Tootfinder

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
Jos\'e Eduardo Zerna Torres, Marios Avgeris, Chrysa Papagianni, Gergely Pongr\'acz, Istv\'an G\'odor, Paola Grosso
https://arxiv.org/abs/2508.13806

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 sw…

Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Network
Nilay Kushawaha, Carlo Alessi, Lorenzo Fruzzetti, Egidio Falotico
https://arxiv.org/abs/2508.14100

Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Network
Deep learning provides a powerful method for modeling the dynamics of soft robots, offering advantages over traditional analytical approaches that require precise knowledge of the robot's structure, material properties, and other physical characteristics. Given the inherent complexity and non-linearity of these systems, extracting such details can be challenging. The mappings learned in one domain cannot be directly transferred to another domain with different physical properties. This challeng…

Excited to be travelling to Paris next week (train obviously) to attend the Sunbelt conference of the International Network of Social Network Analysis
It's my first Sunbelt since 2019 and I'm really looking forward to connecting with people and learning about latest research
#Sunbelt2025

Learning from user's behaviour of some well-known congested traffic networks
Isolda Cardoso, Lucas Venturato, Jorgelina Walpen
https://arxiv.org/abs/2508.14804 https://

Learning from user's behaviour of some well-known congested traffic networks
We consider the problem of predicting users' behavior of a congested traffic network under an equilibrium condition, the traffic assignment problem. We propose a two-stage machine learning approach which couples a neural network with a fixed point algorithm, and we evaluate its performance along several classical congested traffic networks.

Machine Learning-aided Optimal Control of a noisy qubit
Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli, Elisabetta Paladino, Giuseppe Falci
https://arxiv.org/abs/2507.14085

Machine Learning-aided Optimal Control of a noisy qubit
We apply a graybox machine-learning framework to model and control a qubit undergoing Markovian and non-Markovian dynamics from environmental noise. The approach combines physics-informed equations with a lightweight transformer neural network based on the self-attention mechanism. The model is trained on simulated data and learns an effective operator that predicts observables accurately, even in the presence of memory effects. We benchmark both non-Gaussian random-telegraph noise and Gaussian…

Learning the non-Markovian features of subsystem dynamics
Michele Coppola, Mari Carmen Ba\~nuls, Zala Lenar\v{c}i\v{c}
https://arxiv.org/abs/2507.14133 htt…

Learning the non-Markovian features of subsystem dynamics
The dynamics of local observables in a quantum many-body system can be formally described in the language of open systems. The problem is that the bath representing the complement of the local subsystem generally does not allow the common simplifications often crucial for such a framework. Leveraging tensor network calculations and optimization tools from machine learning, we extract and characterize the dynamical maps for single- and two-site subsystems embedded in an infinite quantum Ising ch…

Improving Deep Learning for Accelerated MRI With Data Filtering
Kang Lin, Anselm Krainovic, Kun Wang, Reinhard Heckel
https://arxiv.org/abs/2508.13822 https://

Improving Deep Learning for Accelerated MRI With Data Filtering
Deep neural networks achieve state-of-the-art results for accelerated MRI reconstruction. Most research on deep learning based imaging focuses on improving neural network architectures trained and evaluated on fixed and homogeneous training and evaluation data. In this work, we investigate data curation strategies for improving MRI reconstruction. We assemble a large dataset of raw k-space data from 18 public sources consisting of 1.1M images and construct a diverse evaluation set comprising 48…

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
Cameron T. Pratt, Zhijie Qu, Joel N. Bregman
https://arxiv.org/abs/2507.13400

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
The thermal Sunyaev$-$Zel'dovich (SZ) effect offers a unique probe of the hot and diffuse universe that could help close the missing baryon problem. Traditional extractions of the SZ effect, however, exhibit systematic noise that may lead to unreliable results. In this work, we provide an alternative solution using a three-dimensional Attention Nested U-Net trained end-to-end with supervised learning. Our labeled data consists of simulated SZ signals injected into $\textit{Planck}$ frequency ma…

Stochastic synaptic dynamics under learning
Jakob Stubenrauch, Naomi Auer, Richard Kempter, Benjamin Lindner
https://arxiv.org/abs/2508.13846 https://arxiv…

Stochastic synaptic dynamics under learning
Learning is based on synaptic plasticity, which drives and is driven by neural activity. Because pre- and postsynaptic spiking activity is shaped by randomness, the synaptic weights follow a stochastic process, requiring a probabilistic framework to capture the noisy synaptic dynamics. We consider a paradigmatic supervised learning example: a presynaptic neural population impinging in a sequence of episodes on a recurrent network of integrate-and-fire neurons through synapses undergoing spike-t…

BaMANI: Bayesian Multi-Algorithm causal Network Inference
Habibolla Latifizadeh, Anika C. Pirkey, Alanna Gould, David J. Klinke II
https://arxiv.org/abs/2508.11741 https://

BaMANI: Bayesian Multi-Algorithm causal Network Inference
Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also bear an opaque imprint of the specific computational algorithm used. Following a ``wisdom of the crowds" strategy, we developed an ensemble learning approach …

Addressing the ML Domain Adaptation Problem for Networking: Realistic and Controllable Training Data Generation with NetReplica
Jaber Daneshamooz, Jessica Nguyen, William Chen, Sanjay Chandrasekaran, Satyandra Guthula, Ankit Gupta, Arpit Gupta, Walter Willinger
https://arxiv.org/abs/2507.13476

Addressing the ML Domain Adaptation Problem for Networking: Realistic and Controllable Training Data Generation with NetReplica
Machine learning models in networking suffer from the domain adaptation problem; models trained in one domain often fail when deployed in different production environments. This paper presents the design and implementation of NetReplica, a system that addresses this challenge by generating training datasets with two critical properties: realism in protocol dynamics and controllability of network conditions. NetReplica models networks as collections of bottleneck links with specific attributes, …

QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography
Hao Fang, Sihao Teng, Hao Yu, Siyi Yuan, Huaiwu He, Zhe Liu, Yunjie Yang
https://arxiv.org/abs/2507.14031

QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography
Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution, making it suitable for bedside monitoring. However, its inherently ill-posed inverse problem poses significant challenges for accurate image reconstruction. Deep learning (DL)-based approaches have shown promise but often rely on complex network architectures with a large number of parameters, limiting efficiency and scalability. Here, we propose an Ultra-Lightweight Quantum…

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
Wuyang Zhang, Yexin Tian, Xiandong Meng, Mengjie Wang, Junliang Du
https://arxiv.org/abs/2508.14427

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
This paper addresses the problems of missing reasoning chains and insufficient entity-level semantic understanding in large language models when dealing with tasks that require structured knowledge. It proposes a fine-tuning algorithm framework based on knowledge graph injection. The method builds on pretrained language models and introduces structured graph information for auxiliary learning. A graph neural network is used to encode entities and their relations, constructing a graph-based sema…

A segmented robot grasping perception neural network for edge AI
Casper Br\"ocheler, Thomas Vroom, Derrick Timmermans, Alan van den Akker, Guangzhi Tang, Charalampos S. Kouzinopoulos, Rico M\"ockel
https://arxiv.org/abs/2507.13970

A segmented robot grasping perception neural network for edge AI
Robotic grasping, the ability of robots to reliably secure and manipulate objects of varying shapes, sizes and orientations, is a complex task that requires precise perception and control. Deep neural networks have shown remarkable success in grasp synthesis by learning rich and abstract representations of objects. When deployed at the edge, these models can enable low-latency, low-power inference, making real-time grasping feasible in resource-constrained environments. This work implements Hea…

Machine learning-based classification of variable stars using phase-folded light curves
Almat Akhmetali, Alisher Zhunuskanov, Timur Namazbayev, Marat Zaidyn, Aknur Sakan, Dana Turlykozhayeva, Nurzhan Ussipov
https://arxiv.org/abs/2508.11964

Machine learning-based classification of variable stars using phase-folded light curves
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques continues to grow. Traditional methods often rely on manual feature extraction and selection, which can be labor-intensive and less effective for managing extensive datasets. In this study, we present a convolutional neural network (CNN)-based method for cla…

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
Wenxuan Ye, Xueli An, Onur Ayan, Junfan Wang, Xueqiang Yan, Georg Carle
https://arxiv.org/abs/2508.13625

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
Large models, renowned for superior performance, outperform smaller ones even without billion-parameter scales. While mobile network servers have ample computational resources to support larger models than client devices, privacy constraints prevent clients from directly sharing their raw data. Federated Learning (FL) enables decentralized clients to collaboratively train a shared model by exchanging model parameters instead of transmitting raw data. Yet, it requires a uniform model architectur…

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Zhuo Liu, Tao Chen
https://arxiv.org/abs/2508.11659

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing im-plementations of EP suffer from instability and prohibi-tively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically plau-sible Feedback-regulated REsidual recurrent neural network (FRE-RNN) and study its learning performance…

Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
Linlin Wang, Wei Wang, Dezhao Wang, Shanwen Wang
https://arxiv.org/abs/2506.15125 http…

Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
With the applicability of optical fiber-based distributed acoustic sensing (DAS) systems, effective signal processing and analysis approaches are needed to promote its popularization in the field of intelligent transportation systems (ITS). This paper presents a signal denoising algorithm using a hybrid deep-learning network (HDLNet). Without annotated data and time-consuming labeling, this self-supervised network runs in parallel, combining an autoencoder for denoising (DAE) and a long short-t…

Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of Synthesized Speech Under Distribution Shifts
Ashi Garg, Zexin Cai, Henry Li Xinyuan, Leibny Paola Garc\'ia-Perera, Kevin Duh, Sanjeev Khudanpur, Matthew Wiesner, Nicholas Andrews
https://arxiv.org/abs/2508.13320

Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of Synthesized Speech Under Distribution Shifts
We address the challenge of detecting synthesized speech under distribution shifts -- arising from unseen synthesis methods, speakers, languages, or audio conditions -- relative to the training data. Few-shot learning methods are a promising way to tackle distribution shifts by rapidly adapting on the basis of a few in-distribution samples. We propose a self-attentive prototypical network to enable more robust few-shot adaptation. To evaluate our approach, we systematically compare the performa…

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Laura Boggia, Carlos Cocha, Fotis Giasemis, Joachim Hansen, Patin Inkaew, Kaare Endrup Iversen, Pratik Jawahar, Henrique Pineiro Monteagudo, Micol Olocco, Sten Astrand, Martino Borsato, Leon Bozianu, Steven Schramm, the SMARTHEP Network

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionised many aspects of the data processing pipeline at collider experiments including the Large Hadron Collider (LHC). In this whitepaper, we discuss the increasingly crucial role that ML plays in real-time analysis (RTA) at the LHC, namely in the context of the unique challenges posed by the trigger systems of the large …

How does the #brain transfer #MotorSkills between hands?
This study reveals that transfer relies on re-expressing the neural patterns established during initial learning in distributed higher-order brain areas,
offering new insights into learning

Transfer of motor learning is associated with patterns of activity in the default mode network
How does the brain transfer motor skills between hands? This study reveals that transfer relies on re-expressing the neural patterns established during initial learning in distributed higher-order brain areas, offering new insights into learning generalization.

Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
Jose L. Bonilla, Krzysztof M. Graczyk, Artur M. Ankowski, Rwik Dharmapal Banerjee, Beata E. Kowal, Hemant Prasad, Jan T. Sobczyk
https://arxiv.org/abs/2508.12987

Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
We utilize transfer learning to extrapolate the physics knowledge encoded in a Generative Adversarial Network (GAN) model trained on synthetic charged-current (CC) neutrino-carbon inclusive scattering data. This base model is adapted to generate CC inclusive scattering events (lepton kinematics only) for neutrino-argon and antineutrino-carbon interactions. Furthermore, we assess the effectiveness of transfer learning in re-optimizing a custom model when new data comes from a different neutrino-…

A Hybrid Neural Network -- Polynomial Series Scheme for Learning Invariant Manifolds of Discrete Dynamical Systems
Dimitrios G. Patsatzis, Nikolaos Kazantzis, Ioannis G. Kevrekidis, Constantinos Siettos
https://arxiv.org/abs/2506.13950

A Hybrid Neural Network -- Polynomial Series Scheme for Learning Invariant Manifolds of Discrete Dynamical Systems
We propose a hybrid machine learning scheme to learn -- in physics-informed and numerical analysis-informed fashion -- invariant manifolds (IM) of discrete maps for constructing reduced-order models (ROMs) for dynamical systems. The proposed scheme combines polynomial series with shallow neural networks, exploiting the complementary strengths of both approaches. Polynomials enable an efficient and accurate modeling of ROMs with guaranteed local exponential convergence rate around the fixed poin…

Determining the chemical potential via universal density functional learning
Florian Samm\"uller, Matthias Schmidt
https://arxiv.org/abs/2506.15608 ht…

Determining the chemical potential via universal density functional learning
We demonstrate that the machine learning of density functionals allows one to determine simultaneously the equilibrium chemical potential across simulation datasets of inhomogeneous classical fluids. Minimization of an implicit loss function based on an Euler-Lagrange equation yields both the universal one-body direct correlation functional, which is represented locally by a neural network, as well as the system-specific unknown chemical potential values. The method can serve as an efficient al…

Learning to See Through Flare
Xiaopeng Peng, Heath Gemar, Erin Fleet, Kyle Novak, Abbie Watnik, Grover Swartzlander
https://arxiv.org/abs/2508.13907 https://

Learning to See Through Flare
Machine vision systems are susceptible to laser flare, where unwanted intense laser illumination blinds and distorts its perception of the environment through oversaturation or permanent damage to sensor pixels. We introduce NeuSee, the first computational imaging framework for high-fidelity sensor protection across the full visible spectrum. It jointly learns a neural representation of a diffractive optical element (DOE) and a frequency-space Mamba-GAN network for image restoration. NeuSee sys…

IoT Malware Network Traffic Detection using Deep Learning and GraphSAGE Models
Nikesh Prajapati, Bimal Karki, Saroj Gopali, Akbar Siami Namin
https://arxiv.org/abs/2507.10758

IoT Malware Network Traffic Detection using Deep Learning and GraphSAGE Models
This paper intends to detect IoT malicious attacks through deep learning models and demonstrates a comprehensive evaluation of the deep learning and graph-based models regarding malicious network traffic detection. The models particularly are based on GraphSAGE, Bidirectional encoder representations from transformers (BERT), Temporal Convolutional Network (TCN) as well as Multi-Head Attention, together with Bidirectional Long Short-Term Memory (BI-LSTM) Multi-Head Attention and BI-LSTM and LSTM…

CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient Fine-Tuning
Saisai Xia, Wenhao Wang, Zihao Wang, Yuhui Zhang, Yier Jin, Dan Meng, Rui Hou
https://arxiv.org/abs/2508.12264

CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient Fine-Tuning
Publicly available large pretrained models (i.e., backbones) and lightweight adapters for parameter-efficient fine-tuning (PEFT) have become standard components in modern machine learning pipelines. However, preserving the privacy of both user inputs and fine-tuned adapters -- often trained on sensitive data -- during inference remains a significant challenge. Applying cryptographic techniques, such as multi-party computation (MPC), to PEFT settings still incurs substantial encrypted computatio…

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
Yimian Ding, Jingzehua Xu, Guanwen Xie, Shuai Zhang, Yi Li
https://arxiv.org/abs/2506.15082

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental ad…

Learning to Maximize Quantum Neural Network Expressivity via Effective Rank
Juan Yao
https://arxiv.org/abs/2506.15375 https://arxiv.o…

Learning to Maximize Quantum Neural Network Expressivity via Effective Rank
Quantum neural networks (QNNs) are widely employed as ansätze for solving variational problems, where their expressivity directly impacts performance. Yet, accurately characterizing QNN expressivity remains an open challenge, impeding the optimal design of quantum circuits. In this work, we introduce the effective rank, denoted as $κ$, as a novel quantitative measure of expressivity. Specifically, $κ$ captures the number of effectively independent parameters among all the variational paramet…

Search for Z/2 eigenfunctions on the sphere using machine learning
Andriy Haydys, Willem Adriaan Salm
https://arxiv.org/abs/2507.13122 https://

Search for Z/2 eigenfunctions on the sphere using machine learning
We use machine learning to search for examples of Z/2 eigenfunctions on the 2-sphere. For this we created a multivalued version of a feedforward deep neural network, and we implemented it using the JAX library. We found Z/2 eigenfunctions for three cases: In the first two cases we fixed the branch points at the vertices of a tetrahedron and at a cube respectively. In a third case, we allowed the AI to move the branch points around and, in the end, it positioned the branch points at the vertices…

Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Ziheng Chen, Po T. Wang, Mina Ibrahim, Shivali Baveja, Rong Mu, An H. Do, Zoran Nenadic
https://arxiv.org/abs/2506.14120

Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Lengthy subject- or session-specific data acquisition and calibration remain a key barrier to deploying electroencephalography (EEG)-based brain-computer interfaces (BCIs) outside the laboratory. Previous work has shown that cross subject, cross-session invariant features exist in EEG. We propose a transfer learning pipeline based on a two-layer convolutional neural network (CNN) that leverages these invariants to reduce the burden of data acquisition and calibration. A baseline model is traine…

Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases
Md. Tanvir Alam, Md. Ahasanul Alam, Md Mahmudur Rahman, Md. Mosaddek Khan
https://arxiv.org/abs/2507.12562

Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases
Relational databases (RDBs) are ubiquitous in enterprise and real-world applications. Flattening the database poses challenges for deep learning models that rely on fixed-size input representations to capture relational semantics from the structured nature of relational data. Graph neural networks (GNNs) have been proposed to address this, but they often oversimplify relational structures by modeling all the tuples as monolithic nodes and ignoring intra-tuple associations. In this work, we prop…

Expansive Natural Neural Gradient Flows for Energy Minimization
Wolfgang Dahmen, Wuchen Li, Yuankai Teng, Zhu Wang
https://arxiv.org/abs/2507.13475 https:/…

Expansive Natural Neural Gradient Flows for Energy Minimization
This paper develops expansive gradient dynamics in deep neural network-induced mapping spaces. Specifically, we generate tools and concepts for minimizing a class of energy functionals in an abstract Hilbert space setting covering a wide scope of applications such as PDEs-based inverse problems and supervised learning. The approach hinges on a Hilbert space metric in the full diffeomorphism mapping space, which could be viewed as a generalized Wasserstein-2 metric. We then study a projection gr…

An ECC-based Fault Tolerance Approach for DNNs
Mohsen Raji, Mohammad Zaree, Kimia Soroush
https://arxiv.org/abs/2508.12347 https://arxiv.org/pdf/2508.12347…

An ECC-based Fault Tolerance Approach for DNNs
Deep Neural Network (DNN) has achieve great success in solving a wide range of machine learning problems. Recently, they have been deployed in datacenters (potentially for business-critical or industrial applications) and safety-critical systems such as self-driving cars. So, their correct functionality in the presence of potential bit-flip errors on DNN parameters stored in memories plays the key role in their applicability in safety-critical applications. In this paper, a fault tolerance appr…

Beyond ReLU: Chebyshev-DQN for Enhanced Deep Q-Networks
Saman Yazdannik, Morteza Tayefi, Shamim Sanisales
https://arxiv.org/abs/2508.14536 https://arxiv.or…

Beyond ReLU: Chebyshev-DQN for Enhanced Deep Q-Networks
The performance of Deep Q-Networks (DQN) is critically dependent on the ability of its underlying neural network to accurately approximate the action-value function. Standard function approximators, such as multi-layer perceptrons, may struggle to efficiently represent the complex value landscapes inherent in many reinforcement learning problems. This paper introduces a novel architecture, the Chebyshev-DQN (Ch-DQN), which integrates a Chebyshev polynomial basis into the DQN framework to create…

Learning-Based Interface for Semantic Communication with Bit Importance Awareness
Wenzheng Kong, Wenyi Zhang
https://arxiv.org/abs/2507.12850 https://

Learning-Based Interface for Semantic Communication with Bit Importance Awareness
Joint source-channel coding (JSCC) is an effective approach for semantic communication. However, current JSCC methods are difficult to integrate with existing communication network architectures, where application and network providers are typically different entities. Recently, a novel paradigm termed Split DeepJSCC has been under consideration to address this challenge. Split DeepJSCC employs a bit-level interface that enables separate design of source and channel codes, ensuring compatibilit…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning
Simon P. von der Maase
https://arxiv.org/abs/2506.14817

Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning
Forecasting violent conflict at high spatial and temporal resolution remains a central challenge for both researchers and policymakers. This study presents a novel neural network architecture for forecasting three distinct types of violence -- state-based, non-state, and one-sided -- at the subnational (priogrid-month) level, up to 36 months in advance. The model jointly performs classification and regression tasks, producing both probabilistic estimates and expected magnitudes of future events…

Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?
Mohammed Saqr, Kamila Misiejuk, Sonsoles L\'opez-Pernas
https://arxiv.org/abs/2508.10919

Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?
While research on human-AI collaboration exists, it mainly examined language learning and used traditional counting methods with little attention to evolution and dynamics of collaboration on cognitively demanding tasks. This study examines human-AI interactions while solving a complex problem. Student-AI interactions were qualitatively coded and analyzed with transition network analysis, sequence analysis and partial correlation networks as well as comparison of frequencies using chi-square an…

Fully Spiking Actor-Critic Neural Network for Robotic Manipulation
Liwen Zhang, Heng Deng, Guanghui Sun
https://arxiv.org/abs/2508.12038 https://arxiv.org/…

Fully Spiking Actor-Critic Neural Network for Robotic Manipulation
This study proposes a hybrid curriculum reinforcement learning (CRL) framework based on a fully spiking neural network (SNN) for 9-degree-of-freedom robotic arms performing target reaching and grasping tasks. To reduce network complexity and inference latency, the SNN architecture is simplified to include only an input and an output layer, which shows strong potential for resource-constrained environments. Building on the advantages of SNNs-high inference speed, low energy consumption, and spik…

Theory-informed neural networks for particle physics
Barry M. Dillon, Michael Spannowsky
https://arxiv.org/abs/2507.13447 https://arx…

Theory-informed neural networks for particle physics
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision process. A transformer-based Deep Q-Network, rewarded at each step by the logarithmic change in the tree-level matrix element, learns to map final-state particles to partons. Because the reward derives solely from first-principles theory, the resulting policy is label-free and fully interpretable, allowing every reconstru…

A Multimodal Data Fusion Generative Adversarial Network for Real Time Underwater Sound Speed Field Construction
Wei Huang, Yuqiang Huang, Yanan Wu, Tianhe Xu, Junting Wang, Hao Zhang
https://arxiv.org/abs/2507.11812

A Multimodal Data Fusion Generative Adversarial Network for Real Time Underwater Sound Speed Field Construction
Sound speed profiles (SSPs) are essential parameters underwater that affects the propagation mode of underwater signals and has a critical impact on the energy efficiency of underwater acoustic communication and accuracy of underwater acoustic positioning. Traditionally, SSPs can be obtained by matching field processing (MFP), compressive sensing (CS), and deep learning (DL) methods. However, existing methods mainly rely on on-site underwater sonar observation data, which put forward strict req…

ATLAS: AI-Native Receiver Test-and-Measurement by Leveraging AI-Guided Search
Mauro Belgiovine, Suyash Pradhan, Johannes Lange, Michael L\"ohning, Kaushik Chowdhury
https://arxiv.org/abs/2508.12204

ATLAS: AI-Native Receiver Test-and-Measurement by Leveraging AI-Guided Search
Industry adoption of Artificial Intelligence (AI)-native wireless receivers, or even modular, Machine Learning (ML)-aided wireless signal processing blocks, has been slow. The main concern is the lack of explainability of these trained ML models and the significant risks posed to network functionalities in case of failures, especially since (i) testing on every exhaustive case is infeasible and (ii) the data used for model training may not be available. This paper proposes ATLAS, an AI-guided a…

Low-latency Forecasts of Kilonova Light Curves for Rubin and ZTF
Natalya Plestkova, Niharika Sravan, R. Weizmann Kiendrebeogo, Michael W. Coughlin, Derek Davis, Andrew Toivonen, Theophile Jegou du Laz, Tom\'as Ahumada, Tyler Barna, George Helou, Roger Smith, Ben Rusholme, Russ R. Laher, Ashish A. Mahabal
https://arxiv.org/ab…

Low-latency Forecasts of Kilonova Light Curves for Rubin and ZTF
Follow-up of gravitational-wave events by wide-field surveys is a crucial tool for the discovery of electromagnetic counterparts to gravitational wave sources, such as kilonovae. Machine learning tools can play an important role in aiding search efforts. We have developed a public tool to predict kilonova light curves using simulated low-latency alert data from the International Gravitational Wave Network during observing runs 4 (O4) and 5 (O5). It uses a bidirectional long-short-term memory (L…

Asymmetric Network Games: $\alpha$-Potential Function and Learning
Kiran Rokade, Adit Jain, Francesca Parise, Vikram Krishnamurthy, Eva Tardos
https://arxiv.org/abs/2508.06619 h…

Asymmetric Network Games: $α$-Potential Function and Learning
In a network game, players interact over a network and the utility of each player depends on his own action and on an aggregate of his neighbours' actions. Many real world networks of interest are asymmetric and involve a large number of heterogeneous players. This paper analyzes static network games using the framework of $α$-potential games. Under mild assumptions on the action sets (compact intervals) and the utility functions (twice continuously differentiable) of the players, we derive an…

Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network
Mehrshad Eskandarpour, Saba Pirahmadian, Parham Soltani, Hossein Soleimani
https://arxiv.org/abs/2508.12707

Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network
Energy in Wireless Sensor Networks (WSNs) is critical to network lifetime and data delivery. However, the primary impediment to the durability and dependability of these sensor nodes is their short battery life. Currently, power-saving algorithms such as clustering and routing algorithms have improved energy efficiency in standard protocols. This paper proposes a clustering-based routing approach for creating an adaptive, energy-efficient mechanism. Our system employs a multi-step clustering st…

Learning magic in the Schwinger model
Samuel Crew, Hsueh Hao Lu
https://arxiv.org/abs/2508.09640 https://arxiv.org/pdf/2508.09640

Learning magic in the Schwinger model
We demonstrate the use of variational neural network quantum states to study non-stabilizerness in qubit-regularised quantum field theory. Applying the methodology recently introduced by Sinibaldi et al., we numerically compute the stabilizer Rényi entropy of ground states of the Schwinger model with a topological term. We examine how the magic content of these states depends on the separation between external probe charges, providing insight into the classical hardness of simulating gauge the…

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
Jiaming Yu, Le Liang, Hao Ye, Shi Jin
https://arxiv.org/abs/2506.14187

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
High-density Wi-Fi deployments often result in significant co-channel interference, which degrades overall network performance. To address this issue, coordination of multi access points (APs) has been considered to enable coordinated spatial reuse (CSR) in next generation wireless local area networks. This paper tackles the challenge of downlink spatial reuse in Wi-Fi networks, specifically in scenarios involving overlapping basic service sets, by employing hierarchical multi-agent reinforceme…

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Vincent Lannurien, Cam\'elia Slimani, Louis Morge-Rollet, Laurent Lemarchand, David Espes, Fr\'ed\'eric Le Roy, Jalil Boukhobza
https://arxiv.org/abs/2506.11800

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Swarms of drones are gaining more and more autonomy and efficiency during their missions. However, security threats can disrupt their missions' progression. To overcome this problem, Network Intrusion Detection Systems ((N)IDS) are promising solutions to detect malicious behavior on network traffic. However, modern NIDS rely on resource-hungry machine learning techniques, that can be difficult to deploy on a swarm of drones. The goal of the DISPEED project is to leverage the heterogeneity (exec…

Machine learning analysis of Photometric data from the Dark Energy Survey
Elcio Abdalla, Filipe B. Abdalla, Alessandro Marins, Amilcar Queiroz, Rafael M. Ribeiro, Alex S. C. Souza
https://arxiv.org/abs/2508.10191

Machine learning analysis of Photometric data from the Dark Energy Survey
In order to retrieve cosmological parameters from photometric surveys, we need to estimate the distribution of the photometric redshift in the sky with excellent accuracy. We use and apply three different machine learning methods to publicly available Dark Energy Survey data release 2 (DR2): a) Artificial Neural Network for photometric redshifts (ANNz2); b) Gaussian processes for photometric redshifts (GPz); and c) Keras, a deep learning application programming interface in Python. We compare t…

IDS-Net: A novel framework for few-shot photovoltaic power prediction with interpretable dynamic selection and feature information fusion
Hang Fan, Weican Liu, Zuhan Zhang, Ying Lu, Wencai Run, Dunnan Liu
https://arxiv.org/abs/2507.12745

IDS-Net: A novel framework for few-shot photovoltaic power prediction with interpretable dynamic selection and feature information fusion
With the growing demand for renewable energy, countries are accelerating the construction of photovoltaic (PV) power stations. However, accurately forecasting power data for newly constructed PV stations is extremely challenging due to limited data availability. To this end, we propose a novel interpretable dynamic selection network (IDS-Net) based on feature information fusion to achieve accurate few-shot prediction. This transfer learning framework primarily consists of two parts. In the firs…

The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty
Mario Brcic
https://arxiv.org/abs/2508.05867 https://arxiv.org/pd…

The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty
The advent of continuously learning Artificial Intelligence (AI) assistants marks a paradigm shift from episodic interactions to persistent, memory-driven relationships. This paper introduces the concept of "Cognitive Sovereignty", the ability of individuals, groups, and nations to maintain autonomous thought and preserve identity in the age of powerful AI systems, especially those that hold their deep personal memory. It argues that the primary risk of these technologies transcends traditional…

Memorisation and forgetting in a learning Hopfield neural network: bifurcation mechanisms, attractors and basins
Adam E. Essex (Loughborough University, England), Natalia B. Janson (Loughborough University, England), Rachel A. Norris (Loughborough University, England), Alexander G. Balanov (Loughborough University, England)
https://arxiv.o…

Memorisation and forgetting in a learning Hopfield neural network: bifurcation mechanisms, attractors and basins
Despite explosive expansion of artificial intelligence based on artificial neural networks (ANNs), these are employed as "black boxes'', as it is unclear how, during learning, they form memories or develop unwanted features, including spurious memories and catastrophic forgetting. Much research is available on isolated aspects of learning ANNs, but due to their high dimensionality and non-linearity, their comprehensive analysis remains a challenge. In ANNs, knowledge is thought to reside in con…

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Yonatan Kurniawan (Department of Physics and Astronomy, Brigham Young University, Provo, Utah, USA), Mingjian Wen (Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China), Ellad B. Tadmor (Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, Minnesota, USA), Mark K. Transtru…

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful tools for large-scale materials modeling. The accuracy of MLIPs is typically validated on a held-out dataset of \emph{ab initio} energies and atomic forces. However, accuracy on these small-scale properties does not guarantee reliability for emergent, system-level behavior -- precisely the regime where atomistic simula…

Unraveling the Biomarker Prospects of High-Altitude Diseases: Insights from Biomolecular Event Network Constructed using Text Mining
Balu Bhasuran, Sabenabanu Abdulkadhar, Jeyakumar Natarajan
https://arxiv.org/abs/2507.10953

Unraveling the Biomarker Prospects of High-Altitude Diseases: Insights from Biomolecular Event Network Constructed using Text Mining
High-altitude diseases (HAD), encompassing acute mountain sickness (AMS), high-altitude cerebral edema (HACE), and high-altitude pulmonary edema (HAPE), are triggered by hypobaric hypoxia at elevations above 2,500 meters. These conditions pose significant health risks, yet the molecular mechanisms remain insufficiently understood. In this study, we developed a biomolecular event extraction pipeline integrating supervised machine learning with feature-based and multiscale Laplacian graph kernels…

A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
Chao Feng, Alberto Huertas Celdran, Jing Han, Heqing Ren, Xi Cheng, Zien Zeng, Lucas Krauter, Gerome Bovet, Burkhard Stiller
https://arxiv.org/abs/2507.13313

A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
This paper introduces a dataset and experimental study for decentralized federated learning (DFL) applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls, file system activities, resource usage, kernel events, input/output events, and network records. These records were aggregated into 30-second windows, resulting in 342,106 features used for model trai…

A Framework to Pinpoint Bottlenecks in Emerging Solar Cells and Disordered Devices via Differential Machine Learning
Cai Williams, Chen Wang, Alexander Ehm, Dietrich R. T. Zahn, Maria Saladina, Carsten Deibel, Roderick C. I. Mackenzie
https://arxiv.org/abs/2507.11740

A Framework to Pinpoint Bottlenecks in Emerging Solar Cells and Disordered Devices via Differential Machine Learning
A key challenge in the development of materials for the next generation of solar cells, sensors and transistors is linking macroscopic device performance to underlying microscopic properties. For years, fabrication of devices has been faster than our ability to characterize them. This has led to a random walk of material development, with new materials being proposed faster than our understanding. We present two neural network-based methods for extracting key material parameters, including char…

Catching Bid-rigging Cartels with Graph Attention Neural Networks
David Imhof, Emanuel W Viklund, Martin Huber
https://arxiv.org/abs/2507.12369 https://arx…

Catching Bid-rigging Cartels with Graph Attention Neural Networks
We propose a novel application of graph attention networks (GATs), a type of graph neural network enhanced with attention mechanisms, to develop a deep learning algorithm for detecting collusive behavior, leveraging predictive features suggested in prior research. We test our approach on a large dataset covering 13 markets across seven countries. Our results show that predictive models based on GATs, trained on a subset of the markets, can be effectively transferred to other markets, achieving …

Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
Eugene Kofi Okrah Denteh, Andrews Danyo, Joshua Kofi Asamoah, Blessing Agyei Kyem, Armstrong Aboah
https://arxiv.org/abs/2506.12456

Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio-demographics, and travel behavior dynamics. The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data to accurately forecast future spatial transformations. The study also introduces a demographics prediction component which…

Unifying equivalences across unsupervised learning, network science, and imaging/network neuroscience
Mika Rubinov
https://arxiv.org/abs/2508.10045 https://

Unifying equivalences across unsupervised learning, network science, and imaging/network neuroscience
Modern scientific fields face the challenge of integrating a wealth of data, analyses, and results. We recently showed that a neglect of this integration can lead to circular analyses and redundant explanations. Here, we help advance scientific integration by describing equivalences that unify diverse analyses of datasets and networks. We describe equivalences across analyses of clustering and dimensionality reduction, network centrality and dynamics, and popular models in imaging and network n…

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning
Ashish Gautam, Prasanna Date, Shruti Kulkarni, Robert Patton, Thomas Potok
https://arxiv.org/abs/2506.14138

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning
Spiking Neural Networks (SNNs) are computational models inspired by the structure and dynamics of biological neuronal networks. Their event-driven nature enables them to achieve high energy efficiency, particularly when deployed on neuromorphic hardware platforms. Unlike conventional Artificial Neural Networks (ANNs), which primarily rely on layered architectures, SNNs naturally support a wide range of connectivity patterns, from traditional layered structures to small-world graphs characterize…

Weak TransNet: A Petrov-Galerkin based neural network method for solving elliptic PDEs
Zhihang Xu, Min Wang, Zhu Wang
https://arxiv.org/abs/2506.14812 http…

Weak TransNet: A Petrov-Galerkin based neural network method for solving elliptic PDEs
While deep learning has achieved remarkable success in solving partial differential equations (PDEs), it still faces significant challenges, particularly when the PDE solutions have low regularity or singularities. To address these issues, we propose the Weak TransNet (WTN) method, based on a Petrov-Galerkin formulation, for solving elliptic PDEs in this work, though its framework may extend to other classes of equations. Specifically, the neural feature space defined by TransNet (Zhang et al.,…

Online Training and Pruning of Deep Reinforcement Learning Networks
Valentin Frank Ingmar Guenter, Athanasios Sideris
https://arxiv.org/abs/2507.11975 http…

Online Training and Pruning of Deep Reinforcement Learning Networks
Scaling deep neural networks (NN) of reinforcement learning (RL) algorithms has been shown to enhance performance when feature extraction networks are used but the gained performance comes at the significant expense of increased computational and memory complexity. Neural network pruning methods have successfully addressed this challenge in supervised learning. However, their application to RL is underexplored. We propose an approach to integrate simultaneous training and pruning within advance…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Managemen...
Li, Madhukumar, Li, Liu, Teng, Wu, Wang, Yan, Simeonidou

Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Anup Mishra, \v{C}edomir Stefanovi\'c, Xiuqiang Xu, Petar Popovski, Israel Leyva-Mayorga
https://arxiv.org/abs/2506.15273

Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Flexible and efficient wireless resource sharing across heterogeneous services is a key objective for future wireless networks. In this context, we investigate the performance of a system where latency-constrained internet-of-things (IoT) devices coexist with a broadband user. The base station adopts a grant-free access framework to manage resource allocation, either through orthogonal radio access network (RAN) slicing or by allowing shared access between services. For the IoT users, we propos…

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach
Thomas Feys, Liesbet Van der Perre, Fran\c{c}ois Rottenberg
https://arxiv.org/abs/2507.10634

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach
Massive MIMO systems are moving toward increased numbers of radio frequency chains, higher carrier frequencies and larger bandwidths. As such, digital-to-analog converters (DACs) are becoming a bottleneck in terms of hardware complexity and power consumption. In this work, non-linear precoding for coarsely quantized downlink massive MIMO is studied. Given the NP-hard nature of this problem, a graph neural network (GNN) is proposed that directly outputs the precoded quantized vector based on the…

Observable Optimization for Precision Theory: Machine Learning Energy Correlators
Arindam Bhattacharya, Katherine Fraser, Matthew D. Schwartz
https://arxiv.org/abs/2508.10988 ht…

Observable Optimization for Precision Theory: Machine Learning Energy Correlators
The practice of collider physics typically involves the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In any cases, such as classification or anomaly detection, the observable can be arbitrarily complicated, such as the output of a neural network. However, for precision measurements, the observable must correspond to something computable systematically beyond the level of current simulation tools. In this work, we demonstrate t…

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Afra Kilic, Kim Batselier
https://arxiv.org/abs/2507.11136 …

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model complexity hyperparameters like tensor rank and feature dimensions, often through trial-and-error or computationally costly methods like cross-validation. We propose Bayesian Tensor Network Kernel Machines, a fully probabil…

CIRO7.2: A Material Network with Circularity of -7.2 and Reinforcement-Learning-Controlled Robotic Disassembler
Federico Zocco, Monica Malvezzi
https://arxiv.org/abs/2506.11748

CIRO7.2: A Material Network with Circularity of -7.2 and Reinforcement-Learning-Controlled Robotic Disassembler
The competition over natural reserves of minerals is expected to increase in part because of the linear-economy paradigm based on take-make-dispose. Simultaneously, the linear economy considers end-of-use products as waste rather than as a resource, which results in large volumes of waste whose management remains an unsolved problem. Since a transition to a circular economy can mitigate these open issues, in this paper we begin by enhancing the notion of circularity based on compartmental dynam…

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems
Gubio G. de Lima, Iann Cunha, Leonardo Kleber Castelano
https://arxiv.org/abs/2507.12552

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems
Accurate characterization of quantum systems is essential for the development of quantum technologies, particularly in the noisy intermediate-scale quantum (NISQ) era. While traditional methods for Hamiltonian learning and noise characterization often require extensive measurements and scale poorly with system size, machine learning approaches offer promising alternatives. In this work, we extend the inverse physics-informed neural network (referred to as PINNverse) framework to open quantum sy…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Preston G. Waldrop, Dimitrios Psaltis, Tong Zhao
https://arxiv.org/abs/2506.11194 http…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Ray tracing algorithms that compute pulse profiles from rotating neutron stars are essential tools for constraining neutron-star properties with data from missions such as NICER. However, the high computational cost of these simulations presents a significant bottleneck for inference algorithms that require millions of evaluations, such as Markov Chain Monte Carlo methods. In this work, we develop a residual neural network model that accelerates this calculation by predicting the observed flux …

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

Collective Communication Profiling of Modern-day Machine Learning Workloads
Jit Gupta, Andrew Li, Tarun Banka, Ariel Cohen, T. Sridhar, Raj Yavatkar
https://arxiv.org/abs/2507.07117

Collective Communication Profiling of Modern-day Machine Learning Workloads
Machine Learning jobs, carried out on large number of distributed high performance systems, involve periodic communication using operations like AllReduce, AllGather, and Broadcast. These operations may create high bandwidth and bursty traffic patterns, leading to network congestion and packet loss, thus impacting the performance of these jobs. Hence it is imperative to analyze these patterns, which can be helpful in provisioning network resources depending on the type of machine learning workl…

Vector Representations of Vessel Trees
James Batten, Michiel Schaap, Matthew Sinclair, Ying Bai, Ben Glocker
https://arxiv.org/abs/2506.11163 https://

Vector Representations of Vessel Trees
We introduce a novel framework for learning vector representations of tree-structured geometric data focusing on 3D vascular networks. Our approach employs two sequentially trained Transformer-based autoencoders. In the first stage, the Vessel Autoencoder captures continuous geometric details of individual vessel segments by learning embeddings from sampled points along each curve. In the second stage, the Vessel Tree Autoencoder encodes the topology of the vascular network as a single vector r…

Making Effective Decisions: Machine Learning and the Ecogame in 1970
Catherine Mason
https://arxiv.org/abs/2508.07027 https://arxiv.org/pdf/2508.07027

Making Effective Decisions: Machine Learning and the Ecogame in 1970
This paper considers Ecogame, an innovative art project of 1970, whose creators believed in a positive vision of a technological future; an understanding, posited on cybernetics, of a future that could be participatory via digital means, and therefore more democratised. Using simulation and early machine learning techniques over a live network, Ecogame combined the power of visual art with cybernetic concepts of adaptation, feedback, and control to propose that behaviour had implications for th…

Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
DongNyeong Heo, Daniela Noemi Rim, Heeyoul Choi
https://arxiv.org/abs/2506.13153

Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
An internet network service provider manages its network with multiple objectives, such as high quality of service (QoS) and minimum computing resource usage. To achieve these objectives, a reinforcement learning-based (RL) algorithm has been proposed to train its network management agent. Usually, their algorithms optimize their agents with respect to a single static reward formulation consisting of multiple objectives with fixed importance factors, which we call preferences. However, in pract…

Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models
Eunchi Kim, Paula Hartnagel, Barbara Urbano, Leonard Christen, Thomas Kirchartz
https://arxiv.org/abs/2506.13308

Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models
Machine learning has emerged as a promising approach for estimating material parameters in solar cells. Traditional methods for parameter extraction often rely on time-consuming numerical simulations that fail to capture the full complexity of the parameter space and discard valuable information from suboptimal simulations. In this study, we introduce a novel workflow for parameter estimation in organic solar cells based on a combination of numerical simulations and neural networks. The workflo…

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
Lixin Jia, Zhiqing Guo, Gaobo Yang, Liejun Wang, Keqin Li
https://arxiv.org/abs/2508.10741

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
The emergence of deepfake technology has introduced a range of societal problems, garnering considerable attention. Current deepfake detection methods perform well on specific datasets, but exhibit poor performance when applied to datasets with unknown forgery techniques. Moreover, as the gap between emerging and traditional forgery techniques continues to widen, cross-domain detection methods that rely on common forgery traces are becoming increasingly ineffective. This situation highlights th…

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
HyeYoung Lee, Muhammad Nadeem, Pavel Tsoi
https://arxiv.org/abs/2507.10622 h…

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
The rapid expansion of Internet of Things (IoT) networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging the Mel-frequency cepstral coefficients (MFCC) and ResNet-18, a deep learning model known for its effectiveness in feature extraction and image-based tasks. Learnable MFCCs enable adaptive spectral f…

Leveraging Quantum Layers in Classical Neural Networks
Silvie Ill\'esov\'a
https://arxiv.org/abs/2507.12505 https://arxiv.org…

Leveraging Quantum Layers in Classical Neural Networks
Hybrid quantum-classical neural networks represent a promising frontier in the search for improved machine learning models. This thesis explores the integration of quantum layers within classical convolutional neural network architectures, aiming to leverage quantum entanglement and feature mapping to enhance learning capabilities. A detailed methodology for constructing and training such hybrid models is presented, using PyTorch and Qiskit Machine Learning frameworks. Experiments investigate t…

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution
Kalana Wijegunarathna, Kristin Stock, Christopher B. Jones
https://arxiv.org/abs/2508.06584

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution
The development, integration, and maintenance of geospatial databases rely heavily on efficient and accurate matching procedures of Geospatial Entity Resolution (ER). While resolution of points-of-interest (POIs) has been widely addressed, resolution of entities with diverse geometries has been largely overlooked. This is partly due to the lack of a uniform technique for embedding heterogeneous geometries seamlessly into a neural network framework. Existing neural approaches simplify complex ge…

Range-Angle Likelihood Maps for Indoor Positioning Using Deep Neural Networks
Muhammad Ammad, Paul Schwarzbach, Michael Schultz, Oliver Michler
https://arxiv.org/abs/2508.12746 …

Range-Angle Likelihood Maps for Indoor Positioning Using Deep Neural Networks
Accurate and high precision of the indoor positioning is as important as ensuring reliable navigation in outdoor environments. Using the state-of-the-art deep learning models provides better reliability and accuracy to navigate and monitor the accurate positions in the aircraft cabin environment. We utilize the simulated aircraft cabin environment measurements and propose a residual neural network (ResNet) model to predict the accurate positions inside the cabin. The measurements include the ra…

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li, Zhihui Xu, Jingfeng Jiang, Chen Zhao
https://arxiv.org/abs/2506.11454

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of …

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
Deqing Song, Weimin Yang, Maryam Rezayati, Hans Wernher van de Venn
https://arxiv.org/abs/2508.12435

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors. We evaluated various convolutional neural network (CNN) architectures and collected two datasets to study the impact of data representation and model architecture on the recognition accuracy. Our results show that spectrogram-based representatio…

Developing a Transferable Federated Network Intrusion Detection System
Abu Shafin Mohammad Mahdee Jameel, Shreya Ghosh, Aly El Gamal
https://arxiv.org/abs/2508.09060 https://

Developing a Transferable Federated Network Intrusion Detection System
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown attacks using knowledge from known attacks. To this end, we develop algorithms to maximize the number of transferability relationships. We propose a Convolutional Neural Network (CNN) model, along wi…

Improving Wi-Fi Network Performance Prediction with Deep Learning Models
Gabriele Formis, Amanda Ericson, Stefan Forsstrom, Kyi Thar, Gianluca Cena, Stefano Scanzio
https://arxiv.org/abs/2507.11168

Improving Wi-Fi Network Performance Prediction with Deep Learning Models
The increasing need for robustness, reliability, and determinism in wireless networks for industrial and mission-critical applications is the driver for the growth of new innovative methods. The study presented in this work makes use of machine learning techniques to predict channel quality in a Wi-Fi network in terms of the frame delivery ratio. Predictions can be used proactively to adjust communication parameters at runtime and optimize network operations for industrial applications. Methods…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…

KAN-HAR: A Human activity recognition based on Kolmogorov-Arnold Network
Mohammad Alikhani
https://arxiv.org/abs/2508.11186 https://arxiv.org/pdf/2508.1118…

KAN-HAR: A Human activity recognition based on Kolmogorov-Arnold Network
Human Activity Recognition (HAR) plays a critical role in numerous applications, including healthcare monitoring, fitness tracking, and smart environments. Traditional deep learning (DL) approaches, while effective, often require extensive parameter tuning and may lack interpretability. In this work, we investigate the use of a single three-axis accelerometer and the Kolmogorov--Arnold Network (KAN) for HAR tasks, leveraging its ability to model complex nonlinear relationships with improved int…

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
Tianlong Yu, Lihong Liu, Ziyi Zhou, Fudu Xing, Kailong Wang, Yang Yang
https://arxiv.org/abs/2508.10701

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
The exploitation of 1 day or n day vulnerabilities poses severe threats to networked devices due to massive deployment scales and delayed patching (average Mean Time To Patch exceeds 60 days). Existing defenses, including host based patching and network based filtering, are inadequate due to limited scalability across diverse devices, compatibility issues especially with embedded or legacy systems, and error prone deployment process (manual patch validation). To address these issues, we introdu…

MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Jianfei Jiang, Qiankun Liu, Haochen Yu, Hongyuan Liu, Liyong Wang, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2507.11333

MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Learning-based Multi-View Stereo (MVS) methods aim to predict depth maps for a sequence of calibrated images to recover dense point clouds. However, existing MVS methods often struggle with challenging regions, such as textureless regions and reflective surfaces, where feature matching fails. In contrast, monocular depth estimation inherently does not require feature matching, allowing it to achieve robust relative depth estimation in these regions. To bridge this gap, we propose MonoMVSNet, a …

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Yifei Tang, Hongjie Jiang, Changhong Jing, Hieu Pham, Shuqiang Wang
https://arxiv.org/abs/2506.11671

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Functional brain network analysis has become an indispensable tool for brain disease analysis. It is profoundly impacted by deep learning methods, which can characterize complex connections between ROIs. However, the research on foundation models of brain network is limited and constrained to a single dimension, which restricts their extensive application in neuroscience. In this study, we propose a fine-tuned brain network model for brain disease diagnosis. It expands brain region representati…

D2Q Synchronizer: Distributed SDN Synchronization for Time Sensitive Applications
Ioannis Panitsas, Akrit Mudvari, Leandros Tassiulas
https://arxiv.org/abs/2508.11475 https://…

D2Q Synchronizer: Distributed SDN Synchronization for Time Sensitive Applications
In distributed Software-Defined Networking (SDN), distributed SDN controllers require synchronization to maintain a global network state. Despite the availability of synchronization policies for distributed SDN architectures, most policies do not consider joint optimization of network and user performance. In this work, we propose a reinforcement learning-based algorithm called D2Q Synchronizer, to minimize long-term network costs by strategically offloading time-sensitive tasks to cost-effecti…

COLI: A Hierarchical Efficient Compressor for Large Images
Haoran Wang, Hanyu Pei, Yang Lyu, Kai Zhang, Li Li, Feng-Lei Fan
https://arxiv.org/abs/2507.11443

COLI: A Hierarchical Efficient Compressor for Large Images
The escalating adoption of high-resolution, large-field-of-view imagery amplifies the need for efficient compression methodologies. Conventional techniques frequently fail to preserve critical image details, while data-driven approaches exhibit limited generalizability. Implicit Neural Representations (INRs) present a promising alternative by learning continuous mappings from spatial coordinates to pixel intensities for individual images, thereby storing network weights rather than raw pixels a…

BERTector: Intrusion Detection Based on Joint-Dataset Learning
Haoyang Hu, Xun Huang, Chenyu Wu, Shiwen Liu, Zhichao Lian, Shuangquan Zhang
https://arxiv.org/abs/2508.10327 http…

BERTector: Intrusion Detection Based on Joint-Dataset Learning
Intrusion detection systems (IDS) are facing challenges in generalization and robustness due to the heterogeneity of network traffic and the diversity of attack patterns. To address this issue, we propose a new joint-dataset training paradigm for IDS and propose a scalable BERTector framework based on BERT. BERTector integrates three key components: NSS-Tokenizer for traffic-aware semantic tokenization, supervised fine-tuning with a hybrid dataset, and low-rank adaptation (LoRA) for efficient t…

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
Ming Wang, Zhaoyang Duan, Dong Xue, Fangzhou Liu, Zhongheng Zhang
https://arxiv.org/abs/2507.08050 https://arxiv.org/pdf/2507.08050 https://arxiv.org/html/2507.08050
arXiv:2507.08050v1 Announce Type: new
Abstract: The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreover, patient privacy concerns complicate the direct sharing of local medical data across institutions, and existing centralized data-driven approaches, which rely on amounts of available data, often compromise data privacy. This study proposes a federated few-shot learning framework with privacy-preserving mechanisms to address the issues of limited labeled data and privacy protection in diagnosing respiratory diseases. In particular, a meta-stochastic gradient descent algorithm is proposed to mitigate the overfitting problem that arises from insufficient data when employing traditional gradient descent methods for neural network training. Furthermore, to ensure data privacy against gradient leakage, differential privacy noise from a standard Gaussian distribution is integrated into the gradients during the training of private models with local data, thereby preventing the reconstruction of medical images. Given the impracticality of centralizing respiratory disease data dispersed across various medical institutions, a weighted average algorithm is employed to aggregate local diagnostic models from different clients, enhancing the adaptability of a model across diverse scenarios. Experimental results show that the proposed method yields compelling results with the implementation of differential privacy, while effectively diagnosing respiratory diseases using data from different structures, categories, and distributions.
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https://arxiv.org/abs/2507.10903

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