id stringlengths 64 64 | published stringlengths 19 25 | title stringlengths 7 262 | description stringlengths 6 54.4k | link stringlengths 31 227 | category stringclasses 6
values | image stringlengths 3 247 |
|---|---|---|---|---|---|---|
d3bfe270ede0d223402d8eafb74c4e03be0c5c819dbc3c8e062e4e6935cb8a86 | 2026-01-13T00:00:00-05:00 | Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classification | arXiv:2601.07790v1 Announce Type: new Abstract: System logs are crucial for monitoring and diagnosing modern computing infrastructure, but their scale and complexity require reliable and efficient automated interpretation. Since severity levels are predefined metadata in system log messages, having a model merely class... | https://arxiv.org/abs/2601.07790 | Academic Papers | svg |
631fed5da3fa9d7f75c97aabe78116648d08d7efef1c3bcca9a2ae7df57ce6da | 2026-01-13T00:00:00-05:00 | Necessary and Sufficient Conditions for the Existence of an LU Factorization for General Rank Deficient Matrices | arXiv:2601.07791v1 Announce Type: new Abstract: We establish necessary and sufficient conditions for the existence of an LU factorization $A=LU$ for an arbitrary square matrix $A$, including singular and rank-deficient cases, without the use of row or column permutations. We prove that such a factorization exists if an... | https://arxiv.org/abs/2601.07791 | Academic Papers | svg |
4dd3d404a212f96f148fdc21f104afe4e36c79490b89b451ee794c6d84fa680e | 2026-01-13T00:00:00-05:00 | Kinship Data Benchmark for Multi-hop Reasoning | arXiv:2601.07794v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on their ability to perform multi-hop reasoning, i.e., to combine multiple pieces of information into a coherent inference. We introduce KinshipQA, a benchmark designed to probe this capability through reasoning over... | https://arxiv.org/abs/2601.07794 | Academic Papers | svg |
834580f5794123a2bd809d56f31cf699359d86bf08750c9126e8f247eb8e095e | 2026-01-13T00:00:00-05:00 | Vision-Language Model for Accurate Crater Detection | arXiv:2601.07795v1 Announce Type: new Abstract: The European Space Agency (ESA), driven by its ambitions on planned lunar missions with the Argonaut lander, has a profound interest in reliable crater detection, since craters pose a risk to safe lunar landings. This task is usually addressed with automated crater detect... | https://arxiv.org/abs/2601.07795 | Academic Papers | svg |
4fce96f39111a2fd12b4329b0e9fd5ed7b678e0144a9f7f50448ec36f548205d | 2026-01-13T00:00:00-05:00 | Learning Through Dialogue: Unpacking the Dynamics of Human-LLM Conversations on Political Issues | arXiv:2601.07796v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users' learning and engagement are understudied. We analyze the linguistic and interactional features from both LLM and participant chats ... | https://arxiv.org/abs/2601.07796 | Academic Papers | svg |
a1e5dd23829a9c25bc2405513992e880b3f97e3bfe00841f7a8949bdd11ce2a7 | 2026-01-13T00:00:00-05:00 | Lossy Source Coding with Broadcast Side Information | arXiv:2601.07797v1 Announce Type: new Abstract: This paper considers the source coding problem with broadcast side information. The side information is sent to two receivers through a noisy broadcast channel. We provide an outer bound of the rate--distortion--bandwidth (RDB) quadruples and achievable RDB quadruples whe... | https://arxiv.org/abs/2601.07797 | Academic Papers | svg |
ea52fb645045dea86706d759ff55af33778a314296c0617b2e935a3060004b8a | 2026-01-13T00:00:00-05:00 | Exchange Is All You Need for Remote Sensing Change Detection | arXiv:2601.07805v1 Announce Type: new Abstract: Remote sensing change detection fundamentally relies on the effective fusion and discrimination of bi-temporal features. Prevailing paradigms typically utilize Siamese encoders bridged by explicit difference computation modules, such as subtraction or concatenation, to id... | https://arxiv.org/abs/2601.07805 | Academic Papers | svg |
25cc3aa101a27413ef11096e5062675d510b968c2062a6600c50b7315b3a618c | 2026-01-13T00:00:00-05:00 | The Confidence Trap: Gender Bias and Predictive Certainty in LLMs | arXiv:2601.07806v1 Announce Type: new Abstract: The increased use of Large Language Models (LLMs) in sensitive domains leads to growing interest in how their confidence scores correspond to fairness and bias. This study examines the alignment between LLM-predicted confidence and human-annotated bias judgments. Focusing... | https://arxiv.org/abs/2601.07806 | Academic Papers | svg |
053276e809c0875976f89296d5166cd53d904b89fb55fdb8394fbe4ac2912a81 | 2026-01-13T00:00:00-05:00 | More Images, More Problems? A Controlled Analysis of VLM Failure Modes | arXiv:2601.07812v1 Announce Type: new Abstract: Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities, yet their proficiency in understanding and reasoning over multiple images remains largely unexplored. While existing benchmarks have initiated the evaluation of multi-image models, a comprehen... | https://arxiv.org/abs/2601.07812 | Academic Papers | svg |
e074c892026a20f0272c7a3cd8d38fcae6b86f35e44baf8820d5e67bfd77815b | 2026-01-13T00:00:00-05:00 | Data-driven control of hydraulic impact hammers under strict operational and control constraints | arXiv:2601.07813v1 Announce Type: new Abstract: This paper presents a data-driven methodology for the control of static hydraulic impact hammers, also known as rock breakers, which are commonly used in the mining industry. The task addressed in this work is that of controlling the rock-breaker so its end-effector reach... | https://arxiv.org/abs/2601.07813 | Academic Papers | svg |
626762d5fbe75a3571afeed4bdd4c8f6e104f7050c569827d1957ca98488720d | 2026-01-13T00:00:00-05:00 | Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests | arXiv:2601.07820v1 Announce Type: new Abstract: In human conversation, both interlocutors play an active role in maintaining mutual understanding. When addressees are uncertain about what speakers mean, for example, they can request clarification. It is an open question for language models whether they can assume a sim... | https://arxiv.org/abs/2601.07820 | Academic Papers | svg |
466920c0f9845c71209bbf7e648e89feec8fa114882956cd179c57a0a42cb074 | 2026-01-13T00:00:00-05:00 | Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation | arXiv:2601.07821v1 Announce Type: new Abstract: Post-training algorithms based on deep reinforcement learning can push the limits of robotic models for specific objectives, such as generalizability, accuracy, and robustness. However, Intervention-requiring Failures (IR Failures) (e.g., a robot spilling water or breakin... | https://arxiv.org/abs/2601.07821 | Academic Papers | svg |
b18783ff60c6b0b43fa109d3dda9a906f513b24ea52ca845e0df79da73fd6090 | 2026-01-13T00:00:00-05:00 | Video Generation Models in Robotics - Applications, Research Challenges, Future Directions | arXiv:2601.07823v1 Announce Type: new Abstract: Video generation models have emerged as high-fidelity models of the physical world, capable of synthesizing high-quality videos capturing fine-grained interactions between agents and their environments conditioned on multi-modal user inputs. Their impressive capabilities ... | https://arxiv.org/abs/2601.07823 | Academic Papers | svg |
87d83e2b187fdb0bb10f641e0ec8f56b4633e11f0460fa3e4f6f802cebf7a5a1 | 2026-01-13T00:00:00-05:00 | Tensor Algebra Processing Primitives (TAPP): Towards a Standard for Tensor Operations | arXiv:2601.07827v1 Announce Type: new Abstract: To address the absence of a universal standard interface for tensor operations, we introduce the Tensor Algebra Processing Primitives (TAPP), a C-based interface designed to decouple the application layer from hardware-specific implementations. We provide a mathematical f... | https://arxiv.org/abs/2601.07827 | Academic Papers | svg |
946b4ae44da84acfffca28489bf64247041cd4292ab546f36729bee1586eb68b | 2026-01-13T00:00:00-05:00 | Optimal Learning Rate Schedule for Balancing Effort and Performance | arXiv:2601.07830v1 Announce Type: new Abstract: Learning how to learn efficiently is a fundamental challenge for biological agents and a growing concern for artificial ones. To learn effectively, an agent must regulate its learning speed, balancing the benefits of rapid improvement against the costs of effort, instabil... | https://arxiv.org/abs/2601.07830 | Academic Papers | svg |
f344c56bedb43f0ef7d4000f345d790589cf0cfc67f094f6a1b1df6ac93e8ed0 | 2026-01-13T00:00:00-05:00 | MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-Head | arXiv:2601.07832v1 Announce Type: new Abstract: While the Transformer architecture dominates many fields, its quadratic self-attention complexity hinders its use in large-scale applications. Linear attention offers an efficient alternative, but its direct application often degrades performance, with existing fixes typi... | https://arxiv.org/abs/2601.07832 | Academic Papers | svg |
60e622288bab94a6776424db54df579322231bd4ec8650375ceb159c088f9c47 | 2026-01-13T00:00:00-05:00 | Tuning-free Visual Effect Transfer across Videos | arXiv:2601.07833v1 Announce Type: new Abstract: We present RefVFX, a new framework that transfers complex temporal effects from a reference video onto a target video or image in a feed-forward manner. While existing methods excel at prompt-based or keyframe-conditioned editing, they struggle with dynamic temporal effec... | https://arxiv.org/abs/2601.07833 | Academic Papers | svg |
4b93f0d1f0101e8a19c51f2f5518ba521ee90e3d5f6f4c84e2126434060a6d5a | 2026-01-13T00:00:00-05:00 | SecureCAI: Injection-Resilient LLM Assistants for Cybersecurity Operations | arXiv:2601.07835v1 Announce Type: new Abstract: Large Language Models have emerged as transformative tools for Security Operations Centers, enabling automated log analysis, phishing triage, and malware explanation; however, deployment in adversarial cybersecurity environments exposes critical vulnerabilities to prompt ... | https://arxiv.org/abs/2601.07835 | Academic Papers | svg |
7f75621e877a26e12fc96fc12915f0408ef0bc694a3b33d60a75aa9c4e337d13 | 2026-01-13T00:00:00-05:00 | Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach | arXiv:2509.07820v1 Announce Type: cross Abstract: The rise of large reasoning language models (LRLMs) has unlocked new potential for solving complex tasks. These models operate with a thinking budget, that is, a predefined number of reasoning tokens used to arrive at a solution. We propose a novel approach, inspired by... | https://arxiv.org/abs/2509.07820 | Academic Papers | svg |
c262e8bc3ac2748ecd05dfa1ed59d24eb811541d2f2ffca8208cdba2b17271c4 | 2026-01-13T00:00:00-05:00 | Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment | arXiv:2511.08399v1 Announce Type: cross Abstract: Most multimodal models treat every negative pair alike, ignoring the ambiguous negatives that differ from the positive by only a small detail. We propose Boundary-Aware Curriculum with Local Attention (BACL), a lightweight add-on that turns these borderline cases into a... | https://arxiv.org/abs/2511.08399 | Academic Papers | svg |
579928b0c3559e11f886a62b30873fde7de837394e5324188d31029660d09254 | 2026-01-13T00:00:00-05:00 | SmartSplat: Feature-Smart Gaussians for Scalable Compression of Ultra-High-Resolution Images | arXiv:2512.20377v1 Announce Type: cross Abstract: Recent advances in generative AI have accelerated the production of ultra-high-resolution visual content, posing significant challenges for efficient compression and real-time decoding on end-user devices. Inspired by 3D Gaussian Splatting, recent 2D Gaussian image mode... | https://arxiv.org/abs/2512.20377 | Academic Papers | svg |
cef3af540cca6c8b3fe8853593e0677a0dd2e8e4191b913f60c22379dd399e0e | 2026-01-13T00:00:00-05:00 | Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing | arXiv:2601.00020v2 Announce Type: cross Abstract: Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are strongly affected by non-stationary neural signals that vary across sessions and individuals, limiting the generalization of subject-agnostic models and motivating adaptive and personalized learning... | https://arxiv.org/abs/2601.00020 | Academic Papers | svg |
412c22c91c96c067f45ddc9acaff37f7d62a3f352f506e0fdab7c0fd82dd8405 | 2026-01-13T00:00:00-05:00 | Efficient GPU-computing simulation platform JAX-PF for differentiable phase field model | arXiv:2601.06079v1 Announce Type: cross Abstract: We present JAX-PF, an open-source, GPU-accelerated, and differentiable Phase Field (PF) software package, supporting both explicit and implicit time stepping schemes. Leveraging the modern computing architecture JAX, JAX-PF achieves high performance through array progra... | https://arxiv.org/abs/2601.06079 | Academic Papers | svg |
d55c2a15070868841b4da8e3598544f1032c983a9965c5d72026d1c704155b5a | 2026-01-13T00:00:00-05:00 | First Multi-Constellation Observations of Navigation Satellite Signals in the Lunar Domain by Post-Processing L1/L5 IQ Snapshots | arXiv:2601.06081v1 Announce Type: cross Abstract: The use of Global Navigation Satellite Systems (GNSS) to increase spacecraft autonomy for orbit determination has gained renewed momentum following the Lunar GNSS Receiver Experiment (LuGRE), which demonstrated feasible onboard GPS and Galileo signal reception and track... | https://arxiv.org/abs/2601.06081 | Academic Papers | svg |
73d51db08f3526cf135c2a9fe5ed306b0310b6505bf89a5a9769f354d48141e5 | 2026-01-13T00:00:00-05:00 | PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction | arXiv:2601.06088v1 Announce Type: cross Abstract: Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied. Current large language models (LLMs) have shown remarkable potential in various domains, exhibiting expert-level performance th... | https://arxiv.org/abs/2601.06088 | Academic Papers | svg |
da432364dd22848596687967c3da06490a2568bd6dd5496651fb23fdcba604ce | 2026-01-13T00:00:00-05:00 | Auditory Filter Behavior and Updated Estimated Constants | arXiv:2601.06094v1 Announce Type: cross Abstract: Filters from the Gammatone family are often used to model auditory signal processing, but the filter constant values used to mimic human hearing are largely set to values based on historical psychoacoustic data collected several decades ago. Here, we move away from this... | https://arxiv.org/abs/2601.06094 | Academic Papers | svg |
9e473eb556f87dbdea61c0b01d51c41ad321abeae50d96ba46e545833c37df4d | 2026-01-13T00:00:00-05:00 | Emergent Complexity in Nuclear Reaction Networks: A Study of Stellar Nucleosynthesis through Chemical Organization Theory | arXiv:2601.06143v1 Announce Type: cross Abstract: We explore the emergence of complex structures within reaction networks, focusing on nuclear reaction networks relevant to stellar nucleosynthesis. The work presents a theoretical framework rooted in Chemical Organization Theory (COT) to characterize how stable, self-su... | https://arxiv.org/abs/2601.06143 | Academic Papers | svg |
c445ebd7d312747153aaf1037556a6fa51ac86610523ad09f75dd6cf7125adb4 | 2026-01-13T00:00:00-05:00 | Certificate for Orthogonal Equivalence of Real Polynomials by Polynomial-Weighted Principal Component Analysis | arXiv:2601.06148v1 Announce Type: cross Abstract: Suppose that $f(x) \in \mathbb{R}[x_1,\dots, x_n]$ and $g(x) \in \mathbb{R}[x_1,\dots, x_n]$ are two real polynomials of degree $d$ in $n$ variables. If the polynomials $f$ and $g$ are the same up to orthogonal symmetry a natural question is then what element of the ort... | https://arxiv.org/abs/2601.06148 | Academic Papers | svg |
6f075702dc413ff7b5f222cd147afa4b595e1fd3586b79624b62b3d59a874e0e | 2026-01-13T00:00:00-05:00 | Deep Joint Source-Channel Coding for Wireless Video Transmission with Asymmetric Context | arXiv:2601.06170v1 Announce Type: cross Abstract: In this paper, we propose a high-efficiency deep joint source-channel coding (JSCC) method for video transmission based on conditional coding with asymmetric context. The conditional coding-based neural video compression requires to predict the encoding and decoding con... | https://arxiv.org/abs/2601.06170 | Academic Papers | svg |
a19e8d9f9410c7a4c6b42fde0e35902c55c85a0fd8e2d03f5d45045457eba1dc | 2026-01-13T00:00:00-05:00 | A First Course in Sparse Optimization | arXiv:2601.06173v1 Announce Type: cross Abstract: This article aims to provide a comprehensive overview of sparse optimization, with a focus on both sparse signal recovery and sparse regularization techniques. We will begin by exploring the foundations of sparse optimization, delving into the mathematical tools and mod... | https://arxiv.org/abs/2601.06173 | Academic Papers | svg |
c8f40d566474665133d2d3dce5a87229ce4def4667d894af04eacea37f342ded | 2026-01-13T00:00:00-05:00 | FastSLM: Hierarchical Frame Q-Former for Effective Speech Modality Adaptation | arXiv:2601.06199v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have demonstrated human-expert-level capabilities, driving significant interest in their potential for achieving artificial general intelligence (AGI). In particular, there is growing momentum in adapting LLMs to various m... | https://arxiv.org/abs/2601.06199 | Academic Papers | svg |
b131bddeb2e248d08d17afb8453a8271320134aae009771efe6c60d2355d8556 | 2026-01-13T00:00:00-05:00 | Real-Time Image Processing Algorithms for Embedded Systems | arXiv:2601.06243v1 Announce Type: cross Abstract: Embedded vision systems need efficient and robust image processing algorithms to perform real-time, with resource-constrained hardware. This research investigates image processing algorithms, specifically edge detection, corner detection, and blob detection, that are im... | https://arxiv.org/abs/2601.06243 | Academic Papers | svg |
31c3aa1020dd8b590ed0014f5bdda1d11b75aa90f6480a07a647c3b1f03dedf9 | 2026-01-13T00:00:00-05:00 | Hard Constraint Projection in a Physics Informed Neural Network | arXiv:2601.06244v1 Announce Type: cross Abstract: In this work, we embed hard constraints in a physics informed neural network (PINN) which predicts solutions to the 2D incompressible Navier Stokes equations. We extend the hard constraint method introduced by Chen et al. (arXiv:2012.06148) from a linear PDE to a strong... | https://arxiv.org/abs/2601.06244 | Academic Papers | svg |
7ae971ecd1d9cba2d9dc325b2865bd3277f35054fb5f4a431b7472f2367207e5 | 2026-01-13T00:00:00-05:00 | Gamma2Patterns: Deep Cognitive Attention Region Identification and Gamma-Alpha Pattern Analysis | arXiv:2601.06257v1 Announce Type: cross Abstract: Deep cognitive attention is characterized by heightened gamma oscillations and coordinated visual behavior. Despite the physiological importance of these mechanisms, computational studies rarely synthesize these modalities or identify the neural regions most responsible... | https://arxiv.org/abs/2601.06257 | Academic Papers | svg |
f00f28cdb26f7354cf0af36ed24851195af8c58c713f4364b6c6fab574e2f10d | 2026-01-13T00:00:00-05:00 | Performance Analysis of DCT, Hadamard, and PCA in Block-Based Image Compression | arXiv:2601.06273v1 Announce Type: cross Abstract: Block based image compression relies on transform coding to concentrate signal energy into a small number of coefficients. While classical codecs use fixed transforms such as the Discrete Cosine Transform (DCT), data driven methods such as Principal Component Analysis (... | https://arxiv.org/abs/2601.06273 | Academic Papers | svg |
c4b8456ff0a46637562dbd1d3a9e26c389aa6197bc8b825a4e5c73506720af0e | 2026-01-13T00:00:00-05:00 | Timing Fragility Aware Selective Hardening of RISCV Soft Processors on SRAM Based FPGAs | arXiv:2601.06308v1 Announce Type: cross Abstract: Selective hardening is widely employed to improve the reliability of FPGA based soft processors while limiting the overhead of full redundancy. However, existing approaches primarily rely on architectural criticality or functional fault analysis, overlooking the impact ... | https://arxiv.org/abs/2601.06308 | Academic Papers | svg |
cde8df50f6451a4136b002ba5a09d617c971bf1daa247ccc455ebd6a58a9d40d | 2026-01-13T00:00:00-05:00 | Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers | arXiv:2601.06360v1 Announce Type: cross Abstract: Current histopathological grading of prostate cancer relies primarily on glandular architecture, largely overlooking the tumor microenvironment. Here, we present PROTAS, a deep learning framework that quantifies reactive stroma (RS) in routine hematoxylin and eosin (H&a... | https://arxiv.org/abs/2601.06360 | Academic Papers | svg |
ecbf3ad819407bc56096d92f1e5546e2ef1f196b5f613ec66f1716c57e79b46a | 2026-01-13T00:00:00-05:00 | The Replicator-Optimization Mechanism: A Scale-Relative Formalism for Persistence-Conditioned Dynamics with Application to Consent-Based Metaethics | arXiv:2601.06363v1 Announce Type: cross Abstract: This paper formalizes a widely used dynamical class--replicator-mutator dynamics and Price-style selection-and-transmission--and makes explicit the modeling choices (scale, atomic unit, interaction topology, transmission kernel) that determine how this class instantiate... | https://arxiv.org/abs/2601.06363 | Academic Papers | svg |
6ac8146a1b25172a5416139c7f79bf3826455819ef03b6f3ef04948547a30d3c | 2026-01-13T00:00:00-05:00 | A Linear Combination of Unitaries Decomposition for the Laplace Operator | arXiv:2601.06370v1 Announce Type: cross Abstract: We provide novel linear combination of unitaries decompositions for a class of discrete elliptic differential operators. Specifically, Poisson problems augmented with periodic, Dirichlet, Neumann, Robin, and mixed boundary conditions are considered on the unit interval ... | https://arxiv.org/abs/2601.06370 | Academic Papers | svg |
7bf4553d37ed84e64896f132c06bb8d3db4b958b23a00f3de2bf0d073beb86f5 | 2026-01-13T00:00:00-05:00 | Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing | arXiv:2601.06392v1 Announce Type: cross Abstract: We introduce CL-QAS, a continual quantum architecture search framework that mitigates the challenges of costly amplitude encoding and catastrophic forgetting in variational quantum circuits. The method uses Tensor-Train encoding to efficiently compress high-dimensional ... | https://arxiv.org/abs/2601.06392 | Academic Papers | svg |
03d22126f97835a21c8fcdc340e9f7f752a67bae151ef15bb0c3ab80a7062416 | 2026-01-13T00:00:00-05:00 | On a Gradient Approach to Chebyshev Center Problems with Applications to Function Learning | arXiv:2601.06434v1 Announce Type: cross Abstract: We introduce $\textsf{gradOL}$, the first gradient-based optimization framework for solving Chebyshev center problems, a fundamental challenge in optimal function learning and geometric optimization. $\textsf{gradOL}$ hinges on reformulating the semi-infinite problem as... | https://arxiv.org/abs/2601.06434 | Academic Papers | svg |
47b1283e9d12612676b5c2b02aaae350d92eb51a42ebdae2df7f5cf34ed9b1e5 | 2026-01-13T00:00:00-05:00 | Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators | arXiv:2601.06462v1 Announce Type: cross Abstract: Applying Physics-Informed Gaussian Process Regression to the eigenvalue problem $(\mathcal{L}-\lambda)u = 0$ poses a fundamental challenge, where the null source term results in a trivial predictive mean and a degenerate marginal likelihood. Drawing inspiration from sys... | https://arxiv.org/abs/2601.06462 | Academic Papers | svg |
4abd207e4b1f06812f73d6cdcaeeea5c51fe75ea6047cd005cee750bac087792 | 2026-01-13T00:00:00-05:00 | R$^3$D: Regional-guided Residual Radar Diffusion | arXiv:2601.06465v1 Announce Type: cross Abstract: Millimeter-wave radar enables robust environment perception in autonomous systems under adverse conditions yet suffers from sparse, noisy point clouds with low angular resolution. Existing diffusion-based radar enhancement methods either incur high learning complexity b... | https://arxiv.org/abs/2601.06465 | Academic Papers | svg |
42a8bd6e9ae1588e44ab89b483c9d5f642d94071e56cec87fec469500f1e210f | 2026-01-13T00:00:00-05:00 | Joint Impact of ADC and Fronthaul Quantization in Cell-Free Massive MIMO-OFDM Uplink | arXiv:2601.06483v1 Announce Type: cross Abstract: In the uplink of a cell-free massive MIMO system, quantization affects performance in two key domains: the time-domain distortion introduced by finite-resolution analog-to-digital converters (ADCs) at the access points (APs), and the fronthaul quantization of signals se... | https://arxiv.org/abs/2601.06483 | Academic Papers | svg |
b79b99972aa84fc0a747c5dfb706beacb6c5aaa355678d8f0ef55dd47ca9b34d | 2026-01-13T00:00:00-05:00 | Cell-Free Massive MIMO with Hardware-Impaired Wireless Fronthaul | arXiv:2601.06486v1 Announce Type: cross Abstract: Cell-free massive MIMO (multiple-input multiple-output) enhances spectral and energy efficiency compared to conventional cellular networks by enabling joint transmission and reception across a large number of distributed access points (APs). Since these APs are envision... | https://arxiv.org/abs/2601.06486 | Academic Papers | svg |
d07d382848407c6c710631063222ddc02f0c8606aaf592386d0cc122d6659b44 | 2026-01-13T00:00:00-05:00 | Inference-Time Alignment for Diffusion Models via Doob's Matching | arXiv:2601.06514v1 Announce Type: cross Abstract: Inference-time alignment for diffusion models aims to adapt a pre-trained diffusion model toward a target distribution without retraining the base score network, thereby preserving the generative capacity of the base model while enforcing desired properties at the infer... | https://arxiv.org/abs/2601.06514 | Academic Papers | svg |
46c78926a18e0839577d4435b402cf3b01250c05c98c39ea1885705f41d4f46e | 2026-01-13T00:00:00-05:00 | Resource-constrained Project Scheduling with Time-of-Use Energy Tariffs and Machine States: A Logic-based Benders Decomposition Approach | arXiv:2601.06542v1 Announce Type: cross Abstract: In this paper, we investigate the Resource-Constrained Project Scheduling Problem (RCPSP) with time-of-use energy tariffs (TOU) and machine states, a variant of RCPSP for production scheduling where energy price is part of the criteria and one machine is highly energy-d... | https://arxiv.org/abs/2601.06542 | Academic Papers | svg |
e521b9fe1ab8430939f1a8dcce6ad5ef118f7229af740b2ee53e5fd1dd51d593 | 2026-01-13T00:00:00-05:00 | Lightweight Resolution-Aware Audio Deepfake Detection via Cross-Scale Attention and Consistency Learning | arXiv:2601.06560v1 Announce Type: cross Abstract: Audio deepfake detection has become increasingly challenging due to rapid advances in speech synthesis and voice conversion technologies, particularly under channel distortions, replay attacks, and real-world recording conditions. This paper proposes a resolution-aware ... | https://arxiv.org/abs/2601.06560 | Academic Papers | svg |
3191bce4fa3a71611169ebe21e8955115ca6d7923f37bed1fe9be4845c426dea | 2026-01-13T00:00:00-05:00 | Stereo Audio Rendering for Personal Sound Zones Using a Binaural Spatially Adaptive Neural Network (BSANN) | arXiv:2601.06621v1 Announce Type: cross Abstract: A binaural rendering framework for personal sound zones (PSZs) is proposed to enable multiple head-tracked listeners to receive fully independent stereo audio programs. Current PSZ systems typically rely on monophonic rendering and therefore cannot control the left and ... | https://arxiv.org/abs/2601.06621 | Academic Papers | svg |
e1f3e1358e3021b6a6e7ec014f15a2a692f2041faf4c90cc966494e161fedb75 | 2026-01-13T00:00:00-05:00 | A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking | arXiv:2601.06645v1 Announce Type: cross Abstract: Artificial intelligence holds strong potential to support clinical decision making in intensive care units where timely and accurate risk assessment is critical. However, many existing models focus on isolated outcomes or limited data types, while clinicians integrate l... | https://arxiv.org/abs/2601.06645 | Academic Papers | svg |
05fdbf8dc4292e9bd5e51bfe72002ae6834c4f06e8efab2c2b8265c660730ae1 | 2026-01-13T00:00:00-05:00 | Dereverberation Filter by Deconvolution with Frequency Bin Specific Faded Impulse Response | arXiv:2601.06662v1 Announce Type: cross Abstract: This work introduces a robust single-channel inverse filter for dereverberation of non-ideal recordings, validated on real audio. The developed method focuses on the calculation and modification of a discrete impulse response in order to filter the characteristics from ... | https://arxiv.org/abs/2601.06662 | Academic Papers | svg |
4068a0f55a9a28d425a6ca99e2bd80890ca65641d098ee45436977488f8a7a37 | 2026-01-13T00:00:00-05:00 | Diffusion Models with Heavy-Tailed Targets: Score Estimation and Sampling Guarantees | arXiv:2601.06715v1 Announce Type: cross Abstract: Score-based diffusion models have become a powerful framework for generative modeling, with score estimation as a central statistical bottleneck. Existing guarantees for score estimation largely focus on light-tailed targets or rely on restrictive assumptions such as co... | https://arxiv.org/abs/2601.06715 | Academic Papers | svg |
94fa27836c086f02e5fc8c6739c78e0d99210fbb9ecf841b55807da2e2df47b9 | 2026-01-13T00:00:00-05:00 | USFetal: Tools for Fetal Brain Ultrasound Compounding | arXiv:2601.06726v1 Announce Type: cross Abstract: Ultrasound offers a safe, cost-effective, and widely accessible technology for fetal brain imaging, making it especially suitable for routine clinical use. However, it suffers from view-dependent artifacts, operator variability, and a limited field of view, which make i... | https://arxiv.org/abs/2601.06726 | Academic Papers | svg |
0e74f316dd364b84d58c13c6cd8fcadb03af9c998dcfa90604fa8b2807e71eeb | 2026-01-13T00:00:00-05:00 | Non-Abelian qLDPC: TQFT Formalism, Addressable Gauging Measurement and Application to Magic State Fountain on 2D Product Codes | arXiv:2601.06736v1 Announce Type: cross Abstract: A fundamental problem of fault-tolerant quantum computation with quantum low-density parity-check (qLDPC) codes is the tradeoff between connectivity and universality. It is widely believed that in order to perform native logical non-Clifford gates, one needs to resort t... | https://arxiv.org/abs/2601.06736 | Academic Papers | svg |
b91e4097e2ba16390565543bec3eb75995ffba98ce8b450fbd84cad9e610b80a | 2026-01-13T00:00:00-05:00 | Water Demand Maximization: Quick Recovery of Nonlinear Physics Solutions | arXiv:2601.06755v1 Announce Type: cross Abstract: Determining the maximum demand a water distribution network can satisfy is crucial for ensuring reliable supply and planning network expansion. This problem, typically formulated as a mixed-integer nonlinear program (MINLP), is computationally challenging. A common stra... | https://arxiv.org/abs/2601.06755 | Academic Papers | svg |
f28c99f90f2a930c8d790988a06600a256fdeb705c205f8f848614284e5b4d80 | 2026-01-13T00:00:00-05:00 | Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies | arXiv:2601.06782v1 Announce Type: cross Abstract: Individualized treatment regimes (ITRs) aim to improve clinical outcomes by assigning treatment based on patient-specific characteristics. However, existing methods often struggle with high-dimensional covariates, limiting accuracy, interpretability, and real-world appl... | https://arxiv.org/abs/2601.06782 | Academic Papers | svg |
b3f6dc811e9016ad70822f93965ea1bb52f9d432184bb072c1878e9d60bc384a | 2026-01-13T00:00:00-05:00 | Constrained Density Estimation via Optimal Transport | arXiv:2601.06830v1 Announce Type: cross Abstract: A novel framework for density estimation under expectation constraints is proposed. The framework minimizes the Wasserstein distance between the estimated density and a prior, subject to the constraints that the expected value of a set of functions adopts or exceeds giv... | https://arxiv.org/abs/2601.06830 | Academic Papers | svg |
3b07b88d81a943e7cdd9fdbcae42a30e55b42cccd7246c9937f30ad7b44ff92b | 2026-01-13T00:00:00-05:00 | Deep Learning Based Channel Extrapolation for Dual-Band Massive MIMO Systems | arXiv:2601.06858v1 Announce Type: cross Abstract: Future wireless communication systems will increasingly rely on the integration of millimeter wave (mmWave) and sub-6 GHz bands to meet heterogeneous demands on high-speed data transmission and extensive coverage. To fully exploit the benefits of mmWave bands in massive... | https://arxiv.org/abs/2601.06858 | Academic Papers | svg |
01444dc943365259d39b027156f3223eb570bf45046c7a22c5636df84c15c863 | 2026-01-13T00:00:00-05:00 | Surface Dean--Kawasaki equations | arXiv:2601.06863v1 Announce Type: cross Abstract: We consider stochastic particle dynamics on hypersurfaces represented in Monge gauge parametrization. Starting from the underlying Langevin system, we derive the surface Dean-Kawasaki (DK) equation and formulate it in the martingale sense. The resulting SPDE explicitly ... | https://arxiv.org/abs/2601.06863 | Academic Papers | svg |
93569ac1a93788f1802ec244eebf1d4a4d5e27818cd06b8a00a26cd366678508 | 2026-01-13T00:00:00-05:00 | TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding | arXiv:2601.06896v1 Announce Type: cross Abstract: We present TagSpeech, a unified LLM-based framework that utilizes Temporal Anchor Grounding for joint multi-speaker ASR and diarization. The framework is built on two key designs: (1) decoupled semantic and speaker streams fine-tuned via Serialized Output Training (SOT)... | https://arxiv.org/abs/2601.06896 | Academic Papers | svg |
c44340d77d2692b461094a1949ca244e624c780df2877d188ad9de7b0626fa31 | 2026-01-13T00:00:00-05:00 | The Impact of Anisotropic Covariance Structure on the Training Dynamics and Generalization Error of Linear Networks | arXiv:2601.06961v1 Announce Type: cross Abstract: The success of deep neural networks largely depends on the statistical structure of the training data. While learning dynamics and generalization on isotropic data are well-established, the impact of pronounced anisotropy on these crucial aspects is not yet fully unders... | https://arxiv.org/abs/2601.06961 | Academic Papers | svg |
a05fe7213ace404d4361f43e37704882c71cc6a03834efbc014bfeaacc70eba6 | 2026-01-13T00:00:00-05:00 | Benchmarking Autonomy in Scientific Experiments: A Hierarchical Taxonomy for Autonomous Large-Scale Facilities | arXiv:2601.06978v1 Announce Type: cross Abstract: The transition from automated data collection to fully autonomous discovery requires a shared vocabulary to benchmark progress. While the automotive industry relies on the SAE J3016 standard, current taxonomies for autonomous science presuppose an owner-operator model t... | https://arxiv.org/abs/2601.06978 | Academic Papers | svg |
e8545e0e9818e3d0641b795357884683368531b7881474dc02944c21b56625a5 | 2026-01-13T00:00:00-05:00 | Match Made with Matrix Completion: Efficient Learning under Matching Interference | arXiv:2601.06982v1 Announce Type: cross Abstract: Matching markets face increasing needs to learn the matching qualities between demand and supply for effective design of matching policies. In practice, the matching rewards are high-dimensional due to the growing diversity of participants. We leverage a natural low-ran... | https://arxiv.org/abs/2601.06982 | Academic Papers | svg |
bcb0f0217142b44fb5d93602fdcf1ed786ff3f8f7a2967f530991448367e35de | 2026-01-13T00:00:00-05:00 | Unity Forests: Improving Interaction Modelling and Interpretability in Random Forests | arXiv:2601.07003v1 Announce Type: cross Abstract: Random forests (RFs) are widely used for prediction and variable importance analysis and are often believed to capture any types of interactions via recursive splitting. However, since the splits are chosen locally, interactions are only reliably captured when at least ... | https://arxiv.org/abs/2601.07003 | Academic Papers | svg |
8d033234e332752294a665ab6b0a74c13cb3eafe50ea91b45ffd43f3829ef9d9 | 2026-01-13T00:00:00-05:00 | Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation | arXiv:2601.07013v1 Announce Type: cross Abstract: Traditional filtering algorithms for state estimation -- such as classical Kalman filtering, unscented Kalman filtering, and particle filters - show performance degradation when applied to nonlinear systems whose uncertainty follows arbitrary non-Gaussian, and potential... | https://arxiv.org/abs/2601.07013 | Academic Papers | svg |
5ec8fb7d3aa7d108231be4b2ec58b53971e05f9135d8e73b1fb14d9fe6856a43 | 2026-01-13T00:00:00-05:00 | Local EGOP for Continuous Index Learning | arXiv:2601.07061v1 Announce Type: cross Abstract: We introduce the setting of continuous index learning, in which a function of many variables varies only along a small number of directions at each point. For efficient estimation, it is beneficial for a learning algorithm to adapt, near each point $x$, to the subspace ... | https://arxiv.org/abs/2601.07061 | Academic Papers | svg |
5a75dc57c0b04c2374a767312d754b68ce7b2ff2cb2828ef041403b85754b9fd | 2026-01-13T00:00:00-05:00 | Robust Mean Estimation under Quantization | arXiv:2601.07074v1 Announce Type: cross Abstract: We consider the problem of mean estimation under quantization and adversarial corruption. We construct multivariate robust estimators that are optimal up to logarithmic factors in two different settings. The first is a one-bit setting, where each bit depends only on a s... | https://arxiv.org/abs/2601.07074 | Academic Papers | svg |
5f1246654042a4a0dd1d82af18bf2ab7ce8c871327d576024e61b0a3cba208ec | 2026-01-13T00:00:00-05:00 | Primal-Dual algorithms for Abstract convex functions with respect to quadratic functions | arXiv:2601.07076v1 Announce Type: cross Abstract: We consider the saddle point problem where the objective functions are abstract convex with respect to the class of quadratic functions. We propose primal-dual algorithms using the corresponding abstract proximal operator and investigate the convergence under certain re... | https://arxiv.org/abs/2601.07076 | Academic Papers | svg |
ec5f6127f559d09dfa7f2681e90f05f86bc11ae43f9fb90be94f0638fa84e8f4 | 2026-01-13T00:00:00-05:00 | Adaptive Robust Control for Uncertain Systems with Ellipsoid-Set Learning | arXiv:2601.07079v1 Announce Type: cross Abstract: Despite the celebrated success of stochastic control approaches for uncertain systems, such approaches are limited in the ability to handle non-Gaussian uncertainties. This work presents an adaptive robust control for linear uncertain systems, whose process noise, obser... | https://arxiv.org/abs/2601.07079 | Academic Papers | svg |
057b524b04dc73df64b4fa74460ba56832283d452fe2a4e3e2b317f7cf71fe8a | 2026-01-13T00:00:00-05:00 | Robust Bayesian Optimization via Tempered Posteriors | arXiv:2601.07094v1 Announce Type: cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function such as expected improvement (EI). In practice, BO often concentrates evaluations near the current incumbent, cau... | https://arxiv.org/abs/2601.07094 | Academic Papers | svg |
9e7a023d2f1e7e5d21fe80eb20bf76f6ad403cbf0731bbf628783f221639e1ec | 2026-01-13T00:00:00-05:00 | Symmetry Breaking, Hysteresis, and Convergence to the Mean Voter in two-party Spatial Competition | arXiv:2601.07108v1 Announce Type: cross Abstract: Classical spatial models of two-party competition typically predict convergence to the median voter, yet real-world party systems often exhibit persistent and asymmetric polarization. We develop a spatial model of two-party competition in which voters evaluate parties t... | https://arxiv.org/abs/2601.07108 | Academic Papers | svg |
d66d7496459ff317e1224b69f2d25b6198df1e7059dce36f29d73cb964cd1154 | 2026-01-13T00:00:00-05:00 | The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories | arXiv:2601.07130v1 Announce Type: cross Abstract: Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very Large Array (ngVLA) take shape. Under increasingly demanding power cons... | https://arxiv.org/abs/2601.07130 | Academic Papers | svg |
f9172d240f6330fd74e4d614bbbcb26b0507f3a230d47075584296f2eee01cab | 2026-01-13T00:00:00-05:00 | Optimal Transport under Group Fairness Constraints | arXiv:2601.07144v1 Announce Type: cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT), we introduce a novel notion of group fairness requiring that the probability of matching two individuals from any two given grou... | https://arxiv.org/abs/2601.07144 | Academic Papers | svg |
8686278beb113e291f4fbfb202aabf3ed455c613a1a2061b4beaec413893f378 | 2026-01-13T00:00:00-05:00 | Approximate FKG inequalities for phase-bound spin systems | arXiv:2601.07169v1 Announce Type: cross Abstract: The FKG inequality is an invaluable tool in monotone spin systems satisfying the FKG lattice condition, which provides positive correlations for all coordinate-wise increasing functions of spins. However, the FKG lattice condition is somewhat brittle and is not preserve... | https://arxiv.org/abs/2601.07169 | Academic Papers | svg |
80255c6a150a35a14aa1f8512ad430c876f1df560b6a4188132dffc9d71c40ea | 2026-01-13T00:00:00-05:00 | On Lie Groups Preserving Subspaces of Degenerate Clifford Algebras | arXiv:2601.07191v1 Announce Type: cross Abstract: This paper introduces Lie groups in degenerate geometric (Clifford) algebras that preserve four fundamental subspaces determined by the grade involution and reversion under the adjoint and twisted adjoint representations. We prove that these Lie groups can be equivalent... | https://arxiv.org/abs/2601.07191 | Academic Papers | svg |
e3fa7404553e5fd45da6f9158c1ad52a783254e7c36996e73fc2336e6d454f94 | 2026-01-13T00:00:00-05:00 | The ICASSP 2026 Automatic Song Aesthetics Evaluation Challenge | arXiv:2601.07237v1 Announce Type: cross Abstract: This paper summarizes the ICASSP 2026 Automatic Song Aesthetics Evaluation (ASAE) Challenge, which focuses on predicting the subjective aesthetic scores of AI-generated songs. The challenge consists of two tracks: Track 1 targets the prediction of the overall musicality... | https://arxiv.org/abs/2601.07237 | Academic Papers | svg |
ae4885c944809cc38d78361533f5bd00b5e89f0f66504eec27032ab0b4cc3ec2 | 2026-01-13T00:00:00-05:00 | Multi-environment Invariance Learning with Missing Data | arXiv:2601.07247v1 Announce Type: cross Abstract: Learning models that can handle distribution shifts is a key challenge in domain generalization. Invariance learning, an approach that focuses on identifying features invariant across environments, improves model generalization by capturing stable relationships, which m... | https://arxiv.org/abs/2601.07247 | Academic Papers | svg |
f62cecbab7decd4d21b8f0aeb20d72c2bb3c6156512bb6ba04fd7aa320e47be6 | 2026-01-13T00:00:00-05:00 | Robust maximum hands-off optimal control: existence, maximum principle, and $L^{0}$-$L^1$ equivalence | arXiv:2601.07256v1 Announce Type: cross Abstract: This work advances the maximum hands-off sparse control framework by developing a robust counterpart for constrained linear systems with parametric uncertainties. The resulting optimal control problem minimizes an $L^{0}$ objective subject to an uncountable, compact fam... | https://arxiv.org/abs/2601.07256 | Academic Papers | svg |
922a16427c20aa9f728369d17b2e6bbfbde7bcfe166122f64cd590ff360a6834 | 2026-01-13T00:00:00-05:00 | Covariance-Driven Regression Trees: Reducing Overfitting in CART | arXiv:2601.07281v1 Announce Type: cross Abstract: Decision trees are powerful machine learning algorithms, widely used in fields such as economics and medicine for their simplicity and interpretability. However, decision trees such as CART are prone to overfitting, especially when grown deep or the sample size is small... | https://arxiv.org/abs/2601.07281 | Academic Papers | svg |
a9559d429f5799df0ad0103e388eeea33c161c2703755cc934d32f0920b03f1e | 2026-01-13T00:00:00-05:00 | Condorcet's Paradox as Non-Orientability | arXiv:2601.07283v1 Announce Type: cross Abstract: Preference cycles are prevalent in problems of decision-making, and are contradictory when preferences are assumed to be transitive. This contradiction underlies Condorcet's Paradox, a pioneering result of Social Choice Theory, wherein intuitive and seemingly desirable ... | https://arxiv.org/abs/2601.07283 | Academic Papers | svg |
3424cbd23f56093723541affdc5d50cbbf33d3bd92ffc1d4e53160cba3db295c | 2026-01-13T00:00:00-05:00 | Variational Approximations for Robust Bayesian Inference via Rho-Posteriors | arXiv:2601.07325v1 Announce Type: cross Abstract: The $\rho$-posterior framework provides universal Bayesian estimation with explicit contamination rates and optimal convergence guarantees, but has remained computationally difficult due to an optimization over reference distributions that precludes intractable posterio... | https://arxiv.org/abs/2601.07325 | Academic Papers | svg |
7d892a398f43aafbba9b91679235883b99212854384498b4b33f6b478dcf93fb | 2026-01-13T00:00:00-05:00 | Convergence Rate Analysis of the AdamW-Style Shampoo: Unifying One-sided and Two-Sided Preconditioning | arXiv:2601.07326v1 Announce Type: cross Abstract: This paper studies the AdamW-style Shampoo optimizer, an effective implementation of classical Shampoo that notably won the external tuning track of the AlgoPerf neural network training algorithm competition. Our analysis unifies one-sided and two-sided preconditioning ... | https://arxiv.org/abs/2601.07326 | Academic Papers | svg |
93b046c0ac40a88dae82a7060f554446aa3ef4c1c81ec7b6158d97066017eafd | 2026-01-13T00:00:00-05:00 | Efficient Convolutional Forward Model for Passive Acoustic Mapping and Temporal Monitoring | arXiv:2601.07356v1 Announce Type: cross Abstract: Passive acoustic mapping (PAM) is a key imaging technique for characterizing cavitation activity in therapeutic ultrasound applications. Recent model-based beamforming algorithms offer high reconstruction quality and strong physical interpretability. However, their comp... | https://arxiv.org/abs/2601.07356 | Academic Papers | svg |
fde661dc74c222a610b5edd093d4478c8c5422d3a81b2752d26710fc684b3b2e | 2026-01-13T00:00:00-05:00 | Optimizing the Design of a Simple Three-Sphere Magnetic Microswimmer | arXiv:2601.07370v1 Announce Type: cross Abstract: When swimming at low Reynolds numbers, inertial effects are negligible and reciprocal movements cannot induce net motion. Instead, symmetry breaking is necessary to achieve net propulsion. Directed swimming can be supported by magnetic fields, which simultaneously provi... | https://arxiv.org/abs/2601.07370 | Academic Papers | svg |
c76016c12ad878edb1e53f40405b8006189453480962d61d2c2be672a363f1a2 | 2026-01-13T00:00:00-05:00 | Layerwise goal-oriented adaptivity for neural ODEs: an optimal control perspective | arXiv:2601.07397v1 Announce Type: cross Abstract: In this work, we propose a novel layerwise adaptive construction method for neural network architectures. Our approach is based on a goal--oriented dual-weighted residual technique for the optimal control of neural differential equations. This leads to an ordinary diffe... | https://arxiv.org/abs/2601.07397 | Academic Papers | svg |
9a2a471f8af8b45a59e9e2f9cdd632f6b27cccc7283ad17538d6b3fe592fe8f7 | 2026-01-13T00:00:00-05:00 | Position: Don't be Afraid of Over-Smoothing And Over-Squashing | arXiv:2601.07419v1 Announce Type: cross Abstract: Over-smoothing and over-squashing have been extensively studied in the literature on Graph Neural Networks (GNNs) over the past years. We challenge this prevailing focus in GNN research, arguing that these phenomena are less critical for practical applications than assu... | https://arxiv.org/abs/2601.07419 | Academic Papers | svg |
8111c571f9dfba03bd34921c551de2265a1663345f0c1d440a2e77819cef067f | 2026-01-13T00:00:00-05:00 | Nonquadratic global asymptotic stability certificates for saturated linear feedbacks | arXiv:2601.07431v1 Announce Type: cross Abstract: We establish sufficient conditions for positive (semi-)definiteness, with or without radial unboundedness, for nonquadratic Lyapunov function constructed as sign-indefinite quadratic forms involving the state and the deadzone of a suitable input. We then use these condi... | https://arxiv.org/abs/2601.07431 | Academic Papers | svg |
2a09142500e6abf219d16daab54a9485f2bab328cb81751fd245e7a14fa5c286 | 2026-01-13T00:00:00-05:00 | PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation | arXiv:2601.07436v1 Announce Type: cross Abstract: We propose physics-informed digital twin (PIDT): a fiber parameter estimation approach that combines a parameterized split-step method with a physics-informed loss. PIDT improves accuracy and convergence speed with lower complexity compared to previous neural operators. | https://arxiv.org/abs/2601.07436 | Academic Papers | svg |
f8cceb57c5288d79952d09866fa3f4975ee723d2078eb80e5e9318d0dc0439f3 | 2026-01-13T00:00:00-05:00 | Advanced computing for reproducibility of astronomy Big Data Science, with a showcase of AMIGA and the SKA Science prototype | arXiv:2601.07439v1 Announce Type: cross Abstract: The Square Kilometre Array Observatory (SKAO) faces un- precedented technological challenges due to the vast scale and complexity of its data. This paper provides an overview of research by the AMIGA group to address these computing and reproducibility challenges. We pr... | https://arxiv.org/abs/2601.07439 | Academic Papers | svg |
66686a711979c3268f94daad1077bf13c16b243277e94fa733aa079a4e668e03 | 2026-01-13T00:00:00-05:00 | Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management | arXiv:2601.07514v1 Announce Type: cross Abstract: This paper investigates the integration of machine learning forecasts of intervention durations into a stochastic variant of the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). In particular, we exploit tree-based gradient boosting (XGBoost) trained on e... | https://arxiv.org/abs/2601.07514 | Academic Papers | svg |
a970df61bd1e1e579e0980f62301596778ad38a5bc88c181ed0dea7710bb46ab | 2026-01-13T00:00:00-05:00 | Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization | arXiv:2601.07519v1 Announce Type: cross Abstract: Fully convolutional networks have become the backbone of modern medical imaging due to their ability to learn multi-scale representations and perform end-to-end inference. Yet their potential for slice-to-volume reconstruction (SVR), the task of jointly estimating 3D an... | https://arxiv.org/abs/2601.07519 | Academic Papers | svg |
edf67956bd2bfdef458e82cd26af9d9fdfb48768fb9a4ca528b882359258445c | 2026-01-13T00:00:00-05:00 | Nonparametric Kernel Clustering with Bandit Feedback | arXiv:2601.07535v1 Announce Type: cross Abstract: Clustering with bandit feedback refers to the problem of partitioning a set of items, where the clustering algorithm can sequentially query the items to receive noisy observations. The problem is formally posed as the task of partitioning the arms of an N-armed stochast... | https://arxiv.org/abs/2601.07535 | Academic Papers | svg |
8bdc470a032e4ef1426eacbfaafad0120c43949bc795d5ff74ec3d4918fcebdb | 2026-01-13T00:00:00-05:00 | A Model of Artificial Jagged Intelligence | arXiv:2601.07573v1 Announce Type: cross Abstract: Generative AI systems often display highly uneven performance across tasks that appear ``nearby'': they can be excellent on one prompt and confidently wrong on another with only small changes in wording or context. We call this phenomenon Artificial Jagged Intelligence ... | https://arxiv.org/abs/2601.07573 | Academic Papers | svg |
280ae1b131071bdccb98d687be177e989ad0e9521a78beb0a94fe8722cb09429 | 2026-01-13T00:00:00-05:00 | Large Language Models for Physics Instrument Design | arXiv:2601.07580v1 Announce Type: cross Abstract: We study the use of large language models (LLMs) for physics instrument design and compare their performance to reinforcement learning (RL). Using only prompting, LLMs are given task constraints and summaries of prior high-scoring designs and propose complete detector c... | https://arxiv.org/abs/2601.07580 | Academic Papers | svg |
15977846df6c447e73053b2233304115d863da2fca06eaba57d05e7924aab553 | 2026-01-13T00:00:00-05:00 | Machine learning nonequilibrium phase transitions in charge-density wave insulators | arXiv:2601.07583v1 Announce Type: cross Abstract: Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machine learning framework for adiabatic lattice dynamics coupled to nonequilibrium electrons, ... | https://arxiv.org/abs/2601.07583 | Academic Papers | svg |
8ad7ebdc28eaff7b0d25164b7d5957db636a5f2ddfa9f83127d1ecc8afe732e0 | 2026-01-13T00:00:00-05:00 | Temporal-Aligned Meta-Learning for Risk Management: A Stacking Approach for Multi-Source Credit Scoring | arXiv:2601.07588v1 Announce Type: cross Abstract: This paper presents a meta-learning framework for credit risk assessment of Italian Small and Medium Enterprises (SMEs) that explicitly addresses the temporal misalignment of credit scoring models. The approach aligns financial statement reference dates with evaluation ... | https://arxiv.org/abs/2601.07588 | Academic Papers | svg |
d264411e7b17cd10337955be99b9144c8bc3956db5f74b3419a26e1d2890f208 | 2026-01-13T00:00:00-05:00 | Aggregating swarms through morphology handling design contingencies: from the sweet spot to a rich expressivity | arXiv:2601.07610v1 Announce Type: cross Abstract: Morphological computing, the use of the physical design of a robot to ease the realization of a given task has been proven to be a relevant concept in the context of swarm robotics. Here we demonstrate both experimentally and numerically, that the success of such a stra... | https://arxiv.org/abs/2601.07610 | Academic Papers | svg |
c5d585f210d215b0a7dcd98ab4b3d0235564f36529269b95fef43e2602b61e46 | 2026-01-13T00:00:00-05:00 | Scattering at Interluminal Interfaces | arXiv:2601.06073v1 Announce Type: new Abstract: Scattering at interluminal modulation interfaces, where a sharp space-time perturbation moves at a velocity lying between the wave velocities of the two surrounding media, has remained an open problem for decades. This regime is somewhat reminiscent of the Cherenkov regim... | https://arxiv.org/abs/2601.06073 | Academic Papers | svg |
3d9075e0d73f0980b3cfade93e6b0466580a44f726d1a7bada1e469fc3bd21ff | 2026-01-13T00:00:00-05:00 | A Polarization Hall Effect in Hydrated DNA | arXiv:2601.06089v1 Announce Type: new Abstract: Understanding how biological soft matter responds to electromagnetic fields under ambient conditions remains a central challenge, as thermal fluctuations are generally expected to suppress long-range organization. Here, we report that hydrated DNA exhibits a reproducible ... | https://arxiv.org/abs/2601.06089 | Academic Papers | svg |
5ec8c29ae5d8fd858b1343347f1da16aa3d75c55a822a433280905b0d9728dc5 | 2026-01-13T00:00:00-05:00 | Energetic vs Inference-Based Invisibility: Fisher-Information Analysis of Two-Layer Acoustic Near-Cloaks | arXiv:2601.06091v1 Announce Type: new Abstract: Near-cloaks based on passive coatings can strongly suppress scattered-field energy in a narrow frequency band, yet an observer's ability to infer object parameters from noisy measurements need not decrease proportionally. We develop a fully theoretical two-dimensional (2D... | https://arxiv.org/abs/2601.06091 | Academic Papers | svg |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.