Tootfinder

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
Mohammad Firas Sada, John J. Graham, Mahidhar Tatineni, Dmitry Mishin, Thomas A. DeFanti, Frank W\"urthwein
https://arxiv.org/abs/2507.00428

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
As machine learning (ML) applications become integral to modern network operations, there is an increasing demand for network programmability that enables low-latency ML inference for tasks such as Quality of Service (QoS) prediction and anomaly detection in cybersecurity. ML models provide adaptability through dynamic weight adjustments, making Programming Protocol-independent Packet Processors (P4)-programmable FPGA SmartNICs an ideal platform for investigating In-Network Machine Learning (IN…

Auto-Doubly Robust Estimation of Causal Effects on a Network
Jizhou Liu, Dake Zhang, Eric J. Tchetgen Tchetgen
https://arxiv.org/abs/2506.23332 https://

Auto-Doubly Robust Estimation of Causal Effects on a Network
This paper develops new methods for causal inference in observational studies on a single large network of interconnected units, addressing two key challenges: long-range dependence among units and the presence of general interference. We introduce a novel network version of Augmented Inverse Propensity Weighted, which combines propensity score and outcome models defined on the network to achieve doubly robust identification and estimation of both direct and spillover causal effects. Under a ne…

Plan-Based Scalable Online Virtual Network Embedding
Oleg Kolosov, David Breitgand, Dean H. Lorenz, Gala Yadgar
https://arxiv.org/abs/2507.00237 https://…

Plan-Based Scalable Online Virtual Network Embedding
Network virtualization allows hosting applications with diverse computation and communication requirements on shared edge infrastructure. Given a set of requests for deploying virtualized applications, the edge provider has to deploy a maximum number of them to the underlying physical network, subject to capacity constraints. This challenge is known as the virtual network embedding (VNE) problem: it models applications as virtual networks, where virtual nodes represent functions and virtual lin…

This https://arxiv.org/abs/2505.15304 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Saliency-Aware Quantized Imitation Learning for Efficient Robotic Control
Deep neural network (DNN)-based policy models, such as vision-language-action (VLA) models, excel at automating complex decision-making from multi-modal inputs. However, scaling these models greatly increases computational overhead, complicating deployment in resource-constrained settings like robot manipulation and autonomous driving. To address this, we propose Saliency-Aware Quantized Imitation Learning (SQIL), which combines quantization-aware training with a selective loss-weighting strate…

Phylogenetic network models as graphical models
Seth Sullivant
https://arxiv.org/abs/2507.23056 https://arxiv.org/pdf/2507.23056

Phylogenetic network models as graphical models
The displayed tree phylogenetic network model is shown to sit as a natural submodel of the graphical model associated to a directed acyclic graph (DAG). This representation allows to derive a number of results about the displayed tree model. In particular, the concept of a local modification to a DAG model is developed and applied to the displayed tree model. As an application, some nonidentifiability issues related to the displayed tree models are highlighted as they relate to reticulation edg…

This https://arxiv.org/abs/2311.16139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as social network analysis and fraud detection for financial services. At the heart of these networks are the edges, which are crucial in guiding GNN models' predictions. In many scenarios, these edges represent sensitive information, such as personal associations or financial dealings, which require privacy assurance. However, their contributions to GNN mo…

Community-Based Efficient Algorithms for User-Driven Competitive Influence Maximization in Social Networks
Rahul Kumar Gautam
https://arxiv.org/abs/2506.23179

Community-Based Efficient Algorithms for User-Driven Competitive Influence Maximization in Social Networks
Nowadays, people in the modern world communicate with their friends, relatives, and colleagues through the internet. Persons/nodes and communication/edges among them form a network. Social media networks are a type of network where people share their views with the community. There are several models that capture human behavior, such as a reaction to the information received from friends or relatives. The two fundamental models of information diffusion widely discussed in the social networks ar…

Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems
Xinquan Wang, Fenghao Zhu, Zhaohui Yang, Chongwen Huang, Xiaoming Chen, Zhaoyang Zhang, Sami Muhaidat, M\'erouane Debbah
https://arxiv.org/abs/2506.24009

Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems
Large artificial intelligence (AI) models offer revolutionary potential for future wireless systems, promising unprecedented capabilities in network optimization and performance. However, current paradigms largely overlook crucial physical interactions. This oversight means they primarily rely on offline datasets, leading to difficulties in handling real-time wireless dynamics and non-stationary environments. Furthermore, these models often lack the capability for active environmental probing. …

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Jisoo Kim, Chu-Hsuan Lin, Alberto Ceballos-Arroyo, Ping Liu, Huaizu Jiang, Shrikanth Yadav, Qi Wan, Lei Qin, Geoffrey S Young
https://arxiv.org/abs/2507.00832

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D convolutional neural network-transformer hybrid (3D-CN…

This https://arxiv.org/abs/2410.13230 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Starbucks-v2: Improved Training for 2D Matryoshka Embeddings
2D Matryoshka training enables a single embedding model to generate sub-network representations across different layers and embedding dimensions, offering adaptability to diverse computational and task constraints. However, its effectiveness remains well below that of individually trained models of equivalent sizes. To address this, we propose Starbucks, a new training strategy for Matryoshka-style embedding models that combines structured fine-tuning with masked autoencoder (MAE) pre-training.…

Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille
https://arxiv.org/abs/2506.22443

Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists through neural optimization, applied for the first time to radar-based hand gesture recognition (HGR). We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI), evaluating a…

This https://arxiv.org/abs/2409.15683 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum DeepONet: Neural operators accelerated by quantum computing
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new condition in a single forward pass. However, classical DeepONet entails quadratic complexity concerni…

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
Agnideep Aich, Sameera Hewage, Md Monzur Murshed, Ashit Baran Aich, Amanda Mayeaux, Asim K. Dey, Kumer P. Das, Bruce Wade
https://arxiv.org/abs/2505.24006

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing both typical spatial patterns and extreme dependencies. By embedding the dual-tail novel Archimedean copula viz. A2 directly into the network's weight initialization, A2-SBNN naturally models complex spatial relationships, including rare co-movements in the data. The model is trained through a calibration-driven process …

Learning Constraints Directly from Network Data
Hongyu H\`e, Minhao Jin, Maria Apostolaki
https://arxiv.org/abs/2506.23964 https://ar…

Learning Constraints Directly from Network Data
Network data conforms to a wide range of rules that arise from protocols, design principles, and deployment decisions (e.g., a packet's queuing delay must be less than its end-to-end delay). Formalizing such rules as logic constraints can (i) improve the quality of synthetic data, (ii) reduce the brittleness of machine learning (ML) models, and (iii) improve semantic understanding of network measurements. However, these benefits remain out of reach if rule extraction is manual or solely reliant…

Adaptive network dynamics and behavioral contagion in multi-state drug use propagation
Hsuan-Wei Lee, Yi-Hsuan Huang, Nishant Malik
https://arxiv.org/abs/2506.21766

Adaptive network dynamics and behavioral contagion in multi-state drug use propagation
Addictive behavior spreads through social networks via feedback among choice, peer pressure, and shifting ties, a process that eludes standard epidemic models. We present a comprehensive multi-state network model that integrates utility-based behavioral transitions with adaptive network rewiring, capturing the co-evolutionary dynamics between drug use patterns and social structure. Our framework distinguishes four distinct individual states by combining drug use behavior with addiction status, …

A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domains
Matteo Calaf\`a, Tito Andriollo, Allan P. Engsig-Karup, Cheol-Ho Jeong
https://arxiv.org/abs/2507.22678

A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domains
Physics-informed holomorphic neural networks (PIHNNs) have recently emerged as efficient surrogate models for solving differential problems. By embedding the underlying problem structure into the network, PIHNNs require training only to satisfy boundary conditions, often resulting in significantly improved accuracy and computational efficiency compared to traditional physics-informed neural networks (PINNs). In this work, we improve and extend the application of PIHNNs to two-dimensional proble…

From Large-scale Audio Tagging to Real-Time Explainable Emergency Vehicle Sirens Detection
Stefano Giacomelli, Marco Giordano, Claudia Rinaldi, Fabio Graziosi
https://arxiv.org/abs/2506.23437

From Large-scale Audio Tagging to Real-Time Explainable Emergency Vehicle Sirens Detection
Accurate recognition of Emergency Vehicle (EV) sirens is critical for the integration of intelligent transportation systems, smart city monitoring systems, and autonomous driving technologies. Modern automatic solutions are limited by the lack of large scale, curated datasets and by the computational demands of state of the art sound event detection models. This work introduces E2PANNs (Efficient Emergency Pre trained Audio Neural Networks), a lightweight Convolutional Neural Network architectu…

Graph neural networks for residential location choice: connection to classical logit models
Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Yuqi Zhou, Shenhao Wang
https://arxiv.org/abs/2507.21334

Graph neural networks for residential location choice: connection to classical logit models
Researchers have adopted deep learning for classical discrete choice analysis as it can capture complex feature relationships and achieve higher predictive performance. However, the existing deep learning approaches cannot explicitly capture the relationship among choice alternatives, which has been a long-lasting focus in classical discrete choice models. To address the gap, this paper introduces Graph Neural Network (GNN) as a novel framework to analyze residential location choice. The GNN-ba…

Last week, we continued our #ISE2025 lecture on distributional semantics with the introduction of neural language models (NLMs) and compared them to traditional statistical n-gram models.
Benefits of NLMs:
- Capturing Long-Range Dependencies
- Computational and Statistical Tractability
- Improved Generalisation
- Higher Accuracy
@…

Topological weight and structural diversity of polydisperse chromatin loop networks
Andrea Bonato, Enrico Carlon, Sergey Kitaev, Davide Marenduzzo, Enzo Orlandini
https://arxiv.org/abs/2507.00520

Topological weight and structural diversity of polydisperse chromatin loop networks
Current biophysical models for transcriptionally active chromatin view this as a polymer with sticky sites, mimicking transcription units such as promoters and enhancers which interact via the binding of multivalent complexes of chromatin-binding proteins. It has been demonstrated that this model spontaneously leads to microphase separation, resulting in the formation of a network of loops with transcription units serving as anchors. Here, we demonstrate how to compute the topological weights o…

Learning-Based Hybrid Neural Receiver for 6G-V2X Communications
Osama Saleem, Mohammed Alfaqawi, Pierre Merdrignac, Abdelaziz Bensrhair, Soheyb Ribouh
https://arxiv.org/abs/2506.21983

Learning-Based Hybrid Neural Receiver for 6G-V2X Communications
Neural receiver models are proposed to jointly optimize multiple functionalities of wireless receivers; however, a comprehensive receiver model that replaces the entire physical layer blocks has not yet been presented in the literature. In this work, we introduce a novel hybrid neural receiver (H-NR) built on Transformer encoder blocks and Graph Neural Network (GNN), as part of an end-to-end wireless communication framework. In our communication framework, we assume vehicle to network (V2N) upl…

Neural network extraction of chromo-electric and chromo-magnetic gluon masses
Jie Mei, Lingxiao Wang, Mei Huang
https://arxiv.org/abs/2507.22012 https://ar…

Neural network extraction of chromo-electric and chromo-magnetic gluon masses
We present a neural network approach to separate the contributions of chromo-electric and chromo-magnetic gluons within the quasi-particle framework. Using dual residual networks, we extract temperature-dependent masses from SU(3) lattice thermodynamic data of pressure and trace anomaly. After incorporating physics regularizations, the trained models reproduce lattice results with high accuracy over $T/T_c \in [1,10]$, capturing both the crossover behavior near $T_c$ and linear scaling at high …

This https://arxiv.org/abs/2410.00059 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method
Illegitimate reproduction, distribution and derivation of Deep Neural Network (DNN) models can inflict economic loss, reputation damage and even privacy infringement. Passive DNN intellectual property (IP) protection methods such as watermarking and fingerprinting attempt to prove the ownership upon IP violation, but they are often too late to stop catastrophic damage of IP abuse and too feeble against strong adversaries. In this paper, we propose IDEA, an Inverse Domain Expert Adaptation based…

Design and Analysis of Power Consumption Models for Open-RAN Architectures
Urooj Tariq, Rishu Raj, Dan Kilper
https://arxiv.org/abs/2505.24552 https://

Design and Analysis of Power Consumption Models for Open-RAN Architectures
The open radio access network (O-RAN) Alliance developed an architecture and specifications for open and disaggregated cellular networks including many elements that are being widely adopted and implemented in both commercial and research networks. In this paper, we develop transaction-based power consumption models of a centralized O-RAN architecture based on commercial hardware and considering the full end-to-end data path from the radio unit to the data center. We focus on recent fanout limi…

Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization
Pol Francesch Huc, Emily Bates, Simone D'Amico
https://arxiv.org/abs/2507.19459 https:…

Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization
The advent of novel view synthesis techniques such as NeRF and 3D Gaussian Splatting (3DGS) has enabled learning precise 3D models only from posed monocular images. Although these methods are attractive, they hold two major limitations that prevent their use in space applications: they require poses during training, and have high computational cost at training and inference. To address these limitations, this work contributes: (1) a Convolutional Neural Network (CNN) based primitive initializer…

Physics-informed conditional diffusion model for generalizable elastic wave-mode separation
Shijun Cheng, Xinru Mu, Tariq Alkhalifah
https://arxiv.org/abs/2506.23007

Physics-informed conditional diffusion model for generalizable elastic wave-mode separation
Traditional elastic wavefield separation methods, while accurate, often demand substantial computational resources, especially for large geological models or 3D scenarios. Purely data-driven neural network approaches can be more efficient, but may fail to generalize and maintain physical consistency due to the absence of explicit physical constraints. Here, we propose a physics-informed conditional diffusion model for elastic wavefield separation that seamlessly integrates domain-specific physi…

Parametrized Multi-Agent Routing via Deep Attention Models
Salar Basiri, Dhananjay Tiwari, Srinivasa M. Salapaka
https://arxiv.org/abs/2507.22338 https://a…

Parametrized Multi-Agent Routing via Deep Attention Models
We propose a scalable deep learning framework for parametrized sequential decision-making (ParaSDM), where multiple agents jointly optimize discrete action policies and shared continuous parameters. A key subclass of this setting arises in Facility-Location and Path Optimization (FLPO), where multi-agent systems must simultaneously determine optimal routes and facility locations, aiming to minimize the cumulative transportation cost within the network. FLPO problems are NP-hard due to their mix…

Less is More: Data Curation Matters in Scaling Speech Enhancement
Chenda Li, Wangyou Zhang, Wei Wang, Robin Scheibler, Kohei Saijo, Samuele Cornell, Yihui Fu, Marvin Sach, Zhaoheng Ni, Anurag Kumar, Tim Fingscheidt, Shinji Watanabe, Yanmin Qian
https://arxiv.org/abs/2506.23859

Less is More: Data Curation Matters in Scaling Speech Enhancement
The vast majority of modern speech enhancement systems rely on data-driven neural network models. Conventionally, larger datasets are presumed to yield superior model performance, an observation empirically validated across numerous tasks in other domains. However, recent studies reveal diminishing returns when scaling speech enhancement data. We focus on a critical factor: prevalent quality issues in ``clean'' training labels within large-scale datasets. This work re-examines this phenomenon a…

CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Tiancheng Chen, Ales Kubicek, Langwen Huang, Torsten Hoefler
https://arxiv.org/abs/2507.00217

CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Training large language models (LLMs) now requires resources that exceed a single datacenter, making cross-datacenter strategies increasingly crucial. We present CrossPipe, a framework designed to optimize model training across geographically distributed datacenters by explicitly modeling and mitigating the impact of network latency and limited bandwidth. It enables unified analysis and optimization incorporating both pipeline parallelism (PP) and opportunities for overlapping data parallelism …

New pre-print! #ai
**Universal pre-training by iterated random computation.**
⌨️🐒 A monkey behind a typewriter will produce the collected works of Shakespeare eventually.
💻🐒 But what if we put a monkey behind a computer?
⌨️🐒 needs to be lucky enough to type all characters of all of Shakespeare correctly. 💻🐒 only needs to be lucky enough to type a program for Shakespeare.

Universal pre-training by iterated random computation
We investigate the use of randomly generated data for the sake of pre-training a model. We justify this approach theoretically from the perspective of algorithmic complexity, building on recent research that shows that sequence models can be trained to approximate Solomonoff induction. We derive similar, but complementary theoretical results. We show empirically that synthetically generated data can be used to pre-train a model before the data is seen. We replicate earlier results that models t…

Accuracy and Security-Guaranteed Participant Selection and Beamforming Design for RIS-Assisted Federated Learning
Mengru Wu, Yu Gao, Weidang Lu, Huimei Han, Lei Sun, Wanli Ni
https://arxiv.org/abs/2507.00388

Accuracy and Security-Guaranteed Participant Selection and Beamforming Design for RIS-Assisted Federated Learning
Federated learning (FL) has emerged as an effective approach for training neural network models without requiring the sharing of participants' raw data, thereby addressing data privacy concerns. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted FL framework in the presence of eavesdropping, where partial edge devices are selected to participate in the FL training process. In contrast, the remaining devices serve as cooperative jammers by transmitting jamming signals …

Unified Network-Based Representation of BIM Models for Embedding Semantic, Spatial, and Topological Data
Jin Han, Xin-Zheng Lu, Jia-Rui Lin
https://arxiv.org/abs/2505.22670

Unified Network-Based Representation of BIM Models for Embedding Semantic, Spatial, and Topological Data
Building Information Modeling (BIM) has revolutionized the construction industry by providing a comprehensive digital representation of building structures throughout their lifecycle. However, existing research lacks effective methods for capturing the complex spatial and topological relationships between components in BIM models, which are essential for understanding design patterns and enhancing decision-making. This study proposes a unified network-based representation method that integrates…

A Novel Discrete Memristor-Coupled Heterogeneous Dual-Neuron Model and Its Application in Multi-Scenario Image Encryption
Yi Zou, Mengjiao Wang, Xinan Zhang, Herbert Ho-Ching Iu
https://arxiv.org/abs/2505.24294

A Novel Discrete Memristor-Coupled Heterogeneous Dual-Neuron Model and Its Application in Multi-Scenario Image Encryption
Simulating brain functions using neural networks is an important area of research. Recently, discrete memristor-coupled neurons have attracted significant attention, as memristors effectively mimic synaptic behavior, which is essential for learning and memory. This highlights the biological relevance of such models. This study introduces a discrete memristive heterogeneous dual-neuron network (MHDNN). The stability of the MHDNN is analyzed with respect to initial conditions and a range of neuro…

This https://arxiv.org/abs/2501.15081 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?
Large Language Models (LLMs) have shown impressive capabilities in language understanding and reasoning across diverse domains. Recently, there has been increasing interests in utilizing LLMs not merely as assistants in optimization tasks, but as active optimizers, particularly for network-structured combinatorial problems. However, before LLMs can be reliably deployed in this role, a fundamental question must be addressed: Can LLMs iteratively manipulate solutions that consistently adhere to p…

LLMs Between the Nodes: Community Discovery Beyond Vectors
Ekta Gujral, Apurva Sinha
https://arxiv.org/abs/2507.22955 https://arxiv.org/pdf/2507.22955

LLMs Between the Nodes: Community Discovery Beyond Vectors
Community detection in social network graphs plays a vital role in uncovering group dynamics, influence pathways, and the spread of information. Traditional methods focus primarily on graph structural properties, but recent advancements in Large Language Models (LLMs) open up new avenues for integrating semantic and contextual information into this task. In this paper, we present a detailed investigation into how various LLM-based approaches perform in identifying communities within social grap…

Cascades on Constrained Multiplex Networks
Christian Kluge, Christian Kuehn
https://arxiv.org/abs/2505.24631 https://arxiv.org/pdf/25…

Cascades on Constrained Multiplex Networks
We consider a version of the Watts cascade model on directed multiplex configuration model networks, and present a detailed analysis of the cascade size, single-seed cascade probability and cascade condition. We then introduce a smaller class of network models that we call constrained multiplex networks, which allows us to induce patterns in the node activity, i.e. in the participation of nodes on different layers. We find that the choice of induced patterns affects the phase transitions of the…

Cardiorespiratory coupling improves cardiac pumping efficiency in heart failure
Josh Border, Andrew Lefevre, Vishal Jain, Alain Nogaret
https://arxiv.org/abs/2507.00597

Cardiorespiratory coupling improves cardiac pumping efficiency in heart failure
Recent trials of a neuronal pacemaker have shown that cardiac pumping efficiency increases when respiratory sinus arrhythmia (RSA) is artificially restored in animal models of heart failure. This novel device sheds new light on the functional role of RSA, which has long been debated, by allowing the strength of cardiorespiratory coupling to be artificially varied. Here we show that RSA minimizes the cardiac power dissipated within the cardiovascular network. The cardiorespiratory system is foun…

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…

ARCH-COMP25 Category Report: Stochastic Models
Alessandro Abate, Omid Akbarzadeh, Henk A. P. Blom, Sofie Haesaert, Sina Hassani, Abolfazl Lavaei, Frederik Baymler Mathiesen, Rahul Misra, Amy Nejati, Mathis Niehage, Fie {\O}rum, Anne Remke, Behrad Samari, Ruohan Wang, Rafal Wisniewski, Ben Wooding, Mahdieh Zaker
https://arxiv.org…

ARCH-COMP25 Category Report: Stochastic Models
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year's edition of the competition. In particular, this report introduces three recently developed software tools, a new water distribution network benchmark, and a collection of simplified benchmarks intended to facilitate further comparison…

This https://arxiv.org/abs/2505.19305 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Leveraging Large Language Models to Contextualize Network Measurements
With the worldwide growth of remote communication and telepresence, network measurements form a cornerstone of effective performance assessment and diagnostics for Internet users. Most often, users seek for overall connection performance measurement using publicly available tools (also known as `speed tests') that provide an overview of their connection's throughput and latency. However, extracting meaningful insights from these measurements remains a challenging task for a non-technical audien…

Efficient optimization of variational tensor-network approach to three-dimensional statistical systems
Xia-Ze Xu, Tong-Yu Lin, Guang-Ming Zhang
https://arxiv.org/abs/2506.19339

Efficient optimization of variational tensor-network approach to three-dimensional statistical systems
Variational tensor network optimization has become a powerful tool for studying classical statistical models in two dimensions. However, its application to three-dimensional systems remains limited, primarily due to the high computational cost associated with evaluating the free energy density and its gradient. This process requires contracting a triple-layer tensor network composed of a projected entangled pair operator and projected entangled pair states. In this paper, we employ a split corn…

This https://arxiv.org/abs/2503.02041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems
Deep learning has been extensively employed as a powerful function approximator for modeling physics-based problems described by partial differential equations (PDEs). Despite their popularity, standard deep learning models often demand prohibitively large computational resources and yield limited accuracy when scaling to large-scale, high-dimensional physical problems. Their black-box nature further hinders the application in industrial problems where interpretability and high precision are cr…

Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks
Riccardo Basilone, Eleonora Bergamin, Federica Fanelli, Egor Kotov, Kevin Morelle, Alisa Klamm, Manal Nhili, Joshua Rosen, Andrew Schendl, Olena Holubowska, Andrew Renninger, Kamil Smolak
https://arxiv.org/abs/2506.22138

Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks
This study examines the complex movement patterns and behavioral characteristics of wild boars using GPS telemetry data collected over a two-month period. Our methodological approach centers on the application of a Hidden Markov Model (HMM) to discern distinct behavioral states embedded within the trajectories. Furthermore, the study aimed to construct behavioral networks, derived from these segmented trajectories. The resultant network structures showed that the hidden behavioral patterns are …

Predicting Parkinson's Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers
Ran Tong, Lanruo Wang, Tong Wang, Wei Yan
https://arxiv.org/abs/2507.20058

Predicting Parkinson's Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers
Predicting Parkinson's Disease (PD) progression is crucial, and voice biomarkers offer a non-invasive method for tracking symptom severity (UPDRS scores) through telemonitoring. Analyzing this longitudinal data is challenging due to within-subject correlations and complex, nonlinear patient-specific progression patterns. This study benchmarks LMMs against two advanced hybrid approaches: the Generalized Neural Network Mixed Model (GNMM) (Mandel 2021), which embeds a neural network within a GLMM …

This https://arxiv.org/abs/2411.06268 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting
Power system networks are often modeled as homogeneous graphs, which limits the ability of graph neural network (GNN) to capture individual generator features at the same nodes. By introducing the proposed virtual node-splitting strategy, generator-level attributes like costs, limits, and ramp rates can be fully captured by GNN models, improving GNN's learning capacity and prediction accuracy. Optimal power flow (OPF) problem is used for real-time grid operations. Limited timeframe motivates st…

SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization
Manno Versluis, Yizhuo Wu, Chang Gao
https://arxiv.org/abs/2506.16591

SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization
Digital predistortion (DPD) is crucial for linearizing radio frequency (RF) power amplifiers (PAs), improving signal integrity and efficiency in wireless systems. Neural network (NN)-based DPD methods surpass traditional polynomial models but face computational challenges limiting their practical deployment. This paper introduces SparseDPD, an FPGA accelerator employing a spatially sparse phase-normalized time-delay neural network (PNTDNN), optimized through unstructured pruning to reduce compu…

LLM coding is the opposite of DRY
An important principle in software engineering is DRY: Don't Repeat Yourself. We recognize that having the same code copied in more than one place is bad for several reasons:
1. It makes the entire codebase harder to read.
2. It increases maintenance burden, since any problems in the duplicated code need to be solved in more than one place.
3. Because it becomes possible for the copies to drift apart if changes to one aren't transferred to the other (maybe the person making the change has forgotten there was a copy) it makes the code more error-prone and harder to debug.
All modern programming languages make it almost entirely unnecessary to repeat code: we can move the repeated code into a "function" or "module" and then reference it from all the different places it's needed. At a larger scale, someone might write an open-source "library" of such functions or modules and instead of re-implementing that functionality ourselves, we can use their code, with an acknowledgement. Using another person's library this way is complicated, because now you're dependent on them: if they stop maintaining it or introduce bugs, you've inherited a problem, but still, you could always copy their project and maintain your own version, and it would be not much more work than if you had implemented stuff yourself from the start. It's a little more complicated than this, but the basic principle holds, and it's a foundational one for software development in general and the open-source movement in particular. The network of "citations" as open-source software builds on other open-source software and people contribute patches to each others' projects is a lot of what makes the movement into a community, and it can lead to collaborations that drive further development. So the DRY principle is important at both small and large scales.
Unfortunately, the current crop of hyped-up LLM coding systems from the big players are antithetical to DRY at all scales:
- At the library scale, they train on open source software but then (with some unknown frequency) replicate parts of it line-for-line *without* any citation [1]. The person who was using the LLM has no way of knowing that this happened, or even any way to check for it. In theory the LLM company could build a system for this, but it's not likely to be profitable unless the courts actually start punishing these license violations, which doesn't seem likely based on results so far and the difficulty of finding out that the violations are happening. By creating these copies (and also mash-ups, along with lots of less-problematic stuff), the LLM users (enabled and encouraged by the LLM-peddlers) are directly undermining the DRY principle. If we see what the big AI companies claim to want, which is a massive shift towards machine-authored code, DRY at the library scale will effectively be dead, with each new project simply re-implementing the functionality it needs instead of every using a library. This might seem to have some upside, since dependency hell is a thing, but the downside in terms of comprehensibility and therefore maintainability, correctness, and security will be massive. The eventual lack of new high-quality DRY-respecting code to train the models on will only make this problem worse.
- At the module & function level, AI is probably prone to re-writing rather than re-using the functions or needs, especially with a workflow where a human prompts it for many independent completions. This part I don't have direct evidence for, since I don't use LLM coding models myself except in very specific circumstances because it's not generally ethical to do so. I do know that when it tries to call existing functions, it often guesses incorrectly about the parameters they need, which I'm sure is a headache and source of bugs for the vibe coders out there. An AI could be designed to take more context into account and use existing lookup tools to get accurate function signatures and use them when generating function calls, but even though that would probably significantly improve output quality, I suspect it's the kind of thing that would be seen as too-baroque and thus not a priority. Would love to hear I'm wrong about any of this, but I suspect the consequences are that any medium-or-larger sized codebase written with LLM tools will have significant bloat from duplicate functionality, and will have places where better use of existing libraries would have made the code simpler. At a fundamental level, a principle like DRY is not something that current LLM training techniques are able to learn, and while they can imitate it from their training sets to some degree when asked for large amounts of code, when prompted for many smaller chunks, they're asymptotically likely to violate it.
I think this is an important critique in part because it cuts against the argument that "LLMs are the modern compliers, if you reject them you're just like the people who wanted to keep hand-writing assembly code, and you'll be just as obsolete." Compilers actually represented a great win for abstraction, encapsulation, and DRY in general, and they supported and are integral to open source development, whereas LLMs are set to do the opposite.
[1] to see what this looks like in action in prose, see the example on page 30 of the NYTimes copyright complaint against OpenAI (#AI #GenAI #LLMs #VibeCoding

OFCnetLLM: Large Language Model for Network Monitoring and Alertness
Hong-Jun Yoon, Mariam Kiran, Danial Ebling, Joe Breen
https://arxiv.org/abs/2507.22711 https://

OFCnetLLM: Large Language Model for Network Monitoring and Alertness
The rapid evolution of network infrastructure is bringing new challenges and opportunities for efficient network management, optimization, and security. With very large monitoring databases becoming expensive to explore, the use of AI and Generative AI can help reduce costs of managing these datasets. This paper explores the use of Large Language Models (LLMs) to revolutionize network monitoring management by addressing the limitations of query finding and pattern analysis. We leverage LLMs to …

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
Nikolaus Salvatore, Qiong Zhang
https://arxiv.org/abs/2506.17424

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
Past work has long recognized the important role of context in guiding how humans search their memory. While context-based memory models can explain many memory phenomena, it remains unclear why humans develop such architectures over possible alternatives in the first place. In this work, we demonstrate that foundational architectures in neural machine translation -- specifically, recurrent neural network (RNN)-based sequence-to-sequence models with attention -- exhibit mechanisms that directly…

Volatility Spillovers and Interconnectedness in OPEC Oil Markets: A Network-Based log-ARCH Approach
Fay\c{c}al Djebari, Kahina Mehidi, Khelifa Mazouz, Philipp Otto
https://arxiv.org/abs/2507.15046

Volatility Spillovers and Interconnectedness in OPEC Oil Markets: A Network-Based log-ARCH Approach
This paper examines several network-based volatility models for oil prices, capturing spillovers among OPEC oil-exporting countries by embedding novel network structures into ARCH-type models. We apply a network-based log-ARCH framework that incorporates weight matrices derived from time-series clustering and model-implied distances into the conditional variance equation. These weight matrices are constructed from return data and standard multivariate GARCH model outputs (CCC, DCC, and GO-GARCH…

X changes its developer agreement to prevent third parties from using "the X API or X Content to fine-tune or train a foundation or frontier model" (Ivan Mehta/TechCrunch)
https://techcrunch.com/2025/06/05/x-changes-i…

X changes its terms to bar training of AI models using its content | TechCrunch
Social network X has changed its developer agreement to prevent third parties from using the platform's content to train large language models.

This https://arxiv.org/abs/2505.16508 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Edge-First Language Model Inference: Models, Metrics, and Tradeoffs
The widespread adoption of Language Models (LMs) across industries is driving interest in deploying these services across the computing continuum, from the cloud to the network edge. This shift aims to reduce costs, lower latency, and improve reliability and privacy. Small Language Models (SLMs), enabled by advances in model compression, are central to this shift, offering a path to on-device inference on resource-constrained edge platforms. This work examines the interplay between edge and clo…

The chemical yields of stars in the range 9-15 Msun
Marco Limongi, Lorenzo Roberti, Agnese Falla, Alessandro Chieffi, Ken'ichi Nomoto
https://arxiv.org/abs/2505.22030

The chemical yields of stars in the range 9-15 Msun
In Limongi et al. (2024) we presented and discussed the main evolutionary properties and final fate of stars in the mass range 7-15 Msun. The evolutions of those models were computed by means of a medium size nuclear network that guaranteed a proper calculation of the nuclear energy generation and hence a good modeling of the physical evolution of these stars. In the present paper, we extend this study by computing the detailed chemical yields of stars in the mass range 9-15 Msun, i.e., those s…

Nature of Data in Pre-Trained Large Language Models
https://fpf.org/blog/nature-of-data-in-pre-trained-large-language-models/
@…

Network classification through random walks
Gonzalo Travieso, Joao Merenda, Odemir M. Bruno
https://arxiv.org/abs/2505.21706 https://…

Network classification through random walks
Network models have been widely used to study diverse systems and analyze their dynamic behaviors. Given the structural variability of networks, an intriguing question arises: Can we infer the type of system represented by a network based on its structure? This classification problem involves extracting relevant features from the network. Existing literature has proposed various methods that combine structural measurements and dynamical processes for feature extraction. In this study, we introd…

Inferring viscoplastic models from velocity fields: a physics-informed neural network approach
Martin Lardy, Sham Tlili, Simon Gsell
https://arxiv.org/abs/2506.17681

Inferring viscoplastic models from velocity fields: a physics-informed neural network approach
Fluid-like materials are ubiquitous, spanning from living biological tissues to geological formations, and across scales ranging from micrometers to kilometers. Inferring their rheological properties remains a major challenge, particularly when traditional rheometry fails to capture their complex, three-dimensional, and often heterogeneous behavior. This difficulty is exacerbated by system size, boundary conditions, and other material-specific physical, chemical, or thermal constraints. In this…

Zero Forcing on Iterated Graph Models
Christopher Brice, Erin Meger, Nhat-Dinh Nguyen, Allen Rakhamimov, Abigail Raz
https://arxiv.org/abs/2507.12579 https…

Zero Forcing on Iterated Graph Models
Modeling how information travels throughout a network has vast applications across social sciences, cybersecurity, and graph-based neural networks. In this paper, we consider the zero forcing model for information diffusion on iterative deterministic complex network models. In particular, we continue the exploration of the Iterative Local Transitive (ILT) model and the Iterative Local Anti-Transitive (ILAT) model, both introduced by Bonato et. al. in 2009 and 2017, respectively. These models us…

This https://arxiv.org/abs/2501.15081 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?
Large Language Models (LLMs) have shown impressive capabilities in language understanding and reasoning across diverse domains. Recently, there has been increasing interests in utilizing LLMs not merely as assistants in optimization tasks, but as active optimizers, particularly for network-structured combinatorial problems. However, before LLMs can be reliably deployed in this role, a fundamental question must be addressed: Can LLMs iteratively manipulate solutions that consistently adhere to p…

ChatPD: An LLM-driven Paper-Dataset Networking System
Anjie Xu, Ruiqing Ding, Leye Wang
https://arxiv.org/abs/2505.22349 https://arxi…

ChatPD: An LLM-driven Paper-Dataset Networking System
Scientific research heavily depends on suitable datasets for method validation, but existing academic platforms with dataset management like PapersWithCode suffer from inefficiencies in their manual workflow. To overcome this bottleneck, we present a system, called ChatPD, that utilizes Large Language Models (LLMs) to automate dataset information extraction from academic papers and construct a structured paper-dataset network. Our system consists of three key modules: \textit{paper collection},…

Quality assessment of 3D human animation: Subjective and objective evaluation
Rim Rekik, Stefanie Wuhrer, Ludovic Hoyet, Katja Zibrek, Anne-H\'el\`ene Olivier
https://arxiv.org/abs/2505.23301

Quality assessment of 3D human animation: Subjective and objective evaluation
Virtual human animations have a wide range of applications in virtual and augmented reality. While automatic generation methods of animated virtual humans have been developed, assessing their quality remains challenging. Recently, approaches introducing task-oriented evaluation metrics have been proposed, leveraging neural network training. However, quality assessment measures for animated virtual humans that are not generated with parametric body models have yet to be developed. In this contex…

TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction
St\'ephane d'Ascoli, J\'er\'emy Rapin, Yohann Benchetrit, Hubert Banville, Jean-R\'emi King
https://arxiv.org/abs/2507.22229

TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction
Historically, neuroscience has progressed by fragmenting into specialized domains, each focusing on isolated modalities, tasks, or brain regions. While fruitful, this approach hinders the development of a unified model of cognition. Here, we introduce TRIBE, the first deep neural network trained to predict brain responses to stimuli across multiple modalities, cortical areas and individuals. By combining the pretrained representations of text, audio and video foundational models and handling th…

Exploring MLLMs Perception of Network Visualization Principles
Jacob Miller, Markus Wallinger, Ludwig Felder, Timo Brand, Henry F\"orster, Johannes Zink, Chunyang Chen, Stephen Kobourov
https://arxiv.org/abs/2506.14611

Exploring MLLMs Perception of Network Visualization Principles
In this paper, we test whether Multimodal Large Language Models (MLLMs) can match human-subject performance in tasks involving the perception of properties in network layouts. Specifically, we replicate a human-subject experiment about perceiving quality (namely stress) in network layouts using GPT-4o and Gemini-2.5. Our experiments show that giving MLLMs exactly the same study information as trained human participants results in a similar performance to human experts and exceeds the performanc…

Symmetry breaking in minimum dissipation networks
Aarathi Parameswaran, Iva Ba\v{c}i\'c, Andrea Benigni, Dirk Witthaut
https://arxiv.org/abs/2505.24818

Symmetry breaking in minimum dissipation networks
Both natural and man-made supply networks exhibit universal structural patterns, such as the formation of loops. These patterns can be understood in terms of optimization models, assuming that biological networks evolved to optimal states and technical networks are designed to function optimally. In this article, we analyze networks that minimize dissipation under a research constraint. We demonstrate spontaneous symmetry breaking in optimal network structures as a function of resource scaling.…

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Zhaoyang Xu, Yunbo Liu
https://arxiv.org/abs/2506.19877 https…

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models - Multi-Layer Perceptron (MLP), 1D Convolutional Neural Network (CNN), One-Class Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) - on the CICIDS2017 dataset under two scenarios: detecting known attack types and generalizing to previously unseen threats. O…

Bayesian Modeling for Aggregated Relational Data: A Unified Perspective
Owen G. Ward, Anna L. Smith, Tian Zheng
https://arxiv.org/abs/2506.21353 https://…

Bayesian Modeling for Aggregated Relational Data: A Unified Perspective
Aggregated relational data is widely collected to study social network theory. It has been used to address a variety of key problems in fields such as sociology, public health and economics. ARD models enable researchers to estimate the size of hidden populations, estimate personal network sizes, understand global network structures and fit complex latent variable models to massive network data. Many of the successes of ARD models have been driven by the utilisation of Bayesian modeling, which …

Empirical Models of the Time Evolution of SPX Option Prices
Alessio Brini, David A. Hsieh, Patrick Kuiper, Sean Moushegian, David Ye
https://arxiv.org/abs/2506.17511

Empirical Models of the Time Evolution of SPX Option Prices
The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical models, including neural network, random forest, and linear regression. These models use the observed characteristics of the options as inputs -- their price, moneyness and time-to-maturity, as well as a small set of external inputs, such as the SPX and its past history, dividend …

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
Junjie Xu, Jiahao Zhang, Mangal Prakash, Xiang Zhang, Suhang Wang
https://arxiv.org/abs/2506.19862

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
Geometric graph neural networks (GNNs) that respect E(3) symmetries have achieved strong performance on small molecule modeling, but they face scalability and expressiveness challenges when applied to large biomolecules such as RNA and proteins. These systems require models that can simultaneously capture fine-grained atomic interactions, long-range dependencies across spatially distant components, and biologically relevant hierarchical structure, such as atoms forming residues, which in turn f…

Learning Heat Transport Kernels Using a Nonlocal Heat Transport Theory-Informed Neural Network
Mufei Luo, Charles Heaton, Yizhen Wang, Daniel Plummer, Mila Fitzgerald, Francesco Miniati, Sam M. Vinko, Gianluca Gregori
https://arxiv.org/abs/2506.16619

Learning Heat Transport Kernels Using a Nonlocal Heat Transport Theory-Informed Neural Network
We present a data-driven framework for the modeling of nonlocal heat transport in plasmas using a nonlocal theory informed neural network trained on kinetic Particle-in-Cell simulations that span both local and nonlocal regimes. The model learns spatiotemporal heat flux kernels directly from simulation data, capturing dynamic transport behaviors beyond the reach of classical formulations. Unlike time-independent kernel models such as Luciani Mora Virmont and Schurtz Nicolaï Busquet models, our…

Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification
Daniel Sadasivan, Isaac Cordero, Andrew Graham, Cecilia Marsh, Daniel Kupcho, Melana Mourad, Maxim Mai
https://arxiv.org/abs/2507.18824

Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification
Simulation Based Inference (SBI) is shown to yield more accurate resonance parameter estimates than traditional chi-squared minimization in certain cases of model misspecification, demonstrated through a case study of pi-pi scattering and the rho(770) resonance. Models fit to some data sets using chi-squared minimization can predict inaccurate pole positions for the rho(770), while SBI provides more robust predictions across the same models and data. This result is significant both as a proof o…

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation
Muhammad Daud, Philippe Charton, Cedric Damour, Jingbo Wang, Frederic Cadet
https://arxiv.org/abs/2506.16921

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation
Understanding the relationship between protein sequences and their functions is fundamental to protein engineering, but this task is hindered by the combinatorially vast sequence space and the experimental noise inherent in fitness measurements. In this study, we present a novel framework that models the sequence landscape as a hypercube $H(k,2)$ and integrates wavelet-based signal denoising with a graph convolutional neural network (GCN) to predict protein fitness across rugged fitness landsca…

Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
Jingwei Kang, Maarten de Rijke, Santiago de Leon-Martinez, Harrie Oosterhuis
https://arxiv.org/abs/2506.18548

Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
Click models are a well-established for modeling user interactions with web interfaces. Previous work has mainly focused on traditional single-list web search settings; this includes existing surveys that introduced categorizations based on the first generation of probabilistic graphical model (PGM) click models that have become standard. However, these categorizations have become outdated, as their conceptualizations are unable to meaningfully compare PGM with neural network (NN) click models …

DynamiX: Large-Scale Dynamic Social Network Simulator
Yanhui Sun, Wu Liu, Wentao Wang, Hantao Yao, Jiebo Luo, Yongdong Zhang
https://arxiv.org/abs/2507.19929 https://

DynamiX: Large-Scale Dynamic Social Network Simulator
Understanding the intrinsic mechanisms of social platforms is an urgent demand to maintain social stability. The rise of large language models provides significant potential for social network simulations to capture attitude dynamics and reproduce collective behaviors. However, existing studies mainly focus on scaling up agent populations, neglecting the dynamic evolution of social relationships. To address this gap, we introduce DynamiX, a novel large-scale social network simulator dedicated t…

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
Qifei Cui, Xinyu Lu
https://arxiv.org/abs/2506.21245 https://

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
This work introduces a novel framework for brain tumor segmentation leveraging pre-trained GANs and Unet architectures. By combining a global anomaly detection module with a refined mask generation network, the proposed model accurately identifies tumor-sensitive regions and iteratively enhances segmentation precision using adversarial loss constraints. Multi-modal MRI data and synthetic image augmentation are employed to improve robustness and address the challenge of limited annotated dataset…

Perfect Clustering in Very Sparse Diverse Multiplex Networks
Marianna Pensky
https://arxiv.org/abs/2507.19423 https://arxiv.org/pdf/2507.19423

Perfect Clustering in Very Sparse Diverse Multiplex Networks
The paper studies the DIverse MultiPLEx Signed Generalized Random Dot Product Graph (DIMPLE-SGRDPG) network model (Pensky (2024)), where all layers of the network have the same collection of nodes. In addition, all layers can be partitioned into groups such that the layers in the same group are embedded in the same ambient subspace but otherwise matrices of connection probabilities can be all different. This setting includes majority of multilayer network models as its particular cases. The key…

From Continuous to Discrete: Cross-Domain Collaborative General Speech Enhancement via Hierarchical Language Models
Zhaoxi Mu, Rilin Chen, Andong Li, Meng Yu, Xinyu Yang, Dong Yu
https://arxiv.org/abs/2507.19062

BLUE: Bi-layer Heterogeneous Graph Fusion Network for Avian Influenza Forecasting
Jing Du, Haley Stone, Yang Yang, Ashna Desai, Hao Xue, Andreas Z\"ufle, Chandini Raina MacIntyre, Flora D. Salim
https://arxiv.org/abs/2505.22692

BLUE: Bi-layer Heterogeneous Graph Fusion Network for Avian Influenza Forecasting
Accurate forecasting of avian influenza outbreaks within wild bird populations requires models that account for complex, multi-scale transmission patterns driven by various factors. Spatio-temporal GNN-based models have recently gained traction for infection forecasting due to their ability to capture relations and flow between spatial regions, but most existing frameworks rely solely on spatial connections and their connections. This overlooks valuable genetic information at the case level, su…

Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis
Milan Maksimovic, Anna Bohdanets, Immaculate Motsi-Omoijiade, Guido Governatori, Ivan S. Maksymov
https://arxiv.org/abs/2507.18645

Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis
Prior work has demonstrated that incorporating well-known quantum tunnelling (QT) probability into neural network models effectively captures important nuances of human perception, particularly in the recognition of ambiguous objects and sentiment analysis. In this paper, we employ novel QT-based neural networks and assess their effectiveness in distinguishing customised CIFAR-format images of military and civilian vehicles, as well as sentiment, using a proprietary military-specific vocabulary…

Just saw this:
#AI can mean a lot of things these days, but lots of the popular meanings imply a bevy of harms that I definitely wouldn't feel are worth a cute fish game. In fact, these harms are so acute that even "just" playing into the AI hype becomes its own kind of harm (it's similar to blockchain in that way).
@… noticed that the authors claim the code base is 80% AI generated, which is a red flag because people with sound moral compasses wouldn't be using AI to "help" write code in the first place. The authors aren't by some miracle people who couldn't build this app without help, in case that influences your thinking about it: they have the skills to write the code themselves, although it likely would have taken longer (but also been better).
I was more interested in the fish-classification AI, and how much it might be dependent on datacenters. Thankfully, a quick glance at the code confirms they're using ONNX and running a self-trained neural network on your device. While the exponentially-increasing energy & water demands of datacenters to support billion-parameter models are a real concern, this is not that. Even a non-AI game can burn a lot of cycles on someone's phone, and I don't think there's anything to complain about energy-wise if we're just using cycles on the end user's device as long as we're not having them keep it on for hours crunching numbers like blockchain stuff does. Running whatever stuff locally while the user is playing a game is a negligible environmental concern, unlike, say, calling out to ChatGPT where you're directly feeding datacenter demand. Since they claimed to have trained the network themselves, and since it's actually totally reasonable to make your own dataset for this and get good-enough-for-a-silly-game results with just a few hundred examples, I don't have any ethical objections to the data sourcing or training processes either. Hooray! This is finally an example of "ethical use of neutral networks" that I can hold up as an example of what people should be doing instead of the BS they are doing.
But wait... Remember what I said about feeding the AI hype being its own form of harm? Yeah, between using AI tools for coding and calling their classifier "AI" in a way that makes it seem like the same kind of thing as ChatGPT et al., they're leaning into the hype rather than helping restrain it. And that means they're causing harm. Big AI companies can point to them and say "look AI enables cute things you like" when AI didn't actually enable it. So I'm feeling meh about this cute game and won't be sharing it aside from this post. If you love the cute fish, you don't really have to feel bad for playing with it, but I'd feel bad for advertising it without a disclaimer.

Regression Analysis of Reciprocity in Directed Networks
Rui Feng, Chenlei Leng
https://arxiv.org/abs/2507.21469 https://arxiv.org/pdf/2507.21469

Regression Analysis of Reciprocity in Directed Networks
Reciprocity--the tendency of individuals to form mutual ties--is a fundamental structural feature of many directed networks. Despite its ubiquity, reciprocity remains insufficiently integrated into statistical network models, particularly in relation to covariate information. In this paper, we introduce the $R^{2}$-Model, a novel and flexible framework that explicitly models reciprocity while incorporating covariate effects. Built upon a generalized $p_1$ model, our framework accommodates both …

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Majumder Haider, Imtiaz Ahmed, Zoheb Hassan, Kamrul Hasan, H. Vincent Poor
https://arxiv.org/abs/2506.18293

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions. Digital twins (DTs) offer a powerful tool for network management by creating high-fidelity virtual replicas that model real-time network behavior, while large language models (LLMs) enhance decision-making through their advanced generalization and contextual rea…

iPLAN: Redefining Indoor Wireless Network Planning Through Large Language Models
Jinbo Hou, Stefanos Bakirtzis, Kehai Qiu, Sichong Liao, Hui Song, Haonan Hu, Kezhi Wang, Jie Zhang
https://arxiv.org/abs/2507.19096

iPLAN: Redefining Indoor Wireless Network Planning Through Large Language Models
Efficient indoor wireless network (IWN) planning is crucial for providing high-quality 5G in-building services. However, traditional meta-heuristic and artificial intelligence-based planning methods face significant challenges due to the intricate interplay between indoor environments (IEs) and IWN demands. In this article, we present an indoor wireless network Planning with large LANguage models (iPLAN) framework, which integrates multi-modal IE representations into large language model (LLM)-…

A resource-centric, task-based approach to quantum network control
Alexander Pirker, Belen Munoz, Wolfgang D\"ur
https://arxiv.org/abs/2507.12030 http…

A resource-centric, task-based approach to quantum network control
Quantum networks exhibit fundamental differences from their classical counterparts. These differences necessitate novel principles when organizing, managing, and operating them. Here we propose an unconventional approach to organize and manage the operations of quantum network devices. Instead of a hierarchical scheme using layers, like in classical networks and present quantum network stack models, we propose a resource-centric task-based scheme. In this scheme, quantum applications pose objec…

eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
Paul R. B. Houssel, Siamak Layeghy, Priyanka Singh, Marius Portmann
https://arxiv.org/abs/2507.16241

eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems (NIDS) by leveraging Large Language Models (LLMs). In our proposed framework, flows labelled as malicious by NIDS are initially processed through a module called the Prompt Augmenter. This module extracts contextual information and Cyber Threat Intelligence (CTI)-related knowledge from these flows. This enriched, context-specific data is then integrated with an input…

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
Chen Zeno, Hila Manor, Greg Ongie, Nir Weinberger, Tomer Michaeli, Daniel Soudry
https://arxiv.org/abs/2506.19031

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
While diffusion models generate high-quality images via probability flow, the theoretical understanding of this process remains incomplete. A key question is when probability flow converges to training samples or more general points on the data manifold. We analyze this by studying the probability flow of shallow ReLU neural network denoisers trained with minimal $\ell^2$ norm. For intuition, we introduce a simpler score flow and show that for orthogonal datasets, both flows follow similar traj…

Bayesian unanchored additive models for component network meta-analysis
Augustine Wigle, Audrey B\'eliveau
https://arxiv.org/abs/2507.16047 https://

Bayesian unanchored additive models for component network meta-analysis
Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several statistical aspects of CNMA. First, by introducing a unified notation, we establish that currently available methods differ in the way they assume additivity, an important distinction that has been overlooked so far in the literature. In particular, one model uses a mo…

Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
Vinura Galwaduge, Jagath Samarabandu
https://arxiv.org/abs/2507.17161

Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
Modern network intrusion detection systems (NIDS) frequently utilize the predictive power of complex deep learning models. However, the "black-box" nature of such deep learning methods adds a layer of opaqueness that hinders the proper understanding of detection decisions, trust in the decisions and prevent timely countermeasures against such attacks. Explainable AI (XAI) methods provide a solution to this problem by providing insights into the causes of the predictions. The majority of the exi…

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Ahmad Mustafa, Reza Rastegar, Ghassan AlRegib
https://arxiv.org/abs/2506.19687 …

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Prostate gland segmentation from T2-weighted MRI is a critical yet challenging task in clinical prostate cancer assessment. While deep learning-based methods have significantly advanced automated segmentation, most conventional approaches-particularly 2D convolutional neural networks (CNNs)-fail to leverage inter-slice anatomical continuity, limiting their accuracy and robustness. Fully 3D models offer improved spatial coherence but require large amounts of annotated data, which is often imprac…

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Yisu Wang, Xinjiao Li, Ruilong Wu, Huangxun Chen, Dirk Kutscher
https://arxiv.org/abs/2506.16235

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Training large-scale distributed machine learning models imposes considerable demands on network infrastructure, often resulting in sudden traffic spikes that lead to congestion, increased latency, and reduced throughput, which would ultimately affect convergence times and overall training performance. While gradient compression techniques are commonly employed to alleviate network load, they frequently compromise model accuracy due to the loss of gradient information. This paper introduces N…

Efficient and Distortion-less Spectrum Multiplexer via Neural Network-based Filter Banks
Jiazhao Wang, Wenchao Jiang
https://arxiv.org/abs/2507.17106 https://

Efficient and Distortion-less Spectrum Multiplexer via Neural Network-based Filter Banks
Spectrum multiplexer enables simultaneous transmission of multiple narrow-band IoT signals through gateway devices, thereby enhancing overall spectrum utilization. We propose a novel solution based on filter banks that offer increased efficiency and minimal distortion compared with conventional methods. We follow a model-driven approach to integrate the neural networks into the filter bank design by interpreting the neural network models as filter banks. The proposed NN-based filter banks can l…

Task-Specific Zero-shot Quantization-Aware Training for Object Detection
Changhao Li, Xinrui Chen, Ji Wang, Kang Zhao, Jianfei Chen
https://arxiv.org/abs/2507.16782

Task-Specific Zero-shot Quantization-Aware Training for Object Detection
Quantization is a key technique to reduce network size and computational complexity by representing the network parameters with a lower precision. Traditional quantization methods rely on access to original training data, which is often restricted due to privacy concerns or security challenges. Zero-shot Quantization (ZSQ) addresses this by using synthetic data generated from pre-trained models, eliminating the need for real training data. Recently, ZSQ has been extended to object detection. Ho…

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
Haonan An, Guang Hua, Hangcheng Cao, Zhengru Fang, Guowen Xu, Susanto Rahardja, Yuguang Fang
https://arxiv.org/abs/2507.18034

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
The intellectual property of deep generative networks (GNets) can be protected using a cascaded hiding network (HNet) which embeds watermarks (or marks) into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box (called operation network, or ONet), with only the generated and marked outputs from HNet being released to end users and deemed secure, in this paper, we reveal an overlooked vulnerability in such systems. Specifically, we show that t…

Explicit Residual-Based Scalable Image Coding for Humans and Machines
Yui Tatsumi, Ziyue Zeng, Hiroshi Watanabe
https://arxiv.org/abs/2506.19297 https://…

Explicit Residual-Based Scalable Image Coding for Humans and Machines
Scalable image compression is a technique that progressively reconstructs multiple versions of an image for different requirements. In recent years, images have increasingly been consumed not only by humans but also by image recognition models. This shift has drawn growing attention to scalable image compression methods that serve both machine and human vision (ICMH). Many existing models employ neural network-based codecs, known as learned image compression, and have made significant strides i…

SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization
Hao Zhang, Shuo Shao, Song Li, Zhenyu Zhong, Yan Liu, Zhan Qin, Kui Ren
https://arxiv.org/abs/2506.16981

SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization
End-point monitoring solutions are widely deployed in today's enterprise environments to support advanced attack detection and investigation. These monitors continuously record system-level activities as audit logs and provide deep visibility into security events. Unfortunately, existing methods of semantic analysis based on audit logs have low granularity, only reaching the system call level, making it difficult to effectively classify highly covert behaviors. Additionally, existing works main…

Selective Quantization Tuning for ONNX Models
Nikolaos Louloudakis, Ajitha Rajan
https://arxiv.org/abs/2507.12196 https://arxiv.org/p…

Selective Quantization Tuning for ONNX Models
Quantization is a process that reduces the precision of deep neural network models to lower model size and computational demands, often at the cost of accuracy. However, fully quantized models may exhibit sub-optimal performance below acceptable levels and face deployment challenges on low-end hardware accelerators due to practical constraints. To address these issues, quantization can be selectively applied to only a subset of layers, but selecting which layers to exclude is non-trivial. To th…

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
Bokai Yang
https://arxiv.org/abs/2506.14244 https://

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
In the network literature, a wide range of statistical models have been proposed to exploit structural patterns in the data. Therefore, model selection between different models is a fundamental problem. Cross-validation is a powerful candidate to solve this problem, and Li et al. have already proposed an edge-sampling procedure to choose the number of communities in the block model framework. In this paper, we propose a penalized edge-sampling cross-validation framework for nested network model…

RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Isaac Marroqui Penalva, Enrique Tom\'as Mart\'inez Beltr\'an, Manuel Gil P\'erez, Alberto Huertas Celdr\'an
https://arxiv.org/abs/2506.19892

RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects peers for model aggregation. Malicious nodes may exploit this autonomy by sending corrupted models (model poisoning), delaying model submissions (delay attack), or flooding the network with excessive messages, negatively affecting system performance. Existing solutions often depend on rigid configurations or addition…

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…

Intent-Based Network for RAN Management with Large Language Models
Fransiscus Asisi Bimo, Maria Amparo Canaveras Galdon, Chun-Kai Lai, Ray-Guang Cheng, Edwin K. P. Chong
https://arxiv.org/abs/2507.14230

Intent-Based Network for RAN Management with Large Language Models
Advanced intelligent automation becomes an important feature to deal with the increased complexity in managing wireless networks. This paper proposes a novel automation approach of intent-based network for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs). The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN by integrating LLMs wit…

Fractality of Wireless Mesh Networks: Dimensional Effects on Network Performance
Marat Zaidyn, Sayat Akhtanov, Dana Turlykozhayeva, Symbat Temesheva, Almat Akhmetali, Alisher Skabylov, Nurzhan Ussipov
https://arxiv.org/abs/2506.19366

Fractality of Wireless Mesh Networks: Dimensional Effects on Network Performance
Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. In this study, we present a novel algorithm that constructs WMN topologies with tunable fractal dimensions, allowing precise control over spatial self-si…

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
Sabrine Ennaji, Elhadj Benkhelifa, Luigi V. Mancini
https://arxiv.org/abs/2506.20576

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theoretical advancements and practical application, particularly in structured data like network traffic, where interdependent features complicate effective adversarial manipulations. Moreover, ambiguity in current approaches restricts reproducibility and limits progress in this field. Hence, existing defenses often fail to…