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arXiv preprints from January 1, 2026 through July 28, 2026 — 13:04:32 EST

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Posted in cs.CV · 2026-01-16 · Ruibang Li, Guan Luo, Yiwei Zhang, Jin Gao, Bing Li, Weiming Hu

SoLA-Vision: Fine-grained Layer-wise Linear Softmax Hybrid Attention

Standard softmax self-attention excels in vision tasks but incurs quadratic complexity O(N^2), limiting high-resolution deployment. Linear attention reduces the cost to O(N), yet its compressed state representations can impair modeling capacity and accuracy. We present an analytical study that contrasts linear and softmax attention...

💬 0 commentsarXiv:2601.11164v1PDF
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Posted in eess.AS · 2026-01-16 · Zhuoyue Gao, Xiaohui Wang, Xiaocui Yang, Wen Zhang, Daling Wang, Shi Feng, Yifei Zhang

ES4R: Speech Encoding Based on Prepositive Affective Modeling for Empathetic Response Generation

Empathetic speech dialogue requires not only understanding linguistic content but also perceiving rich paralinguistic information such as prosody, tone, and emotional intensity for affective understandings. Existing speech-to-speech large language models either rely on ASR transcription or use encoders to extract latent...

💬 0 commentsarXiv:2601.16225v1PDF
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Posted in cs.LG · 2026-01-16 · P. Sánchez, K. Reyes, B. Radu, E. Fernández

LSTM VS. Feed-Forward Autoencoders for Unsupervised Fault Detection in Hydraulic Pumps

Unplanned failures in industrial hydraulic pumps can halt production and incur substantial costs. We explore two unsupervised autoencoder (AE) schemes for early fault detection: a feed-forward model that analyses individual sensor snapshots and a Long Short-Term Memory (LSTM) model that captures short temporal windows. Both networks...

💬 0 commentsarXiv:2601.11163v1PDF
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Posted in math.PR · 2026-01-16 · Laurent Decreusefond, Antonin Jacquet

Rate of convergence of the conditioned random walk towards the Brownian bridge

We study the rate of convergence of two discrete processes towards the Brownian bridge: the random walk conditioned to be zero at time 2n and the empirical process which appears in the Glivencko-Cantelli theorem. Combining a functional Stein method with a Radon-Nikodym representation of the bridge, we bound the Fortet-Mourier distance...

💬 0 commentsarXiv:2601.11162v1PDF
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Posted in cs.LG · 2026-01-16 · Pascal Schlachter, Bin Yang

GMM-COMET: Continual Source-Free Universal Domain Adaptation via a Mean Teacher and Gaussian Mixture Model-Based Pseudo-Labeling

Unsupervised domain adaptation tackles the problem that domain shifts between training and test data impair the performance of neural networks in many real-world applications. Thereby, in realistic scenarios, the source data may no longer be available during adaptation, and the label space of the target domain may differ from the...

💬 0 commentsarXiv:2601.11161v1PDF
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Posted in cs.LG · 2026-01-16 · Claudia Plant, Lena G. M. Bauer, Christian Böhm

Clustering High-dimensional Data: Balancing Abstraction and Representation Tutorial at AAAI 2026

How to find a natural grouping of a large real data set? Clustering requires a balance between abstraction and representation. To identify clusters, we need to abstract from superfluous details of individual objects. But we also need a rich representation that emphasizes the key features shared by groups of objects that distinguish...

💬 0 commentsarXiv:2601.11160v1PDF
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Posted in cs.LG · 2026-01-16 · Yichun Yang, Longlong Lin, Rong-Hua Li, Meihao Liao, Guoren Wang

Theoretically and Practically Efficient Resistance Distance Computation on Large Graphs

The computation of resistance distance is pivotal in a wide range of graph analysis applications, including graph clustering, link prediction, and graph neural networks. Despite its foundational importance, efficient algorithms for computing resistance distances on large graphs are still lacking. Existing state-of-the-art (SOTA)...

💬 0 commentsarXiv:2601.11159v1PDF
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Posted in cs.DM · 2026-01-16 · Indrajit Paul, Ashok Kumar Das

Vertex ordering characterizations of interval r-graphs

An r-partite graph is an interval r-graph if corresponding to each vertex we can assign an interval of the real line such that two vertices u and v of different partite sets are adjacent if and only if their corresponding intervals intersect. In this paper, we provide two vertex-ordering characterizations of interval r-graphs and...

💬 0 commentsarXiv:2601.11158v2PDF
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Posted in math.NA · 2026-01-16 · Zeyu Dong, Aqin Xiao, Guojian Yin, Junfeng Yin

Adaptive Randomized Extended Bregman-Kaczmarz Method for Combined Optimization Problems

Combined optimization problems that couple data-fidelity and regularization terms arise naturally in a wide range of inverse problems. In this paper, we study an adaptive randomized averaging block extended Bregman-Kaczmarz (aRABEBK) method for solving such problems. The proposed method incorporates iteration-wise relaxation...

💬 0 commentsarXiv:2601.11157v1PDF
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Posted in q-bio.NC · 2026-01-16 · Francesco Chiappone, Davide Marocco, Nicola Milano

Large Language Models as Simulative Agents for Neurodivergent Adult Psychometric Profiles

Adult neurodivergence, including Attention-Deficit/Hyperactivity Disorder (ADHD), high-functioning Autism Spectrum Disorder (ASD), and Cognitive Disengagement Syndrome (CDS), is marked by substantial symptom overlap that limits the discriminant sensitivity of standard psychometric instruments. While recent work suggests that Large...

💬 0 commentsarXiv:2601.15319v1PDF
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Posted in cs.DC · 2026-01-16 · Niklas Kowallik, Trever Schirmer, David Bermbach

Konflux: Optimized Function Fusion for Serverless Applications

Function-as-a-Service (FaaS) has become a central paradigm in serverless cloud computing, yet optimizing FaaS deployments remains challenging. Using function fusion, multiple functions can be combined into a single deployment unit, which can be used to reduce cost and latency of complex serverless applications comprising multiple...

💬 0 commentsarXiv:2601.11156v1PDF
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Posted in math.AP · 2026-01-16 · Harald Garcke, Kei Fong Lam, Robert Nürnberg, Andrea Signori

On a Mullins-Sekerka model for the growth of active droplets modelling protocells: Stability analysis and numerical computations

Mullins-Sekerka models with chemical reactions can lead to scenarios where droplets grow, become unstable, split, grow and undergo further division. These grow and division cycles have been proposed as a model for protocells and are believed to play a fundamental role in living systems by providing chemical compartments which are...

💬 0 commentsarXiv:2601.11155v1PDF
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Posted in cs.LG · 2026-01-16 · P. Sánchez, K. Reyes, B. Radu, E. Fernández

Assesing the Viability of Unsupervised Learning with Autoencoders for Predictive Maintenance in Helicopter Engines

Unplanned engine failures in helicopters can lead to severe operational disruptions, safety hazards, and costly repairs. To mitigate these risks, this study compares two predictive maintenance strategies for helicopter engines: a supervised classification pipeline and an unsupervised anomaly detection approach based on autoencoders...

💬 0 commentsarXiv:2601.11154v1PDF
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Posted in cs.GT · 2026-01-16 · Naoyuki Kamiyama

Non-uniformly Stable Common Independent Sets

In this paper, we consider a matroid generalization of the stable matching problem. In particular, we consider the setting where preferences may contain ties. For this generalization, we propose a polynomial-time algorithm for the problem of checking the existence of a common independent set satisfying non-uniform stability, which is...

💬 0 commentsarXiv:2601.11153v1PDF
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Posted in math.NA · 2026-01-16 · Yujun Zhu, Min Li, Yulan Ning, Ju Ming

An efficient solver based on low-rank approximation and Neumann matrix series for unsteady diffusion-type partial differential equations with random coefficients

In this paper, we develop an efficient numerical solver for unsteady diffusion-type partial differential equations with random coefficients. A major computational challenge in such problems lies in repeatedly handling large-scale linear systems arising from spatial and temporal discretizations under uncertainty. To address this issue,...

💬 0 commentsarXiv:2601.11152v1PDF
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Posted in cs.IR · 2026-01-16 · Ji Dai, Quan Fang, Jun Hu, Desheng Cai, Yang Yang, Can Zhao

Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation

Multimedia recommendation systems leverage user-item interactions and multimodal information to capture user preferences, enabling more accurate and personalized recommendations. Despite notable advancements, existing approaches still face two critical limitations: first, shallow modality fusion often relies on simple concatenation,...

💬 0 commentsarXiv:2601.11151v1PDF
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Posted in eess.IV · 2026-01-16 · Srinivas Miriyala, Sowmya Vajrala, Sravanth Kodavanti

Towards Efficient Image Deblurring for Edge Deployment

Image deblurring is a critical stage in mobile image signal processing pipelines, where the ability to restore fine structures and textures must be balanced with real-time constraints on edge devices. While recent deep networks such as transformers and activation-free architectures achieve state-of-the-art (SOTA) accuracy, their...

💬 0 commentsarXiv:2601.11685v1PDF
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Posted in astro-ph.CO · 2026-01-16 · Nathan Cohen, Jan Hamann, Ameek Malhotra

Bayesian optimisation for Bayesian evidence (BOBE) -- a fast and efficient likelihood emulator for model selection

The formalism of Bayesian model selection provides a very elegant way of ranking different physical models in terms of how compatible they are with a given set of observed data. However, its practical application is often hampered by the challenge of having to compute the Bayesian evidence - a multi-dimensional integral over the...

💬 0 commentsarXiv:2601.11150v1PDF
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Posted in cs.IT · 2026-01-16 · Lei Xie, Hengtao He, Jun Tong, Fan Liu, Shenghui Song

Sensing Mutual Information for Communication Signal with Deterministic Pilots and Random Data Payloads

The recent emergence of the integrated sensing and communication (ISAC) framework has sparked significant interest in quantifying the sensing capabilities inherent in communication signals. However, existing literature has mainly focused on scenarios involving either purely random or purely deterministic waveforms. This overlooks a...

💬 0 commentsarXiv:2601.11149v1PDF
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Posted in q-bio.QM · 2026-01-16 · Ruben Taieb, René Bruno, Pascal Chanu, Jin Yan Jin, Sébastien Benzekry

Mechanistic Learning for Survival Prediction in NSCLC Using Routine Blood Biomarkers and Tumor Kinetics

Background Predicting overall survival (OS) in non-small cell lung cancer (NSCLC) is essential for clinical decision-making and drug development. While tumor and blood test markers kinetics are intrinsically linked, their joint dynamics and relationship to OS remain unknown. Methods We developed a mechanistic model capturing the...

💬 0 commentsarXiv:2601.11148v1PDF
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Posted in cs.AI · 2026-01-16 · Zixu Wang, Bingbing Xu, Yige Yuan, Huawei Shen, Xueqi Cheng

Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems

Multi-Agent Systems (MAS) built on large language models typically solve complex tasks by coordinating multiple agents through workflows. Existing approaches generates workflows either at task level or query level, but their relative costs and benefits remain unclear. After rethinking and empirical analyses, we show that query-level...

💬 0 commentsarXiv:2601.11147v1PDF
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Posted in math.AP · 2026-01-16 · Kewen Bu, Youjun Deng, Yan Jiang, Kai Zhang

Inverse Spectral Problem With Low Regularity Refractive Index

This article investigates the unique determination of a radial refractive index n from spectral data. First, we demonstrate that for piecewise twice continuously differentiable functions, n is not uniquely determined by the special transmission eigenvalues associated with radially symmetric eigenfunctions. Subsequently we prove that...

💬 0 commentsarXiv:2601.11146v1PDF
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Posted in math.NA · 2026-01-16 · Jean-Paul Chehab, Gaspard Kemlin, Marcos Raydan, Yousef Saad

Eigenvector-based acceleration strategies for gradient-type methods

Several strategies are described and analyzed to speed-up gradient-type methods when applied to the minimization of strictly convex quadratics and strictly convex functions. The proposed techniques focus on relaxing the traditional optimal step length associated with gradient methods, including the steepest descent (SD) and the...

💬 0 commentsarXiv:2601.11145v1PDF
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Posted in cs.IR · 2026-01-16 · Yuejie Li, Ke Yang, Tao Wang, Bolin Chen, Bowen Li, Chengjun Mao

Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration

Graph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing methods are often challenged by navigating large-scale hierarchical graphs, optimizing retrieval paths, and balancing exploration-exploitation dynamics,...

💬 0 commentsarXiv:2601.11144v3PDF
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Posted in cs.RO · 2026-01-16 · Minho Lee, Hyeonseok Kim, Jin Tak Kim, Sangshin Park, Jeong Hyun Lee, Jungsan Cho, Jemin Hwangbo

Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model

The simulation-to-reality (sim-to-real) transfer of large-scale hydraulic robots presents a significant challenge in robotics because of the inherent slow control response and complex fluid dynamics. The complex dynamics result from the multiple interconnected cylinder structure and the difference in fluid rates of the cylinders....

💬 0 commentsarXiv:2601.11143v1PDF