Tootfinder

Visual Detector Compression via Location-Aware Discriminant Analysis
Qizhen Lan, Jung Im Choi, Qing Tian
https://arxiv.org/abs/2509.17968 https://arxiv.org…

Visual Detector Compression via Location-Aware Discriminant Analysis
Deep neural networks are powerful, yet their high complexity greatly limits their potential to be deployed on billions of resource-constrained edge devices. Pruning is a crucial network compression technique, yet most existing methods focus on classification models, with limited attention to detection. Even among those addressing detection, there is a lack of utilization of essential localization information. Also, many pruning methods passively rely on pre-trained models, in which useful and u…

Growing unlabeled networks
Harrison Hartle, Brennan Klein, Dmitri Krioukov, P. L. Krapivsky
https://arxiv.org/abs/2509.17200 https://arxiv.org/pdf/2509.172…

Growing unlabeled networks
Models of growing networks are a central topic in network science. In these models, vertices are usually labeled by their arrival time, distinguishing even those node pairs whose structural roles are identical. In contrast, unlabeled networks encode only structure, so unlabeled growth rules must be defined in terms of structurally distinguishable outcomes; network symmetries therefore play a key role in unlabeled growth dynamics. Here, we introduce and study models of growing unlabeled trees, d…

Train to Defend: First Defense Against Cryptanalytic Neural Network Parameter Extraction Attacks
Ashley Kurian, Aydin Aysu
https://arxiv.org/abs/2509.16546 https://

Train to Defend: First Defense Against Cryptanalytic Neural Network Parameter Extraction Attacks
Neural networks are valuable intellectual property due to the significant computational cost, expert labor, and proprietary data involved in their development. Consequently, protecting their parameters is critical not only for maintaining a competitive advantage but also for enhancing the model's security and privacy. Prior works have demonstrated the growing capability of cryptanalytic attacks to scale to deeper models. In this paper, we present the first defense mechanism against cryptanalyti…

CMOS Implementation of Field Programmable Spiking Neural Network for Hardware Reservoir Computing
Ckristian Duran, Nanako Kimura, Zolboo Byambadorj, Tetsuya Iizuka
https://arxiv.org/abs/2509.17355

CMOS Implementation of Field Programmable Spiking Neural Network for Hardware Reservoir Computing
The increasing complexity and energy demands of large-scale neural networks, such as Deep Neural Networks (DNNs) and Large Language Models (LLMs), challenge their practical deployment in edge applications due to high power consumption, area requirements, and privacy concerns. Spiking Neural Networks (SNNs), particularly in analog implementations, offer a promising low-power alternative but suffer from noise sensitivity and connectivity limitations. This work presents a novel CMOS-implemented fi…

Optimizing Split Federated Learning with Unstable Client Participation
Wei Wei, Zheng Lin, Xihui Liu, Hongyang Du, Dusit Niyato, Xianhao Chen
https://arxiv.org/abs/2509.17398 ht…

Optimizing Split Federated Learning with Unstable Client Participation
To enable training of large artificial intelligence (AI) models at the network edge, split federated learning (SFL) has emerged as a promising approach by distributing computation between edge devices and a server. However, while unstable network environments pose significant challenges to SFL, prior schemes often overlook such an effect by assuming perfect client participation, rendering them impractical for real-world scenarios. In this work, we develop an optimization framework for SFL with …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot

Topological Mechanics of Entangled Networks
Juntao Huang, Jiabin Liu, Shaoting Lin
https://arxiv.org/abs/2509.17813 https://arxiv.org/pdf/2509.17813…

Topological Mechanics of Entangled Networks
Entangled networks are ubiquitous in tissues, polymers, and fabrics. However, their mechanics remain insufficiently understood due to the complexity of the topological constraints at the network level. Here, we develop a mathematical framework that models entangled networks as graphs, capturing topological constraints of entanglements. We prove that entanglements reduce system energy by enabling uniform tension along chains crossing entanglements and by redistributing stress through sliding. Un…

QASTAnet: A DNN-based Quality Metric for Spatial Audio
Adrien Llave, Emma Granier, Gr\'egory Pallone
https://arxiv.org/abs/2509.16715 https://arxiv.org…

QASTAnet: A DNN-based Quality Metric for Spatial Audio
In the development of spatial audio technologies, reliable and shared methods for evaluating audio quality are essential. Listening tests are currently the standard but remain costly in terms of time and resources. Several models predicting subjective scores have been proposed, but they do not generalize well to real-world signals. In this paper, we propose QASTAnet (Quality Assessment for SpaTial Audio network), a new metric based on a deep neural network, specialized on spatial audio (ambison…

On a Conjecture of Schilling Regarding the Coupon Collector's Problem
Daniel Berend, Tomer Sher
https://arxiv.org/abs/2509.17201 https://arxiv.org/pdf/…

On a Conjecture of Schilling Regarding the Coupon Collector's Problem
This article addresses a conjecture by Schilling concerning the optimality of the uniform distribution in the generalized Coupon Collector's Problem (CCP) where, in each round, a subset (package) of $s$ coupons is drawn from a total of $n$ distinct coupons. While the classical CCP (with single-coupon draws) is well understood, the group-draw variant - where packages of size $s$ are drawn - presents new challenges and has applications in areas such as biological network models. Schilling conje…

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Roberto Doriguzzi-Corin, Petr Sabel, Silvio Cretti, Silvio Ranise
https://arxiv.org/abs/2509.17836

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Machine Learning (ML) techniques have shown strong potential for network traffic analysis; however, their effectiveness depends on access to representative, up-to-date datasets, which is limited in cybersecurity due to privacy and data-sharing restrictions. To address this challenge, Federated Learning (FL) has recently emerged as a novel paradigm that enables collaborative training of ML models across multiple clients while ensuring that sensitive data remains local. Nevertheless, Federated Av…

Bee Maps, a decentralized mapping project powered by Hivemapper on the Solana blockchain, raised $32M to expand its network by distributing AI-enabled dashcams (CoinDesk)
https://www.coindesk.com/tech/2025/10/06/bee-maps-r…

Bee Maps Raises $32M to Scale Solana-Powered Decentralized Mapping Network
The fresh capital will be used to distribute more devices, enhance AI models that process and update map features, and boost contributor incentives.

Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy
https://arxiv.org/abs/2510.10321

Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks. Our hybrid framework combines these graph representations with light-weight (<4B) local LLMs, uniting topological features with semantic reasoning while avoiding the cost and privacy concerns of large cloud models. Evaluated on…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
toXiv_bot_toot

"All in all, these machine learning models are so good that they’ve almost completely supplanted traditional methods for detecting and phase-picking earthquakes, especially for smaller magnitudes."
"In the last five years, an AI-based workflow has almost completely replaced one of the fundamental tasks in seismology for the better."
(It's not LLMs)

“Like putting on glasses for the first time”—how AI improves earthquake detection
AI is “comically good” at detecting small earthquakes—here’s why that matters.

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
Maral Doctorarastoo, Katherine A. Flanigan, Mario Berg\'es, Christopher McComb
https://arxiv.org/abs/2510.10954

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which ne…

Geometric Model Selection for Latent Space Network Models: Hypothesis Testing via Multidimensional Scaling and Resampling Techniques
Jieyun Wang, Anna L. Smith
https://arxiv.org/abs/2510.06136

Geometric Model Selection for Latent Space Network Models: Hypothesis Testing via Multidimensional Scaling and Resampling Techniques
Latent space models assume that network ties are more likely between nodes that are closer together in an underlying latent space. Euclidean space is a popular choice for the underlying geometry, but hyperbolic geometry can mimic more realistic patterns of ties in complex networks. To identify the underlying geometry, past research has applied non-Euclidean extensions of multidimensional scaling (MDS) to the observed geodesic distances: the shortest path lengths between nodes. The difference in…

Bounds of Validity for Bifurcations of Equilibria in a Class of Networked Dynamical Systems
Pranav Gupta, Ravi Banavar, Anastasia Bizyaeva
https://arxiv.org/abs/2510.10215 https…

Bounds of Validity for Bifurcations of Equilibria in a Class of Networked Dynamical Systems
Local bifurcation analysis plays a central role in understanding qualitative transitions in networked nonlinear dynamical systems, including dynamic neural network and opinion dynamics models. In this article we establish explicit bounds of validity for the classification of bifurcation diagrams in two classes of continuous-time networked dynamical systems, analogous in structure to the Hopfield and the Firing Rate dynamic neural network models. Our approach leverages recent advances in computi…

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
C\^ome Cattin, Thomas Pl\'e, Olivier Adjoua, Nicola\"i Gouraud, Louis Lagard\`ere, Jean-Philip Piquemal
https://arxiv.org/abs/2510.06562

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
We present a strategy to accelerate molecular dynamics simulations using foundation neural network models. To do so, we apply a dual-level neural network multi-time-step (MTS) strategy where the target accurate potential is coupled to a simpler but faster model obtained via a distillation process. Thus, the 3.5 Å-cutoff distilled model is sufficient to capture the fast varying forces, i.e. mainly bonded interactions, from the accurate potential allowing its use in a reversible reference system…

High-resolution Photo Enhancement in Real-time: A Laplacian Pyramid Network
Feng Zhang, Haoyou Deng, Zhiqiang Li, Lida Li, Bin Xu, Qingbo Lu, Zisheng Cao, Minchen Wei, Changxin Gao, Nong Sang, Xiang Bai
https://arxiv.org/abs/2510.11613

High-resolution Photo Enhancement in Real-time: A Laplacian Pyramid Network
Photo enhancement plays a crucial role in augmenting the visual aesthetics of a photograph. In recent years, photo enhancement methods have either focused on enhancement performance, producing powerful models that cannot be deployed on edge devices, or prioritized computational efficiency, resulting in inadequate performance for real-world applications. To this end, this paper introduces a pyramid network called LLF-LUT++, which integrates global and local operators through closed-form Laplacia…

Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory
Einar Urdshals, Edmund Lau, Jesse Hoogland, Stan van Wingerden, Daniel Murfet
https://arxiv.org/abs/2510.12077

Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory
We study neural network compressibility by using singular learning theory to extend the minimum description length (MDL) principle to singular models like neural networks. Through extensive experiments on the Pythia suite with quantization, factorization, and other compression techniques, we find that complexity estimates based on the local learning coefficient (LLC) are closely, and in some cases, linearly correlated with compressibility. Our results provide a path toward rigorously evaluating…

Pretraining in Actor-Critic Reinforcement Learning for Robot Motion Control
Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter
https://arxiv.org/abs/2510.12363 https://

Pretraining in Actor-Critic Reinforcement Learning for Robot Motion Control
The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years. However, in the domain of reinforcement learning (RL) for robot motion control, individual skills are often learned from scratch despite the high likelihood that some generalizable knowledge is shared across all task-specific policies belonging to a single robot embodiment. This work aims to define a paradigm for pretraining neural network models that enc…

Mean-field analysis of a neural network with stochastic STDP
Pascal Helson, Etienne Tanr\'e, Romain Veltz
https://arxiv.org/abs/2510.02545 https://arxi…

Mean-field analysis of a neural network with stochastic STDP
Analysing biological spiking neural network models with synaptic plasticity has proven to be challenging both theoretically and numerically. In a network with N all-to-all connected neurons, the number of synaptic connections is on the order of $N^2$, making these models computationally demanding. Furthermore, the intricate coupling between neuron and synapse dynamics, along with the heterogeneity generated by plasticity, hinder the use of classic theoretical tools such as mean-field or slow-fa…

I wrote a blog post about the often stated but never explained assumption that communities in graphs should always be connected.
This “connected cluster axiom” is inconsistent with statistical significance and null models that underlie the most widely employed methods.
https://skewed.de/lab/posts/connected-

Microstructure sensitive recurrent neural network surrogate model of crystal plasticity
Michael D. Atkinson, Michael D. White, Adam J. Plowman, Pratheek Shanthraj
https://arxiv.org/abs/2510.06904

Microstructure sensitive recurrent neural network surrogate model of crystal plasticity
The development of next-generation structural materials for harsh environments requires rapid assessment of mechanical performance and its dependence on microstructure. While full-field crystal plasticity (CP) models provide detailed insights, the high computational cost limits their use with uncertainty quantification workflows and in component-scale simulation. Surrogate models based on recurrent neural networks (RNNs) have shown promise in reproducing history-dependent mechanical behaviour b…

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin, Ivan Kireev, Andrey Savchenko
https://arxiv.org/abs/2510.09566

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their application to specific tasks. Solving such problems is crucial because the need for scalable and resource-efficient models is increasing. To address these challenges, we have developed a new automated machine learning (AutoML) framework, Parameter Efficient Training with Robust Automation (PETRA). It applies evolutionary op…

Textual interpretation of transient image classifications from large language models
Fiorenzo Stoppa, Turan Bulmus, Steven Bloemen, Stephen J. Smartt, Paul J. Groot, Paul Vreeswijk, Ken W. Smith
https://arxiv.org/abs/2510.06931

Textual interpretation of transient image classifications from large language models
Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders interpretability. Here we show that large language models (LLMs) can approach the performance level of a convolutional neural network on thr…

Signed network models for portfolio optimization
Bibhas Adhikari
https://arxiv.org/abs/2510.05377 https://arxiv.org/pdf/2510.05377

Signed network models for portfolio optimization
In this work, we consider weighted signed network representations of financial markets derived from raw or denoised correlation matrices, and examine how negative edges can be exploited to reduce portfolio risk. We then propose a discrete optimization scheme that reduces the asset selection problem to a desired size by building a time series of signed networks based on asset returns. To benchmark our approach, we consider two standard allocation strategies: Markowitz's mean-variance optimizatio…

Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM
Biyao Zhang, Mingkai Zheng, Debargha Ganguly, Xuecen Zhang, Vikash Singh, Vipin Chaudhary, Zhao Zhang
https://arxiv.org/abs/2509.22832

Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM
Training Large Language Models(LLMs) is one of the most compute-intensive tasks in high-performance computing. Predicting end-to-end training time for multi-billion parameter models distributed across hundreds of GPUs remains challenging due to complex interactions between transformer components, parallelism strategies(data, model, pipeline, tensor), and multi-tier communication. Learned models require costly sampling, while analytical models often struggle with real-world network and hardware …

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Baptiste Hilaire, Emmanouil Karystinaios, Gerhard Widmer
https://arxiv.org/abs/2509.26521

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we introduce MUSE-Explainer, a new method that helps reveal how music Graph Neural Network models make decisions by providing clear, human-friendly explanations. Our approach generates counterfactual explanations by making small, meaningful changes to musical score graphs that alter a model's prediction while ensuring the …

Detecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network
Xin Liu, Rongwu Xu, Xinyi Jia, Jason Liao, Jiao Sun, Ling Huang, Wei Xu
https://arxiv.org/abs/2510.01801

Detecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network
The rise of large language models (LLMs) has enabled the generation of highly persuasive spam reviews that closely mimic human writing. These reviews pose significant challenges for existing detection systems and threaten the credibility of online platforms. In this work, we first create three realistic LLM-generated spam review datasets using three distinct LLMs, each guided by product metadata and genuine reference reviews. Evaluations by GPT-4.1 confirm the high persuasion and deceptive pote…

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
Nisith Dissanayake (University of Moratuwa), Uthayasanker Thayasivam (University of Moratuwa)
https://arxiv.org/abs/2510.12455

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems (IDS) often struggle to maintain high accuracy across both frequent and rare attacks, leading to increased false negatives for minority classes. To address this, we propose a hybrid anomaly detection framework that integrates specialized deep learning models with an ensemble meta-c…

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
Junhan Zhu, Hesong Wang, Mingluo Su, Zefang Wang, Huan Wang
https://arxiv.org/abs/2510.06751 https://

OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to them due to the iterative denoising nature of diffusion models. To bridge the gap, this paper presents OBS-Diff, a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models. Specifically, (i) OBS-Diff revitalizes the classic Optimal Brain Surgeon…

Complexity of Activity Patterns in a Bio-Inspired Hopfield-Type Network in Different Topologies
Marco Cafiso, Paolo Paradisi
https://arxiv.org/abs/2509.18758 https://

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models
Linfeng Wang, Susana Campino, Taane G. Clark, Jody E. Phelan
https://arxiv.org/abs/2510.08703

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models
Positive selection drives the emergence of adaptive mutations in Mycobacterium tuberculosis, shaping drug resistance, transmissibility, and virulence. Phylogenetic trees capture evolutionary relationships among isolates and provide a natural framework for detecting such adaptive signals. We present a phylogeny-guided graph attention network (GAT) approach, introducing a method for converting SNP-annotated phylogenetic trees into graph structures suitable for neural network analysis. Using 500 M…

Optimal estimation of a factorizable density using diffusion models with ReLU neural networks
Jianqing Fan, Yihong Gu, Ximing Li
https://arxiv.org/abs/2510.03994 https://…

Optimal estimation of a factorizable density using diffusion models with ReLU neural networks
This paper investigates the score-based diffusion models for density estimation when the target density admits a factorizable low-dimensional nonparametric structure. To be specific, we show that when the log density admits a $d^*$-way interaction model with $β$-smooth components, the vanilla diffusion model, which uses a fully connected ReLU neural network for score matching, can attain optimal $n^{-β/(2β+d^*)}$ statistical rate of convergence in total variation distance. This is, to the be…

When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI
Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye
https://arxiv.org/abs/2510.06903 htt…

When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI
As artificial intelligence (AI) enters the agentic era, large language models (LLMs) are increasingly deployed as autonomous agents that interact with one another rather than operate in isolation. This shift raises a fundamental question: how do machine agents behave in interdependent environments where outcomes depend not only on their own choices but also on the coordinated expectations of peers? To address this question, we study LLM agents in a canonical network-effect game, where economic …

Efficient Domain Generalization in Wireless Networks with Scarce Multi-Modal Data
Minsu Kim, Walid Saad, Dour Calin
https://arxiv.org/abs/2510.04359 https://

Efficient Domain Generalization in Wireless Networks with Scarce Multi-Modal Data
In 6G wireless networks, multi-modal ML models can be leveraged to enable situation-aware network decisions in dynamic environments. However, trained ML models often fail to generalize under domain shifts when training and test data distributions are different because they often focus on modality-specific spurious features. In practical wireless systems, domain shifts occur frequently due to dynamic channel statistics, moving obstacles, or hardware configuration. Thus, there is a need for learn…

Cooperation in Bilateral Generalized Network Creation
Hans Gawendowicz, Pascal Lenzner, Lukas Weyand
https://arxiv.org/abs/2510.00239 https://arxiv.org/pdf…

Cooperation in Bilateral Generalized Network Creation
Studying the impact of cooperation in strategic settings is one of the cornerstones of algorithmic game theory. Intuitively, allowing more cooperation yields equilibria that are more beneficial for the society of agents. However, for many games it is still an open question how much cooperation is actually needed to ensure socially good equilibria. We contribute to this research endeavor by analyzing the benefits of cooperation in a network formation game that models the creation of communicatio…

Universal Model of Urban Street Networks
https://arxiv.org/abs/2509.21931

Universal Model of Urban Street Networks
Analyzing 9,000 urban areas' street networks, we identify properties, including extreme betweenness centrality heterogeneity, that typical spatial network models fail to explain. Accordingly we propose a universal, parsimonious, generative model based on a two-step mechanism that begins with a spanning tree as a backbone then iteratively adds edges to match empirical degree distributions. Controlled by a single parameter representing lattice-equivalent node density, it accurately reproduces key…

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GFSR-Net: Guided Focus via Segment-Wise Relevance Network for Interpretable Deep Learning in Medical Imaging
Jhonatan Contreras, Thomas Bocklitz
https://arxiv.org/abs/2510.01919

GFSR-Net: Guided Focus via Segment-Wise Relevance Network for Interpretable Deep Learning in Medical Imaging
Deep learning has achieved remarkable success in medical image analysis, however its adoption in clinical practice is limited by a lack of interpretability. These models often make correct predictions without explaining their reasoning. They may also rely on image regions unrelated to the disease or visual cues, such as annotations, that are not present in real-world conditions. This can reduce trust and increase the risk of misleading diagnoses. We introduce the Guided Focus via Segment-Wise R…

Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks
Patrick Gerard, Luca Luceria, Leonardo Blas, Emilio Ferrara
https://arxiv.org/abs/2510.09464

Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks
Online narratives spread unevenly across platforms, with content emerging on one site often appearing on others, hours, days or weeks later. Existing cross-platform information diffusion models often treat platforms as isolated systems, disregarding cross-platform activity that might make these patterns more predictable. In this work, we frame cross-platform prediction as a network proximity problem: rather than tracking individual users across platforms or relying on brittle signals like share…

Applications of Machine Learning in Constraining Multi-Scalar Models
Darius Jur\v{c}iukonis
https://arxiv.org/abs/2509.24092 https://arxiv.org/pdf/2509.240…

Applications of Machine Learning in Constraining Multi-Scalar Models
Machine learning techniques are used to predict theoretical constraints such as unitarity and boundedness from below in extensions of the Standard Model. This approach has proven effective for models incorporating additional SU(2) scalar multiplets, in particular the quadruplet and sixplet cases. High predictive performance is achieved through the use of suitable neural network architectures and well-prepared training datasets. Moreover, machine learning provides a substantial computational adv…

GeoPipe: a Geo-distributed LLM Training Framework with enhanced Pipeline Parallelism in a Lossless RDMA-enabled Datacenter Optical Transport Network
Jun Dai, Xiaorun Wang, Kexiong Fang, Zheng Yang, Yuefeng Ji, Jiawei Zhang
https://arxiv.org/abs/2510.12064

GeoPipe: a Geo-distributed LLM Training Framework with enhanced Pipeline Parallelism in a Lossless RDMA-enabled Datacenter Optical Transport Network
The proliferation of Large Language Models (LLMs) with exponentially growing parameters is making cross-data center (DC) training an inevitable trend. However, viable strategies for extending single-DC training frameworks to multi-DC environments remain underdeveloped. We experimentally demonstrate, for the first time, a high-performance geo-distributed LLMs training framework across multiple DCs interconnected by a lossless, remote direct memory access (RDMA) enabled Datacenter Optical Transpo…

Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training
Haixin Zhao, Kaixuan Yang, Nilesh Madhu
https://arxiv.org/abs/2510.11395 https://

Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training
To further reduce the complexity of lightweight speech enhancement models, we introduce a gating-based Dynamically Slimmable Network (DSN). The DSN comprises static and dynamic components. For architecture-independent applicability, we introduce distinct dynamic structures targeting the commonly used components, namely, grouped recurrent neural network units, multi-head attention, convolutional, and fully connected layers. A policy module adaptively governs the use of dynamic parts at a frame-w…

Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Shihe Zhou, Ruikun Li, Huandong Wang, Yong Li
https://arxiv.org/abs/2510.07957 https://

Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Forecasting state evolution of network systems, such as the spread of information on social networks, is significant for effective policy interventions and resource management. However, the underlying propagation dynamics constantly shift with new topics or events, which are modeled as changing coefficients of the underlying dynamics. Deep learning models struggle to adapt to these out-of-distribution shifts without extensive new data and retraining. To address this, we present Zero-Shot Foreca…

Joint Neural SDF Reconstruction and Semantic Segmentation for CAD Models
Shen Fan, Przemyslaw Musialski
https://arxiv.org/abs/2510.03837 https://arxiv.org/…

Joint Neural SDF Reconstruction and Semantic Segmentation for CAD Models
We propose a simple, data-efficient pipeline that augments an implicit reconstruction network based on neural SDF-based CAD parts with a part-segmentation head trained under PartField-generated supervision. Unlike methods tied to fixed taxonomies, our model accepts meshes with any number of parts and produces coherent, geometry-aligned labels in a single pass. We evaluate on randomly sampled CAD meshes from the ABC dataset with intentionally varied part cardinalities, including over-segmented s…

Commutative algebra neural network reveals genetic origins of diseases
JunJie Wee, Faisal Suwayyid, Mushal Zia, Hongsong Feng, Yuta Hozumi, Guo-Wei Wei
https://arxiv.org/abs/2509.26566

Commutative algebra neural network reveals genetic origins of diseases
Genetic mutations can disrupt protein structure, stability, and solubility, contributing to a wide range of diseases. Existing predictive models often lack interpretability and fail to integrate physical and chemical interactions critical to molecular mechanisms. Moreover, current approaches treat disease association, stability changes, and solubility alterations as separate tasks, limiting model generalizability. In this study, we introduce a unified framework based on multiscale commutative a…

👾 Unsupervised Instance Segmentation with Superpixels
#cs #arxiv

Unsupervised Instance Segmentation with Superpixels
Instance segmentation is essential for numerous computer vision applications, including robotics, human-computer interaction, and autonomous driving. Currently, popular models bring impressive performance in instance segmentation by training with a large number of human annotations, which are costly to collect. For this reason, we present a new framework that efficiently and effectively segments objects without the need for human annotations. Firstly, a MultiCut algorithm is applied to self-sup…

Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Jinyuan Liu, Zihang Chen, Zhu Liu, Zhiying Jiang, Long Ma, Xin Fan, Risheng Liu
https://arxiv.org/abs/2510.09343

Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradations, such as noise, contrast, and blurring, making it difficult to handle coupled degradations. Meanwhile, all-in-one enhancement methods, commonly applied to RGB sensors, often demonstrate limited effectiveness due to the significant differences in imaging models. In sight of this, we first revisit the imaging mecha…

Residual-Informed Learning of Solutions to Algebraic Loops
Felix Brandt, Andreas Heuermann, Philip Hannebohm, Bernhard Bachmann
https://arxiv.org/abs/2510.09317 https://

Residual-Informed Learning of Solutions to Algebraic Loops
This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid on…

DisCoCLIP: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding
Kin Ian Lo, Hala Hawashin, Mina Abbaszadeh, Tilen Limback-Stokin, Hadi Wazni, Mehrnoosh Sadrzadeh
https://arxiv.org/abs/2509.21287

DisCoCLIP: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding
Recent vision-language models excel at large-scale image-text alignment but often neglect the compositional structure of language, leading to failures on tasks that hinge on word order and predicate-argument structure. We introduce DisCoCLIP, a multimodal encoder that combines a frozen CLIP vision transformer with a novel tensor network text encoder that explicitly encodes syntactic structure. Sentences are parsed with a Combinatory Categorial Grammar parser to yield distributional word tensors…

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth
Mohid Farooqi, Ingmar B\"osing, Conrad G. Tetsassi Feugmo
https://arxiv.org/abs/2510.02872

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth
Physics-informed neural networks (PINNs) offer a novel AI-driven framework for integrating physical laws directly into neural network models, facilitating the solution of complex multiphysics problems in materials engineering. This study systematically explores the application of PINNs to simulate oxide film layer growth in halide-free solutions using the point defect model (PDM). We identify and analyze four key failure modes in this context: imbalanced loss components across different physica…

Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
Anis Bekri, Amar Abane, Abdella Battou, Saddek Bensalem
https://arxiv.org/abs/2509.22834 htt…

Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). To address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval…

Rebuild AC Power Flow Models with Graph Attention Networks
Yuting Hu, Jinjun Xiong
https://arxiv.org/abs/2509.22733 https://arxiv.org/pdf/2509.22733…

Rebuild AC Power Flow Models with Graph Attention Networks
A full power flow (PF) model is a complete representation of the physical power network. Traditional model-based methods rely on the full PF model to implement power flow analysis. In practice, however, some PF model parameters can be inaccurate or even unavailable due to the uncertainties or dynamics in the power systems. Moreover, because the power network keeps evolving with possibly changing topology, the generalizability of a PF model to different network sizes and typologies should be con…

Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks
Achraf Zinihi
https://arxiv.org/abs/2509.22760 https://

Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks
We develop a physics-informed neural network (PINN) framework for parameter estimation in fractional-order SEIRD epidemic models. By embedding the Caputo fractional derivative into the network residuals via the L1 discretization scheme, our method simultaneously reconstructs epidemic trajectories and infers both epidemiological parameters and the fractional memory order $α$. The fractional formulation extends classical integer-order models by capturing long-range memory effects in disease prog…

Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
KiHyun Nam, Jongmin Choi, Hyeongkeun Lee, Jungwoo Heo, Joon Son Chung
https://arxiv.org/abs/2510.11330

Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
Contrastive audio-language pretraining yields powerful joint representations, yet a persistent audio-text modality gap limits the benefits of coupling multimodal encoders with large language models (LLMs). We present Diffusion-Link, a diffusion-based modality-bridging module that generatively maps audio embeddings into the text-embedding distribution. The module is trained at the output embedding from the frozen multimodal encoder and implemented as a lightweight network with three residual MLP…

Understanding How Network Geometry Influences Diffusion Processes in Complex Networks: A Focus on Cryptocurrency Blockchains and Critical Infrastructure Networks
S M Mustaquim, Asim K. Dey, Abhijit Mandal
https://arxiv.org/abs/2509.23450

Understanding How Network Geometry Influences Diffusion Processes in Complex Networks: A Focus on Cryptocurrency Blockchains and Critical Infrastructure Networks
This study provides essential insights into how diffusion processes unfold in complex networks, with a focus on cryptocurrency blockchains and infrastructure networks. The structural properties of these networks, such as hub-dominated, heavy-tailed topology, network motifs, and node centrality, significantly influence diffusion speed and reach. Using epidemic diffusion models, specifically the Kertesz threshold model and the Susceptible-Infected (SI) model, we analyze key factors affecting diff…

Replaced article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple ...
Cattin, Pl\'e, Adjoua, Gouraud, Lagard\`ere, Piquemal

The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain
Adrian Kosowski, Przemys{\l}aw Uzna\'nski, Jan Chorowski, Zuzanna Stamirowska, Micha{\l} Bartoszkiewicz
https://arxiv.org/abs/2509.26507

The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain
The relationship between computing systems and the brain has served as motivation for pioneering theoreticians since John von Neumann and Alan Turing. Uniform, scale-free biological networks, such as the brain, have powerful properties, including generalizing over time, which is the main barrier for Machine Learning on the path to Universal Reasoning Models. We introduce `Dragon Hatchling' (BDH), a new Large Language Model architecture based on a scale-free biologically inspired network of \$…

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
Pranav Sambhu, Om Guin, Madhav Sambhu, Jinho Cha
https://arxiv.org/abs/2510.07681

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
This study evaluates whether integrating curriculum learning with diffusion-based synthetic augmentation can enhance the detection of difficult pulmonary nodules in chest radiographs, particularly those with low size, brightness, and contrast, which often challenge conventional AI models due to data imbalance and limited annotation. A Faster R-CNN with a Feature Pyramid Network (FPN) backbone was trained on a hybrid dataset comprising expert-labeled NODE21 (1,213 patients; 52.4 percent male; me…

Travel Bans vs. Social Distancing: A Mathematical Analysis
Christian Borgs, Karissa Huang, Geng Zhao
https://arxiv.org/abs/2510.08895 https://arxiv.org/pdf…

Travel Bans vs. Social Distancing: A Mathematical Analysis
As the world grows increasingly connected, infectious disease transmission and outbreaks become a pressing global concern for public health officials and policymakers. While policy interventions to contain and prevent the spread of disease have been proposed and implemented, there has been little rigorous quantitative analysis of the effectiveness of such interventions. In this paper, we study the susceptible-infected-recovered (SIR) infection process on a dynamic network model that models two …

ExpIDS: A Drift-adaptable Network Intrusion Detection System With Improved Explainability
Ayush Kumar, Kar Wai Fok, Vrizlynn L. L. Thing
https://arxiv.org/abs/2509.20767 https:/…

ExpIDS: A Drift-adaptable Network Intrusion Detection System With Improved Explainability
Despite all the advantages associated with Network Intrusion Detection Systems (NIDSs) that utilize machine learning (ML) models, there is a significant reluctance among cyber security experts to implement these models in real-world production settings. This is primarily because of their opaque nature, meaning it is unclear how and why the models make their decisions. In this work, we design a deep learning-based NIDS, ExpIDS to have high decision tree explanation fidelity, i.e., the prediction…

FinGAN: An Interpretable RSS Generation Network for Scalable Fingerprint Localization
Jiaming Zhang, Jiajun He, Jie Zhang, Okan Yurduseven
https://arxiv.org/abs/2509.26286 https…

FinGAN: An Interpretable RSS Generation Network for Scalable Fingerprint Localization
This work introduces FinGAN, a robust received signal strength (RSS) data generator designed to expand RSS fingerprint datasets. Compared to existing generative adversarial models that either rely on known reference positions (RPs) or depend on predefined priors, FinGAN learns the latent information between RPs and RSS values by maximizing the mutual information between the generated RSS data and the RPs, enabling an end-to-end RSS generation directly from RPs. This allows us to accurately gene…

Homophily-induced Emergence of Biased Structures in LLM-based Multi-Agent AI Systems
Aliakbar Mehdizadeh, Martin Hilbert
https://arxiv.org/abs/2510.02637 https://

Homophily-induced Emergence of Biased Structures in LLM-based Multi-Agent AI Systems
This study examines how interactions among artificially intelligent (AI) agents, guided by large language models (LLMs), drive the evolution of collective network structures. We ask LLM-driven agents to grow a network by informing them about current link constellations. Our observations confirm that agents consistently apply a preferential attachment mechanism, favoring connections to nodes with higher degrees. We systematically solicited more than a million decisions from four different LLMs, …

DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks
Laurent Brisson (IMT Atlantique - DSD), C\'ecile Bothorel (IMT Atlantique - DSD), Nicolas Duminy (IMT Atlantique, IMT Atlantique - DSD)
https://arxiv.org/abs/2510.06245

DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks
Graph models help understand network dynamics and evolution. Creating graphs with controlled topology and embedded partitions is a common strategy for evaluating community detection algorithms. However, existing benchmarks often overlook the need to track the evolution of communities in real-world networks. To address this, a new community-centered model is proposed to generate customizable evolving community structures where communities can grow, shrink, merge, split, appear or disappear. This…

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, Merim Dzaferagic, John D. Kelleher
https://arxiv.org/abs/2510.08303

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven by multi-vendor deployments, hardware upgrades, and evolving service requirements. To address this growing need for flexible learning in non-stationary environments, this vision paper highlights Adaptive Random Forests (ARFs) as a reliable solution for dynamic feature adaptation in communication network scenarios. We…

Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning
Carlos G\"uemes-Palau, Miquel Ferriol-Galm\'es, Jordi Paillisse-Vilanova, Albert L\'opez-Bresc\'o, Pere Barlet-Ros, Albert Cabellos-Aparicio
https://arxiv.org/abs/2510.00956

Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning
Machine Learning (ML)-based network models provide fast and accurate predictions for complex network behaviors but require substantial training data. Collecting such data from real networks is often costly and limited, especially for critical scenarios like failures. As a result, researchers commonly rely on simulated data, which reduces accuracy when models are deployed in real environments. We propose a hybrid approach leveraging transfer learning to combine simulated and real-world data. Usi…

Unpacking Musical Symbolism in Online Communities: Content-Based and Network-Centric Approaches
Kajwan Ziaoddini
https://arxiv.org/abs/2510.00006 https://a…

Unpacking Musical Symbolism in Online Communities: Content-Based and Network-Centric Approaches
This paper examines how musical symbolism is produced and circulated in online communities by combining content-based music analysis with a lightweight network perspective on lyrics. Using a curated corpus of 275 chart-topping songs enriched with audio descriptors (energy, danceability, loudness, liveness, valence, acousticness, speechiness, popularity) and full lyric transcripts, we build a reproducible pipeline that (i) quantifies temporal trends in sonic attributes, (ii) models lexical salie…

MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis
Hongyu Zhu, Lin Chen, Mounim A. El-Yacoubi, Mingsheng Shang
https://arxiv.org/abs/2510.11579 https://

MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) aims to identify and interpret human emotions by integrating information from heterogeneous data sources such as text, video, and audio. While deep learning models have advanced in network architecture design, they remain heavily limited by scarce multimodal annotated data. Although Mixup-based augmentation improves generalization in unimodal tasks, its direct application to MSA introduces critical challenges: random mixing often amplifies label ambiguity and…

Computational Advances in Taste Perception: From Ion Channels to Neural Coding
Vladimir A. Lazovsky, Sergey V. Stasenko, Victor B. Kazantsev
https://arxiv.org/abs/2510.00010 htt…

Computational Advances in Taste Perception: From Ion Channels to Neural Coding
Recent advances in computational neuroscience demand models that balance biophysical realism with scalability. We present a hybrid neuron model combining the biophysical fidelity of Hodgkin-Huxley (HH) dynamics for taste receptor cells with the computational efficiency of Izhikevich spiking neurons for large-network simulations. Our framework incorporates biomorphic taste cell models, featuring modality-specific receptor dynamics (T1R/T2R, ENaC, PKD) and Goldman-Hodgkin-Katz (GHK)-driven ion cu…

Decade-long Emission Forecasting with an Ensemble Model in Taiwan
Gordon Hung, Salinna Abdullah
https://arxiv.org/abs/2510.05548 https://arxiv.org/pdf/2510…

Decade-long Emission Forecasting with an Ensemble Model in Taiwan
Taiwan's high population and heavy dependence on fossil fuels have led to severe air pollution, with the most prevalent greenhouse gas being carbon dioxide (CO2). There-fore, this study presents a reproducible and comprehensive case study comparing 21 of the most commonly employed time series models in forecasting emissions, analyzing both univariate and multivariate approaches. Among these, Feedforward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest Regressor (RFR) achie…

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
Peter Wauyo, Dalia Bwiza, Alain Murara, Edwin Mugume, Eric Umuhoza
https://arxiv.org/abs/2510.02165

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
This research introduces a multimodal system designed to detect fraud and fare evasion in public transportation by analyzing closed circuit television (CCTV) and audio data. The proposed solution uses the Vision Transformer for Video (ViViT) model for video feature extraction and the Audio Spectrogram Transformer (AST) for audio analysis. The system implements a Tensor Fusion Network (TFN) architecture that explicitly models unimodal and bimodal interactions through a 2-fold Cartesian product. …

Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation
Ian Taylor, Juliane Mueller, Julie Bessac
https://arxiv.org/abs/2509.21711 https://

Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation
As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develo…

Harmonic fields and the mechanical response of a cellular monolayer to ablation
Oliver E. Jensen, Christopher K. Revell
https://arxiv.org/abs/2510.05193 https://

Harmonic fields and the mechanical response of a cellular monolayer to ablation
Multicellular tissues, such as the epithelium coating a developing embryo, often combine complex tissue shapes with heterogeneity in the spatial arrangement of individual cells. Discrete approximations, such as the cell vertex model, can accommodate these geometric features, but techniques for analysis of such models are underdeveloped. Here, we express differential operators defined on a network representing a monolayer of confluent cells in the framework of discrete exterior calculus, conside…

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
Alexandru Ioni\c{t}\u{a}, Andreea Ioni\c{t}\u{a}
https://arxiv.org/abs/2509.21497

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
With the increased interest in artificial intelligence, Machine Learning as a Service provides the infrastructure in the Cloud for easy training, testing, and deploying models. However, these systems have a major privacy issue: uploading sensitive data to the Cloud, especially during training. Therefore, achieving secure Neural Network training has been on many researchers' minds lately. More and more solutions for this problem are built around a main pillar: Functional Encryption (FE). Althoug…

jmstate, a Flexible Python Package for Multi-State Joint Modeling
F\'elix Laplante, Christophe Ambroise, Estelle Kuhn, Sarah Lemler
https://arxiv.org/abs/2510.07128 https://…

jmstate, a Flexible Python Package for Multi-State Joint Modeling
Classical joint modeling approaches often rely on competing risks or recurrent event formulations to account for complex real-world processes involving evolving longitudinal markers and discrete event occurrences. However, these frameworks typically capture only limited aspects of the underlying event dynamics. Multi-state joint models offer a more flexible alternative by representing full event histories through a network of possible transitions, including recurrent cycles and terminal absor…

Phase Aware Ear-Conditioned Learning for Multi-Channel Binaural Speaker Separation
Ruben Johnson Robert Jeremiah, Peyman Goli, Steven van de Par
https://arxiv.org/abs/2510.11366

Phase Aware Ear-Conditioned Learning for Multi-Channel Binaural Speaker Separation
Separating competing speech in reverberant environments requires models that preserve spatial cues while maintaining separation efficiency. We present a Phase-aware Ear-conditioned speaker Separation network using eight microphones (PEASE-8) that consumes complex STFTs and directly introduces a raw-STFT input to the early decoder layer, bypassing the entire encoder pathway to improve reconstruction. The model is trained end-to-end with an SI-SDR-based objective against direct-path ear targets, …

A Theoretical Analysis of Discrete Flow Matching Generative Models
Maojiang Su, Mingcheng Lu, Jerry Yao-Chieh Hu, Shang Wu, Zhao Song, Alex Reneau, Han Liu
https://arxiv.org/abs/2509.22623

A Theoretical Analysis of Discrete Flow Matching Generative Models
We provide a theoretical analysis for end-to-end training Discrete Flow Matching (DFM) generative models. DFM is a promising discrete generative modeling framework that learns the underlying generative dynamics by training a neural network to approximate the transformative velocity field. Our analysis establishes a clear chain of guarantees by decomposing the final distribution estimation error. We first prove that the total variation distance between the generated and target distributions is c…

EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Mohammad Hossein Samaei, Faryad Darabi Sahneh, Lee W. Cohnstaedt, Caterina Scoglio
https://arxiv.org/abs/2510.00024

EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Large Language Models (LLMs) offer new opportunities to automate complex interdisciplinary research domains. Epidemic modeling, characterized by its complexity and reliance on network science, dynamical systems, epidemiology, and stochastic simulations, represents a prime candidate for leveraging LLM-driven automation. We introduce \textbf{EpidemIQs}, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modelin…

PRISM: Progressive Rain removal with Integrated State-space Modeling
Pengze Xue, Shanwen Wang, Fei Zhou, Yan Cui, Xin Sun
https://arxiv.org/abs/2509.26413 https://

PRISM: Progressive Rain removal with Integrated State-space Modeling
Image deraining is an essential vision technique that removes rain streaks and water droplets, enhancing clarity for critical vision tasks like autonomous driving. However, current single-scale models struggle with fine-grained recovery and global consistency. To address this challenge, we propose Progressive Rain removal with Integrated State-space Modeling (PRISM), a progressive three-stage framework: Coarse Extraction Network (CENet), Frequency Fusion Network (SFNet), and Refine Network (RNe…

SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph
Huizhe Zhang, Jintang Li, Yuchang Zhu, Liang Chen, Li Kuang
https://arxiv.org/abs/2509.21342

SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph
Graph Neural Networks (GNNs) are exemplary deep models designed for graph data. Message passing mechanism enables GNNs to effectively capture graph topology and push the performance boundaries across various graph tasks. However, the trend of developing such complex machinery for graph representation learning has become unsustainable on large-scale graphs. The computational and time overhead make it imperative to develop more energy-efficient GNNs to cope with the explosive growth of real-world…

An LLM-based Agentic Framework for Accessible Network Control
Samuel Lin, Jiawei Zhou, Minlan Yu
https://arxiv.org/abs/2509.20600 https://arxiv.org/pdf/250…

An LLM-based Agentic Framework for Accessible Network Control
Traditional approaches to network management have been accessible only to a handful of highly-trained network operators with significant expert knowledge. This creates barriers for lay users to easily manage their networks without resorting to experts. With recent development of powerful large language models (LLMs) for language comprehension, we design a system to make network management accessible to a broader audience of non-experts by allowing users to converse with networks in natural lang…

Neural Network Based Framework for Passive Intermodulation Cancellation in MIMO Systems
Xiaolong Li, Zhi-qin John Xu, Peiting You, Yifei Zhu
https://arxiv.org/abs/2509.19382 htt…

Neural Network Based Framework for Passive Intermodulation Cancellation in MIMO Systems
Passive intermodulation (PIM) has emerged as a critical source of self-interference in modern MIMO-OFDM systems, especially under the stringent requirements of 5G and beyond. Conventional cancellation methods often rely on complex nonlinear models with limited scalability and high computational cost. In this work, we propose a lightweight deep learning framework for PIM cancellation that leverages depthwise separable convolutions and dilated convolutions to efficiently capture nonlinear depende…

A Hybrid GNN-IZR Framework for Fast and Empirically Robust AC Power Flow Analysis in Radial Distribution Systems
Mohamed Shamseldein
https://arxiv.org/abs/2510.04264 https://

A Hybrid GNN-IZR Framework for Fast and Empirically Robust AC Power Flow Analysis in Radial Distribution Systems
The Alternating Current Power Flow (ACPF) problem forces a trade-off between the speed of data-driven models and the reliability of analytical solvers. This paper introduces a hybrid framework that synergizes a Graph Neural Network (GNN) with the Implicit Z-Bus Recursive (IZR) method, a robust, non-iterative solver for radial distribution networks. The framework employs a physics-informed GNN for rapid initial predictions and invokes the IZR solver as a failsafe for stressed cases identified by…

EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
https://arxiv.org/abs/2509.21061

EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task
Fine-grained classification models are designed to focus on the relevant details necessary to distinguish highly similar classes, particularly when intra-class variance is high and inter-class variance is low. Most existing models rely on part annotations such as bounding boxes, part locations, or textual attributes to enhance classification performance, while others employ sophisticated techniques to automatically extract attention maps. We posit that part-based approaches, including automatic…

Knowledge-Defined and Twin-Assisted Network Management for 6G
Tu\u{g}\c{c}e Bilen, Mehmet \"Ozdem
https://arxiv.org/abs/2509.23398 https://arxiv.org/p…

Knowledge-Defined and Twin-Assisted Network Management for 6G
The increasing complexity, dynamism, and heterogeneity of 6G networks demand management systems that can reason proactively and generalize beyond pre-defined cases. In this paper, we propose a modular, knowledge-defined architecture that integrates Digital Twin models with semantic reasoning and zero-shot learning to enable autonomous decision-making for previously unseen network scenarios. Real-time data streams are used to maintain synchronized virtual replicas of the physical network, which …

Adaptive Bayesian Joint Latent Space Modeling via Cumulative Shrinkage
Bin Lv, Yincai Tang, Siliang Zhang
https://arxiv.org/abs/2509.18580 https://arxiv.or…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Yuyao Wang, Yu-Hung Cheng, Debarghya Mukherjee, Huimin Cheng
https://arxiv.org/abs/2510.05527 htt…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, accurate graphon estimation typically requires a large graph, whereas in practice, one often only observes a small-sized network. One approach to addressing this issue is to adopt a transfer learning framework, which aims to improve estimation in a small target graph by leveragi…

Integrated Sensing, Communication, and Positioning in Cellular Vehicular Networks
Xin Tong, Zhaoyang Zhang, Yuzhi Yang, Yu Ge, Zhaohui Yang, Henk Wymeersch, M\'erouane Debbah
https://arxiv.org/abs/2510.02939

Integrated Sensing, Communication, and Positioning in Cellular Vehicular Networks
In this correspondence, a novel integrated sensing and communication (ISAC) framework is proposed to accomplish data communication, vehicle positioning, and environment sensing simultaneously in a cellular vehicular network. By incorporating the vehicle positioning problem with the existing computational-imaging-based ISAC models, we formulate a special integrated sensing, communication, and positioning problem in which the unknowns are highly coupled. To mitigate the rank deficiency and make i…

Introducing Large Language Models in the Design Flow of Time Sensitive Networking
Rubi Debnath, Luxi Zhao, Mohammadreza Barzegaran, Sebastian Steinhorst
https://arxiv.org/abs/2509.26368

Introducing Large Language Models in the Design Flow of Time Sensitive Networking
The growing demand for real-time, safety-critical systems has significantly increased both the adoption and complexity of Time Sensitive Networking (TSN). Configuring an optimized TSN network is highly challenging, requiring careful planning, design, verification, validation, and deployment. Large Language Models (LLMs) have recently demonstrated strong capabilities in solving complex tasks, positioning them as promising candidates for automating end-to-end TSN deployment, referred to as TSN or…

XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Daniel Palenicek, Florian Vogt, Joe Watson, Ingmar Posner, Jan Peters
https://arxiv.org/abs/2509.25174

XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Sample efficiency is a central property of effective deep reinforcement learning algorithms. Recent work has improved this through added complexity, such as larger models, exotic network architectures, and more complex algorithms, which are typically motivated purely by empirical performance. We take a more principled approach by focusing on the optimization landscape of the critic network. Using the eigenspectrum and condition number of the critic's Hessian, we systematically investigate the i…

MsFIN: Multi-scale Feature Interaction Network for Traffic Accident Anticipation
Tongshuai Wu, Chao Lu, Ze Song, Yunlong Lin, Sizhe Fan, Xuemei Chen
https://arxiv.org/abs/2509.19227

Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network
Pin-Jui Huang, Yu-Hsuan Liao, SooHeon Kim, NoSeong Park, JongBae Park, DongMyung Shin
https://arxiv.org/abs/2509.19896

Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network
Computational models that predict cellular phenotypic responses to chemical and genetic perturbations can accelerate drug discovery by prioritizing therapeutic hypotheses and reducing costly wet-lab iteration. However, extracting biologically meaningful and batch-robust cell painting representations remains challenging. Conventional self-supervised and contrastive learning approaches often require a large-scale model and/or a huge amount of carefully curated data, still struggling with batch ef…

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Yining Jiang, Wenyun Xu, Qingyu Song, Yuling Lin, Xuanhao Liu, Xiaoqiang Zheng, Qiang Su, Lizhao You, Lu Tang, Wangjian Feng, Linghe Kong, Qiao Xiang, Jiwu Shu
https://arxiv.org/abs/2509.21074

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Reproducing networking research is a critical but challenging task due to the scarcity of open-source code. While Large Language Models (LLMs) can automate code generation, current approaches lack the generalizability required for the diverse networking field. To address this, we propose RePro, a semi-automated reproduction framework that leverages advanced prompt engineering to reproduce network systems from their research papers. RePro combines few-shot in-context learning with Structured and…

Holistic Order Prediction in Natural Scenes
Pierre Musacchio, Hyunmin Lee, Jaesik Park
https://arxiv.org/abs/2510.01704 https://arxiv.org/pdf/2510.01704

Holistic Order Prediction in Natural Scenes
Even in controlled settings, understanding instance-wise geometries is a challenging task for a wide range of visual models. Although specialized systems exist, modern arts rely on expensive input formats (category labels, binary segmentation masks) and inference costs (a quadratic amount of forward passes). We mitigate these limitations by proposing InstaFormer, a network capable of holistic order prediction. That is, solely given an input RGB image, InstaFormer returns the full occlusion and …

Towards Adversarial Training under Hyperspectral Images
Weihua Zhang, Chengze Jiang, Jie Gui, Lu Dong
https://arxiv.org/abs/2510.01014 https://arxiv.org/pd…

Towards Adversarial Training under Hyperspectral Images
Recent studies have revealed that hyperspectral classification models based on deep learning are highly vulnerable to adversarial attacks, which pose significant security risks. Although several approaches have attempted to enhance adversarial robustness by modifying network architectures, these methods often rely on customized designs that limit scalability and fail to defend effectively against strong attacks. To address these challenges, we introduce adversarial training to the hyperspectral…

LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model
Haozhe Jia, Wenshuo Chen, Yuqi Lin, Yang Yang, Lei Wang, Mang Ning, Bowen Tian, Songning Lai, Nanqian Jia, Yifan Chen, Yutao Yue
https://arxiv.org/abs/2509.25304

LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model
While current diffusion-based models, typically built on U-Net architectures, have shown promising results on the text-to-motion generation task, they still suffer from semantic misalignment and kinematic artifacts. Through analysis, we identify severe gradient attenuation in the deep layers of the network as a key bottleneck, leading to insufficient learning of high-level features. To address this issue, we propose \textbf{LUMA} (\textit{\textbf{L}ow-dimension \textbf{U}nified \textbf{M}otion …