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Mobility-as-a-Service (MaaS) integrates different transport modalities and
can support more personalisation of travellers' journey planning based on their
individual preferences, behaviours and wishes. To fully achieve the potential
of MaaS, a range of AI (including machine learning and data mining) algorithms
are needed to learn personal requirements and needs, to optimise journey
planning of each traveller and all travellers as a whole, to help transport
service operators and relevant governmental bodies to operate and plan their
services, and to detect and prevent cyber attacks from various threat actors
including dishonest and malicious travellers and transport operators. The
increasing use of different AI and data processing algorithms in both
centralised and distributed settings opens the MaaS ecosystem up to diverse
cyber and privacy attacks at both the AI algorithm level and the connectivity
surfaces. In this paper, we present the first comprehensive review on the
coupling between AI-driven MaaS design and the diverse cyber security
challenges related to cyber attacks and countermeasures. In particular, we
focus on how current and emerging AI-facilitated privacy risks (profiling,
inference, and third-party threats) and adversarial AI attacks (evasion,
extraction, and gamification) may impact the MaaS ecosystem. These risks often
combine novel attacks (e.g., inverse learning) with traditional attack vectors
(e.g., man-in-the-middle attacks), exacerbating the risks for the wider
participation actors and the emergence of new business models. | arXiv |
NGC 5139 ($\omega$ Cen), is the closest candidate of a Nuclear Star Cluster
that has been stripped of its host galaxy in the Milky Way. Despite extensive
studies through the last decades, many open questions about the cluster remain,
including the properties of the binary population. In this study we use MUSE
multi-epoch spectroscopy to identify binary systems in $\omega$ Cen. The
observations span 8 years, with a total of 312 248 radial velocity measurements
for 37 225 stars. Following the removal of known photometric variables, we
identify 275 stars that show RV variations, corresponding to a discovery
fraction of 1.4$\pm$0.1%. Using dedicated simulations, we find that our data is
sensitive to 70$\pm$10% of the binaries expected in the sample, resulting in a
completeness-corrected binary fraction of 2.1$\pm$0.4% in the central region of
$\omega$ Cen. We find similar binary fractions for all stellar evolutionary
stages covered by our data, the only notable exception being the blue straggler
stars, which show an enhanced binary fraction. We also find no distinct
correlation with distance from the cluster centre, indicating a limited amount
of mass segregation within the half-light radius of $\omega$ Cen. | arXiv |
The dissociation of quarkonium states with different binding energies
produced in heavy-ion collisions is a powerful probe for investigating the
formation and properties of the quark-gluon plasma. The ratio of production
cross-sections of $\psi(2S)$ and $J/\psi$ mesons times the ratio of their
branching fractions into the dimuon final state is measured as a function of
centrality using data collected by the LHCb detector in PbPb collisions at
$\sqrt{s_{\text{NN}}}$ = 5.02 TeV. The measured ratio shows no dependence on
the collision centrality, and is compared to the latest theory predictions and
to the recent measurements in literature. | arXiv |
We define the Super-Unique-Tarski problem, which is a Tarski instance in
which all slices are required to have a unique fixed point. We show that
Super-Unique-Tarski lies in UEOPL under promise-preserving reductions. | arXiv |
We assess the prospects for detecting gravitational wave echoes arising due
to the quantum nature of black hole horizons with LISA. In a recent proposal,
Bekenstein's black hole area quantization is connected to a discrete absorption
spectrum for black holes in the context of gravitational radiation.
Consequently, for incoming radiation at the black hole horizon, not all
frequencies are absorbed, raising the possibility that the unabsorbed radiation
is reflected, producing an echo-like signal closely following the binary
coalescence waveform. In this work, we further develop this proposal by
introducing a robust, phenomenologically motivated model for black hole
reflectivity. Using this model, we calculate the resulting echoes for an
ensemble of Numerical Relativity waveforms and examine their detectability with
the LISA space-based interferometer. Our analysis demonstrates promising
detection prospects and shows that, upon detection, LISA provides a direct
probe of the Bekenstein-Hawking entropy. In addition, we find that the
information extractable from LISA data offers valuable constraints on a wide
range of quantum gravity theories. | arXiv |
There is a persistent $\sim 5 \sigma$ tension between the value of the Hubble
constant, as derived from either the local distance ladder or the cosmic
microwave background, signaling either unaccounted for systematics in the
measurements or `new physics'. Determining the Hubble constant using Type Ia
supernovae requires non-trivial and accurate corrections for dust extinction.
To circumvent this obstacle, we here determine the Hubble constant from blue,
and hence presumably unextinguished, supernovae, only. For two different
compilations of Type Ia supernova data and lightcurve fitting methods we find
that the derived Hubble constant is consistently lower by $\sim$ 3 km s$^{-1}$
Mpc$^{-1}$ ($\sim 70$ km s$^{-1}$ Mpc$^{-1}$), and within 1 $\sigma$ of the
Cosmic Microwave Background measurement, when using only blue supernovae as
opposed to using all supernovae. Although the number of blue calibrating Type
Ia supernovae is small, this indicates potential systematic effects in dust
corrections in standard supernova cosmology. Upcoming major transient surveys
will discover numerous unextinguished SNe~Ia, and thus be able to increase
precision of the Hubble constant measured from blue SNe~Ia, heralding a
promising path toward resolving the Hubble constant tension. | arXiv |
To address the challenges of high computational costs and long-distance
dependencies in exist ing video understanding methods, such as CNNs and
Transformers, this work introduces RWKV to the video domain in a novel way. We
propose a LSTM CrossRWKV (LCR) framework, designed for spatiotemporal
representation learning to tackle the video understanding task. Specifically,
the proposed linear complexity LCR incorporates a novel Cross RWKV gate to
facilitate interaction be tween current frame edge information and past
features, enhancing the focus on the subject through edge features and globally
aggregating inter-frame features over time. LCR stores long-term mem ory for
video processing through an enhanced LSTM recurrent execution mechanism. By
leveraging the Cross RWKV gate and recurrent execution, LCR effectively
captures both spatial and temporal features. Additionally, the edge information
serves as a forgetting gate for LSTM, guiding long-term memory management.Tube
masking strategy reduces redundant information in food and reduces
overfitting.These advantages enable LSTM CrossRWKV to set a new benchmark in
video under standing, offering a scalable and efficient solution for
comprehensive video analysis. All code and models are publicly available. | arXiv |
Extended gravitational models have gained large attention in the last couple
of decades. In this work, we examine the solution space of vacuum, static, and
spherically symmetric spacetimes within $F(R)$ theories, introducing novel
methods that reduce the vacuum equations to a single second-order equation. For
the first time, we derive analytic expressions for the metric functions in
terms of the arbitrary functional $F(R)$, providing detailed insight into how
the gravitational action impacts the structure of spacetime. We analyze
conditions under which solutions are asymptotically flat, regular at the core,
and contain an event horizon, obtaining explicit expressions for entropy,
temperature, and specific heat in terms of $F(R)$. By using a single metric
degree of freedom, we identify the most general solution and examine its
(un)physical properties, showing that resolving singularities is not possible
within this restricted framework in vacuum. For the general case involving two
metric functions, we use several approximation schemes to explore corrections
to Schwarzschild-(anti)de Sitter spacetimes, finding that $F(R)$ extensions to
General Relativity induce instabilities that are not negligible. Finally,
through an analysis of axial perturbations, we derived a general expression for
the potential of quasinormal modes of a black hole as a function of the
arbitrary Lagrangian. | arXiv |
This paper presents a data-driven min-max model predictive control (MPC)
scheme for linear parameter-varying (LPV) systems. Contrary to existing
data-driven LPV control approaches, we assume that the scheduling signal is
unknown during offline data collection and online system operation. Assuming a
quadratic matrix inequality (QMI) description for the scheduling signal, we
develop a novel data-driven characterization of the consistent system matrices
using only input-state data. The proposed data-driven min-max MPC minimizes a
tractable upper bound on the worst-case cost over the consistent system
matrices set and over all scheduling signals satisfying the QMI. The proposed
approach guarantees recursive feasibility, closed-loop exponential stability
and constraint satisfaction if it is feasible at the initial time. We
demonstrate the effectiveness of the proposed method in simulation. | arXiv |
The butterfly diagram of the solar cycle exhibits a poleward migration of the
diffuse magnetic field resulting from the decay of trailing sunspots. It is one
component of what is sometimes referred to as the "rush to the poles". We
investigate under which conditions the rush to the poles can be reproduced in
flux-transport Babcock-Leighton dynamo models. We identify three main ways to
reproduce it: a flux emergence probability that decreases rapidly with
latitude; a threshold in subsurface toroidal field strength between slow and
fast emergence; and an emergence rate based on magnetic buoyancy. We find that
all three mechanisms lead to solar-like butterfly diagrams, but which present
notable differences between them. The shape of the butterfly diagram is very
sensitive to model parameters for the threshold prescription, while most models
incorporating magnetic buoyancy converge to very similar butterfly diagrams,
with butterfly wings widths of $\lesssim\pm 30^\circ$, in very good agreement
with observations. With turbulent diffusivities above $35~\text{km}^2/\text{s}$
but below about $40~\text{km}^2/\text{s}$, buoyancy models are strikingly
solar-like. The threshold and magnetic buoyancy prescriptions make the models
non-linear and as such can saturate the dynamo through latitudinal quenching.
The period of the models involving buoyancy is independent of the source term
amplitude, but emergence loss increases it by $\simeq 60\%$. For the rush to
the poles to be visible, a mechanism suppressing (enhancing) emergences at high
(low) latitudes must operate. It is not sufficient that the toroidal field be
stored at low latitudes for emergences to be limited to low latitudes. From
these models we infer that the Sun is not in the advection-dominated regime,
but also not in the diffusion-dominated regime. The cycle period is set through
a balance between advection, diffusion and flux emergence. | arXiv |
The main challenges hindering the adoption of deep learning-based systems in
clinical settings are the scarcity of annotated data and the lack of
interpretability and trust in these systems. Concept Bottleneck Models (CBMs)
offer inherent interpretability by constraining the final disease prediction on
a set of human-understandable concepts. However, this inherent interpretability
comes at the cost of greater annotation burden. Additionally, adding new
concepts requires retraining the entire system. In this work, we introduce a
novel two-step methodology that addresses both of these challenges. By
simulating the two stages of a CBM, we utilize a pretrained Vision Language
Model (VLM) to automatically predict clinical concepts, and a Large Language
Model (LLM) to generate disease diagnoses based on the predicted concepts. We
validate our approach on three skin lesion datasets, demonstrating that it
outperforms traditional CBMs and state-of-the-art explainable methods, all
without requiring any training and utilizing only a few annotated examples. The
code is available at
https://github.com/CristianoPatricio/2-step-concept-based-skin-diagnosis. | arXiv |
We study all the ways that a given convex body in $d$ dimensions can break
into countably many pieces that move away from each other rigidly at constant
velocity, with no rotation or shearing. The initial velocity field is locally
constant, but may be continuous and/or fail to be integrable. For any choice of
mass-velocity pairs for the pieces, such a motion can be generated by the
gradient of a convex potential that is affine on each piece. We classify such
potentials in terms of a countable version of a theorem of Alexandrov for
convex polytopes, and prove a stability theorem. For bounded velocities, there
is a bijection between the mass-velocity data and optimal transport flows
(Wasserstein geodesics) that are locally incompressible.
Given any rigidly breaking velocity field that is the gradient of a
continuous potential, the convexity of the potential is established under any
of several conditions, such as the velocity field being continuous, the
potential being semi-convex, the mass measure generated by a convexified
transport potential being absolutely continuous, or there being a finite number
of pieces. Also we describe a number of curious and paradoxical examples having
fractal structure. | arXiv |
Document-level Event Argument Extraction (EAE) faces two challenges due to
increased input length: 1) difficulty in distinguishing semantic boundaries
between events, and 2) interference from redundant information. To address
these issues, we propose two methods. The first method introduces the Co and
Structure Event Argument Extraction model (CsEAE) based on Small Language
Models (SLMs). CsEAE includes a co-occurrences-aware module, which integrates
information about all events present in the current input through context
labeling and co-occurrences event prompts extraction. Additionally, CsEAE
includes a structure-aware module that reduces interference from redundant
information by establishing structural relationships between the sentence
containing the trigger and other sentences in the document. The second method
introduces new prompts to transform the extraction task into a generative task
suitable for Large Language Models (LLMs), addressing gaps in EAE performance
using LLMs under Supervised Fine-Tuning (SFT) conditions. We also fine-tuned
multiple datasets to develop an LLM that performs better across most datasets.
Finally, we applied insights from CsEAE to LLMs, achieving further performance
improvements. This suggests that reliable insights validated on SLMs are also
applicable to LLMs. We tested our models on the Rams, WikiEvents, and MLEE
datasets. The CsEAE model achieved improvements of 2.1\%, 2.3\%, and 3.2\% in
the Arg-C F1 metric compared to the baseline, PAIE~\cite{PAIE}. For LLMs, we
demonstrated that their performance on document-level datasets is comparable to
that of SLMs~\footnote{All code is available at
https://github.com/simon-p-j-r/CsEAE}. | arXiv |
Let $M$ be a compact complex $n$-manifold. A Gauduchon metric is a Hermitian
metric whose fundamental 2-form $\omega$ satisfies the equation
$dd^c(\omega^{n-1})=0$. Paul Gauduchon has proven that any Hermitian metric is
conformally equivalent to a Gauduchon metric, which is unique (up to a constant
multiplier) in its conformal class. Then $d^c(\omega^{n-1})$ is a closed
$(2n-1)$-form; the set of cohomology classes of all such forms, called the
Lee-Gauduchon cone, is a convex cone, superficially similar to the Kahler cone.
We prove that the Lee-Gauduchon cone is a bimeromorphic invariant, and compute
it for several classes of non-Kahler manifolds. | arXiv |
Background: Screening trials require large sample sizes and long
time-horizons to demonstrate mortality reductions. We recently proposed
increasing statistical power by testing stored control-arm specimens, called
the Intended Effect (IE) design. To evaluate feasibility of the IE design, the
US National Cancer Institute (NCI) is collecting blood specimens in the
control-arm of the NCI Vanguard Multicancer Detection pilot feasibility trial.
However, key assumptions of the IE design require more investigation and
relaxation. Methods: We relax the IE design to (1) reduce costs by testing only
a stratified sample of control-arm specimens by incorporating
inverse-probability sampling weights, (2) correct for potential loss-of-signal
in stored control-arm specimens, and (3) correct for non-compliance with
control-arm specimen collections. We also examine sensitivity to unintended
effects of screening. Results: In simulations, testing all primary-outcome
control-arm specimens and a 50% sample of the rest maintains nearly all the
power of the IE while only testing half the control-arm specimens. Power
remains increased from the IE analysis (versus the standard analysis) even if
unintended effects exist. The IE design is robust to some loss-of-signal
scenarios, but otherwise requires retest-positive fractions that correct bias
at a small loss of power. The IE can be biased and lose power under control-arm
non-compliance scenarios, but corrections correct bias and can increase power.
Conclusions: The IE design can be made more cost-efficient and robust to
loss-of-signal. Unintended effects will not typically reduce the power gain
over the standard trial design. Non-compliance with control-arm specimen
collections can cause bias and loss of power that can be mitigated by
corrections. Although promising, practical experience with the IE design in
screening trials is necessary. | arXiv |
Game solving is the process of finding the theoretical outcome for a game,
assuming that all player choices are optimal. This paper focuses on a technique
that can reduce the heuristic search space significantly for 7x7 Killall-Go. In
Go and Killall-Go, live patterns are stones that are protected from opponent
capture. Mutual life, also referred to as seki, is when both players' stones
achieve life by sharing liberties with their opponent. Whichever player
attempts to capture the opponent first will leave their own stones vulnerable.
Therefore, it is critical to recognize seki patterns to avoid putting oneself
in jeopardy. Recognizing seki can reduce the search depth significantly. In
this paper, we enumerate all seki patterns up to a predetermined area size,
then store these patterns into a seki table. This allows us to recognize seki
during search, which significantly improves solving efficiency for the game of
Killall-Go. Experiments show that a day-long, unsolvable position can be solved
in 482 seconds with the addition of a seki table. For general positions, a 10%
to 20% improvement in wall clock time and node count is observed. | arXiv |
In this work, we present a novel Koopman spectrum-based reachability
verification method for nonlinear systems. Contrary to conventional methods
that focus on characterizing all potential states of a dynamical system over a
presupposed time span, our approach seeks to verify the reachability by
assessing the non-emptiness of estimated time-to-reach intervals without
engaging in the explicit computation of reachable set. Based on the spectral
analysis of the Koopman operator, we reformulate the problem of verifying
existence of reachable trajectories into the problem of determining feasible
time-to-reach bounds required for system reachability. By solving linear
programming (LP) problems, our algorithm can effectively estimate all potential
time intervals during which a dynamical system can enter (and exit) target sets
from given initial sets over an unbounded time horizon. Finally, we demonstrate
our method in challenging settings, such as verifying the reachability between
non-convex or even disconnected sets, as well as backward reachability and
multiple entries into target sets. Additionally, we validate its applicability
in addressing real-world challenges and scalability to high-dimensional systems
through case studies in verifying the reachability of the cart-pole and
multi-agent consensus systems. | arXiv |
This study explores the effectiveness of Large Language Models in meal
planning, focusing on their ability to identify and decompose compound
ingredients. We evaluated three models-GPT-4o, Llama-3 (70b), and Mixtral
(8x7b)-to assess their proficiency in recognizing and breaking down complex
ingredient combinations. Preliminary results indicate that while Llama-3 (70b)
and GPT-4o excels in accurate decomposition, all models encounter difficulties
with identifying essential elements like seasonings and oils. Despite strong
overall performance, variations in accuracy and completeness were observed
across models. These findings underscore LLMs' potential to enhance
personalized nutrition but highlight the need for further refinement in
ingredient decomposition. Future research should address these limitations to
improve nutritional recommendations and health outcomes. | arXiv |
Rotation is an important, yet poorly-modelled phenomenon of stellar structure
and evolution. Accurate estimates of internal rotation rates are therefore
valuable for constraining stellar evolution models. We aim to assess the
accuracy of asteroseismic estimates of internal rotation rates and how these
depend on the fundamental stellar parameters. We apply the recently-developed
method called extended-MOLA inversions to infer localised estimates of internal
rotation rates of synthetic observations of red giants. We search for suitable
reference stellar models following a grid-based approach, and assess the
robustness of the resulting inferences to the choice of reference model. We
find that matching the mixed mode pattern between the observation and the
reference model is an important criterion to select suitable reference models.
We propose to i) select a set of reference models based on the correlation
between the observed rotational splittings and the mode-trapping parameter ii)
compute rotation rates for all these models iii) use the mean value obtained
across the whole set as the estimate of the internal rotation rates. We find
that the effect of a near surface perturbation in the synthetic observations on
the rotation rates estimated based on the correlation between the observed
rotational splittings and the mode-trapping parameter is negligible. We
conclude that when using an ensemble of reference models, constructed based on
matching the mixed mode pattern, the input rotation rates can be recovered
across a range of fundamental stellar parameters like mass, mixing-length
parameter and composition. Further, red-giant rotation rates determined in this
way are also independent of a near surface perturbation of stellar structure. | arXiv |
The design and optimization of optical components, such as Bragg gratings,
are critical for applications in telecommunications, sensing, and photonic
circuits. To overcome the limitations of traditional design methods that rely
heavily on computationally intensive simulations and large datasets, we propose
an integrated methodology that significantly reduces these burdens while
maintaining high accuracy in predicting optical response. First, we employ a
Bayesian optimization technique to strategically select a limited yet
informative number of simulation points from the design space, ensuring that
each contributes maximally to the model's performance. Second, we utilize
singular value decomposition to effectively parametrize the entire reflectance
spectra into a reduced set of coefficients, allowing us to capture all
significant spectral features without losing crucial information. Finally, we
apply XGBoost, a robust machine learning algorithm, to predict the entire
reflectance spectra from the reduced dataset. The combination of Bayesian
optimization for data selection, SVD for full-spectrum fitting, and XGBoost for
predictive modeling provides a powerful, generalizable framework for the design
of optical components. | arXiv |
We review the multivariate holomorphic functional calculus for tuples in a
commutative Banach algebra and establish a simple "na\"ive" extension to
commuting tuples in a general Banach algebra. The approach is na\"ive in the
sense that the na\"ively defined joint spectrum maybe too big. The advantage of
the approach is that the functional calculus then is given by a simple concrete
formula from which all its continuity properties can easily be derived.
We apply this framework to multivariate functions arising as divided
differences of a univariate function. This provides a rich set of examples to
which our na\"ive calculus applies. Foremost, we offer a natural and
straightforward proof of the Connes-Moscovici Rearrangement Lemma in the
context of the multivariate holomorphic functional calculus. Secondly, we show
that the Daletski-Krein type noncommutative Taylor expansion is a natural
consequence of our calculus. Also Magnus' Theorem which gives a nonlinear
differential equation for the $\log$ of the solutions to a linear matrix ODE
follows naturally and easily from our calculus. Finally, we collect various
combinatorial related formulas. | arXiv |
Differential privacy (DP) is a formal notion that restricts the privacy
leakage of an algorithm when running on sensitive data, in which
privacy-utility trade-off is one of the central problems in private data
analysis. In this work, we investigate the fundamental limits of differential
privacy in online learning algorithms and present evidence that separates three
types of constraints: no DP, pure DP, and approximate DP. We first describe a
hypothesis class that is online learnable under approximate DP but not online
learnable under pure DP under the adaptive adversarial setting. This indicates
that approximate DP must be adopted when dealing with adaptive adversaries. We
then prove that any private online learner must make an infinite number of
mistakes for almost all hypothesis classes. This essentially generalizes
previous results and shows a strong separation between private and non-private
settings since a finite mistake bound is always attainable (as long as the
class is online learnable) when there is no privacy requirement. | arXiv |
We explore the phenomenology of Weinberg's $Z_2\times Z_2$ symmetric
three-Higgs-doublet potential, allowing for spontaneous violation of CP due to
complex vacuum expectation values. An overview of all possible ways of
satisfying the stationary-point conditions is given, with one, two or three
non-vanishing vacuum expectation values, together with conditions for CP
conservation in terms of basis invariants. All possible ways of satisfying the
conditions for CP conservation are given. Scans of allowed parameter regions
are given, together with measures of CP violation, in terms of the invariants.
The light states identified in an earlier paper are further explored in terms
of their CP-violating couplings. Loop-induced CP violation in $WWZ$ couplings,
as well as charge-asymmetric scattering are also commented on. | arXiv |
In this paper, we revisit the concept of noncommuting common causes; refute
two objections raised against them, the triviality objection and the lack of
causal explanatory force; and explore how their existence modifies the EPR
argument. More specifically, we show that 1) product states screening off all
quantum correlations do not compromise noncommuting common causal explanations;
2) noncommuting common causes can satisfy the law of total probability; 3)
perfect correlations can have indeterministic noncommuting common causes; and,
as a combination of the above claims, 4) perfect correlations can have
noncommuting common causes which are both nontrivial and satisfy the law of
total probability. | arXiv |
Given a symmetric monoidal stable $\infty$-category $\mathcal{C}$ which is
rigidly-compactly generated and a set of compact objects $\mathcal{K}$ of
$\mathcal{C}$, one can form the subcategories of $\mathcal{K}$-complete and
$\mathcal{K}$-local objects. The goal of this paper is to explain how to
recover $\mathcal{C}$ from its $\mathcal{K}$-local and $\mathcal{K}$-complete
subcategories while retaining the symmetric monoidal structure. Specializing to
the case where $\mathcal{C}$ is the $\infty$-category of $G$-spectra for a
finite group $G$, our result can be viewed as a symmetric monoidal variant of
the isotropy separation decomposition, a version of which appeared previously
in work of Krause. | arXiv |
We analyze the universality and generalization of graph neural networks
(GNNs) on attributed graphs, i.e., with node attributes. To this end, we
propose pseudometrics over the space of all attributed graphs that describe the
fine-grained expressivity of GNNs. Namely, GNNs are both Lipschitz continuous
with respect to our pseudometrics and can separate attributed graphs that are
distant in the metric. Moreover, we prove that the space of all attributed
graphs is relatively compact with respect to our metrics. Based on these
properties, we prove a universal approximation theorem for GNNs and
generalization bounds for GNNs on any data distribution of attributed graphs.
The proposed metrics compute the similarity between the structures of
attributed graphs via a hierarchical optimal transport between computation
trees. Our work extends and unites previous approaches which either derived
theory only for graphs with no attributes, derived compact metrics under which
GNNs are continuous but without separation power, or derived metrics under
which GNNs are continuous and separate points but the space of graphs is not
relatively compact, which prevents universal approximation and generalization
analysis. | arXiv |
In the acquisition of Magnetic Resonance (MR) images shorter scan times lead
to higher image noise. Therefore, automatic image denoising using deep learning
methods is of high interest. MR images containing line-like structures such as
roots or vessels yield special characteristics as they display connected
structures and yield sparse information. For this kind of data, it is important
to consider voxel neighborhoods when training a denoising network. In this
paper, we translate the Perceptual Loss to 3D data by comparing feature maps of
untrained networks in the loss function as done previously for 2D data. We
tested the performance of untrained Perceptual Loss (uPL) on 3D image denoising
of MR images displaying brain vessels (MR angiograms - MRA) and images of plant
roots in soil. We investigate the impact of various uPL characteristics such as
weight initialization, network depth, kernel size, and pooling operations on
the results. We tested the performance of the uPL loss on four Rician noise
levels using evaluation metrics such as the Structural Similarity Index Metric
(SSIM). We observe, that our uPL outperforms conventional loss functions such
as the L1 loss or a loss based on the Structural Similarity Index Metric
(SSIM). The uPL network's initialization is not important, while network depth
and pooling operations impact denoising performance. E.g. for both datasets a
network with five convolutional layers led to the best performance while a
network with more layers led to a performance drop. We also find that small uPL
networks led to better or comparable results than using large networks such as
VGG. We observe superior performance of our loss for both datasets, all noise
levels, and three network architectures. In conclusion, for images containing
line-like structures, uPL is an alternative to other loss functions for 3D
image denoising. | arXiv |
In the rapidly evolving landscape of cyber security, intelligent chatbots are
gaining prominence. Artificial Intelligence, Machine Learning, and Natural
Language Processing empower these chatbots to handle user inquiries and deliver
threat intelligence. This helps cyber security knowledge readily available to
both professionals and the public. Traditional rule-based chatbots often lack
flexibility and struggle to adapt to user interactions. In contrast, Large
Language Model-based chatbots offer contextually relevant information across
multiple domains and adapt to evolving conversational contexts. In this work,
we develop IntellBot, an advanced cyber security Chatbot built on top of
cutting-edge technologies like Large Language Models and Langchain alongside a
Retrieval-Augmented Generation model to deliver superior capabilities. This
chatbot gathers information from diverse data sources to create a comprehensive
knowledge base covering known vulnerabilities, recent cyber attacks, and
emerging threats. It delivers tailored responses, serving as a primary hub for
cyber security insights. By providing instant access to relevant information
and resources, this IntellBot enhances threat intelligence, incident response,
and overall security posture, saving time and empowering users with knowledge
of cyber security best practices. Moreover, we analyzed the performance of our
copilot using a two-stage evaluation strategy. We achieved BERT score above 0.8
by indirect approach and a cosine similarity score ranging from 0.8 to 1, which
affirms the accuracy of our copilot. Additionally, we utilized RAGAS to
evaluate the RAG model, and all evaluation metrics consistently produced scores
above 0.77, highlighting the efficacy of our system. | arXiv |
Twists are defects that are used to encode and process quantum information in
topological codes like surface and color codes. Color codes can host three
basic types of twists viz., charge-permuting, color-permuting and domino
twists. In this paper, we study domino twists from the viewpoint of
computation. Specifically, we give a systematic construction for domino twists
in qubit color codes. We also present protocols for measurement of logical
qubits. Finally, we show that all Clifford gates can be implemented by braiding
twists. | arXiv |
In this study, we report a detailed calculation of the static dipole
polarizabilities for group 12 elements using the finite-field approach combined
with the relativistic coupled-cluster method, including single, double, and
perturbative triple excitations. We examine three types of relativistic effects
on dipole polarizabilities: scalar-relativistic, spin-orbit coupling (SOC), and
fully relativistic Dirac-Coulomb contributions. The final recommended
polarizability values, with their associated uncertainties, are $37.95 \pm
0.72$ for Zn, $45.68 \pm 1.16$ for Cd, $34.04 \pm 0.67$ for Hg, and $27.92 \pm
0.24$ for Cn. Our results align closely with the recommended values in the 2018
Table of static dipole polarizabilities for neutral atoms [Mol. Phys.
\textbf{117}, 1200 (2019)], while providing reduced uncertainties for Cd and
Cn. The analysis indicates that scalar-relativistic effects are the dominant
relativistic contributions to atomic dipole polarizabilities for these atoms,
with SOC effects found to be negligible. Furthermore, we evaluate the influence
of electron correlation across all relativistic regimes, underscoring its
critical role in the precise determination of dipole polarizabilities. | arXiv |
A partition is finitary if all its blocks are finite. For a cardinal
$\mathfrak{a}$ and a natural number $n$, let $\mathrm{fin}(\mathfrak{a})$ and
$\mathscr{B}_{n}(\mathfrak{a})$ be the cardinalities of the set of finite
subsets and the set of finitary partitions with exactly $n$ non-singleton
blocks of a set which is of cardinality $\mathfrak{a}$, respectively. In this
paper, we prove in $\mathsf{ZF}$ (without the axiom of choice) that for all
infinite cardinals $\mathfrak{a}$ and all non-zero natural numbers $n$, \[
(2^{\mathscr{B}_{n}(\mathfrak{a})})^{\aleph_0}=2^{\mathscr{B}_{n}(\mathfrak{a})}
\] and \[
2^{\mathrm{fin}(\mathfrak{a})^n}=2^{\mathscr{B}_{2^n-1}(\mathfrak{a})}. \] It
is also proved consistent with $\mathsf{ZF}$ that there exists an infinite
cardinal $\mathfrak{a}$ such that \[
2^{\mathscr{B}_{1}(\mathfrak{a})}<2^{\mathscr{B}_{2}(\mathfrak{a})}<2^{\mathscr{B}_{3}(\mathfrak{a})}<\cdots<2^{\mathrm{fin}(\mathrm{fin}(\mathfrak{a}))}.
\] | arXiv |
By cross-matching the eclipsing binary catalog from TESS with that from
LAMOST MRS, semi-detached eclipsing binaries with radial velocities coverage
spanning more than 0.3 phases were authenticated. The absolute parameters for
these systems were determined by simultaneous modeling of light curves and
radial velocities using the Wilson-Devinney program. Additionally, the secular
orbital variations were further analyzed using O-C curves. Eight semi-detached
eclipsing binaries have been identified. Among them, seven feature primary
stars situated within the main-sequence band, while their secondaries are all
in evolved stages. This suggests that these systems likely originated as
detached binaries and have undergone a reversal of the mass ratio. However, TIC
428257299 is an exception where the primary is Roche lobe-filling, and its
secondary has experienced mass loss events. Additionally, TIC 8677671 and TIC
318217844 demonstrate secular cyclical changes of orbital periods.
Specifically, for TIC 8677671, the cyclical change could result from magnetic
activity or a third body which is likely to be compact, with a mass of at least
2.97 M$_{\odot}$. | arXiv |
Practical superconducting nanowire single photon detectors (SNSPDs)
demonstrate a strong trade-off between detection sensitivity and the reset
time. Often, there are wide variations in sensitivity and response times from
SNSPDs of the same superconducting material. Here, using detailed physical
models, we show that the dirtiness in a superconductor enforces a sensitivity
and bandwidth trade-off in all practical devices. More importantly, a certain
degree of dirtiness is a necessary requirement for achieving single photon
detection. Under typical bias conditions close to the transition setpoints, the
minimum number of photons required to register a voltage pulse decreases by the
dirtiness parameter (Ioffe-Regel parameter) and the reset time of SNSPD
increases by the same dirtiness parameter, thereby giving a constant value for
the sensitivity-bandwidth product. The constant is weakly modified by biasing
current and the temperature. Since dirtiness in the superconducting nanowire is
a physically controllable parameter with an important bearing on the final
response of an SNSPD, this work opens new opportunities to develop SNSPD
devices with engineered sensitivity-bandwidth setpoint as dictated by an
application. | arXiv |
Facial landmark detection is a fundamental problem in computer vision for
many downstream applications. This paper introduces a new facial landmark
detector based on vision transformers, which consists of two unique designs:
Dual Vision Transformer (D-ViT) and Long Skip Connections (LSC). Based on the
observation that the channel dimension of feature maps essentially represents
the linear bases of the heatmap space, we propose learning the interconnections
between these linear bases to model the inherent geometric relations among
landmarks via Channel-split ViT. We integrate such channel-split ViT into the
standard vision transformer (i.e., spatial-split ViT), forming our Dual Vision
Transformer to constitute the prediction blocks. We also suggest using long
skip connections to deliver low-level image features to all prediction blocks,
thereby preventing useful information from being discarded by intermediate
supervision. Extensive experiments are conducted to evaluate the performance of
our proposal on the widely used benchmarks, i.e., WFLW, COFW, and 300W,
demonstrating that our model outperforms the previous SOTAs across all three
benchmarks. | arXiv |
Contemporary machine learning models, such as language models, are powerful,
but come with immense resource requirements both at training and inference
time. It has been shown that decoder-only language models can be trained to a
competitive state with ternary weights (1.58 bits per weight), facilitating
efficient inference. Here, we start our exploration with non-transformer model
architectures, investigating 1.58-bit training for multi-layer perceptrons and
graph neural networks. Then, we explore 1.58-bit training in other
transformer-based language models, namely encoder-only and encoder-decoder
models. Our results show that in all of these settings, 1.58-bit training is on
par with or sometimes even better than the standard 32/16-bit models. | arXiv |
Let $E$ be a subset in $\mathbb{F}_p^2$ and $S$ be a subset in the special
linear group $SL_2(\mathbb{F}_p)$ or the $1$-dimensional Heisenberg linear
group $\mathbb{H}_1(\mathbb{F}_p)$. We define $S(E):= \bigcup_{\theta \in S}
\theta (E)$. In this paper, we provide optimal conditions on $S$ and $E$ such
that the set $S(E)$ covers a positive proportion of all elements in the plane
$\mathbb{F}_p$. When the sizes of $S$ and $E$ are small, we prove structural
theorems that guarantee that $|S(E)|\gg |E|^{1+\epsilon}$ for some
$\epsilon>0$. The main ingredients in our proofs are novel results on algebraic
incidence-type structures associated with the groups, in which energy estimates
play a crucial role. The higher-dimensional version will also be discussed in
this paper. | arXiv |
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized
human-machine interactions by seamlessly integrating various forms of data.
Developing a universal spoken language model that comprehends a wide range of
natural language instructions is critical for bridging communication gaps and
facilitating more intuitive interactions. However, the absence of a
comprehensive evaluation benchmark poses a significant challenge. We present
Dynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive
evaluation of instruction-based universal speech models. Building upon the
first generation, this second version incorporates 125 new tasks contributed
collaboratively by the global research community, expanding the benchmark to a
total of 180 tasks, making it the largest benchmark for speech and audio
evaluation. While the first generation of Dynamic-SUPERB was limited to
classification tasks, Dynamic-SUPERB Phase-2 broadens its evaluation
capabilities by introducing a wide array of novel and diverse tasks, including
regression and sequence generation, across speech, music, and environmental
audio. Evaluation results indicate that none of the models performed well
universally. SALMONN-13B excelled in English ASR, while WavLLM demonstrated
high accuracy in emotion recognition, but current models still require further
innovations to handle a broader range of tasks. We will soon open-source all
task data and the evaluation pipeline. | arXiv |
In this paper, we define double almost-Riordan arrays and find that the set
of all double almost-Riordan arrays forms a group, called the double
almost-Riordan group. We also obtain the sequence characteristics of double
almost-Riordan arrays and give the production matrices for double
almost-Riordan arrays. We define the compression of double almost-Riordan
arrays and present their sequence characterization. Finally we give a
characteristic for the total positivity of double Riordan arrays, by using
which we discuss the total positivity for several double almost-Riordan arrays. | arXiv |
We establish global well-posedness for both the defocusing and focusing
complex-valued modified Korteweg--de Vries equations on the real line in
modulation spaces $M_p^{s,2}(\mathbb{R})$, for all $1\leq p<\infty$ and $0\leq
s<3/2-1/p$. We will also show that such solutions admit global-in-time bounds
in these spaces and that equicontinuous sets of initial data lead to
equicontinuous ensembles of orbits. Indeed, such information forms a crucial
part of our well-posedness argument. | arXiv |
In streaming media services, video transcoding is a common practice to
alleviate bandwidth demands. Unfortunately, traditional methods employing a
uniform rate factor (RF) across all videos often result in significant
inefficiencies. Content-adaptive encoding (CAE) techniques address this by
dynamically adjusting encoding parameters based on video content
characteristics. However, existing CAE methods are often tightly coupled with
specific encoding strategies, leading to inflexibility. In this paper, we
propose a model that predicts both RF-quality and RF-bitrate curves, which can
be utilized to derive a comprehensive bitrate-quality curve. This approach
facilitates flexible adjustments to the encoding strategy without necessitating
model retraining. The model leverages codec features, content features, and
anchor features to predict the bitrate-quality curve accurately. Additionally,
we introduce an anchor suspension method to enhance prediction accuracy.
Experiments confirm that the actual quality metric (VMAF) of the compressed
video stays within 1 of the target, achieving an accuracy of 99.14%. By
incorporating our quality improvement strategy with the rate-quality curve
prediction model, we conducted online A/B tests, obtaining both +0.107%
improvements in video views and video completions and +0.064% app duration
time. Our model has been deployed on the Xiaohongshu App. | arXiv |
During intracranial aneurysm (IA) treatment with Diverters (FDs), the
device/parent artery diameters ratio may influence the ability of the device to
induce aneurysm healing response. Oversized FDs are safer to deploy but may not
induce enough hemodynamic resistance to ensure aneurysm occlusion. Methods
based on Computational Fluid Dynamics (CFD) could allow optimal device
selection but are time-consuming and inadequate for intra-operative guidance.
To address this limitation, we propose to investigate a method for optimal FD
selection using Angiographic Parametric Imaging (API) and machine learning
(ML). We selected 128 pre-treatment angiographic sequences of IAs which
demonstrated full occlusion at six months follow-up. For each IA, we extracted
five API parameters from the aneurysm dome and normalized them to the feeding
artery corresponding parameters. We dichotomized the dataset based on the FD/
proximal artery diameter ratio as undersized, if the ratio<1 or if multiple FDs
were used and oversized otherwise. Single API parameter and ML analysis were
used to determine whether API parameters could be used to determine the need
for FD under-sizing (i.e., increased flow resistance). Classification accuracy
was assessed using area under the receiver operator characteristic (AUROC). In
total we identified 51 and 77 cases for the undersized and oversized cohorts
respectively. Single API parameter analysis yielded an inadequate AUROC ~0.5
while machine learning using all five API parameters yielded and AUROC of 0.72. | arXiv |
We present Fox-1, a series of small language models (SLMs) consisting of
Fox-1-1.6B and Fox-1-1.6B-Instruct-v0.1. These models are pre-trained on 3
trillion tokens of web-scraped document data and fine-tuned with 5 billion
tokens of instruction-following and multi-turn conversation data. Aiming to
improve the pre-training efficiency, Fox-1-1.6B model introduces a novel
3-stage data curriculum across all the training data with 2K-8K sequence
length. In architecture design, Fox-1 features a deeper layer structure, an
expanded vocabulary, and utilizes Grouped Query Attention (GQA), offering a
performant and efficient architecture compared to other SLMs. Fox-1 achieves
better or on-par performance in various benchmarks compared to StableLM-2-1.6B,
Gemma-2B, Qwen1.5-1.8B, and OpenELM1.1B, with competitive inference speed and
throughput. The model weights have been released under the Apache 2.0 license,
where we aim to promote the democratization of LLMs and make them fully
accessible to the whole open-source community. | arXiv |
Distractions in mixed reality (MR) environments can significantly influence
user experience, affecting key factors such as presence, reaction time,
cognitive load, and Break in Presence (BIP). Presence measures immersion,
reaction time captures user responsiveness, cognitive load reflects mental
effort, and BIP represents moments when attention shifts from the virtual to
the real world, breaking immersion. However, the effects of distractions on
these elements remain insufficiently explored. To address this gap, we have
presented a theoretical model to understand how congruent and incongruent
distractions affect all these constructs. We conducted a within-subject study
(N=54) where participants performed image-sorting tasks under different
distraction conditions. Our findings show that incongruent distractions
significantly increase cognitive load, slow reaction times, and elevate BIP
frequency, with presence mediating these effects. | arXiv |
We study the eigenfunctions of the classical Liouville operator and
investigate the conditions they must obey to be separable as a product state.
We point out that the conditions for separability are equivalent to
requirements of Liouville's integrability theorem, this is, the eigenfunctions
are separable if and only if the system is integrable. On the other hand, if
the classical system is not integrable, then the eigenfunctions are entangled
in all canonical coordinates. This results in a link between the classical
notions of chaos and integrability with mathematical concepts that are usually
restricted to quantum mechanics. | arXiv |
We associate a sequence of positive integers, termed the type sequence, with
a cochordal graph. Using this type sequence, we compute all graded Betti
numbers of its edge ideal. We then classify all positive integer $n$ such that
the zero divisor graph of $\mathbb{Z}/n \mathbb{Z}$ is cochordal and determine
all the graded Betti numbers of its edge ideal. | arXiv |
The Pettitt test has been widely used in climate change and hydrological
analyzes. However, studies evidence difficulties of this test in detecting
change points, especially in small samples. This study presents a bootstrap
application of the Pettitt test, which is numerically compared with the
classical Pettitt test by an extensive Monte Carlo simulation study. The
proposed test outperforms the classical test in all simulated scenarios. An
application of the tests is conducted in the historical series of naturalized
flows of the Itaipu Hydroelectric plant in Brazil, where several studies have
shown a change point in the 70s. When the series is split into shorter series,
to simulate small sample actual situations, the proposed test is more powerful
than the classical Pettitt test to detect the change point. The proposed test
can be an important tool to detect abrupt changes in water availability,
supporting hydroclimatological resources decision making. | arXiv |
Out-of-Time-Order Correlation function measures transport properties of
dynamical systems. They are ubiquitously used to measure quantum mechanical
quantities, such as scrambling times, criticality in phase transitions, and
detect onset of thermalisation. We characterise the computational complexity of
estimating OTOCs over all eigenstates and show it is Complete for the One Clean
Qubit model (DQC1). We then generalise our setup to establish DQC1-Completeness
of N-time Correlation functions over all eigenstates. Building on previous
results, the DQC1-Completeness of OTOCs and N-time Correlation functions then
allows us to highlight a dichotomy between query complexity and circuit
complexity of estimating correlation functions. | arXiv |
Context. Star-forming regions are gaining considerable interest in the
high-energy astrophysics community as possible Galactic particle accelerators.
In general, the role of electrons has not been fully considered in this kind of
cosmic-ray source. However, the intense radiation fields inside these regions
might make electrons significant gamma-ray contributors. Aims. We study the
young and compact star-forming region NGC 3603, a well known gamma-ray emitter.
Our intention is to test whether its gamma-ray emission can be produced by
cosmic-ray electrons. Methods. We build a novel model by creating realistic 3D
distributions of the gas and the radiation field in the region. We introduce
these models into PICARD to perform cosmic-ray transport simulations and
produce gamma-ray emission maps. The results are compared with a dedicated
Fermi Large Area Telescope data analysis at high energies. We also explore the
radio and neutrino emissions of the system. Results. We improve the existing
upper limits of the NGC 3603 gamma-ray source extension. Although the gamma-ray
spectrum is well reproduced with the injection of CR protons, it requires
nearly 30\% acceleration efficiency. In addition, the resulting extension of
the simulated hadronic source is in mild tension with the extension data upper
limit. The radio data disfavours the lepton-only scenario. Finally, combining
both populations, the results are consistent with all observables, although the
exact contributions are ambiguous. | arXiv |
Recent research suggests that the failures of Vision-Language Models (VLMs)
at visual reasoning often stem from erroneous agreements -- when semantically
distinct images are ambiguously encoded by the CLIP image encoder into
embeddings with high cosine similarity. In this paper, we show that erroneous
agreements are not always the main culprit, as Multimodal Large Language Models
(MLLMs) can still extract distinct information from them. For instance, when
distinguishing objects on the left vs right in the What'sUp benchmark, the CLIP
image embeddings of the left/right pairs have an average cosine similarity
$>0.99$, and CLIP performs at random chance; but LLaVA-1.5-7B, which uses the
same CLIP image encoder, achieves nearly $100\%$ accuracy. We find that the
extractable information in CLIP image embeddings is likely obscured by CLIP's
inadequate vision-language alignment: Its matching score learned by the
contrastive objective might not capture all diverse image-text correspondences.
We also study the MMVP benchmark, on which prior work has shown that LLaVA-1.5
cannot distinguish image pairs with high cosine similarity. We observe a
performance gain brought by attending more to visual input through an
alternative decoding algorithm. Further, the accuracy significantly increases
if the model can take both images as input to emphasize their nuanced
differences. Both findings indicate that LLaVA-1.5 did not utilize extracted
visual information sufficiently. In conclusion, our findings suggest that while
improving image encoders could benefit VLMs, there is still room to enhance
models with a fixed image encoder by applying better strategies for extracting
and utilizing visual information. | arXiv |
Value-based reinforcement learning (RL) can in principle learn effective
policies for a wide range of multi-turn problems, from games to dialogue to
robotic control, including via offline RL from static previously collected
datasets. However, despite the widespread use of policy gradient methods to
train large language models for single turn tasks (e.g., question answering),
value-based methods for multi-turn RL in an off-policy or offline setting have
proven particularly challenging to scale to the setting of large language
models. This setting requires effectively leveraging pretraining, scaling to
large architectures with billions of parameters, and training on large
datasets, all of which represent major challenges for current value-based RL
methods. In this work, we propose a novel offline RL algorithm that addresses
these drawbacks, casting Q-learning as a modified supervised fine-tuning (SFT)
problem where the probabilities of tokens directly translate to Q-values. In
this way we obtain an algorithm that smoothly transitions from maximizing the
likelihood of the data during pretraining to learning a near-optimal Q-function
during finetuning. Our algorithm has strong theoretical foundations, enjoying
performance bounds similar to state-of-the-art Q-learning methods, while in
practice utilizing an objective that closely resembles SFT. Because of this,
our approach can enjoy the full benefits of the pretraining of language models,
without the need to reinitialize any weights before RL finetuning, and without
the need to initialize new heads for predicting values or advantages.
Empirically, we evaluate our method on both pretrained LLMs and VLMs, on a
variety of tasks including both natural language dialogue and robotic
manipulation and navigation from images. | arXiv |
Data assimilation (DA) combines partial observations with a dynamical model
to improve state estimation. Filter-based DA uses only past and present data
and is the prerequisite for real-time forecasts. Smoother-based DA exploits
both past and future observations. It aims to fill in missing data, provide
more accurate estimations, and develop high-quality datasets. However, the
standard smoothing procedure requires using all historical state estimations,
which is storage-demanding, especially for high-dimensional systems. This paper
develops an adaptive-lag online smoother for a large class of complex dynamical
systems with strong nonlinear and non-Gaussian features, which has important
applications to many real-world problems. The adaptive lag allows the DA to
utilize only observations within a nearby window, significantly reducing
computational storage. Online lag adjustment is essential for tackling
turbulent systems, where temporal autocorrelation varies significantly over
time due to intermittency, extreme events, and nonlinearity. Based on the
uncertainty reduction in the estimated state, an information criterion is
developed to systematically determine the adaptive lag. Notably, the
mathematical structure of these systems facilitates the use of closed analytic
formulae to calculate the online smoother and the adaptive lag, avoiding
empirical tunings as in ensemble-based DA methods. The adaptive online smoother
is applied to studying three important scientific problems. First, it helps
detect online causal relationships between state variables. Second, its
advantage of computational storage is illustrated via Lagrangian DA, a
high-dimensional nonlinear problem. Finally, the adaptive smoother advances
online parameter estimation with partial observations, emphasizing the role of
the observed extreme events in accelerating convergence. | arXiv |
Quantum state change can not occurs instantly, but the speed of quantum
evolution is limited to an upper bound value, called quantum speed limit (QSL).
Engineering QSL is an important task for quantum information and computation
science and technologies. This paper devotes to engineering QSL and quantum
correlation in simple two-qubit system suffering dephasing via Periodic
Dynamical Decoupling (PDD) method in both Markovian and non-Markovian dynamical
regimes. The results show that when decoupling pulses are applied to both
qubits this method removes all undesirable effects of the dephasing process,
completely. Applying the PDD on only one of the qubits also works but with
lower efficiency. Additionally, ultra-high speedup of the quantum processes
become possible during the pulse application period, for enough large number of
pulses. The results is useful for high speed quantum gate implementation
application. | arXiv |
Is it possible to comprehensively destroy a piece of quantum information, so
that nothing is left behind except the memory of whether one had it at one
point? For example, various works, most recently Morimae, Poremba, and Yamakawa
(TQC 2024), show how to construct a signature scheme with certified deletion
where a user who deletes a signature on m cannot later produce a signature for
m. However, in all of the existing schemes, even after deletion the user is
still able keep irrefutable evidence that m was signed, and thus they do not
fully capture the spirit of deletion.
In this work, we initiate the study of certified deniability in order to
obtain a more comprehensive notion of deletion. Certified deniability uses a
simulation-based security definition, ensuring that any information the user
has kept after deletion could have been learned without being given the
deleteable object to begin with; meaning that deletion leaves no trace behind!
We define and construct two non-interactive primitives that satisfy certified
deniability in the quantum random oracle model: signatures and non-interactive
zero-knowledge arguments (NIZKs). As a consequence, for example, it is not
possible to delete a signature/NIZK and later provide convincing evidence that
it used to exist. Notably, our results utilize uniquely quantum phenomena to
bypass the celebrated result of Pass (CRYPTO, 2003) showing that deniable NIZKs
are impossible even in the random oracle model. | arXiv |
The Gaussian process (GP) is a widely used probabilistic machine learning
method for stochastic function approximation, stochastic modeling, and
analyzing real-world measurements of nonlinear processes. Unlike many other
machine learning methods, GPs include an implicit characterization of
uncertainty, making them extremely useful across many areas of science,
technology, and engineering. Traditional implementations of GPs involve
stationary kernels (also termed covariance functions) that limit their
flexibility and exact methods for inference that prevent application to data
sets with more than about ten thousand points. Modern approaches to address
stationarity assumptions generally fail to accommodate large data sets, while
all attempts to address scalability focus on approximating the Gaussian
likelihood, which can involve subjectivity and lead to inaccuracies. In this
work, we explicitly derive an alternative kernel that can discover and encode
both sparsity and nonstationarity. We embed the kernel within a fully Bayesian
GP model and leverage high-performance computing resources to enable the
analysis of massive data sets. We demonstrate the favorable performance of our
novel kernel relative to existing exact and approximate GP methods across a
variety of synthetic data examples. Furthermore, we conduct space-time
prediction based on more than one million measurements of daily maximum
temperature and verify that our results outperform state-of-the-art methods in
the Earth sciences. More broadly, having access to exact GPs that use
ultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods
to truly compete with a wide variety of machine learning methods. | arXiv |
We introduce average-distortion sketching for metric spaces. As in
(worst-case) sketching, these algorithms compress points in a metric space
while approximately recovering pairwise distances. The novelty is studying
average-distortion: for any fixed (yet, arbitrary) distribution $\mu$ over the
metric, the sketch should not over-estimate distances, and it should
(approximately) preserve the average distance with respect to draws from $\mu$.
The notion generalizes average-distortion embeddings into $\ell_1$ [Rabinovich
'03, Kush-Nikolov-Tang '21] as well as data-dependent locality-sensitive
hashing [Andoni-Razenshteyn '15, Andoni-Naor-Nikolov-et-al. '18], which have
been recently studied in the context of nearest neighbor search.
$\bullet$ For all $p \in [1, \infty)$ and any $c$ larger than a fixed
constant, we give an average-distortion sketch for $([\Delta]^d, \ell_p)$ with
approximation $c$ and bit-complexity $\text{poly}(cp \cdot 2^{p/c} \cdot
\log(d\Delta))$, which is provably impossible in (worst-case) sketching.
$\bullet$ As an application, we improve on the approximation of
sublinear-time data structures for nearest neighbor search over $\ell_p$ (for
large $p > 2$). The prior best approximation was $O(p)$
[Andoni-Naor-Nikolov-et-al. '18, Kush-Nikolov-Tang '21], and we show it can be
any $c$ larger than a fixed constant (irrespective of $p$) by using
$n^{\text{poly}(cp \cdot 2^{p/c})}$ space.
We give some evidence that $2^{\Omega(p/c)}$ space may be necessary by giving
a lower bound on average-distortion sketches which produce a certain
probabilistic certificate of farness (which our sketches crucially rely on). | arXiv |
Radiology report generation (RRG) aims to create free-text radiology reports
from clinical imaging. Grounded radiology report generation (GRRG) extends RRG
by including the localisation of individual findings on the image. Currently,
there are no manually annotated chest X-ray (CXR) datasets to train GRRG
models. In this work, we present a dataset called PadChest-GR
(Grounded-Reporting) derived from PadChest aimed at training GRRG models for
CXR images. We curate a public bi-lingual dataset of 4,555 CXR studies with
grounded reports (3,099 abnormal and 1,456 normal), each containing complete
lists of sentences describing individual present (positive) and absent
(negative) findings in English and Spanish. In total, PadChest-GR contains
7,037 positive and 3,422 negative finding sentences. Every positive finding
sentence is associated with up to two independent sets of bounding boxes
labelled by different readers and has categorical labels for finding type,
locations, and progression. To the best of our knowledge, PadChest-GR is the
first manually curated dataset designed to train GRRG models for understanding
and interpreting radiological images and generated text. By including detailed
localization and comprehensive annotations of all clinically relevant findings,
it provides a valuable resource for developing and evaluating GRRG models from
CXR images. PadChest-GR can be downloaded under request from
https://bimcv.cipf.es/bimcv-projects/padchest-gr/ | arXiv |
In $(2+1)d$ topological quantum field theory, topological entanglement
entropy (TEE) can be computed using the replica and surgery methods. We
classify all bipartitions on a torus and propose a general method for
calculating their corresponding TEEs. For each bipartition, the TEEs for
different ground states are bounded by a topological quantity, termed the
intrinsic TEE, which depends solely on the number of entanglement interfaces $
\pi_{\partial A}$, $S_{\text{iTEE}}(A) = - \pi_{\partial A} \ln \mathcal{D}$
with $\mathcal{D}$ being the total quantum dimension. We derive a modified form
of strong subadditivity (SSA) for the intrinsic TEE, with the modification
depending on the genus $g_X$ of the subregions $X$, $S_{\text{iTEE}}(A) +
S_{\text{iTEE}}(B) - S_{\text{iTEE}}(A\cup B) - S_{\text{iTEE}}(A\cap B) \geq
-2\ln \mathcal{D} (g_A + g_B - g_{A\cup B} - g_{A\cap B})$. Additionally, we
show that SSA for the full TEE holds when the intersection number between torus
knots of the subregions is not equal to one. When the intersection number is
one, the SSA condition is satisfied if and only if $\sum_a |\psi_a|^2 (\ln
S_{0a} - \ln |\psi_a|) + |S\psi_a|^2 (\ln S_{0a} - \ln |S\psi_a|) \geq 2 \ln
\mathcal{D}$, with $S$ being the modular $S$-matrix and $\psi_a$ being the
probability amplitudes. This condition has been verified for unitary modular
categories up to rank $11$, while counterexamples have been found in
non-pseudo-unitary modular categories, such as the Yang-Lee anyon. | arXiv |
The LIGO, Virgo and KAGRA gravitational wave observatories are currently
undertaking their O4 observing run offering the opportunity to discover new
electromagnetic counterparts to gravitational wave events. We examine the
capability of the Neil Gehrels Swift Observatory (Swift) to respond to these
triggers, primarily binary neutron star mergers, with both the UV/Optical
Telescope (UVOT) and the X-ray Telescope (XRT). We simulate Swift's response to
a trigger under different strategies using model skymaps, convolving these with
the 2MPZ catalogue to produce an ordered list of observing fields, deriving the
time taken for Swift to reach the correct field and simulating the instrumental
responses to modelled kilonovae and short gamma-ray burst afterglows. We find
that UVOT using the $u$ filter with an exposure time of order 120 s is optimal
for most follow-up observations and that we are likely to detect counterparts
in $\sim6$% of all binary neutron star triggers. We find that the gravitational
wave 90% error area and measured distance to the trigger allow us to select
optimal triggers to follow-up. Focussing on sources less than 300 Mpc away or
500 Mpc if the error area is less than a few hundred square degrees, distances
greater than previously assumed, offer the best opportunity for discovery by
Swift with $\sim5 - 30$% of triggers having detection probabilities $\geq 0.5$.
At even greater distances, we can further optimise our follow-up by adopting a
longer 250 s or 500 s exposure time. | arXiv |
The origin of the binary black hole mergers observed by LIGO-Virgo-KAGRA
(LVK) remains an open question. We calculate the merger rate from primordial
black holes (PBHs) within the density spike around supermassive black holes
(SMBHs) at the center of galaxies. We show that the merger rate within the
spike is comparable to that within the wider dark matter halo. We also
calculate the extreme mass ratio inspiral (EMRI) signal from PBHs hosted within
the density spike spiralling into their host SMBHs due to GW emission. We
predict that LISA may detect $\sim10^4$ of these EMRIs with signal-to-noise
ratio of 5 within a 4-year observation run, if all dark matter is made up of
PBHs. Uncertainties in our rates come from the uncertain mass fraction of PBHs
within the dark matter spike, relative to the host central SMBHs, which defines
the parameter space LISA can constrain. | arXiv |
The large eccentricities of cold Jupiters and the existence of hot Jupiters
have long challenged theories of planet formation. A proposed solution to both
of these puzzles is high-eccentricity migration, in which an initially cold
Jupiter is excited to high eccentricities before being tidally circularized.
Secular perturbations from an inclined stellar companion are a potential source
of eccentricity oscillations, a phenomenon known as the Eccentric Kozai-Lidov
(EKL) mechanism. Previous studies have found that the cold Jupiter eccentricity
distribution produced by EKL is inconsistent with observations. However, these
studies assumed all planets start on circular orbits. Here, we revisit this
question, considering that an initial period of planet-planet scattering on
$\sim$Myr timescales likely places planets on slightly eccentric orbits before
being modulated by EKL on $\sim$Myr-Gyr timescales. Small initial
eccentricities can have a dramatic effect by enabling EKL to act at lower
inclinations. We numerically integrate the secular hierarchical three-body
equations of motion, including general relativity and tides, for populations of
cold giant planets in stellar binaries with varied initial eccentricity
distributions. For populations with modest initial mean eccentricities, the
simulated eccentricity distribution produced by EKL is statistically consistent
with the observed eccentricities of cold single-planet systems. The lower
eccentricities in a multi-planet control sample suggest that planetary
companions quench stellar EKL. We show that scattering alone is unlikely to
reproduce the present-day eccentricity distribution. We also show that the
anisotropic inclination distribution produced by EKL may lead radial velocity
measurements to underestimate giant planet masses. | arXiv |
This paper proposes ProEdit - a simple yet effective framework for
high-quality 3D scene editing guided by diffusion distillation in a novel
progressive manner. Inspired by the crucial observation that multi-view
inconsistency in scene editing is rooted in the diffusion model's large
feasible output space (FOS), our framework controls the size of FOS and reduces
inconsistency by decomposing the overall editing task into several subtasks,
which are then executed progressively on the scene. Within this framework, we
design a difficulty-aware subtask decomposition scheduler and an adaptive 3D
Gaussian splatting (3DGS) training strategy, ensuring high quality and
efficiency in performing each subtask. Extensive evaluation shows that our
ProEdit achieves state-of-the-art results in various scenes and challenging
editing tasks, all through a simple framework without any expensive or
sophisticated add-ons like distillation losses, components, or training
procedures. Notably, ProEdit also provides a new way to control, preview, and
select the "aggressivity" of editing operation during the editing process. | arXiv |
Recently, breakthroughs in video modeling have allowed for controllable
camera trajectories in generated videos. However, these methods cannot be
directly applied to user-provided videos that are not generated by a video
model. In this paper, we present ReCapture, a method for generating new videos
with novel camera trajectories from a single user-provided video. Our method
allows us to re-generate the reference video, with all its existing scene
motion, from vastly different angles and with cinematic camera motion. Notably,
using our method we can also plausibly hallucinate parts of the scene that were
not observable in the reference video. Our method works by (1) generating a
noisy anchor video with a new camera trajectory using multiview diffusion
models or depth-based point cloud rendering and then (2) regenerating the
anchor video into a clean and temporally consistent reangled video using our
proposed masked video fine-tuning technique. | arXiv |
This work presents a modification of the self-attention dynamics proposed by
Geshkovski et al. (arXiv:2312.10794) to better reflect the practically
relevant, causally masked attention used in transformer architectures for
generative AI. This modification translates into an interacting particle system
that cannot be interpreted as a mean-field gradient flow. Despite this loss of
structure, we significantly strengthen the results of Geshkovski et al.
(arXiv:2312.10794) in this context: While previous rigorous results focused on
cases where all three matrices (Key, Query, and Value) were scaled identities,
we prove asymptotic convergence to a single cluster for arbitrary key-query
matrices and a value matrix equal to the identity. Additionally, we establish a
connection to the classical R\'enyi parking problem from combinatorial geometry
to make initial theoretical steps towards demonstrating the existence of
meta-stable states. | arXiv |
The classification of topological phases of matter is a fundamental challenge
in quantum many-body physics, with applications to quantum technology.
Recently, this classification has been extended to the setting of Adaptive
Finite-Depth Local Unitary (AFDLU) circuits which allow global classical
communication. In this setting, the trivial phase is the collection of all
topological states that can be prepared via AFDLU. Here, we propose a complete
classification of the trivial phase by showing how to prepare all solvable
anyon theories that admit a gapped boundary via AFDLU, extending recent results
on solvable groups. Our construction includes non-Abelian anyons with
irrational quantum dimensions, such as Ising anyons, and more general acyclic
anyons. Specifically, we introduce a sequential gauging procedure, with an
AFDLU implementation, to produce a string-net ground state in any topological
phase described by a solvable anyon theory with gapped boundary. In addition,
we introduce a sequential ungauging and regauging procedure, with an AFDLU
implementation, to apply string operators of arbitrary length for anyons and
symmetry twist defects in solvable anyon theories. We apply our procedure to
the quantum double of the group $S_3$ and to several examples that are beyond
solvable groups, including the doubled Ising theory, the $\mathbb{Z}_3$
Tambara-Yamagami string-net, and doubled $SU(2)_4$ anyons. | arXiv |
We present new advances in achieving exponential quantum speedups for solving
optimization problems by low-depth quantum algorithms. Specifically, we focus
on families of combinatorial optimization problems that exhibit symmetry and
contain planted solutions. We rigorously prove that the 1-step Quantum
Approximate Optimization Algorithm (QAOA) can achieve a success probability of
$\Omega(1/\sqrt{n})$, and sometimes $\Omega(1)$, for finding the exact solution
in many cases. Furthermore, we construct near-symmetric optimization problems
by randomly sampling the individual clauses of symmetric problems, and prove
that the QAOA maintains a strong success probability in this setting even when
the symmetry is broken. Finally, we construct various families of
near-symmetric Max-SAT problems and benchmark state-of-the-art classical
solvers, discovering instances where all known classical algorithms require
exponential time. Therefore, our results indicate that low-depth QAOA could
achieve an exponential quantum speedup for optimization problems. | arXiv |
Zero-shot coordination (ZSC) is a popular setting for studying the ability of
reinforcement learning (RL) agents to coordinate with novel partners. Prior ZSC
formulations assume the $\textit{problem setting}$ is common knowledge: each
agent knows the underlying Dec-POMDP, knows others have this knowledge, and so
on ad infinitum. However, this assumption rarely holds in complex real-world
settings, which are often difficult to fully and correctly specify. Hence, in
settings where this common knowledge assumption is invalid, agents trained
using ZSC methods may not be able to coordinate well. To address this
limitation, we formulate the $\textit{noisy zero-shot coordination}$ (NZSC)
problem. In NZSC, agents observe different noisy versions of the ground truth
Dec-POMDP, which are assumed to be distributed according to a fixed noise
model. Only the distribution of ground truth Dec-POMDPs and the noise model are
common knowledge. We show that a NZSC problem can be reduced to a ZSC problem
by designing a meta-Dec-POMDP with an augmented state space consisting of all
the ground-truth Dec-POMDPs. For solving NZSC problems, we propose a simple and
flexible meta-learning method called NZSC training, in which the agents are
trained across a distribution of coordination problems - which they only get to
observe noisy versions of. We show that with NZSC training, RL agents can be
trained to coordinate well with novel partners even when the (exact) problem
setting of the coordination is not common knowledge. | arXiv |
Let $F$ be a non-archimedean local field of characteristic zero. If $F$ has
even residual characteristic, we assume $F/\mathbb{Q}_2$ is unramified. Let $V$
be a depth zero, irreducible, nongeneric supercuspidal representation of
$GSp(4, F)$. We calculate the dimensions of the spaces of Siegel-invariant
vectors in $V$ of level $\mathfrak{p}^n$ for all $n\geq0$. | arXiv |
Misinformation is a complex societal issue, and mitigating solutions are
difficult to create due to data deficiencies. To address this problem, we have
curated the largest collection of (mis)information datasets in the literature,
totaling 75. From these, we evaluated the quality of all of the 36 datasets
that consist of statements or claims. We assess these datasets to identify
those with solid foundations for empirical work and those with flaws that could
result in misleading and non-generalizable results, such as insufficient label
quality, spurious correlations, or political bias. We further provide
state-of-the-art baselines on all these datasets, but show that regardless of
label quality, categorical labels may no longer give an accurate evaluation of
detection model performance. We discuss alternatives to mitigate this problem.
Overall, this guide aims to provide a roadmap for obtaining higher quality data
and conducting more effective evaluations, ultimately improving research in
misinformation detection. All datasets and other artifacts are available at
https://misinfo-datasets.complexdatalab.com/. | arXiv |
Time-varying inhomogeneities on stellar surfaces constitute one of the
largest sources of radial velocity (RV) error for planet detection and
characterization. We show that stellar variations, because they manifest on
coherent, rotating surfaces, give rise to changes that are complex but useably
compact and coherent in the spectral domain. Methods for disentangling stellar
signals in RV measurements benefit from modeling the full domain of spectral
pixels. We simulate spectra of spotted stars using starry and construct a
simple spectrum projection space that is sensitive to the orientation and size
of stellar surface features. Regressing measured RVs in this projection space
reduces RV scatter by 60-80% while preserving planet shifts. We note that
stellar surface variability signals do not manifest in spectral changes that
are purely orthogonal to a Doppler shift or exclusively asymmetric in line
profiles; enforcing orthogonality or focusing exclusively on asymmetric
features will not make use of all the information present in the spectra. We
conclude with a discussion of existing and possible implementations on real
data based on the presented compact, coherent framework for stellar signal
mitigation. | arXiv |
We provide a complete classification of the integrability and
nonintegrability of the spin-1 bilinear-biquadratic model with a uniaxial
anisotropic field, which includes the Heisenberg model and the
Affleck-Kennedy-Lieb-Tasaki model. It is rigorously shown that all systems,
except for the known integrable systems, are nonintegrable, meaning that they
do not have nontrivial local conserved quantities. In particular, this result
guarantees the nonintegrability of the Affleck-Kennedy-Lieb-Tasaki model, which
is a fundamental assumption for quantum many-body scarring. Furthermore, we
give simple necessary conditions for integrability in an extended model of the
bilinear-biquadratic model with anisotropic interactions. Our result has
accomplished a breakthrough in nonintegrability proofs by expanding their scope
to spin-1 systems. | arXiv |
There is great interest in fine-tuning frontier large language models (LLMs)
to inject new information and update existing knowledge. While commercial LLM
fine-tuning APIs from providers such as OpenAI and Google promise flexible
adaptation for various applications, the efficacy of fine-tuning remains
unclear. In this study, we introduce FineTuneBench, an evaluation framework and
dataset for understanding how well commercial fine-tuning APIs can successfully
learn new and updated knowledge. We analyze five frontier LLMs with
commercially available fine-tuning APIs, including GPT-4o and Gemini 1.5 Pro,
on their effectiveness in two settings: (1) ingesting novel information, such
as recent news events and new people profiles, and (2) updating existing
knowledge, such as updated medical guidelines and code frameworks. Our results
reveal substantial shortcomings in all the models' abilities to effectively
learn new information through fine-tuning, with an average generalization
accuracy of 37% across all models. When updating existing knowledge, such as
incorporating medical guideline updates, commercial fine-tuning APIs show even
more limited capability (average generalization accuracy of 19%). Overall,
fine-tuning GPT-4o mini is the most effective for infusing new knowledge and
updating knowledge, followed by GPT-3.5 Turbo and GPT-4o. The fine-tuning APIs
for Gemini 1.5 Flesh and Gemini 1.5 Pro are unable to learn new knowledge or
update existing knowledge. These findings underscore a major shortcoming in
using current commercial fine-tuning services to achieve reliable knowledge
infusion in common scenarios. We open source the FineTuneBench dataset at
https://github.com/kevinwu23/StanfordFineTuneBench. | arXiv |
Galaxy mergers are a key driver of galaxy formation and evolution, including
the triggering of AGN and star formation to a still unknown degree. We thus
investigate the impact of galaxy mergers on star formation and AGN activity
using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields
(CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and
MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We
focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess
in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$
and projected separations $r_p < 100$ kpc, compared to non-merger samples. We
find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25
\pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z =
[4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically
significant enhancements in galaxy sSFR or stellar mass are observed at any
projected separation or redshift bin. We also compare our observational results
with predictions from the SC-SAM simulation and find no evidence of star
formation enhancement in the simulations at any separation range. Finally, we
examine the AGN fraction and AGN excess, finding that the fraction of AGNs in
AGN-galaxy pairs, relative to the total AGN population, is
$3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative
to the overall galaxy population at the same redshift. We find that nearly all
AGNs have a companion within 100 kpc and observe an excess AGN fraction in
close-pair samples compared to non-merger samples. This excess is found to be
$1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT
diagram and photometric SED selection, respectively. | arXiv |
Using optical technology for current injection and electromagnetic emission
simplifies the comparison between materials. Here, we inject current into
monolayer graphene and bulk gallium arsenide (GaAs) using two-color quantum
interference and detect the emitted electric field by electro-optic sampling.
We find the amplitude of emitted terahertz (THz) radiation scales in the same
way for both materials even though they differ in dimension, band gap, atomic
composition, symmetry and lattice structure. In addition, we observe the same
mapping of the current direction to the light characteristics. With no
electrodes for injection or detection, our approach will allow electron
scattering timescales to be directly measured. We envisage that it will enable
exploration of new materials suitable for generating terahertz magnetic fields. | arXiv |
This paper investigates the impact of noise in the quantum query model, a
fundamental framework for quantum algorithms. We focus on the scenario where
the oracle is subject to non-unitary (or irreversible) noise, specifically
under the \textit{faulty oracle} model, where the oracle fails with a constant
probability and acts as identity. Regev and Schiff (ICALP'08) showed that
quantum advantage is lost for the search problem under this noise model. Our
main result shows that every quantum query algorithm can be made robust in this
noise model with a roughly quadratic blow-up in query complexity, thereby
preserving quantum speedup for all problems where the quantum advantage is
super-cubic. This is the first non-trivial robustification of quantum query
algorithms against an oracle that is noisy. | arXiv |
Fine-grained alignment between videos and text is challenging due to complex
spatial and temporal dynamics in videos. Existing video-based Large Multimodal
Models (LMMs) handle basic conversations but struggle with precise pixel-level
grounding in videos. To address this, we introduce VideoGLaMM, a LMM designed
for fine-grained pixel-level grounding in videos based on user-provided textual
inputs. Our design seamlessly connects three key components: a Large Language
Model, a dual vision encoder that emphasizes both spatial and temporal details,
and a spatio-temporal decoder for accurate mask generation. This connection is
facilitated via tunable V-L and L-V adapters that enable close Vision-Language
(VL) alignment. The architecture is trained to synchronize both spatial and
temporal elements of video content with textual instructions. To enable
fine-grained grounding, we curate a multimodal dataset featuring detailed
visually-grounded conversations using a semiautomatic annotation pipeline,
resulting in a diverse set of 38k video-QA triplets along with 83k objects and
671k masks. We evaluate VideoGLaMM on three challenging tasks: Grounded
Conversation Generation, Visual Grounding, and Referring Video Segmentation.
Experimental results show that our model consistently outperforms existing
approaches across all three tasks. | arXiv |
The generalized hydrodynamics (GHD) equation is the equivalent of the Euler
equations of hydrodynamics for integrable models. Systems of hyperbolic
equations such as the Euler equations usually develop shocks and are plagued by
problems of uniqueness. We establish for the first time the existence and
uniqueness of solutions to the full GHD equation and the absence of shocks,
from a large class of initial conditions with bounded occupation function. We
assume only absolute integrability of the two-body scattering shift. In
applications to quantum models of fermionic type, this includes all commonly
used physical initial states, such as locally thermal states and zero-entropy
states. We show in particular that differentiable initial conditions give
differentiable solutions at all times and that weak initial conditions such as
the Riemann problem have unique weak solutions which preserve entropy. For this
purpose, we write the GHD equation as a new fixed-point problem (announced in a
companion paper). We show that the fixed point exists, is unique, and is
approached, under an iterative solution procedure, in the Banach topology on
functions of momenta. | arXiv |
In this paper, we study the optimal control for an SEIR model adapted to the
vaccination strategy of susceptible individuals. There are factors associated
with a vaccination campaign that make this strategy not only a public health
issue but also an economic one. In this case, optimal control is important as
it minimizes implementation costs. We consider the availability of two vaccines
with different efficacy levels, and the control indicates when each vaccine
should be used. The optimal strategy specifies in all cases how vaccine
purchases should be distributed. For similar efficacy values, we perform a
sensitivity analysis on parameters that depend on the intrinsic characteristics
of the vaccines. Additionally, we investigate the behavior of the number of
infections under the optimal vaccination strategy. | arXiv |
In this paper we study flow problems on temporal networks, where edge
capacities and travel times change over time. We consider a network with $n$
nodes and $m$ edges where the capacity and length of each edge is a piecewise
constant function, and use $\mu=\Omega(m)$ to denote the total number of pieces
in all of the $2m$ functions. Our goal is to design exact algorithms for
various flow problems that run in time polynomial in the parameter $\mu$.
Importantly, the algorithms we design are strongly polynomial, i.e. have no
dependence on the capacities, flow value, or the time horizon of the flow
process, all of which can be exponentially large relative to the other
parameters; and return an integral flow when all input parameters are integral.
Our main result is an algorithm for checking feasibility of a dynamic
transshipment problem on temporal networks -- given multiple sources and sinks
with supply and demand values, is it possible to satisfy the desired supplies
and demands within a given time horizon? We develop a fast ($O(\mu^3)$ time)
algorithm for this feasibility problem when the input network has a certain
canonical form, by exploiting the cut structure of the associated time expanded
network. We then adapt an approach of \cite{hoppe2000} to show how other flow
problems on temporal networks can be reduced to the canonical format.
For computing dynamic transshipments on temporal networks, this results in a
$O(\mu^7)$ time algorithm, whereas the previous best integral exact algorithm
runs in time $\tilde O(\mu^{19})$. We achieve similar improvements for other
flow problems on temporal networks. | arXiv |
Large language models (LLMs) for code have become indispensable in various
domains, including code generation, reasoning tasks and agent systems. While
open-access code LLMs are increasingly approaching the performance levels of
proprietary models, high-quality code LLMs suitable for rigorous scientific
investigation, particularly those with reproducible data processing pipelines
and transparent training protocols, remain limited. The scarcity is due to
various challenges, including resource constraints, ethical considerations, and
the competitive advantages of keeping models advanced. To address the gap, we
introduce OpenCoder, a top-tier code LLM that not only achieves performance
comparable to leading models but also serves as an "open cookbook" for the
research community. Unlike most prior efforts, we release not only model
weights and inference code, but also the reproducible training data, complete
data processing pipeline, rigorous experimental ablation results, and detailed
training protocols for open scientific research. Through this comprehensive
release, we identify the key ingredients for building a top-tier code LLM: (1)
code optimized heuristic rules for data cleaning and methods for data
deduplication, (2) recall of text corpus related to code and (3) high-quality
synthetic data in both annealing and supervised fine-tuning stages. By offering
this level of openness, we aim to broaden access to all aspects of a top-tier
code LLM, with OpenCoder serving as both a powerful model and an open
foundation to accelerate research, and enable reproducible advancements in code
AI. | arXiv |
We study $\varepsilon$-stability in continuous logic. We first consider
stability in a model, where we obtain a definability of types result with a
better approximation than that in the literature. We also prove forking
symmetry for $\varepsilon$-stability and briefly discuss finitely satisfiable
types. We then do a short survey of $\varepsilon$-stability in a theory.
Finally, we consider the map that takes each formula to its "degree" of
stability in a given theory and show that it is a seminorm. All of this is done
in the context of a first-order formalism that allows predicates to take values
in arbitrary compact metric spaces. | arXiv |
Cosmological parameters and dark energy (DE) behavior are generally
constrained assuming \textit{a priori} models. We work out a model-independent
reconstruction to bound the key cosmological quantities and the DE evolution.
Through the model-independent \textit{B\'ezier interpolation} method, we
reconstruct the Hubble rate from the observational Hubble data and derive
analytic expressions for the distances of galaxy clusters, type Ia supernovae,
and uncorrelated baryonic acoustic oscillation (BAO) data. In view of the
discrepancy between Sloan Digital Sky Survey (SDSS) and Dark Energy
Spectroscopic Instrument (DESI) BAO data, they are kept separate in two
distinct analyses. Correlated BAO data are employed to break the baryonic--dark
matter degeneracy. All these interpolations enable us to single out and
reconstruct the DE behavior with the redshift $z$ in a totally
model-independent way. In both analyses, with SDSS-BAO or DESI-BAO data sets,
the constraints agree at $1$--$\sigma$ confidence level (CL) with the flat
$\Lambda$CDM model. The Hubble constant tension appears solved in favor of the
Planck satellite value. The reconstructed DE behavior exhibits deviations at
small $z$ ($>1$--$\sigma$ CL), but agrees ($<1$--$\sigma$ CL) with the
cosmological constant paradigm at larger $z$. Our method hints for a slowly
evolving DE, consistent with a cosmological constant at early times. | arXiv |
We consider quantum circuit models where the gates are drawn from arbitrary
gate ensembles given by probabilistic distributions over certain gate sets and
circuit architectures, which we call stochastic quantum circuits. Of main
interest in this work is the speed of convergence of stochastic circuits with
different gate ensembles and circuit architectures to unitary t-designs. A key
motivation for this theory is the varying preference for different gates and
circuit architectures in different practical scenarios. In particular, it
provides a versatile framework for devising efficient circuits for implementing
$t$-designs and relevant applications including random circuit and scrambling
experiments, as well as benchmarking the performance of gates and circuit
architectures. We examine various important settings in depth. A key aspect of
our study is an "ironed gadget" model, which allows us to systematically
evaluate and compare the convergence efficiency of entangling gates and circuit
architectures. Particularly notable results include i) gadgets of two-qubit
gates with KAK coefficients
$\left(\frac{\pi}{4}-\frac{1}{8}\arccos(\frac{1}{5}),\frac{\pi}{8},\frac{1}{8}\arccos(\frac{1}{5})\right)$
(which we call $\chi$ gates) directly form exact 2- and 3-designs; ii) the
iSWAP gate family achieves the best efficiency for convergence to 2-designs
under mild conjectures with numerical evidence, even outperforming the
Haar-random gate, for generic many-body circuits; iii) iSWAP + complete graph
achieve the best efficiency for convergence to 2-designs among all graph
circuits. A variety of numerical results are provided to complement our
analysis. We also derive robustness guarantees for our analysis against gate
perturbations. Additionally, we provide cursory analysis on gates with higher
locality and found that the Margolus gate outperforms various other well-known
gates. | arXiv |
New metal-organic frameworks (MOFs) are periodically synthesized all over the
world due to the wide range of societally and environmentally relevant
applications they possess. However, the mechanisms and thermodynamics
associated to MOF self-assembly are poorly understood because of the
difficulties in studying such a multi-scale process with molecular-level
resolution. In this work, we performed well-tempered metadynamics simulations
of the early nucleation and late growth steps of the self-assembly of ZIF-4
using a reactive force field. We found that the formation of building blocks is
a complex, multi-step process that involves changes in the coordination of the
metal ion. Saturating the ligand coordination of a metal ion is more
energetically favorable during growth than during early nucleation. The
addition of a fourth ligand is less exergonic than it is for the first three
and the associated free energy is highly dependent on the local environment of
the undercoordinated metal ion. The stability of this bond depends on the
strength of the solvent--metal ion interaction. Incorporating a ligand to a
ZIF-1 crystal is less favorable compared to the more stable ZIF-4 polymorph.
Milder differences were found when comparing the growth of (100), (010) and
(001) ZIF-4 surfaces. | arXiv |
The $\sigma_{t}$-irregularity (or sigma total index) is a graph invariant
which is defined as $\sigma_{t}(G)=\sum_{\{u,v\}\subseteq
V(G)}(d(u)-d(v))^{2},$ where $d(z)$ denotes the degree of $z$. This
irregularity measure was proposed by R\' {e}ti [Appl. Math. Comput. 344-345
(2019) 107-115], and recently rediscovered by Dimitrov and Stevanovi\'c [Appl.
Math. Comput. 441 (2023) 127709]. In this paper we remark that
$\sigma_{t}(G)=n^{2}\cdot Var(G)$, where $Var(G)$ is the degree variance of the
graph. Based on this observation, we characterize irregular graphs with maximum
$\sigma_{t}$-irregularity. We show that among all connected graphs on $n$
vertices, the split graphs $S_{\lceil\frac{n}{4}\rceil,
\lfloor\frac{3n}{4}\rfloor }$ and $S_{\lfloor\frac{n}{4}\rfloor,
\lceil\frac{3n}{4}\rceil }$ have the maximum $\sigma_{t}$-irregularity, and
among all complete bipartite graphs on $n$ vertices, either the complete
bipartite graph $K_{\lfloor\frac{n}{4}(2-\sqrt{2})\rfloor,
\lceil\frac{n}{4}(2+\sqrt{2})\rceil }$ or
$K_{\lceil\frac{n}{4}(2-\sqrt{2})\rceil, \lfloor\frac{n}{4}(2+\sqrt{2})\rfloor
}$ has the maximum sigma total index. Moreover, various upper and lower bounds
for $\sigma_{t}$-irregularity are provided; in this direction we give a
relation between the graph energy $\mathcal{E}(G)$ and sigma total index
$\sigma_{t}(G)$ and give another proof of two results by Dimitrov and
Stevanovi\'c. Applying Fiedler's characterization of the largest and the second
smallest Laplacian eigenvalue of the graph, we also establish new relationships
between $\sigma_{t}$ and $\sigma$. We conclude the paper with two conjectures. | arXiv |
This paper combines a techno-economic energy system model with an econometric
model to maximise electricity price forecasting accuracy. The proposed
combination model is tested on the German day-ahead wholesale electricity
market. Our paper also benchmarks the results against several econometric
alternatives. Lastly, we demonstrate the economic value of improved price
estimators maximising the revenue from an electric storage resource. The
results demonstrate that our integrated model improves overall forecasting
accuracy by 18 %, compared to available literature benchmarks. Furthermore, our
robustness checks reveal that a) the Ensemble Deep Neural Network model
performs best in our dataset and b) adding output from the techno-economic
energy systems model as econometric model input improves the performance of all
econometric models. The empirical relevance of the forecast improvement is
confirmed by the results of the exemplary storage optimisation, in which the
integration of the techno-economic energy system model leads to a revenue
increase of up to 10 %. | arXiv |
Title: Sentiment Analysis of Spanish Political Party Communications on
Twitter Using Pre-trained Language Models
Authors: Chuqiao Song, Shunzhang Chen, Xinyi Cai, Hao Chen
Comments: 21 pages, 6 figures
Abstract: This study investigates sentiment patterns within Spanish political
party communications on Twitter by leveraging BETO and RoBERTuito, two
pre-trained language models optimized for Spanish text. Using a dataset of
tweets from major Spanish political parties: PSOE, PP, Vox, Podemos, and
Ciudadanos, spanning 2019 to 2024, this research analyzes sentiment
distributions and explores the relationship between sentiment expression and
party ideology. The findings indicate that both models consistently identify a
predominant Neutral sentiment across all parties, with significant variations
in Negative and Positive sentiments that align with ideological distinctions.
Specifically, Vox exhibits higher levels of Negative sentiment, while PSOE
demonstrates relatively high Positive sentiment, supporting the hypothesis that
emotional appeals in political messaging reflect ideological stances. This
study underscores the potential of pre-trained language models for non-English
sentiment analysis on social media, providing insights into sentiment dynamics
that shape public discourse within Spain's multi-party political system.
Keywords: Spanish politics, sentiment analysis, pre-trained language models,
Twitter, BETO, RoBERTuito, political ideology, multi-party system | arXiv |
We show that every Born Lie algebra can be obtained by the bicross product
construction starting from two pseudo-Riemannian Lie algebras. We then obtain a
classification of all Lie algebras up to dimension four and all six-dimensional
nilpotent Lie algebras admitting an integrable Born structure. Finally, we
study the curvature properties of the pseudo-Riemannian metrics of the
integrable Born structures obtained in our classification results. | arXiv |
In many repeated auction settings, participants care not only about how
frequently they win but also how their winnings are distributed over time. This
problem arises in various practical domains where avoiding congested demand is
crucial, such as online retail sales and compute services, as well as in
advertising campaigns that require sustained visibility over time. We introduce
a simple model of this phenomenon, modeling it as a budgeted auction where the
value of a win is a concave function of the time since the last win. This
implies that for a given number of wins, even spacing over time is optimal. We
also extend our model and results to the case when not all wins result in
"conversions" (realization of actual gains), and the probability of conversion
depends on a context. The goal is to maximize and evenly space conversions
rather than just wins.
We study the optimal policies for this setting in second-price auctions and
offer learning algorithms for the bidders that achieve low regret against the
optimal bidding policy in a Bayesian online setting. Our main result is a
computationally efficient online learning algorithm that achieves $\tilde
O(\sqrt T)$ regret. We achieve this by showing that an infinite-horizon Markov
decision process (MDP) with the budget constraint in expectation is essentially
equivalent to our problem, even when limiting that MDP to a very small number
of states. The algorithm achieves low regret by learning a bidding policy that
chooses bids as a function of the context and the system's state, which will be
the time elapsed since the last win (or conversion). We show that
state-independent strategies incur linear regret even without uncertainty of
conversions. We complement this by showing that there are state-independent
strategies that, while still having linear regret, achieve a $(1-\frac 1 e)$
approximation to the optimal reward. | arXiv |
We propose the use of the Extended Kalman Filter (EKF) for online data
assimilation and update of a dynamic model, preliminary identified through the
Sparse Identification of Nonlinear Dynamics (SINDy). This data-driven technique
may avoid biases due to incorrect modelling assumptions and exploits SINDy to
approximate the system dynamics leveraging a predefined library of functions,
where active terms are selected and weighted by a sparse set of coefficients.
This results in a physically-sound and interpretable dynamic model allowing to
reduce epistemic uncertainty often affecting machine learning approaches.
Treating the SINDy model coefficients as random variables, we propose to update
them while acquiring (possibly noisy) system measurements, thus enabling the
online identification of time-varying systems. These changes can stem from,
e.g., varying operational conditions or unforeseen events. The EKF performs
model adaptation through joint state-parameters estimation, with the Jacobian
matrices required to computed the model sensitivity inexpensively evaluated
from the SINDy model formulation. The effectiveness of this approach is
demonstrated through three case studies: (i) a Lokta-Volterra model in which
all parameters simultaneously evolve during the observation period; (ii) a
Selkov model where the system undergoes a bifurcation not seen during the SINDy
training; (iii) a MEMS arch exhibiting a 1:2 internal resonance. The ability of
EKF of recovering inactivated functional terms from the SINDy library, or
discarding unnecessary contribution, is also highlighted. Based on the
presented applications, this method shows strong promise for handling
time-varying nonlinear dynamic systems possibly experiencing bifurcating
behaviours. | arXiv |
We study the robust regulation of labour contracts in moral hazard problems.
A firm offers a contract to incentivise production by an agent protected by
limited liability. A regulator chooses the set of permissible contracts to (i)
improve efficiency and (ii) protect the worker. The regulator ignores the
agent's productive actions and the firm's costs and evaluates regulation by its
worst-case regret. The regret-minimising regulation imposes a linear minimum
wage, allowing all contracts above this linear threshold. The slope of the
minimum contract balances the worker's protection - by ensuring they receive a
minimal share of the production - and the necessary flexibility for incentive
provision. | arXiv |
The present article is concerned with the nonlinear approximation of
functions in the Sobolev space H^q with respect to a tensor-product, or
hyperbolic wavelet basis on the unit n-cube. Here, q is a real number, which is
not necessarily positive. We derive Jackson and Bernstein inequalities to
obtain that the approximation classes contain Besov spaces of hybrid
regularity. Especially, we show that all functions that can be approximated by
classical wavelets are also approximable by tensor-product wavelets at least at
the same rate. In particular, this implies that for nonnegative regularity, the
classical Besov spaces of regularity q+sn, integrability and weak index t, with
1/t = s + 1/2, are included in the Besov spaces of hybrid regularity with
isotropic regularity q and additional mixed regularity s. | arXiv |
As implemented in the commercialized device modeling software, the four-state
nonradiative multi-phonon model has attracted intensive attention in the past
decade for describing the physics in negative bias temperature instability
(NBTI) and other reliability issues of Si/SiO$_\text{2}$ MOSFET devices. It was
proposed initially based on the assumption that the oxygen vacancy defects
(V$_\text{O}$) in SiO$_\text{2}$ dielectric layer are bistable in the Si-dimer
and back-projected structures during carrier capture and emission. Through
high-throughput first-principles structural search, we found V$_\text{O}$ on
non-equivalent O sites in amorphous SiO$_\text{2}$ can take 4 types of
structural configurations in neutral state and 7 types of configurations in +1
charged state after capturing holes, which produce a wide range of charge-state
transition levels for trapping holes. The finding contrasts the
structural-bistability assumption and makes the four-state model invalid for
most of O sites. To describe the reliability physics accurately, we propose an
all-state model to consider all these structural configurations as well as all
the carrier capture/emission transitions and thermal transitions between them.
With the all-state model, we show that the V$_\text{O}$ defects play important
roles in causing NBTI, which challenges the recent studies that discarded
V$_\text{O}$ as a possible hole trap in NBTI. Our systematical calculations on
the diversified V$_\text{O}$ properties and the all-state model provide the
microscopic foundation for describing the reliability physics of MOSFETs and
other transistors accurately. | arXiv |
The detection of cosmic antideuterons ($\overline{\rm D}$) at kinetic
energies below a few GeV/n could provide a smoking gun signature for dark
matter (DM). However, the theoretical uncertainties of coalescence models have
represented so far one of the main limiting factors for precise predictions of
the $\overline{\rm D}$ flux. In this Letter we present a novel calculation of
the $\overline{\rm D}$ source spectra, based on the Wigner formalism, for which
we implement the Argonne $v_{18}$ antideuteron wavefunction that does not have
any free parameters related to the coalescence process. We show that the
Argonne Wigner model excellently reproduces the $\overline{\rm D}$ multiplicity
measured by ALEPH at the $Z$-boson pole, which is usually adopted to tune the
coalescence models based on different approaches. Our analysis is based on
Pythia~8 Monte Carlo event generator and the state-of-the-art Vincia shower
algorithm. We succeed, with our model, to reduce the current theoretical
uncertainty on the prediction of the $\overline{\rm D}$ source spectra to a few
percent, for $\overline{\rm D}$ kinetic energies relevant to DM searches with
GAPS and AMS, and for DM masses above a few tens of GeV. This result implies
that the theoretical uncertainties due to the coalescence process are no longer
the main limiting factor in the predictions. We provide the tabulated source
spectra for all the relevant DM annihilation/decay channels and DM masses
between 5 GeV and 100 TeV, on the CosmiXs github repository
(https://github.com/ajueid/CosmiXs.git). | arXiv |
Deep regression models are used in a wide variety of safety-critical
applications, but are vulnerable to backdoor attacks. Although many defenses
have been proposed for classification models, they are ineffective as they do
not consider the uniqueness of regression models. First, the outputs of
regression models are continuous values instead of discretized labels. Thus,
the potential infected target of a backdoored regression model has infinite
possibilities, which makes it impossible to be determined by existing defenses.
Second, the backdoor behavior of backdoored deep regression models is triggered
by the activation values of all the neurons in the feature space, which makes
it difficult to be detected and mitigated using existing defenses. To resolve
these problems, we propose DRMGuard, the first defense to identify if a deep
regression model in the image domain is backdoored or not. DRMGuard formulates
the optimization problem for reverse engineering based on the unique
output-space and feature-space characteristics of backdoored deep regression
models. We conduct extensive evaluations on two regression tasks and four
datasets. The results show that DRMGuard can consistently defend against
various backdoor attacks. We also generalize four state-of-the-art defenses
designed for classifiers to regression models, and compare DRMGuard with them.
The results show that DRMGuard significantly outperforms all those defenses. | arXiv |
We consider the space of all configurations of finitely many (potentially
nested) circles in the plane. We prove that this space is aspherical, and
compute the fundamental group of each of its connected components. It turns out
these fundamental groups are obtained as iterated semdirect products of braid
groups, with the structure for each component dictated by a finite rooted tree.
These groups can be viewed as "braided" versions of the automorphism groups of
such trees. We also discuss connections to statistical mechanics, topological
data analysis, and geometric group theory. | arXiv |
We present a framework for learning a single policy capable of producing all
quadruped gaits and transitions. The framework consists of a policy trained
with deep reinforcement learning (DRL) to modulate the parameters of a system
of abstract oscillators (i.e. Central Pattern Generator), whose output is
mapped to joint commands through a pattern formation layer that sets the gait
style, i.e. body height, swing foot ground clearance height, and foot offset.
Different gaits are formed by changing the coupling between different
oscillators, which can be instantaneously selected at any velocity by a user.
With this framework, we systematically investigate which gait should be used at
which velocity, and when gait transitions should occur from a Cost of Transport
(COT), i.e. energy-efficiency, point of view. Additionally, we note how gait
style changes as a function of locomotion speed for each gait to keep the most
energy-efficient locomotion. While the currently most popular gait (trot) does
not result in the lowest COT, we find that considering different co-dependent
metrics such as mean base velocity and joint acceleration result in different
`optimal' gaits than those that minimize COT. We deploy our controller in
various hardware experiments, showing all 9 typical quadruped animal gaits, and
demonstrate generalizability to unseen gaits during training, and robustness to
leg failures. Video results can be found at https://youtu.be/OLoWSX_R868. | arXiv |
We present a comprehensive first-principles analysis of the thermoelectric
transport properties of hole-doped pyrite FeS$_2$ that includes electron-phonon
interactions. This work was motivated by the observed variations in the
magnitude of thermopower reported in previous experimental and theoretical
studies of hole-doped FeS$_2$ systems. Our calculations reveal that hole-doped
FeS$_2$ exhibits large positive room-temperature thermopower across all doping
levels, with a room-temperature thermopower of 608 $\mu$V/K at a low
hole-doping concentration of 10$^{19}$ cm$^{-3}$. This promising thermopower
finding prompted a comprehensive investigation of other key thermoelectric
parameters governing the thermoelectric figure of merit $ZT$. The calculated
electrical conductivity is modest and remains below 10$^5$ S/m at
room-temperature for all doping levels, limiting the achievable power factor.
Furthermore, the thermal conductivity is found to be phonon driven, with a high
room-temperature lattice thermal conductivity of 40.5 W/mK. Consequently, the
calculated $ZT$ remains below 0.1, suggesting that hole-doped FeS$_2$ may not a
viable candidate for effective thermoelectric applications despite its
promising thermopower. | arXiv |
The advantage of quantum protocols lies in the inherent properties of the
shared quantum states. These states are sometimes provided by sources that are
not trusted, and therefore need to be verified. Finding secure and efficient
quantum state verification protocols remains a big challenge, and recent works
illustrate trade-offs between efficiency and security for different groups of
states in restricted settings. However, whether a universal trade-off exists
for all quantum states and all verification strategies remains unknown. In this
work, we instantiate the categorical composable cryptography framework to show
a fundamental limit for quantum state verification for all cut-and-choose
approaches used to verify arbitrary quantum states. Our findings show that the
prevailing cut-and-choose techniques cannot lead to quantum state verification
protocols that are both efficient and secure. | arXiv |
Let $n$ be a positive integer. The Diophantine equation $n(x_1+x_2+\dots
+x_n)=x_1x_2\dots x_n$, $1 \le x_1\le x_2\le \dots \le x_n$ is called
Erd\H{o}s's last equation. We prove that $x_n\to \infty $ as $n\to \infty$ and
determine all tuples $(n,x_1,\dots ,x_n)$ with $x_n\le 10$. | arXiv |
Surveys are an indispensable source of data for applied economic research;
however, their reliance on self-reported information can introduce bias,
especially if core variables such as personal income are misreported. To assess
the extent and impact of this misreporting bias, we compare self-reported wages
from the German Socio-Economic Panel (SOEP) with administrative wages from
social security records (IEB) for the same individuals. Using a novel and
unique data linkage (SOEP-ADIAB), we identify a modest but economically
significant reporting bias, with SOEP respondents underreporting their
administrative wages by about 7.3%. This misreporting varies systematically
with individual, household, and especially job and firm characteristics. In
replicating common empirical analyses in which wages serve as either dependent
or independent variables, we find that misreporting is consequential for some,
but not all estimated relationships. It turns out to be inconsequential for
examining the returns to education, but relevant for analyzing the gender wage
gap. In addition we find that misreporting bias can significantly affect the
results when wage is used as the independent variable. Specifically, estimates
of the wage-satisfaction relationship are substantially overestimated when
based on survey data, although this bias is mitigated when focusing on
interpersonal changes. Our findings underscore that survey-based measures of
individual wages can significantly bias commonly estimated empirical
relationships. They also demonstrate the enormous research potential of linked
administrative-survey data. | arXiv |