Tootfinder

Random Effects Models for Understanding Variability and Association between Brain Functional and Structural Connectivity
Lingyi Peng, Qiaochu Wang, Yaotian Wang, Jie He, Xu Zou, Shuoran Li, Dana L. Tudorascu, David J. Schaeffer, Lauren Schaeffer, Diego Szczupak, Emily S. Rothwell, Stacey J. Sukoff Rizzo, Gregory W. Carter, Afonso C. Silva, Tingting Zhang
https:…

Random Effects Models for Understanding Variability and Association between Brain Functional and Structural Connectivity
The human brain is organized as a complex network, where connections between regions are characterized by both functional connectivity (FC) and structural connectivity (SC). While previous studies have primarily focused on network-level FC-SC correlations (i.e., the correlation between FC and SC across all edges within a predefined network), edge-level correlations (i.e., the correlation between FC and SC across subjects at each edge) has received comparatively little attention. In this study, …

The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering
Bruce Mizrach, Nathaniel Yoshida
https://arxiv.org/abs/2508.04003 https://arxiv.org/pd…

The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering
Two MEV builders now produce nearly 80\% of Ethereum blocks. Block builders have the ability to reorder transactions on the blockchain in a way that can be harmful to participants. We estimate they would pay in the aggregate nearly \$14 million per month to ensure that they remained in the first quartile of the block. Sandwich attacks, in which a transaction is front-run, are frequent, averaging more than one per block. Gas fees on these transactions pay for nearly 15\% of the MEV payments to t…

Quantum Convolutional Neural Network with Nonlinear Effects and Barren Plateau Mitigation
Pei-Kun Yang
https://arxiv.org/abs/2508.02459 https://arxiv.org/p…

Quantum Convolutional Neural Network with Nonlinear Effects and Barren Plateau Mitigation
Quantum neural networks (QNNs) leverage quantum entanglement and superposition to enable large-scale parallel linear computation, offering a potential solution to the scalability limits of classical deep learning. However, their practical deployment is hampered by two key challenges: the lack of intrinsic nonlinear operations and the barren plateau phenomenon. We propose a quantum convolutional neural network (QCNN) architecture that simultaneously addresses both issues. Nonlinear effects are i…

Sequence-Based Deep Learning for Handover Optimization in Dense Urban Cellular Network
Muhammad Kabeer, Rosdiadee Nordin, Mehran Behjati, Lau Sian Lun
https://arxiv.org/abs/2510.02958

Sequence-Based Deep Learning for Handover Optimization in Dense Urban Cellular Network
Efficient handover management remains a critical challenge in dense urban cellular networks, where high cell density, user mobility, and diverse service demands increase the likelihood of unnecessary handovers and ping-pong effects. This paper leverages a real-world, multi-operator drive-test dataset of 30,925 labelled records collected within a 2 km area around Sunway City to investigate sequence-based deep learning approaches for handover detection and avoidance. We formulate handover predict…

Bayesian Network Propensity Score to Evaluate Treatment Effects in Observational Studies
Clelia Di Serio, Federica Cugnata, Pier Luigi Conti, Alberto Briganti, Fulvia Mecatti, Paola Vicard, Paola Maria Vittoria Rancoita
https://arxiv.org/abs/2509.03194

Bayesian Network Propensity Score to Evaluate Treatment Effects in Observational Studies
This paper focuses on the Bayesian Network Propensity Score (BNPS), a novel approach for estimating treatment effects in observational studies characterized by unknown (and likely unbalanced) designs and complex dependency structures among covariates. Traditional methods, such as logistic regression, often impose rigid parametric assumptions that may lead to misspecification errors, compromising causal inference. Recent classical and machine learning alternatives, such as boosted CART, random f…

Structure Maintained Representation Learning Neural Network for Causal Inference
Yang Sun, Wenbin Lu, Yi-Hui Zhou
https://arxiv.org/abs/2508.01865 https://…

Structure Maintained Representation Learning Neural Network for Causal Inference
Recent developments in causal inference have greatly shifted the interest from estimating the average treatment effect to the individual treatment effect. In this article, we improve the predictive accuracy of representation learning and adversarial networks in estimating individual treatment effects by introducing a structure keeper which maintains the correlation between the baseline covariates and their corresponding representations in the high dimensional space. We train a discriminator at …

Latin American network on electromagnetic effects in strongly interacting matter: Contribution to the update of the Latin American Strategy for High Energy, Cosmology and Astroparticle Physics
Ana Mizher, Alejandro Ayala
https://arxiv.org/abs/2508.00771

Latin American network on electromagnetic effects in strongly interacting matter: Contribution to the update of the Latin American Strategy for High Energy, Cosmology and Astroparticle Physics
An accurate characterization of the quark-gluon plasma requires understanding of how electromagnetic effects affect the processes mediated by the strong force. All the scenarios in which the plasma emerges, either in nature or in the laboratory, involve strong electromagnetic fields. The early universe, compact astrophysical objects, or ultra-relativistic heavy-ion collisions harbor the most intense fields we know. Researches from the Latin America region have made a substantial contribution on…

Shear-driven memory effects in carbon black gels
Julien Bauland, Thomas Gibaud
https://arxiv.org/abs/2508.02239 https://arxiv.org/pdf/2508.02239

Shear-driven memory effects in carbon black gels
In recent years, significant effort has been devoted to developing smart materials whose mechanical properties can adapt under physical stimuli. Particulate colloidal gels, which behave as solids but can also flow under stress, have emerged as promising candidates. Resulting from the attractive interaction between their constituents, their network architecture exhibit solid-like properties even at very low volume fractions. This structural flexibility allows them to adopt various configurations…

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
Shengjiao Ji, Yujin Zhang, Zihan Zou, Bin Jiang, Jun Jiang, Yi Luo, Wei Hu
https://arxiv.org/abs/2510.04227

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
Accurate and efficient simulation of infrared (IR) and Raman spectra is essential for molecular identification and structural analysis. Traditional quantum chemistry methods based on the harmonic approximation neglect anharmonicity and nuclear quantum effects, while ab initio molecular dynamics (AIMD) remains computationally expensive. Here, we integrate our deep equivariant tensor attention network (DetaNet) with a velocity-Verlet integrator to enable fast and accurate machine learning molecul…

Distributionally Robust Cascading Risk Quantification in Multi-Agent Rendezvous: Effects of Time Delay and Network Connectivity
Vivek Pandey, Nader Motee
https://arxiv.org/abs/2507.23489

Distributionally Robust Cascading Risk Quantification in Multi-Agent Rendezvous: Effects of Time Delay and Network Connectivity
Achieving safety in autonomous multi-agent systems, particularly in time-critical tasks like rendezvous, is a critical challenge. In this paper, we propose a distributionally robust risk framework for analyzing cascading failures in multi-agent rendezvous. To capture the complex interactions between network connectivity, system dynamics, and communication delays, we use a time-delayed network model as a benchmark. We introduce a conditional distributionally robust functional to quantify cascadi…

RIS-Aided Near-Field Channel Estimation under Mutual Coupling and Spatial Correlation
Ahmad Dkhan, Simon Tarboush, Hadi Sarieddeen, Tareq Y. Al-Naffouri
https://arxiv.org/abs/2508.01828

RIS-Aided Near-Field Channel Estimation under Mutual Coupling and Spatial Correlation
The integration of reconfigurable intelligent surfaces (RIS) with extremely large multiple-input multiple-output (MIMO) arrays at the base station has emerged as a key enabler for enhancing wireless network performance. However, this setup introduces high-dimensional channel matrices, leading to increased computational complexity and pilot overhead in channel estimation. Mutual coupling (MC) effects among densely packed unit cells, spatial correlation, and near-field propagation conditions furt…

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
Obasho M, Shambhavi Jaiswal, Santanu Das, Krishna Mohan Parattu
https://arxiv.org/abs/2509.00139

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
Extracting cosmological information from microwave sky observations requires accurate estimation of the underlying Cosmic Microwave Background (CMB) by removing foreground contamination, instrumental noise, and the effects of beam convolution. In this work, we develop a machine learning-based approach for CMB reconstruction using a generative adversarial network (GAN) architecture, where the generator is modeled as a U-Net-based convolutional neural network. To train the network, we generate …

Consistent DAG selection for Bayesian causal discovery under general error distributions
Anamitra Chaudhuri, Anirban Bhattacharya, Yang Ni
https://arxiv.org/abs/2508.00993 https…

Consistent DAG selection for Bayesian causal discovery under general error distributions
We consider the problem of learning the underlying causal structure among a set of variables, which are assumed to follow a Bayesian network or, more specifically, a linear recursive structural equation model (SEM) with the associated errors being independent and allowed to be non-Gaussian. A Bayesian hierarchical model is proposed to identify the true data-generating directed acyclic graph (DAG) structure where the nodes and edges represent the variables and the direct causal effects, respecti…

Rotational Symmetry-Breaking effects in the Kuramoto model
Antonio Mihara, Rene O. Medrano-T
https://arxiv.org/abs/2509.04157 https://arxiv.org/pdf/2509.04…

Rotational Symmetry-Breaking effects in the Kuramoto model
We study the bifurcations and phase diagram for a network of identical Kuramoto oscillators with a coupling that explicitly breaks the rotational symmetry of the equations. Applying the Watanabe-Strogatz ansatz, the original N-dimensional dynamics of the network collapses to a system of dimension two. Our analytical exploration uncovers bifurcation mechanisms, including transcritical, saddle-node, heteroclinic, Hopf, and Bogdanov-Takens bifurcations, that dictate transitions between collective …

Signal from Noise Signal from Noise: A Neural Network-Based Denoising Approach for Measuring Global Financial Spillovers
Abdullah Karasan, \"Ozge Sezgin Alp
https://arxiv.org/abs/2509.01156

Signal from Noise Signal from Noise: A Neural Network-Based Denoising Approach for Measuring Global Financial Spillovers
Filtering signal from noise is fundamental to accurately assessing spillover effects in financial markets. This study investigates denoised return and volatility spillovers across a diversified set of markets, spanning developed and developing economies as well as key asset classes, using a neural network-based denoising architecture. By applying denoising to the covariance matrices prior to spillover estimation, we disentangle signal from noise. Our analysis covers the period from late 2014 to…

Triadic Network Formation
Chris Muris, Cavit Pakel
https://arxiv.org/abs/2509.26420 https://arxiv.org/pdf/2509.26420

Triadic Network Formation
We study estimation and inference for triadic link formation with dyad-level fixed effects in a nonlinear binary choice logit framework. Dyad-level effects provide a richer and more realistic representation of heterogeneity across pairs of dimensions (e.g. importer--exporter, importer--product, exporter--product), yet their sheer number creates a severe incidental parameter problem. We propose a novel ``hexad logit'' estimator and establish its consistency and asymptotic normality. Identificati…

Securing Heterogeneous Network (HetNet) Communications for Wildfire Management: Mitigating the Effects of Adversarial and Environmental Threats
Nesrine Benchoubane, Olfa Ben Yahia, William Ferguson, Gurkan Gur, Sumit Chakravarty, Gregory Falco, Gunes Karabulut Kurt
https://arxiv.org/abs/2508.16761 …

Securing Heterogeneous Network (HetNet) Communications for Wildfire Management: Mitigating the Effects of Adversarial and Environmental Threats
In the face of adverse environmental conditions and cyber threats, robust communication systems for critical applications such as wildfire management and detection demand secure and resilient architectures. This paper presents a novel framework that considers both adversarial factors, building resilience into a heterogeneous network (HetNet) integrating Low Earth Orbit (LEO) satellite constellation with High-Altitude Platform Ground Stations (HAPGS) and Low-Altitude Platforms (LAPS), tailored t…

Dynamics of Infection Spread and Hotspot Growth in Bi-Pathogen Networks
Alyssa Yu, Laura P. Schaposnik
https://arxiv.org/abs/2509.03374 https://arxiv.org/p…

Dynamics of Infection Spread and Hotspot Growth in Bi-Pathogen Networks
Understanding the spatio-temporal evolution of epidemics with multiple pathogens requires not only new theoretical models but also careful analysis of their practical consequences. Building on the Multiplex Bi-Virus Reaction-Diffusion framework (MBRD) introduced in our companion paper, we investigate how the super-infection model (MBRD-SI) and the co-infection model (MBRD-CI) behave under different epidemiological and network conditions. Through numerical experiments, we study the effects of pa…

Causal Spatial Quantile Regression
Yan Gong, Reetam Majumder, Brian J. Reich, Rapha\"el Huser
https://arxiv.org/abs/2509.02294 https://arxiv.org/pdf/2…

Causal Spatial Quantile Regression
Treatment effects in a wide range of economic, environmental, and epidemiological applications often vary across space, and understanding the heterogeneity of causal effects across space and outcome quantiles is a critical challenge in spatial causal inference. To effectively capture spatial heterogeneity in distributional treatment effects, we propose a novel semiparametric neural network-based causal framework leveraging deep spatial quantile regression and then construct a plug-in estimator …

📊 Full observability: Log every state change, measure latency, generate audit trails
🎨 #PHP8 features: String-backed enums for states/events, readonly value objects for context data
🔄 Domain events pattern: Emit events to outbox, handle side effects idempotently with retries
📋 Easy testing: Unit test state transitions without hitting database or network
📈 Auto-documentation: …

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

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

Twin Peaks: Dual-Head Architecture for Structure-Free Prediction of Protein-Protein Binding Affinity and Mutation Effects
Supantha Dey, Ratul Chowdhury
https://arxiv.org/abs/2509.22950

Twin Peaks: Dual-Head Architecture for Structure-Free Prediction of Protein-Protein Binding Affinity and Mutation Effects
We present a novel dual-head deep learning architecture for protein-protein interaction modeling that enables simultaneous prediction of binding affinity ($ΔG$) and mutation-induced affinity changes ($ΔΔG$) using only protein sequence information. Our approach offers a significant advancement over existing methods by employing specialized prediction heads that operate on a shared representation network, allowing direct and optimized prediction of both values. To ensure robust generalization,…

Fermion Discretization Effects in the Two-Flavor Lattice Schwinger Model: A Study with Matrix Product States
Tim Schw\"agerl, Karl Jansen, Stefan K\"uhn
https://arxiv.org/abs/2509.02329

Fermion Discretization Effects in the Two-Flavor Lattice Schwinger Model: A Study with Matrix Product States
We present a comprehensive tensor network study of staggered, Wilson, and twisted mass fermions in the Hamiltonian formulation, using the massive two-flavor Schwinger model as a benchmark. Particular emphasis is placed on twisted mass fermions, whose properties in this context have not been systematically explored before. We confirm the expected O(a) improvement in the free theory and observe that this improvement persists in the interacting case. By leveraging an electric-field-based method fo…

Difference-in-Differences Under Network Interference
Kuan Sun, Zhiguo Xiao
https://arxiv.org/abs/2509.24259 https://arxiv.org/pdf/2509.24259

Difference-in-Differences Under Network Interference
This paper develops doubly robust estimators for direct (DATT) and spillover (SATT) average treatment effects on the treated in network-based difference-in-differences (DiD) designs. Unlike standard DiD methods, the proposed approach explicitly accommodates treatment spillovers and high-dimensional network confounding arising from complex inter-unit dependencies. Identification relies on a conditional parallel-trends assumption that holds after adjusting for high-dimensional network confounders…

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
Ali Abu-Nada, Subhashish Banerjee
https://arxiv.org/abs/2509.22758

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
We present a \emph{novel} and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory using only local ancilla measurements. A system qubit is coherently coupled to an ancilla via a symmetric XY Hamiltonian; the ancilla interacts with a noisy environment and is the only qubit we measure. A feedforward neural network, trained on short sliding windows of supplementary data from the past, forecasts the observable system $\langle Z_{(S)…

LLM-Supported Content Analysis of Motivated Reasoning on Climate Change
Yuheun Kim, Qiaoyi Liu, Jeff Hemsley
https://arxiv.org/abs/2508.21305 https://arxiv…

LLM-Supported Content Analysis of Motivated Reasoning on Climate Change
Public discourse around climate change remains polarized despite scientific consensus on anthropogenic climate change (ACC). This study examines how "believers" and "skeptics" of ACC differ in their YouTube comment discourse. We analyzed 44,989 comments from 30 videos using a large language model (LLM) as a qualitative annotator, identifying ten distinct topics. These annotations were combined with social network analysis to examine engagement patterns. A linear mixed-effects model showed that …

AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society
Tatsuru Kikuchi
https://arxiv.org/abs/2507.19911

AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society
This paper develops the first comprehensive theoretical and empirical framework for analyzing AI-driven spatial distribution dynamics in metropolitan areas undergoing demographic transition. We extend New Economic Geography by formalizing five novel AI-specific mechanisms: algorithmic learning spillovers, digital infrastructure returns, virtual agglomeration effects, AI-human complementarity, and network externalities. Using Tokyo as our empirical laboratory, we implement rigorous causal identi…

Novel Approaches to Artificial Intelligence Development Based on the Nearest Neighbor Method
I. I. Priezzhev, D. A. Danko, A. V. Shubin
https://arxiv.org/abs/2508.18953 https://…

Novel Approaches to Artificial Intelligence Development Based on the Nearest Neighbor Method
Modern neural network technologies, including large language models, have achieved remarkable success in various applied artificial intelligence applications, however, they face a range of fundamental limitations. Among them are hallucination effects, high computational complexity of training and inference, costly fine-tuning, and catastrophic forgetting issues. These limitations significantly hinder the use of neural networks in critical areas such as medicine, industrial process management, a…

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Hongbo Liu, Siyi Li, Zheng Yu
https://arxiv.org/abs/2508.18766 https://

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships among drugs, targets, and biological entities. In this work, we propose HGNN-DDI, a heterogeneous graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources. HGNN-DDI leverages graph representation learning to…

Bayesian optimization and nonlocal effects method for {\alpha} decay of superheavy nuclei based on CPPM
Xuanpeng Xiao, Panpan Qi, Gongming Yu, Haitao Yang, Qiang Hu
https://arxiv.org/abs/2507.19091

Bayesian optimization and nonlocal effects method for α decay of superheavy nuclei based on CPPM
We have integrated nonlocal effects with the Bayesian neural network (BNN) approach to enhance the prediction accuracy of $α$ decay half-lives. The results indicate that incorporating nonlocal effects significantly impacts half-life calculations, while the BNN method substantially improves prediction accuracy and demonstrates strong extrapolation capabilities. Using Shapley Additive Explanations (SHAP), we conducted a quantitative comparison among the four input feature parameters in the BNN, …

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties
Minxuan He, Claudius Krause, Daohan Wang
https://arxiv.org/abs/2509.00672 https://

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties
We present a dedicated graph neural network (GNN)-based methodology for the extraction of the Higgs boson signal strength $μ$, incorporating systematic uncertainties. The architecture features two branches: a deterministic GNN that processes kinematic variables unaffected by nuisance parameters, and an uncertainty-aware GNN that handles inputs modulated by systematic effects through gated attention-based message passing. Their outputs are fused to produce classification scores for signal-backg…

Just had to upgrade my solo instance of hosted Mastodon from the smallest plan (est. 5 active users) to the next size (est. 20 active users), due to database storage exhaustion.
I’m still happy to pay someone else to deal with Ruby, and it’s certainly worth $9/month…
…and I’m also happy when it happens, but this is totally the fault of the network effects of the occasional @…

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…

Binary choice logit models with general fixed effects for panel and network data
Kevin Dano, Bo E. Honor\'e, Martin Weidner
https://arxiv.org/abs/2508.11556 https://

Binary choice logit models with general fixed effects for panel and network data
This paper systematically analyzes and reviews identification strategies for binary choice logit models with fixed effects in panel and network data settings. We examine both static and dynamic models with general fixed-effect structures, including individual effects, time trends, and two-way or dyadic effects. A key challenge is the incidental parameter problem, which arises from the increasing number of fixed effects as the sample size grows. We explore two main strategies for eliminating nui…

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 …

Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems
V. Slavin, O. Kryvchikov, D. Laptev
https://arxiv.org/abs/2507.17509 https://

Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems
We present a graph-based deep learning framework for predicting the magnetic properties of quasi-one-dimensional Ising spin systems. The lattice geometry is encoded as a graph and processed by a graph neural network (GNN) followed by fully connected layers. The model is trained on Monte Carlo simulation data and accurately reproduces key features of the magnetization curve, including plateaus, critical transition points, and the effects of geometric frustration. It captures both local motifs an…

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Joshua R. Jandrell, Mitchell A. Cox
https://arxiv.org/abs/2508.19751 https://

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Modelling the effects of perturbations on optical fields often requires learning highly oscillatory complex-valued functions. Standard multi-layer perceptrons (MLPs) struggle with this task due to an inherent spectral bias, preventing them from fitting high-frequency sinusoids. To overcome this, we incorporate Fourier features - a set of predefined sinusoids dependent on the perturbation - as an additional network input. This reframes the learning problem from approximating a complex function t…

SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Peican Zhu, Yubo Jing, Le Cheng, Bin Chen, Xiaodong Cui, Lianwei Wu, Keke Tang
https://arxiv.org/abs/2507.13415

SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Previous studies on multimodal fake news detection mainly focus on the alignment and integration of cross-modal features, as well as the application of text-image consistency. However, they overlook the semantic enhancement effects of large multimodal models and pay little attention to the emotional features of news. In addition, people find that fake news is more inclined to contain negative emotions than real ones. Therefore, we propose a novel Semantic Enhancement and Emotional Reasoning (SE…

Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
Walker Gosrich, Saurav Agarwal, Kashish Garg, Siddharth Mayya, Matthew Malencia, Mark Yim, Vijay Kumar
https://arxiv.org/abs/2509.15052

Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
We propose a new formulation for the multi-robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra-task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP-hard; hence, we propose network flow-based algorithms to approximat…

Surveying the Operational Cybersecurity and Supply Chain Threat Landscape when Developing and Deploying AI Systems
Michael R Smith, Joe Ingram
https://arxiv.org/abs/2508.20307 h…

Surveying the Operational Cybersecurity and Supply Chain Threat Landscape when Developing and Deploying AI Systems
The rise of AI has transformed the software and hardware landscape, enabling powerful capabilities through specialized infrastructures, large-scale data storage, and advanced hardware. However, these innovations introduce unique attack surfaces and objectives which traditional cybersecurity assessments often overlook. Cyber attackers are shifting their objectives from conventional goals like privilege escalation and network pivoting to manipulating AI outputs to achieve desired system effects, …

Modeling Bilateral Lymphatic Head and Neck Tumour Progression for Personalized Elective Target Volume Definition
Kristoffer Moos, Anne Ivalu Sander Holm, Yoel Perez Haas, Roman Ludwig, Jesper Grau Eriksen, Stine Sofia Korreman
https://arxiv.org/abs/2509.24911

Modeling Bilateral Lymphatic Head and Neck Tumour Progression for Personalized Elective Target Volume Definition
Objective: Large irradiated volumes are a major contributor to severe side-effects in patients with head and neck cancer undergoing curatively intended radiotherapy. We propose a data-driven approach for defining the elective clinical target volume (CTV-E) on a patient-specific basis, with the potential to reduce irradiated volumes compared to standard guidelines. Approach: We introduce a bilateral Bayesian Network (BN), trained on a large cohort, to estimate the patient-specific risk of undete…

Temporal network analysis via a degree-corrected Cox model
Yuguo Chen, Lianqiang Qu, Jinfeng Xu, Ting Yan, Yunpeng Zhou
https://arxiv.org/abs/2507.19868 https://

Temporal network analysis via a degree-corrected Cox model
Temporal dynamics, characterised by time-varying degree heterogeneity and homophily effects, are often exhibited in many real-world networks. As observed in an MIT Social Evolution study, the in-degree and out-degree of the nodes show considerable heterogeneity that varies with time. Concurrently, homophily effects, which explain why nodes with similar characteristics are more likely to connect with each other, are also time-dependent. To facilitate the exploration and understanding of these dy…

Effects of Epoxy Composition on the Thermal and Network Properties of Crosslinked Thermosets: A Molecular-Dynamics Study
Venkata Rama Manoj Pola, Alexey Lyulin
https://arxiv.org/abs/2507.11453

Effects of Epoxy Composition on the Thermal and Network Properties of Crosslinked Thermosets: A Molecular-Dynamics Study
In this study, we investigate the influence of resin composition on the thermal and structural properties of crosslinked epoxy networks using molecular-dynamics simulations. The systems studied are based on mixtures of diglycidyl ether of bisphenol A (DGEBA) and butylated epoxy resin (BER), cured with diethyltoluenediamine (DETDA). A multi-step crosslinking algorithm was used to generate highly crosslinked networks. We systematically explored how the addition of BER affects the network topology…

Estimating Peer Effects Using Partial Network Data
Vincent Boucher, Aristide Houndetoungan
https://arxiv.org/abs/2509.08145 https://arxiv.org/pdf/2509.0814…

Estimating Peer Effects Using Partial Network Data
We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer effects on students' academic ach…

Measuring Social Media Network Effects
Sinan Aral, Seth G Benzell, Avinash Collis, Christos Nicolaides
https://arxiv.org/abs/2507.04545 https://

Measuring Social Media Network Effects
We use representative, incentive-compatible online choice experiments involving 19,923 Facebook, Instagram, LinkedIn, and X users in the US to provide the first large-scale, empirical measurement of local network effects in the digital economy. Our analysis reveals social media platform value ranges from $78 to $101 per consumer, per month, on average, and that 20-34% of that value is explained by local network effects. We also find 1) stronger ties are more valuable on Facebook and Instagram, …

Discovery of energy landscapes towards optimized quantum transport: Environmental effects and long-range tunneling
Maggie Lawrence, Matthew Pocrnic, Erin Fung, Juan Carrasquilla, Erik M. Gauger, Dvira Segal
https://arxiv.org/abs/2508.09371

Discovery of energy landscapes towards optimized quantum transport: Environmental effects and long-range tunneling
Carrier transport in quantum networks is governed by a variety of factors, including network dimensionality and connectivity, on-site energies, couplings between sites and whether they are short- or long-range, and environmental effects. In this work, we identify classes of quasi-one-dimensional chains with energy profiles that optimize carrier transport under such influences. Specifically, we optimize on-site energies using Optax's optimistic gradient descent and AdaMax algorithms, enabled by …

Nonreciprocal random networks and their percolation properties
Chanania Steinbock
https://arxiv.org/abs/2509.05253 https://arxiv.org/pdf/2509.05253

Nonreciprocal random networks and their percolation properties
We study the effects of nonreciprocity and network structure on percolation. To this end, we investigate nonreciprocal random networks - directed networks for which the probability of a link occurring from node i to node j differs from the probability of the reverse link occurring from node j to node i. We analytically determine the degree and percolation properties of such networks with exactly two types of link probability, demonstrating that whether the networks are structured such that the …

GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks
Muhammad Hadir Khan, Matthew Guthaus
https://arxiv.org/abs/2507.05681 https://

GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks
Clock meshes are essential in high-performance VLSI systems for minimizing skew and handling PVT variations, but analyzing them is difficult due to reconvergent paths, multi-source driving, and input mesh buffer skew. SPICE simulations are accurate but slow; yet simplified models miss key effects like slew and input skew. We propose GATMesh, a Graph Neural Network (GNN)-based framework that models the clock mesh as a graph with augmented structural and physical features. Trained on SPICE data, …

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
Osman Tokluoglu, Enver Cavus, Ebrahim Bedeer, Halim Yanikomeroglu
https://arxiv.org/abs/2507.15291

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier l…

Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
Rodrigo Moreira, Larissa F. Rodrigues Moreira, Fl\'avio de Oliveira Silva
https://arxiv.org/abs/2507.17850

Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using mathematical models, queue theories, or even Artificial Intelligence (AI). This paper elucidates the effects of chaotic workloads, generated by Distributed Denial of Service (DDoS) on different Network Functi…

Effects of multiple cycles on the resistance distance of a strand in a homogeneous polymer network
Erica Uehara, Tetsuo Deguchi
https://arxiv.org/abs/2507.06476

Effects of multiple cycles on the resistance distance of a strand in a homogeneous polymer network
We show that the resistance distance between a pair of adjacent vertices in a phantom network generated randomly by a Monte-Carlo method depends on the existence of short cycles around it. Here we assume that phantom networks have no fixed points but their centers of mass are located at a point. The resistance distance corresponds to the mean-square deviation of the end-to-end vector along the strand connecting the adjacent vertices. We generate random networks with fixed valency $f$ but differ…

Dyadic data with ordered outcome variables
Chris Muris, Cavit Pakel, Qichen Zhang
https://arxiv.org/abs/2507.16689 https://arxiv.org/…

Dyadic data with ordered outcome variables
We consider ordered logit models for directed network data that allow for flexible sender and receiver fixed effects that can vary arbitrarily across outcome categories. This structure poses a significant incidental parameter problem, particularly challenging under network sparsity or when some outcome categories are rare. We develop the first estimation method for this setting by extending tetrad-differencing conditional maximum likelihood (CML) techniques from binary choice network models. Th…

Weak Identification in Peer Effects Estimation
William W. Wang, Ali Jadbabaie
https://arxiv.org/abs/2508.04897 https://arxiv.org/pdf/2508.04897

Weak Identification in Peer Effects Estimation
It is commonly accepted that some phenomena are social: for example, individuals' smoking habits often correlate with those of their peers. Such correlations can have a variety of explanations, such as direct contagion or shared socioeconomic circumstances. The network linear-in-means model is a workhorse statistical model which incorporates these peer effects by including average neighborhood characteristics as regressors. Although the model's parameters are identifiable under mild structural …

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 …

The Impact of Shared Telecom Infrastructure on Digital Connectivity and Inclusion
Georges V. Houngbonon, Marc Ivaldi, Emil Palikot, Davide Strusani
https://arxiv.org/abs/2507.19693

The Impact of Shared Telecom Infrastructure on Digital Connectivity and Inclusion
Nearly half the world remains offline, and capital scarcity stalls new network buildouts. Sharing existing mobile towers could accelerate connectivity. We assemble data on 107 tower-sharing deals in 28 low-income countries (2008-20) and estimate staggered difference-in-differences effects. Two years after a transaction covering over 1,000 towers, the PPP-adjusted mobile-price index falls USD 1.60 (s.e. 1.10) from a baseline of USD 3.16, while data prices drop USD 1.00 (0.29), baseline USD 3.41 …

Verification of Visual Controllers via Compositional Geometric Transformations
Alexander Estornell, Leonard Jung, Michael Everett
https://arxiv.org/abs/2507.04523

Verification of Visual Controllers via Compositional Geometric Transformations
Perception-based neural network controllers are increasingly used in autonomous systems that rely on visual inputs to operate in the real world. Ensuring the safety of such systems under uncertainty is challenging. Existing verification techniques typically focus on Lp-bounded perturbations in the pixel space, which fails to capture the low-dimensional structure of many real-world effects. In this work, we introduce a novel verification framework for perception-based controllers that can genera…

When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
Ming Tang, Xiaowen Huang, Jitao Sang
https://arxiv.org/abs/2508.11516

When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
Recommender systems increasingly suffer from echo chambers and user homogenization, systemic distortions arising from the dynamic interplay between algorithmic recommendations and human behavior. While prior work has studied these phenomena through the lens of algorithmic bias or social network structure, we argue that the psychological mechanisms of users and the closed-loop interaction between users and recommenders are critical yet understudied drivers of these emergent effects. To bridge th…

Dynamic effects of electric field in hybrid coupling thermosensitive neuronal network
Ediline L. F. Nguessap (Department of Physics, FFCLRP, Sao Paulo, Brazil), Antonio C. Roque (Department of Physics, FFCLRP, Sao Paulo, Brazil), Fernando F. Ferreira (Department of Physics, FFCLRP, Sao Paulo, Brazil)
https://arxiv.org/abs/2509.14910…

Dynamic effects of electric field in hybrid coupling thermosensitive neuronal network
The dynamic of thermosensitive neuronal networks under the influence of external electric fields is explored, focusing on hybrid coupling models that incorporate both electrical and chemical synapses. Numerical simulations reveal a variety of complex behaviors, including coherent, incoherent, and chimera states, which are influenced by the frequency of the external field and the cell size. Specificaly chemical coupling is shown to enhances the appearance of a typical chimera states called trave…

Cluster breaking and reconfiguration effects in 12 Lambda B hypernucleus
Jiaqi Tian, Mengjiao Lyu, Akinobu Dote, Zheng Cheng, Takayuki Myo, Masahiro Isaka, Hisashi Horiuchi, Hiroki Takemoto, Hiroshi Toki, Niu Wan, Qing Zhao
https://arxiv.org/abs/2508.11937

Cluster breaking and reconfiguration effects in 12 Lambda B hypernucleus
We investigate the cluster-breaking effect and spatial distribution of negative-parity states in the 12 Lambda B hypernucleus using the Hyper-Brink model with cluster-breaking(CB-Hyper-Brink) optimized via Control Neural Network (Ctrl.NN). The results demonstrate that the inclusion of cluster-breaking is essential for accurately reproducing the observed low-lying energy levels and for making reliable predictions of the Hoyle-analog state 1-4 in 12 Lambda B. Cluster-breaking manifests as strong …

Simulation-Prior Independent Neural Unfolding Procedure
Anja Butter, Theo Heimel, Nathan Huetsch, Michael Kagan, Tilman Plehn
https://arxiv.org/abs/2507.15084

Simulation-Prior Independent Neural Unfolding Procedure
Machine learning allows unfolding high-dimensional spaces without binning at the LHC. The new SPINUP method extracts the unfolded distribution based on a neural network encoding the forward mapping, making it independent of the prior from the simulated training data. It is made efficient through neural importance sampling, and ensembling can be used to estimate the effect of information loss in the forward process. We showcase SPINUP for unfolding detector effects on jet substructure observable…

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…

Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts
Matthew Sprintson, Edward Oughton
https://arxiv.org/abs/2507.19989

Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts
It is estimated that over one-fourth of US households experienced a power outage in 2023, costing on average US $\$150$ Bn annually, with $87\%$ of outages caused by natural hazards. Indeed, numerous studies have examined the macroeconomic impact of power network interruptions, employing a wide variety of modeling methods and data parameterization techniques, which warrants further investigation. In this paper, we quantify the macroeconomic effects of three significant natural hazard-induced US…

Less is more: removing a single bond increases the toughness of elastic networks
Antoine Sanner, Luca Michel, Alessandra Lingua, David S. Kammer
https://arxiv.org/abs/2507.13806

Less is more: removing a single bond increases the toughness of elastic networks
We investigate how the removal of a single bond affects the fracture behavior of triangular spring networks, whereby we systematically vary the position of the removed bond. Our simulations show that removing the bond has two contrasting effects on the fracture energy for initiation of crack propagation and on the fracture energy for failure of the entire network. A single missing bond can either lower or raise the initiation fracture energy, depending on its placement relative to the crack tip…

Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime
Alberto Aziani, Michael V. Lo Giudice, Ali Shadman Yazdi
https://arxiv.org/abs/2507.07884

Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime
Is demand for conspiracy theories online linked to real-world hate crimes? By analyzing online search trends for 36 racially and politically-charged conspiracy theories in Michigan (2015-2019), we employ a one-dimensional convolutional neural network (1D-CNN) to predict hate crime occurrences offline. A subset of theories including the Rothschilds family, Q-Anon, and The Great Replacement improves prediction accuracy, with effects emerging two to three weeks after fluctuations in searches. Howe…

Tomography-assisted noisy quantum circuit simulator using matrix product density operators
Wei-guo Ma, Yun-Hao Shi, Kai Xu, Heng Fan
https://arxiv.org/abs/2508.07610 https://

Tomography-assisted noisy quantum circuit simulator using matrix product density operators
In recent years, efficient quantum circuit simulations incorporating ideal noise assumptions have relied on tensor network simulators, particularly leveraging the matrix product density operator (MPDO) framework. However, experiments on real noisy intermediate-scale quantum (NISQ) devices often involve complex noise profiles, encompassing uncontrollable elements and instrument-specific effects such as crosstalk. To address these challenges, we employ quantum process tomography (QPT) techniques …

Block entanglement bounds distribution of regionally localized entanglement
Jithin G. Krishnan, Aditi Sen De, Amit Kumar Pal
https://arxiv.org/abs/2508.05431 https://

Block entanglement bounds distribution of regionally localized entanglement
In quantum networks, eliminating connections between nodes is crucial to mitigate the effects of decoherence, often achieved by performing measurements on nodes that are idle, or vulnerable to noise. To characterize the entanglement content of the resulting smaller network, we introduce the notion of ``regionally localized entanglement", defined as the average entanglement concentrated over a two-qubit region in a multi-qubit system. Hence, the total regionally localized entanglement can be obt…