyu Zong, Yueyu Hu, Yao Wang, Yong Liu},
journal={arXiv preprint arXiv:2409.18236},
year={2024},
archivePrefix={arXiv},
eprint={2409.18236},
primaryClass={cs.CV cs.LG cs.MM eess.IV}
}
|
li2024spatial
|
arxiv-662517
|
2409.18237
|
Learning Beamforming in Cell-Free Massive MIMO ISAC Systems
|
<|reference_start|>Learning Beamforming in Cell-Free Massive MIMO ISAC Systems: Beamforming design is critical for the efficient operation of integrated sensing and communication (ISAC) MIMO systems. ISAC beamforming design in cell-free massive MIMO systems, compared to colocated MIMO systems, is more challenging due to the additional complexity of the distributed large number of access points (APs). To address this problem, this paper first shows that graph neural networks (GNNs) are a suitable machine learning framework. Then, it develops a novel heterogeneous GNN model inspired by the specific characteristics of the cell-free ISAC MIMO systems. This model enables the low-complexity scaling of the cell-free ISAC system and does not require full retraining when additional APs are added or removed. Our results show that the proposed architecture can achieve near-optimal performance, and applies well to various network structures.<|reference_end|>
|
arxiv
|
@article{demirhan2024learning,
title={Learning Beamforming in Cell-Free Massive MIMO ISAC Systems},
author={Umut Demirhan and Ahmed Alkhateeb},
journal={arXiv preprint arXiv:2409.18237},
year={2024},
archivePrefix={arXiv},
eprint={2409.18237},
primaryClass={cs.IT eess.SP math.IT}
}
|
demirhan2024learning
|
arxiv-662518
|
2409.18239
|
Towards sub-millisecond latency real-time speech enhancement models on hearables
|
<|reference_start|>Towards sub-millisecond latency real-time speech enhancement models on hearables: Low latency models are critical for real-time speech enhancement applications, such as hearing aids and hearables. However, the sub-millisecond latency space for resource-constrained hearables remains underexplored. We demonstrate speech enhancement using a computationally efficient minimum-phase FIR filter, enabling sample-by-sample processing to achieve mean algorithmic latency of 0.32 ms to 1.25 ms. With a single microphone, we observe a mean SI-SDRi of 4.1 dB. The approach shows generalization with a DNSMOS increase of 0.2 on unseen audio recordings. We use a lightweight LSTM-based model of 644k parameters to generate FIR taps. We benchmark that our system can run on low-power DSP with 388 MIPS and mean end-to-end latency of 3.35 ms. We provide a comparison with baseline low-latency spectral masking techniques. We hope this work will enable a better understanding of latency and can be used to improve the comfort and usability of hearables.<|reference_end|>
|
arxiv
|
@article{dementyev2024towards,
title={Towards sub-millisecond latency real-time speech enhancement models on
hearables},
author={Artem Dementyev, Chandan K. A. Reddy, Scott Wisdom, Navin Chatlani,
John R. Hershey, Richard F.Lyon},
journal={arXiv preprint arXiv:2409.18239},
year={2024},
archivePrefix={arXiv},
eprint={2409.18239},
primaryClass={cs.SD cs.LG eess.AS}
}
|
dementyev2024towards
|
arxiv-662519
|
2409.18240
|
Measuring Research Interest Similarity with Transition Probabilities
|
<|reference_start|>Measuring Research Interest Similarity with Transition Probabilities: We propose a method to measure the similarity of papers and authors by simulating a literature search procedure on citation networks, which is an information retrieval inspired conceptualization of similarity. This transition probability (TP) based approach does not require a curated classification system, avoids clustering complications, and provides a continuous measure of similarity. We perform testing scenarios to explore several versions of the general TP concept and the Node2vec machine-learning technique. We found that TP measures outperform Node2vec in mapping the macroscopic structure of fields. The paper provides a general discussion of how to implement TP similarity measurement, with a particular focus on how to utilize publication-level information to approximate the research interest similarity of individual scientists. This paper is accompanied by a Python package capable of calculating all the tested metrics.<|reference_end|>
|
arxiv
|
@article{varga2024measuring,
title={Measuring Research Interest Similarity with Transition Probabilities},
author={Attila Varga, Sadamori Kojaku, Filipi Nascimento Silva},
journal={arXiv preprint arXiv:2409.18240},
year={2024},
archivePrefix={arXiv},
eprint={2409.18240},
primaryClass={cs.DL cs.SI stat.AP}
}
|
varga2024measuring
|
arxiv-662520
|
2409.18244
|
Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks
|
<|reference_start|>Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks: Deploying machine learning (ML) in dynamic data-driven applications systems (DDDAS) can improve the security of industrial control systems (ICS). However, ML-based DDDAS are vulnerable to adversarial attacks because adversaries can alter the input data slightly so that the ML models predict a different result. In this paper, our goal is to build a resilient edge machine learning (reML) architecture that is designed to withstand adversarial attacks by performing Data Air Gap Transformation (DAGT) to anonymize data feature spaces using deep neural networks and randomize the ML models used for predictions. The reML is based on the Resilient DDDAS paradigm, Moving Target Defense (MTD) theory, and TinyML and is applied to combat adversarial attacks on ICS. Furthermore, the proposed approach is power-efficient and privacy-preserving and, therefore, can be deployed on power-constrained devices to enhance ICS security. This approach enables resilient ML inference at the edge by shifting the computation from the computing-intensive platforms to the resource-constrained edge devices. The incorporation of TinyML with TensorFlow Lite ensures efficient resource utilization and, consequently, makes reML suitable for deployment in various industrial control environments. Furthermore, the dynamic nature of reML, facilitated by the resilient DDDAS development environment, allows for continuous adaptation and improvement in response to emerging threats. Lastly, we evaluate our approach on an ICS dataset and demonstrate that reML provides a viable and effective solution for resilient ML inference at the edge devices.<|reference_end|>
|
arxiv
|
@article{yao2024development,
title={Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial
Attacks},
author={Likai Yao, Qinxuan Shi, Zhanglong Yang, Sicong Shao, Salim Hariri},
journal={arXiv preprint arXiv:2409.18244},
year={2024},
archivePrefix={arXiv},
eprint={2409.18244},
primaryClass={cs.CR cs.LG}
}
|
yao2024development
|
arxiv-662521
|
2409.18248
|
Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehicles
|
<|reference_start|>Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehicles: Ensuring autonomous vehicle (AV) security remains a critical concern. An area of paramount importance is the study of physical-world adversarial examples (AEs) aimed at exploiting vulnerabilities in perception systems. However, most of the prevailing research on AEs has neglected considerations of stealthiness and legality, resulting in scenarios where human drivers would promptly intervene or attackers would be swiftly detected and punished. These limitations hinder the applicability of such examples in real-life settings. In this paper, we introduce a novel approach to generate AEs using what we term negative shadows: deceptive patterns of light on the road created by strategically blocking sunlight, which then cast artificial lane-like patterns. These shadows are inconspicuous to a driver while deceiving AV perception systems, particularly those reliant on lane detection algorithms. By prioritizing the stealthy nature of attacks to minimize driver interventions and ensuring their legality from an attacker's standpoint, a more plausible range of scenarios is established. In multiple scenarios, including at low speeds, our method shows a high safety violation rate. Using a 20-meter negative shadow, it can direct a vehicle off-road with a 100% violation rate at speeds over 10 mph. Other attack scenarios, such as causing collisions, can be performed with at least 30 meters of negative shadow, achieving a 60-100% success rate. The attack also maintains an average stealthiness of 83.6% as measured through a human subject experiment, ensuring its efficacy in covert settings.<|reference_end|>
|
arxiv
|
@article{mohajeransari2024discovering,
title={Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection
of Autonomous Vehicles},
author={Pedram MohajerAnsari, Alkim Domeke, Jan de Voor, Arkajyoti Mitra,
Grace Johnson, Amir Salarpour, Habeeb Olufowobi, Mohammad Hamad, and Mert D.
Pes'e},
journal={arXiv preprint arXiv:2409.18248},
year={2024},
archivePrefix={arXiv},
eprint={2409.18248},
primaryClass={cs.CR}
}
|
mohajeransari2024discovering
|
arxiv-662522
|
2409.18249
|
Bridging the Protection Gap: Innovative Approaches to Shield Older Adults from AI-Enhanced Scams
|
<|reference_start|>Bridging the Protection Gap: Innovative Approaches to Shield Older Adults from AI-Enhanced Scams: Artificial Intelligence (AI) is rapidly gaining popularity as individuals, groups, and organizations discover and apply its expanding capabilities. Generative AI creates or alters various content types including text, image, audio, and video that are realistic and challenging to identify as AI-generated constructs. However, guardrails preventing malicious use of AI are easily bypassed. Numerous indications suggest that scammers are already using AI to enhance already successful scams, improving scam effectiveness, speed and credibility, while reducing detectability of scams that target older adults, who are known to be slow to adopt new technologies. Through hypothetical cases analysis of two leading scams, the tech support scams and the romance scams, this paper explores the future of AI in scams affecting older adults by identifying current vulnerabilities and recommending updated defensive measures focusing the establishment of a reliable support network offering elevated support to increase confidence and ability to defend against AI-enhanced scams.<|reference_end|>
|
arxiv
|
@article{herrera2024bridging,
title={Bridging the Protection Gap: Innovative Approaches to Shield Older
Adults from AI-Enhanced Scams},
author={LD Herrera, London Van Sickle, Ashley Podhradsky},
journal={arXiv preprint arXiv:2409.18249},
year={2024},
archivePrefix={arXiv},
eprint={2409.18249},
primaryClass={cs.CR}
}
|
herrera2024bridging
|
arxiv-662523
|
2409.18253
|
UAV-Assisted Self-Supervised Terrain Awareness for Off-Road Navigation
|
<|reference_start|>UAV-Assisted Self-Supervised Terrain Awareness for Off-Road Navigation: Terrain awareness is an essential milestone to enable truly autonomous off-road navigation. Accurately predicting terrain characteristics allows optimizing a vehicle's path against potential hazards. Recent methods use deep neural networks to predict traversability-related terrain properties in a self-supervised manner, relying on proprioception as a training signal. However, onboard cameras are inherently limited by their point-of-view relative to the ground, suffering from occlusions and vanishing pixel density with distance. This paper introduces a novel approach for self-supervised terrain characterization using an aerial perspective from a hovering drone. We capture terrain-aligned images while sampling the environment with a ground vehicle, effectively training a simple predictor for vibrations, bumpiness, and energy consumption. Our dataset includes 2.8 km of off-road data collected in forest environment, comprising 13 484 ground-based images and 12 935 aerial images. Our findings show that drone imagery improves terrain property prediction by 21.37 % on the whole dataset and 37.35 % in high vegetation, compared to ground robot images. We conduct ablation studies to identify the main causes of these performance improvements. We also demonstrate the real-world applicability of our approach by scouting an unseen area with a drone, planning and executing an optimized path on the ground.<|reference_end|>
|
arxiv
|
@article{fortin2024uav-assisted,
title={UAV-Assisted Self-Supervised Terrain Awareness for Off-Road Navigation},
author={Jean-Michel Fortin, Olivier Gamache, William Fecteau, Effie Daum,
William Larriv'ee-Hardy, Franc{c}ois Pomerleau, Philippe Gigu`ere},
journal={arXiv preprint arXiv:2409.18253},
year={2024},
archivePrefix={arXiv},
eprint={2409.18253},
primaryClass={cs.RO}
}
|
fortin2024uav-assisted
|
arxiv-662524
|
2409.18254
|
Evaluation of Cluster Id Assignment Schemes with ABCDE
|
<|reference_start|>Evaluation of Cluster Id Assignment Schemes with ABCDE: A cluster id assignment scheme labels each cluster of a clustering with a distinct id. The goal of id assignment is semantic id stability, which means that, whenever possible, a cluster for the same underlying concept as that of a historical cluster should ideally receive the same id as the historical cluster. Semantic id stability allows the users of a clustering to refer to a concept's cluster with an id that is stable across clusterings/time. This paper treats the problem of evaluating the relative merits of id assignment schemes. In particular, it considers a historical clustering with id assignments, and a new clustering with ids assigned by a baseline and an experiment. It produces metrics that characterize both the magnitude and the quality of the id assignment diffs between the baseline and the experiment. That happens by transforming the problem of cluster id assignment into a problem of cluster membership, and evaluating it with ABCDE. ABCDE is a sophisticated and scalable technique for evaluating differences in cluster membership in real-world applications, where billions of items are grouped into millions of clusters, and some items are more important than others. The paper also describes several generalizations to the basic evaluation setup for id assignment schemes. For example, it is fairly straightforward to evaluate changes that simultaneously mutate cluster memberships and cluster ids. The ideas are generously illustrated with examples.<|reference_end|>
|
arxiv
|
@article{van staden2024evaluation,
title={Evaluation of Cluster Id Assignment Schemes with ABCDE},
author={Stephan van Staden},
journal={arXiv preprint arXiv:2409.18254},
year={2024},
archivePrefix={arXiv},
eprint={2409.18254},
primaryClass={cs.IR}
}
|
van staden2024evaluation
|
arxiv-662525
|
2409.18256
|
Amodal Instance Segmentation with Diffusion Shape Prior Estimation
|
<|reference_start|>Amodal Instance Segmentation with Diffusion Shape Prior Estimation: Amodal Instance Segmentation (AIS) presents an intriguing challenge, including the segmentation prediction of both visible and occluded parts of objects within images. Previous methods have often relied on shape prior information gleaned from training data to enhance amodal segmentation. However, these approaches are susceptible to overfitting and disregard object category details. Recent advancements highlight the potential of conditioned diffusion models, pretrained on extensive datasets, to generate images from latent space. Drawing inspiration from this, we propose AISDiff with a Diffusion Shape Prior Estimation (DiffSP) module. AISDiff begins with the prediction of the visible segmentation mask and object category, alongside occlusion-aware processing through the prediction of occluding masks. Subsequently, these elements are inputted into our DiffSP module to infer the shape prior of the object. DiffSP utilizes conditioned diffusion models pretrained on extensive datasets to extract rich visual features for shape prior estimation. Additionally, we introduce the Shape Prior Amodal Predictor, which utilizes attention-based feature maps from the shape prior to refine amodal segmentation. Experiments across various AIS benchmarks demonstrate the effectiveness of our AISDiff.<|reference_end|>
|
arxiv
|
@article{tran2024amodal,
title={Amodal Instance Segmentation with Diffusion Shape Prior Estimation},
author={Minh Tran, Khoa Vo, Tri Nguyen, Ngan Le},
journal={arXiv preprint arXiv:2409.18256},
year={2024},
archivePrefix={arXiv},
eprint={2409.18256},
primaryClass={cs.CV}
}
|
tran2024amodal
|
arxiv-662526
|
2409.18257
|
Developing a Dual-Stage Vision Transformer Model for Lung Disease Classification
|
<|reference_start|>Developing a Dual-Stage Vision Transformer Model for Lung Disease Classification: Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06\% when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.<|reference_end|>
|
arxiv
|
@article{mazumder2024developing,
title={Developing a Dual-Stage Vision Transformer Model for Lung Disease
Classification},
author={Anirudh Mazumder, Jianguo Liu},
journal={arXiv preprint arXiv:2409.18257},
year={2024},
archivePrefix={arXiv},
eprint={2409.18257},
primaryClass={eess.IV cs.CV}
}
|
mazumder2024developing
|
arxiv-662527
|
2409.18260
|
PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond
|
<|reference_start|>PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond: For automatic human figure drawing (HFD) assessment tasks, such as diagnosing autism spectrum disorder (ASD) using HFD images, the clarity and explainability of a model decision are crucial. Existing pixel-level attribution-based explainable AI (XAI) approaches demand considerable effort from users to interpret the semantic information of a region in an image, which can be often time-consuming and impractical. To overcome this challenge, we propose a part contribution evaluation based model explanation (PCEvE) framework. On top of the part detection, we measure the Shapley Value of each individual part to evaluate the contribution to a model decision. Unlike existing attribution-based XAI approaches, the PCEvE provides a straightforward explanation of a model decision, i.e., a part contribution histogram. Furthermore, the PCEvE expands the scope of explanations beyond the conventional sample-level to include class-level and task-level insights, offering a richer, more comprehensive understanding of model behavior. We rigorously validate the PCEvE via extensive experiments on multiple HFD assessment datasets. Also, we sanity-check the proposed method with a set of controlled experiments. Additionally, we demonstrate the versatility and applicability of our method to other domains by applying it to a photo-realistic dataset, the Stanford Cars.<|reference_end|>
|
arxiv
|
@article{lee2024pceve:,
title={PCEvE: Part Contribution Evaluation Based Model Explanation for Human
Figure Drawing Assessment and Beyond},
author={Jongseo Lee, Geo Ahn, Seong Tae Kim, Jinwoo Choi},
journal={arXiv preprint arXiv:2409.18260},
year={2024},
archivePrefix={arXiv},
eprint={2409.18260},
primaryClass={cs.CV cs.AI}
}
|
lee2024pceve:
|
arxiv-662528
|
2409.18261
|
Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation
|
<|reference_start|>Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation: 6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize across unseen instances within the same category. However, this generalization is limited by the narrow range of categories covered by existing datasets, such as NOCS, which also tend to overlook common real-world challenges like occlusion. To tackle these challenges, we introduce Omni6D, a comprehensive RGBD dataset featuring a wide range of categories and varied backgrounds, elevating the task to a more realistic context. 1) The dataset comprises an extensive spectrum of 166 categories, 4688 instances adjusted to the canonical pose, and over 0.8 million captures, significantly broadening the scope for evaluation. 2) We introduce a symmetry-aware metric and conduct systematic benchmarks of existing algorithms on Omni6D, offering a thorough exploration of new challenges and insights. 3) Additionally, we propose an effective fine-tuning approach that adapts models from previous datasets to our extensive vocabulary setting. We believe this initiative will pave the way for new insights and substantial progress in both the industrial and academic fields, pushing forward the boundaries of general 6D pose estimation.<|reference_end|>
|
arxiv
|
@article{zhang2024omni6d:,
title={Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object
Pose Estimation},
author={Mengchen Zhang, Tong Wu, Tai Wang, Tengfei Wang, Ziwei Liu, Dahua Lin},
journal={arXiv preprint arXiv:2409.18261},
year={2024},
archivePrefix={arXiv},
eprint={2409.18261},
primaryClass={cs.CV cs.AI}
}
|
zhang2024omni6d:
|
arxiv-662529
|
2409.18263
|
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
|
<|reference_start|>DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking: Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of distractors for multiple-choice questions (MCQ) remains a challenging task. In this work, we present a simple, generic framework for distractor generation using readily available Pre-trained Language Models (PLMs). Unlike previous methods, our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Building upon previous research, we introduce a two-stage framework consisting of candidate generation and candidate selection. Our proposed distractor generation framework outperforms previous methods without the need for training or fine-tuning. Human evaluations confirm that our approach produces more effective and engaging distractors. The related codebase is publicly available at https://github.com/obss/disgem.<|reference_end|>
|
arxiv
|
@article{cavusoglu2024disgem:,
title={DisGeM: Distractor Generation for Multiple Choice Questions with Span
Masking},
author={Devrim Cavusoglu, Secil Sen, Ulas Sert},
journal={arXiv preprint arXiv:2409.18263},
year={2024},
archivePrefix={arXiv},
eprint={2409.18263},
primaryClass={cs.CL cs.LG}
}
|
cavusoglu2024disgem:
|
arxiv-662530
|
2409.18265
|
Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning
|
<|reference_start|>Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning: Exemplar-Free Class Incremental Learning (EFCIL) tackles the problem of training a model on a sequence of tasks without access to past data. Existing state-of-the-art methods represent classes as Gaussian distributions in the feature extractor's latent space, enabling Bayes classification or training the classifier by replaying pseudo features. However, we identify two critical issues that compromise their efficacy when the feature extractor is updated on incremental tasks. First, they do not consider that classes' covariance matrices change and must be adapted after each task. Second, they are susceptible to a task-recency bias caused by dimensionality collapse occurring during training. In this work, we propose AdaGauss -- a novel method that adapts covariance matrices from task to task and mitigates the task-recency bias owing to the additional anti-collapse loss function. AdaGauss yields state-of-the-art results on popular EFCIL benchmarks and datasets when training from scratch or starting from a pre-trained backbone. The code is available at: https://github.com/grypesc/AdaGauss.<|reference_end|>
|
arxiv
|
@article{rypeść2024task-recency,
title={Task-recency bias strikes back: Adapting covariances in Exemplar-Free
Class Incremental Learning},
author={Grzegorz Rype's'c, Sebastian Cygert, Tomasz Trzci'nski,
Bart{l}omiej Twardowski},
journal={arXiv preprint arXiv:2409.18265},
year={2024},
archivePrefix={arXiv},
eprint={2409.18265},
primaryClass={cs.LG cs.CV}
}
|
rypeść2024task-recency
|
arxiv-662531
|
2409.18266
|
Predicting Muscle Thickness Deformation from Muscle Activation Patterns: A Dual-Attention Framework
|
<|reference_start|>Predicting Muscle Thickness Deformation from Muscle Activation Patterns: A Dual-Attention Framework: Understanding the relationship between muscle activation and thickness deformation is critical for diagnosing muscle-related diseases and monitoring muscle health. Although ultrasound technique can measure muscle thickness change during muscle movement, its application in portable devices is limited by wiring and data collection challenges. Surface electromyography (sEMG), on the other hand, records muscle bioelectrical signals as the muscle activation. This paper introduced a deep-learning approach to leverage sEMG signals for muscle thickness deformation prediction, eliminating the need for ultrasound measurement. Using a dual-attention framework combining self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data. Experimental results with six healthy subjects showed that the approach could accurately predict muscle excursion with an average precision of 0.923$\pm$0.900mm, which shows that this method can facilitate real-time portable muscle health monitoring, showing potential for applications in clinical diagnostics, sports science, and rehabilitation.<|reference_end|>
|
arxiv
|
@article{lan2024predicting,
title={Predicting Muscle Thickness Deformation from Muscle Activation Patterns:
A Dual-Attention Framework},
author={Bangyu Lan, Kenan Niu},
journal={arXiv preprint arXiv:2409.18266},
year={2024},
archivePrefix={arXiv},
eprint={2409.18266},
primaryClass={eess.SP cs.LG}
}
|
lan2024predicting
|
arxiv-662532
|
2409.18267
|
Using dynamic loss weighting to boost improvements in forecast stability
|
<|reference_start|>Using dynamic loss weighting to boost improvements in forecast stability: Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecasting was proposed to include forecast stability as an additional optimization objective, next to accuracy. It was shown that more stable forecasts can be obtained without harming accuracy by minimizing a composite loss function that contains both a forecast error and a forecast instability component, with a static hyperparameter to control the impact of stability. In this paper, we empirically investigate whether further improvements in stability can be obtained without compromising accuracy by applying dynamic loss weighting algorithms, which change the loss weights during training. We show that some existing dynamic loss weighting methods achieve this objective. However, our proposed extension to the Random Weighting approach -- Task-Aware Random Weighting -- shows the best performance.<|reference_end|>
|
arxiv
|
@article{caljon2024using,
title={Using dynamic loss weighting to boost improvements in forecast stability},
author={Daan Caljon, Jeff Vercauteren, Simon De Vos, Wouter Verbeke, Jente Van
Belle},
journal={arXiv preprint arXiv:2409.18267},
year={2024},
archivePrefix={arXiv},
eprint={2409.18267},
primaryClass={cs.LG stat.ML}
}
|
caljon2024using
|
arxiv-662533
|
2409.18268
|
Leader Selection and Follower Association for UE-centric Distributed Learning in Future Wireless Networks
|
<|reference_start|>Leader Selection and Follower Association for UE-centric Distributed Learning in Future Wireless Networks: User equipment (UE) devices with high compute performance acting on data with dynamic and stochastic nature to train Artificial Intelligence/Machine Learning (AI/ML) models call for real-time, agile distributed machine learning (DL) algorithms. Consequently, we focus on UE-centric DL algorithms where UEs initiate requests to adapt AI/ML models for better performance, e.g., locally refined AI/ML models among a set of headsets or smartphones. This new setup requires selecting a set of UEs as aggregators (here called leaders) and another set as followers, where all UEs update their models based on their local data, and followers share theirs with leaders for aggregation. From a networking perspective, the first question is how to select leaders and associate followers efficiently. This results in a high dimensional mixed integer programming problem and involves internal UE state information and state information among UEs, called external state information in this paper. To address this challenge, we introduce two new indices: a Leader Internal Index (LII), which is a function of the internal states of each device, demonstrating the willingness to be a leader such as battery life and AI/hardware accelerators, and a Leader eXternal Index (LXI), which is a function of external state information among UEs, such as trust, channel condition, and any aspect relevant for associating a follower with a leader. These two indices transform the highly complex leader selection and follower association problem into a better tractable formulation. More importantly, LIIs and LXIs allow to keep the internal and external state information of this problem inside of each device without compromising users' privacy.<|reference_end|>
|
arxiv
|
@article{parsaeefard2024leader,
title={Leader Selection and Follower Association for UE-centric Distributed
Learning in Future Wireless Networks},
author={Saeedeh Parsaeefard, Sabine Roessel, Anousheh Gholami Ghavamabad,
Robert Zaus, Bernhard Raaf},
journal={arXiv preprint arXiv:2409.18268},
year={2024},
archivePrefix={arXiv},
eprint={2409.18268},
primaryClass={cs.NI}
}
|
parsaeefard2024leader
|
arxiv-662534
|
2409.18269
|
Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling
|
<|reference_start|>Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling: Prophet inequality concerns a basic optimal stopping problem and states that simple threshold stopping policies -- i.e., accepting the first reward larger than a certain threshold -- can achieve tight $\frac{1}{2}$-approximation to the optimal prophet value. Motivated by its economic applications, this paper studies the robustness of this approximation to natural strategic manipulations in which each random reward is associated with a self-interested player who may selectively reveal his realized reward to the searcher in order to maximize his probability of being selected. We say a threshold policy is $\alpha$(-strategically)-robust if it (a) achieves the $\alpha$-approximation to the prophet value for strategic players; and (b) meanwhile remains a $\frac{1}{2}$-approximation in the standard non-strategic setting. Starting with a characterization of each player's optimal information revealing strategy, we demonstrate the intrinsic robustness of prophet inequalities to strategic reward signaling through the following results: (1) for arbitrary reward distributions, there is a threshold policy that is $\frac{1-\frac{1}{e}}{2}$-robust, and this ratio is tight; (2) for i.i.d. reward distributions, there is a threshold policy that is $\frac{1}{2}$-robust, which is tight for the setting; and (3) for log-concave (but non-identical) reward distributions, the $\frac{1}{2}$-robustness can also be achieved under certain regularity assumptions.<|reference_end|>
|
arxiv
|
@article{tang2024intrinsic,
title={Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling},
author={Wei Tang, Haifeng Xu, Ruimin Zhang, Derek Zhu},
journal={arXiv preprint arXiv:2409.18269},
year={2024},
archivePrefix={arXiv},
eprint={2409.18269},
primaryClass={cs.GT}
}
|
tang2024intrinsic
|
arxiv-662535
|
2409.18272
|
SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems
|
<|reference_start|>SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems: In computational engineering, enhancing the simulation speed and efficiency is a perpetual goal. To fully take advantage of neural network techniques and hardware, we present the SLiding-window Initially-truncated Dynamic-response Estimator (SLIDE), a deep learning-based method designed to estimate output sequences of mechanical or multibody systems with primarily, but not exclusively, forced excitation. A key advantage of SLIDE is its ability to estimate the dynamic response of damped systems without requiring the full system state, making it particularly effective for flexible multibody systems. The method truncates the output window based on the decay of initial effects, such as damping, which is approximated by the complex eigenvalues of the systems linearized equations. In addition, a second neural network is trained to provide an error estimation, further enhancing the methods applicability. The method is applied to a diverse selection of systems, including the Duffing oscillator, a flexible slider-crank system, and an industrial 6R manipulator, mounted on a flexible socket. Our results demonstrate significant speedups from the simulation up to several millions, exceeding real-time performance substantially.<|reference_end|>
|
arxiv
|
@article{manzl2024slide:,
title={SLIDE: A machine-learning based method for forced dynamic response
estimation of multibody systems},
author={Peter Manzl, Alexander Humer, Qasim Khadim, Johannes Gerstmayr},
journal={arXiv preprint arXiv:2409.18272},
year={2024},
archivePrefix={arXiv},
eprint={2409.18272},
primaryClass={cs.LG}
}
|
manzl2024slide:
|
arxiv-662536
|
2409.18273
|
Autonomous Excavation of Challenging Terrain using Oscillatory Primitives and Adaptive Impedance Control
|
<|reference_start|>Autonomous Excavation of Challenging Terrain using Oscillatory Primitives and Adaptive Impedance Control: This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a standard penetrate-drag-scoop motion pattern. Inspired by human excavation strategies, our approach incorporates oscillatory rotation elements -- including swivel, twist, and dive motions -- to break up compacted, tangled grains and reduce jamming. We also present an adaptive impedance control method, the Reactive Attractor Impedance Controller (RAIC), that adapts a motion trajectory to unexpected forces during loading in a manner that tracks a trajectory closely when loads are low, but avoids excessive loads when significant resistance is met. Our method is evaluated on four terrains using a robotic arm, demonstrating improved excavation performance across multiple metrics, including volume scooped, protective stop rate, and trajectory completion percentage.<|reference_end|>
|
arxiv
|
@article{franceschini2024autonomous,
title={Autonomous Excavation of Challenging Terrain using Oscillatory
Primitives and Adaptive Impedance Control},
author={Noah Franceschini, Pranay Thangeda, Melkior Ornik, Kris Hauser},
journal={arXiv preprint arXiv:2409.18273},
year={2024},
archivePrefix={arXiv},
eprint={2409.18273},
primaryClass={cs.RO}
}
|
franceschini2024autonomous
|
arxiv-662537
|
2409.18275
|
Transitioning Together: Collaborative Work in Adolescent Chronic Illness Management
|
<|reference_start|>Transitioning Together: Collaborative Work in Adolescent Chronic Illness Management: Adolescents with chronic illnesses need to learn self-management skills in preparation for the transition from pediatric to adult healthcare, which is associated with negative health outcomes for youth. However, few studies have explored how adolescents in a pre-transition stage practice self-management and collaborative management with their parents. Through interviews with 15 adolescents (aged 15-17), we found that adolescents managed mundane self-care tasks and experimented with lifestyle changes to be more independent, which sometimes conflicted with their parents' efforts to ensure their safety. Adolescents and their parents also performed shared activities that provided adolescents with the opportunity to learn and practice self-management skills. Based on our findings, we discuss considerations for technology design to facilitate transition and promote parent-adolescent collaboration in light of these tensions.<|reference_end|>
|
arxiv
|
@article{zehrung2024transitioning,
title={Transitioning Together: Collaborative Work in Adolescent Chronic Illness
Management},
author={Rachael Zehrung, Madhu Reddy, Yunan Chen},
journal={arXiv preprint arXiv:2409.18275},
year={2024},
archivePrefix={arXiv},
eprint={2409.18275},
primaryClass={cs.HC}
}
|
zehrung2024transitioning
|
arxiv-662538
|
2409.18276
|
Galerkin Method of Regularized Stokeslets for Procedural Fluid Flow with Control Curves
|
<|reference_start|>Galerkin Method of Regularized Stokeslets for Procedural Fluid Flow with Control Curves: We present a new procedural incompressible velocity field authoring tool, which lets users design a volumetric flow by directly specifying velocity along control curves. Our method combines the Method of Regularized Stokeslets with Galerkin discretization. Based on the highly viscous Stokes flow assumption, we find the force along a given set of curves that satisfies the velocity constraints along them. We can then evaluate the velocity anywhere inside the surrounding infinite 2D or 3D domain. We also show the extension of our method to control the angular velocity along control curves. Compared to a collocation discretization, our method is not very sensitive to the vertex sampling rate along control curves and only requires a small linear system solve.<|reference_end|>
|
arxiv
|
@article{sugimoto2024galerkin,
title={Galerkin Method of Regularized Stokeslets for Procedural Fluid Flow with
Control Curves},
author={Ryusuke Sugimoto, Jeff Lait, Christopher Batty, Toshiya Hachisuka},
journal={arXiv preprint arXiv:2409.18276},
year={2024},
doi={10.1145/3681758.3698019},
archivePrefix={arXiv},
eprint={2409.18276},
primaryClass={cs.GR cs.NA math.NA}
}
|
sugimoto2024galerkin
|
arxiv-662539
|
2409.18280
|
easylayout: an R package for interactive force-directed layouts within RStudio
|
<|reference_start|>easylayout: an R package for interactive force-directed layouts within RStudio: Motivation Network visualization is critical for effective communication in various fields of knowledge. Currently, a gap separates network manipulation from network visualization in programming environments. Users often export network data to be laid out in external interactive software, like Cytoscape and Gephi. We argue the current R package ecosystem lacks an interactive layout engine well integrated with common data analysis workflows. Results We present easylayout, an R package that bridges network manipulation and visualization by leveraging interactive force simulations within the IDE itself (e.g., RStudio, VSCode). It is not yet another visualization library, but instead aims to interconnect existing libraries and streamline their usage into the R ecosystem. easylayout takes an igraph object and serializes it into a web application integrated with the IDE's interface through a Shiny server. The app lays out the network by simulating attraction and repulsion forces. Simulation parameters can be adjusted in real-time. An editing mode allows moving and rotating nodes. The implementation aims for performance, so that even lower-end devices are able to work with relatively large networks. Once the user finishes tweaking the layout, it is sent back to the R session to be plotted through popular libraries like ggraph, igraph or even the base package itself. The current implementation focuses on the R ecosystem, but using web technologies makes it easily portable to similar environments, like Python/Jupyter Notebooks. We expect this tool to reduce the time spent searching for suitable network layouts, ultimately allowing researchers to generate more compelling figures. Availability and implementation easylayout is freely available under an MIT license on GitHub (https://github.com/dalmolingroup/easylayout). The package is implemented in R/Shiny and JavaScript/Svelte.<|reference_end|>
|
arxiv
|
@article{imparato2024easylayout:,
title={easylayout: an R package for interactive force-directed layouts within
RStudio},
author={Danilo Oliveira Imparato, Jo~ao Vitor F. Cavalcante, Rodrigo J. S.
Dalmolin},
journal={arXiv preprint arXiv:2409.18280},
year={2024},
archivePrefix={arXiv},
eprint={2409.18280},
primaryClass={stat.OT cs.SI q-bio.MN}
}
|
imparato2024easylayout:
|
arxiv-662540
|
2409.18281
|
Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach
|
<|reference_start|>Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach: This paper analyzes a downlink C-NOMA scenario where a base station (BS) is deployed to serve a pair of users equipped with movable antenna (MA) technology. The user with better channel conditions with the BS will be able to transmit the signal to the other user providing an extra transmission resource and enhancing performance. Both users are equipped with a receiving MA each and a transmitting MA for the relaying user. In this regard, we formulate an optimization problem with the objective of maximizing the achievable sum rate by jointly determining the beamforming vector at the BS, the transmit power at the device and the positions of the MAs while meeting the quality of service (QoS) constraints. Due to the non-convex structure of the formulated problem and the randomness in the channels we adopt a deep deterministic policy gradient (DDPG) approach, a reinforcement learning (RL) algorithm capable of dealing with continuous state and action spaces. Numerical results demonstrate the superiority of the presented model compared to the other benchmark schemes showing gains reaching 45% compared to the NOMA enabled MA scheme and 60% compared to C-NOMA model with fixed antennas. The solution approach showed 93% accuracy compared to the optimal solution.<|reference_end|>
|
arxiv
|
@article{amhaz2024optimizing,
title={Optimizing Downlink C-NOMA Transmission with Movable Antennas: A
DDPG-based Approach},
author={Ali Amhaz, Mohamed Elhattab, Chadi Assi and Sanaa Sharafeddine},
journal={arXiv preprint arXiv:2409.18281},
year={2024},
archivePrefix={arXiv},
eprint={2409.18281},
primaryClass={eess.SY cs.SY}
}
|
amhaz2024optimizing
|
arxiv-662541
|
2409.18282
|
Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study
|
<|reference_start|>Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study: Beta-amyloid positron emission tomography (A$\beta$-PET) imaging has become a critical tool in Alzheimer's disease (AD) research and diagnosis, providing insights into the pathological accumulation of amyloid plaques, one of the hallmarks of AD. However, the high cost, limited availability, and exposure to radioactivity restrict the widespread use of A$\beta$-PET imaging, leading to a scarcity of comprehensive datasets. Previous studies have suggested that structural magnetic resonance imaging (MRI), which is more readily available, may serve as a viable alternative for synthesizing A$\beta$-PET images. In this study, we propose an approach to utilize 3D diffusion models to synthesize A$\beta$-PET images from T1-weighted MRI scans, aiming to overcome the limitations associated with direct PET imaging. Our method generates high-quality A$\beta$-PET images for cognitive normal cases, although it is less effective for mild cognitive impairment (MCI) patients due to the variability in A$\beta$ deposition patterns among subjects. Our preliminary results suggest that incorporating additional data, such as a larger sample of MCI cases and multi-modality information including clinical and demographic details, cognitive and functional assessments, and longitudinal data, may be necessary to improve A$\beta$-PET image synthesis for MCI patients.<|reference_end|>
|
arxiv
|
@article{lyu2024synthesizing,
title={Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A
Preliminary Study},
author={Qing Lyu, Jin Young Kim, Jeongchul Kim, and Christopher T Whitlow},
journal={arXiv preprint arXiv:2409.18282},
year={2024},
archivePrefix={arXiv},
eprint={2409.18282},
primaryClass={eess.IV cs.CV physics.med-ph}
}
|
lyu2024synthesizing
|
arxiv-662542
|
2409.18286
|
Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
|
<|reference_start|>Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing: This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies. We highlight their effectiveness and limitations in object detection within various transportation scenarios. The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions. Building on this, we proposed empirical analysis for testing MLLMs on three real-world transportation problems that include object detection tasks namely, road safety attributes extraction, safety-critical event detection, and visual reasoning of thermal images. Our findings provide a detailed assessment of MLLM performance, uncovering both strengths and areas for improvement. Finally, we discuss practical limitations and challenges of MLLMs in enhancing object detection in transportation, thereby offering a roadmap for future research and development in this critical area.<|reference_end|>
|
arxiv
|
@article{ashqar2024advancing,
title={Advancing Object Detection in Transportation with Multimodal Large
Language Models (MLLMs): A Comprehensive Review and Empirical Testing},
author={Huthaifa I. Ashqar, Ahmed Jaber, Taqwa I. Alhadidi, and Mohammed
Elhenawy},
journal={arXiv preprint arXiv:2409.18286},
year={2024},
archivePrefix={arXiv},
eprint={2409.18286},
primaryClass={cs.CV cs.AI cs.CL}
}
|
ashqar2024advancing
|
arxiv-662543
|
2409.18289
|
Criticality and Safety Margins for Reinforcement Learning
|
<|reference_start|>Criticality and Safety Margins for Reinforcement Learning: State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during deployment, where it might be advantageous to call out to a human overseer for help. Efforts to gauge the criticality of different points in time have been developed, but their accuracy is not well established due to a lack of ground truth, and they are not designed to be easily interpretable by end users. Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users. We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions. We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality. Safety margins make these interpretable, when defined as the number of random actions for which performance loss will not exceed some tolerance with high confidence. We demonstrate this approach in several environment-agent combinations; for an A3C agent in an Atari Beamrider environment, the lowest 5% of safety margins contain 47% of agent losses; i.e., supervising only 5% of decisions could potentially prevent roughly half of an agent's errors. This criticality framework measures the potential impacts of bad decisions, even before those decisions are made, allowing for more effective debugging and oversight of autonomous agents.<|reference_end|>
|
arxiv
|
@article{grushin2024criticality,
title={Criticality and Safety Margins for Reinforcement Learning},
author={Alexander Grushin, Walt Woods, Alvaro Velasquez, Simon Khan},
journal={arXiv preprint arXiv:2409.18289},
year={2024},
archivePrefix={arXiv},
eprint={2409.18289},
primaryClass={cs.LG cs.AI cs.SY eess.SY}
}
|
grushin2024criticality
|
arxiv-662544
|
2409.18290
|
Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs Clinical Teams
|
<|reference_start|>Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs Clinical Teams: In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden on healthcare workflows, consuming considerable time for clinical care teams. To address this, we introduce RadOnc-GPT, a specialized Large Language Model (LLM) powered by GPT-4 that has been designed with a focus on radiotherapeutic treatment of prostate cancer with advanced prompt engineering, and specifically designed to assist in generating responses. We integrated RadOnc-GPT with patient electronic health records (EHR) from both the hospital-wide EHR database and an internal, radiation-oncology-specific database. RadOnc-GPT was evaluated on 158 previously recorded in-basket message interactions. Quantitative natural language processing (NLP) analysis and two grading studies with clinicians and nurses were used to assess RadOnc-GPT's responses. Our findings indicate that RadOnc-GPT slightly outperformed the clinical care team in "Clarity" and "Empathy," while achieving comparable scores in "Completeness" and "Correctness." RadOnc-GPT is estimated to save 5.2 minutes per message for nurses and 2.4 minutes for clinicians, from reading the inquiry to sending the response. Employing RadOnc-GPT for in-basket message draft generation has the potential to alleviate the workload of clinical care teams and reduce healthcare costs by producing high-quality, timely responses.<|reference_end|>
|
arxiv
|
@article{hao2024retrospective,
title={Retrospective Comparative Analysis of Prostate Cancer In-Basket
Messages: Responses from Closed-Domain LLM vs. Clinical Teams},
author={Yuexing Hao, Jason M. Holmes, Jared Hobson, Alexandra Bennett, Daniel
K. Ebner, David M. Routman, Satomi Shiraishi, Samir H. Patel, Nathan Y. Yu,
Chris L. Hallemeier, Brooke E. Ball, Mark R. Waddle, and Wei Liu},
journal={arXiv preprint arXiv:2409.18290},
year={2024},
archivePrefix={arXiv},
eprint={2409.18290},
primaryClass={cs.AI cs.CY}
}
|
hao2024retrospective
|
arxiv-662545
|
2409.18291
|
Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control
|
<|reference_start|>Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control: This paper is directed towards the food crystal quality control area for manufacturing, focusing on efficiently predicting food crystal counts and size distributions. Previously, manufacturers used the manual counting method on microscopic images of food liquid products, which requires substantial human effort and suffers from inconsistency issues. Food crystal segmentation is a challenging problem due to the diverse shapes of crystals and their surrounding hard mimics. To address this challenge, we propose an efficient instance segmentation method based on object detection. Experimental results show that the predicted crystal counting accuracy of our method is comparable with existing segmentation methods, while being five times faster. Based on our experiments, we also define objective criteria for separating hard mimics and food crystals, which could benefit manual annotation tasks on similar dataset.<|reference_end|>
|
arxiv
|
@article{ji2024efficient,
title={Efficient Microscopic Image Instance Segmentation for Food Crystal
Quality Control},
author={Xiaoyu Ji, Jan P Allebach, Ali Shakouri, Fengqing Zhu},
journal={arXiv preprint arXiv:2409.18291},
year={2024},
archivePrefix={arXiv},
eprint={2409.18291},
primaryClass={cs.CV}
}
|
ji2024efficient
|
arxiv-662546
|
2409.18293
|
Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs
|
<|reference_start|>Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs: We present an autonomous aerial system for safe and efficient through-the-canopy fruit counting. Aerial robot applications in large-scale orchards face significant challenges due to the complexity of fine-tuning flight paths based on orchard layouts, canopy density, and plant variability. Through-the-canopy navigation is crucial for minimizing occlusion by leaves and branches but is more challenging due to the complex and dense environment compared to traditional over-the-canopy flights. Our system addresses these challenges by integrating: i) a high-fidelity simulation framework for optimizing flight trajectories, ii) a low-cost autonomy stack for canopy-level navigation and data collection, and iii) a robust workflow for fruit detection and counting using RGB images. We validate our approach through fruit counting with canopy-level aerial images and by demonstrating the autonomous navigation capabilities of our experimental vehicle.<|reference_end|>
|
arxiv
|
@article{yang2024towards,
title={Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting
with UAVs},
author={Teaya Yang, Roman Ibrahimov, Mark W. Mueller},
journal={arXiv preprint arXiv:2409.18293},
year={2024},
archivePrefix={arXiv},
eprint={2409.18293},
primaryClass={cs.RO}
}
|
yang2024towards
|
arxiv-662547
|
2409.18295
|
Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications
|
<|reference_start|>Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications: Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction or transformation techniques to compress the data. Previous approaches use local information from a single target field when predicting target data points, limiting their potential to achieve higher compression ratios. In this paper, we identified significant cross-field correlations within scientific datasets. We propose a novel hybrid prediction model that utilizes CNN to extract cross-field information and combine it with existing local field information. Our solution enhances the prediction accuracy of lossy compressors, leading to improved compression ratios without compromising data quality. We evaluate our solution on three scientific datasets, demonstrating its ability to improve compression ratios by up to 25% under specific error bounds. Additionally, our solution preserves more data details and reduces artifacts compared to baseline approaches.<|reference_end|>
|
arxiv
|
@article{liu2024enhancing,
title={Enhancing Lossy Compression Through Cross-Field Information for
Scientific Applications},
author={Youyuan Liu, Wenqi Jia, Taolue Yang, Miao Yin, Sian Jin},
journal={arXiv preprint arXiv:2409.18295},
year={2024},
archivePrefix={arXiv},
eprint={2409.18295},
primaryClass={cs.LG cs.AI cs.DC}
}
|
liu2024enhancing
|
arxiv-662548
|
2409.18297
|
Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation
|
<|reference_start|>Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation: We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations of flattening and folding 44 unique garments across 8 categories, Flat'n'Fold surpasses prior datasets in size, scope, and diversity. Our dataset uniquely captures the entire manipulation process from crumpled to folded states, providing synchronized multi-view RGB-D images, point clouds, and action data, including hand or gripper positions and rotations. We quantify the dataset's diversity and complexity compared to existing benchmarks and show that our dataset features natural and diverse manipulations of real-world demonstrations of human and robot demonstrations in terms of visual and action information. To showcase Flat'n'Fold's utility, we establish new benchmarks for grasping point prediction and subtask decomposition. Our evaluation of state-of-the-art models on these tasks reveals significant room for improvement. This underscores Flat'n'Fold's potential to drive advances in robotic perception and manipulation of deformable objects. Our dataset can be downloaded at https://cvas-ug.github.io/flat-n-fold<|reference_end|>
|
arxiv
|
@article{zhuang2024flat'n'fold:,
title={Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and
Manipulation},
author={Lipeng Zhuang, Shiyu Fan, Yingdong Ru, Florent Audonnet, Paul
Henderson, Gerardo Aragon-Camarasa},
journal={arXiv preprint arXiv:2409.18297},
year={2024},
archivePrefix={arXiv},
eprint={2409.18297},
primaryClass={cs.RO cs.AI cs.CV}
}
|
zhuang2024flat'n'fold:
|
arxiv-662549
|
2409.18298
|
Causality-based Subject and Task Fingerprints using fMRI Time-series Data
|
<|reference_start|>Causality-based Subject and Task Fingerprints using fMRI Time-series Data: Recently, there has been a revived interest in system neuroscience causation models due to their unique capability to unravel complex relationships in multi-scale brain networks. In this paper, our goal is to verify the feasibility and effectiveness of using a causality-based approach for fMRI fingerprinting. Specifically, we propose an innovative method that utilizes the causal dynamics activities of the brain to identify the unique cognitive patterns of individuals (e.g., subject fingerprint) and fMRI tasks (e.g., task fingerprint). The key novelty of our approach stems from the development of a two-timescale linear state-space model to extract 'spatio-temporal' (aka causal) signatures from an individual's fMRI time series data. To the best of our knowledge, we pioneer and subsequently quantify, in this paper, the concept of 'causal fingerprint.' Our method is well-separated from other fingerprint studies as we quantify fingerprints from a cause-and-effect perspective, which are then incorporated with a modal decomposition and projection method to perform subject identification and a GNN-based (Graph Neural Network) model to perform task identification. Finally, we show that the experimental results and comparisons with non-causality-based methods demonstrate the effectiveness of the proposed methods. We visualize the obtained causal signatures and discuss their biological relevance in light of the existing understanding of brain functionalities. Collectively, our work paves the way for further studies on causal fingerprints with potential applications in both healthy controls and neurodegenerative diseases.<|reference_end|>
|
arxiv
|
@article{song2024causality-based,
title={Causality-based Subject and Task Fingerprints using fMRI Time-series
Data},
author={Dachuan Song, Li Shen, Duy Duong-Tran, Xuan Wang},
journal={arXiv preprint arXiv:2409.18298},
year={2024},
archivePrefix={arXiv},
eprint={2409.18298},
primaryClass={cs.LG cs.SY eess.SY}
}
|
song2024causality-based
|
arxiv-662550
|
2409.18300
|
SOAR: Self-supervision Optimized UAV Action Recognition with Efficient Object-Aware Pretraining
|
<|reference_start|>SOAR: Self-supervision Optimized UAV Action Recognition with Efficient Object-Aware Pretraining: We introduce SOAR, a novel Self-supervised pretraining algorithm for aerial footage captured by Unmanned Aerial Vehicles (UAVs). We incorporate human object knowledge throughout the pretraining process to enhance UAV video pretraining efficiency and downstream action recognition performance. This is in contrast to prior works that primarily incorporate object information during the fine-tuning stage. Specifically, we first propose a novel object-aware masking strategy designed to retain the visibility of certain patches related to objects throughout the pretraining phase. Second, we introduce an object-aware loss function that utilizes object information to adjust the reconstruction loss, preventing bias towards less informative background patches. In practice, SOAR with a vanilla ViT backbone, outperforms best UAV action recognition models, recording a 9.7% and 21.4% boost in top-1 accuracy on the NEC-Drone and UAV-Human datasets, while delivering an inference speed of 18.7ms per video, making it 2x to 5x faster. Additionally, SOAR obtains comparable accuracy to prior self-supervised learning (SSL) methods while requiring 87.5% less pretraining time and 25% less memory usage<|reference_end|>
|
arxiv
|
@article{xian2024soar:,
title={SOAR: Self-supervision Optimized UAV Action Recognition with Efficient
Object-Aware Pretraining},
author={Ruiqi Xian, Xiyang Wu, Tianrui Guan, Xijun Wang, Boqing Gong and
Dinesh Manocha},
journal={arXiv preprint arXiv:2409.18300},
year={2024},
archivePrefix={arXiv},
eprint={2409.18300},
primaryClass={cs.CV cs.AI cs.LG cs.RO}
}
|
xian2024soar:
|
arxiv-662551
|
2409.18301
|
Harnessing Wavelet Transformations for Generalizable Deepfake Forgery Detection
|
<|reference_start|>Harnessing Wavelet Transformations for Generalizable Deepfake Forgery Detection: The evolution of digital image manipulation, particularly with the advancement of deep generative models, significantly challenges existing deepfake detection methods, especially when the origin of the deepfake is obscure. To tackle the increasing complexity of these forgeries, we propose \textbf{Wavelet-CLIP}, a deepfake detection framework that integrates wavelet transforms with features derived from the ViT-L/14 architecture, pre-trained in the CLIP fashion. Wavelet-CLIP utilizes Wavelet Transforms to deeply analyze both spatial and frequency features from images, thus enhancing the model's capability to detect sophisticated deepfakes. To verify the effectiveness of our approach, we conducted extensive evaluations against existing state-of-the-art methods for cross-dataset generalization and detection of unseen images generated by standard diffusion models. Our method showcases outstanding performance, achieving an average AUC of 0.749 for cross-data generalization and 0.893 for robustness against unseen deepfakes, outperforming all compared methods. The code can be reproduced from the repo: \url{https://github.com/lalithbharadwajbaru/Wavelet-CLIP}<|reference_end|>
|
arxiv
|
@article{baru2024harnessing,
title={Harnessing Wavelet Transformations for Generalizable Deepfake Forgery
Detection},
author={Lalith Bharadwaj Baru, Shilhora Akshay Patel, Rohit Boddeda},
journal={arXiv preprint arXiv:2409.18301},
year={2024},
archivePrefix={arXiv},
eprint={2409.18301},
primaryClass={cs.CV cs.AI cs.LG}
}
|
baru2024harnessing
|
arxiv-662552
|
2409.18303
|
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging
|
<|reference_start|>Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging: Introduction: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction to obtain high-quality metabolic maps. Methods: Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm$^3$ isotropic resolution with acquisition times between 4:11-9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to conventional iterative Total Generalized Variation reconstruction using image and spectral quality metrics. Results: Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial-spectral quality and metabolite quantification with 12%-45% (P<0.05) higher signal-to-noise and 8%-50% (P<0.05) smaller Cramer-Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Conclusion: Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications.<|reference_end|>
|
arxiv
|
@article{weiser2024deep-er:,
title={Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution
neurometabolic imaging},
author={Paul Weiser, Georg Langs, Wolfgang Bogner, Stanislav Motyka, Bernhard
Strasser, Polina Golland, Nalini Singh, Jorg Dietrich, Erik Uhlmann, Tracy
Batchelor, Daniel Cahill, Malte Hoffmann, Antoine Klauser, Ovidiu C.
Andronesi},
journal={arXiv preprint arXiv:2409.18303},
year={2024},
archivePrefix={arXiv},
eprint={2409.18303},
primaryClass={eess.IV cs.LG}
}
|
weiser2024deep-er:
|
arxiv-662553
|
2409.18304
|
Multi-platoon car-following models with flexible platoon sizes and communication levels
|
<|reference_start|>Multi-platoon car-following models with flexible platoon sizes and communication levels: In this paper, we extend a single platoon car-following (CF) model to some multi-platoon CF models for connected and autonomous vehicles (CAVs) with flexible platoon size and communication level. Specifically, we consider forward and backward communication methods between platoons with delays. Some general results of linear stability are mathematically proven, and numerical simulations are performed to illustrate the effects of platoon sizes and communication levels, as well as to demonstrate the potential for stabilizing human-driven vehicles (HDVs) in mixed traffic conditions. The simulation results are consistent with theoretical analysis, and demonstrate that in the ring road scenario, CAV platoons can stabilize certain percentage of HDVs. This paper can provide suggestions for the design of communication system of autonomous vehicles (AVs), and management of mixed traffic flow of CAVs and HDVs.<|reference_end|>
|
arxiv
|
@article{hui2024multi-platoon,
title={Multi-platoon car-following models with flexible platoon sizes and
communication levels},
author={Shouwei Hui, Michael Zhang},
journal={arXiv preprint arXiv:2409.18304},
year={2024},
archivePrefix={arXiv},
eprint={2409.18304},
primaryClass={eess.SY cs.SY}
}
|
hui2024multi-platoon
|
arxiv-662554
|
2409.18307
|
On the Strong Converse Exponent of the Classical Soft Covering
|
<|reference_start|>On the Strong Converse Exponent of the Classical Soft Covering: In this paper, we provide a lower and an upper bound for the strong converse exponent of the soft covering problem in the classical setting. This exponent characterizes the slowest achievable convergence speed of the total variation to one when a code with a rate below mutual information is applied to a discrete memoryless channel for synthesizing a product output distribution. We employ a type-based approach and additionally propose an equivalent form of our upper bound using the R\'enyi mutual information. Future works include tightening these two bounds to determine the exact bound of the strong converse exponent.<|reference_end|>
|
arxiv
|
@article{he2024on,
title={On the Strong Converse Exponent of the Classical Soft Covering},
author={Xingyi He and S. Sandeep Pradhan},
journal={arXiv preprint arXiv:2409.18307},
year={2024},
archivePrefix={arXiv},
eprint={2409.18307},
primaryClass={cs.IT math.IT}
}
|
he2024on
|
arxiv-662555
|
2409.18313
|
Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation
|
<|reference_start|>Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation: There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhouse of large-scale non-parametric knowledge, however existing techniques do not directly transfer to the embodied domain, which is multimodal, data is highly correlated, and perception requires abstraction. To address these challenges, we introduce Embodied-RAG, a framework that enhances the foundational model of an embodied agent with a non-parametric memory system capable of autonomously constructing hierarchical knowledge for both navigation and language generation. Embodied-RAG handles a full range of spatial and semantic resolutions across diverse environments and query types, whether for a specific object or a holistic description of ambiance. At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail. This hierarchical organization allows the system to efficiently generate context-sensitive outputs across different robotic platforms. We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 200 explanation and navigation queries across 19 environments, highlighting its promise for general-purpose non-parametric system for embodied agents.<|reference_end|>
|
arxiv
|
@article{xie2024embodied-rag:,
title={Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and
Generation},
author={Quanting Xie, So Yeon Min, Tianyi Zhang, Kedi Xu, Aarav Bajaj, Ruslan
Salakhutdinov, Matthew Johnson-Roberson, Yonatan Bisk},
journal={arXiv preprint arXiv:2409.18313},
year={2024},
archivePrefix={arXiv},
eprint={2409.18313},
primaryClass={cs.RO cs.AI cs.LG}
}
|
xie2024embodied-rag:
|
arxiv-662556
|
2409.18314
|
Realistic Evaluation of Model Merging for Compositional Generalization
|
<|reference_start|>Realistic Evaluation of Model Merging for Compositional Generalization: Merging has become a widespread way to cheaply combine individual models into a single model that inherits their capabilities and attains better performance. This popularity has spurred rapid development of many new merging methods, which are typically validated in disparate experimental settings and frequently differ in the assumptions made about model architecture, data availability, and computational budget. In this work, we characterize the relative merits of different merging methods by evaluating them in a shared experimental setting and precisely identifying the practical requirements of each method. Specifically, our setting focuses on using merging for compositional generalization of capabilities in image classification, image generation, and natural language processing. Additionally, we measure the computational costs of different merging methods as well as how they perform when scaling the number of models being merged. Taken together, our results clarify the state of the field of model merging and provide a comprehensive and rigorous experimental setup to test new methods.<|reference_end|>
|
arxiv
|
@article{tam2024realistic,
title={Realistic Evaluation of Model Merging for Compositional Generalization},
author={Derek Tam, Yash Kant, Brian Lester, Igor Gilitschenski, and Colin
Raffel},
journal={arXiv preprint arXiv:2409.18314},
year={2024},
archivePrefix={arXiv},
eprint={2409.18314},
primaryClass={cs.LG cs.CL cs.CV}
}
|
tam2024realistic
|
arxiv-662557
|
2409.18316
|
Towards the Mitigation of Confirmation Bias in Semi-supervised Learning: a Debiased Training Perspective
|
<|reference_start|>Towards the Mitigation of Confirmation Bias in Semi-supervised Learning: a Debiased Training Perspective: Semi-supervised learning (SSL) commonly exhibits confirmation bias, where models disproportionately favor certain classes, leading to errors in predicted pseudo labels that accumulate under a self-training paradigm. Unlike supervised settings, which benefit from a rich, static data distribution, SSL inherently lacks mechanisms to correct this self-reinforced bias, necessitating debiased interventions at each training step. Although the generation of debiased pseudo labels has been extensively studied, their effective utilization remains underexplored. Our analysis indicates that data from biased classes should have a reduced influence on parameter updates, while more attention should be given to underrepresented classes. To address these challenges, we introduce TaMatch, a unified framework for debiased training in SSL. TaMatch employs a scaling ratio derived from both a prior target distribution and the model's learning status to estimate and correct bias at each training step. This ratio adjusts the raw predictions on unlabeled data to produce debiased pseudo labels. In the utilization phase, these labels are differently weighted according to their predicted class, enhancing training equity and minimizing class bias. Additionally, TaMatch dynamically adjust the target distribution in response to the model's learning progress, facilitating robust handling of practical scenarios where the prior distribution is unknown. Empirical evaluations show that TaMatch significantly outperforms existing state-of-the-art methods across a range of challenging image classification tasks, highlighting the critical importance of both the debiased generation and utilization of pseudo labels in SSL.<|reference_end|>
|
arxiv
|
@article{wang2024towards,
title={Towards the Mitigation of Confirmation Bias in Semi-supervised Learning:
a Debiased Training Perspective},
author={Yu Wang, Yuxuan Yin, Peng Li},
journal={arXiv preprint arXiv:2409.18316},
year={2024},
archivePrefix={arXiv},
eprint={2409.18316},
primaryClass={cs.LG stat.ML}
}
|
wang2024towards
|
arxiv-662558
|
2409.18317
|
Survey of Moving Target Defense in Power Grids: Design Principles, Tradeoffs, and Future Directions
|
<|reference_start|>Survey of Moving Target Defense in Power Grids: Design Principles, Tradeoffs, and Future Directions: Moving target defense (MTD) in power grids is an emerging defense technique that has gained prominence in the recent past. It aims to solve the long-standing problem of securing the power grid against stealthy attacks. The key idea behind MTD is to introduce periodic/event-triggered controlled changes to the power grid's SCADA network/physical plant, thereby invalidating the knowledge attackers use for crafting stealthy attacks. In this paper, we provide a comprehensive overview of this topic and classify the different ways in which MTD is implemented in power grids. We further introduce the guiding principles behind the design of MTD, key performance metrics, and the associated trade-offs in MTD and identify the future development of MTD for power grid security.<|reference_end|>
|
arxiv
|
@article{lakshminarayana2024survey,
title={Survey of Moving Target Defense in Power Grids: Design Principles,
Tradeoffs, and Future Directions},
author={Subhash Lakshminarayana, Yexiang Chen, Charalambos Konstantinou,
Daisuke Mashima, Anurag K. Srivastava},
journal={arXiv preprint arXiv:2409.18317},
year={2024},
archivePrefix={arXiv},
eprint={2409.18317},
primaryClass={eess.SY cs.SY}
}
|
lakshminarayana2024survey
|
arxiv-662559
|
2409.18318
|
Modelling cooperating failure-resilient Processes
|
<|reference_start|>Modelling cooperating failure-resilient Processes: Cycloids are particular Petri nets for modelling processes of actions or events. They belong to the fundaments of Petri's general systems theory and have very different interpretations, ranging from Einstein's relativity theory and elementary information processing gates to the modelling of interacting sequential processes. The subclass of regular cycloids describes cooperating sequential processes. Such cycloids are extended to cover failure resilience.<|reference_end|>
|
arxiv
|
@article{valk2024modelling,
title={Modelling cooperating failure-resilient Processes},
author={R"udiger Valk},
journal={arXiv preprint arXiv:2409.18318},
year={2024},
archivePrefix={arXiv},
eprint={2409.18318},
primaryClass={cs.DC}
}
|
valk2024modelling
|
arxiv-662560
|
2409.18319
|
Cross-Institutional Structured Radiology Reporting for Lung Cancer Screening Using a Dynamic Template-Constrained Large Language Model
|
<|reference_start|>Cross-Institutional Structured Radiology Reporting for Lung Cancer Screening Using a Dynamic Template-Constrained Large Language Model: Structured radiology reporting is advantageous for optimizing clinical workflows and patient outcomes. Current LLMs in creating structured reports face the challenges of formatting errors, content hallucinations, and privacy leakage concerns when uploaded to external servers. We aim to develop an enhanced open-source LLM for creating structured and standardized LCS reports from free-text descriptions. After institutional IRB approvals, 5,442 de-identified LCS reports from two institutions were retrospectively analyzed. 500 reports were randomly selected from the two institutions evenly and then manually labeled for evaluation. Two radiologists from the two institutions developed a standardized template including 29 features for lung nodule reporting. We proposed template-constrained decoding to enhance state-of-the-art open-source LLMs, including LLAMA, Qwen, and Mistral. The LLM performance was extensively evaluated in terms of F1 score, confidence interval, McNemar test, and z-test. Based on the structured reports created from the large-scale dataset, a nodule-level retrieval system was prototyped and an automatic statistical analysis was performed. Our software, vLLM-structure, is publicly available for local deployment with enhanced LLMs. Our template-constrained decoding approach consistently enhanced the LLM performance on multi-institutional datasets, with neither formatting errors nor content hallucinations. Our method improved the best open-source LLAMA-3.1 405B by up to 10.42%, and outperformed GPT-4o by 17.19%. A novel nodule retrieval system was successfully prototyped and demonstrated on a large-scale multimodal database using our enhanced LLM technologies. The automatically derived statistical distributions were closely consistent with the prior findings in terms of nodule type, location, size, status, and Lung-RADS.<|reference_end|>
|
arxiv
|
@article{niu2024development,
title={Development and Validation of a Dynamic-Template-Constrained Large
Language Model for Generating Fully-Structured Radiology Reports},
author={Chuang Niu, Parisa Kaviani, Qing Lyu, Mannudeep K. Kalra, Christopher
T. Whitlow, Ge Wang},
journal={arXiv preprint arXiv:2409.18319},
year={2024},
archivePrefix={arXiv},
eprint={2409.18319},
primaryClass={cs.AI cs.CL}
}
|
niu2024development
|
arxiv-662561
|
2409.18321
|
Local Prediction-Powered Inference
|
<|reference_start|>Local Prediction-Powered Inference: To infer a function value on a specific point $x$, it is essential to assign higher weights to the points closer to $x$, which is called local polynomial / multivariable regression. In many practical cases, a limited sample size may ruin this method, but such conditions can be improved by the Prediction-Powered Inference (PPI) technique. This paper introduced a specific algorithm for local multivariable regression using PPI, which can significantly reduce the variance of estimations without enlarge the error. The confidence intervals, bias correction, and coverage probabilities are analyzed and proved the correctness and superiority of our algorithm. Numerical simulation and real-data experiments are applied and show these conclusions. Another contribution compared to PPI is the theoretical computation efficiency and explainability by taking into account the dependency of the dependent variable.<|reference_end|>
|
arxiv
|
@article{gu2024local,
title={Local Prediction-Powered Inference},
author={Yanwu Gu and Dong Xia},
journal={arXiv preprint arXiv:2409.18321},
year={2024},
archivePrefix={arXiv},
eprint={2409.18321},
primaryClass={stat.ML cs.LG stat.ME}
}
|
gu2024local
|
arxiv-662562
|
2409.18324
|
Input-Dependent Power Usage in GPUs
|
<|reference_start|>Input-Dependent Power Usage in GPUs: GPUs are known to be power-hungry, and due to the boom in artificial intelligence, they are currently the major contributors to the high power demands of upcoming datacenters. Most GPU usage in these popular workloads consist of large general matrix-matrix multiplications (GEMMs), which have therefore been optimized to achieve high utilization of hardware resources. In this work, we show that modifying the input data to GEMMs, while maintaining the matrix shapes and sizes can notably change the power consumption of these kernels. We experiment with four kinds of input variations: value distribution, bit similarity, placement, and sparsity, across different data types. Our findings indicate that these variations can change the GPU power usage during GEMM by almost 40%. We hypothesize that input-dependent power usage variations occur due to changes in the number of bit flips in the GPUs. We propose leveraging this property through compiler and scheduler optimizations to manage power and reduce energy consumption.<|reference_end|>
|
arxiv
|
@article{gregersen2024input-dependent,
title={Input-Dependent Power Usage in GPUs},
author={Theo Gregersen, Pratyush Patel, Esha Choukse},
journal={arXiv preprint arXiv:2409.18324},
year={2024},
archivePrefix={arXiv},
eprint={2409.18324},
primaryClass={cs.AI}
}
|
gregersen2024input-dependent
|
arxiv-662563
|
2409.18326
|
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks
|
<|reference_start|>Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks: With the increasing adoption of metal additive manufacturing (AM), researchers and practitioners are turning to data-driven approaches to optimise printing conditions. Cross-sectional images of melt tracks provide valuable information for tuning process parameters, developing parameter scaling data, and identifying defects. Here we present an image segmentation neural network that automatically identifies and measures melt track dimensions from a cross-section image. We use a U-Net architecture to train on a data set of 62 pre-labelled images obtained from different labs, machines, and materials coupled with image augmentation. When neural network hyperparameters such as batch size and learning rate are properly tuned, the learned model shows an accuracy for classification of over 99% and an F1 score over 90%. The neural network exhibits robustness when tested on images captured by various users, printed on different machines, and acquired using different microscopes. A post-processing module extracts the height and width of the melt pool, and the wetting angles. We discuss opportunities to improve model performance and avenues for transfer learning, such as extension to other AM processes such as directed energy deposition.<|reference_end|>
|
arxiv
|
@article{shah2024automated,
title={Automated Segmentation and Analysis of Microscopy Images of Laser Powder
Bed Fusion Melt Tracks},
author={Aagam Shah, Reimar Weissbach, David A. Griggs, A. John Hart, Elif
Ertekin, Sameh Tawfick},
journal={arXiv preprint arXiv:2409.18326},
year={2024},
archivePrefix={arXiv},
eprint={2409.18326},
primaryClass={cs.CV physics.app-ph}
}
|
shah2024automated
|
arxiv-662564
|
2409.18327
|
Accelerated gradient descent for high frequency Model Predictive Control
|
<|reference_start|>Accelerated gradient descent for high frequency Model Predictive Control: The recent promises of Model Predictive Control in robotics have motivated the development of tailored second-order methods to solve optimal control problems efficiently. While those methods benefit from strong convergence properties, tailored efficient implementations are challenging to derive. In this work, we study the potential effectiveness of first-order methods and show on a torque controlled manipulator that they can equal the performances of second-order methods.<|reference_end|>
|
arxiv
|
@article{zhang2024accelerated,
title={Accelerated gradient descent for high frequency Model Predictive Control},
author={Jianghan Zhang, Armand Jordana and Ludovic Righetti},
journal={arXiv preprint arXiv:2409.18327},
year={2024},
archivePrefix={arXiv},
eprint={2409.18327},
primaryClass={cs.RO}
}
|
zhang2024accelerated
|
arxiv-662565
|
2409.18328
|
Quasi-Orthogonal Runge-Kutta Projection Methods
|
<|reference_start|>Quasi-Orthogonal Runge-Kutta Projection Methods: A wide range of physical phenomena exhibit auxiliary admissibility criteria, such as conservation of entropy or various energies, which arise implicitly under exact solution of their governing PDEs. However, standard temporal schemes, such as classical Runge-Kutta (RK) methods, do not enforce these constraints, leading to a loss of accuracy and stability. Projection is an efficient way to address this shortcoming by correcting the RK solution at the end of each time step. Here we introduce a novel projection method for explicit RK schemes, called a \textit{quasi-orthogonal} projection method. This method can be employed for systems containing a single (not necessarily convex) invariant functional, for dissipative systems, and for the systems containing multiple invariants. It works by projecting the orthogonal search direction(s) into the solution space spanned by the RK stage derivatives. With this approach linear invariants of the problem are preserved, the time step size remains fixed, additional computational cost is minimal, and these optimal search direction(s) preserve the order of accuracy of the base RK method. This presents significant advantages over existing projection methods. Numerical results demonstrate that these properties are observed in practice for a range of applications.<|reference_end|>
|
arxiv
|
@article{najafian2024quasi-orthogonal,
title={Quasi-Orthogonal Runge-Kutta Projection Methods},
author={Mohammad R. Najafian, Brian C. Vermeire},
journal={arXiv preprint arXiv:2409.18328},
year={2024},
archivePrefix={arXiv},
eprint={2409.18328},
primaryClass={math.NA cs.NA}
}
|
najafian2024quasi-orthogonal
|
arxiv-662566
|
2409.18329
|
Harnessing and modulating chaos to sample from neural generative models
|
<|reference_start|>Harnessing and modulating chaos to sample from neural generative models: Chaos is generic in strongly-coupled recurrent networks of model neurons, and thought to be an easily accessible dynamical regime in the brain. While neural chaos is typically seen as an impediment to robust computation, we show how such chaos might play a functional role in allowing the brain to learn and sample from generative models. We construct architectures that combine a classic model of neural chaos either with a canonical generative modeling architecture or with energy-based models of neural memory. We show that these architectures have appealing properties for sampling, including easy biologically-plausible control of sampling rates via overall gain modulation.<|reference_end|>
|
arxiv
|
@article{chaudhuri2024harnessing,
title={Harnessing and modulating chaos to sample from neural generative models},
author={Rishidev Chaudhuri, Vivek Handebagh},
journal={arXiv preprint arXiv:2409.18329},
year={2024},
archivePrefix={arXiv},
eprint={2409.18329},
primaryClass={q-bio.NC cs.NE nlin.CD}
}
|
chaudhuri2024harnessing
|
arxiv-662567
|
2409.18330
|
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
|
<|reference_start|>DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors: Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for expensive online learning. Despite strong generalization in some respects, agents are often remarkably brittle to minor visual variations in control-irrelevant factors such as the background or camera viewpoint. In this paper, we present theDeepMind Control Visual Benchmark (DMC-VB), a dataset collected in the DeepMind Control Suite to evaluate the robustness of offline RL agents for solving continuous control tasks from visual input in the presence of visual distractors. In contrast to prior works, our dataset (a) combines locomotion and navigation tasks of varying difficulties, (b) includes static and dynamic visual variations, (c) considers data generated by policies with different skill levels, (d) systematically returns pairs of state and pixel observation, (e) is an order of magnitude larger, and (f) includes tasks with hidden goals. Accompanying our dataset, we propose three benchmarks to evaluate representation learning methods for pretraining, and carry out experiments on several recently proposed methods. First, we find that pretrained representations do not help policy learning on DMC-VB, and we highlight a large representation gap between policies learned on pixel observations and on states. Second, we demonstrate when expert data is limited, policy learning can benefit from representations pretrained on (a) suboptimal data, and (b) tasks with stochastic hidden goals. Our dataset and benchmark code to train and evaluate agents are available at: https://github.com/google-deepmind/dmc_vision_benchmark.<|reference_end|>
|
arxiv
|
@article{ortiz2024dmc-vb:,
title={DMC-VB: A Benchmark for Representation Learning for Control with Visual
Distractors},
author={Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach, Swaroop Guntupalli,
Carter Wendelken, Ahmad Humayun, Guangyao Zhou, Sivaramakrishnan Swaminathan,
Miguel L'azaro-Gredilla, Kevin Murphy},
journal={arXiv preprint arXiv:2409.18330},
year={2024},
archivePrefix={arXiv},
eprint={2409.18330},
primaryClass={cs.LG}
}
|
ortiz2024dmc-vb:
|
arxiv-662568
|
2409.18332
|
Benchmarking Graph Conformal Prediction: Empirical Analysis, Scalability, and Theoretical Insights
|
<|reference_start|>Benchmarking Graph Conformal Prediction: Empirical Analysis, Scalability, and Theoretical Insights: Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has built upon this approach for conformal graph prediction. The nascent nature of these explorations has led to conflicting choices for implementations, baselines, and method evaluation. In this work, we analyze the design choices made in the literature and discuss the tradeoffs associated with existing methods. Building on the existing implementations for existing methods, we introduce techniques to scale existing methods to large-scale graph datasets without sacrificing performance. Our theoretical and empirical results justify our recommendations for future scholarship in graph conformal prediction.<|reference_end|>
|
arxiv
|
@article{maneriker2024benchmarking,
title={Benchmarking Graph Conformal Prediction: Empirical Analysis,
Scalability, and Theoretical Insights},
author={Pranav Maneriker, Aditya T. Vadlamani, Anutam Srinivasan, Yuntian He,
Ali Payani, Srinivasan Parthasarathy},
journal={arXiv preprint arXiv:2409.18332},
year={2024},
archivePrefix={arXiv},
eprint={2409.18332},
primaryClass={cs.LG stat.ML}
}
|
maneriker2024benchmarking
|
arxiv-662569
|
2409.18333
|
A Framework for Standardizing Similarity Measures in a Rapidly Evolving Field
|
<|reference_start|>A Framework for Standardizing Similarity Measures in a Rapidly Evolving Field: Similarity measures are fundamental tools for quantifying the alignment between artificial and biological systems. However, the diversity of similarity measures and their varied naming and implementation conventions makes it challenging to compare across studies. To facilitate comparisons and make explicit the implementation choices underlying a given code package, we have created and are continuing to develop a Python repository that benchmarks and standardizes similarity measures. The goal of creating a consistent naming convention that uniquely and efficiently specifies a similarity measure is not trivial as, for example, even commonly used methods like Centered Kernel Alignment (CKA) have at least 12 different variations, and this number will likely continue to grow as the field evolves. For this reason, we do not advocate for a fixed, definitive naming convention. The landscape of similarity measures and best practices will continue to change and so we see our current repository, which incorporates approximately 100 different similarity measures from 14 packages, as providing a useful tool at this snapshot in time. To accommodate the evolution of the field we present a framework for developing, validating, and refining naming conventions with the goal of uniquely and efficiently specifying similarity measures, ultimately making it easier for the community to make comparisons across studies.<|reference_end|>
|
arxiv
|
@article{cloos2024a,
title={A Framework for Standardizing Similarity Measures in a Rapidly Evolving
Field},
author={Nathan Cloos, Guangyu Robert Yang, Christopher J. Cueva},
journal={arXiv preprint arXiv:2409.18333},
year={2024},
archivePrefix={arXiv},
eprint={2409.18333},
primaryClass={q-bio.NC cs.LG}
}
|
cloos2024a
|
arxiv-662570
|
2409.18335
|
A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies
|
<|reference_start|>A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies: Despite recent advancements in AI and NLP, negotiation remains a difficult domain for AI agents. Traditional game theoretic approaches that have worked well for two-player zero-sum games struggle in the context of negotiation due to their inability to learn human-compatible strategies. On the other hand, approaches that only use human data tend to be domain-specific and lack the theoretical guarantees provided by strategies grounded in game theory. Motivated by the notion of fairness as a criterion for optimality in general sum games, we propose a negotiation framework called FDHC which incorporates fairness into both the reward design and search to learn human-compatible negotiation strategies. Our method includes a novel, RL+search technique called LGM-Zero which leverages a pre-trained language model to retrieve human-compatible offers from large action spaces. Our results show that our method is able to achieve more egalitarian negotiation outcomes and improve negotiation quality.<|reference_end|>
|
arxiv
|
@article{shea2024a,
title={A Fairness-Driven Method for Learning Human-Compatible Negotiation
Strategies},
author={Ryan Shea and Zhou Yu},
journal={arXiv preprint arXiv:2409.18335},
year={2024},
archivePrefix={arXiv},
eprint={2409.18335},
primaryClass={cs.AI cs.CL}
}
|
shea2024a
|
arxiv-662571
|
2409.18336
|
DeBaRA: Denoising-Based 3D Room Arrangement Generation
|
<|reference_start|>DeBaRA: Denoising-Based 3D Room Arrangement Generation: Generating realistic and diverse layouts of furnished indoor 3D scenes unlocks multiple interactive applications impacting a wide range of industries. The inherent complexity of object interactions, the limited amount of available data and the requirement to fulfill spatial constraints all make generative modeling for 3D scene synthesis and arrangement challenging. Current methods address these challenges autoregressively or by using off-the-shelf diffusion objectives by simultaneously predicting all attributes without 3D reasoning considerations. In this paper, we introduce DeBaRA, a score-based model specifically tailored for precise, controllable and flexible arrangement generation in a bounded environment. We argue that the most critical component of a scene synthesis system is to accurately establish the size and position of various objects within a restricted area. Based on this insight, we propose a lightweight conditional score-based model designed with 3D spatial awareness at its core. We demonstrate that by focusing on spatial attributes of objects, a single trained DeBaRA model can be leveraged at test time to perform several downstream applications such as scene synthesis, completion and re-arrangement. Further, we introduce a novel Self Score Evaluation procedure so it can be optimally employed alongside external LLM models. We evaluate our approach through extensive experiments and demonstrate significant improvement upon state-of-the-art approaches in a range of scenarios.<|reference_end|>
|
arxiv
|
@article{maillard2024debara:,
title={DeBaRA: Denoising-Based 3D Room Arrangement Generation},
author={L'eopold Maillard, Nicolas Sereyjol-Garros, Tom Durand, Maks
Ovsjanikov},
journal={arXiv preprint arXiv:2409.18336},
year={2024},
archivePrefix={arXiv},
eprint={2409.18336},
primaryClass={cs.CV}
}
|
maillard2024debara:
|
arxiv-662572
|
2409.18337
|
Photon Inhibition for Energy-Efficient Single-Photon Imaging
|
<|reference_start|>Photon Inhibition for Energy-Efficient Single-Photon Imaging: Single-photon cameras (SPCs) are emerging as sensors of choice for various challenging imaging applications. One class of SPCs based on the single-photon avalanche diode (SPAD) detects individual photons using an avalanche process; the raw photon data can then be processed to extract scene information under extremely low light, high dynamic range, and rapid motion. Yet, single-photon sensitivity in SPADs comes at a cost -- each photon detection consumes more energy than that of a CMOS camera. This avalanche power significantly limits sensor resolution and could restrict widespread adoption of SPAD-based SPCs. We propose a computational-imaging approach called \emph{photon inhibition} to address this challenge. Photon inhibition strategically allocates detections in space and time based on downstream inference task goals and resource constraints. We develop lightweight, on-sensor computational inhibition policies that use past photon data to disable SPAD pixels in real-time, to select the most informative future photons. As case studies, we design policies tailored for image reconstruction and edge detection, and demonstrate, both via simulations and real SPC captured data, considerable reduction in photon detections (over 90\% of photons) while maintaining task performance metrics. Our work raises the question of ``which photons should be detected?'', and paves the way for future energy-efficient single-photon imaging.<|reference_end|>
|
arxiv
|
@article{koerner2024photon,
title={Photon Inhibition for Energy-Efficient Single-Photon Imaging},
author={Lucas J. Koerner, Shantanu Gupta, Atul Ingle, Mohit Gupta},
journal={arXiv preprint arXiv:2409.18337},
year={2024},
archivePrefix={arXiv},
eprint={2409.18337},
primaryClass={eess.IV cs.CV physics.ins-det}
}
|
koerner2024photon
|
arxiv-662573
|
2409.18338
|
AQMLator -- An Auto Quantum Machine Learning E-Platform
|
<|reference_start|>AQMLator -- An Auto Quantum Machine Learning E-Platform: A successful Machine Learning (ML) model implementation requires three main components: training dataset, suitable model architecture and training procedure. Given dataset and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on automatic architecture search -- a meta method that aims at moving human from ML system design process. The success of ML and the development of quantum computing (QC) in recent years led to a birth of new fascinating field called Quantum Machine Learning (QML) that, amongst others, incorporates quantum computers into ML models. In this paper we present AQMLator, an Auto Quantum Machine Learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. This way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.<|reference_end|>
|
arxiv
|
@article{rybotycki2024aqmlator,
title={AQMLator -- An Auto Quantum Machine Learning E-Platform},
author={Tomasz Rybotycki, Piotr Gawron},
journal={arXiv preprint arXiv:2409.18338},
year={2024},
archivePrefix={arXiv},
eprint={2409.18338},
primaryClass={quant-ph cs.LG}
}
|
rybotycki2024aqmlator
|
arxiv-662574
|
2409.18339
|
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models
|
<|reference_start|>AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models: Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks. In addition to their cognitive intelligence, exploring their capabilities in emotional intelligence is also crucial, as it enables more natural and empathetic conversational AI. Recent studies have shown LLMs' capability in recognizing emotions, but they often focus on single emotion labels and overlook the complex and ambiguous nature of human emotions. This study is the first to address this gap by exploring the potential of LLMs in recognizing ambiguous emotions, leveraging their strong generalization capabilities and in-context learning. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition. Experiments conducted using three datasets indicate significant potential for LLMs in recognizing ambiguous emotions, and highlight the substantial benefits of including context information. Furthermore, our findings indicate that LLMs demonstrate a high degree of effectiveness in recognizing less ambiguous emotions and exhibit potential for identifying more ambiguous emotions, paralleling human perceptual capabilities.<|reference_end|>
|
arxiv
|
@article{hong2024aer-llm:,
title={AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language
Models},
author={Xin Hong, Yuan Gong, Vidhyasaharan Sethu, Ting Dang},
journal={arXiv preprint arXiv:2409.18339},
year={2024},
archivePrefix={arXiv},
eprint={2409.18339},
primaryClass={cs.CL cs.AI}
}
|
hong2024aer-llm:
|
arxiv-662575
|
2409.18340
|
DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning
|
<|reference_start|>DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning: Unsupervised domain adaptation (UDA) is essential for medical image segmentation, especially in cross-modality data scenarios. UDA aims to transfer knowledge from a labeled source domain to an unlabeled target domain, thereby reducing the dependency on extensive manual annotations. This paper presents DRL-STNet, a novel framework for cross-modality medical image segmentation that leverages generative adversarial networks (GANs), disentangled representation learning (DRL), and self-training (ST). Our method leverages DRL within a GAN to translate images from the source to the target modality. Then, the segmentation model is initially trained with these translated images and corresponding source labels and then fine-tuned iteratively using a combination of synthetic and real images with pseudo-labels and real labels. The proposed framework exhibits superior performance in abdominal organ segmentation on the FLARE challenge dataset, surpassing state-of-the-art methods by 11.4% in the Dice similarity coefficient and by 13.1% in the Normalized Surface Dice metric, achieving scores of 74.21% and 80.69%, respectively. The average running time is 41 seconds, and the area under the GPU memory-time curve is 11,292 MB. These results indicate the potential of DRL-STNet for enhancing cross-modality medical image segmentation tasks.<|reference_end|>
|
arxiv
|
@article{lin2024drl-stnet:,
title={DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical
Image Segmentation via Disentangled Representation Learning},
author={Hui Lin, Florian Schiffers, Santiago L'opez-Tapia, Neda Tavakoli,
Daniel Kim, Aggelos K. Katsaggelos},
journal={arXiv preprint arXiv:2409.18340},
year={2024},
archivePrefix={arXiv},
eprint={2409.18340},
primaryClass={eess.IV cs.AI cs.CV}
}
|
lin2024drl-stnet:
|
arxiv-662576
|
2409.18341
|
Does End-to-End Autonomous Driving Really Need Perception Tasks?
|
<|reference_start|>Does End-to-End Autonomous Driving Really Need Perception Tasks?: End-to-End Autonomous Driving (E2EAD) methods typically rely on supervised perception tasks to extract explicit scene information (e.g., objects, maps). This reliance necessitates expensive annotations and constrains deployment and data scalability in real-time applications. In this paper, we introduce SSR, a novel framework that utilizes only 16 navigation-guided tokens as Sparse Scene Representation, efficiently extracting crucial scene information for E2EAD. Our method eliminates the need for supervised sub-tasks, allowing computational resources to concentrate on essential elements directly related to navigation intent. We further introduce a temporal enhancement module that employs a Bird's-Eye View (BEV) world model, aligning predicted future scenes with actual future scenes through self-supervision. SSR achieves state-of-the-art planning performance on the nuScenes dataset, demonstrating a 27.2\% relative reduction in L2 error and a 51.6\% decrease in collision rate to the leading E2EAD method, UniAD. Moreover, SSR offers a 10.9$\times$ faster inference speed and 13$\times$ faster training time. This framework represents a significant leap in real-time autonomous driving systems and paves the way for future scalable deployment. Code will be released at \url{https://github.com/PeidongLi/SSR}.<|reference_end|>
|
arxiv
|
@article{li2024does,
title={Does End-to-End Autonomous Driving Really Need Perception Tasks?},
author={Peidong Li, Dixiao Cui},
journal={arXiv preprint arXiv:2409.18341},
year={2024},
archivePrefix={arXiv},
eprint={2409.18341},
primaryClass={cs.CV}
}
|
li2024does
|
arxiv-662577
|
2409.18342
|
Exploring Time-Space trade-offs for synchronized in Lilliput
|
<|reference_start|>Exploring Time-Space trade-offs for synchronized in Lilliput: In the context of Project Lilliput, which attempts to reduce the size of object header in the HotSpot Java Virtual Machine (JVM), we explore a curated set of synchronization algorithms. Each of the algorithms could serve as a potential replacement implementation for the "synchronized" construct in HotSpot. Collectively, the algorithms illuminate trade-offs in space-time properties. The key design decisions are where to locate synchronization metadata (monitor fields), how to map from an object to those fields, and the lifecycle of the monitor information. The reader is assumed to be familiar with current HotSpot implementation of "synchronized" as well as the Compact Java Monitors (CJM) design and Project Lilliput.<|reference_end|>
|
arxiv
|
@article{dice2024exploring,
title={Exploring Time-Space trade-offs for synchronized in Lilliput},
author={Dave Dice and Alex Kogan},
journal={arXiv preprint arXiv:2409.18342},
year={2024},
archivePrefix={arXiv},
eprint={2409.18342},
primaryClass={cs.OS}
}
|
dice2024exploring
|
arxiv-662578
|
2409.18343
|
Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving
|
<|reference_start|>Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving: A major challenge in autonomous vehicle research is modeling agent behaviors, which has critical applications including constructing realistic and reliable simulations for off-board evaluation and forecasting traffic agents motion for onboard planning. While supervised learning has shown success in modeling agents across various domains, these models can suffer from distribution shift when deployed at test-time. In this work, we improve the reliability of agent behaviors by closed-loop fine-tuning of behavior models with reinforcement learning. Our method demonstrates improved overall performance, as well as improved targeted metrics such as collision rate, on the Waymo Open Sim Agents challenge. Additionally, we present a novel policy evaluation benchmark to directly assess the ability of simulated agents to measure the quality of autonomous vehicle planners and demonstrate the effectiveness of our approach on this new benchmark.<|reference_end|>
|
arxiv
|
@article{peng2024improving,
title={Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving},
author={Zhenghao Peng, Wenjie Luo, Yiren Lu, Tianyi Shen, Cole Gulino, Ari
Seff, Justin Fu},
journal={arXiv preprint arXiv:2409.18343},
year={2024},
archivePrefix={arXiv},
eprint={2409.18343},
primaryClass={cs.AI}
}
|
peng2024improving
|
arxiv-662579
|
2409.18345
|
A Generalized LLM-Augmented BIM Framework: Application to a Speech-to-BIM system
|
<|reference_start|>A Generalized LLM-Augmented BIM Framework: Application to a Speech-to-BIM system: Performing building information modeling (BIM) tasks is a complex process that imposes a steep learning curve and a heavy cognitive load due to the necessity of remembering sequences of numerous commands. With the rapid advancement of large language models (LLMs), it is foreseeable that BIM tasks, including querying and managing BIM data, 4D and 5D BIM, design compliance checking, or authoring a design, using written or spoken natural language (i.e., text-to-BIM or speech-to-BIM), will soon supplant traditional graphical user interfaces. This paper proposes a generalized LLM-augmented BIM framework to expedite the development of LLM-enhanced BIM applications by providing a step-by-step development process. The proposed framework consists of six steps: interpret-fill-match-structure-execute-check. The paper demonstrates the applicability of the proposed framework through implementing a speech-to-BIM application, NADIA-S (Natural-language-based Architectural Detailing through Interaction with Artificial Intelligence via Speech), using exterior wall detailing as an example.<|reference_end|>
|
arxiv
|
@article{lee2024a,
title={A Generalized LLM-Augmented BIM Framework: Application to a
Speech-to-BIM system},
author={Ghang Lee, Suhyung Jang, Seokho Hyun},
journal={arXiv preprint arXiv:2409.18345},
year={2024},
number={http://itc.scix.net/paper/w78-2024-155},
archivePrefix={arXiv},
eprint={2409.18345},
primaryClass={cs.CL cs.AI cs.HC}
}
|
lee2024a
|
arxiv-662580
|
2409.18346
|
MultiClimate: Multimodal Stance Detection on Climate Change Videos
|
<|reference_start|>MultiClimate: Multimodal Stance Detection on Climate Change Videos: Climate change (CC) has attracted increasing attention in NLP in recent years. However, detecting the stance on CC in multimodal data is understudied and remains challenging due to a lack of reliable datasets. To improve the understanding of public opinions and communication strategies, this paper presents MultiClimate, the first open-source manually-annotated stance detection dataset with $100$ CC-related YouTube videos and $4,209$ frame-transcript pairs. We deploy state-of-the-art vision and language models, as well as multimodal models for MultiClimate stance detection. Results show that text-only BERT significantly outperforms image-only ResNet50 and ViT. Combining both modalities achieves state-of-the-art, $0.747$/$0.749$ in accuracy/F1. Our 100M-sized fusion models also beat CLIP and BLIP, as well as the much larger 9B-sized multimodal IDEFICS and text-only Llama3 and Gemma2, indicating that multimodal stance detection remains challenging for large language models. Our code, dataset, as well as supplementary materials, are available at https://github.com/werywjw/MultiClimate.<|reference_end|>
|
arxiv
|
@article{wang2024multiclimate:,
title={MultiClimate: Multimodal Stance Detection on Climate Change Videos},
author={Jiawen Wang, Longfei Zuo, Siyao Peng, Barbara Plank},
journal={arXiv preprint arXiv:2409.18346},
year={2024},
archivePrefix={arXiv},
eprint={2409.18346},
primaryClass={cs.CL cs.CV}
}
|
wang2024multiclimate:
|
arxiv-662581
|
2409.18347
|
Progress Towards Submersible Microrobots: A Novel 13-mg Low-Power SMA-Based Actuator for Underwater Propulsion
|
<|reference_start|>Progress Towards Submersible Microrobots: A Novel 13-mg Low-Power SMA-Based Actuator for Underwater Propulsion: We introduce a new low-power 13-mg microactuator driven by shape-memory alloy (SMA) wires for underwater operation. The development of this device was motivated by the recent creation of microswimmers such as the FRISHBot, WaterStrider, VLEIBot, VLEIBot+, and VLEIBot++. The first four of these robots, ranging from 30 to 90 mg, function tethered to an electrical power supply while the last platform is an 810-mg fully autonomous system. These five robots are driven by dry SMA-based microactuators first developed for microrobotic crawlers such as the SMALLBug and SMARTI. As shown in this abstract, dry SMA-based actuators do not operate efficiently under water due to high heat-transfer rates in this medium; for example, the actuators that drive the VLEIBot++ require about 40 mW of average power at 1 Hz in dry air while requiring about 900 mW of average power at 1 Hz in water. In contrast, the microactuator presented in this abstract consumes about 150 mW of average power at 1 Hz in both dry air and water; additionally, it can be excited directly using an onboard battery through simple power electronics implemented on a custom-built printed circuit board (PCB). This technological breakthrough was enabled by the integration of a soft structure that encapsulates the SMA wires that drive the actuator in order to passively control the rates of heat transfer. The results presented here represent preliminary, yet compelling, experimental evidence that the proposed actuation approach will enable the development of fully autonomous and controllable submersible microswimmers. To accomplish this objective, we will evolve the current version of the VLEIBot++ and introduce new bioinspired underwater propulsion mechanisms.<|reference_end|>
|
arxiv
|
@article{longwell2024progress,
title={Progress Towards Submersible Microrobots: A Novel 13-mg Low-Power
SMA-Based Actuator for Underwater Propulsion},
author={Cody R. Longwell, Conor K. Trygstad, Francisco M. F. R. Goncalves, Ke
Xu, and Nestor O. Perez-Arancibia},
journal={arXiv preprint arXiv:2409.18347},
year={2024},
archivePrefix={arXiv},
eprint={2409.18347},
primaryClass={cs.RO}
}
|
longwell2024progress
|
arxiv-662582
|
2409.18351
|
Tracking Software Security Topics
|
<|reference_start|>Tracking Software Security Topics: Software security incidents occur everyday and thousands of software security reports are announced each month. Thus, it is difficult for software security researchers, engineers, and other stakeholders to follow software security topics of their interests in real-time. In this paper, we propose, SOSK, a novel tool for this problem. SOSK allows a user to import a collection of software security reports. It pre-processes and extracts the most important keywords from the textual description of the reports. Based on the similarity of embedding vectors of keywords, SOSK can expand and/or refine a keyword set from a much smaller set of user-provided keywords. Thus, SOSK allows users to define any topic of their interests and retrieve security reports relevant to that topic effectively. Our preliminary evaluation shows that SOSK can expand keywords and retrieve reports relevant to user requests.<|reference_end|>
|
arxiv
|
@article{vu2024tracking,
title={Tracking Software Security Topics},
author={Phong Minh Vu and Tung Thanh Nguyen},
journal={arXiv preprint arXiv:2409.18351},
year={2024},
archivePrefix={arXiv},
eprint={2409.18351},
primaryClass={cs.SE cs.AI cs.CR cs.IR}
}
|
vu2024tracking
|
arxiv-662583
|
2409.18352
|
A New 10-mg SMA-Based Fast Bimorph Actuator for Microrobotics
|
<|reference_start|>A New 10-mg SMA-Based Fast Bimorph Actuator for Microrobotics: We present a new millimeter-scale bimorph actuator for microrobotic applications, driven by feedforward controlled shape-memory alloy (SMA) wires. The device weighs 10 mg, measures 14 mm in length, and occupies a volume of 4.8 mm3, which makes it the lightest and smallest fully functional SMA-based bimorph actuator for microrobotics developed to date. The experimentally measured operational bandwidth is on the order of 20 Hz, and the unimorph and bimorph maximum low-frequency displacement outputs are on the order of 3.5 and 7 mm, respectively. To test and demonstrate the functionality and suitability of the actuator for microrobotics, we developed the Fish-&-Ribbon-Inspired Small Swimming Harmonic roBot (FRISSHBot). Loosely inspired by carangiformes, the FRISSHBot leverages fluid-structure interaction (FSI) phenomena to propel itself forward, weighs 30 mg, measures 34 mm in length, operates at frequencies of up to 4 Hz, and swims at speeds of up to 3.06 mm/s (0.09 Bl/s). This robot is the lightest and smallest swimmer with onboard actuation developed to date.<|reference_end|>
|
arxiv
|
@article{trygstad2024a,
title={A New 10-mg SMA-Based Fast Bimorph Actuator for Microrobotics},
author={Conor K. Trygstad, Elijah K. Blankenship, and Nestor O.
Perez-Arancibia},
journal={arXiv preprint arXiv:2409.18352},
year={2024},
archivePrefix={arXiv},
eprint={2409.18352},
primaryClass={cs.RO}
}
|
trygstad2024a
|
arxiv-662584
|
2409.18353
|
Energy Efficient Beamforming Training in Terahertz Communication Systems
|
<|reference_start|>Energy Efficient Beamforming Training in Terahertz Communication Systems: Terahertz (THz) enables promising Tbps-level wireless transmission thanks to its prospect of ultra-huge spectrum utilization and narrow beamforming in the next sixth-generation (6G) communication system. Compared to millimeter wave (mmWave), THz intrinsically possesses compellingly severer molecular absorption and high pathloss serving confined coverage area. These defects should be well conquered under the employment of ultra-thin 3D beamforming with enormous deployed antennas with high beam gains. However, pencil-beams require substantially high overhead of time and power to train its optimal THz beamforming direction. We propose an energy efficient (EE) oriented THz beamforming (EETBF) scheme by separating the original complex problem into beamforming training (EETBF-BT) acquirement and learning-enabled training power assignment (EETBF-PA). The historical beam data is employed to update next beam selection policy. The performance results have demonstrated that the proposed EETBF outperforms the existing benchmarks leveraging full beam search, iterative search, linear/binary search as well as non-power-control based mechanism in open literature. Our proposed EETBF scheme results in the lowest training latency and power consumption, achieving the highest effective rate and EE performance.<|reference_end|>
|
arxiv
|
@article{shen2024energy,
title={Energy Efficient Beamforming Training in Terahertz Communication Systems},
author={Li-Hsiang Shen, Kai-Ten Feng, Lie-Liang Yang},
journal={arXiv preprint arXiv:2409.18353},
year={2024},
archivePrefix={arXiv},
eprint={2409.18353},
primaryClass={cs.IT eess.SP math.IT}
}
|
shen2024energy
|
arxiv-662585
|
2409.18355
|
SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement
|
<|reference_start|>SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement: Cone Beam Computed Tomography (CBCT) finds diverse applications in medicine. Ensuring high image quality in CBCT scans is essential for accurate diagnosis and treatment delivery. Yet, the susceptibility of CBCT images to noise and artifacts undermines both their usefulness and reliability. Existing methods typically address CBCT artifacts through image-to-image translation approaches. These methods, however, are limited by the artifact types present in the training data, which may not cover the complete spectrum of CBCT degradations stemming from variations in imaging protocols. Gathering additional data to encompass all possible scenarios can often pose a challenge. To address this, we present SinoSynth, a physics-based degradation model that simulates various CBCT-specific artifacts to generate a diverse set of synthetic CBCT images from high-quality CT images without requiring pre-aligned data. Through extensive experiments, we demonstrate that several different generative networks trained on our synthesized data achieve remarkable results on heterogeneous multi-institutional datasets, outperforming even the same networks trained on actual data. We further show that our degradation model conveniently provides an avenue to enforce anatomical constraints in conditional generative models, yielding high-quality and structure-preserving synthetic CT images.<|reference_end|>
|
arxiv
|
@article{pang2024sinosynth:,
title={SinoSynth: A Physics-based Domain Randomization Approach for
Generalizable CBCT Image Enhancement},
author={Yunkui Pang, Yilin Liu, Xu Chen, Pew-Thian Yap, Jun Lian},
journal={arXiv preprint arXiv:2409.18355},
year={2024},
archivePrefix={arXiv},
eprint={2409.18355},
primaryClass={cs.CV}
}
|
pang2024sinosynth:
|
arxiv-662586
|
2409.18356
|
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
|
<|reference_start|>FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration: Recently, federated learning has attracted much attention as a privacy-preserving integrated analysis that enables integrated analysis of data held by multiple institutions without sharing raw data. On the other hand, federated learning requires iterative communication across institutions and has a big challenge for implementation in situations where continuous communication with the outside world is extremely difficult. In this study, we propose a federated data collaboration learning (FedDCL), which solves such communication issues by combining federated learning with recently proposed non-model share-type federated learning named as data collaboration analysis. In the proposed FedDCL framework, each user institution independently constructs dimensionality-reduced intermediate representations and shares them with neighboring institutions on intra-group DC servers. On each intra-group DC server, intermediate representations are transformed to incorporable forms called collaboration representations. Federated learning is then conducted between intra-group DC servers. The proposed FedDCL framework does not require iterative communication by user institutions and can be implemented in situations where continuous communication with the outside world is extremely difficult. The experimental results show that the performance of the proposed FedDCL is comparable to that of existing federated learning.<|reference_end|>
|
arxiv
|
@article{imakura2024feddcl:,
title={FedDCL: a federated data collaboration learning as a hybrid-type
privacy-preserving framework based on federated learning and data
collaboration},
author={Akira Imakura, Tetsuya Sakurai},
journal={arXiv preprint arXiv:2409.18356},
year={2024},
archivePrefix={arXiv},
eprint={2409.18356},
primaryClass={cs.LG cs.CR}
}
|
imakura2024feddcl:
|
arxiv-662587
|
2409.18359
|
Generative AI for fast and accurate Statistical Computation of Fluids
|
<|reference_start|>Generative AI for fast and accurate Statistical Computation of Fluids: We present a generative AI algorithm for addressing the challenging task of fast, accurate and robust statistical computation of three-dimensional turbulent fluid flows. Our algorithm, termed as GenCFD, is based on a conditional score-based diffusion model. Through extensive numerical experimentation with both incompressible and compressible fluid flows, we demonstrate that GenCFD provides very accurate approximation of statistical quantities of interest such as mean, variance, point pdfs, higher-order moments, while also generating high quality realistic samples of turbulent fluid flows and ensuring excellent spectral resolution. In contrast, ensembles of operator learning baselines which are trained to minimize mean (absolute) square errors regress to the mean flow. We present rigorous theoretical results uncovering the surprising mechanisms through which diffusion models accurately generate fluid flows. These mechanisms are illustrated with solvable toy models that exhibit the relevant features of turbulent fluid flows while being amenable to explicit analytical formulas.<|reference_end|>
|
arxiv
|
@article{molinaro2024generative,
title={Generative AI for fast and accurate Statistical Computation of Fluids},
author={Roberto Molinaro, Samuel Lanthaler, Bogdan Raoni'c, Tobias Rohner,
Victor Armegioiu, Zhong Yi Wan, Fei Sha, Siddhartha Mishra, Leonardo
Zepeda-N'u~nez},
journal={arXiv preprint arXiv:2409.18359},
year={2024},
archivePrefix={arXiv},
eprint={2409.18359},
primaryClass={cs.LG cs.NA math.NA physics.flu-dyn}
}
|
molinaro2024generative
|
arxiv-662588
|
2409.18360
|
Architecture for Protecting Data Privacy in Decentralized Social Networks
|
<|reference_start|>Architecture for Protecting Data Privacy in Decentralized Social Networks: Centralized social networks have experienced a transformative impact on our digital era communication, connection, and information-sharing information. However, it has also raised significant concerns regarding users' privacy and individual rights. In response to these concerns, this paper proposes a novel Decentralized Social Network employing Blockchain technology and Decentralized Storage Networks completed by Access Control Smart Contracts. The initial phase comprises a comprehensive literature review, delving into decentralized social networks, explaining the review methodology, and presenting the resulting findings. Building upon these findings and an analysis of previous research gaps, we propose a novel architecture for decentralized social networks. In conclusion, the principal results highlight the benefit of our decentralized social network to protect user privacy. Moreover, the users have all rights to their posted information following the General Data Protection Regulation (GDPR).<|reference_end|>
|
arxiv
|
@article{cao2024architecture,
title={Architecture for Protecting Data Privacy in Decentralized Social
Networks},
author={Quang Cao, Katerina Vgena, Aikaterini-Georgia Mavroeidi, Christos
Kalloniatis, Xun Yi, Son Hoang Dau},
journal={arXiv preprint arXiv:2409.18360},
year={2024},
archivePrefix={arXiv},
eprint={2409.18360},
primaryClass={cs.CR}
}
|
cao2024architecture
|
arxiv-662589
|
2409.18361
|
iWalker: Imperative Visual Planning for Walking Humanoid Robot
|
<|reference_start|>iWalker: Imperative Visual Planning for Walking Humanoid Robot: Humanoid robots, with the potential to perform a broad range of tasks in environments designed for humans, have been deemed crucial for the basis of general AI agents. When talking about planning and controlling, although traditional models and task-specific methods have been extensively studied over the past few decades, they are inadequate for achieving the flexibility and versatility needed for general autonomy. Learning approaches, especially reinforcement learning, are powerful and popular nowadays, but they are inherently "blind" during training, relying heavily on trials in simulation without proper guidance from physical principles or underlying dynamics. In response, we propose a novel end-to-end pipeline that seamlessly integrates perception, planning, and model-based control for humanoid robot walking. We refer to our method as iWalker, which is driven by imperative learning (IL), a self-supervising neuro-symbolic learning framework. This enables the robot to learn from arbitrary unlabeled data, significantly improving its adaptability and generalization capabilities. In experiments, iWalker demonstrates effectiveness in both simulated and real-world environments, representing a significant advancement toward versatile and autonomous humanoid robots.<|reference_end|>
|
arxiv
|
@article{lin2024iwalker:,
title={iWalker: Imperative Visual Planning for Walking Humanoid Robot},
author={Xiao Lin, Yuhao Huang, Taimeng Fu, Xiaobin Xiong, Chen Wang},
journal={arXiv preprint arXiv:2409.18361},
year={2024},
archivePrefix={arXiv},
eprint={2409.18361},
primaryClass={cs.RO cs.SY eess.SY}
}
|
lin2024iwalker:
|
arxiv-662590
|
2409.18364
|
Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images
|
<|reference_start|>Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images: 3D human shape reconstruction under severe occlusion due to human-object or human-human interaction is a challenging problem. Parametric models i.e., SMPL(-X), which are based on the statistics across human shapes, can represent whole human body shapes but are limited to minimally-clothed human shapes. Implicit-function-based methods extract features from the parametric models to employ prior knowledge of human bodies and can capture geometric details such as clothing and hair. However, they often struggle to handle misaligned parametric models and inpaint occluded regions given a single RGB image. In this work, we propose a novel pipeline, MHCDIFF, Multi-hypotheses Conditioned Point Cloud Diffusion, composed of point cloud diffusion conditioned on probabilistic distributions for pixel-aligned detailed 3D human reconstruction under occlusion. Compared to previous implicit-function-based methods, the point cloud diffusion model can capture the global consistent features to generate the occluded regions, and the denoising process corrects the misaligned SMPL meshes. The core of MHCDIFF is extracting local features from multiple hypothesized SMPL(-X) meshes and aggregating the set of features to condition the diffusion model. In the experiments on CAPE and MultiHuman datasets, the proposed method outperforms various SOTA methods based on SMPL, implicit functions, point cloud diffusion, and their combined, under synthetic and real occlusions. Our code is publicly available at https://donghwankim0101.github.io/projects/mhcdiff/ .<|reference_end|>
|
arxiv
|
@article{kim2024multi-hypotheses,
title={Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human
Reconstruction from Occluded Images},
author={Donghwan Kim, Tae-Kyun Kim},
journal={arXiv preprint arXiv:2409.18364},
year={2024},
archivePrefix={arXiv},
eprint={2409.18364},
primaryClass={cs.CV cs.AI cs.LG}
}
|
kim2024multi-hypotheses
|
arxiv-662591
|
2409.18365
|
Defect Prediction with Content-based Features
|
<|reference_start|>Defect Prediction with Content-based Features: Traditional defect prediction approaches often use metrics that measure the complexity of the design or implementing code of a software system, such as the number of lines of code in a source file. In this paper, we explore a different approach based on content of source code. Our key assumption is that source code of a software system contains information about its technical aspects and those aspects might have different levels of defect-proneness. Thus, content-based features such as words, topics, data types, and package names extracted from a source code file could be used to predict its defects. We have performed an extensive empirical evaluation and found that: i) such content-based features have higher predictive power than code complexity metrics and ii) the use of feature selection, reduction, and combination further improves the prediction performance.<|reference_end|>
|
arxiv
|
@article{pham2024defect,
title={Defect Prediction with Content-based Features},
author={Hung Viet Pham and Tung Thanh Nguyen},
journal={arXiv preprint arXiv:2409.18365},
year={2024},
archivePrefix={arXiv},
eprint={2409.18365},
primaryClass={cs.SE cs.CL cs.LG}
}
|
pham2024defect
|
arxiv-662592
|
2409.18370
|
Discovery and inversion of the viscoelastic wave equation in inhomogeneous media
|
<|reference_start|>Discovery and inversion of the viscoelastic wave equation in inhomogeneous media: In scientific machine learning, the task of identifying partial differential equations accurately from sparse and noisy data poses a significant challenge. Current sparse regression methods may identify inaccurate equations on sparse and noisy datasets and are not suitable for varying coefficients. To address this issue, we propose a hybrid framework that combines two alternating direction optimization phases: discovery and embedding. The discovery phase employs current well-developed sparse regression techniques to preliminarily identify governing equations from observations. The embedding phase implements a recurrent convolutional neural network (RCNN), enabling efficient processes for time-space iterations involved in discretized forms of wave equation. The RCNN model further optimizes the imperfect sparse regression results to obtain more accurate functional terms and coefficients. Through alternating update of discovery-embedding phases, essential physical equations can be robustly identified from noisy and low-resolution measurements. To assess the performance of proposed framework, numerical experiments are conducted on various scenarios involving wave equation in elastic/viscoelastic and homogeneous/inhomogeneous media. The results demonstrate that the proposed method exhibits excellent robustness and accuracy, even when faced with high levels of noise and limited data availability in both spatial and temporal domains.<|reference_end|>
|
arxiv
|
@article{chen2024discovery,
title={Discovery and inversion of the viscoelastic wave equation in
inhomogeneous media},
author={Su Chen, Yi Ding, Hiroe Miyake, Xiaojun Li},
journal={arXiv preprint arXiv:2409.18370},
year={2024},
archivePrefix={arXiv},
eprint={2409.18370},
primaryClass={cs.LG physics.geo-ph}
}
|
chen2024discovery
|
arxiv-662593
|
2409.18371
|
A model-constrained Discontinuous Galerkin Network (DGNet) for Compressible Euler Equations with Out-of-Distribution Generalization
|
<|reference_start|>A model-constrained Discontinuous Galerkin Network (DGNet) for Compressible Euler Equations with Out-of-Distribution Generalization: Real-time accurate solutions of large-scale complex dynamical systems are critically needed for control, optimization, uncertainty quantification, and decision-making in practical engineering and science applications, particularly in digital twin contexts. In this work, we develop a model-constrained discontinuous Galerkin Network (DGNet) approach, an extension to our previous work [Model-constrained Tagent Slope Learning Approach for Dynamical Systems], for compressible Euler equations with out-of-distribution generalization. The core of DGNet is the synergy of several key strategies: (i) leveraging time integration schemes to capture temporal correlation and taking advantage of neural network speed for computation time reduction; (ii) employing a model-constrained approach to ensure the learned tangent slope satisfies governing equations; (iii) utilizing a GNN-inspired architecture where edges represent Riemann solver surrogate models and nodes represent volume integration correction surrogate models, enabling capturing discontinuity capacity, aliasing error reduction, and mesh discretization generalizability; (iv) implementing the input normalization technique that allows surrogate models to generalize across different initial conditions, boundary conditions, and solution orders; and (v) incorporating a data randomization technique that not only implicitly promotes agreement between surrogate models and true numerical models up to second-order derivatives, ensuring long-term stability and prediction capacity, but also serves as a data generation engine during training, leading to enhanced generalization on unseen data. To validate the effectiveness, stability, and generalizability of our novel DGNet approach, we present comprehensive numerical results for 1D and 2D compressible Euler equation problems.<|reference_end|>
|
arxiv
|
@article{van nguyen2024a,
title={A model-constrained Discontinuous Galerkin Network (DGNet) for
Compressible Euler Equations with Out-of-Distribution Generalization},
author={Hai Van Nguyen (1), Jau-Uei Chen (1), William Cole Nockolds (2),
Wesley Lao (2), and Tan Bui-Thanh (1 and 2) ((1) Department of Aerospace
Engineering and Engineering Mechanics, the University of Texas at Austin,
Texas (2) The Oden Institute for Computational Engineering and Sciences, the
University of Texas at Austin, Texas)},
journal={arXiv preprint arXiv:2409.18371},
year={2024},
archivePrefix={arXiv},
eprint={2409.18371},
primaryClass={stat.ML cs.LG stat.CO}
}
|
van nguyen2024a
|
arxiv-662594
|
2409.18372
|
You Only Speak Once to See
|
<|reference_start|>You Only Speak Once to See: Grounding objects in images using visual cues is a well-established approach in computer vision, yet the potential of audio as a modality for object recognition and grounding remains underexplored. We introduce YOSS, "You Only Speak Once to See," to leverage audio for grounding objects in visual scenes, termed Audio Grounding. By integrating pre-trained audio models with visual models using contrastive learning and multi-modal alignment, our approach captures speech commands or descriptions and maps them directly to corresponding objects within images. Experimental results indicate that audio guidance can be effectively applied to object grounding, suggesting that incorporating audio guidance may enhance the precision and robustness of current object grounding methods and improve the performance of robotic systems and computer vision applications. This finding opens new possibilities for advanced object recognition, scene understanding, and the development of more intuitive and capable robotic systems.<|reference_end|>
|
arxiv
|
@article{yang2024you,
title={You Only Speak Once to See},
author={Wenhao Yang, Jianguo Wei, Wenhuan Lu, Lei Li},
journal={arXiv preprint arXiv:2409.18372},
year={2024},
archivePrefix={arXiv},
eprint={2409.18372},
primaryClass={cs.CV}
}
|
yang2024you
|
arxiv-662595
|
2409.18374
|
Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks
|
<|reference_start|>Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks: Generative models based on latent variables, such as generative adversarial networks (GANs) and variational auto-encoders (VAEs), have gained lots of interests due to their impressive performance in many fields. However, many data such as natural images usually do not populate the ambient Euclidean space but instead reside in a lower-dimensional manifold. Thus an inappropriate choice of the latent dimension fails to uncover the structure of the data, possibly resulting in mismatch of latent representations and poor generative qualities. Towards addressing these problems, we propose a novel framework called the latent Wasserstein GAN (LWGAN) that fuses the Wasserstein auto-encoder and the Wasserstein GAN so that the intrinsic dimension of the data manifold can be adaptively learned by a modified informative latent distribution. We prove that there exist an encoder network and a generator network in such a way that the intrinsic dimension of the learned encoding distribution is equal to the dimension of the data manifold. We theoretically establish that our estimated intrinsic dimension is a consistent estimate of the true dimension of the data manifold. Meanwhile, we provide an upper bound on the generalization error of LWGAN, implying that we force the synthetic data distribution to be similar to the real data distribution from a population perspective. Comprehensive empirical experiments verify our framework and show that LWGAN is able to identify the correct intrinsic dimension under several scenarios, and simultaneously generate high-quality synthetic data by sampling from the learned latent distribution.<|reference_end|>
|
arxiv
|
@article{qiu2024adaptive,
title={Adaptive Learning of the Latent Space of Wasserstein Generative
Adversarial Networks},
author={Yixuan Qiu, Qingyi Gao, Xiao Wang},
journal={arXiv preprint arXiv:2409.18374},
year={2024},
archivePrefix={arXiv},
eprint={2409.18374},
primaryClass={stat.ML cs.AI cs.LG stat.ME}
}
|
qiu2024adaptive
|
arxiv-662596
|
2409.18375
|
AM-MTEEG: Multi-task EEG classification based on impulsive associative memory
|
<|reference_start|>AM-MTEEG: Multi-task EEG classification based on impulsive associative memory: Electroencephalogram-based brain-computer interface (BCI) has potential applications in various fields, but their development is hindered by limited data and significant cross-individual variability. Inspired by the principles of learning and memory in the human hippocampus, we propose a multi-task (MT) classification model, called AM-MTEEG, which combines learning-based impulsive neural representations with bidirectional associative memory (AM) for cross-individual BCI classification tasks. The model treats the EEG classification of each individual as an independent task and facilitates feature sharing across individuals. Our model consists of an impulsive neural population coupled with a convolutional encoder-decoder to extract shared features and a bidirectional associative memory matrix to map features to class. Experimental results in two BCI competition datasets show that our model improves average accuracy compared to state-of-the-art models and reduces performance variance across individuals, and the waveforms reconstructed by the bidirectional associative memory provide interpretability for the model's classification results. The neuronal firing patterns in our model are highly coordinated, similarly to the neural coding of hippocampal neurons, indicating that our model has biological similarities.<|reference_end|>
|
arxiv
|
@article{li2024am-mteeg:,
title={AM-MTEEG: Multi-task EEG classification based on impulsive associative
memory},
author={Junyan Li, Bin Hu, Zhi-Hong Guan},
journal={arXiv preprint arXiv:2409.18375},
year={2024},
archivePrefix={arXiv},
eprint={2409.18375},
primaryClass={cs.NE q-bio.NC}
}
|
li2024am-mteeg:
|
arxiv-662597
|
2409.18382
|
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
|
<|reference_start|>CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models: Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates the achievement of complex policies by progressively increasing the task difficulty during training. However, designing effective curricula for a specific task often requires extensive domain knowledge and human intervention, which limits its applicability across various domains. Our core idea is that large language models (LLMs), with their extensive training on diverse language data and ability to encapsulate world knowledge, present significant potential for efficiently breaking down tasks and decomposing skills across various robotics environments. Additionally, the demonstrated success of LLMs in translating natural language into executable code for RL agents strengthens their role in generating task curricula. In this work, we propose CurricuLLM, which leverages the high-level planning and programming capabilities of LLMs for curriculum design, thereby enhancing the efficient learning of complex target tasks. CurricuLLM consists of: (Step 1) Generating sequence of subtasks that aid target task learning in natural language form, (Step 2) Translating natural language description of subtasks in executable task code, including the reward code and goal distribution code, and (Step 3) Evaluating trained policies based on trajectory rollout and subtask description. We evaluate CurricuLLM in various robotics simulation environments, ranging from manipulation, navigation, and locomotion, to show that CurricuLLM can aid learning complex robot control tasks. In addition, we validate humanoid locomotion policy learned through CurricuLLM in real-world. The code is provided in https://github.com/labicon/CurricuLLM<|reference_end|>
|
arxiv
|
@article{ryu2024curricullm:,
title={CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot
Skills using Large Language Models},
author={Kanghyun Ryu, Qiayuan Liao, Zhongyu Li, Koushil Sreenath, Negar Mehr},
journal={arXiv preprint arXiv:2409.18382},
year={2024},
archivePrefix={arXiv},
eprint={2409.18382},
primaryClass={cs.RO cs.LG cs.SY eess.SY}
}
|
ryu2024curricullm:
|
arxiv-662598
|
2409.18383
|
AquaMILR+: Design of an untethered limbless robot for complex aquatic terrain navigation
|
<|reference_start|>AquaMILR+: Design of an untethered limbless robot for complex aquatic terrain navigation: This paper presents AquaMILR+, an untethered limbless robot designed for agile navigation in complex aquatic environments. The robot features a bilateral actuation mechanism that models musculoskeletal actuation in many anguilliform swimming organisms which propagates a moving wave from head to tail allowing open fluid undulatory swimming. This actuation mechanism employs mechanical intelligence, enhancing the robot's maneuverability when interacting with obstacles. AquaMILR+ also includes a compact depth control system inspired by the swim bladder and lung structures of eels and sea snakes. The mechanism, driven by a syringe and telescoping leadscrew, enables depth and pitch control-capabilities that are difficult for most anguilliform swimming robots to achieve. Additional structures, such as fins and a tail, further improve stability and propulsion efficiency. Our tests in both open water and indoor 2D and 3D heterogeneous aquatic environments highlight AquaMILR+'s capabilities and suggest a promising system for complex underwater tasks such as search and rescue and deep-sea exploration.<|reference_end|>
|
arxiv
|
@article{fernandez2024aquamilr+:,
title={AquaMILR+: Design of an untethered limbless robot for complex aquatic
terrain navigation},
author={Matthew Fernandez, Tianyu Wang, Galen Tunnicliffe, Donoven Dortilus,
Peter Gunnarson, John O. Dabiri, Daniel I. Goldman},
journal={arXiv preprint arXiv:2409.18383},
year={2024},
archivePrefix={arXiv},
eprint={2409.18383},
primaryClass={cs.RO}
}
|
fernandez2024aquamilr+:
|
arxiv-662599
|
2409.18385
|
Robo-CSK-Organizer: Commonsense Knowledge to Organize Detected Objects for Multipurpose Robots
|
<|reference_start|>Robo-CSK-Organizer: Commonsense Knowledge to Organize Detected Objects for Multipurpose Robots: This paper presents a system called Robo-CSK-Organizer that infuses commonsense knowledge from a classical knowledge based to enhance the context recognition capabilities of robots so as to facilitate the organization of detected objects by classifying them in a task-relevant manner. It is particularly useful in multipurpose robotics. Unlike systems relying solely on deep learning tools such as ChatGPT, the Robo-CSK-Organizer system stands out in multiple avenues as follows. It resolves ambiguities well, and maintains consistency in object placement. Moreover, it adapts to diverse task-based classifications. Furthermore, it contributes to explainable AI, hence helping to improve trust and human-robot collaboration. Controlled experiments performed in our work, simulating domestic robotics settings, make Robo-CSK-Organizer demonstrate superior performance while placing objects in contextually relevant locations. This work highlights the capacity of an AI-based system to conduct commonsense-guided decision-making in robotics closer to the thresholds of human cognition. Hence, Robo-CSK-Organizer makes positive impacts on AI and robotics.<|reference_end|>
|
arxiv
|
@article{hidalgo2024robo-csk-organizer:,
title={Robo-CSK-Organizer: Commonsense Knowledge to Organize Detected Objects
for Multipurpose Robots},
author={Rafael Hidalgo, Jesse Parron, Aparna S. Varde, Weitian Wang},
journal={Springer IEMTRONICS 2024 conference},
year={2024},
archivePrefix={arXiv},
eprint={2409.18385},
primaryClass={cs.RO cs.AI}
}
|
hidalgo2024robo-csk-organizer:
|
arxiv-662600
|
2409.18386
|
ChARLES: Change-Aware Recovery of Latent Evolution Semantics in Relational Data
|
<|reference_start|>ChARLES: Change-Aware Recovery of Latent Evolution Semantics in Relational Data: Data-driven decision-making is at the core of many modern applications, and understanding the data is critical in supporting trust in these decisions. However, data is dynamic and evolving, just like the real-world entities it represents. Thus, an important component of understanding data is analyzing and drawing insights from the changes it undergoes. Existing methods for exploring data change list differences exhaustively, which are not interpretable by humans and lack salient insights regarding change trends. For example, an explanation that semantically summarizes changes to highlight gender disparities in performance rewards is more human-consumable than a long list of employee salary changes. We demonstrate ChARLES, a system that derives semantic summaries of changes between two snapshots of an evolving database, in an effective, concise, and interpretable way. Our key observation is that, while datasets often evolve through point and other small-batch updates, rich data features can reveal latent semantics that can intuitively summarize the changes. Under the hood, ChARLES compares database versions, infers feasible transformations by fitting multiple regression lines over different data partitions to derive change summaries, and ranks them. ChARLES allows users to customize it to obtain their preferred explanation by navigating the accuracy-interpretability tradeoff, and offers a proof of concept for reasoning about data evolution over real-world datasets.<|reference_end|>
|
arxiv
|
@article{he2024charles:,
title={ChARLES: Change-Aware Recovery of Latent Evolution Semantics in
Relational Data},
author={Shiyi He, Alexandra Meliou, Anna Fariha},
journal={arXiv preprint arXiv:2409.18386},
year={2024},
archivePrefix={arXiv},
eprint={2409.18386},
primaryClass={cs.DB}
}
|
he2024charles:
|
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