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2501.18170
|
Continually Evolved Multimodal Foundation Models for Cancer Prognosis
|
cs.LG
|
Cancer prognosis is a critical task that involves predicting patient outcomes
and survival rates. To enhance prediction accuracy, previous studies have
integrated diverse data modalities, such as clinical notes, medical images, and
genomic data, leveraging their complementary information. However, existing
approaches face two major limitations. First, they struggle to incorporate
newly arrived data with varying distributions into training, such as patient
records from different hospitals, thus rendering sub-optimal generalizability
and limited utility in real-world applications. Second, most multimodal
integration methods rely on simplistic concatenation or task-specific
pipelines, which fail to capture the complex interdependencies across
modalities. To address these, we propose a continually evolving multi-modal
foundation model. Extensive experiments on the TCGA dataset demonstrate the
effectiveness of our approach, highlighting its potential to advance cancer
prognosis by enabling robust and adaptive multimodal integration.
|
2501.18174
|
Advancing Personalized Federated Learning: Integrative Approaches with
AI for Enhanced Privacy and Customization
|
cs.LG eess.SP
|
In the age of data-driven decision making, preserving privacy while providing
personalized experiences has become paramount. Personalized Federated Learning
(PFL) offers a promising framework by decentralizing the learning process, thus
ensuring data privacy and reducing reliance on centralized data repositories.
However, the integration of advanced Artificial Intelligence (AI) techniques
within PFL remains underexplored. This paper proposes a novel approach that
enhances PFL with cutting-edge AI methodologies including adaptive
optimization, transfer learning, and differential privacy. We present a model
that not only boosts the performance of individual client models but also
ensures robust privacy-preserving mechanisms and efficient resource utilization
across heterogeneous networks. Empirical results demonstrate significant
improvements in model accuracy and personalization, along with stringent
privacy adherence, as compared to conventional federated learning models. This
work paves the way for a new era of truly personalized and privacy-conscious AI
systems, offering significant implications for industries requiring compliance
with stringent data protection regulations.
|
2501.18177
|
Investigating Tax Evasion Emergence Using Dual Large Language Model and
Deep Reinforcement Learning Powered Agent-based Simulation
|
cs.IR cs.CY cs.MA
|
Tax evasion, usually the largest component of an informal economy, is a
persistent challenge over history with significant socio-economic implications.
Many socio-economic studies investigate its dynamics, including influencing
factors, the role and influence of taxation policies, and the prediction of the
tax evasion volume over time. These studies assumed such behavior is given, as
observed in the real world, neglecting the "big bang" of such activity in a
population. To this end, computational economy studies adopted developments in
computer simulations, in general, and recent innovations in artificial
intelligence (AI), in particular, to simulate and study informal economy
appearance in various socio-economic settings. This study presents a novel
computational framework to examine the dynamics of tax evasion and the
emergence of informal economic activity. Employing an agent-based simulation
powered by Large Language Models and Deep Reinforcement Learning, the framework
is uniquely designed to allow informal economic behaviors to emerge
organically, without presupposing their existence or explicitly signaling
agents about the possibility of evasion. This provides a rigorous approach for
exploring the socio-economic determinants of compliance behavior. The
experimental design, comprising model validation and exploratory phases,
demonstrates the framework's robustness in replicating theoretical economic
behaviors. Findings indicate that individual personality traits, external
narratives, enforcement probabilities, and the perceived efficiency of public
goods provision significantly influence both the timing and extent of informal
economic activity. The results underscore that efficient public goods provision
and robust enforcement mechanisms are complementary; neither alone is
sufficient to curtail informal activity effectively.
|
2501.18178
|
Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte
Carlo
|
eess.SP cs.LG stat.ML
|
This paper considers the problem of estimating chirp parameters from a noisy
mixture of chirps. While a rich body of work exists in this area, challenges
remain when extending these techniques to chirps of higher order polynomials.
We formulate this as a non-convex optimization problem and propose a modified
Langevin Monte Carlo (LMC) sampler that exploits the average curvature of the
objective function to reliably find the minimizer. Results show that our
Curvature-guided LMC (CG-LMC) algorithm is robust and succeeds even in low SNR
regimes, making it viable for practical applications.
|
2501.18179
|
Tunable Multilayer Surface Plasmon Resonance Biosensor for Trace-Level
Toxin Detection
|
eess.SY cs.SY
|
This paper presents a comprehensive study on a novel multilayer surface
plasmon resonance (SPR) biosensor designed for detecting trace-level toxins in
liquid samples with exceptional precision and efficiency. Leveraging the
Kretschmann configuration, the proposed design integrates advanced
two-dimensional materials, including black phosphorus (BP) and transition metal
dichalcogenides (TMDs), to significantly enhance the performance metrics of the
sensor. Key innovations include the optimization of sensitivity through precise
material layering, minimization of full-width at half-maximum (FWHM) to improve
signal resolution, and maximization of the figure of merit (FoM) for superior
detection accuracy. Numerical simulations are employed to validate the
structural and functional enhancements of the biosensor. The results
demonstrate improved interaction between the evanescent field and the analyte,
enabling detection at trace concentrations with higher specificity. This
biosensor is poised to contribute to advancements in biochemical sensing,
environmental monitoring, and other critical applications requiring
high-sensitivity toxin detection.
|
2501.18183
|
Decentralized Projection-free Online Upper-Linearizable Optimization
with Applications to DR-Submodular Optimization
|
math.OC cs.CC cs.LG stat.ML
|
We introduce a novel framework for decentralized projection-free
optimization, extending projection-free methods to a broader class of
upper-linearizable functions. Our approach leverages decentralized optimization
techniques with the flexibility of upper-linearizable function frameworks,
effectively generalizing traditional DR-submodular function optimization. We
obtain the regret of $O(T^{1-\theta/2})$ with communication complexity of
$O(T^{\theta})$ and number of linear optimization oracle calls of
$O(T^{2\theta})$ for decentralized upper-linearizable function optimization,
for any $0\le \theta \le 1$. This approach allows for the first results for
monotone up-concave optimization with general convex constraints and
non-monotone up-concave optimization with general convex constraints. Further,
the above results for first order feedback are extended to zeroth order,
semi-bandit, and bandit feedback.
|
2501.18184
|
Genetic Algorithm with Border Trades (GAB)
|
cs.LG cs.NE stat.CO
|
This paper introduces a novel approach to improving Genetic Algorithms (GA)
in large or complex problem spaces by incorporating new chromosome patterns in
the breeding process through border trade activities. These strategies increase
chromosome diversity, preventing premature convergence and enhancing the GA's
ability to explore the solution space more effectively. Empirical evidence
demonstrates significant improvements in convergence behavior. This approach
offers a promising pathway to addressing challenges in optimizing large or
complex problem domains.
|
2501.18187
|
In-Context Learning of Polynomial Kernel Regression in Transformers with
GLU Layers
|
cs.LG cs.AI
|
Transformer-based models have demonstrated remarkable ability in in-context
learning (ICL), where they can adapt to unseen tasks from a prompt with a few
examples, without requiring parameter updates. Recent research has provided
insight into how linear Transformers can perform ICL by implementing gradient
descent estimators. In particular, it has been shown that the optimal linear
self-attention (LSA) mechanism can implement one step of gradient descent with
respect to a linear least-squares objective when trained on random linear
regression tasks.
However, the theoretical understanding of ICL for nonlinear function classes
remains limited. In this work, we address this gap by first showing that LSA is
inherently restricted to solving linear least-squares objectives and thus, the
solutions in prior works cannot readily extend to nonlinear ICL tasks. To
overcome this limitation, drawing inspiration from modern architectures, we
study a mechanism that combines LSA with GLU-like feed-forward layers and show
that this allows the model to perform one step of gradient descent on a
polynomial kernel regression. Further, we characterize the scaling behavior of
the resulting Transformer model, highlighting the necessary model size to
effectively handle quadratic ICL tasks. Our findings highlight the distinct
roles of attention and feed-forward layers in nonlinear ICL and identify key
challenges when extending ICL to nonlinear function classes.
|
2501.18189
|
Neural Network Modeling of Microstructure Complexity Using Digital
Libraries
|
cs.LG cond-mat.mtrl-sci cs.CE nlin.PS physics.comp-ph
|
Microstructure evolution in matter is often modeled numerically using field
or level-set solvers, mirroring the dual representation of spatiotemporal
complexity in terms of pixel or voxel data, and geometrical forms in vector
graphics. Motivated by this analog, as well as the structural and event-driven
nature of artificial and spiking neural networks, respectively, we evaluate
their performance in learning and predicting fatigue crack growth and Turing
pattern development. Predictions are made based on digital libraries
constructed from computer simulations, which can be replaced by experimental
data to lift the mathematical overconstraints of physics. Our assessment
suggests that the leaky integrate-and-fire neuron model offers superior
predictive accuracy with fewer parameters and less memory usage, alleviating
the accuracy-cost tradeoff in contrast to the common practices in computer
vision tasks. Examination of network architectures shows that these benefits
arise from its reduced weight range and sparser connections. The study
highlights the capability of event-driven models in tackling problems with
evolutionary bulk-phase and interface behaviors using the digital library
approach.
|
2501.18190
|
Economic Rationality under Specialization: Evidence of Decision Bias in
AI Agents
|
cs.AI
|
In the study by Chen et al. (2023) [01], the large language model GPT
demonstrated economic rationality comparable to or exceeding the average human
level in tasks such as budget allocation and risk preference. Building on this
finding, this paper further incorporates specialized agents, such as
biotechnology experts and economists, for a horizontal comparison to explore
whether specialization can enhance or maintain economic rationality equivalent
to that of GPT in similar decision-making scenarios. The results indicate that
when agents invest more effort in specialized fields, their decision-making
behavior is more prone to 'rationality shift,' specifically manifested as
increased violations of GARP (Generalized Axiom of Revealed Preference),
decreased CCEI (Critical Cost Efficiency Index), and more significant decision
deviations under high-risk conditions. In contrast, GPT and more generalized
basic agents maintain a more stable and consistent level of rationality across
multiple tasks. This study reveals the inherent conflict between specialization
and economic rationality, providing new insights for constructing AI
decision-making systems that balance specialization and generalization across
various scenarios.
|
2501.18192
|
Machine Learning Fairness for Depression Detection using EEG Data
|
cs.CV cs.LG eess.SP
|
This paper presents the very first attempt to evaluate machine learning
fairness for depression detection using electroencephalogram (EEG) data. We
conduct experiments using different deep learning architectures such as
Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks,
and Gated Recurrent Unit (GRU) networks across three EEG datasets: Mumtaz,
MODMA and Rest. We employ five different bias mitigation strategies at the
pre-, in- and post-processing stages and evaluate their effectiveness. Our
experimental results show that bias exists in existing EEG datasets and
algorithms for depression detection, and different bias mitigation methods
address bias at different levels across different fairness measures.
|
2501.18196
|
GDformer: Going Beyond Subsequence Isolation for Multivariate Time
Series Anomaly Detection
|
cs.LG
|
Unsupervised anomaly detection of multivariate time series is a challenging
task, given the requirements of deriving a compact detection criterion without
accessing the anomaly points. The existing methods are mainly based on
reconstruction error or association divergence, which are both confined to
isolated subsequences with limited horizons, hardly promising unified
series-level criterion. In this paper, we propose the Global
Dictionary-enhanced Transformer (GDformer) with a renovated dictionary-based
cross attention mechanism to cultivate the global representations shared by all
normal points in the entire series. Accordingly, the cross-attention maps
reflect the correlation weights between the point and global representations,
which naturally leads to the representation-wise similarity-based detection
criterion. To foster more compact detection boundary, prototypes are introduced
to capture the distribution of normal point-global correlation weights.
GDformer consistently achieves state-of-the-art unsupervised anomaly detection
performance on five real-world benchmark datasets. Further experiments validate
the global dictionary has great transferability among various datasets. The
code is available at https://github.com/yuppielqx/GDformer.
|
2501.18197
|
Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting
Their Limitations
|
cs.LG cs.DB
|
In this work, we dive into the fundamental challenges of evaluating Text2SQL
solutions and highlight potential failure causes and the potential risks of
relying on aggregate metrics in existing benchmarks. We identify two largely
unaddressed limitations in current open benchmarks: (1) data quality issues in
the evaluation data, mainly attributed to the lack of capturing the
probabilistic nature of translating a natural language description into a
structured query (e.g., NL ambiguity), and (2) the bias introduced by using
different match functions as approximations for SQL equivalence.
To put both limitations into context, we propose a unified taxonomy of all
Text2SQL limitations that can lead to both prediction and evaluation errors. We
then motivate the taxonomy by providing a survey of Text2SQL limitations using
state-of-the-art Text2SQL solutions and benchmarks. We describe the causes of
limitations with real-world examples and propose potential mitigation solutions
for each category in the taxonomy. We conclude by highlighting the open
challenges encountered when deploying such mitigation strategies or attempting
to automatically apply the taxonomy.
|
2501.18199
|
HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation
|
cs.LG cs.AI
|
This paper introduces the Hierarchical Kolmogorov-Arnold Network (HKAN), a
novel network architecture that offers a competitive alternative to the
recently proposed Kolmogorov-Arnold Network (KAN). Unlike KAN, which relies on
backpropagation, HKAN adopts a randomized learning approach, where the
parameters of its basis functions are fixed, and linear aggregations are
optimized using least-squares regression. HKAN utilizes a hierarchical
multi-stacking framework, with each layer refining the predictions from the
previous one by solving a series of linear regression problems. This
non-iterative training method simplifies computation and eliminates sensitivity
to local minima in the loss function. Empirical results show that HKAN delivers
comparable, if not superior, accuracy and stability relative to KAN across
various regression tasks, while also providing insights into variable
importance. The proposed approach seamlessly integrates theoretical insights
with practical applications, presenting a robust and efficient alternative for
neural network modeling.
|
2501.18200
|
Characterization of Permanent Magnet Synchronous Machines based on
semi-analytic model reduction for drive cycle analysis
|
cs.CE
|
The characterization of an interior permanent magnet synchronous machine
(IPMSM) requires numerical analysis of the nonlinear magnetic motor model in
different load conditions. To obtain the case-specific best machine behavior, a
strategy for the determination of stator input current amplitude and angle is
employed for all possible load torques given a limited terminal current
amplitude and DC bus voltage. Various losses are calculated using state of the
art loss models. The electromagnetic performance of the electric machine is
stored in lookup tables. These can then be used for the drive cycle analysis of
the electric drive train in the design and optimization stages.
To avoid the use of a dedicated mesh generator in the numerical analysis,
volumetric spline-based models are suggested.With this approach, the mesh can
be generated directly from the Computer Aided Design (CAD) geometry. This
enables an automatic adaption of the grid following a geometry perturbation.
With this the approximated solution is kept consistent over the different
iterations of an overlying optimization, improving its convergence behavior.
|
2501.18201
|
Neural Operator based Reinforcement Learning for Control of first-order
PDEs with Spatially-Varying State Delay
|
cs.AI cs.SY eess.SY
|
Control of distributed parameter systems affected by delays is a challenging
task, particularly when the delays depend on spatial variables. The idea of
integrating analytical control theory with learning-based control within a
unified control scheme is becoming increasingly promising and advantageous. In
this paper, we address the problem of controlling an unstable first-order
hyperbolic PDE with spatially-varying delays by combining PDE backstepping
control strategies and deep reinforcement learning (RL). To eliminate the
assumption on the delay function required for the backstepping design, we
propose a soft actor-critic (SAC) architecture incorporating a DeepONet to
approximate the backstepping controller. The DeepONet extracts features from
the backstepping controller and feeds them into the policy network. In
simulations, our algorithm outperforms the baseline SAC without prior
backstepping knowledge and the analytical controller.
|
2501.18202
|
On Scaling Neurosymbolic Programming through Guided Logical Inference
|
cs.AI
|
Probabilistic neurosymbolic learning seeks to integrate neural networks with
symbolic programming. Many state-of-the-art systems rely on a reduction to the
Probabilistic Weighted Model Counting Problem (PWMC), which requires computing
a Boolean formula called the logical provenance.However, PWMC is \\#P-hard, and
the number of clauses in the logical provenance formula can grow exponentially,
creating a major bottleneck that significantly limits the applicability of PNL
solutions in practice.We propose a new approach centered around an exact
algorithm DPNL, that enables bypassing the computation of the logical
provenance.The DPNL approach relies on the principles of an oracle and a
recursive DPLL-like decomposition in order to guide and speed up logical
inference.Furthermore, we show that this approach can be adapted for
approximate reasoning with $\epsilon$ or $(\epsilon, \delta)$ guarantees,
called ApproxDPNL.Experiments show significant performance gains.DPNL enables
scaling exact inference further, resulting in more accurate models.Further,
ApproxDPNL shows potential for advancing the scalability of neurosymbolic
programming by incorporating approximations even further, while simultaneously
ensuring guarantees for the reasoning process.
|
2501.18203
|
Joint Design and Pricing of Extended Warranties for Multiple Automobiles
with Different Price Bands
|
math.OC cs.SY eess.SY
|
Extended warranties (EWs) are significant source of revenue for
capital-intensive products like automobiles. Such products consist of multiple
subsystems, providing flexibility in EW customization, for example, bundling a
tailored set of subsystems in an EW contract. This, in turn, enables the
creation of a service menu with different EW contract options. From the
perspective of a third-party EW provider servicing a fleet of automobile
brands, we develop a novel model to jointly optimize the design and pricing of
EWs in order to maximize the profit. Specifically, the problem is to determine
which contracts should be included in the EW menu and identify the appropriate
price for each contract. As the complexity of the joint optimization problem
increases exponentially with the number of subsystems, two solution approaches
are devised to solve the problem. The first approach is based on a
mixed-integer second-order cone programming reformulation, which guarantees
optimality but is applicable only for a small number of subsystems. The second
approach utilizes a two-step iteration process, offering enhanced computational
efficiency in scenarios with a large number of subsystems. Through numerical
experiments, the effectiveness of our model is validated, particularly in
scenarios characterized by high failure rates and a large number of subsystems.
|
2501.18205
|
Contextually Structured Token Dependency Encoding for Large Language
Models
|
cs.CL
|
Token representation strategies within large-scale neural architectures often
rely on contextually refined embeddings, yet conventional approaches seldom
encode structured relationships explicitly within token interactions.
Self-attention mechanisms effectively capture dynamic contextual dependencies,
but their reliance on learned weight distributions limits the preservation of
long-range hierarchical structures in generated sequences. Dependency-aware
token encoding introduces a structured approach to embedding initialization,
ensuring that relational constraints are embedded within token representations
rather than inferred solely through attention dynamics. The proposed encoding
mechanism refines token interactions through dependency-weighted attention
computations, ensuring that syntactic and semantic dependencies are retained
across multiple processing layers. Empirical evaluations indicate reductions in
perplexity across diverse linguistic benchmarks, suggesting improvements in
contextual coherence and predictive consistency in autoregressive text
generation. Computational efficiency assessments reveal a moderate increase in
memory consumption and training time, attributed to additional matrix
computations within the encoding module, yet scalability remains feasible
within conventional transformer architectures. Structured encoding enhances
lexical variation and dependency retention, reinforcing linguistic coherence
without requiring external syntactic annotations or auxiliary training
objectives. Statistical comparisons highlight improvements in dependency
alignment, particularly in longer sequences where conventional self-attention
models exhibit degradation in hierarchical consistency. Sentence length
distributions indicate a reduction in abrupt phrase transitions, further
supporting the hypothesis that explicit dependency encoding facilitates more
structured phrase generation.
|
2501.18210
|
Hashtag Re-Appropriation for Audience Control on Recommendation-Driven
Social Media Xiaohongshu (rednote)
|
cs.HC cs.CY cs.IR cs.SI
|
Algorithms have played a central role in personalized recommendations on
social media. However, they also present significant obstacles for content
creators trying to predict and manage their audience reach. This issue is
particularly challenging for marginalized groups seeking to maintain safe
spaces. Our study explores how women on Xiaohongshu (rednote), a
recommendation-driven social platform, proactively re-appropriate hashtags
(e.g., #Baby Supplemental Food) by using them in posts unrelated to their
literal meaning. The hashtags were strategically chosen from topics that would
be uninteresting to the male audience they wanted to block. Through a
mixed-methods approach, we analyzed the practice of hashtag re-appropriation
based on 5,800 collected posts and interviewed 24 active users from diverse
backgrounds to uncover users' motivations and reactions towards the
re-appropriation. This practice highlights how users can reclaim agency over
content distribution on recommendation-driven platforms, offering insights into
self-governance within algorithmic-centered power structures.
|
2501.18216
|
Behavior Modeling Space Reconstruction for E-Commerce Search
|
cs.IR
|
Delivering superior search services is crucial for enhancing customer
experience and driving revenue growth. Conventionally, search systems model
user behaviors by combining user preference and query item relevance
statically, often through a fixed logical 'and' relationship. This paper
reexamines existing approaches through a unified lens using both causal graphs
and Venn diagrams, uncovering two prevalent yet significant issues: entangled
preference and relevance effects, and a collapsed modeling space. To surmount
these challenges, our research introduces a novel framework, DRP, which
enhances search accuracy through two components to reconstruct the behavior
modeling space. Specifically, we implement preference editing to proactively
remove the relevance effect from preference predictions, yielding untainted
user preferences. Additionally, we employ adaptive fusion, which dynamically
adjusts fusion criteria to align with the varying patterns of relevance and
preference, facilitating more nuanced and tailored behavior predictions within
the reconstructed modeling space. Empirical validation on two public datasets
and a proprietary search dataset underscores the superiority of our proposed
methodology, demonstrating marked improvements in performance over existing
approaches.
|
2501.18219
|
Revisiting $\Psi$DONet: microlocally inspired filters for
incomplete-data tomographic reconstructions
|
math.OC cs.CV cs.LG
|
In this paper, we revisit a supervised learning approach based on unrolling,
known as $\Psi$DONet, by providing a deeper microlocal interpretation for its
theoretical analysis, and extending its study to the case of sparse-angle
tomography. Furthermore, we refine the implementation of the original
$\Psi$DONet considering special filters whose structure is specifically
inspired by the streak artifact singularities characterizing tomographic
reconstructions from incomplete data. This allows to considerably lower the
number of (learnable) parameters while preserving (or even slightly improving)
the same quality for the reconstructions from limited-angle data and providing
a proof-of-concept for the case of sparse-angle tomographic data.
|
2501.18220
|
On-Line Learning for Planning and Control of Underactuated Robots with
Uncertain Dynamics
|
cs.RO
|
We present an iterative approach for planning and controlling motions of
underactuated robots with uncertain dynamics. At its core, there is a learning
process which estimates the perturbations induced by the model uncertainty on
the active and passive degrees of freedom. The generic iteration of the
algorithm makes use of the learned data in both the planning phase, which is
based on optimization, and the control phase, where partial feedback
linearization of the active dofs is performed on the model updated on-line. The
performance of the proposed approach is shown by comparative simulations and
experiments on a Pendubot executing various types of swing-up maneuvers. Very
few iterations are typically needed to generate dynamically feasible
trajectories and the tracking control that guarantees their accurate execution,
even in the presence of large model uncertainties.
|
2501.18223
|
Exploring Large Protein Language Models in Constrained Evaluation
Scenarios within the FLIP Benchmark
|
cs.LG cs.AI
|
In this study, we expand upon the FLIP benchmark-designed for evaluating
protein fitness prediction models in small, specialized prediction tasks-by
assessing the performance of state-of-the-art large protein language models,
including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse
benchmarks such as ProteinGym, which cover a broad spectrum of tasks, FLIP
focuses on constrained settings where data availability is limited. This makes
it an ideal framework to evaluate model performance in scenarios with scarce
task-specific data. We investigate whether recent advances in protein language
models lead to significant improvements in such settings. Our findings provide
valuable insights into the performance of large-scale models in specialized
protein prediction tasks.
|
2501.18229
|
GPD: Guided Polynomial Diffusion for Motion Planning
|
cs.RO
|
Diffusion-based motion planners are becoming popular due to their
well-established performance improvements, stemming from sample diversity and
the ease of incorporating new constraints directly during inference. However, a
primary limitation of the diffusion process is the requirement for a
substantial number of denoising steps, especially when the denoising process is
coupled with gradient-based guidance. In this paper, we introduce, diffusion in
the parametric space of trajectories, where the parameters are represented as
Bernstein coefficients. We show that this representation greatly improves the
effectiveness of the cost function guidance and the inference speed. We also
introduce a novel stitching algorithm that leverages the diversity in
diffusion-generated trajectories to produce collision-free trajectories with
just a single cost function-guided model. We demonstrate that our approaches
outperform current SOTA diffusion-based motion planners for manipulators and
provide an ablation study on key components.
|
2501.18232
|
Free-T2M: Frequency Enhanced Text-to-Motion Diffusion Model With
Consistency Loss
|
cs.CV
|
Rapid progress in text-to-motion generation has been largely driven by
diffusion models. However, existing methods focus solely on temporal modeling,
thereby overlooking frequency-domain analysis. We identify two key phases in
motion denoising: the **semantic planning stage** and the **fine-grained
improving stage**. To address these phases effectively, we propose
**Fre**quency **e**nhanced **t**ext-**to**-**m**otion diffusion model
(**Free-T2M**), incorporating stage-specific consistency losses that enhance
the robustness of static features and improve fine-grained accuracy. Extensive
experiments demonstrate the effectiveness of our method. Specifically, on
StableMoFusion, our method reduces the FID from **0.189** to **0.051**,
establishing a new SOTA performance within the diffusion architecture. These
findings highlight the importance of incorporating frequency-domain insights
into text-to-motion generation for more precise and robust results.
|
2501.18236
|
RIS-assisted Physical Layer Security
|
cs.IT eess.SP math.IT
|
We propose a reconfigurable intelligent surface (RIS)-assisted wiretap
channel, where the RIS is strategically deployed to provide a spatial
separation to the transmitter, and orthogonal combiners are employed at the
legitimate receiver to extract the data streams from the direct and
RIS-assisted links. Then we derive the achievable secrecy rate under semantic
security for the RIS-assisted channel and design an algorithm for the secrecy
rate optimization problem. The simulation results show the effects of total
transmit power, the location and number of eavesdroppers on the security
performance.
|
2501.18237
|
Arbitrary Data as Images: Fusion of Patient Data Across Modalities and
Irregular Intervals with Vision Transformers
|
cs.CV cs.AI
|
A patient undergoes multiple examinations in each hospital stay, where each
provides different facets of the health status. These assessments include
temporal data with varying sampling rates, discrete single-point measurements,
therapeutic interventions such as medication administration, and images. While
physicians are able to process and integrate diverse modalities intuitively,
neural networks need specific modeling for each modality complicating the
training procedure. We demonstrate that this complexity can be significantly
reduced by visualizing all information as images along with unstructured text
and subsequently training a conventional vision-text transformer. Our approach,
Vision Transformer for irregular sampled Multi-modal Measurements (ViTiMM), not
only simplifies data preprocessing and modeling but also outperforms current
state-of-the-art methods in predicting in-hospital mortality and phenotyping,
as evaluated on 6,175 patients from the MIMIC-IV dataset. The modalities
include patient's clinical measurements, medications, X-ray images, and
electrocardiography scans. We hope our work inspires advancements in
multi-modal medical AI by reducing the training complexity to (visual) prompt
engineering, thus lowering entry barriers and enabling no-code solutions for
training. The source code will be made publicly available.
|
2501.18243
|
Statistical multi-metric evaluation and visualization of LLM system
predictive performance
|
stat.AP cs.CL cs.LG
|
The evaluation of generative or discriminative large language model
(LLM)-based systems is often a complex multi-dimensional problem. Typically, a
set of system configuration alternatives are evaluated on one or more benchmark
datasets, each with one or more evaluation metrics, which may differ between
datasets. We often want to evaluate -- with a statistical measure of
significance -- whether systems perform differently either on a given dataset
according to a single metric, on aggregate across metrics on a dataset, or
across datasets. Such evaluations can be done to support decision-making, such
as deciding whether a particular system component change (e.g., choice of LLM
or hyperparameter values) significantly improves performance over the current
system configuration, or, more generally, whether a fixed set of system
configurations (e.g., a leaderboard list) have significantly different
performances according to metrics of interest. We present a framework
implementation that automatically performs the correct statistical tests,
properly aggregates the statistical results across metrics and datasets (a
nontrivial task), and can visualize the results. The framework is demonstrated
on the multi-lingual code generation benchmark CrossCodeEval, for several
state-of-the-art LLMs.
|
2501.18246
|
Ground Awareness in Deep Learning for Large Outdoor Point Cloud
Segmentation
|
cs.CV
|
This paper presents an analysis of utilizing elevation data to aid outdoor
point cloud semantic segmentation through existing machine-learning networks in
remote sensing, specifically in urban, built-up areas. In dense outdoor point
clouds, the receptive field of a machine learning model may be too small to
accurately determine the surroundings and context of a point. By computing
Digital Terrain Models (DTMs) from the point clouds, we extract the relative
elevation feature, which is the vertical distance from the terrain to a point.
RandLA-Net is employed for efficient semantic segmentation of large-scale point
clouds. We assess its performance across three diverse outdoor datasets
captured with varying sensor technologies and sensor locations. Integration of
relative elevation data leads to consistent performance improvements across all
three datasets, most notably in the Hessigheim dataset, with an increase of 3.7
percentage points in average F1 score from 72.35% to 76.01%, by establishing
long-range dependencies between ground and objects. We also explore additional
local features such as planarity, normal vectors, and 2D features, but their
efficacy varied based on the characteristics of the point cloud. Ultimately,
this study underscores the important role of the non-local relative elevation
feature for semantic segmentation of point clouds in remote sensing
applications.
|
2501.18250
|
Dynamic Model Fine-Tuning For Extreme MIMO CSI Compression
|
cs.IT eess.SP math.IT
|
Efficient channel state information (CSI) compression is crucial in frequency
division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems
due to excessive feedback overhead. Recently, deep learning-based compression
techniques have demonstrated superior performance across various data types,
including CSI. However, these approaches often experience performance
degradation when the data distribution changes due to their limited
generalization capabilities. To address this challenge, we propose a model
fine-tuning approach for CSI feedback in massive MIMO systems. The idea is to
fine-tune the encoder/decoder network models in a dynamic fashion using the
recent CSI samples. First, we explore encoder-only fine-tuning, where only the
encoder parameters are updated, leaving the decoder and latent parameters
unchanged. Next, we consider full-model fine-tuning, where the encoder and
decoder models are jointly updated. Unlike encoder-only fine-tuning, full-model
fine-tuning requires the updated decoder and latent parameters to be
transmitted to the decoder side. To efficiently handle this, we propose
different prior distributions for model updates, such as uniform and truncated
Gaussian to entropy code them together with the compressed CSI and account for
additional feedback overhead imposed by conveying the model updates. Moreover,
we incorporate quantized model updates during fine-tuning to reflect the impact
of quantization in the deployment phase. Our results demonstrate that
full-model fine-tuning significantly enhances the rate-distortion (RD)
performance of neural CSI compression. Furthermore, we analyze how often the
full-model fine-tuning should be applied in a new wireless environment and
identify an optimal period interval for achieving the best RD trade-off.
|
2501.18251
|
How to Select Datapoints for Efficient Human Evaluation of NLG Models?
|
cs.CL
|
Human evaluation is the gold-standard for evaluating text generation models.
It is also expensive, and to fit budgetary constraints, a random subset of the
test data is often chosen in practice. The randomly selected data may not
accurately represent test performance, making this approach economically
inefficient for model comparison. Thus, in this work, we develop a suite of
selectors to get the most informative datapoints for human evaluation while
taking the evaluation costs into account. We show that selectors based on
variance in automated metric scores, diversity in model outputs, or Item
Response Theory outperform random selection. We further develop an approach to
distill these selectors to the scenario where the model outputs are not yet
available. In particular, we introduce source-based estimators, which predict
item usefulness for human evaluation just based on the source texts. We
demonstrate the efficacy of our selectors in two common NLG tasks, machine
translation and summarization, and show that up to only ~50% of the test data
is needed to produce the same evaluation result as the entire data. Our
implementations are published in the subset2evaluate package.
|
2501.18258
|
PDE-DKL: PDE-constrained deep kernel learning in high dimensionality
|
cs.LG cs.AI stat.ML
|
Many physics-informed machine learning methods for PDE-based problems rely on
Gaussian processes (GPs) or neural networks (NNs). However, both face
limitations when data are scarce and the dimensionality is high. Although GPs
are known for their robust uncertainty quantification in low-dimensional
settings, their computational complexity becomes prohibitive as the
dimensionality increases. In contrast, while conventional NNs can accommodate
high-dimensional input, they often require extensive training data and do not
offer uncertainty quantification. To address these challenges, we propose a
PDE-constrained Deep Kernel Learning (PDE-DKL) framework that combines DL and
GPs under explicit PDE constraints. Specifically, NNs learn a low-dimensional
latent representation of the high-dimensional PDE problem, reducing the
complexity of the problem. GPs then perform kernel regression subject to the
governing PDEs, ensuring accurate solutions and principled uncertainty
quantification, even when available data are limited. This synergy unifies the
strengths of both NNs and GPs, yielding high accuracy, robust uncertainty
estimates, and computational efficiency for high-dimensional PDEs. Numerical
experiments demonstrate that PDE-DKL achieves high accuracy with reduced data
requirements. They highlight its potential as a practical, reliable, and
scalable solver for complex PDE-based applications in science and engineering.
|
2501.18265
|
Collecting Cost-Effective, High-Quality Truthfulness Assessments with
LLM Summarized Evidence
|
cs.IR cs.CL cs.HC
|
With the degradation of guardrails against mis- and disinformation online, it
is more critical than ever to be able to effectively combat it. In this paper,
we explore the efficiency and effectiveness of using crowd-sourced truthfulness
assessments based on condensed, large language model (LLM) generated summaries
of online sources. We compare the use of generated summaries to the use of
original web pages in an A/B testing setting, where we employ a large and
diverse pool of crowd-workers to perform the truthfulness assessment. We
evaluate the quality of assessments, the efficiency with which assessments are
performed, and the behavior and engagement of participants. Our results
demonstrate that the Summary modality, which relies on summarized evidence,
offers no significant change in assessment accuracy over the Standard modality,
while significantly increasing the speed with which assessments are performed.
Workers using summarized evidence produce a significantly higher number of
assessments in the same time frame, reducing the cost needed to acquire
truthfulness assessments. Additionally, the Summary modality maximizes both the
inter-annotator agreements as well as the reliance on and perceived usefulness
of evidence, demonstrating the utility of summarized evidence without
sacrificing the quality of assessments.
|
2501.18268
|
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data
Acquisition
|
cs.LG
|
To generate accurate and reliable predictions, modern AI systems need to
combine data from multiple modalities, such as text, images, audio,
spreadsheets, and time series. Multi-modal data introduces new opportunities
and challenges for disentangling uncertainty: it is commonly assumed in the
machine learning community that epistemic uncertainty can be reduced by
collecting more data, while aleatoric uncertainty is irreducible. However, this
assumption is challenged in modern AI systems when information is obtained from
different modalities. This paper introduces an innovative data acquisition
framework where uncertainty disentanglement leads to actionable decisions,
allowing sampling in two directions: sample size and data modality. The main
hypothesis is that aleatoric uncertainty decreases as the number of modalities
increases, while epistemic uncertainty decreases by collecting more
observations. We provide proof-of-concept implementations on two multi-modal
datasets to showcase our data acquisition framework, which combines ideas from
active learning, active feature acquisition and uncertainty quantification.
|
2501.18269
|
MAMS: Model-Agnostic Module Selection Framework for Video Captioning
|
cs.CV cs.AI
|
Multi-modal transformers are rapidly gaining attention in video captioning
tasks. Existing multi-modal video captioning methods typically extract a fixed
number of frames, which raises critical challenges. When a limited number of
frames are extracted, important frames with essential information for caption
generation may be missed. Conversely, extracting an excessive number of frames
includes consecutive frames, potentially causing redundancy in visual tokens
extracted from consecutive video frames. To extract an appropriate number of
frames for each video, this paper proposes the first model-agnostic module
selection framework in video captioning that has two main functions: (1)
selecting a caption generation module with an appropriate size based on visual
tokens extracted from video frames, and (2) constructing subsets of visual
tokens for the selected caption generation module. Furthermore, we propose a
new adaptive attention masking scheme that enhances attention on important
visual tokens. Our experiments on three different benchmark datasets
demonstrate that the proposed framework significantly improves the performance
of three recent video captioning models.
|
2501.18270
|
The iToBoS dataset: skin region images extracted from 3D total body
photographs for lesion detection
|
eess.IV cs.AI cs.CV
|
Artificial intelligence has significantly advanced skin cancer diagnosis by
enabling rapid and accurate detection of malignant lesions. In this domain,
most publicly available image datasets consist of single, isolated skin lesions
positioned at the center of the image. While these lesion-centric datasets have
been fundamental for developing diagnostic algorithms, they lack the context of
the surrounding skin, which is critical for improving lesion detection. The
iToBoS dataset was created to address this challenge. It includes 16,954 images
of skin regions from 100 participants, captured using 3D total body
photography. Each image roughly corresponds to a $7 \times 9$ cm section of
skin with all suspicious lesions annotated using bounding boxes. Additionally,
the dataset provides metadata such as anatomical location, age group, and sun
damage score for each image. This dataset aims to facilitate training and
benchmarking of algorithms, with the goal of enabling early detection of skin
cancer and deployment of this technology in non-clinical environments.
|
2501.18271
|
Pre-Trained Vision-Language Model Selection and Reuse for Downstream
Tasks
|
cs.LG cs.AI
|
Pre-trained Vision-Language Models (VLMs) are becoming increasingly popular
across various visual tasks, and several open-sourced VLM variants have been
released. However, selecting the best-performing pre-trained VLM for a specific
downstream task is challenging since no single VLM can achieve promising
performance on all downstream tasks, and evaluating all available VLMs is
impossible due to time and data limitations. To address this problem, this
paper proposes a novel paradigm to select and reuse VLM for downstream tasks,
called Model Label Learning (MLL). The proposal contains three key modules:
\emph{model labeling}, which assigns labels to each VLM to describe their
specialty and utility; \emph{model selection}, which matches the requirements
of the target task with model labels; and \emph{model reuse}, which applies
selected VLMs to the target task in an ensemble manner. The proposal is highly
computationally efficient and growable since the model labeling process is
completed target task independent and the ability could grow with the number of
candidate VLMs. We also introduce a new benchmark for evaluating VLM selection
methods, including 49 VLMs and 17 target task datasets. Experimental results
clearly demonstrate the effectiveness of the proposed method for selecting and
reusing VLMs.
|
2501.18277
|
Sebra: Debiasing Through Self-Guided Bias Ranking
|
cs.LG
|
Ranking samples by fine-grained estimates of spuriosity (the degree to which
spurious cues are present) has recently been shown to significantly benefit
bias mitigation, over the traditional binary biased-\textit{vs}-unbiased
partitioning of train sets. However, this spuriosity ranking comes with the
requirement of human supervision. In this paper, we propose a debiasing
framework based on our novel \ul{Se}lf-Guided \ul{B}ias \ul{Ra}nking
(\emph{Sebra}), that mitigates biases (spurious correlations) via an automatic
ranking of data points by spuriosity within their respective classes. Sebra
leverages a key local symmetry in Empirical Risk Minimization (ERM) training --
the ease of learning a sample via ERM inversely correlates with its
spuriousity; the fewer spurious correlations a sample exhibits, the harder it
is to learn, and vice versa. However, globally across iterations, ERM tends to
deviate from this symmetry. Sebra dynamically steers ERM to correct this
deviation, facilitating the sequential learning of attributes in increasing
order of difficulty, \ie, decreasing order of spuriosity. As a result, the
sequence in which Sebra learns samples naturally provides spuriousity rankings.
We use the resulting fine-grained bias characterization in a contrastive
learning framework to mitigate biases from multiple sources. Extensive
experiments show that Sebra consistently outperforms previous state-of-the-art
unsupervised debiasing techniques across multiple standard benchmarks,
including UrbanCars, BAR, CelebA, and ImageNet-1K. Code, pre-trained models,
and training logs are available at https://kadarsh22.github.io/sebra_iclr25/.
|
2501.18278
|
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation
Learning via Biochemical Reaction Networks
|
cs.LG
|
The challenge in computational biology and drug discovery lies in creating
comprehensive representations of proteins and molecules that capture their
intrinsic properties and interactions. Traditional methods often focus on
unimodal data, such as protein sequences or molecular structures, limiting
their ability to capture complex biochemical relationships. This work enhances
these representations by integrating biochemical reactions encompassing
interactions between molecules and proteins. By leveraging reaction data
alongside pre-trained embeddings from state-of-the-art protein and molecule
models, we develop ReactEmbed, a novel method that creates a unified embedding
space through contrastive learning. We evaluate ReactEmbed across diverse
tasks, including drug-target interaction, protein-protein interaction, protein
property prediction, and molecular property prediction, consistently surpassing
all current state-of-the-art models. Notably, we showcase ReactEmbed's
practical utility through successful implementation in lipid nanoparticle-based
drug delivery, enabling zero-shot prediction of blood-brain barrier
permeability for protein-nanoparticle complexes. The code and comprehensive
database of reaction pairs are available for open use at
\href{https://github.com/amitaysicherman/ReactEmbed}{GitHub}.
|
2501.18280
|
Jailbreaking LLMs' Safeguard with Universal Magic Words for Text
Embedding Models
|
cs.CL cs.AI cs.LG cs.NE
|
The security issue of large language models (LLMs) has gained significant
attention recently, with various defense mechanisms developed to prevent
harmful outputs, among which safeguards based on text embedding models serve as
a fundamental defense. Through testing, we discover that the distribution of
text embedding model outputs is significantly biased with a large mean.
Inspired by this observation, we propose novel efficient methods to search for
universal magic words that can attack text embedding models. The universal
magic words as suffixes can move the embedding of any text towards the bias
direction, therefore manipulate the similarity of any text pair and mislead
safeguards. By appending magic words to user prompts and requiring LLMs to end
answers with magic words, attackers can jailbreak the safeguard. To eradicate
this security risk, we also propose defense mechanisms against such attacks,
which can correct the biased distribution of text embeddings in a train-free
manner.
|
2501.18282
|
Leveraging Sparsity for Sample-Efficient Preference Learning: A
Theoretical Perspective
|
cs.LG
|
This paper considers the sample-efficiency of preference learning, which
models and predicts human choices based on comparative judgments. The minimax
optimal estimation rate $\Theta(d/n)$ in traditional estimation theory requires
that the number of samples $n$ scales linearly with the dimensionality of the
feature space $d$. However, the high dimensionality of the feature space and
the high cost of collecting human-annotated data challenge the efficiency of
traditional estimation methods. To remedy this, we leverage sparsity in the
preference model and establish sharp estimation rates. We show that under the
sparse random utility model, where the parameter of the reward function is
$k$-sparse, the minimax optimal rate can be reduced to $\Theta(k/n \log(d/k))$.
Furthermore, we analyze the $\ell_{1}$-regularized estimator and show that it
achieves near-optimal rate under mild assumptions on the Gram matrix.
Experiments on synthetic data and LLM alignment data validate our theoretical
findings, showing that sparsity-aware methods significantly reduce sample
complexity and improve prediction accuracy.
|
2501.18283
|
Random Feature Representation Boosting
|
stat.ML cs.LG
|
We introduce Random Feature Representation Boosting (RFRBoost), a novel
method for constructing deep residual random feature neural networks (RFNNs)
using boosting theory. RFRBoost uses random features at each layer to learn the
functional gradient of the network representation, enhancing performance while
preserving the convex optimization benefits of RFNNs. In the case of MSE loss,
we obtain closed-form solutions to greedy layer-wise boosting with random
features. For general loss functions, we show that fitting random feature
residual blocks reduces to solving a quadratically constrained least squares
problem. We demonstrate, through numerical experiments on 91 tabular datasets
for regression and classification, that RFRBoost significantly outperforms
traditional RFNNs and end-to-end trained MLP ResNets, while offering
substantial computational advantages and theoretical guarantees stemming from
boosting theory.
|
2501.18287
|
Mining for Species, Locations, Habitats, and Ecosystems from Scientific
Papers in Invasion Biology: A Large-Scale Exploratory Study with Large
Language Models
|
cs.CL cs.AI cs.DL
|
This paper presents an exploratory study that harnesses the capabilities of
large language models (LLMs) to mine key ecological entities from invasion
biology literature. Specifically, we focus on extracting species names, their
locations, associated habitats, and ecosystems, information that is critical
for understanding species spread, predicting future invasions, and informing
conservation efforts. Traditional text mining approaches often struggle with
the complexity of ecological terminology and the subtle linguistic patterns
found in these texts. By applying general-purpose LLMs without domain-specific
fine-tuning, we uncover both the promise and limitations of using these models
for ecological entity extraction. In doing so, this study lays the groundwork
for more advanced, automated knowledge extraction tools that can aid
researchers and practitioners in understanding and managing biological
invasions.
|
2501.18288
|
Normalizing flows for SU($N$) gauge theories employing singular value
decomposition
|
hep-lat cs.LG
|
We present a progress report on the use of normalizing flows for generating
gauge field configurations in pure SU(N) gauge theories. We discuss how the
singular value decomposition can be used to construct gauge-invariant
quantities, which serve as the building blocks for designing gauge-equivariant
transformations of SU(N) gauge links. Using this novel approach, we build
representative models for the SU(3) Wilson action on a \( 4^4 \) lattice with
\( \beta = 1 \). We train these models and provide an analysis of their
performance, highlighting the effectiveness of the new technique for
gauge-invariant transformations. We also provide a comparison between the
efficiency of the proposed algorithm and the spectral flow of Wilson loops.
|
2501.18291
|
CueTip: An Interactive and Explainable Physics-aware Pool Assistant
|
cs.AI cs.HC
|
We present an interactive and explainable automated coaching assistant called
CueTip for a variant of pool/billiards. CueTip's novelty lies in its
combination of three features: a natural-language interface, an ability to
perform contextual, physics-aware reasoning, and that its explanations are
rooted in a set of predetermined guidelines developed by domain experts. We
instrument a physics simulator so that it generates event traces in natural
language alongside traditional state traces. Event traces lend themselves to
interpretation by language models, which serve as the interface to our
assistant. We design and train a neural adaptor that decouples tactical choices
made by CueTip from its interactivity and explainability allowing it to be
reconfigured to mimic any pool playing agent. Our experiments show that CueTip
enables contextual query-based assistance and explanations while maintaining
the strength of the agent in terms of win rate (improving it in some
situations). The explanations generated by CueTip are physically-aware and
grounded in the expert rules and are therefore more reliable.
|
2501.18292
|
Citation Recommendation based on Argumentative Zoning of User Queries
|
cs.IR cs.CL cs.DL
|
Citation recommendation aims to locate the important papers for scholars to
cite. When writing the citing sentences, the authors usually hold different
citing intents, which are referred to citation function in citation analysis.
Since argumentative zoning is to identify the argumentative and rhetorical
structure in scientific literature, we want to use this information to improve
the citation recommendation task. In this paper, a multi-task learning model is
built for citation recommendation and argumentative zoning classification. We
also generated an annotated corpus of the data from PubMed Central based on a
new argumentative zoning schema. The experimental results show that, by
considering the argumentative information in the citing sentence, citation
recommendation model will get better performance.
|
2501.18294
|
A Comprehensive Analysis on Machine Learning based Methods for Lung
Cancer Level Classification
|
cs.CV cs.AI
|
Lung cancer is a major issue in worldwide public health, requiring early
diagnosis using stable techniques. This work begins a thorough investigation of
the use of machine learning (ML) methods for precise classification of lung
cancer stages. A cautious analysis is performed to overcome overfitting issues
in model performance, taking into account minimum child weight and learning
rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM,
Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF),
CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted.
Furthermore, the correlation between features and targets is examined using the
deep neural network (DNN) model and thus their capability in detecting complex
patternsis established. It is argued that several ML models can be capable of
classifying lung cancer stages with great accuracy. In spite of the complexity
of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic
Regression excel with superior performance. The models perform better than the
others in lung cancer prediction on the complete set of comparative metrics
like accuracy, precision, recall, and F-1 score
|
2501.18296
|
Extending the design space of ontologization practices: Using bCLEARer
as an example
|
cs.AI
|
Our aim in this paper is to outline how the design space for the
ontologization process is richer than current practice would suggest. We point
out that engineering processes as well as products need to be designed - and
identify some components of the design. We investigate the possibility of
designing a range of radically new practices, providing examples of the new
practices from our work over the last three decades with an outlier
methodology, bCLEARer. We also suggest that setting an evolutionary context for
ontologization helps one to better understand the nature of these new practices
and provides the conceptual scaffolding that shapes fertile processes. Where
this evolutionary perspective positions digitalization (the evolutionary
emergence of computing technologies) as the latest step in a long evolutionary
trail of information transitions. This reframes ontologization as a strategic
tool for leveraging the emerging opportunities offered by digitalization.
|
2501.18298
|
Update Estimation and Scheduling for Over-the-Air Federated Learning
with Energy Harvesting Devices
|
cs.LG cs.DC
|
We study over-the-air (OTA) federated learning (FL) for energy harvesting
devices with heterogeneous data distribution over wireless fading multiple
access channel (MAC). To address the impact of low energy arrivals and data
heterogeneity on global learning, we propose user scheduling strategies.
Specifically, we develop two approaches: 1) entropy-based scheduling for known
data distributions and 2) least-squares-based user representation estimation
for scheduling with unknown data distributions at the parameter server. Both
methods aim to select diverse users, mitigating bias and enhancing convergence.
Numerical and analytical results demonstrate improved learning performance by
reducing redundancy and conserving energy.
|
2501.18299
|
Model-Free RL Agents Demonstrate System 1-Like Intentionality
|
cs.AI
|
This paper argues that model-free reinforcement learning (RL) agents, while
lacking explicit planning mechanisms, exhibit behaviours that can be analogised
to System 1 ("thinking fast") processes in human cognition. Unlike model-based
RL agents, which operate akin to System 2 ("thinking slow") reasoning by
leveraging internal representations for planning, model-free agents react to
environmental stimuli without anticipatory modelling. We propose a novel
framework linking the dichotomy of System 1 and System 2 to the distinction
between model-free and model-based RL. This framing challenges the prevailing
assumption that intentionality and purposeful behaviour require planning,
suggesting instead that intentionality can manifest in the structured, reactive
behaviours of model-free agents. By drawing on interdisciplinary insights from
cognitive psychology, legal theory, and experimental jurisprudence, we explore
the implications of this perspective for attributing responsibility and
ensuring AI safety. These insights advocate for a broader, contextually
informed interpretation of intentionality in RL systems, with implications for
their ethical deployment and regulation.
|
2501.18308
|
Zero Estimation Cost Strategy for Witsenhausen Counterexample with
Causal Encoder
|
cs.IT math.IT
|
We propose a zero estimation cost (ZEC) scheme for causal-encoding
noncausal-decoding vector-valued Witsenhausen counterexample based on the
coordination coding result. In contrast to source coding, our goal is to
communicate a controlled system state. The introduced ZEC scheme is a joint
control-communication approach that transforms the system state into a sequence
that can be efficiently communicated using block coding. Numerical results show
that our approach significantly reduces the power required for achieving
zero-estimation-cost state reconstruction at the decoder. In the second part,
we introduce a more general non-zero estimation cost (Non-ZEC) scheme. We
observe numerically that the Non-ZEC scheme operates as a time-sharing
mechanism between the two-point strategy and the ZEC scheme. Overall, by
leveraging block-coding gain, our proposed methods substantially improve the
power-estimation trade-off for Witsenhausen counterexample.
|
2501.18309
|
Knowledge in multi-robot systems: an interplay of dynamics, computation
and communication
|
cs.LO cs.DC cs.RO
|
We show that the hybrid systems perspective of distributed multi-robot
systems is compatible with logical models of knowledge already used in
distributed computing, and demonstrate its usefulness by deriving sufficient
epistemic conditions for exploration and gathering robot tasks to be solvable.
We provide a separation of the physical and computational aspects of a robotic
system, allowing us to decouple the problems related to each and directly use
methods from control theory and distributed computing, fields that are
traditionally distant in the literature. Finally, we demonstrate a novel
approach for reasoning about the knowledge in multi-robot systems through a
principled method of converting a switched hybrid dynamical system into a
temporal-epistemic logic model, passing through an abstract state machine
representation. This creates space for methods and results to be exchanged
across the fields of control theory, distributed computing and
temporal-epistemic logic, while reasoning about multi-robot systems.
|
2501.18310
|
Efficient Neural Theorem Proving via Fine-grained Proof Structure
Analysis
|
cs.LG cs.AI
|
The synergy between deep learning models and traditional automation tools
plays a pivotal role in developing robust neural theorem provers (NTPs).
However, for proof synthesis with LLMs, previous work applies automation tools
either only when the model explicitly calls the method, or only at a single
granularity level, failing to fully exploit the power of built-in tactics and
off-the-shelf automated theorem provers. In this work, we propose ProofAug, a
novel theorem proving method that enjoys superior sample efficiency through
equipping proof-generation LLMs with automation methods in different
granularities via fine-grained structure analysis of model-generated proof
proposals. Furthermore, ProofAug serves as a versatile plug-and-play module
that seamlessly integrates with any tree-search algorithm, enabling our
construction of an efficient recursive proving (ERP) module to further enhance
performance. The superiority of our method is validated on the miniF2F-test
benchmark using the open-source deepseek-math-7b-base model and the Isabelle
proof assistant. Notably, by additionally employing a mixed prompting strategy,
we achieve a cumulative pass rate of 66.0% after curation of the dataset (61.9%
for the original version), setting a new SOTA across all proof languages with a
total sample budget of only 2100. Our code is available at
https://github.com/haoxiongliu/ProofAug.
|
2501.18313
|
Simulation of microstructures and machine learning
|
cs.CV
|
Machine learning offers attractive solutions to challenging image processing
tasks. Tedious development and parametrization of algorithmic solutions can be
replaced by training a convolutional neural network or a random forest with a
high potential to generalize. However, machine learning methods rely on huge
amounts of representative image data along with a ground truth, usually
obtained by manual annotation. Thus, limited availability of training data is a
critical bottleneck. We discuss two use cases: optical quality control in
industrial production and segmenting crack structures in 3D images of concrete.
For optical quality control, all defect types have to be trained but are
typically not evenly represented in the training data. Additionally, manual
annotation is costly and often inconsistent. It is nearly impossible in the
second case: segmentation of crack systems in 3D images of concrete. Synthetic
images, generated based on realizations of stochastic geometry models, offer an
elegant way out. A wide variety of structure types can be generated. The within
structure variation is naturally captured by the stochastic nature of the
models and the ground truth is for free. Many new questions arise. In
particular, which characteristics of the real image data have to be met to
which degree of fidelity.
|
2501.18314
|
AGAV-Rater: Adapting Large Multimodal Model for AI-Generated
Audio-Visual Quality Assessment
|
cs.MM cs.CV cs.SD eess.AS
|
Many video-to-audio (VTA) methods have been proposed for dubbing silent
AI-generated videos. An efficient quality assessment method for AI-generated
audio-visual content (AGAV) is crucial for ensuring audio-visual quality.
Existing audio-visual quality assessment methods struggle with unique
distortions in AGAVs, such as unrealistic and inconsistent elements. To address
this, we introduce AGAVQA, the first large-scale AGAV quality assessment
dataset, comprising 3,382 AGAVs from 16 VTA methods. AGAVQA includes two
subsets: AGAVQA-MOS, which provides multi-dimensional scores for audio quality,
content consistency, and overall quality, and AGAVQA-Pair, designed for optimal
AGAV pair selection. We further propose AGAV-Rater, a LMM-based model that can
score AGAVs, as well as audio and music generated from text, across multiple
dimensions, and selects the best AGAV generated by VTA methods to present to
the user. AGAV-Rater achieves state-of-the-art performance on AGAVQA,
Text-to-Audio, and Text-to-Music datasets. Subjective tests also confirm that
AGAV-Rater enhances VTA performance and user experience. The project page is
available at https://agav-rater.github.io.
|
2501.18315
|
Surface Defect Identification using Bayesian Filtering on a 3D Mesh
|
cs.CV cs.RO
|
This paper presents a CAD-based approach for automated surface defect
detection. We leverage the a-priori knowledge embedded in a CAD model and
integrate it with point cloud data acquired from commercially available stereo
and depth cameras. The proposed method first transforms the CAD model into a
high-density polygonal mesh, where each vertex represents a state variable in
3D space. Subsequently, a weighted least squares algorithm is employed to
iteratively estimate the state of the scanned workpiece based on the captured
point cloud measurements. This framework offers the potential to incorporate
information from diverse sensors into the CAD domain, facilitating a more
comprehensive analysis. Preliminary results demonstrate promising performance,
with the algorithm achieving convergence to a sub-millimeter standard deviation
in the region of interest using only approximately 50 point cloud samples. This
highlights the potential of utilising commercially available stereo cameras for
high-precision quality control applications.
|
2501.18318
|
Estimating unknown dynamics and cost as a bilinear system with
Koopman-based Inverse Optimal Control
|
eess.SY cs.SY math.DS
|
In this work, we address the challenge of approximating unknown system
dynamics and costs by representing them as a bilinear system using
Koopman-based Inverse Optimal Control (IOC). Using optimal trajectories, we
construct a bilinear control system in transformed state variables through a
modified Extended Dynamic Mode Decomposition with control (EDMDc) that
maintains exact dynamical equivalence with the original nonlinear system. We
derive Pontryagin's Maximum Principle (PMP) optimality conditions for this
system, which closely resemble those of the inverse Linear Quadratic Regulator
(LQR) problem due to the consistent control input and state independence from
the control. This similarity allows us to apply modified inverse LQR theory,
offering a more tractable and robust alternative to nonlinear Inverse Optimal
Control methods, especially when dealing with unknown dynamics. Our approach
also benefits from the extensive analytical properties of bilinear control
systems, providing a solid foundation for further analysis and application. We
demonstrate the effectiveness of the proposed method through theoretical
analysis, simulation studies and a robotic experiment, highlighting its
potential for broader applications in the approximation and design of control
systems.
|
2501.18320
|
Leveraging LLM Agents for Automated Optimization Modeling for SASP
Problems: A Graph-RAG based Approach
|
cs.AI eess.SP
|
Automated optimization modeling (AOM) has evoked considerable interest with
the rapid evolution of large language models (LLMs). Existing approaches
predominantly rely on prompt engineering, utilizing meticulously designed
expert response chains or structured guidance. However, prompt-based techniques
have failed to perform well in the sensor array signal processing (SASP) area
due the lack of specific domain knowledge. To address this issue, we propose an
automated modeling approach based on retrieval-augmented generation (RAG)
technique, which consists of two principal components: a multi-agent (MA)
structure and a graph-based RAG (Graph-RAG) process. The MA structure is
tailored for the architectural AOM process, with each agent being designed
based on principles of human modeling procedure. The Graph-RAG process serves
to match user query with specific SASP modeling knowledge, thereby enhancing
the modeling result. Results on ten classical signal processing problems
demonstrate that the proposed approach (termed as MAG-RAG) outperforms several
AOM benchmarks.
|
2501.18322
|
A Unified Perspective on the Dynamics of Deep Transformers
|
cs.LG math.AP
|
Transformers, which are state-of-the-art in most machine learning tasks,
represent the data as sequences of vectors called tokens. This representation
is then exploited by the attention function, which learns dependencies between
tokens and is key to the success of Transformers. However, the iterative
application of attention across layers induces complex dynamics that remain to
be fully understood. To analyze these dynamics, we identify each input sequence
with a probability measure and model its evolution as a Vlasov equation called
Transformer PDE, whose velocity field is non-linear in the probability measure.
Our first set of contributions focuses on compactly supported initial data. We
show the Transformer PDE is well-posed and is the mean-field limit of an
interacting particle system, thus generalizing and extending previous analysis
to several variants of self-attention: multi-head attention, L2 attention,
Sinkhorn attention, Sigmoid attention, and masked attention--leveraging a
conditional Wasserstein framework. In a second set of contributions, we are the
first to study non-compactly supported initial conditions, by focusing on
Gaussian initial data. Again for different types of attention, we show that the
Transformer PDE preserves the space of Gaussian measures, which allows us to
analyze the Gaussian case theoretically and numerically to identify typical
behaviors. This Gaussian analysis captures the evolution of data anisotropy
through a deep Transformer. In particular, we highlight a clustering phenomenon
that parallels previous results in the non-normalized discrete case.
|
2501.18324
|
A Video-grounded Dialogue Dataset and Metric for Event-driven Activities
|
cs.CV cs.CL
|
This paper presents VDAct, a dataset for a Video-grounded Dialogue on
Event-driven Activities, alongside VDEval, a session-based context evaluation
metric specially designed for the task. Unlike existing datasets, VDAct
includes longer and more complex video sequences that depict a variety of
event-driven activities that require advanced contextual understanding for
accurate response generation. The dataset comprises 3,000 dialogues with over
30,000 question-and-answer pairs, derived from 1,000 videos with diverse
activity scenarios. VDAct displays a notably challenging characteristic due to
its broad spectrum of activity scenarios and wide range of question types.
Empirical studies on state-of-the-art vision foundation models highlight their
limitations in addressing certain question types on our dataset. Furthermore,
VDEval, which integrates dialogue session history and video content summaries
extracted from our supplementary Knowledge Graphs to evaluate individual
responses, demonstrates a significantly higher correlation with human
assessments on the VDAct dataset than existing evaluation metrics that rely
solely on the context of single dialogue turns.
|
2501.18328
|
CodeBrain: Impute Any Brain MRI via Instance-specific Scalar-quantized
Codes
|
cs.CV cs.AI
|
MRI imputation aims to synthesize the missing modality from one or more
available ones, which is highly desirable since it reduces scanning costs and
delivers comprehensive MRI information to enhance clinical diagnosis. In this
paper, we propose a unified model, CodeBrain, designed to adapt to various
brain MRI imputation scenarios. The core design lies in casting various
inter-modality transformations as a full-modality code prediction task. To this
end, CodeBrain is trained in two stages: Reconstruction and Code Prediction.
First, in the Reconstruction stage, we reconstruct each MRI modality, which is
mapped into a shared latent space followed by a scalar quantization. Since such
quantization is lossy and the code is low dimensional, another MRI modality
belonging to the same subject is randomly selected to generate common features
to supplement the code and boost the target reconstruction. In the second
stage, we train another encoder by a customized grading loss to predict the
full-modality codes from randomly masked MRI samples, supervised by the
corresponding quantized codes generated from the first stage. In this way, the
inter-modality transformation is achieved by mapping the instance-specific
codes in a finite scalar space. We evaluated the proposed CodeBrain model on
two public brain MRI datasets (i.e., IXI and BraTS 2023). Extensive experiments
demonstrate that our CodeBrain model achieves superior imputation performance
compared to four existing methods, establishing a new state of the art for
unified brain MRI imputation. Codes will be released.
|
2501.18331
|
Stream-Based Monitoring of Algorithmic Fairness
|
cs.LG cs.LO cs.SE
|
Automatic decision and prediction systems are increasingly deployed in
applications where they significantly impact the livelihood of people, such as
for predicting the creditworthiness of loan applicants or the recidivism risk
of defendants. These applications have given rise to a new class of
algorithmic-fairness specifications that require the systems to decide and
predict without bias against social groups. Verifying these specifications
statically is often out of reach for realistic systems, since the systems may,
e.g., employ complex learning components, and reason over a large input space.
In this paper, we therefore propose stream-based monitoring as a solution for
verifying the algorithmic fairness of decision and prediction systems at
runtime. Concretely, we present a principled way to formalize algorithmic
fairness over temporal data streams in the specification language RTLola and
demonstrate the efficacy of this approach on a number of benchmarks. Besides
synthetic scenarios that particularly highlight its efficiency on streams with
a scaling amount of data, we notably evaluate the monitor on real-world data
from the recidivism prediction tool COMPAS.
|
2501.18337
|
Unfaithful Probability Distributions in Binary Triple of Causality
Directed Acyclic Graph
|
stat.ML cs.AI cs.LG
|
Faithfulness is the foundation of probability distribution and graph in
causal discovery and causal inference. In this paper, several unfaithful
probability distribution examples are constructed in three--vertices binary
causality directed acyclic graph (DAG) structure, which are not faithful to
causal DAGs described in J.M.,Robins,et al. Uniform consistency in causal
inference. Biometrika (2003),90(3): 491--515. And the general unfaithful
probability distribution with multiple independence and conditional
independence in binary triple causal DAG is given.
|
2501.18344
|
Transfer Learning of Surrogate Models: Integrating Domain Warping and
Affine Transformations
|
cs.LG cs.AI
|
Surrogate models provide efficient alternatives to computationally demanding
real-world processes but often require large datasets for effective training. A
promising solution to this limitation is the transfer of pre-trained surrogate
models to new tasks. Previous studies have investigated the transfer of
differentiable and non-differentiable surrogate models, typically assuming an
affine transformation between the source and target functions. This paper
extends previous research by addressing a broader range of transformations,
including linear and nonlinear variations. Specifically, we consider the
combination of an unknown input warping, such as one modelled by the beta
cumulative distribution function, with an unspecified affine transformation.
Our approach achieves transfer learning by employing a limited number of data
points from the target task to optimize these transformations, minimizing
empirical loss on the transfer dataset. We validate the proposed method on the
widely used Black-Box Optimization Benchmark (BBOB) testbed and a real-world
transfer learning task from the automobile industry. The results underscore the
significant advantages of the approach, revealing that the transferred
surrogate significantly outperforms both the original surrogate and the one
built from scratch using the transfer dataset, particularly in data-scarce
scenarios.
|
2501.18350
|
Joint Power and Spectrum Orchestration for D2D Semantic Communication
Underlying Energy-Efficient Cellular Networks
|
eess.SY cs.SY
|
Semantic communication (SemCom) has been recently deemed a promising
next-generation wireless technique to enable efficient spectrum savings and
information exchanges, thus naturally introducing a novel and practical network
paradigm where cellular and device-to-device (D2D) SemCom approaches coexist.
Nevertheless, the involved wireless resource management becomes complicated and
challenging due to the unique semantic performance measurements and
energy-consuming semantic coding mechanism. To this end, this paper jointly
investigates power control and spectrum reuse problems for energy-efficient D2D
SemCom cellular networks. Concretely, we first model the user preference-aware
semantic triplet transmission and leverage a novel metric of semantic value to
identify the semantic information importance conveyed in SemCom. Then, we
define the additional power consumption from semantic encoding in conjunction
with basic power amplifier dissipation to derive the overall system energy
efficiency (semantics/Joule). Next, we formulate an energy efficiency
maximization problem for joint power and spectrum allocation subject to several
SemCom-related and practical constraints. Afterward, we propose an optimal
resource management solution by employing the fractional-to-subtractive problem
transformation and decomposition while developing a three-stage method with
theoretical analysis of its optimality guarantee and computational complexity.
Numerical results demonstrate the adequate performance superiority of our
proposed solution compared with different benchmarks.
|
2501.18351
|
Dual-BEV Nav: Dual-layer BEV-based Heuristic Path Planning for Robotic
Navigation in Unstructured Outdoor Environments
|
cs.RO
|
Path planning with strong environmental adaptability plays a crucial role in
robotic navigation in unstructured outdoor environments, especially in the case
of low-quality location and map information. The path planning ability of a
robot depends on the identification of the traversability of global and local
ground areas. In real-world scenarios, the complexity of outdoor open
environments makes it difficult for robots to identify the traversability of
ground areas that lack a clearly defined structure. Moreover, most existing
methods have rarely analyzed the integration of local and global traversability
identifications in unstructured outdoor scenarios. To address this problem, we
propose a novel method, Dual-BEV Nav, first introducing Bird's Eye View (BEV)
representations into local planning to generate high-quality traversable paths.
Then, these paths are projected onto the global traversability map generated by
the global BEV planning model to obtain the optimal waypoints. By integrating
the traversability from both local and global BEV, we establish a dual-layer
BEV heuristic planning paradigm, enabling long-distance navigation in
unstructured outdoor environments. We test our approach through both public
dataset evaluations and real-world robot deployments, yielding promising
results. Compared to baselines, the Dual-BEV Nav improved temporal distance
prediction accuracy by up to $18.7\%$. In the real-world deployment, under
conditions significantly different from the training set and with notable
occlusions in the global BEV, the Dual-BEV Nav successfully achieved a
65-meter-long outdoor navigation. Further analysis demonstrates that the local
BEV representation significantly enhances the rationality of the planning,
while the global BEV probability map ensures the robustness of the overall
planning.
|
2501.18355
|
Multilayered Intelligent Reflecting Surface for Long-Range Underwater
Acoustic Communication
|
eess.AS cs.SD cs.SY eess.SP eess.SY
|
This article introduces a multilayered acoustic reconfigurable intelligent
surface (ML-ARIS) architecture designed for the next generation of underwater
communications. ML-ARIS incorporates multiple layers of piezoelectric material
in each acoustic reflector, with the load impedance of each layer independently
adjustable via a control circuit. This design increases the flexibility in
generating reflected signals with desired amplitudes and orthogonal phases,
enabling passive in-phase and quadrature (IQ) modulation using a single
acoustic reflector. Such a feature enables precise beam steering, enhancing
sound levels in targeted directions while minimizing interference in
surrounding environments. Extensive simulations and tank experiments were
conducted to verify the feasibility of ML-ARIS. The experimental results
indicate that implementing IQ modulation with a multilayer structure is indeed
practical in real-world scenarios, making it possible to use a single
reflection unit to generate reflected waves with high-resolution amplitudes and
phases.
|
2501.18356
|
State Stream Transformer (SST) : Emergent Metacognitive Behaviours
Through Latent State Persistence
|
cs.LG cs.AI cs.CL
|
We introduce the State Stream Transformer (SST), a novel LLM architecture
that reveals emergent reasoning behaviours and capabilities latent in
pretrained weights through addressing a fundamental limitation in traditional
transformer models: the lack of latent computational continuity across
autoregressive generations in the state space. SST introduces a sliding window
latent state (FFN) cache with weighted decay that maintains and evolves
persistent latent processes throughout autoregressive generations. Through
controlled experiments comparing base and SST architectures using the same
frozen weights, we demonstrate that this architectural modification alone
enables enhanced reasoning capabilities which appear best explained by some
form of potential higher-order processing, as evidenced by emergent
metacognitive behaviours. These behaviours persist under controlled conditions
designed to eliminate confounding factors such as stochastic variation or
learned response patterns. Analysis of latent state distributions and
processing dynamics provides evidence that it is solely the 'state stream' that
is responsible for these phenomena. In quantitative evaluations, the SST
achieves substantial performance improvements over the base model on two
reasoning benchmarks, reaching 89.01\% accuracy on GSM-8K (0-shot) and 91.04\%
on ARC Challenge (0-shot CoT). These findings indicate that persistent
computation in the latent state space enables fundamentally different
information processing and internal reasoning strategies, with implications for
our understanding of artificial intelligence systems.
|
2501.18357
|
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A
Comprehensive Graph Representation Framework from Local to Global
|
cs.LG
|
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in
various graph representation learning tasks. However, most existing GNNs focus
primarily on capturing local information through explicit graph convolution,
often neglecting global message-passing. This limitation hinders the
establishment of a collaborative interaction between global and local
information, which is crucial for comprehensively understanding graph data. To
address these challenges, we propose a novel framework called Comprehensive
Graph Representation Learning (ComGRL). ComGRL integrates local information
into global information to derive powerful representations. It achieves this by
implicitly smoothing local information through flexible graph contrastive
learning, ensuring reliable representations for subsequent global exploration.
Then ComGRL transfers the locally derived representations to a multi-head
self-attention module, enhancing their discriminative ability by uncovering
diverse and rich global correlations. To further optimize local information
dynamically under the self-supervision of pseudo-labels, ComGRL employs a
triple sampling strategy to construct mixed node pairs and applies reliable
Mixup augmentation across attributes and structure for local contrastive
learning. This approach broadens the receptive field and facilitates
coordination between local and global representation learning, enabling them to
reinforce each other. Experimental results across six widely used graph
datasets demonstrate that ComGRL achieves excellent performance in node
classification tasks. The code could be available at
https://github.com/JinluWang1002/ComGRL.
|
2501.18359
|
Contextual Online Decision Making with Infinite-Dimensional Functional
Regression
|
stat.ML cs.LG
|
Contextual sequential decision-making problems play a crucial role in machine
learning, encompassing a wide range of downstream applications such as bandits,
sequential hypothesis testing and online risk control. These applications often
require different statistical measures, including expectation, variance and
quantiles. In this paper, we provide a universal admissible algorithm framework
for dealing with all kinds of contextual online decision-making problems that
directly learns the whole underlying unknown distribution instead of focusing
on individual statistics. This is much more difficult because the dimension of
the regression is uncountably infinite, and any existing linear contextual
bandits algorithm will result in infinite regret. To overcome this issue, we
propose an efficient infinite-dimensional functional regression oracle for
contextual cumulative distribution functions (CDFs), where each data point is
modeled as a combination of context-dependent CDF basis functions. Our analysis
reveals that the decay rate of the eigenvalue sequence of the design integral
operator governs the regression error rate and, consequently, the utility
regret rate. Specifically, when the eigenvalue sequence exhibits a polynomial
decay of order $\frac{1}{\gamma}\ge 1$, the utility regret is bounded by
$\tilde{\mathcal{O}}\Big(T^{\frac{3\gamma+2}{2(\gamma+2)}}\Big)$. By setting
$\gamma=0$, this recovers the existing optimal regret rate for contextual
bandits with finite-dimensional regression and is optimal under a stronger
exponential decay assumption. Additionally, we provide a numerical method to
compute the eigenvalue sequence of the integral operator, enabling the
practical implementation of our framework.
|
2501.18361
|
Video-based Surgical Tool-tip and Keypoint Tracking using Multi-frame
Context-driven Deep Learning Models
|
cs.CV
|
Automated tracking of surgical tool keypoints in robotic surgery videos is an
essential task for various downstream use cases such as skill assessment,
expertise assessment, and the delineation of safety zones. In recent years, the
explosion of deep learning for vision applications has led to many works in
surgical instrument segmentation, while lesser focus has been on tracking
specific tool keypoints, such as tool tips. In this work, we propose a novel,
multi-frame context-driven deep learning framework to localize and track tool
keypoints in surgical videos. We train and test our models on the annotated
frames from the 2015 EndoVis Challenge dataset, resulting in state-of-the-art
performance. By leveraging sophisticated deep learning models and multi-frame
context, we achieve 90\% keypoint detection accuracy and a localization RMS
error of 5.27 pixels. Results on a self-annotated JIGSAWS dataset with more
challenging scenarios also show that the proposed multi-frame models can
accurately track tool-tip and tool-base keypoints, with ${<}4.2$-pixel RMS
error overall. Such a framework paves the way for accurately tracking surgical
instrument keypoints, enabling further downstream use cases. Project and
dataset webpage: https://tinyurl.com/mfc-tracker
|
2501.18362
|
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and
Understanding
|
cs.AI cs.CL cs.CV cs.LG
|
We introduce MedXpertQA, a highly challenging and comprehensive benchmark to
evaluate expert-level medical knowledge and advanced reasoning. MedXpertQA
includes 4,460 questions spanning 17 specialties and 11 body systems. It
includes two subsets, Text for text evaluation and MM for multimodal
evaluation. Notably, MM introduces expert-level exam questions with diverse
images and rich clinical information, including patient records and examination
results, setting it apart from traditional medical multimodal benchmarks with
simple QA pairs generated from image captions. MedXpertQA applies rigorous
filtering and augmentation to address the insufficient difficulty of existing
benchmarks like MedQA, and incorporates specialty board questions to improve
clinical relevance and comprehensiveness. We perform data synthesis to mitigate
data leakage risk and conduct multiple rounds of expert reviews to ensure
accuracy and reliability. We evaluate 16 leading models on MedXpertQA.
Moreover, medicine is deeply connected to real-world decision-making, providing
a rich and representative setting for assessing reasoning abilities beyond
mathematics and code. To this end, we develop a reasoning-oriented subset to
facilitate the assessment of o1-like models.
|
2501.18363
|
Robust Online Conformal Prediction under Uniform Label Noise
|
cs.LG
|
Conformal prediction is an emerging technique for uncertainty quantification
that constructs prediction sets guaranteed to contain the true label with a
predefined probability. Recent work develops online conformal prediction
methods that adaptively construct prediction sets to accommodate distribution
shifts. However, existing algorithms typically assume perfect label accuracy
which rarely holds in practice. In this work, we investigate the robustness of
online conformal prediction under uniform label noise with a known noise rate,
in both constant and dynamic learning rate schedules. We show that label noise
causes a persistent gap between the actual mis-coverage rate and the desired
rate $\alpha$, leading to either overestimated or underestimated coverage
guarantees. To address this issue, we propose Noise Robust Online Conformal
Prediction (dubbed NR-OCP) by updating the threshold with a novel robust
pinball loss, which provides an unbiased estimate of clean pinball loss without
requiring ground-truth labels. Our theoretical analysis shows that NR-OCP
eliminates the coverage gap in both constant and dynamic learning rate
schedules, achieving a convergence rate of $\mathcal{O}(T^{-1/2})$ for both
empirical and expected coverage errors under uniform label noise. Extensive
experiments demonstrate the effectiveness of our method by achieving both
precise coverage and improved efficiency.
|
2501.18365
|
RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against
Retrieval Defects
|
cs.CL cs.IR
|
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by
integrating external knowledge retrieved from a knowledge base. However, its
effectiveness is fundamentally constrained by the reliability of both the
retriever and the knowledge base. In real-world scenarios, imperfections in
these components often lead to the retrieval of noisy, irrelevant, or
misleading counterfactual information, ultimately undermining the
trustworthiness of RAG systems. To address this challenge, we propose Robust
Fine-Tuning (RbFT), a method designed to enhance the resilience of LLMs against
retrieval defects through two targeted fine-tuning tasks. Experimental results
demonstrate that RbFT significantly improves the robustness of RAG systems
across diverse retrieval conditions, surpassing existing methods while
maintaining high inference efficiency and compatibility with other robustness
techniques.
|
2501.18367
|
A Learnable Multi-views Contrastive Framework with Reconstruction
Discrepancy for Medical Time-Series
|
cs.LG cs.AI
|
In medical time series disease diagnosis, two key challenges are
identified.First, the high annotation cost of medical data leads to overfitting
in models trained on label-limited, single-center datasets. To address this, we
propose incorporating external data from related tasks and leveraging AE-GAN to
extract prior knowledge,providing valuable references for downstream tasks.
Second, many existing studies employ contrastive learning to derive more
generalized medical sequence representations for diagnostic tasks, usually
relying on manually designed diverse positive and negative sample
pairs.However, these approaches are complex, lack generalizability, and fail to
adaptively capture disease-specific features across different conditions.To
overcome this, we introduce LMCF (Learnable Multi-views Contrastive Framework),
a framework that integrates a multi-head attention mechanism and adaptively
learns representations from different views through inter-view and intra-view
contrastive learning strategies.Additionally, the pre-trained AE-GAN is used to
reconstruct discrepancies in the target data as disease probabilities, which
are then integrated into the contrastive learning process.Experiments on three
target datasets demonstrate that our method consistently outperforms seven
other baselines, highlighting its significant impact on healthcare applications
such as the diagnosis of myocardial infarction, Alzheimer's disease, and
Parkinson's disease.
|
2501.18369
|
A Cartesian Encoding Graph Neural Network for Crystal Structures
Property Prediction: Application to Thermal Ellipsoid Estimation
|
cs.LG
|
In diffraction-based crystal structure analysis, thermal ellipsoids,
quantified via Anisotropic Displacement Parameters (ADPs), are critical yet
challenging to determine. ADPs capture atomic vibrations, reflecting thermal
and structural properties, but traditional computation is often expensive. This
paper introduces CartNet, a novel graph neural network (GNN) for efficiently
predicting crystal properties by encoding atomic geometry into Cartesian
coordinates alongside the crystal temperature. CartNet integrates a neighbour
equalization technique to emphasize covalent and contact interactions, and a
Cholesky-based head to ensure valid ADP predictions. We also propose a
rotational SO(3) data augmentation strategy during training to handle unseen
orientations. An ADP dataset with over 200,000 experimental crystal structures
from the Cambridge Structural Database (CSD) was curated to validate the
approach. CartNet significantly reduces computational costs and outperforms
existing methods in ADP prediction by 10.87%, while delivering a 34.77%
improvement over theoretical approaches. We further evaluated CartNet on other
datasets covering formation energy, band gap, total energy, energy above the
convex hull, bulk moduli, and shear moduli, achieving 7.71% better results on
the Jarvis Dataset and 13.16% on the Materials Project Dataset. These gains
establish CartNet as a state-of-the-art solution for diverse crystal property
predictions. Project website and online demo: https://www.ee.ub.edu/cartnet
|
2501.18373
|
Function Encoders: A Principled Approach to Transfer Learning in Hilbert
Spaces
|
cs.LG
|
A central challenge in transfer learning is designing algorithms that can
quickly adapt and generalize to new tasks without retraining. Yet, the
conditions of when and how algorithms can effectively transfer to new tasks is
poorly characterized. We introduce a geometric characterization of transfer in
Hilbert spaces and define three types of inductive transfer: interpolation
within the convex hull, extrapolation to the linear span, and extrapolation
outside the span. We propose a method grounded in the theory of function
encoders to achieve all three types of transfer. Specifically, we introduce a
novel training scheme for function encoders using least-squares optimization,
prove a universal approximation theorem for function encoders, and provide a
comprehensive comparison with existing approaches such as transformers and
meta-learning on four diverse benchmarks. Our experiments demonstrate that the
function encoder outperforms state-of-the-art methods on four benchmark tasks
and on all three types of transfer.
|
2501.18374
|
Proofs for Folklore Theorems on the Radon-Nikodym Derivative
|
cs.IT math.HO math.IT math.ST stat.ML stat.TH
|
Rigorous statements and formal proofs are presented for both foundational and
advanced folklore theorems on the Radon-Nikodym derivative. The cases of
product and marginal measures are carefully considered; and the hypothesis
under which the statements hold are rigorously enumerated.
|
2501.18376
|
Cracks in concrete
|
cs.CV eess.IV stat.AP
|
Finding and properly segmenting cracks in images of concrete is a challenging
task. Cracks are thin and rough and being air filled do yield a very weak
contrast in 3D images obtained by computed tomography. Enhancing and segmenting
dark lower-dimensional structures is already demanding. The heterogeneous
concrete matrix and the size of the images further increase the complexity. ML
methods have proven to solve difficult segmentation problems when trained on
enough and well annotated data. However, so far, there is not much 3D image
data of cracks available at all, let alone annotated. Interactive annotation is
error-prone as humans can easily tell cats from dogs or roads without from
roads with cars but have a hard time deciding whether a thin and dark structure
seen in a 2D slice continues in the next one. Training networks by synthetic,
simulated images is an elegant way out, bears however its own challenges. In
this contribution, we describe how to generate semi-synthetic image data to
train CNN like the well known 3D U-Net or random forests for segmenting cracks
in 3D images of concrete. The thickness of real cracks varies widely, both,
within one crack as well as from crack to crack in the same sample. The
segmentation method should therefore be invariant with respect to scale
changes. We introduce the so-called RieszNet, designed for exactly this
purpose. Finally, we discuss how to generalize the ML crack segmentation
methods to other concrete types.
|
2501.18377
|
Using Read Promotion and Mixed Isolation Levels for Performant Yet
Serializable Execution of Transaction Programs
|
cs.DB
|
We propose a theory that can determine the lowest isolation level that can be
allocated to each transaction program in an application in a
mixed-isolation-level setting, to guarantee that all executions will be
serializable and thus preserve all integrity constraints, even those that are
not explicitly declared. This extends prior work applied to completely known
transactions, to deal with the realistic situation where transactions are
generated by running programs with parameters that are not known in advance.
Using our theory, we propose an optimization method that allows for high
throughput while ensuring that all executions are serializable. Our method is
based on searching for application code modifications that are
semantics-preserving while improving the isolation level allocation. We
illustrate our approach to the SmallBank benchmark.
|
2501.18381
|
Implicit Riemannian Optimism with Applications to Min-Max Problems
|
math.OC cs.LG
|
We introduce a Riemannian optimistic online learning algorithm for Hadamard
manifolds based on inexact implicit updates. Unlike prior work, our method can
handle in-manifold constraints, and matches the best known regret bounds in the
Euclidean setting with no dependence on geometric constants, like the minimum
curvature. Building on this, we develop algorithms for g-convex, g-concave
smooth min-max problems on Hadamard manifolds. Notably, one method nearly
matches the gradient oracle complexity of the lower bound for Euclidean
problems, for the first time.
|
2501.18382
|
Rydberg Atomic Quantum Receivers for the Multi-User MIMO Uplink
|
eess.SP cs.IT math.IT
|
Rydberg atomic quantum receivers exhibit great potential in assisting
classical wireless communications due to their outstanding advantages in
detecting radio frequency signals. To realize this potential, we integrate a
Rydberg atomic quantum receiver into a classical multi-user multiple-input
multiple-output (MIMO) scheme to form a multi-user Rydberg atomic quantum MIMO
(RAQ-MIMO) system for the uplink. To study this system, we first construct an
equivalent baseband signal model, which facilitates convenient system design,
signal processing and optimizations. We then study the ergodic achievable rates
under both the maximum ratio combining (MRC) and zero-forcing (ZF) schemes by
deriving their tight lower bounds. We next compare the ergodic achievable rates
of the RAQ-MIMO and the conventional massive MIMO schemes by offering a
closed-form expression for the difference of their ergodic achievable rates,
which allows us to directly compare the two systems. Our results show that
RAQ-MIMO allows the average transmit power of users to be $\sim 20$ dBm lower
than that of the conventional massive MIMO. Viewed from a different
perspective, an extra $\sim 7$ bits/s/Hz/user rate becomes achievable by ZF
RAQ-MIMO, when equipping $50 \sim 500$ receive elements for receiving $1 \sim
100$ user signals at an enough transmit power (e.g., $\ge 20$ dBm).
|
2501.18385
|
Performance guarantees for optimization-based state estimation using
turnpike properties
|
math.OC cs.SY eess.SY
|
In this paper, we develop novel accuracy and performance guarantees for
optimal state estimation of general nonlinear systems (in particular, moving
horizon estimation, MHE). Our results rely on a turnpike property of the
optimal state estimation problem, which essentially states that the omniscient
infinite-horizon solution involving all past and future data serves as turnpike
for the solutions of finite-horizon estimation problems involving a subset of
the data. This leads to the surprising observation that MHE problems naturally
exhibit a leaving arc, which may have a strong negative impact on the
estimation accuracy. To address this, we propose a delayed MHE scheme, and we
show that the resulting performance (both averaged and non-averaged) is
approximately optimal and achieves bounded dynamic regret with respect to the
infinite-horizon solution, with error terms that can be made arbitrarily small
by an appropriate choice of the delay. In various simulation examples, we
observe that already a very small delay in the MHE scheme is sufficient to
significantly improve the overall estimation error by 20-25 % compared to
standard MHE (without delay). This finding is of great importance for practical
applications (especially for monitoring, fault detection, and parameter
estimation) where a small delay in the estimation is rather irrelevant but may
significantly improve the estimation results.
|
2501.18388
|
Improved Replicable Boosting with Majority-of-Majorities
|
cs.LG
|
We introduce a new replicable boosting algorithm which significantly improves
the sample complexity compared to previous algorithms. The algorithm works by
doing two layers of majority voting, using an improved version of the
replicable boosting algorithm introduced by Impagliazzo et al. [2022] in the
bottom layer.
|
2501.18394
|
Quantum-Key Distribution using Decoy Pulses to Combat Photon-Number
Splitting by Eavesdropper: An Event-by-Event Impairment Enumeration Approach
for Performance Evaluation and Design
|
quant-ph cs.CR cs.IT cs.NI math.IT
|
Quantum-key distribution (QKD) schemes employing quantum communication links
are typically based on the transmission of weak optical pulses over optical
fibers to setup a secret key between the transmitting and receiving nodes.
Alice transmits optically a random bit stream to the receiver (Bob) through the
photon polarizations or the quadrature components of the lightwaves associated
with the photons, with a secret key remaining implicitly embedded therein.
However, during the above transmission, some eavesdropper (Eve) might attempt
to tap the passing-by photons from the optical fiber links to extract the key.
In one of the popular QKD schemes, along with signal pulses, some additional
decoy pulses are transmitted by Alice, while Eve might use photon-number
splitting (PNS) for eavesdropping. In a typical PNS scheme, (i) the optical
pulses with single photon are blocked by Eve, (ii) from the optical pulses with
two photons, one photon is retained by Eve to carry out eavesdropping operation
and the other is retransmitted to Bob, and (iii) all other pulses with more
than two photons are retransmitted by Eve to Bob without retaining any photon
from them. Extensive theoretical research has been carried out on such QKD
schemes, by employing information-theoretic approach along with computer
simulations and experimental studies. We present a novel event-by-event
impairment enumeration approach to evaluate the overall performance of one such
QKD scheme analytically with due consideration to the physical layer of the
quantum communication links. The proposed approach monitors the impairments of
the propagating optical pulses event-by-event at all possible locations along
the optical fiber link using statistical approach, and provides estimates of
the realizable key generation rate, while assuring an adequate yield ratio
between signal and decoy pulses for the detection of possible eavesdropping.
|
2501.18397
|
A weakly compressible SPH method for RANS simulation of wall-bounded
turbulent flows
|
physics.flu-dyn cs.CE
|
This paper presents a Weakly Compressible Smoothed Particle Hydrodynamics
(WCSPH) method for solving the two-equation Reynolds-Averaged Navier-Stokes
(RANS) model. The turbulent wall-bounded flow with or without mild flow
separation, a crucial flow pattern in engineering applications, yet rarely
explored in the SPH community, is simulated. The inconsistency between the
Lagrangian characteristic and RANS model, mainly due to the intense particle
shear and near-wall discontinuity, is firstly revealed and addressed by the
mainstream and nearwall improvements, respectively. The mainstream
improvements, including Adaptive Riemann-eddy Dissipation (ARD) and Limited
Transport Velocity Formulation (LTVF), address dissipation incompatibility and
turbulent kinetic energy over-prediction issues. The nearwall improvements,
such as the particle-based wall model realization, weighted near-wall
compensation scheme, and constant $y_p$ strategy, improve the accuracy and
stability of the adopted wall model, where the wall dummy particles are still
used for future coupling of solid dynamics. Besides, to perform rigorous
convergence tests, an level-set-based boundary-offset technique is developed to
ensure consistent $y^+$ across different resolutions. The benchmark
wall-bounded turbulent cases, including straight, mildly- and strongly-curved,
and Half Converging and Diverging (HCD) channels are calculated. Good
convergence is, to our best knowledge, firstly achieved for both velocity and
turbulent kinetic energy for the SPH-RANS method. All the results agree well
with the data from the experiments or simulated by the Eulerian methods at
engineering-acceptable resolutions. The proposed method bridges particle-based
and mesh-based RANS models, providing adaptability for other turbulence models
and potential for turbulent fluid-structure interaction (FSI) simulations.
|
2501.18401
|
MatIR: A Hybrid Mamba-Transformer Image Restoration Model
|
cs.CV
|
In recent years, Transformers-based models have made significant progress in
the field of image restoration by leveraging their inherent ability to capture
complex contextual features. Recently, Mamba models have made a splash in the
field of computer vision due to their ability to handle long-range dependencies
and their significant computational efficiency compared to Transformers.
However, Mamba currently lags behind Transformers in contextual learning
capabilities. To overcome the limitations of these two models, we propose a
Mamba-Transformer hybrid image restoration model called MatIR. Specifically,
MatIR cross-cycles the blocks of the Transformer layer and the Mamba layer to
extract features, thereby taking full advantage of the advantages of the two
architectures. In the Mamba module, we introduce the Image Inpainting State
Space (IRSS) module, which traverses along four scan paths to achieve efficient
processing of long sequence data. In the Transformer module, we combine
triangular window-based local attention with channel-based global attention to
effectively activate the attention mechanism over a wider range of image
pixels. Extensive experimental results and ablation studies demonstrate the
effectiveness of our approach.
|
2501.18403
|
Efficient Transformer for High Resolution Image Motion Deblurring
|
cs.CV cs.AI
|
This paper presents a comprehensive study and improvement of the Restormer
architecture for high-resolution image motion deblurring. We introduce
architectural modifications that reduce model complexity by 18.4% while
maintaining or improving performance through optimized attention mechanisms.
Our enhanced training pipeline incorporates additional transformations
including color jitter, Gaussian blur, and perspective transforms to improve
model robustness as well as a new frequency loss term. Extensive experiments on
the RealBlur-R, RealBlur-J, and Ultra-High-Definition Motion blurred (UHDM)
datasets demonstrate the effectiveness of our approach. The improved
architecture shows better convergence behavior and reduced training time while
maintaining competitive performance across challenging scenarios. We also
provide detailed ablation studies analyzing the impact of our modifications on
model behavior and performance. Our results suggest that thoughtful
architectural simplification combined with enhanced training strategies can
yield more efficient yet equally capable models for motion deblurring tasks.
Code and Data Available at: https://github.com/hamzafer/image-deblurring
|
2501.18405
|
Segmentation of cracks in 3d images of fiber reinforced concrete using
deep learning
|
cs.LG
|
Cracks in concrete structures are very common and are an integral part of
this heterogeneous material. Characteristics of cracks induced by standardized
tests yield valuable information about the tested concrete formulation and its
mechanical properties. Observing cracks on the surface of the concrete
structure leaves a wealth of structural information unused. Computed tomography
enables looking into the sample without interfering or destroying the
microstructure. The reconstructed tomographic images are 3d images, consisting
of voxels whose gray values represent local X-ray absorption. In order to
identify voxels belonging to the crack, so to segment the crack structure in
the images, appropriate algorithms need to be developed. Convolutional neural
networks are known to solve this type of task very well given enough and
consistent training data. We adapted a 3d version of the well-known U-Net and
trained it on semi-synthetic 3d images of real concrete samples equipped with
simulated crack structures. Here, we explain the general approach. Moreover, we
show how to teach the network to detect also real crack systems in 3d images of
varying types of real concrete, in particular of fiber reinforced concrete.
|
2501.18407
|
Degree is Important: On Evolving Homogeneous Boolean Functions
|
cs.NE cs.CR
|
Boolean functions with good cryptographic properties like high nonlinearity
and algebraic degree play an important in the security of stream and block
ciphers. Such functions may be designed, for instance, by algebraic
constructions or metaheuristics. This paper investigates the use of
Evolutionary Algorithms (EAs) to design homogeneous bent Boolean functions,
i.e., functions that are maximally nonlinear and whose algebraic normal form
contains only monomials of the same degree. In our work, we evaluate three
genotype encodings and four fitness functions. Our results show that while EAs
manage to find quadratic homogeneous bent functions (with the best method being
a GA leveraging a restricted encoding), none of the approaches result in cubic
homogeneous bent functions.
|
2501.18411
|
Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for
Agents
|
cs.AI astro-ph.IM physics.comp-ph
|
Modern science emerged from reasoning over repeatedly-observed planetary
motions. We present Gravity-Bench-v1, an environment-based benchmark that
challenges AI agents on tasks that parallel this historical development.
Gravity-Bench-v1 evaluates agents on the discovery of physics concealed within
a dynamic environment, using rigorous gravitational dynamics simulations.
Gravity-Bench includes out-of-distribution cases, i.e. with physics that
deviates from the real world, to evaluate true scientific generalization
capabilities. Agents must plan to collect data within an experimental budget
and must perform a dynamic form of data analysis and reasoning to solve tasks
efficiently. Our benchmark admits an open-ended space of solutions. PhD-level
solutions for each task are provided, to calibrate AI performance against human
expertise. Technically at an upper-undergraduate level, our benchmark proves
challenging to baseline AI agents. Gravity-Bench-v1 and planned extensions
should help map out AI progress towards scientific discovery capabilities.
|
2501.18412
|
Real Time Scheduling Framework for Multi Object Detection via Spiking
Neural Networks
|
eess.SY cs.CV cs.NE cs.SY
|
Given the energy constraints in autonomous mobile agents (AMAs), such as
unmanned vehicles, spiking neural networks (SNNs) are increasingly favored as a
more efficient alternative to traditional artificial neural networks. AMAs
employ multi-object detection (MOD) from multiple cameras to identify nearby
objects while ensuring two essential objectives, (R1) timing guarantee and (R2)
high accuracy for safety. In this paper, we propose RT-SNN, the first system
design, aiming at achieving R1 and R2 in SNN-based MOD systems on AMAs.
Leveraging the characteristic that SNNs gather feature data of input image
termed as membrane potential, through iterative computation over multiple
timesteps, RT-SNN provides multiple execution options with adjustable timesteps
and a novel method for reusing membrane potential to support R1. Then, it
captures how these execution strategies influence R2 by introducing a novel
notion of mean absolute error and membrane confidence. Further, RT-SNN develops
a new scheduling framework consisting of offline schedulability analysis for R1
and a run-time scheduling algorithm for R2 using the notion of membrane
confidence. We deployed RT-SNN to Spiking-YOLO, the SNN-based MOD model derived
from ANN-to-SNN conversion, and our experimental evaluation confirms its
effectiveness in meeting the R1 and R2 requirements while providing significant
energy efficiency.
|
2501.18413
|
GBFRS: Robust Fuzzy Rough Sets via Granular-ball Computing
|
cs.AI cs.LG
|
Fuzzy rough set theory is effective for processing datasets with complex
attributes, supported by a solid mathematical foundation and closely linked to
kernel methods in machine learning. Attribute reduction algorithms and
classifiers based on fuzzy rough set theory exhibit promising performance in
the analysis of high-dimensional multivariate complex data. However, most
existing models operate at the finest granularity, rendering them inefficient
and sensitive to noise, especially for high-dimensional big data. Thus,
enhancing the robustness of fuzzy rough set models is crucial for effective
feature selection. Muiti-garanularty granular-ball computing, a recent
development, uses granular-balls of different sizes to adaptively represent and
cover the sample space, performing learning based on these granular-balls. This
paper proposes integrating multi-granularity granular-ball computing into fuzzy
rough set theory, using granular-balls to replace sample points. The
coarse-grained characteristics of granular-balls make the model more robust.
Additionally, we propose a new method for generating granular-balls, scalable
to the entire supervised method based on granular-ball computing. A forward
search algorithm is used to select feature sequences by defining the
correlation between features and categories through dependence functions.
Experiments demonstrate the proposed model's effectiveness and superiority over
baseline methods.
|
2501.18415
|
Consensus statement on the credibility assessment of ML predictors
|
q-bio.QM cs.LG
|
The rapid integration of machine learning (ML) predictors into in silico
medicine has revolutionized the estimation of quantities of interest (QIs) that
are otherwise challenging to measure directly. However, the credibility of
these predictors is critical, especially when they inform high-stakes
healthcare decisions. This position paper presents a consensus statement
developed by experts within the In Silico World Community of Practice. We
outline twelve key statements forming the theoretical foundation for evaluating
the credibility of ML predictors, emphasizing the necessity of causal
knowledge, rigorous error quantification, and robustness to biases. By
comparing ML predictors with biophysical models, we highlight unique challenges
associated with implicit causal knowledge and propose strategies to ensure
reliability and applicability. Our recommendations aim to guide researchers,
developers, and regulators in the rigorous assessment and deployment of ML
predictors in clinical and biomedical contexts.
|
2501.18416
|
Exploring Potential Prompt Injection Attacks in Federated Military LLMs
and Their Mitigation
|
cs.LG
|
Federated Learning (FL) is increasingly being adopted in military
collaborations to develop Large Language Models (LLMs) while preserving data
sovereignty. However, prompt injection attacks-malicious manipulations of input
prompts-pose new threats that may undermine operational security, disrupt
decision-making, and erode trust among allies. This perspective paper
highlights four potential vulnerabilities in federated military LLMs: secret
data leakage, free-rider exploitation, system disruption, and misinformation
spread. To address these potential risks, we propose a human-AI collaborative
framework that introduces both technical and policy countermeasures. On the
technical side, our framework uses red/blue team wargaming and quality
assurance to detect and mitigate adversarial behaviors of shared LLM weights.
On the policy side, it promotes joint AI-human policy development and
verification of security protocols. Our findings will guide future research and
emphasize proactive strategies for emerging military contexts.
|
2501.18417
|
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)
|
cs.LG
|
Anomaly detection is essential for identifying rare and significant events
across diverse domains such as finance, cybersecurity, and network monitoring.
This paper presents Synthetic Anomaly Monitoring (SAM), an innovative approach
that applies synthetic control methods from causal inference to improve both
the accuracy and interpretability of anomaly detection processes. By modeling
normal behavior through the treatment of each feature as a control unit, SAM
identifies anomalies as deviations within this causal framework. We conducted
extensive experiments comparing SAM with established benchmark models,
including Isolation Forest, Local Outlier Factor (LOF), k-Nearest Neighbors
(kNN), and One-Class Support Vector Machine (SVM), across five diverse
datasets, including Credit Card Fraud, HTTP Dataset CSIC 2010, and KDD Cup
1999, among others. Our results demonstrate that SAM consistently delivers
robust performance, highlighting its potential as a powerful tool for real-time
anomaly detection in dynamic and complex environments.
|
2501.18418
|
Task-based Regularization in Penalized Least-Squares for Binary Signal
Detection Tasks in Medical Image Denoising
|
eess.IV cs.CV
|
Image denoising algorithms have been extensively investigated for medical
imaging. To perform image denoising, penalized least-squares (PLS) problems can
be designed and solved, in which the penalty term encodes prior knowledge of
the object being imaged. Sparsity-promoting penalties, such as total variation
(TV), have been a popular choice for regularizing image denoising problems.
However, such hand-crafted penalties may not be able to preserve task-relevant
information in measured image data and can lead to oversmoothed image
appearances and patchy artifacts that degrade signal detectability. Supervised
learning methods that employ convolutional neural networks (CNNs) have emerged
as a popular approach to denoising medical images. However, studies have shown
that CNNs trained with loss functions based on traditional image quality
measures can lead to a loss of task-relevant information in images. Some
previous works have investigated task-based loss functions that employ model
observers for training the CNN denoising models. However, such training
processes typically require a large number of noisy and ground-truth
(noise-free or low-noise) image data pairs. In this work, we propose a
task-based regularization strategy for use with PLS in medical image denoising.
The proposed task-based regularization is associated with the likelihood of
linear test statistics of noisy images for Gaussian noise models. The proposed
method does not require ground-truth image data and solves an individual
optimization problem for denoising each image. Computer-simulation studies are
conducted that consider a multivariate-normally distributed (MVN) lumpy
background and a binary texture background. It is demonstrated that the
proposed regularization strategy can effectively improve signal detectability
in denoised images.
|
2501.18423
|
DeepExtractor: Time-domain reconstruction of signals and glitches in
gravitational wave data with deep learning
|
gr-qc astro-ph.IM cs.LG physics.data-an physics.ins-det
|
Gravitational wave (GW) interferometers, detect faint signals from distant
astrophysical events, such as binary black hole mergers. However, their high
sensitivity also makes them susceptible to background noise, which can obscure
these signals. This noise often includes transient artifacts called "glitches"
that can mimic astrophysical signals or mask their characteristics. Fast and
accurate reconstruction of both signals and glitches is crucial for reliable
scientific inference. In this study, we present DeepExtractor, a deep learning
framework designed to reconstruct signals and glitches with power exceeding
interferometer noise, regardless of their source. We design DeepExtractor to
model the inherent noise distribution of GW interferometers, following
conventional assumptions that the noise is Gaussian and stationary over short
time scales. It operates by predicting and subtracting the noise component of
the data, retaining only the clean reconstruction. Our approach achieves
superior generalization capabilities for arbitrary signals and glitches
compared to methods that directly map inputs to the clean training waveforms.
We validate DeepExtractor's effectiveness through three experiments: (1)
reconstructing simulated glitches injected into simulated detector noise, (2)
comparing performance with the state-of-the-art BayesWave algorithm, and (3)
analyzing real data from the Gravity Spy dataset to demonstrate effective
glitch subtraction from LIGO strain data. DeepExtractor achieves a median
mismatch of only 0.9% for simulated glitches, outperforming several deep
learning baselines. Additionally, DeepExtractor surpasses BayesWave in glitch
recovery, offering a dramatic computational speedup by reconstructing one
glitch sample in approx. 0.1 seconds on a CPU, compared to BayesWave's
processing time of approx. one hour per glitch.
|
2501.18426
|
Guaranteed confidence-band enclosures for PDE surrogates
|
cs.LG cs.AI
|
We propose a method for obtaining statistically guaranteed confidence bands
for functional machine learning techniques: surrogate models which map between
function spaces, motivated by the need build reliable PDE emulators. The method
constructs nested confidence sets on a low-dimensional representation (an SVD)
of the surrogate model's prediction error, and then maps these sets to the
prediction space using set-propagation techniques. The result are
conformal-like coverage guaranteed prediction sets for functional surrogate
models. We use zonotopes as basis of the set construction, due to their well
studied set-propagation and verification properties. The method is model
agnostic and can thus be applied to complex Sci-ML models, including Neural
Operators, but also in simpler settings. We also elicit a technique to capture
the truncation error of the SVD, ensuring the guarantees of the method.
|
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