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**A**: In Section VI, we present numerical examples to demonstrate the performance of
the proposed method**B**: The application of C2-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD to pattern synthesis is presented in Section V**C**: Conclusions are drawn in Section VII. | BAC | BCA | BCA | ABC | Selection 1 |
**A**: Higher altitude indicates larger coverage size as shown in Fig. 1 (c)**B**:
In order to support as many users as possible, UAVs are required to enlarge coverage size, which is equal to enlarge the coverage proportion in the mission area**C**: The utility of coverage size is denoted as | CBA | BAC | BCA | CBA | Selection 2 |
**A**: Guo et al. (2018) provided a review of deep learning based semantic segmentation of images, and divided the literature into three categories: region-based, fully convolutional network (FCN)-based, and weakly supervised segmentation methods. Hu et al. (2018b) summarized the most commonly used RGB-D datasets for semantic segmentation as well as traditional machine learning based methods and deep learning-based network architectures for RGB-D segmentation. Lateef and Ruichek (2019) presented an extensive survey of deep learning architectures, datasets, and evaluation methods for the semantic segmentation of natural images using deep neural networks. Similarly, for medical imaging, Goceri and Goceri (2017) presented an high-level overview of deep learning-based medical image analysis techniques and application areas**B**: Hesamian et al. (2019) presented an overview of the state-of-the-art methods in medical image segmentation using deep learning by covering the literature related to network structures and model training techniques. Karimi et al. (2019) reviewed the literature on techniques to handle label noise in deep learning based medical image analysis and evaluated existing approaches on three medical imaging datasets for segmentation and classification tasks. Zhou et al. (2019b) presented a review of techniques proposed for fusion of medical images from multiple modalities for medical image segmentation**C**: Goceri (2019a) discussed the fully supervised, weakly supervised and transfer learning techniques for training deep neural networks for segmentation of medical images, and also discussed the existing methods for addressing the problems of lack of data and class imbalance. Zhang et al. (2019) presented a review of the approaches to address the problem of small sample sizes in medical image analysis, and divided the literature into five categories including explanation, weakly supervised, transfer learning, and active learning techniques. Tajbakhsh et al. (2020) presented a review of the literature for addressing the challenges of scarce annotations as well as weak annotations (e.g., noisy annotations, image-level labels, sparse annotations, etc.) in medical image segmentation. Similarly, there are several surveys covering the literature on the task of object detection (Wang et al., 2019c; Zou et al., 2019; Borji et al., 2019; Liu et al., 2019b; Zhao et al., 2019), which can also be used to obtain what can be termed as rough localizations of the object(s) of interest. In contrast to the existing surveys, we make the following contributions in this review:
| ACB | ACB | ABC | CBA | Selection 3 |
**A**: (a) The t-UAV subarray partition pattern**B**:
Figure 6: The subarray patterns on the cylinder and the corresponding expanded cylinder**C**: (b) The r-UAV subarray partition pattern with conflict. (c) The r-UAV subarray partition pattern without conflict. (d) The t-UAV subarray partition pattern with beamwidth selection. | ACB | CAB | BAC | ABC | Selection 3 |
**A**:
Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which may be spatially and temporally dependent**B**: The additive and multiplicative communication noises co-exist in communication links. We consider the distributed stochastic subgradient optimization algorithm and prove that if the sequence of random digraphs is conditionally balanced and uniformly conditionally jointly connected, then the states of all local optimizers converge to the same global optimal solution almost surely. The main contributions of our paper are listed as follows.**C**: The local cost functions are not required to be differentiable, nor do their subgradients need to be bounded. The local optimizers can only obtain measurement information of the local subgradients with random noises | ACB | ABC | BAC | BCA | Selection 1 |
**A**: This control framework requires tuning of multiple parameters associated with an extensive number of iterations. We propose a sample-efficient joint tuning algorithm, where the performance metrics associated with the full geometry traversal are modelled as Gaussian processes, and used to form the cost and the constraints in a constrained Bayesian optimization algorithm, where they enable the trade-off between fast traversal, high tracking accuracy, and suppression of vibrations in the system. Data-driven tuning of all the parameters compensates for model imperfections and results in improved performance.
Our numerical results demonstrate that tuning the parameters of the MPCC stage achieves the best performance in terms of time and tracking accuracy.**B**: We use a contouring predictive control approach to optimize the input to a low level controller**C**: This paper demonstrated a hierarchical contour control implementation for the increase of productivity in positioning systems | CBA | BAC | BCA | BCA | Selection 1 |
**A**: To the best of our knowledge, CPP is the first method that enjoys linear convergence under such a general setting.**B**:
We propose CPP – a novel decentralized optimization method with communication compression**C**: The method works under a general class of compression operators and is shown to achieve linear convergence for strongly convex and smooth objective functions over general directed graphs | BCA | CAB | CBA | BAC | Selection 2 |
**A**: Notation—“M1”: vocal melody, “M2”: instrumental melody, “A”: accompaniment.**B**: Each row represents the percentage of notes in an actual class while each column represents a predicted class**C**:
Figure 3: Confusion tables (in %) for two models for three-class melody classification, calculated on the test split of POP9094/44/4{}_{\text{4/4}}start_FLOATSUBSCRIPT 4/4 end_FLOATSUBSCRIPT | BAC | CAB | CBA | ACB | Selection 3 |
**A**: However, such a text-based semantic communication system only measures the performance at the word level instead of the sentence level. Thus, a further investigation about semantic communications for text transmission, named DeepSC, has been carried out in[12] to deal with the semantic error at the sentence level with various length. Moreover, a lite distributed semantic communication system for text transmission, named L-DeepSC, has been proposed in[13] to address the challenge of IoT to perform the intelligent tasks.
**B**: Particularly, an initial research on semantic communication systems for text information has been developed in[11], which directly mitigates the semantic error when achieving Nash equilibrium**C**: Inspired by the end-to-end (E2E) communication systems developed to address the challenges in traditional block-wise commutation systems[9, 10], different types of sources have been considered on E2E semantic communication systems | ACB | ACB | CBA | BCA | Selection 3 |
**A**: Images classified as tumor by a pathologist are labelled as 1 while normal tissue images are labelled 0**B**: In total in this dataset there are 220,177 images of 50 x 50 pixels in three colors**C**: The ratio between the two classes is 70 % normal and 30 % tumor for this dataset (Figure 2).
| ABC | BCA | BCA | BAC | Selection 4 |
**A**: See Supplementary Notes 9 and 10 for details.
**B**: We evaluated the neural étendue expanders using a prototype holographic display**C**: The prototype consists of a HOLOEYE-PLUTO SLM, a 4F system, a DC block, and a camera for imaging the étendue expanded holograms | CBA | ACB | CBA | CAB | Selection 4 |
**A**: Motivated by the dense connection mechanism, Tong et al. (Tong
et al., 2017) proposed an SRDenseNet**B**: SRDenseNet uses not only the layer-level dense connections but also the block-level ones, where the output of each dense block is connected by dense connections**C**: In this way, the low-level features and high-level features are combined and fully used to conduct the reconstruction. In RDN (Zhang | ABC | BAC | CAB | BCA | Selection 1 |
**A**: Last, while the presence of dark blue traces in Fig. 1(d) indicate components of the spectrogram which favour the negative class, the overall dominance of red colours (though not all dark red) indicate a greater support for the positive class (the classifier output correctly indicates bona fide speech).
Plots of SHAP values such as those shown in Fig. 1(c) are not easily visualised without the use of dilation operations or some other such smoothing operations which distort the results. While they offer interesting insights, we need more easily visualised means with which to explore results.**B**: Ignoring for now whether or not the SHAP values are positive or negative, it exhibits a high degree of correlation to the fundamental frequency and harmonics in the spectrogram, indicating the focus of the classifier on these same components**C**: A second visualisation focusing on this specific region is displayed in Fig. 1(d) | CAB | ABC | CBA | BAC | Selection 3 |
**A**:
CBFs that account for uncertainties in the system dynamics have been considered in two ways. The authors in [10] and [11] consider input-to-state safety to quantify possible safety violation**B**: CBFs that account for state estimation uncertainties were proposed in [15] and [16]. Relying on the same notion of measurement robust CBFs as in [15], the authors in [17] present empirical evaluations on a segway. While the notion of ROCBFs that we present in this paper is inspired by measurement-robust CBFs as presented in [15], we also consider uncertainties in the system dynamics and focus on learning valid CBFs from expert demonstrations. Similar to the notion of ROCBF, the authors in [18] consider additive disturbances in the system dynamics and state-estimation errors jointly.**C**: Conversely, the work in [12] proposes robust CBFs to guarantee robust safety by accounting for all permissible errors within an uncertainty set. Input delays within CBFs were discussed in [13, 14] | CAB | BAC | ACB | BCA | Selection 3 |
**A**: Hybrid beamforming can adopt dual polarization and the associated codebook design to improve the system performance [7, 25]. Although polarization multiplexing without spatial diversity is promising [18], polarization diversity can be combined with spatial diversity to further improve the performance of wireless communication systems [15, 16, 17, 1].**B**:
In particular, several recent research works present the benefit of utilizing the polarization domain in recently proposed communication schemes including, but not limited to, MIMO spatial multiplexing [1]; spatial modulation (SM) [2, 3, 4]; non-orthogonal multiple access (NOMA) [5]; and beamforming [6, 7]**C**: It is validated that the deployment of dual-polarized antennas can not only increase channel capacity [19, 14, 21, 24]; but also improve SER [6, 15, 17, 16, 18] | CBA | ACB | ACB | CAB | Selection 4 |
**A**: The objective is to define a compliance measure that quantifies the recovery capabilities of a given regularizer R𝑅Ritalic_R given a model set ΣΣ\Sigmaroman_Σ.**B**:
To define a notion of optimal regularizer, we simply propose to say that an optimal regularizer is a function that optimizes a (hopefully well-chosen) criterion**C**: We call such a criterion, a compliance measure and specifically aim at defining it such that it should be maximized | CAB | BAC | ABC | ACB | Selection 1 |
**A**: We propose Sample Choice Policy (SCP) for few-shot medical landmark detection task, a novel framework for screening out the representative instances to reduce labor on annotation and achieve high performance simultaneously. SCP learns to map the consistent anatomical information into feature spaces by solving a self-supervised proxy task and extracting representative patches from all images in the first stage**B**: Simultaneously, SCP leverages traditional key point detector to pick out valuable patches with a large local variation or steep edges in images as the representatives. Finally, SCP estimates the relevance between images by averaging the similarities between their representative patches, and selects the images with high average relevance with all other images**C**: Our extensive experiments show that SCP outperforms the conventional policies of template selection and, after refinement, achieves state-of-the-art performances for few-shot medical landmark detection. In the future, we plan to further improve the efficiency and accuracy of finding out the best templates by designing deep models that extract more representative features and to explore the idea of template selection for other applications such as image segmentation.
| BCA | CAB | CAB | ABC | Selection 4 |
**A**: Mok and Chung (Mok and Chung, 2022b) proposed a 3-step registration method, which comprises an affine pre-alignment, a convolutional neural network with forward-backward consistency constraint, and a nonlinear instance optimization. First, possible linear misalignments caused by the tumour mass effect were eliminated with the descent-based affine registration method. Second, conditional deep Laplacian pyramid image registration network with forward-backward consistency (DIRAC) (Mok and Chung, 2022a, 2021) was leveraged to jointly estimate the regions with missing correspondence and bidirectional nonlinear displacement fields for the pre-operative and follow-up scans**B**: This step further improved
the robustness and registration accuracy of initial solutions. The non-parametric deformation was controlled by the forward-backward consistency constraint as in the previous step and was updated using an Adam optimizer together with multi-level continuation to avoid local minima. The implementation of DIRAC is available at https://github.com/cwmok/DIRAC.**C**: During the training phase, regions with missing correspondence were excluded from the similarity measure. This reduced the effect of the pathological regions on the registration algorithm in an unsupervised manner. Finally, non-rigid instance optimization with forward-backward consistency constraint was introduced to correct solutions from the previous step that were biased because of insufficient training and discrepancy in distributions | ACB | CAB | BAC | ABC | Selection 1 |
**A**: This implies that AL(τ)x0=0𝐴𝐿𝜏subscript𝑥00AL(\tau)x_{0}=0italic_A italic_L ( italic_τ ) italic_x start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT = 0
for some x0∈ℝ2∖{0}subscript𝑥0superscriptℝ20x_{0}\in\mathbb{R}^{2}\setminus\{0\}italic_x start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ∖ { 0 } and τ∈ℝ>0𝜏subscriptℝabsent0\tau\in\mathbb{R}_{>0}italic_τ ∈ blackboard_R start_POSTSUBSCRIPT > 0 end_POSTSUBSCRIPT**B**: x0∈ℝ2∖{0}subscript𝑥0superscriptℝ20x_{0}\in\mathbb{R}^{2}\setminus\{0\}italic_x start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ∖ { 0 }**C**: However | BAC | CBA | ABC | ABC | Selection 1 |
**A**:
Input-to-state safety (ISSf) [4, 5, 6]: Here the objective is to ensure that the system state trajectories stay away from a predefined unsafe region, or in other words, stay close to safe region**B**: On the other hand, trajectories starting in the unsafe region will be brought close of the safety boundary where closeness is proportional to the size of the input.**C**: Specifically, trajectories moving from safe zone towards unsafe region will violate safety boundary only in a sense proportional to the size of input | ACB | BAC | BCA | BAC | Selection 1 |
**A**: In the following section, we develop our spectrum allocation model and setting, discuss related work, and give a high-level overview of our approach. In §III, we develop our CNN-based deep learning model and associated techniques for spectrum allocation. We discuss our simulation results in §IV, and end with concluding remarks in §VI.**B**:
Paper Organization**C**: The rest of the paper is organized as follows | BCA | ACB | CAB | BAC | Selection 3 |
**A**: The regret bounds summarized in Table 1 are consistent with regret bounds of full-gradient based online optimization algorithms proved in the existing literature [29, 11, 7, 23] under similar settings**B**: This setup is also adopted in some existing works including [40, 8] to achieve less conservative regret bounds. It should also be noted that, although the online algorithms with different update rules share the regret bounds with the same order, the exact coefficient for the regret bounds may still be different.
**C**: Our dynamic regret bounds for strongly convex functions proved in Theorems 6 and 7 might need multiple updates at each time t𝑡titalic_t | ABC | ACB | BAC | BAC | Selection 2 |
**A**: VGG Net Simonyan and Zisserman (2015) further improved upon AlexNet by introducing deeper models with 16 or 19 weight layers, known as VGG16 and VGG19, respectively. However, increasing the depth of CNNs can lead to overfitting Goodfellow et al. (2016). To address this, Inception Net Szegedy et al**B**: (2017) proposed using filters of different sizes within the same level to widen the network rather than making it deeper. Residual Networks (ResNets) He et al. (2016) introduced skip connections to enable the training of even deeper models. DenseNet Huang et al. (2017) parallelized this approach by connecting each layer to all preceding and succeeding layers, addressing the vanishing gradient problem in deep neural networks.**C**:
Convolutional Neural Networks (CNNs) Fukushima and Wake (1991) are widely used neural network architectures in image classification tasks. They efficiently extract and learn image features through convolution and pooling layers. The pioneering CNN architecture, AlexNet Krizhevsky et al. (2012), employed multiple convolutional and fully connected layers, achieving state-of-the-art performance on the ImageNet dataset | BCA | CAB | CAB | CAB | Selection 1 |
**A**:
The libraries recommended for further processing of the WEMAC dataset are the ones we found most useful for data cleaning and filtering for physiological and speech signals**B**: On the one hand, Matlab® was employed for the physiological data processing using the TEAP toolbox 888https://github.com/Gijom/TEAP**C**: On the other hand, for the speech signals, the librosa library999https://librosa.org/doc/latest/index.html facilitates the loading of the speech signals and its processing, and for the extraction of the features the openSMILE library101010https://audeering.github.io/opensmile-python/, DeepSpectrum module111111https://github.com/DeepSpectrum/DeepSpectrum and the VGGish module121212https://github.com/tensorflow/models/tree/master/research/audioset/vggish/ are used. | ABC | BCA | BCA | ACB | Selection 1 |
**A**: Retina images, positive and negative to AMD, from multiple databases having a range of image qualities and lesions were used**B**:
This work has introduced an alternative approach for generating synthetic images for training deep networks and tested it for AMD identification, which consists in using a retinal image quality assessment model [37] and the StyleGAN2-ADA [38]**C**: Ten different GAN architectures were compared to generate synthetic eye-fundus images and the quality was assessed using the Fréchet Inception Distance (FID), two independent clinical experts who were label blinded and deep-learning classification. Different percentages of synthetic data were employed in the augmentation. | CBA | BCA | BAC | BCA | Selection 3 |
**A**:
Once fixed feasible control inputs at the vertices of the invariant set have been computed, a variable structure controller either takes a convex combination of those values by exploiting the vertex reconstruction of any state belonging to such a set, or coincides with a purely linear gain stemming from a triangulation, i.e., a simplicial partition 16, of the underlying set**B**: If the simplicial partition-based implementation is considered, then one has also to account for the complexity of the resulting invariant set, which is typically high 6, 8, 49, 10, 2, 9. These methods can therefore require significant memory to store the vectors and/or matrices describing every simplicial partition and associated linear control gain. As a common drawback affecting both the implementations, however, fixing the input values at the vertices may result in poor control performance for the stabilization task.**C**: These methods therefore require one to solve a linear program (LP) online or to generate a lookup table to identify the region in which the current state resides | ACB | BCA | BCA | CBA | Selection 1 |
**A**: [26] discussed privacy-preserving FL with Secure Aggregation [13] and Differential Privacy [1] for 2D medical image classification tasks**B**: While PPIR focuses on the privacy-preserving formulation of classical image registration methods based on gradient-based optimization, throughout the past years the research community has been steering the attention towards deep learning (DL)-based image registration [42, 27, 46, 9].
Among the medical imaging application of privacy-preserving methodologies, Kaissis et al**C**: However, as highlighted by [26], deploying DL models for privacy-preserving inference nowadays is predominantly achievable through Multi-Party Computation (MPC). This process necessitates multiple servers and incurs significant overhead, primarily attributed to the size of the DL model, especially when handling 3D image registration tasks within a DL-based framework [9]. | CAB | BAC | ABC | CAB | Selection 2 |
**A**: Related Work. Our work follows the previous studies of POMDPs. In general, solving a POMDP is intractable from both the computational and the statistical perspectives (Papadimitriou and Tsitsiklis, 1987; Vlassis et al., 2012; Azizzadenesheli et al., 2016; Guo et al., 2016; Jin et al., 2020a). Given such computational and statistical barriers, previous works attempt to identify tractable POMDPs. In particular, Azizzadenesheli et al. (2016); Guo et al. (2016); Jin et al. (2020a); Liu et al. (2022) consider the tabular POMDPs with (left) invertible emission matrices. Efroni et al**B**: (2016); Guo et al. (2016) analyze POMDPs where an arbitrary policy can conduct efficient exploration. Similarly, Cayci et al. (2022) consider POMDPs with a finite concentrability coefficient (Munos, 2003; Chen and Jiang, 2019), where the visitation density of an arbitrary policy is close to that of the optimal policy. In contrast, Jin et al. (2020a); Efroni et al. (2022); Cai et al. (2022) consider POMDPs where strategic exploration is necessary. In our work, we follow Jin et al. (2020a); Efroni et al. (2022); Cai et al. (2022) and design strategic exploration to attain sample efficiency in solving the POMDPs.**C**: (2022) considers the POMDPs where the state is fully determined by the most recent observations of a fixed length. Cayci et al. (2022) analyze POMDPs where a finite internal state can approximately determine the state. In contrast, we analyze POMDPs with the low-rank transition and allow the state and observation spaces to be arbitrarily large. Meanwhile, our analysis hinges on the future and past sufficiency assumptions, which only require that the density of the state is identified by that of the future and past observations, respectively. In recent work, Cai et al. (2022) also utilizes the low-rank property in the transition. Nevertheless, Cai et al. (2022) assumes that the feature representation of state-action pairs is known, thus relieving the agent from feature learning. In contrast, we aim to recover the efficient state-action representation for planning.
In terms of the necessity of exploration, Azizzadenesheli et al | ACB | ABC | ABC | BCA | Selection 1 |
**A**: This part of the system, as mentioned
above, is fast in comparison to the first part**B**: δl,δm,δnsubscript𝛿𝑙subscript𝛿𝑚subscript𝛿𝑛\delta_{l},\delta_{m},\delta_{n}italic_δ start_POSTSUBSCRIPT italic_l end_POSTSUBSCRIPT , italic_δ start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT , italic_δ start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT**C**: More precisely, one | CBA | BCA | ABC | BAC | Selection 4 |
**A**: Whereafter, Corollary 2 gives more intuitive convergence conditions for the case with Markovian switching graphs and regression matrices. Finally, Theorem 3 establishes an upper bound for the regret of the algorithm by Lemma 3, and Theorem 4 gives a non-asymptotic rate for the algorithm. The proofs of theorems, Proposition 1 and Corollary 2 are in Appendix A, and those of the lemmas in this section are in Appendix B.
**B**: Based on which, Theorem 1 proves the almost sure convergence of the algorithm. Then, Theorem 2 gives intuitive convergence conditions for the case with balanced conditional digraphs by Lemma 2**C**: The convergence and performance analysis of the algorithm (6) are presented in this section. First, Lemma 1 gives a nonnegative supermartingale type inequality of the squared estimation error | BCA | BCA | BCA | CBA | Selection 4 |
**A**: Middle column: Reconstructions using SDGLR for regularization. Right column: Reconstructions using SDGGLR for regularization.**B**:
Figure 10: Image patch denoising and interpolation results (PSNR) using SDGLR versus SDGGLR**C**: Left column: Corrupted images | CAB | BAC | ACB | BCA | Selection 1 |
**A**: For all images the signal intensity (SI) and absolute difference are represented in arbitrary units. The evaluation metrics on the test-set of 3300 images from MRiLab can be seen in Table 1.**B**:
Figures 4 and 4 show examples of axial slices from our testset MRiLab for both models**C**: The figures show the input, ground truth, model’s prediction and the absolute difference between the ground truth and the prediction | BCA | BAC | CAB | ABC | Selection 3 |
**A**: This approach uses a dual-interleaved counter to ensure uninterrupted photon counting during each bit, avoiding the dead time compared to the one sequential counter [45]**B**: Additionally, utilizing an asynchronous detection mechanism for the counters eliminates the need for a high-frequency sampling clock, simplifying the design for low-data rate systems.**C**:
3) An FPGA-based customized design is implemented for photon accumulation | ABC | CAB | ABC | BCA | Selection 4 |
**A**: Once again, the selection of scenarios is intended to underscore the proposal’s robustness under unfavorable conditions, while also incorporating conservative assumptions**B**: These scenarios serve to demonstrate that, from a GN&C perspective, an autonomous robotic spacecraft does not necessarily require extensive navigation campaigns to reduce uncertainties to very low levels**C**: Moreover, even in challenging scenarios, the proposal exhibits resilience, indicating that a robotic spacecraft can handle those difficult conditions. Other algorithms could be developed to identify such challenging scenarios and guide the spacecraft toward more favorable operating conditions.
| BAC | CBA | BCA | ABC | Selection 4 |
**A**: Further, when the UAVs are operating over bodies of water, such as lakes, the strength of the reflected path is stronger than when operating over land.**B**:
It has been observed experimentally that when the UAVs operate in an open field with a floor that is flat enough, the propagation channel follows a two-ray model [95]**C**: As the altitude of the UAVs increases, the strength of the reflected path decreases | CBA | BCA | CAB | CBA | Selection 3 |
**A**: The first is adopted from [Lanzon and Bhowmick, 2023], which provides a class of negative imaginary systems characterised by an LTI auxiliary system and a dynamic supply rate**B**: The example is paraphrased in terms of Definition 2.
**C**: Several motivating examples are provided in this subsection | ABC | BCA | BAC | CBA | Selection 2 |
**A**: The RCBF has a form that is easy to imagine as a barrier, while the ZCBF is defined outside the safe set, allowing the design of control laws with robustness.**B**:
In the context of a CBF, the control objective is to make a specific subset, which is said to be a safe set, on the state space invariance forward in time (namely, forward invariance [2])**C**: There are various types of CBFs, the most commonly used currently are a reciprocal control barrier function (RCBF) [2, 4, 5] and a zeroing control barrier function (ZCBF) [2, 3, 6]: the RCBF is a positive function that diverges from the inside of the safe set toward the boundary, while the ZCBF is a function that is zero at the boundary of the safe set | ABC | CAB | ABC | CBA | Selection 2 |
**A**: Combining the power grid strength quantified by gSCR in this section and the analysis of the voltage source behaviors of GFM converters in Section II, it is once again emphasized that it is necessary to install GFM converters to provide effective voltage source behaviors and thus enhance the power grid strength, which can be quantified by gSCR**B**: On this basis, we will show in the next section that the integration of GFM converters has a similar effect to installing ideal voltage sources (i.e., infinite buses) in series with an equivalent internal impedance in the network**C**: Further, we will derive the closed-form relationship between the gSCR and the capacity ratio between the GFM and the GFL converters to simplify the analysis of how large the capacity should be to meet certain stability margins.
| BCA | ACB | ABC | CAB | Selection 3 |
**A**: To further confirm the efficiency of the proposed GSAU, we compare it with some other FFNs**B**: In Tab. 5, we validate four advanced designs: MLP, Simple Gate, CFF, and our GSAU. The GSAU delivers comparable performance to the powerful CFF while occupying 73% of the parameters and calculations, showing effectiveness.**C**:
Study on FFNs | ABC | BCA | BAC | BAC | Selection 2 |
**A**:
There are a number of exciting future directions here**B**: First, we do not provide formal safety guarantees on the obtained BRTs**C**: We would like to explore recent work on providing safety assurances for DeepReach [25] to overcome this limitation. Addressing parameter uncertainty and non-parameterizable enviornment changes are also promising future directions. In addition, it will be interesting to validate the proposed approach on hardware testbeds. | BAC | ACB | CBA | ABC | Selection 4 |
**A**: After highlighting several advantages of the directive RIS architecture, we shall discuss its disadvantages as compared to the reflective RIS configuration**B**: Switching matrices are used in several applications such as satellite communications [37]. As the frequency and the number of ports increase, however, the losses of signal traces and switches become overwhelming, and designing a printed circuit board (PCB) layout with global interconnections and with minimal signal integrity issues is no easy task.
**C**: In addition, to the need for a (metasurface) lens for analog DFT processing, the major issue is the need for longer RF interconnections (see Fig. 7) and a multistage-switching network for conductive RF routing which is in general quite challenging at high frequencies | BCA | BCA | ACB | CAB | Selection 3 |
**A**: The conversation-type task focuses on understanding the intent, language, and sentiment to provide humans with free-flow conversations**B**:
The speech recognition task can be further divided into conversation-type tasks (e.g., human inquiry) and command-type tasks (e.g., smart home control) depending on the speech content**C**: The command-type task focuses on parsing the specific command over the transmitted speech and then controlling the target device/robot. | ABC | CAB | BAC | CAB | Selection 3 |
**A**: We formulate this sensor placement problem as a bilevel optimization with an upper level that minimizes the number of sensors and chooses sensor alarm thresholds and a lower level that computes the most extreme voltage magnitudes within given ranges of power injection variability**B**:
In this paper, we consider a sensor placement problem which seeks to locate the minimum number of sensors and determine corresponding sensor alarm thresholds in order to reliably identify all possible violations of voltage magnitude limits in a distribution system**C**: This problem additionally aims to reduce the number of false positive alarms, i.e., violations of the sensors’ alarm thresholds that do not correspond to an actual voltage limit violation. | BAC | BCA | ABC | BCA | Selection 1 |
**A**: When a node is far from speech sources, the signal-to-noise ratio (SNR) of the signal collected by the node is low**B**:
Ad-hoc microphone arrays are distributed throughout a large acoustic scene**C**: Our preliminary studies show that (i) SNR strongly affects the accuracy of the DOA estimation at the node, and (ii) taking all nodes with the SNR varying in a large range results in large DOA estimation errors. Therefore, we need to select only the nodes with high SNRs for the 2D coordinate estimation. | BAC | ABC | CBA | ACB | Selection 1 |
**A**: To confirm the CNN’s reduction in performance near obstacles, we moved the robot’s starting position away from the chairs for the simulation in fig**B**: 7(b)**C**: The CNN could then see a less occluded image (Fig. 8 right) and predicted a better waypoint to eventually reach the goal (Fig. 8 left).
| CAB | CBA | CAB | ABC | Selection 4 |
**A**: We find that the stateless-decoder models consistently outperform LSTM-decoder models both in terms of accuracy and speed**B**: We actually have done extensive experiments with RNN-Ts with standard LSTM-decoders as well and all our conclusions about the multi-blank methods still hold.
**C**: We evaluate our methods with a Conformer-RNN-T model with a stateless decoder | BAC | CAB | BAC | BCA | Selection 4 |
**A**: The detection models achieve the best performance on seen test but obtain the worst results on unseen test set at -5dB**B**: However, it is hard to distinguish the real one from the fake at -5dB when a mismatch exists between the unseen test set and the training set.
**C**: When the data distribution of the seen test set matches the training set, it is very easy to discriminate the real from the fake by using known strong acoustic scenes and known obvious distorted manipulation traces at -5dB | ABC | ABC | ACB | BAC | Selection 3 |
**A**: The identification of time varying linear systems (TV-ERA and TV-OKID) also builds on the earlier work on time-varying discrete time system identification [5, 12]. The OKID and TV-OKID explain the usage of an ARMA model to be equivalent to an observer in the loop system, and postulate that the identified observer is a deadbeat observer similar to the work in [13]. **B**:
The pioneering work in system identification for LTI systems is the Ho-Kalman realization theory [6] of which the Eigensystem Realization Algorithm (ERA) algorithm is one of the most popular [4]. Another system identification method, namely, q𝑞qitalic_q-Markov covariance equivalent realization, generates a stable LTI system model that matches the first “q𝑞qitalic_q” Markov parameters of the underlying system and also matches the equivalent steady-state covariance response/ parameters of the identified system [7, 8]. These algorithms assume stable systems so that the response can be modeled using a finite set of parameters relating the past inputs to the current output (moving-average (MA) model)**C**: For lightly damped and marginally stable systems, the length of history to be considered and the parameters to be estimated becomes very long, leading to numerical issues when solving for the parameters. To overcome this issue, the observer Kalman identification algorithm (OKID) [9] uses an ARMA model, rather than an MA model, consisting of past outputs and controls to model the current output. The time-varying counterparts of the ERA and OKID - TV-ERA and TV-OKID - were developed in [10] and [11], respectively | CAB | ACB | BAC | ABC | Selection 1 |
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