Tootfinder

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

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…

Empirical Investigation into Configuring Echo State Networks for Representative Benchmark Problem Domains
Brooke R. Weborg, Gursel Serpen
https://arxiv.org/abs/2508.10887 https:…

Empirical Investigation into Configuring Echo State Networks for Representative Benchmark Problem Domains
This paper examines Echo State Network, a reservoir computer, performance using four different benchmark problems, then proposes heuristics or rules of thumb for configuring the architecture, as well as the selection of parameters and their values, which are applicable to problems within the same domain, to help serve to fill the experience gap needed by those entering this field of study. The influence of various parameter selections and their value adjustments, as well as architectural change…

Gauging the variational optimization of projected entangled-pair states
Wei Tang, Laurens Vanderstraeten, Jutho Haegeman
https://arxiv.org/abs/2508.10822 https://

Gauging the variational optimization of projected entangled-pair states
Projected entangled-pair states (PEPS) constitute a powerful variational ansatz for capturing ground state physics of two-dimensional quantum systems. However, accurately computing and minimizing the energy expectation value remains challenging, in part because the impact of the gauge degrees of freedom that are present in the tensor network representation is poorly understood. We analyze the role of gauge transformations for the case of a U(1)-symmetric PEPS with point group symmetry, thereby …

Coherent control of interacting solid-state spins below the diffraction limit
Haitong Xu, Mehmet T. Uysal, Lukasz Dusanowski, Adam Turflinger, Ashwin Boddeti, Joseph Alexander, Jeff D. Thompson
https://arxiv.org/abs/2508.09122

Coherent control of interacting solid-state spins below the diffraction limit
Optically addressed atomic defects in the solid-state are widely used as single-photon sources and memories for quantum network applications. The solid-state environment allows for a high density of electron and nuclear spins with the potential to form registers for coherent information processing. However, it is challenging to reliably address individual spins at nanometer separations where interactions are large. Rare-earth ions offer a unique solution, as their narrow homogeneous optical lin…

Weekend Reads
* Fingerprinting DPI devices
https://arxiv.org/abs/2509.09081
* Exportation of China's GFW
https://

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities
Users around the world face escalating network interference such as censorship, throttling, and interception, largely driven by the commoditization and growing availability of Deep Packet Inspection (DPI) devices. Once reserved for a few well-resourced nation-state actors, the ability to interfere with traffic at scale is now within reach of nearly any network operator. Despite this proliferation, our understanding of DPIs and their deployments on the Internet remains limited -- being network i…

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng
https://arxiv.org/abs/2507.08800

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
We introduce NeuralOS, a neural framework that simulates graphical user interfaces (GUIs) of operating systems by directly predicting screen frames in response to user inputs such as mouse movements, clicks, and keyboard events. NeuralOS combines a recurrent neural network (RNN), which tracks computer state, with a diffusion-based neural renderer that generates screen images. The model is trained on a large-scale dataset of Ubuntu XFCE recordings, which include both randomly generated interacti…

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Madhushan Ramalingam
https://arxiv.org/abs/2509.10048 https://

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Predictive models are being increasingly used across a wide range of domains, including safety-critical applications such as medical diagnosis and criminal justice. Reliable uncertainty estimation is a crucial task in such settings. Tabular Prior-data Fitted Network (TabPFN) is a recently proposed machine learning foundation model for tabular dataset, which uses a generative transformer architecture. Variational Bayesian Last Layers (VBLL) is a state-of-the-art lightweight variational formulati…

Modeling bias in decision-making attractor networks
Safaan Sadiq
https://arxiv.org/abs/2508.07471 https://arxiv.org/pdf/2508.07471

Modeling bias in decision-making attractor networks
Attractor neural network models of cortical decision-making circuits represent them as dynamical systems in the state space of neural firing rates with the attractors of the network encoding possible decisions. While the attractors of these models are well studied, far less attention is paid to the basins of attraction even though their sizes can be said to encode the biases towards the corresponding decisions. The parameters of an attractor network control both the attractors and the basins of…

Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
Byunghee Lee, Hye Yeon Sin, Joonsung Kang
https://arxiv.org/abs/2507.08896

Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
This study introduces an integrated framework for predictive causal inference designed to overcome limitations inherent in conventional single model approaches. Specifically, we combine a Hidden Markov Model (HMM) for spatial health state estimation with a Multi Task and Multi Graph Convolutional Network (MTGCN) for capturing temporal outcome trajectories. The framework asymmetrically treats temporal and spatial information regarding them as endogenous variables in the outcome regression, and e…

Most #RESTful #APIs aren't really RESTful
https://florian-krae…

Apple delays iPhone Air pre-orders in China, likely over eSIM regulatory issues, replacing the original schedule with "release information to be updated later" (Coco Feng/South China Morning Post)
https://www.scmp.com/tech/big-tec…

Apple postpones iPhone Air in China as Beijing keeps tight grip on eSIM approval
Apple says all three state-owned telecoms network operators would provide eSIM support ‘subject to regulatory approval’.

Advancing the Control of Low-Altitude Wireless Networks: Architecture, Design Principles, and Future Directions
Haijia Jin, Weijie Yuan, Jun Wu, Jiacheng Wang, Dusit Niyato, Xianbin Wang, George K. Karagiannidis, Zhiyun Lin, Yi Gong, Dong In Kim, Athina Petropulu, Maria Sabrina Greco, Abbas Jamalipour, Sumei Sun
https://arxiv.org/abs/2508.…

Advancing the Control of Low-Altitude Wireless Networks: Architecture, Design Principles, and Future Directions
This article introduces a control-oriented low-altitude wireless network (LAWN) that integrates near-ground communications and remote estimation of the internal system state. This integration supports reliable networked control in dynamic aerial-ground environments. First, we introduce the network's modular architecture and key performance metrics. Then, we discuss core design trade-offs across the control, communication, and estimation layers. A case study illustrates closed-loop coordination …

Persuasion Gains and Losses from Peer Communication
Toygar T. Kerman, Anastas P. Tenev, Konstantin Zabarnyi
https://arxiv.org/abs/2509.09099 https://arxiv.…

Persuasion Gains and Losses from Peer Communication
We study a Bayesian persuasion setting in which a sender wants to persuade a critical mass of receivers by revealing partial information about the state to them. The homogeneous binary-action receivers are located on a communication network, and each observes the private messages sent to them and their immediate neighbors. We examine how the sender's expected utility varies with increased communication among receivers. We show that for general families of networks, extending the network can str…

Despite having beencriticized by civil rights groups for using
"cell site simulators" (Stingrays) during the last Trump administration,
ICE continues to use the technology.
Earlier this year, new media publication Straight Arrow News said it had
“mobile network anomalies” around a Washington state protest against ICE raids that were consistent with Stingray use.
Forbes found contract records showing ICE purchased nearly
$1 million worth of “cell s…

How ICE Is Using Fake Cell Towers To Spy On People’s Phones
ICE is using a controversial spy tool to locate smartphones, court records show.

Optimal control of stochastic networks of $M/M/\infty$ queues with linear costs
Giovanni Pugliese Carratelli, Ioannis Lestas
https://arxiv.org/abs/2509.08572 https://

Optimal control of stochastic networks of $M/M/\infty$ queues with linear costs
We consider an arbitrary network of $M/M/\infty$ queues with controlled transitions between queues. We consider optimal control problems where the costs are linear functions of the state and inputs over a finite or infinite horizon. We provide in both cases an explicit characterization of the optimal control policies. We also show that these do not involve state feedback, but they depend on the network topology and system parameters. The results are also illustrated with various examples.

What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets
Meghan Wilkinson, Robert H Thomson
https://arxiv.org/abs/2509.09564 https://

What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets
Supervised machine learning techniques rely on labeled data to achieve high task performance, but this requires the labels to capture some meaningful differences in the underlying data structure. For training network intrusion detection algorithms, most datasets contain a series of attack classes and a single large benign class which captures all non-attack network traffic. A review of intrusion detection papers and guides that explicitly state their data preprocessing steps identified that the…

First-order State Space Model for Lightweight Image Super-resolution
Yujie Zhu, Xinyi Zhang, Yekai Lu, Guang Yang, Faming Fang, Guixu Zhang
https://arxiv.org/abs/2509.08458 http…

First-order State Space Model for Lightweight Image Super-resolution
State space models (SSMs), particularly Mamba, have shown promise in NLP tasks and are increasingly applied to vision tasks. However, most Mamba-based vision models focus on network architecture and scan paths, with little attention to the SSM module. In order to explore the potential of SSMs, we modified the calculation process of SSM without increasing the number of parameters to improve the performance on lightweight super-resolution tasks. In this paper, we introduce the First-order State S…

High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator
Kuan-Ting Lin, Ching-Te Chiu, Jheng-Yi Chang, Shi-Zong Huang, Yu-Ting Li
https://arxiv.org/abs/2509.05688

High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator
Deep convolution Neural Network (DCNN) has been widely used in computer vision tasks. However, for edge devices even inference has too large computational complexity and data access amount. The inference latency of state-of-the-art models are impractical for real-world applications. In this paper, we propose a high utilization energy-aware real-time inference deep convolutional neural network accelerator, which improves the performance of the current accelerators. First, we use the 1x1 size con…

A Neuromorphic Incipient Slip Detection System using Papillae Morphology
Yanhui Lu, Zeyu Deng, Stephen J. Redmond, Efi Psomopoulou, Benjamin Ward-Cherrier
https://arxiv.org/abs/2509.09546

A Neuromorphic Incipient Slip Detection System using Papillae Morphology
Detecting incipient slip enables early intervention to prevent object slippage and enhance robotic manipulation safety. However, deploying such systems on edge platforms remains challenging, particularly due to energy constraints. This work presents a neuromorphic tactile sensing system based on the NeuroTac sensor with an extruding papillae-based skin and a spiking convolutional neural network (SCNN) for slip-state classification. The SCNN model achieves 94.33% classification accuracy across t…

Arrested coarsening, oscillations, and memory from a conserved phase separating nucleator in a self-straining cytoskeletal network
Quentin Bodini--Lefranc, Jakob Schindelwig, Daniel Weidinger, Lucas Engleder, Sebastian F\"urthauer
https://arxiv.org/abs/2509.07181

Arrested coarsening, oscillations, and memory from a conserved phase separating nucleator in a self-straining cytoskeletal network
How do phase separated cellular structures set their size? To elucidate this, we study the dynamics and steady states of a phase separating nucleator that is advected by the self-straining cytoskeletal network which it nucleates. We find (i) that the interplay between transport and the tendency of nucleators to phase separate arrests coarsening; (ii) that the system undergoes damped oscillations towards a patterned steady state with a well defined length scale; (iii) that the system supports a …

MoWE : A Mixture of Weather Experts
Dibyajyoti Chakraborty, Romit Maulik, Peter Harrington, Dallas Foster, Mohammad Amin Nabian, Sanjay Choudhry
https://arxiv.org/abs/2509.09052

MoWE : A Mixture of Weather Experts
Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approach as a novel paradigm to overcome these limitations, not by creating a new forecaster, but by optimally combining the outputs of existing models. The MoWE model is trained with significantly lower computational resources than the individual experts. Our model employs a Vision Transformer-based gating network that dyn…

LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition
Zhining He, Yang Xiao
https://arxiv.org/abs/2508.08925 https://

LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition
Emotion recognition in conversations (ERC) aims to predict the emotional state of each utterance by using multiple input types, such as text and audio. While Transformer-based models have shown strong performance in this task, they often face two major issues: high computational cost and heavy dependence on speaker information. These problems reduce their ability to generalize in real-world conversations. To solve these challenges, we propose LPGNet, a Lightweight network with Parallel attentio…

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

Diagonalizing large-scale quantum many-body Hamiltonians using variational quantum circuit and tensor network
Peng-Fei Zhou, Shuang Qiao, An-Chun Ji, Shi-Ju Ran
https://arxiv.org/abs/2508.06159

Diagonalizing large-scale quantum many-body Hamiltonians using variational quantum circuit and tensor network
Exact diagonalization (ED) is an essential tool for exploring quantum many-body physics but is fundamentally limited by the exponentially-scaled computational complexity. Here, we propose tensor network variational diagonalization (TNVD), which encodes the full eigenenergy spectrum of a quantum many-body Hamiltonian into a matrix product state, and encodes the eigenstates as the evolutions of product states using variational quantum circuit (VQC). Thereby, TNVD reduces the computational complex…

Variational Neural Network Approach to QFT in the Field Basis
Kevin Braga, Nobuo Sato, Adam P. Szczepaniak
https://arxiv.org/abs/2508.00173 https://arxiv.o…

Variational Neural Network Approach to QFT in the Field Basis
We present a variational neural network approach for solving quantum field theories in the field basis, focusing on the free Klein-Gordon model formulated in momentum space. While recent studies have explored neural-network-based variational methods for scalar field theory in position space, a systematic benchmark of the analytically solvable Klein-Gordon ground state -- particularly in the momentum-space field basis -- has been lacking. In this work, we represent the ground-state wavefunctiona…

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities
Diwen Xue, Armin Huremagic, Wayne Wang, Ram Sundara Raman, Roya Ensafi
https://arxiv.org/abs/2509.09081 https:…

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities
Users around the world face escalating network interference such as censorship, throttling, and interception, largely driven by the commoditization and growing availability of Deep Packet Inspection (DPI) devices. Once reserved for a few well-resourced nation-state actors, the ability to interfere with traffic at scale is now within reach of nearly any network operator. Despite this proliferation, our understanding of DPIs and their deployments on the Internet remains limited -- being network i…

This is an interesting news report.
I would add that if this report is even partially accurate we may see a massive temporary decline in beef/meat prices as dairy farmers "reduce" their herds in the face of diminished demand for milk products.
By-the-way, Devin Nunes - yes that Devin Nunes, now head of trump's failing social network/crypto-bank - is a diary farmer in some state or another.

Simulating LLM training workloads for heterogeneous compute and network infrastructure
Sumit Kumar, Arjun Temura, Naman Sharma, Ramanjeet Singh, Meet Dadhania, Praveen Tammana, Satananda Burla, Abed Mohammad Kamaluddin, Rinku Shah
https://arxiv.org/abs/2508.05370

Simulating LLM training workloads for heterogeneous compute and network infrastructure
The growing demand for large-scale GPU clusters in distributed model training presents a significant barrier to innovation, particularly in model optimization, performance tuning, and system-level enhancements. To address this challenge, LLM training simulators are employed to estimate training time and guide design decisions. However, the state-of-the-art LLM training simulators assume homogeneous compute and network infrastructure. In practice, device heterogeneity is inevitable due to resour…

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…

An Event-based State Estimation Approach for Positive Systems with Positive Observers
Bhargavi Chaudhary, Krishanu Nath, Subashish Datta, Indra Narayan Kar
https://arxiv.org/abs/2508.03154

An Event-based State Estimation Approach for Positive Systems with Positive Observers
This article addresses the problem of state observer design for continuous-time linear positive networked systems. Considering the bandwidth constraint in the communication network, an event-measurement-based positive observer design is proposed. The physical interpretation of a positive observer differs from that of a general observer. Its primary goal is to ensure that all state estimates remain non-negative at all times. Using output measurements, a law with weighted sampling error is used t…

Coinbase rolls out DEX trading in its app for US users, excluding New York state, starting with assets on the Base network, with Solana support "coming soon" (Mat Di Salvo/Decrypt)
https://decrypt.co/334308/coinbase-roll…

Coinbase Rolls Out DEX Trading on Its App Starting With Base—And Solana 'Coming Soon'
The company is moving to become an "everything exchange," its CEO said.

Energy Efficient p-Circuits for Generative Neural Networks
Lakshmi A. Ghantasala, Ming-Che Li, Risi Jaiswal, Archisman Ghosh, Behtash Behin-Aein, Joseph Makin, Shreyas Sen, Supriyo Datta
https://arxiv.org/abs/2507.07763

Energy Efficient p-Circuits for Generative Neural Networks
A p-circuit comprising a network of $N$ p-bits can generate samples from a probability distribution with $2^N$ possibilities just like a network of q-bits, though there are interesting fundamental differences. A p-bit is essentially a binary stochastic neuron used in Boltzmann machines which involve the repeated application of a specific elementary operation, whereby each p-bit looks at the state of 'n' other p-bits, computes the probability for being $0$ or $1$, and chooses its next state acco…

Algebraically Observable Physics-Informed Neural Network and its Application to Epidemiological Modelling
Mizuka Komatsu
https://arxiv.org/abs/2508.04590 https://

Algebraically Observable Physics-Informed Neural Network and its Application to Epidemiological Modelling
Physics-Informed Neural Network (PINN) is a deep learning framework that integrates the governing equations underlying data into a loss function. In this study, we consider the problem of estimating state variables and parameters in epidemiological models governed by ordinary differential equations using PINNs. In practice, not all trajectory data corresponding to the population described by models can be measured. Learning PINNs to estimate the unmeasured state variables and epidemiological pa…

NNQS-AFQMC: Neural network quantum states enhanced fermionic quantum Monte Carlo
Zhi-Yu Xiao, Bowen Kan, Huan Ma, Bowen Zhao, Honghui Shang
https://arxiv.org/abs/2507.07540

NNQS-AFQMC: Neural network quantum states enhanced fermionic quantum Monte Carlo
We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wavefunctions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently developed class of variational ansätze capable of flexibly representing many-body wavefunctions, though they often incur a high computational cost during optimization. AFQMC, on the other hand, is a powerful stochastic projector approach for ground-state calculations, but it normally requires an approximate constraint vi…

10th Enemy Encounters Webinar “Rumours and 19th-20th Century Religious Resistance, State Repression and Maoist Campaigns in China”
https://ift.tt/JZbFl9X
APPLY: LGBTQ Religious Archives Network Fall Intership—$1500 Stipend Riot Mueller 07/25/2024 -…
via Input 4 RELCFP

Inference in Spreading Processes with Neural-Network Priors
Davide Ghio, Fabrizio Boncoraglio, Lenka Zdeborov\'a
https://arxiv.org/abs/2509.02073 https://

Inference in Spreading Processes with Neural-Network Priors
Stochastic processes on graphs are a powerful tool for modelling complex dynamical systems such as epidemics. A recent line of work focused on the inference problem where one aims to estimate the state of every node at every time, starting from partial observation of a subset of nodes at a subset of times. In these works, the initial state of the process was assumed to be random i.i.d. over nodes. Such an assumption may not be realistic in practice, where one may have access to a set of covaria…

Balaji revealed his shocking ideas for a tech-governed city
where citizens loyal to tech companies would form a new political tribe clad in
gray t-shirts.
“And if you see another Gray on the street … you do the nod,” he said, during a four-hour talk on the Moment of Zen podcast. “You’re a fellow Gray.”
“Grays should embrace the police, okay? All-in on the police,”
said Balaji Srinivasan.
“What does that mean? That’s, as I said, banquets.
That means …

The Tech Baron Seeking to Purge San Francisco of “Blues”
If Balaji Srinivasan is any guide, then the Silicon Valley plutocrats are definitely not OK.

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Zhiying Li, Guanggang Geng, Jian Weng
https://arxiv.org/abs/2508.05695

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Enterprises are facing increasing risks of insider threats, while existing detection methods are unable to effectively address these challenges due to reasons such as insufficient temporal dynamic feature modeling, computational efficiency and real-time bottlenecks and cross-modal information island problem. This paper proposes a new insider threat detection framework MambaITD based on the Mamba state space model and cross-modal adaptive fusion. First, the multi-source log preprocessing module …

Recurrent State Encoders for Efficient Neural Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Lars Schmidt-Thieme
https://arxiv.org/abs/2509.05084 https://

Recurrent State Encoders for Efficient Neural Combinatorial Optimization
The primary paradigm in Neural Combinatorial Optimization (NCO) are construction methods, where a neural network is trained to sequentially add one solution component at a time until a complete solution is constructed. We observe that the typical changes to the state between two steps are small, since usually only the node that gets added to the solution is removed from the state. An efficient model should be able to reuse computation done in prior steps. To that end, we propose to train a recu…

Adaptive Learning for IRS-Assisted Wireless Networks: Securing Opportunistic Communications Against Byzantine Eavesdroppers
Amirhossein Taherpour, Abbas Taherpour, Tamer Khattab
https://arxiv.org/abs/2508.08206

Adaptive Learning for IRS-Assisted Wireless Networks: Securing Opportunistic Communications Against Byzantine Eavesdroppers
We propose a joint learning framework for Byzantine-resilient spectrum sensing and secure intelligent reflecting surface (IRS)--assisted opportunistic access under channel state information (CSI) uncertainty. The sensing stage performs logit-domain Bayesian updates with trimmed aggregation and attention-weighted consensus, and the base station (BS) fuses network beliefs with a conservative minimum rule, preserving detection accuracy under a bounded number of Byzantine users. Conditioned on the …

Learning To Communicate Over An Unknown Shared Network
Shivangi Agarwal, Adi Asija, Sanjit K. Kaul, Arani Bhattacharya, Saket Anand
https://arxiv.org/abs/2507.06499

Learning To Communicate Over An Unknown Shared Network
As robots (edge-devices, agents) find uses in an increasing number of settings and edge-cloud resources become pervasive, wireless networks will often be shared by flows of data traffic that result from communication between agents and corresponding edge-cloud. In such settings, agent communicating with the edge-cloud is unaware of state of network resource, which evolves in response to not just agent's own communication at any given time but also to communication by other agents, which stays u…

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
Pietro Bartoli, Tommaso Bondini, Christian Veronesi, Andrea Giudici, Niccol\`o Antonello, Franco Zappa
https://arxiv.org/abs/2509.07051

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
Keyword spotting (KWS) is a key enabling technology for hands-free interaction in embedded and IoT devices, where stringent memory and energy constraints challenge the deployment of AI-enabeld devices. In this work, we systematically evaluate and compare several state-of-the-art lightweight neural network architectures, including DS-CNN, LiCoNet, and TENet, alongside our proposed Typman-KWS (TKWS) architecture built upon MobileNet, specifically designed for efficient KWS on microcontroller unit…

Patterns of imbalance states between sub-brain regimes during development in the resting state
Fahimeh Ahmadi, Zahra Moradimanesh, Reza Khosrowabadi, G. Reza Jafari
https://arxiv.org/abs/2508.01307

Patterns of imbalance states between sub-brain regimes during development in the resting state
The functional brain network is the result of neurons and different areas working together in a coordinated manner. The findings indicate that the spatial distribution of anticorrelations in the brain network is not inherent. In this study, using Heider balance theory, we examine the network of imbalanced triangles with negative links in the brain, which undergoes dynamic changes across development. By analyzing the age-related transformations in the connectivity of imbalanced triangles at the …

Role of Plaquette Term in Genuine $2 1$D String Dynamics on Quantum Simulators
Yizhuo Tian, N. S. Srivatsa, Kaidi Xu, Jesse J. Osborne, Umberto Borla, Jad C. Halimeh
https://arxiv.org/abs/2508.05736

Role of Plaquette Term in Genuine $2+1$D String Dynamics on Quantum Simulators
With the advent of quantum simulators of $2+1$D lattice gauge theories (LGTs), a fundamental open question is under what circumstances the observed physics is genuinely $2+1$D rather than effectively $1+1$D. Here, we address this question in the ongoing strong effort to quantum-simulate string dynamics in $2+1$D LGTs on state-of-the-art quantum hardware. Through tensor network simulations and analytic derivations, we show that the plaquette term, which represents a magnetic field and only emerg…

Endogenous Network Structures with Precision and Dimension Choices
Nikhil Kumar
https://arxiv.org/abs/2507.00249 https://arxiv.org/pd…

Endogenous Network Structures with Precision and Dimension Choices
This paper presents a social learning model where the network structure is endogenously determined by signal precision and dimension choices. Agents not only choose the precision of their signals and what dimension of the state to learn about, but these decisions directly determine the underlying network structure on which social learning occurs. We show that under a fixed network structure, the optimal precision choice is sublinear in the agent's stationary influence in the network, and this i…

Temporal social network modeling of mobile connectivity data with graph neural networks
Joel Jaskari, Chandreyee Roy, Fumiko Ogushi, Mikko Saukkoriipi, Jaakko Sahlsten, Kimmo Kaski
https://arxiv.org/abs/2509.03319

Temporal social network modeling of mobile connectivity data with graph neural networks
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people's mobile connectivity data has not been extensively investigated. In the present study, we investigate four snapshot - based temporal GNNs in predicting the phone call and SMS activity between u…

Real-Time String Dynamics in a $2 1$D Non-Abelian Lattice Gauge Theory: String Breaking, Glueball Formation, Baryon Blockade, and Tension Reduction
Giovanni Cataldi, Simone Orlando, Jad C. Halimeh
https://arxiv.org/abs/2509.08868

Real-Time String Dynamics in a $2+1$D Non-Abelian Lattice Gauge Theory: String Breaking, Glueball Formation, Baryon Blockade, and Tension Reduction
Understanding flux string dynamics can provide insight into quark confinement and hadronization. First-principles quantum and numerical simulations have mostly focused on toy-model Abelian lattice gauge theories (LGTs). With the advent of state-of-the-art quantum simulation experiments, it is important to bridge this gap and study string dynamics in non-Abelian LGTs beyond one spatial dimension. Using tensor network methods, we simulate the real-time string dynamics of a $2\!+\!1$D SU$(2)$ Yang…

On Optimality of Private Information in Bayesian Routing Games
Alexia Ambrogio, Leonardo Cianfanelli, Giacomo Como
https://arxiv.org/abs/2509.03357 https://

On Optimality of Private Information in Bayesian Routing Games
We study an information design problem in transportation networks, in the presence of a random state that affects the travel times on the links. An omniscient system planner -- aiming at reducing congestion -- observes the network state realization and sends private messages to the users -- who share a common prior on the network state but do not observe it directly -- in order to nudge them towards a socially desirable behavior. The desired effect of these private signals is to correlate the u…

Dual-Thresholding Heatmaps to Cluster Proposals for Weakly Supervised Object Detection
Yuelin Guo, Haoyu He, Zhiyuan Chen, Zitong Huang, Renhao Lu, Lu Shi, Zejun Wang, Weizhe Zhang
https://arxiv.org/abs/2509.08289

Dual-Thresholding Heatmaps to Cluster Proposals for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) has attracted significant attention in recent years, as it does not require box-level annotations. State-of-the-art methods generally adopt a multi-module network, which employs WSDDN as the multiple instance detection network module and multiple instance refinement modules to refine performance. However, these approaches suffer from three key limitations. First, existing methods tend to generate pseudo GT boxes that either focus only on discriminative …

Check out today's Metacurity for the most critical infosec developments you should know today, including
--Salt Typhoon 'extensively' compromised a state national guard network for months,
--Former US army soldier pleads guilty to hacking telcos,
--Ukraine claims hack of big Russian drone maker,
--Korean insurance giant hit by ransomware attack,
--Trump's bill offers $1b spend on offensive cyber,
--Abacus Market goes dark after suspected rug…

Salt Typhoon 'extensively' compromised a state national guard network for months
Former US army soldier pleads guilty to hacking telcos, Ukraine claims hack of big Russian drone maker, Korean insurance giant hit by ransomware attack, Trump's bill offers $1b spend on offensive cyber, Abacus Market goes dark after suspected rug pull, AsyncRAT has spawned 30+ forks, much more

Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
Vasiliy A. Es'kin, Egor V. Ivanov
https://arxiv.org/abs/2507.04153

Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from a mask are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, which is based on a waveguide method with its most computationally expensive part replaced by a neural network. Numerical experiments on realistic 2D and 3D masks show that the WGNO achieves state-of-the-art accuracy and inference time, providing a hi…

The Reflexive Integrated Information Unit: A Differentiable Primitive for Artificial Consciousness
Gnankan Landry Regis N'guessan, Issa Karambal
https://arxiv.org/abs/2506.13825

The Reflexive Integrated Information Unit: A Differentiable Primitive for Artificial Consciousness
Research on artificial consciousness lacks the equivalent of the perceptron: a small, trainable module that can be copied, benchmarked, and iteratively improved. We introduce the Reflexive Integrated Information Unit (RIIU), a recurrent cell that augments its hidden state $h$ with two additional vectors: (i) a meta-state $μ$ that records the cell's own causal footprint, and (ii) a broadcast buffer $B$ that exposes that footprint to the rest of the network. A sliding-window covariance and a dif…

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Yihao Liu, Savannah P. Hays, Dzung L. Pham, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Aaron Carass, Jerry L. Prince
https://arxiv.org/a…

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation, with deep learning (DL) methods achieving state-of-the-art performance. However, these DL-based methods have yet to simultaneously optimize in-domain accuracy and out-of-domain generalization when trained on a single source with limited data, or their performance has been unsatisfactory. To fill this gap, we propose …

Refining Czech GEC: Insights from a Multi-Experiment Approach
Petr Pechman, Milan Straka, Jana Strakov\'a, Jakub N\'aplava
https://arxiv.org/abs/2506.22402

Refining Czech GEC: Insights from a Multi-Experiment Approach
We present a grammar error correction (GEC) system that achieves state of the art for the Czech language. Our system is based on a neural network translation approach with the Transformer architecture, and its key feature is its real-time synthetic generation pipeline, which dynamically augments sentences with artificial errors by introducing both language-agnostic and Czech-specific errors. We conduct a comprehensive series of experiments, investigating the Czech GEC corpora as bases for synth…

Data-Driven Einstein-Dilaton Model for Pure Yang-Mills Thermodynamics and Glueball Spectrum
Xun Chen, Yidian Chen, Kai Zhou
https://arxiv.org/abs/2507.06729

Data-Driven Einstein-Dilaton Model for Pure Yang-Mills Thermodynamics and Glueball Spectrum
We develop a machine learning assisted holographic model that consistently describes both the equation of state and glueball spectrum of pure Yang-Mills theory, achieved through neural network reconstruction of Einstein-dilaton gravity. Our framework incorporates key non-perturbative constraints of lattice QCD data: the ground ($0^{++}$) and first-excited ($0^{++*}$) scalar glueball masses pins down the infrared (IR) geometry, while entropy density data anchors the ultraviolet (UV) behavior of …

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

Decentralized social network Mastodon says it can’t comply with Mississippi’s age verification law
— the same law that saw rival Bluesky pull out of the state
— because it doesn’t have the means to do so.
The social nonprofit explains that Mastodon doesn’t track its users,
which makes it difficult to enforce such legislation.
Nor does it want to use IP address-based blocks, as those would unfairly impact people who were traveling, it says.
The statement …

Mastodon says it doesn't 'have the means' to comply with age verification laws | TechCrunch
Decentralized social network Mastodon says it cannot comply with age verification laws, like in Mississippi and elsewhere, and says it's up to individual server owners to decide.

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Thomas Jantos, Stephan Weiss, Jan Steinbrener
https://arxiv.org/abs/2509.01583

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects. For example, vision-based object-relative navigation relies on a DL-based 6D object pose predictor to provide the relative pose between the object and the robot as measurements to the robot's state estimator. Accurately knowing the uncertainty inherent in such Deep Neural Network (DNN) based measurements is essential…

Benchmarking the State of Networks with a Low-Cost Method Based on Reservoir Computing
Felix Simon Reimers, Carl-Hendrik Peters, Stefano Nichele
https://arxiv.org/abs/2508.21420

Benchmarking the State of Networks with a Low-Cost Method Based on Reservoir Computing
Using data from mobile network utilization in Norway, we showcase the possibility of monitoring the state of communication and mobility networks with a non-invasive, low-cost method. This method transforms the network data into a model within the framework of reservoir computing and then measures the model's performance on proxy tasks. Experimentally, we show how the performance on these proxies relates to the state of the network. A key advantage of this approach is that it uses readily availa…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Behnoud Shafiezadeh, Amir Mashmool, Farshad Eshghi, Manoochehr Kelarestaghi
https://arxiv.org/abs/2509.06868

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Automatic License-Plate Recognition (ALPR) plays a pivotal role in Intelligent Transportation Systems (ITS) as a fundamental element of Smart Cities. However, due to its high variability, ALPR faces challenging issues more efficiently addressed by deep learning techniques. In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step, coupled with the state-of-the-art You-Only-Look-Once (YOLO)v5 object detection architectures for License-Pl…

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
Ioannis Tzortzis, Themistoklis Charalambous, Charalambos D. Charalambous
https://arxiv.org/abs/2509.04116

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
This paper considers the problem of remote state estimation for Markov jump linear systems in the presence of uncertainty in the posterior mode probabilities. Such uncertainty may arise when the estimator receives noisy or incomplete measurements over an unreliable communication network. To address this challenge, the estimation problem is formulated within a distributionally robust framework, where the true posterior is assumed to lie within a total variation distance ball centered at the nomi…

Classical simulation of noisy quantum circuits via locally entanglement-optimal unravelings
Simon Cichy, Paul K. Faehrmann, Lennart Bittel, Jens Eisert, Hakop Pashayan
https://arxiv.org/abs/2508.05745 …

Classical simulation of noisy quantum circuits via locally entanglement-optimal unravelings
Classical simulations of noisy quantum circuits is instrumental to our understanding of the behavior of real world quantum systems and the identification of regimes where one expects quantum advantage. In this work, we present a highly parallelizable tensor-network-based classical algorithm -- equipped with rigorous accuracy guarantees -- for simulating $n$-qubit quantum circuits with arbitrary single-qubit noise. Our algorithm represents the state of a noisy quantum system by a particular ense…

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE
Koen T. W. Teuwen, Sam Baggen, Emmanuele Zambon, Luca Allodi
https://arxiv.org/abs/2507.01571

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE
Automation in Security Operations Centers (SOCs) plays a prominent role in alert classification and incident escalation. However, automated methods must be robust in the presence of imbalanced input data, which can negatively affect performance. Additionally, automated methods should make explainable decisions. In this work, we evaluate the effect of label imbalance on the classification of network intrusion alerts. As our use-case we employ DeepCASE, the state-of-the-art method for automated a…

Robust Power System State Estimation using Physics-Informed Neural Networks
Solon Falas, Markos Asprou, Charalambos Konstantinou, Maria K. Michael
https://arxiv.org/abs/2507.05874

Robust Power System State Estimation using Physics-Informed Neural Networks
Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness, of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal …

On the Perturbed Projection-Based Distributed Gradient-Descent Algorithm: A Fully-Distributed Adaptive Redesign
Tarek Bazizi, Mohamed Maghenem, Paolo Frasca, Antonio Lor\`ia, Elena Panteley
https://arxiv.org/abs/2509.03443

On the Perturbed Projection-Based Distributed Gradient-Descent Algorithm: A Fully-Distributed Adaptive Redesign
In this work, we revisit a classical distributed gradient-descent algorithm, introducing an interesting class of perturbed multi-agent systems. The state of each subsystem represents a local estimate of a solution to the global optimization problem. Thereby, the network is required to minimize local cost functions, while gathering the local estimates around a common value. Such a complex task suggests the interplay of consensus-based dynamics with gradient-descent dynamics. The latter descent d…

The Effect of Network Topology on the Equilibria of Influence-Opinion Games
Yigit Ege Bayiz, Arash Amini, Radu Marculescu, Ufuk Topcu
https://arxiv.org/abs/2506.22293

The Effect of Network Topology on the Equilibria of Influence-Opinion Games
Online social networks exert a powerful influence on public opinion. Adversaries weaponize these networks to manipulate discourse, underscoring the need for more resilient social networks. To this end, we investigate the impact of network connectivity on Stackelberg equilibria in a two-player game to shape public opinion. We model opinion evolution as a repeated competitive influence-propagation process. Players iteratively inject \textit{messages} that diffuse until reaching a steady state, mo…

Crypto and tech entrepreneurs are attending Network School, founded by Balaji Srinivasan in Forest City, Malaysia, to test his concepts about the Network State (Ryan Weeks/Bloomberg)
https://www.bloomberg.com/news/articles/20

Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices
Yael Segal-Feldman, Ann R. Bradlow, Matthew Goldrick, Joseph Keshet
https://arxiv.org/abs/2508.04857 http…

Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices
Open-vocabulary keyword spotting (KWS) refers to the task of detecting words or terms within speech recordings, regardless of whether they were included in the training data. This paper introduces an open-vocabulary keyword spotting model with state-of-the-art detection accuracy for small-footprint devices. The model is composed of a speech encoder, a target keyword encoder, and a detection network. The speech encoder is either a tiny Whisper or a tiny Conformer. The target keyword encoder is i…

QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction
Tobias Winker, Jinghua Groppe, Sven Groppe
https://arxiv.org/abs/2509.08817 https://arxiv…

QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction
Cardinality estimation is an important part of query optimization in DBMS. We develop a Quantum Cardinality Estimation (QCardEst) approach using Quantum Machine Learning with a Hybrid Quantum-Classical Network. We define a compact encoding for turning SQL queries into a quantum state, which requires only qubits equal to the number of tables in the query. This allows the processing of a complete query with a single variational quantum circuit (VQC) on current hardware. In addition, we compare mu…

Synergistic Localization and Sensing in MIMO-OFDM Systems via Mixed-Integer Bilevel Learning
Zelin Zhu, Kai Yang, Rui Zhang
https://arxiv.org/abs/2507.07118

Synergistic Localization and Sensing in MIMO-OFDM Systems via Mixed-Integer Bilevel Learning
Wireless localization and sensing technologies are essential in modern wireless networks, supporting applications in smart cities, the Internet of Things (IoT), and autonomous systems. High-performance localization and sensing systems are critical for both network efficiency and emerging intelligent applications. Integrating channel state information (CSI) with deep learning has recently emerged as a promising solution. Recent works have leveraged the spatial diversity of multiple input multipl…

Pinching Antenna System (PASS) Enhanced Covert Communications: Against Warden via Sensing
Hao Jiang, Zhaolin Wang, Yuanwei Liu, Arumugam Nallanathan, Zhiguo Ding
https://arxiv.org/abs/2509.06170

Pinching Antenna System (PASS) Enhanced Covert Communications: Against Warden via Sensing
A sensing-aided covert communication network empowered by pinching antenna systems (PASS) is proposed in this work. Unlike conventional fixed-position MIMO arrays, PASS dynamically reconfigures its pinching antennas (PAs) closer to the legitimate user, substantially enhancing covertness. To further secure the adversary's channel state information (CSI), a sensing function is leveraged to track the malicious warden's movements. In particular, this paper first proposes an extended Kalman filter (…

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

Polarization-Controlled Dual-State Distribution of Bell and N00N Entanglement Over a Metro-Scale Commercial Quantum Network
Kazi Reaz, Md Mehdi Hassan, Jacob E. Humberd, Matthew L. Boone, Angel Fraire Estrada, Rick Mukherjee, H. R. Sadeghpour, Girish S. Agarwal, George Siopsis, Tian Li
https://arxiv.org/abs/2509.03701

Polarization-Controlled Dual-State Distribution of Bell and N00N Entanglement Over a Metro-Scale Commercial Quantum Network
We report the first demonstration of multiphoton, dual state entanglement distribution over a metropolitan scale commercial fiber network, implemented on the EPB Bohr IV quantum network in Chattanooga, TN, using an all fiber optic experimental platform. Employing a spatially degenerate, continuous wave type II SPDC biphoton source and fully fiber coupled linear optics, we generated a 4 photon entangled state. Through polarization projective measurements on two locally retained photons, we then …

An Experimental Reservoir-Augmented Foundation Model: 6G O-RAN Case Study
Farhad Rezazadeh, Raymond Zhao, Jiongyu Dai, Amir Ashtari Gargari, Hatim Chergui, Lingjia Liu
https://arxiv.org/abs/2508.07778 …

An Experimental Reservoir-Augmented Foundation Model: 6G O-RAN Case Study
Next-generation open radio access networks (O-RAN) continuously stream tens of key performance indicators (KPIs) together with raw in-phase/quadrature (IQ) samples, yielding ultra-high-dimensional, non-stationary time series that overwhelm conventional transformer architectures. We introduce a reservoir-augmented masked autoencoding transformer (RA-MAT). This time series foundation model employs echo state network (ESN) computing with masked autoencoding to satisfy the stringent latency, energy…

ALICE: An Interpretable Neural Architecture for Generalization in Substitution Ciphers
Jeff Shen, Lindsay Smith
https://arxiv.org/abs/2509.07282 https://ar…

ALICE: An Interpretable Neural Architecture for Generalization in Substitution Ciphers
We present cryptogram solving as an ideal testbed for studying neural network generalization in combinatorially complex domains. In this task, models must decrypt text encoded with substitution ciphers, choosing from 26! possible mappings without explicit access to the cipher. We develop ALICE (an Architecture for Learning Interpretable Cryptogram dEcipherment): a simple encoder-only Transformer that sets a new state-of-the-art for both accuracy and speed on this decryption problem. Surprisingl…

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
Sayan Manna, Mahesh Mohan M R
https://arxiv.org/abs/2509.02374 https://arxiv…

Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
Quantum algorithms operate on quantum states through unitary transformations in high dimensional complex Hilbert space. In this work, we propose a machine learning approach to create the quantum circuit using a single-layer complex-valued neural network. The input and ouput quantum states are provided to the network, which is trained to approximate the output state of a given quantum algorithm. To ensure that the fundamental property of unitarity is preserved throughout the training process, we…

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro
https://arxiv.org/abs/2506.18748

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We demonstrate how a state-augmented graph neural network (GNN) parametrization for the resource allocation policy circumvents the drawbacks of the ubiquitous dual subgradient methods by representing the network configurations (or states) as graphs and viewing dual variables as dynamic inputs t…

WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence Matching
Ronghua Li, Shinan Liu, Haibo Hu, Qingqing Ye, Nick Feamster
https://arxiv.org/abs/2508.03151

WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence Matching
IoT environments such as smart homes are susceptible to privacy inference attacks, where attackers can analyze patterns of encrypted network traffic to infer the state of devices and even the activities of people. While most existing attacks exploit ML techniques for discovering such traffic patterns, they underperform on wireless traffic, especially Wi-Fi, due to its heavy noise and packet losses of wireless sniffing. In addition, these approaches commonly target at distinguishing chunked IoT …

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

Quantum state-transfer and periodicity in discrete-time quantum walk under non-Markovian noise
Monika Rani, Supriyo Dutta, Subhashish Banerjee
https://arxiv.org/abs/2506.24066

Quantum state-transfer and periodicity in discrete-time quantum walk under non-Markovian noise
In quantum communication, quantum state transfer from one location to another in a quantum network plays a prominent role, where the impact of noise could be crucial. The idea of state transfer can be fruitfully associated with quantum walk on graphs. We investigate the consequences of non-Markovian quantum noises on periodicity and state transfer induced by a discrete-time quantum walk on graphs, governed by the Grover coin operator. Different bipartite graphs, such as the path graph, cycle gr…

Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
Molly Wang
https://arxiv.org/abs/2507.22174 https://arxiv.org/pdf/25…

Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
Reinforcement Learning (RL) has become a well-established approach for optimizing packet routing in communication networks. Standard RL algorithms typically are based on the Markov Decision Process (MDP), which assumes that the current state of the environment provides all the necessary information for system evolution and decision-making. However, this Markovian assumption is invalid in many practical scenarios, making the MDP and RL frameworks inadequate to produce the optimal solutions. Addi…

The Role of the Satellite in Quantum Information Networks
Luca Paccard, Valentin Leloup, Luca Lazzarini, Agathe Blaise, Mailys Guerault, Mickael Faugeron, Fabrice Arnal, Mathieu Bertrand, Raphael Aymeric, Michel Sotom, St\'ephanie Molin, Patrick G\'elard, Pierre Besancenot, Cyrille Laborde, Laurent de Forges de Parny, Mathias van den Bossche
https://

The Role of the Satellite in Quantum Information Networks
Quantum Information Networks (QIN) attract increasing interest, as they will enable interconnection of multiple quantum devices in a distributed organization thus enhancing intrinsic computing, sensing, and security capabilities. The core mechanism of a QIN is quantum state swapping, based on teleportation, which consumes quantum entanglement, and which can be seen in this context as a new kind of network resource. The satellite is expected to play a central role for supporting global connectiv…

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Quantum-like states from classical systems
Gregory D. Scholes
https://arxiv.org/abs/2507.00967 https://arxiv.org/pdf/2507.00967

Quantum-like states from classical systems
This work studies how a suitably-designed classical system generates with a quantum-like (QL) state space mediated by a graph. The graph plays a special dual role by directing the topology of the classical network and defining a state space that comprises superpositions of states in a tensor product basis. The basis for constructing QL graphs and their properties is reviewed and extended. An optimization of the graph product is developed to produce a more compact graph with the essential proper…

Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees
Guanqin Zhang, Kota Fukuda, Zhenya Zhang, H. M. N. Dilum Bandara, Shiping Chen, Jianjun Zhao, Yulei Sui
https://arxiv.org/abs/2507.17453

Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees
The vulnerability of neural networks to adversarial perturbations has necessitated formal verification techniques that can rigorously certify the quality of neural networks. As the state-of-the-art, branch and bound (BaB) is a "divide-and-conquer" strategy that applies off-the-shelf verifiers to sub-problems for which they perform better. While BaB can identify the sub-problems that are necessary to be split, it explores the space of these sub-problems in a naive "first-come-first-serve" manner…

Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
https://arxiv.org/abs/2506.22801

Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Quantum imaginary time evolution (QITE) is a powerful method to derive the ground states of the systems. Only the damping of quantum states leads it; hence, reaching the ground state is guaranteed by nature without any external manipulation. Numerous QITE methods by many groups are used to improve speed and accuracy, derive excited states, and solve combined optimization problems. However, the QITE methods have not been used for quantum machine learning to predict the ideal values for multiple …

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted