Tootfinder

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Vu Duc Anh Nguyen, Ziyue Li
https://arxiv.org/abs/2510.09350 https://…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while sim…

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
Yuhua Zhang, Jukka-Pekka Onnela, Shuo Sun, Ruoyu Wang
https://arxiv.org/abs/2510.10508 …

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
Interference--in which a unit's outcome is affected by the treatment of other units--poses significant challenges for the identification and estimation of causal effects. Most existing methods for estimating interference effects assume that the interference networks are known. In many practical settings, this assumption is unrealistic as such networks are typically latent. To address this challenge, we propose a novel framework for identifying and estimating heterogeneous group-level interferen…

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 …

Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
Donghan Hu, Rameen Mahmood, Annabelle David, Danny Yuxing Huang
https://arxiv.org/abs/2510.09763

Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
AI-driven applications have become woven into students' academic and creative workflows, influencing how they learn, write, and produce ideas. Gaining a nuanced understanding of these usage patterns is essential, yet conventional survey and interview methods remain limited by recall bias, self-presentation effects, and the underreporting of habitual behaviors. While ethnographic methods offer richer contextual insights, they often face challenges of scale and reproducibility. To bridge this gap…

Efficient and accurate tensor network algorithm for Anderson impurity problems
Zhijie Sun, Zhenyu Li, Chu Guo
https://arxiv.org/abs/2510.11459 https://arxi…

Efficient and accurate tensor network algorithm for Anderson impurity problems
The Anderson impurity model (AIM) is of fundamental importance in condensed matter physics to study strongly correlated effects. However, accurately solving its long-time dynamics still remains a great numerical challenge. An emergent and rapidly developing numerical strategy to solve the AIM is to represent the Feynman-Vernon influence functional (IF), which encodes all the bath effects on the impurity dynamics, as a matrix product state (MPS) in the temporal domain. The computational cost of …

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…

Uncovering All Highly Credible Binary Treatment Hierarchy Questions in Network Meta-Analysis
Caitlin H. Daly, Chloe Tan, Audrey B\'eliveau
https://arxiv.org/abs/2510.08853 h…

Uncovering All Highly Credible Binary Treatment Hierarchy Questions in Network Meta-Analysis
In recent years, there has been growing research interest in addressing treatment hierarchy questions within network meta-analysis (NMA). In NMAs involving many treatments, the number of possible hierarchy questions becomes prohibitively large. To manage this complexity, previous work has recommended pre-selecting specific hierarchy questions of interest (e.g., ``among options A, B, C, D, E, do treatments A and B have the two best effects in terms of improving outcome X?") and calculating the e…

Interplay of choice and topology in percolation on mediation-driven attachment networks
Nilomber Roy, M. M. B. Sheraj, M. K. Hassan
https://arxiv.org/abs/2510.10076 https://

Interplay of choice and topology in percolation on mediation-driven attachment networks
We investigate bond percolation on mediation-driven attachment (MDA) networks under the generalized Achlioptas process, where $M>1$ candidate bonds are sampled and the one that minimizes the resulting cluster size is selected the best-of-$M$ rule. This framework offers a systematic approach to investigate how network topology and choice mechanisms jointly shape percolation behavior. We analyze the effects of the degree exponent $ω$ and the choice parameter $M$ on the critical point $t_c$ and t…

The impact of observation losses on IVS-R1/R4 VLBI sessions
Tiege McCarthy, Lucia McCallum
https://arxiv.org/abs/2510.11436 https://arxiv.org/pdf/2510.1143…

The impact of observation losses on IVS-R1/R4 VLBI sessions
Global VLBI observations, to measure Earth orientation and station positions, are organised into 24-hour sessions. Each session has a bespoke schedule created, optimised for the particular time period and the station network that is available during it. Due to various factors, whether it be station outages, sensitivity issues or source effects, not all scheduled observations are available, or of sufficient quality, to be included in the final geodetic analysis. In this paper we derive statistic…

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

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

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…

MEGATRON: the impact of non-equilibrium effects and local radiation fields on the circumgalactic medium at cosmic noon
Corentin Cadiou, Harley Katz, Martin P. Rey, Oscar Agertz, Jeremy Blaizot, Alex J. Cameron, Nicholas Choustikov, Julien Devriendt, Uliana Hauk, Gareth C. Jones, Taysun Kimm, Isaac Laseter, Sergio Mart\'in \'Alvarez, Kosei Matsumoto, Camilla T. Nyhagen, Autumn Pearce, Francisco Rodr\'iguez Montero, Joki Rosdahl, V\'ictor Rufo Pastor, Mahsa Sanati, Aayush…

MEGATRON: the impact of non-equilibrium effects and local radiation fields on the circumgalactic medium at cosmic noon
We present three cosmological radiation-hydrodynamic zoom simulations of the progenitor of a Milky Way-mass galaxy from the MEGATRON suite. The simulations combine on-the-fly radiative transfer with a detailed non-equilibrium thermochemical network (81 ions and molecules), resolving the cold and warm gas in the circumgalactic medium (CGM) on spatial scales down to 20 pc and on average 200 pc at cosmic noon. Comparing our full non-equilibrium calculation with local radiation to traditional post-…

A GNAR-Based Framework for Spectral Estimation of Network Time Series: Application to Global Bank Network Connectedness
Cristian F. Jim\'enez-Var\'on, Marina I. Knight
https://arxiv.org/abs/2510.06157

A GNAR-Based Framework for Spectral Estimation of Network Time Series: Application to Global Bank Network Connectedness
Patterns of dependence in financial networks, such as global bank connectedness, evolve over time and across frequencies. Analysing these systems requires statistical tools that jointly capture temporal dynamics and the underlying network topology. This work develops a novel spectral analysis framework for Generalized Network Autoregressive (GNAR) processes, modeling dependencies beyond direct neighbours by incorporating r-stage neighbourhood effects, unlike existing methods that at best rely s…

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…

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…

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 …

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…

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,…

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…

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…

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…

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…

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 …

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…

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 …

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)…

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…

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 …

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…

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…

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…

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…

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…

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…

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…

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 …

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 …

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…