Tootfinder

Exploring Major Transitions in the Evolution of Biological Cognition With Artificial Neural Networks
Konstantinos Voudouris, Andrew Barron, Marta Halina, Colin Klein, Matishalin Patel
https://arxiv.org/abs/2509.13968

Exploring Major Transitions in the Evolution of Biological Cognition With Artificial Neural Networks
Transitional accounts of evolution emphasise a few changes that shape what is evolvable, with dramatic consequences for derived lineages. More recently it has been proposed that cognition might also have evolved via a series of major transitions that manipulate the structure of biological neural networks, fundamentally changing the flow of information. We used idealised models of information flow, artificial neural networks (ANNs), to evaluate whether changes in information flow in a network ca…

Fluid Dynamics and Domain Reconstruction from Noisy Flow Images Using Physics-Informed Neural Networks and Quasi-Conformal Mapping
Han Zhang, Xue-Cheng Tai, Jean-Michel Morel, Raymond H. Chan
https://arxiv.org/abs/2508.11216

Fluid Dynamics and Domain Reconstruction from Noisy Flow Images Using Physics-Informed Neural Networks and Quasi-Conformal Mapping
Blood flow imaging provides important information for hemodynamic behavior within the vascular system and plays an essential role in medical diagnosis and treatment planning. However, obtaining high-quality flow images remains a significant challenge. In this work, we address the problem of denoising flow images that may suffer from artifacts due to short acquisition times or device-induced errors. We formulate this task as an optimization problem, where the objective is to minimize the discrep…

Learning Neural Networks by Neuron Pursuit
Akshay Kumar, Jarvis Haupt
https://arxiv.org/abs/2509.12154 https://arxiv.org/pdf/2509.12154

Learning Neural Networks by Neuron Pursuit
The first part of this paper studies the evolution of gradient flow for homogeneous neural networks near a class of saddle points exhibiting a sparsity structure. The choice of these saddle points is motivated from previous works on homogeneous networks, which identified the first saddle point encountered by gradient flow after escaping the origin. It is shown here that, when initialized sufficiently close to such saddle points, gradient flow remains near the saddle point for a sufficiently lon…

Scattering theory for Stokes flow in complex branched structures
Haiyang Wang, Fredrik Fryklund, Samuel Potter, Leslie Greengard
https://arxiv.org/abs/2509.12500 https://…

Scattering theory for Stokes flow in complex branched structures
Slow, viscous flow in branched structures arises in many biological and engineering settings. Direct numerical simulation of flow in such complicated multi-scale geometry, however, is a computationally intensive task. We propose a scattering theory framework that dramatically reduces this cost by decomposing networks into components connected by short straight channels. Exploiting the phenomenon of rapid return to Poiseuille flow (Saint-Venant's principle in the context of elasticity), we compu…

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Xiaodong Wang, Ping Wang, Zijun He, Mengjie Qin, Xin Yuan
https://arxiv.org/abs/2509.12079 https://

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Coded aperture snapshot spectral imaging (CASSI) retrieves a 3D hyperspectral image (HSI) from a single 2D compressed measurement, which is a highly challenging reconstruction task. Recent deep unfolding networks (DUNs), empowered by explicit data-fidelity updates and implicit deep denoisers, have achieved the state of the art in CASSI reconstruction. However, existing unfolding approaches suffer from uncontrollable reconstruction trajectories, leading to abrupt quality jumps and non-gradual re…

On the Feasibility of Inter-Flow Service Degradation Detection
Balint Bicski, Adrian Pekar
https://arxiv.org/abs/2509.11140 https://arxiv.org/pdf/2509.1114…

On the Feasibility of Inter-Flow Service Degradation Detection
Hardware acceleration in modern networks creates monitoring blind spots by offloading flows to a non-observable state, hindering real-time service degradation (SD) detection. To address this, we propose and formalize a novel inter-flow correlation framework, built on the hypothesis that observable flows can act as environmental sensors for concurrent, non-observable flows. We conduct a comprehensive statistical analysis of this inter-flow landscape, revealing a fundamental trade-off: while the …

Coarse-Grained BCFT Tensor Networks and Holographic Reflected Entropy in 3D Gravity
Ning Bao, Jinwei Chu, Yikun Jiang, Jacob March
https://arxiv.org/abs/2509.10170 https://

Coarse-Grained BCFT Tensor Networks and Holographic Reflected Entropy in 3D Gravity
We use the framework of $\textit{BCFT tensor networks}$ to present a microscopic CFT derivation of the correspondence between reflected entropy (RE) and entanglement wedge cross section (EW) in AdS$_3$/CFT$_2$, for both bipartite and multipartite settings. These fixed-point tensor networks, obtained by triangulating Euclidean CFT path integrals, allow us to explicitly construct the canonical purification via cutting-and-gluing CFT path integrals. Employing modular flow in the large-$c$ limit, w…

Intelligent backpropagated neural networks application on Couette-Poiseuille flow of variable viscosity in a composite porous channel filled with an anisotropic porous layer
Timir Karmakar, Amrita Mandal
https://arxiv.org/abs/2510.08745

Intelligent backpropagated neural networks application on Couette-Poiseuille flow of variable viscosity in a composite porous channel filled with an anisotropic porous layer
This study examines Couette-Poiseuille flow of variable viscosity within a channel that is partially filled with a porous medium. To enhance its practical relevance, we assume that the porous medium is anisotropic with permeability varying in all directions, making it a positive semidefinite matrix in the momentum equation. We assume the Navier-Stokes equations govern the flow in the free flow region, while the Brinkman-Forchheimer-extended Darcy's equation governs the flow inside the porous me…

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

Preserving Core Structures of Social Networks via Information Guided Multi-Step Graph Pruning
Yutong Hu, Bingxin Zhou, Jing Wang, Weishu Zhao, Liang Hong
https://arxiv.org/abs/2510.10499

Preserving Core Structures of Social Networks via Information Guided Multi-Step Graph Pruning
Social networks often contain dense and overlapping connections that obscure their essential interaction patterns, making analysis and interpretation challenging. Identifying the structural backbone of such networks is crucial for understanding community organization, information flow, and functional relationships. This study introduces a multi-step network pruning framework that leverages principles from information theory to balance structural complexity and task-relevant information. The fra…

Directed Cycles as Higher-Order Units of Information Processing in Complex Networks
Hardik Rajpal, Paul Expert, Vaiva Vasiliauskaite
https://arxiv.org/abs/2508.09808 https://

Directed Cycles as Higher-Order Units of Information Processing in Complex Networks
Directed cycles form the fundamental motifs in natural, social and artificial networks, yet their distinct computational roles remain under-explored, particularly in the context of higher-order structure and function. In this work, we investigate how two types of directed cycles - feedforward and feedback - can act as higher-order structures to facilitate the flow and integration of information in sparse random networks, and how these roles depend on the environment of the cycles. Using informa…

Carbon Pricing in Traffic Networks
Svenja M. Griesbach, Tobias Harks, Max Klimm, Michael Markl, Philipp Warode
https://arxiv.org/abs/2508.09280 https://arx…

Carbon Pricing in Traffic Networks
Traffic is a significant source of global carbon emissions. In this paper, we study how carbon pricing can be used to guide traffic towards equilibria that respect given emission budgets. In particular, we consider a general multi-commodity flow model with flow-dependent externalities. These externalities may represent carbon emissions, entering a priced area, or the traversal of paths regulated by tradable credit schemes. We provide a complete characterization of all flows that can be attain…

Explainable Ensemble Learning for Graph-Based Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins, Ali A Ghorbani
https://arxiv.org/abs/2508.09801 https…

Explainable Ensemble Learning for Graph-Based Malware Detection
Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks (GNNs) have shown promise in this domain by modeling rich structural dependencies in graph-based program representations such as control flow graphs (CFGs). However, single-model approaches may suffer from limited generalization and lack interpretability, especially in high-stakes security applications. In this paper, we prop…

On properties of hydraulic equilibria in district heating networks
Ask H\"allstr\"om, Felix Agner, Richard Pates
https://arxiv.org/abs/2510.04524 https://

On properties of hydraulic equilibria in district heating networks
District heating networks are an integral part of the energy system in many countries. In future smart energy systems, they are expected to enhance energy flexibility and support the integration of renewable and waste energy sources. An important aspect of these networks is the control of flow rates, which dictates the heat delivered to consumers. This paper concerns the properties of flow rates in tree-structured district heating networks. We show that under mild assumptions of monotonicity in…

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

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

Crossover between kite growth and vibrational bridging in pillar-assisted controlled formation of carbon nanotube networks
Yuanjia Liu, Taiki Inoue, Yoshihiro Kobayashi
https://arxiv.org/abs/2509.08301

Crossover between kite growth and vibrational bridging in pillar-assisted controlled formation of carbon nanotube networks
Pillar-assisted growth is a technique in which short carbon nanotubes (CNTs) form suspended networks by growing across closely spaced microfabricated pillars. During growth, the CNT tips exhibit vibrations that allow them to bridge the neighboring pillars. To improve the complexity and controllability of the CNT networks, we introduce a kite-growth mechanism in which CNTs are elongated and aligned by gas flow during growth, enabling a longer bridging distance compared to vibrational bridging. B…

Metrics for Assessing Changes in Flow-based Networks
Micha{\l} Rzepka, Piotr Cho{\l}da
https://arxiv.org/abs/2508.09573 https://arxiv.org/pdf/2508.09573

Metrics for Assessing Changes in Flow-based Networks
This paper addresses the challenges of evaluating network performance in the presence of fluctuating traffic patterns, with a particular focus on the impact of peak data rates on network resources. We introduce a set of metrics to quantify network load and measure the impact of individual flows on the overall network state. By analyzing link and flow data through percentile values and sample distributions, and introducing the Utilization Score metric, the research provides insights into resourc…

Optimizing information flow in Gene Regulatory Networks: a geometric perspective
Miguel \'Angel Garc\'ia-Ariza, Alessandro Bravetti, Pablo Padilla, J Roberto Romero-Arias
https://arxiv.org/abs/2509.08167

Optimizing information flow in Gene Regulatory Networks: a geometric perspective
The dynamics of gene regulatory networks is governed by the interaction between deterministic biochemical reactions and molecular noise. To understand how gene regulatory networks process information during cell state transitions, we study stochastic dynamics derived from a Boolean network model via its representation on the parameter space of Gaussian distributions, equipped with the Fisher information metric. This reformulation reveals that the trajectories of optimal information transfer are…

Achieving Universal Approximation and Universal Interpolation via Nonlinearity of Control Families
Yongqiang Cai, Yifei Duan
https://arxiv.org/abs/2510.03676 https://

Achieving Universal Approximation and Universal Interpolation via Nonlinearity of Control Families
A significant connection exists between the controllability of dynamical systems and the approximation capabilities of neural networks, where residual networks and vanilla feedforward neural networks can both be regarded as numerical discretizations of the flow maps of dynamical systems. Leveraging the expressive power of neural networks, prior works have explored various control families $\mathcal{F}$ that enable the flow maps of dynamical systems to achieve either the universal approximation …

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Curvature Flow of Networks with Triple Junction Drag and Grain Rotation
Yuchuan Yang, Selim Esedoglu
https://arxiv.org/abs/2509.06125 https://arxiv.org/pdf…

Curvature Flow of Networks with Triple Junction Drag and Grain Rotation
We prove a local existence result for a PDE system that describes curvature motion of networks with a dynamic boundary condition known as triple junction drag. This model arises in the study of grain boundary evolution in polycrystalline materials. We demonstrate the possibility of a new type of topological change during the evolution of the network. Moreover, we adopt a dynamic surface tension model that depends on the crystallographic orientations of the grains, which are allowed to rotate. L…

Intrinsic training dynamics of deep neural networks
Sibylle Marcotte, Gabriel Peyr\'e, R\'emi Gribonval
https://arxiv.org/abs/2508.07370 https://ar…

Intrinsic training dynamics of deep neural networks
A fundamental challenge in the theory of deep learning is to understand whether gradient-based training in high-dimensional parameter spaces can be captured by simpler, lower-dimensional structures, leading to so-called implicit bias. As a stepping stone, we study when a gradient flow on a high-dimensional variable $θ$ implies an intrinsic gradient flow on a lower-dimensional variable $z = ϕ(θ)$, for an architecture-related function $ϕ$. We express a so-called intrinsic dynamic property and…

Energy-Energy Flow Networks
Arianna Garcia Caffaro, Ian Moult, Chase Shimmin
https://arxiv.org/abs/2510.06314 https://arxiv.org/pdf/2510.06314

Energy-Energy Flow Networks
Jet substructure provides one of the most exciting new approaches for searching for physics in and beyond the Standard Model at the Large Hadron Collider. Modern jet substructure searches are often performed with Neural Network (NN) taggers which study the jets' radiation distributions in great detail, far beyond what is theoretically described by parton shower generators. While this represents a great opportunity, as NNs look deeper into the structure of jets they become increasingly sensitive…

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
https://arxiv.org/abs/2510.11600

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
We present an efficient and realisable active flow control framework with few non-intrusive sensors. The method builds upon data-driven, reduced-order predictive models based on Long-Short-Term Memory (LSTM) networks and efficient gradient-based Model Predictive Control (MPC). The model uses only surface-mounted pressure probes to infer the wake state, and is trained entirely offline on a dataset built with open-loop actuations, thus avoiding the complexities of online learning. Sparsification …

Fractal Flow: Hierarchical and Interpretable Normalizing Flow via Topic Modeling and Recursive Strategy
Binhui Zhang, Jianwei Ma
https://arxiv.org/abs/2508.19750 https://…

Fractal Flow: Hierarchical and Interpretable Normalizing Flow via Topic Modeling and Recursive Strategy
Normalizing Flows provide a principled framework for high-dimensional density estimation and generative modeling by constructing invertible transformations with tractable Jacobian determinants. We propose Fractal Flow, a novel normalizing flow architecture that enhances both expressiveness and interpretability through two key innovations. First, we integrate Kolmogorov-Arnold Networks and incorporate Latent Dirichlet Allocation into normalizing flows to construct a structured, interpretable lat…

Quantum Annealing for Realistic Traffic Flow Optimization: Clustering and Data-Driven QUBO
Ren\'ata Rusn\'akov\'a, Martin Chovanec, Juraj Gazda
https://arxiv.org/abs/2510.06053

Quantum Annealing for Realistic Traffic Flow Optimization: Clustering and Data-Driven QUBO
Managing city traffic is a complex NP-hard problem where traditional methods often fail to scale. We present a data-driven approach that reformulates traffic optimization as a Quadratic Unconstrained Binary Optimization, capturing both congestion reduction and travel-time efficiency. The model integrates simulated realistic mobility data, multiple routing alternatives, and analytically derived penalty constraints. To address large networks, we apply Leiden clustering to preserve critical conges…

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

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

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases
Matteo Poggi, Fabio Tosi
https://arxiv.org/abs/2509.05297 https://arxiv.or…

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases
We present FlowSeek, a novel framework for optical flow requiring minimal hardware resources for training. FlowSeek marries the latest advances on the design space of optical flow networks with cutting-edge single-image depth foundation models and classical low-dimensional motion parametrization, implementing a compact, yet accurate architecture. FlowSeek is trained on a single consumer-grade GPU, a hardware budget about 8x lower compared to most recent methods, and still achieves superior cros…

Poroelasticity of bottlebrush and linear polymer networks
Nolan A. Miller, Alfred J. Crosby
https://arxiv.org/abs/2509.00022 https://arxiv.org/pdf/2509.000…

Poroelasticity of bottlebrush and linear polymer networks
The transport of solvent molecules through soft, swollen networks is critical in both natural and engineered systems. While this poroelastic flow has traditionally been explored in networks where the mesh size is comparable to the solvent molecule size, the effect of network architecture on permeability remains underexplored. Here we investigate solvent transport in linear polymer networks (LPN) and highly elastic bottlebrush elastomer networks (BBN), where the presence of densely grafted sidec…

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory
Yueqi Zhao, Michael M. Fogler, Yi-Zhuang You
https://arxiv.org/abs/2510.06508 …

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory
We introduce RGFlow, a deep neural network-based real-space renormalization group (RG) framework tailored for continuum scalar field theories. Leveraging generative capabilities of flow-based neural networks, RGFlow autonomously learns real-space RG transformations from data without prior knowledge of the underlying model. In contrast to conventional approaches, RGFlow is bijective (information-preserving) and is optimized based on the principle of minimal mutual information. We demonstrate the…

Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields
Kim Bente, Roman Marchant, Fabio Ramos
https://arxiv.org/abs/2510.06286 https://

Mass Conservation on Rails - Rethinking Physics-Informed Learning of Ice Flow Vector Fields
To reliably project future sea level rise, ice sheet models require inputs that respect physics. Embedding physical principles like mass conservation into models that interpolate Antarctic ice flow vector fields from sparse & noisy measurements not only promotes physical adherence but can also improve accuracy and robustness. While physics-informed neural networks (PINNs) impose physics as soft penalties, offering flexibility but no physical guarantees, we instead propose divergence-free neural…

Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Oliver Welin Odeback, Arivazhagan Geetha Balasubramanian, Jonas Schollenberger, Edward Ferdiand, Alistair A. Young, C. Alberto Figueroa, Susanne Schnell, Outi Tammisola, Ricardo Vinuesa, Tobias Granberg, Alexander Fyrdahl, David Marlevi
https://…

Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its clinical application is limited by low spatial resolution and noise, particularly affecting near-wall velocity measurements. Machine learning-based super-resolution has shown promise in addressing these limitations, but challenges remain, not least in recovering near-wall velocities. Generative adversarial networks (GANs) offer a compelling solution, having…

Weight Initialization and Variance Dynamics in Deep Neural Networks and Large Language Models
Yankun Han
https://arxiv.org/abs/2510.09423 https://arxiv.org…

Weight Initialization and Variance Dynamics in Deep Neural Networks and Large Language Models
Weight initialization governs signal propagation and gradient flow at the start of training. This paper offers a theory-grounded and empirically validated study across two regimes: compact ReLU multilayer perceptrons and GPT-2-style transformers. First, a logarithmic sweep of the initial standard deviation maps vanishing and exploding regimes and identifies a broad stability band with standard deviations between 1e-2 and 1e-1. Second, a controlled comparison shows that Kaiming (fan-in) initiali…

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Stavros Orfanoudakis, Frans Oliehoek, Peter Palesnky, Pedro P. Vergara
https://arxiv.org/abs/2510.12335

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing Reinforcement Learning (RL) approaches for smart charging often disregard physical grid constraints or have limited performance for complex large-scale tasks, limiting their scalability and real-world applicability. This paper introduces a physics-informed (PI) RL algorithm that integrates a differentiable power flow m…

Crosslisted article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- Optimizing information flow in Gene Regulatory Networks: a geometric perspective
Miguel \'Angel Garc\'ia-Ariza, Alessandro Bravetti, Pablo Padilla, J Roberto Romero-Arias

On the Stability of Dynamical Multi-Commodity Flow Networks
Davide Sipione, Giacomo Como
https://arxiv.org/abs/2508.17804 https://arxiv.org/pdf/2508.17804

On the Stability of Dynamical Multi-Commodity Flow Networks
We study a class of dynamical multi-commodity flow networks in transportation networks. These are modeled as dynamical systems describing the evolution of the densities of a number of different commodities across the cells of a transportation network. Each cell is characterized by commodity-specific increasing demand functions returning the maximum outflow of each commodity from the cell as a function of the current density of that commodity, as well as a decreasing supply function returning th…

Economic relativity: a cut rule for perimeter valuation in equity ownership networks
Omar Di Marzio
https://arxiv.org/abs/2509.04520 https://arxiv.org/pdf/…

Economic relativity: a cut rule for perimeter valuation in equity ownership networks
We introduce the Cut-Based Valuation (CBV), a unified framework for consolidated value in equity/flow networks. The central idea is that economic value is never absolute: it is always defined relative to an observer Omega, which fixes perimeter, measurement basis, units/FX/PPP, discounting, informational regime, and control rules. Given Omega, the Cut Theorem shows that the consolidated value of a perimeter P depends only on boundary quantities across the cut P -> O, while internal reconnection…

Stability Optimization and Analysis of Energy Flow Networks versus Different Centrality Measurement
Yi Li, Xin Li
https://arxiv.org/abs/2508.16944 https://…

Stability Optimization and Analysis of Energy Flow Networks versus Different Centrality Measurement
Optimizing the stability and control performance of complex networks often hinges on effectively identifying critical nodes for targeted intervention. Due to their inherent complexity and high dimensionality, large-scale energy flow networks, prevalent in domains like power grids, transportation, and financial systems, present unique challenges in selecting optimal nodes for resource allocation. While numerous centrality measurements, such as Katz centrality, eigenvector centrality, closeness c…

When Competition Helps: Achieving Optimal Traffic Flow with Multiple Autonomous Planners
Ivan Geffner, Erez Karpas, Moshe Tennenholtz
https://arxiv.org/abs/2508.07145 https://…

When Competition Helps: Achieving Optimal Traffic Flow with Multiple Autonomous Planners
The inefficiency of selfish routing in congested networks is a classical problem in algorithmic game theory, often captured by the Price of Anarchy (i.e., the ratio between the social cost of decentralized decisions and that of a centrally optimized solution.) With the advent of autonomous vehicles, capable of receiving and executing centrally assigned routes, it is natural to ask whether their deployment can eliminate this inefficiency. At first glance, a central authority could simply compute…

Bi-SCORE for Weighted Bipartite Networks with Application in Knowledge Source Discovery
Zicheng Xie, Rui Pan, Yan Zhang
https://arxiv.org/abs/2508.21467 https://

Bi-SCORE for Weighted Bipartite Networks with Application in Knowledge Source Discovery
Community detection in citation networks offers a powerful approach to understanding knowledge flow and identifying core research areas within academic disciplines. This study focuses on knowledge source discovery in statistics by analyzing a weighted bipartite journal citation network constructed from 16,119 articles published in eight core journals from 2001 to 2023. To capture the inherent asymmetry of citation behavior, we explicitly preserve the bipartite structure of the network, distingu…

Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds
Yili Jin, Ling Pan, Rui-Xiao Zhang, Jiangchuan Liu, Xue Liu
https://arxiv.org/abs/2508.17166

Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds
Multimedia systems underpin modern digital interactions, facilitating seamless integration and optimization of resources across diverse multimedia applications. To meet growing personalization demands, multimedia systems must efficiently manage competing resource needs, adaptive content, and user-specific data handling. This paper introduces Generative Flow Networks (GFlowNets, GFNs) as a brave new framework for enabling personalized multimedia systems. By integrating multi-candidate generative…

Flow-weighted Layered Metric Euclidean Capacitated Steiner Tree Problem
Thomas Bl\"asius, Henrik Cs\"ore, Max G\"ottlicher, Elly Schmidt, Wendy Yi
https://arxiv.org/abs/2508.20041

Flow-weighted Layered Metric Euclidean Capacitated Steiner Tree Problem
Motivated by hierarchical networks, we introduce the Flow-weighted Layered Metric Euclidean Capacitated Steiner Tree (FLaMECaST) problem, a variant of the Euclidean Steiner tree with layered structure and capacity constraints per layer. The goal is to construct a cost-optimal Steiner forest connecting a set of sources to a set of sinks under load-dependent edge costs. We prove that FLaMECaST is NP-hard to approximate, even in restricted cases where all sources lie on a circle. However, assuming…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Osama Mohammed, Jiaxin Pan, Mojtaba Nayyeri, Daniel Hern\'andez, Steffen Staab
https://arxiv.org/abs/2508.03251

Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Modeling evolving interactions among entities is critical in many real-world tasks. For example, predicting driver maneuvers in traffic requires tracking how neighboring vehicles accelerate, brake, and change lanes relative to one another over consecutive frames. Likewise, detecting financial fraud hinges on following the flow of funds through successive transactions as they propagate through the network. Unlike classic time-series forecasting, these settings demand reasoning over who interacts…

Privacy-preserving authentication for military 5G networks
I. D. Lutz, A. M. Hill, M. C. Valenti
https://arxiv.org/abs/2509.01470 https://arxiv.org/pdf/250…

Privacy-preserving authentication for military 5G networks
As 5G networks gain traction in defense applications, ensuring the privacy and integrity of the Authentication and Key Agreement (AKA) protocol is critical. While 5G AKA improves upon previous generations by concealing subscriber identities, it remains vulnerable to replay-based synchronization and linkability threats under realistic adversary models. This paper provides a unified analysis of the standardized 5G AKA flow, identifying several vulnerabilities and highlighting how each exploits pr…

Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory
Sota Nishiyama, Masaaki Imaizumi
https://arxiv.org/abs/2510.01930 https://

Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory
Diagonal linear networks (DLNs) are a tractable model that captures several nontrivial behaviors in neural network training, such as initialization-dependent solutions and incremental learning. These phenomena are typically studied in isolation, leaving the overall dynamics insufficiently understood. In this work, we present a unified analysis of various phenomena in the gradient flow dynamics of DLNs. Using Dynamical Mean-Field Theory (DMFT), we derive a low-dimensional effective process that …

Crosslisted article(s) found for hep-ex. https://arxiv.org/list/hep-ex/new
[1/1]:
- Energy-Energy Flow Networks
Arianna Garcia Caffaro, Ian Moult, Chase Shimmin
…

Quantum Machine Learning for UAV Swarm Intrusion Detection
Kuan-Cheng Chen, Samuel Yen-Chi Chen, Tai-Yue Li, Chen-Yu Liu, Kin K. Leung
https://arxiv.org/abs/2509.01812 https://

Quantum Machine Learning for UAV Swarm Intrusion Detection
Intrusion detection in unmanned-aerial-vehicle (UAV) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we benchmark three quantum-machine-learning (QML) approaches - quantum kernels, variational quantum neural networks (QNNs), and hybrid quantum-trained neural networks (QT-NNs) - against strong classical baselines. All models consume an 8-feature flow representation and are evaluat…

WavInWav: Time-domain Speech Hiding via Invertible Neural Network
Wei Fan, Kejiang Chen, Xiangkun Wang, Weiming Zhang, Nenghai Yu
https://arxiv.org/abs/2510.02915 https://

WavInWav: Time-domain Speech Hiding via Invertible Neural Network
Data hiding is essential for secure communication across digital media, and recent advances in Deep Neural Networks (DNNs) provide enhanced methods for embedding secret information effectively. However, previous audio hiding methods often result in unsatisfactory quality when recovering secret audio, due to their inherent limitations in the modeling of time-frequency relationships. In this paper, we explore these limitations and introduce a new DNN-based approach. We use a flow-based invertible…

Prediction error certification for PINNs: Theory, computation, and application to Stokes flow
Birgit Hillebrecht, Benjamin Unger
https://arxiv.org/abs/2508.07994 https://…

Prediction error certification for PINNs: Theory, computation, and application to Stokes flow
Rigorous error estimation is a fundamental topic in numerical analysis. With the increasing use of physics-informed neural networks (PINNs) for solving partial differential equations, several approaches have been developed to quantify the associated prediction error. In this work, we build upon a semigroup-based framework previously introduced by the authors for estimating the PINN error. While this estimator has so far been limited to academic examples - due to the need to compute quantities r…

Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks
Fulin Xing, Junjie Li, Ze Tao, Fujun Liu, Yong Tan
https://arxiv.org/abs/2508.07633

Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks
Underwater explosion modeling faces a critical challenge of simultaneously resolving shock waves and gas-liquid interfaces, as traditional methods struggle to balance accuracy and computational efficiency. To address this, we develop a physics-informed neural network (PINN) framework featuring a dual-network architecture, that one network learns flow-field variables (pressure, density, velocity) from simulation data, while another network tracks the gas-liquid interface despite lacking direct n…

Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization
Sergii Kavun
https://arxiv.org/abs/2507.22090 https://

Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization
Activation functions are critical components in deep neural networks, directly influencing gradient flow, training stability, and model performance. Traditional functions like ReLU suffer from dead neuron problems, while sigmoid and tanh exhibit vanishing gradient issues. We introduce two novel hybrid activation functions: S3 (Sigmoid-Softsign) and its improved version S4 (smoothed S3). S3 combines sigmoid for negative inputs with softsign for positive inputs, while S4 employs a smooth transiti…

A graph neural network based on feature network for identifying influential nodes
Yanmei Hu, Siyuan Yin, Yihang Wu, Xue Yue, Yue Liu
https://arxiv.org/abs/2508.01278 https://

A graph neural network based on feature network for identifying influential nodes
Identifying influential nodes in complex networks is of great importance, and has many applications in practice. For example, finding influential nodes in e-commerce network can provide merchants with customers with strong purchase intent; identifying influential nodes in computer information system can help locating the components that cause the system break down and identifying influential nodes in these networks can accelerate the flow of information in networks. Thus, a lot of efforts have …

VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns
Lin Pang, Jidong J. Yang
https://arxiv.org/abs/2510.02702 https://

VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns
Understanding how urban residents travel between neighborhoods and destinations is critical for transportation planning, mobility management, and public health. By mining historical origin-to-destination flow patterns with spatial, temporal, and functional relations among urban places, we estimate probabilities of visits from neighborhoods to specific destinations. These probabilities capture neighborhood-level contributions to citywide vehicular and foot traffic, supporting demand estimation, …

Distinguishing Phylogenetic Level-2 Networks with Quartets and Inter-Taxon Quartet Distances
Niels Holtgrefe, Elizabeth S. Allman, Hector Ba\~nos, Leo van Iersel, Vincent Moulton, John A. Rhodes, Kristina Wicke
https://arxiv.org/abs/2507.17308

Distinguishing Phylogenetic Level-2 Networks with Quartets and Inter-Taxon Quartet Distances
The inference of phylogenetic networks, which model complex evolutionary processes including hybridization and gene flow, remains a central challenge in evolutionary biology. Until now, statistically consistent inference methods have been limited to phylogenetic level-1 networks, which allow no interdependence between reticulate events. In this work, we establish the theoretical foundations for a statistically consistent inference method for a much broader class: semi-directed level-2 networks …

DeepGraphLog for Layered Neurosymbolic AI
Adem Kikaj, Giuseppe Marra, Floris Geerts, Robin Manhaeve, Luc De Raedt
https://arxiv.org/abs/2509.07665 https://…

DeepGraphLog for Layered Neurosymbolic AI
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy frameworks like DeepProbLog enforce a fixed flow where symbolic reasoning always follows neural processing. This restricts their ability to model complex dependencies, especially in irregular data structures such as graphs. In this work, we introduce DeepGraphLog, a novel NeSy framework that extends ProbLog with Graph Neural …

Functional mesoscale organization of complex networks
Arsham Ghavasieh, Satyaki Sikdar, Manlio De Domenico, Santo Fortunato
https://arxiv.org/abs/2508.04562 https://

Functional mesoscale organization of complex networks
The network density matrix (NDM) framework, enabling an information-theoretic and multiscale treatment of network flow, has been gaining momentum over the last decade. Benefiting from the counterparts of physical functions such as free energy and entropy, NDM's applications range from estimating how nodes influence network flows across scales the centrality of nodes at the local level to explaining the emergence of structural and functional order. Here, we introduce a generalized notion of the …

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

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

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

N78 Frequency Band Modular RIS Design and Implementation
Sefa Kayrakl{\i}k, Recep Ba\c{s}, Hasan O\u{g}uzhan \c{C}al{\i}\c{s}kan, Samed \c{S}ahino\u{g}lu, Sercan Erdo\u{g}an, \.Ilhami \"Unal, \.Ibrahim H\"okelek, K{\i}van\c{c} Nurdan, Ali G\"or\c{c}in
https://arxiv.org/abs/2509.24355…

N78 Frequency Band Modular RIS Design and Implementation
Reconfigurable intelligent surface (RIS), capable of dynamically controlling wireless propagation characteristics using reflecting antenna elements, is a promising technology for enhancing signal coverage and improving end-user connectivity in next-generation wireless networks. This paper presents a complete design flow of a modular RIS prototype operating at the n78 frequency band, starting from the simulations to the prototype development and testing. An RIS prototype includes one master and …

Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networks
D. Veerababu, Prasanta K. Ghosh
https://arxiv.org/abs/2507.22370 ht…

Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networks
Neural networks constrained by the physical laws emerged as an alternate numerical tool. In this paper, the governing equation that represents the propagation of sound inside a one-dimensional duct carrying a heterogeneous medium is derived. The problem is converted into an unconstrained optimization problem and solved using neural networks. Both the acoustic state variables: acoustic pressure and particle velocity are predicted and validated with the traditional Runge-Kutta solver. The effect …

Convex Pollution Control of Wastewater Treatment Systems
Joshua Taylor
https://arxiv.org/abs/2510.03918 https://arxiv.org/pdf/2510.03918

Convex Pollution Control of Wastewater Treatment Systems
We design a model-predictive controller for managing the actuators in sewer networks. It minimizes flooding and combined-sewer overflow during rain and pollution at other times. To make the problem tractable, we use a convex relaxation of the microbial growth kinetics and a physically motivated linearization of the mass flow bilinearities. With these approximations, the trajectory optimization in each control period is a second-order cone program. In simulation, the controller releases roughly …

Estimation of Hemodynamic Parameters via Physics Informed Neural Networks including Hematocrit Dependent Rheology
Moises Sierpe, Ernesto Castillo, Hernan Mella, Felipe Galarce
https://arxiv.org/abs/2508.03326

Estimation of Hemodynamic Parameters via Physics Informed Neural Networks including Hematocrit Dependent Rheology
Physics-Informed Neural Networks (PINNs) show significant potential for solving inverse problems, especially when observations are limited and sparse, provided that the relevant physical equations are known. We use PINNs to estimate smooth velocity and pressure fields from synthetic 4D flow Magnetic Resonance Imaging (MRI) data. We analyze five non-Newtonian dynamic 3D blood flow cases within a realistic aortic model, covering a range of hematocrit values from anemic to polycythemic conditions.…

QoS-Aware Proportional Fairness Scheduling for Multi-Flow 5G UEs: A Smart Factory Perspective
Mohamed Seliem, Utz Roedig, Cormac Sreenan, Dirk Pesch
https://arxiv.org/abs/2508.21783

QoS-Aware Proportional Fairness Scheduling for Multi-Flow 5G UEs: A Smart Factory Perspective
Private 5G networks are emerging as key enablers for smart factories, where a single device often handles multiple concurrent traffic flows with distinct Quality of Service (QoS) requirements. Existing simulation frameworks, however, lack the fidelity to model such multi-flow behavior at the QoS Flow Identifier (QFI) level. This paper addresses this gap by extending Simu5G to support per-QFI modeling and by introducing a novel QoS-aware Proportional Fairness (QoS-PF) scheduler. The scheduler dy…

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

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

Variable Min-Cut Max-Flow Bounds and Algorithms in Finite Regime
Rivka Gitik, Alejandro Cohen
https://arxiv.org/abs/2507.14852 https://

Variable Min-Cut Max-Flow Bounds and Algorithms in Finite Regime
The maximum achievable capacity from source to destination in a network is limited by the min-cut max-flow bound; this serves as a converse limit. In practice, link capacities often fluctuate due to dynamic network conditions. In this work, we introduce a novel analytical framework that leverages tools from computational geometry to analyze throughput in heterogeneous networks with variable link capacities in a finite regime. Within this model, we derive new performance bounds and demonstrate t…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis
Rahul Agarwal, Mustafa A. Mohamad
https://arxiv.org/abs/2508.04301 https:…

Extreme Event Precursor Prediction in Turbulent Dynamical Systems via CNN-Augmented Recurrence Analysis
We present a general framework to predict precursors to extreme events in turbulent dynamical systems. The approach combines phase-space reconstruction techniques with recurrence matrices and convolutional neural networks to identify precursors to extreme events. We evaluate the framework across three distinct testbed systems: a triad turbulent interaction model, a prototype stochastic anisotropic turbulent flow, and the Kolmogorov flow. This method offers three key advantages: (1) a threshold-…

Hodge Decomposition for Urban Traffic Flow: Limits on Dense OD Graphs and Advantages on Road Networks - Los Angeles Case
Yifei Sun
https://arxiv.org/abs/2509.17203 https://

Hodge Decomposition for Urban Traffic Flow: Limits on Dense OD Graphs and Advantages on Road Networks - Los Angeles Case
I study Hodge decomposition (HodgeRank) for urban traffic flow on two graph representations: dense origin--destination (OD) graphs and road-segment networks. Reproducing the method of Aoki et al., we observe that on dense OD graphs the curl and harmonic components are negligible and the potential closely tracks node divergence, limiting the added value of Hodge potentials. In contrast, on a real road network (UTD19, downtown Los Angeles; 15-minute resolution), potentials differ substantially fr…

Replaced article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Min cost flow on unit capacity networks and convex cost K-flow are as easy as the assignment prob...
Dorit S. Hochbaum

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
Michail Kavousanakis, Gianluca Fabiani, Anastasia Georgiou, Constantinos Siettos, Panagiotis Kevrekidis, Ioannis Kevrekidis
https://arxiv.org/abs/2509.15963

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
In the past, we have presented a systematic computational framework for analyzing self-similar and traveling wave dynamics in nonlinear partial differential equations (PDEs) by dynamically factoring out continuous symmetries such as translation and scaling. This is achieved through the use of time-dependent transformations -- what can be viewed as dynamic pinning conditions -- that render the symmetry-invariant solution stationary or slowly varying in rescaled coordinates. The transformation pr…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Data-driven Neural Networks for Windkessel Parameter Calibration
Benedikt Hoock, Tobias K\"oppl
https://arxiv.org/abs/2509.21206 https://arxiv.org/pdf…

Data-driven Neural Networks for Windkessel Parameter Calibration
In this work, we propose a novel method for calibrating Windkessel (WK) parameters in a dimensionally reduced 1D-0D coupled blood flow model. To this end, we design a data-driven neural network (NN)trained on simulated blood pressures in the left brachial artery. Once trained, the NN emulates the pressure pulse waves across the entire simulated domain, i.e., over time, space and varying WK parameters, with negligible error and computational effort. To calibrate the WK parameters on a measured p…

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
Seungyeon Choi, Hwanhee Kim, Chihyun Park, Dahyeon Lee, Seungyong Lee, Yoonju Kim, Hyoungjoon Park, Sein Kwon, Youngwan Jo, Sanghyun Park
https://arxiv.org/abs/2508.21468

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
Recent advances in Structure-based Drug Design (SBDD) have leveraged generative models for 3D molecular generation, predominantly evaluating model performance by binding affinity to target proteins. However, practical drug discovery necessitates high binding affinity along with synthetic feasibility and selectivity, critical properties that were largely neglected in previous evaluations. To address this gap, we identify fundamental limitations of conventional diffusion-based generative models i…

Resolving Indirect Calls in Binary Code via Cross-Reference Augmented Graph Neural Networks
Haotian Zhang, Kun Liu, Cristian Garces, Chenke Luo, Yu Lei, Jiang Ming
https://arxiv.org/abs/2507.18801

Resolving Indirect Calls in Binary Code via Cross-Reference Augmented Graph Neural Networks
Binary code analysis is essential in scenarios where source code is unavailable, with extensive applications across various security domains. However, accurately resolving indirect call targets remains a longstanding challenge in maintaining the integrity of static analysis in binary code. This difficulty arises because the operand of a call instruction (e.g., call rax) remains unknown until runtime, resulting in an incomplete inter-procedural control flow graph (CFG). Previous approaches have …

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
Bangti Jin, Longjun Wu
https://arxiv.org/abs/2508.21571 https://

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
Physics informed neural networks (PINNs) represent a very popular class of neural solvers for partial differential equations. In practice, one often employs stochastic gradient descent type algorithms to train the neural network. Therefore, the convergence guarantee of stochastic gradient descent is of fundamental importance. In this work, we establish the linear convergence of stochastic gradient descent / flow in training over-parameterized two layer PINNs for a general class of activation fu…

Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
https://arxiv.org/abs/2509.14437 h…

Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
Physics-Informed Neural Networks (PINNs) have emerged as a promising machine learning approach for solving partial differential equations (PDEs). However, PINNs face significant challenges in balancing multi-objective losses, as multiple competing loss terms such as physics residuals, boundary conditions, and initial conditions must be appropriately weighted. While various loss balancing schemes have been proposed, they have been implemented within neural network architectures with fixed activa…

FlowCrypt: Flow-Based Lightweight Encryption with Near-Lossless Recovery for Cloud Photo Privacy
Xiaohui Yang, Ping Ping, Feng Xu
https://arxiv.org/abs/2509.18696 https://

FlowCrypt: Flow-Based Lightweight Encryption with Near-Lossless Recovery for Cloud Photo Privacy
The widespread adoption of smartphone photography has led users to increasingly rely on cloud storage for personal photo archiving and sharing, raising critical privacy concerns. Existing deep learning-based image encryption schemes, typically built upon CNNs or GANs, often depend on traditional cryptographic algorithms and lack inherent architectural reversibility, resulting in limited recovery quality and poor robustness. Invertible neural networks (INNs) have emerged to address this issue by…

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Changhun Kim, Timon Conrad, Redwanul Karim, Julian Oelhaf, David Riebesel, Tom\'as Arias-Vergara, Andreas Maier, Johann J\"ager, Siming Bayer
https://arxiv.org/abs/2509.22458

Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace classic Newton--Raphson (NR) solvers, especially when thousands of scenarios must be evaluated. However, current PIGNNs still need accuracy improvements at parity speed; in particular, the physics loss is inoperative at inference, which can deter operational adoption. We address this with PIGNN-Attn-LS, combining an edge-aware attention mechanism that explicitly encodes line physics via p…

Multi-fidelity Ensemble Kalman Filter algorithms enhanced by Convolutional Neural Networks
Tom Moussie, Paolo Errante, Marcello Meldi
https://arxiv.org/abs/2507.13744

Multi-fidelity Ensemble Kalman Filter algorithms enhanced by Convolutional Neural Networks
The present research work proposes advancement for Data Assimilation strategies using Convolutional Neural Networks (CNN). More precisely, multi-fidelity and multi-level algorithms for the Ensemble Kalman Filter are enhanced by CNN tools, with the objective to reduce the discrepancy in the prediction between ensemble realizations performed with different models. The proposed methodology is assessed via the analysis of the flow through a cascade of NACA 0012 profiles for Reynolds $Re=1\,000$ and…

SDAP-based QoS Flow Multiplexing Support in Simu5G for 5G NR Simulation
Mohamed Seliem, Utz Roedig, Cormac Sreenan, Dirk Pesch
https://arxiv.org/abs/2508.12785 https://

SDAP-based QoS Flow Multiplexing Support in Simu5G for 5G NR Simulation
The Service Data Adaptation Protocol (SDAP) plays a central role in 5G New Radio (NR), acting as a bridge between the core and radio networks, by enabling QoS Flow multiplexing over shared Data Radio Bearers (DRBs). However, most 5G simulation frameworks, including the popular OMNet++-based Simu5G, lack SDAP support, limiting their ability to model realistic QoS behavior. This paper presents a modular, standardscompliant SDAP extension for Simu5G. The implementation includes core elements such …

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations
Tejaswini Sanjay Katale, Lu Gao, Yunpeng Zhang, Alaa Senouci
https://arxiv.org/abs/2510.02503

A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations
Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber-physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bi-level formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed g…

Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow
Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband
https://arxiv.org/abs/2508.16403

Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow
Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear, layout-sensitive behavior and the high computational cost of traditional simulation tools. Existing machine learning (ML) surrogates often require large datasets to generalize across various topologies or to accurately model skewed and multi-modal performance metrics. In this work, a lightweight, data-efficient, and topology-awar…

Rebalancing Modular Transit Systems: A Hierarchical Graph-Based Optimization Framework for Fleet Sizing and Routing
Tina Radvand, Alireza Talebpour, Yanfeng Ouyang
https://arxiv.org/abs/2508.18643

Rebalancing Modular Transit Systems: A Hierarchical Graph-Based Optimization Framework for Fleet Sizing and Routing
This study addresses the rebalancing of empty modular transit pods between scheduled service trips in fixed-route bus systems. A two-stage hierarchical optimization framework is proposed. The first stage determines the minimum fleet size and initial vehicle assignments by solving a maximum matching problem on a bipartite graph, using a GPU-accelerated push-relabel algorithm. The second stage formulates detailed routing as a series of minimum-cost flow problems on time-space networks. To manage …

Forecasting Faculty Placement from Patterns in Co-authorship Networks
Samantha Dies, David Liu, Tina Eliassi-Rad
https://arxiv.org/abs/2507.14696 https://

Forecasting Faculty Placement from Patterns in Co-authorship Networks
Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies have found strong correlations between faculty hiring and attributes such as doctoral department prestige and publication record, they rarely assess whether these associations generalize to individual hiring outcomes, particularly for future candidates outside …

Neural Feature Geometry Evolves as Discrete Ricci Flow
Moritz Hehl, Max von Renesse, Melanie Weber
https://arxiv.org/abs/2509.22362 https://arxiv.org/pdf/2…

Neural Feature Geometry Evolves as Discrete Ricci Flow
Deep neural networks learn feature representations via complex geometric transformations of the input data manifold. Despite the models' empirical success across domains, our understanding of neural feature representations is still incomplete. In this work we investigate neural feature geometry through the lens of discrete geometry. Since the input data manifold is typically unobserved, we approximate it using geometric graphs that encode local similarity structure. We provide theoretical resul…

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks
Murat Arda Onsu, Poonam Lohan, Burak Kantarci, Aisha Syed, Matthew Andrews, Sean Kennedy
https://arxiv.org/abs/2509.21259

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks
Real-time urban traffic surveillance is vital for Intelligent Transportation Systems (ITS) to ensure road safety, optimize traffic flow, track vehicle trajectories, and prevent collisions in smart cities. Deploying edge cameras across urban environments is a standard practice for monitoring road conditions. However, integrating these with intelligent models requires a robust understanding of dynamic traffic scenarios and a responsive interface for user interaction. Although multimodal Large Lan…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
Yongsheng Chen, Wei Guo, Qi Tang, Xinghui Zhong
https://arxiv.org/abs/2508.11911 https://

Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
We introduce a novel data-driven symplectic induced-order modeling (ROM) framework for high-dimensional Hamiltonian systems that unifies latent-space discovery and dynamics learning within a single, end-to-end neural architecture. The encoder-decoder is built from Henon neural networks (HenonNets) and may be augmented with linear SGS-reflector layers. This yields an exact symplectic map between full and latent phase spaces. Latent dynamics are advanced by a symplectic flow map implemented as a …

Deconstructing Mobility Segregation: A Network Analysis of Racialized Flows in Pandemic-Era NYC
Wei-Peng Nie, Tian-Rong Ding, Xiao-Yong Yan, Tao Zhou, Zi-You Gao
https://arxiv.org/abs/2508.17072

Deconstructing Mobility Segregation: A Network Analysis of Racialized Flows in Pandemic-Era NYC
Urban segregation research has long relied on residential patterns, yet growing evidence suggests that racial/ethnic segregation also manifests systematically in mobility behaviors. Leveraging anonymized mobile device data from New York City before and during the COVID-19 pandemic, we develop a network-analytic framework to dissect mobility segregation in racialized flow networks. We examine citywide racial mixing patterns through mixing matrices and assortativity indices, revealing persistent …

Euclidean Approach to Green-Wave Theory Applied to Traffic Signal Networks
Melvin H. Friedman, Brian L. Mark, Nathan H. Gartner
https://arxiv.org/abs/2508.12146 https://

Euclidean Approach to Green-Wave Theory Applied to Traffic Signal Networks
Travel on long arterials with signalized intersections can be inefficient if not coordinated properly. As the number of signals increases, coordination becomes more challenging and traditional progression schemes tend to break down. Long progressions save travel time and fuel, reduce pollution and traffic accidents by providing a smoother flow of traffic. This paper introduces a green-wave theory that can be applied to a network of intersecting arterial roads. It enables uninterrupted flow on a…

End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems
Minhaj Uddin Ahmad, Akid Abrar, Sagar Dasgupta, Mizanur Rahman
https://arxiv.org/abs/2509.16489

End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) have been widely deployed across major metropolitan regions worldwide to improve roadway safety, optimize traffic flow, and reduce environmental impacts. These systems integrate advanced sensors, communication networks, and data analytics to enable real-time traffic monitoring, adaptive signal control, and predictive maintenance. However, such integration significantly broadens the ITS attack surface, exposing critical infrastructures to cyber threats th…

Multiscale super-resolution reconstruction of fluid flows with deep neural networks
Gengchao Yang, Renyu Luo, Qinghe Yao, Peiji Wang, Jinxiu Zhang
https://arxiv.org/abs/2509.14721

Multiscale super-resolution reconstruction of fluid flows with deep neural networks
We present a novel multiscale super-resolution framework (SRLBM) that applies deep learning directly to the mesoscopic density distribution functions of the lattice Boltzmann method for high-fidelity flow reconstruction. Two neural network architectures, a standard convolutional neural network (CNN) and a deeper residual dense network (RDN), are trained to upscale distribution functions from coarse grids by factors of 2, 4 and 8, and then recover velocity, pressure, and vorticity from a single …

Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Ehimare Okoyomon, Arbel Yaniv, Christoph Goebel
https://arxiv.org/abs/2509.25158 https://

Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Voltage prediction in distribution grids is a critical yet difficult task for maintaining power system stability. Machine learning approaches, particularly Graph Neural Networks (GNNs), offer significant speedups but suffer from poor generalization when trained on limited or incomplete data. In this work, we systematically investigate the role of inductive biases in improving a model's ability to reliably learn power flow. Specifically, we evaluate three physics-informed strategies: (i) power-f…

Exploiting Convexity of Neural Networks in Dynamic Operating Envelope Optimization for Distributed Energy Resources
Hongyi Li, Liming Liu, Yunyi Li, Zhaoyu Wang
https://arxiv.org/abs/2508.13090

Exploiting Convexity of Neural Networks in Dynamic Operating Envelope Optimization for Distributed Energy Resources
The increasing penetration of distributed energy resources (DERs) brings opportunities and challenges to the operation of distribution systems. To ensure network integrity, dynamic operating envelopes (DOEs) are issued by utilities to DERs as their time-varying export/import power limits. Due to the non-convex nature of power flow equations, the optimization of DOEs faces a dilemma of solution accuracy and computation efficiency. To bridge this gap, in this paper, we facilitate DOE optimization…

Combining Static and Dynamic Traffic with Delay Guarantees in Time-Sensitive Networking
Lisa Maile, Kai-Steffen Hielscher, Reinhard German
https://arxiv.org/abs/2508.18883 https…

Combining Static and Dynamic Traffic with Delay Guarantees in Time-Sensitive Networking
To support reliable and low-latency communication, Time-Sensitive Networking introduced protocols and interfaces for resource allocation in Ethernet. However, the implementation of these allocation algorithms has not yet been covered by the standards. Our work focuses on deadline-guaranteeing resource allocation for networks with static and dynamic traffic. To achieve this, we combine offline network optimization heuristics with online admission control and, thus, allow for new flow registratio…

Design and Simulation of Fault-Tolerant Network Switching System Using Python-Based Algorithms
Terlumun Gbaden, Mterorga Ukor, Grace Erdoo Ateata
https://arxiv.org/abs/2508.14305

Design and Simulation of Fault-Tolerant Network Switching System Using Python-Based Algorithms
Ensuring uninterrupted data flow in modern networks requires robust fault-tolerant mechanisms, especially in environments where reliability and responsiveness are critical. This paper presents the design and simulation of a fault-tolerant network switching system using Python-based algorithms. A simulated enterprise-level Local Area Network (LAN) was modeled using NetworkX to represent switch-router interconnectivity with redundant links. Fault scenarios, including link failure and congestion, …

Optimal Coordination of Local Flexibility from Electric Vehicles with Social Impact Consideration
Si Chen, Benoit Couraud, Sonam Norbu, Merlinda Andoni, Zafar Iqbal, Sasa Djokic, Desen Kirli, Satria Putra Kanugrahan, Paolo Cherubini, Susan Krumdieck, Valentin Robu, David Flynn
https://arxiv.org/abs/2508.16814…

Optimal Coordination of Local Flexibility from Electric Vehicles with Social Impact Consideration
The integration of renewable energy sources (RES) and the convergence of transport electrification, creates a significant challenge for distribution network management e.g. voltage and frequency violations, particularly in rural and remote areas. This paper investigates how smart charging of electric vehicles (EVs) can help reduce renewable energy curtailment and alleviate stress on local distribution networks. We implement a customised AC Optimal Power Flow (AC OPF) formulation which integrate…