Tootfinder

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…

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

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…

StacKAT: Infinite State Network Verification
Jules Jacobs, Nate Foster, Tobias Kapp\'e, Dexter Kozen, Lily Saada, Alexandra Silva, Jana Wagemaker
https://arxiv.org/abs/2506.13383

StacKAT: Infinite State Network Verification
We develop StacKAT, a network verification language featuring loops, finite state variables, nondeterminism, and - most importantly - access to a stack with accompanying push and pop operations. By viewing the variables and stack as the (parsed) headers and (to-be-parsed) contents of a network packet, StacKAT can express a wide range of network behaviors including parsing, source routing, and telemetry. These behaviors are difficult or impossible to model using existing languages like NetKAT. W…

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

GNSS Spoofing Detection Based on Opportunistic Position Information
Wenjie Liu, Panos Papadimitratos
https://arxiv.org/abs/2506.12580 https://

GNSS Spoofing Detection Based on Opportunistic Position Information
The limited or no protection for civilian Global Navigation Satellite System (GNSS) signals makes spoofing attacks relatively easy. With modern mobile devices often featuring network interfaces, state-of-the-art signals of opportunity (SOP) schemes can provide accurate network positions in replacement of GNSS. The use of onboard inertial sensors can also assist in the absence of GNSS, possibly in the presence of jammers. The combination of SOP and inertial sensors has received limited attention…

Quantum Fisher information from tensor network integration of Lyapunov equation
Gabriela W\'ojtowicz, Susana F. Huelga, Marek M. Rams, Martin B. Plenio
https://arxiv.org/abs/2506.11330

Quantum Fisher information from tensor network integration of Lyapunov equation
The Quantum Fisher Information (QFI) is a geometric measure of state deformation calculated along the trajectory parameterizing an ensemble of quantum states. It serves as a key concept in quantum metrology, where it is linked to the fundamental limit on the precision of the parameter that we estimate. However, the QFI is notoriously difficult to calculate due to its non-linear mathematical form. For mixed states, standard numerical procedures based on eigendecomposition quickly become impracti…

I was curious to see what streaming news media would do today, after three straight weeks of nonstop, utterly pointless coverage of the Pope. Would they finally get back to talking about the ongoing destruction of the American democratic state and the urgent matters piling up?
Naaaaah. Every single network (including, sadly, BBC and Sky News) chose to air the thoroughly disgusting spectacle of TFG and MBS making deals in KSA.
And then: Show trials! Kardashian! Mendezez! Diddy!

Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks
Yinghan Li, Yiming Liu, Wei Yu
https://arxiv.org/abs/2506.18218 …

Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks
This paper proposes an approach that leverages multimodal data by integrating visual images with radio frequency (RF) pilots to optimize user association and beamforming in a downlink wireless cellular network under a max-min fairness criterion. Traditional methods typically optimize wireless system parameters based on channel state information (CSI). However, obtaining accurate CSI requires extensive pilot transmissions, which lead to increased overhead and latency. Moreover, the optimization …

Germany is drawing up plans to rapidly expand its network of bomb-proof bunkers and shelters,
the government’s most senior civilian protection official has said,
warning the state needs to be prepared for an attack from Russia within the next four years.
Ralph Tiesler, the head of the Federal Office of Civil Protection and Disaster Assistance (BBK),
said Europe’s largest economy needed to wake up to the reality of conflict,
and that in its current state Germany w…

Noise-induced stabilization in a chemical reaction network without boundary effects
Andrea Agazzi, Lucie Laurence
https://arxiv.org/abs/2506.12163 https://…

Noise-induced stabilization in a chemical reaction network without boundary effects
We present a chemical reaction network that is unstable under deterministic mass action kinetics, exhibiting finite-time blow-up of trajectories in the interior of the state space, but whose stochastic counterpart is positive recurrent. This provides an example of noise-induced stabilization of the model's dynamics arising due to noise perturbing transversally the divergent trajectories of the system that is independently of boundary effects. The proof is based on a careful decomposition of the…

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
Yimian Ding, Jingzehua Xu, Guanwen Xie, Shuai Zhang, Yi Li
https://arxiv.org/abs/2506.15082

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental ad…

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

Metric Framework of Coherent Activity Patterns Identification in Spiking Neuronal Networks
Daniil Radushev, Olesia Dogonasheva, Boris Gutkin, Denis Zakharov
https://arxiv.org/abs/2506.12291

Metric Framework of Coherent Activity Patterns Identification in Spiking Neuronal Networks
Partial synchronization plays a crucial role in the functioning of neuronal networks: selective, coordinated activation of neurons enables information processing that flexibly adapts to a changing computational context. Since the structure of coherent activity patterns reflects the network's current state, developing automated tools to identify them is a key challenge in neurodynamics. Existing methods for analyzing neuronal dynamics tend to focus on global characteristics of the network, such …

This https://arxiv.org/abs/2505.22083 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
In this work, we introduce the first type of non-Euclidean neural quantum state (NQS) ansatz, in the form of the hyperbolic GRU (a variant of recurrent neural networks (RNNs)), to be used in the Variational Monte Carlo method of approximating the ground state wavefunction for quantum many-body systems. In particular, we examine the performances of NQS ansatzes constructed from both conventional or Euclidean RNN/GRU and from hyperbolic GRU in the prototypical settings of the one- and two-dimensi…

Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning
Simon P. von der Maase
https://arxiv.org/abs/2506.14817

Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning
Forecasting violent conflict at high spatial and temporal resolution remains a central challenge for both researchers and policymakers. This study presents a novel neural network architecture for forecasting three distinct types of violence -- state-based, non-state, and one-sided -- at the subnational (priogrid-month) level, up to 36 months in advance. The model jointly performs classification and regression tasks, producing both probabilistic estimates and expected magnitudes of future events…

Buy it Now, Track Me Later: Attacking User Privacy via Wi-Fi AP Online Auctions
Steven Su, Erik Rye, Robert Beverly, Dave Levin
https://arxiv.org/abs/2506.13052

Buy it Now, Track Me Later: Attacking User Privacy via Wi-Fi AP Online Auctions
Static and hard-coded layer-two network identifiers are well known to present security vulnerabilities and endanger user privacy. In this work, we introduce a new privacy attack against Wi-Fi access points listed on secondhand marketplaces. Specifically, we demonstrate the ability to remotely gather a large quantity of layer-two Wi-Fi identifiers by programmatically querying the eBay marketplace and applying state-of-the-art computer vision techniques to extract IEEE 802.11 BSSIDs from the sell…

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

Neural Network-based Universal Formulas for Control
Pol Mestres, Jorge Cort\'es, Eduardo D. Sontag
https://arxiv.org/abs/2505.24744 https://

Neural Network-based Universal Formulas for Control
We study the problem of designing a controller that satisfies an arbitrary number of affine inequalities at every point in the state space. This is motivated by the use of guardrails in autonomous systems. Indeed, a variety of key control objectives, such as stability, safety, and input saturation, are guaranteed by closed-loop systems whose controllers satisfy such inequalities. Many works in the literature design such controllers as the solution to a state-dependent quadratic program (QP) who…

Discrete vortex-based broadcast mode analysis for modification of dynamic stall
Het D. Patel (Department of Mechanical and Aerospace Engineering, North Carolina State University), Yi Tsung Lee (Department of Mechanical and Aerospace Engineering, North Carolina State University), Ashok Gopalarathnam (Department of Mechanical and Aerospace Engineering, North Carolina State University), Chi-An Yeh (Department of Mechanical and Aerospace Engineering, North Carolina State University)

Discrete vortex-based broadcast mode analysis for modification of dynamic stall
We integrate a discrete vortex method with complex network analysis to strategize dynamic stall mitigation over a pitching airfoil with active flow control. The objective is to inform actuator placement and timing to introduce control inputs during the transient evolution of dynamic stall. To this end, we represent the massively separated flow as a network of discrete vortical elements and quantify the interactions among these vortical nodes by tracking the spread of displacement perturbations …

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Rajpreet Singh, Novak Bo\v{s}kov, Aditya Gudal, Manzoor A. Khan
https://arxiv.org/abs/2506.01571

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Resource allocation and scheduling are a common problem in various distributed systems. Although widely studied, the state-of-the-art solutions either do not scale or lack the expressive power to capture the most complex instances of the problem. To that end, we present a mathematical framework for hypergraph ranking and analysis, unifying graph theory, lattice theory, and semantic analysis. In our fundamental theorem, we prove the existence of partial order on entities of hypergraphs, extendin…

Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
Wei Liu, Rui-Hao Bi, Wenjie Dou
https://arxiv.org/abs/2506.07430

Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
Variational optimization of neural-network quantum state representations has achieved FCI-level accuracy for ground state calculations, yet computing optical properties involving excited states remains challenging. In this work, we present a neural-network-based variational quantum Monte Carlo approach for ab-initio absorption spectra. We leverage parallel batch autoregressive sampling and GPU-supported local energy parallelism to efficiently compute ground states of complex systems. By integra…

This https://arxiv.org/abs/2407.14272 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Global Balance and Systemic Risk in Financial Correlation Networks
The global balance index is used in the network literature to quantify how balanced a signed network is. In this paper we show that the global balance index of financial correlation networks can be used as a systemic risk measure. We define the global balance index of a network starting from a diffusive process that describes how the information spreads across nodes in a network, providing an alternative derivation to the usual combinatorial one. The steady state of this process is the solution…

Equivalence of stationary dynamical solutions in a directed chain and a Delay Differential Equation of neuroscientific relevance
Giulio Colombini, Nicola Guglielmi, Armando Bazzani
https://arxiv.org/abs/2506.11654

Equivalence of stationary dynamical solutions in a directed chain and a Delay Differential Equation of neuroscientific relevance
While synchronized states, and the dynamical pathways through which they emerge, are often regarded as the paradigm to understand the dynamics of information spreading on undirected networks of nonlinear dynamical systems, when we consider directed network architectures, dynamical stationary states can arise. To study this phenomenon we consider the simplest directed network, a single cycle, and excitable FitzHugh-Nagumo (FHN) neurons. We show numerically that a stationary dynamical state emerg…

Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer
Alonso Espinoza, Eric Goles, Marco Montalva-Medel
https://arxiv.org/abs/2506.02525

Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer
Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive analyses. Here, we present a systematic reduction of a 31-node Boolean model that describes cisplatin- and pemetrexed-resistance in non-small-cell lung cancer to a compact 9-node core that exactly reproduces the original attractor landscape. The streamlined network shrinks the state space by four orders of magnitude, enabling rapid exploration of cri…

This https://arxiv.org/abs/2501.07327 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Community Aware Temporal Network Generation
The advantages of temporal networks in capturing complex dynamics, such as diffusion and contagion, has led to breakthroughs in real world systems across numerous fields. In the case of human behavior, face-to-face interaction networks enable us to understand the dynamics of how communities emerge and evolve in time through the interactions, which is crucial in fields like epidemics, sociological studies and urban science. However, state-of-the-art datasets suffer from a number of drawbacks, su…

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
Pralay Kumar Das, Nayana Majumdar, Supratik Mukhopadhyay
https://arxiv.org/abs/2506.02506

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
A multi-class convolutional neural network (CNN) model has been developed using Keras deep learning library in Python for image-based classification of $^{12}$C Hoyle state decay branches from tracking information, recorded by Saha Active Target Time Projection Chamber, SAT-TPC (currently under development). The nuclear events, produced by the 30 MeV $α$-particle beam in the SAT-TPC, filled with Ar + CO$_2$ (90:10) gas mixture at atmospheric pressure, have been considered for training and vali…

Buy it Now, Track Me Later: Attacking User Privacy via Wi-Fi AP Online Auctions
Steven Su, Erik Rye, Robert Beverly, Dave Levin
https://arxiv.org/abs/2506.13052

Buy it Now, Track Me Later: Attacking User Privacy via Wi-Fi AP Online Auctions
Static and hard-coded layer-two network identifiers are well known to present security vulnerabilities and endanger user privacy. In this work, we introduce a new privacy attack against Wi-Fi access points listed on secondhand marketplaces. Specifically, we demonstrate the ability to remotely gather a large quantity of layer-two Wi-Fi identifiers by programmatically querying the eBay marketplace and applying state-of-the-art computer vision techniques to extract IEEE 802.11 BSSIDs from the sell…

Graph neural network model for the era of large atomistic models
Duo Zhang, Anyang Peng, Chun Cai, Wentao Li, Yuanchang Zhou, Jinzhe Zeng, Mingyu Guo, Chengqian Zhang, Bowen Li, Hong Jiang, Tong Zhu, Weile Jia, Linfeng Zhang, Han Wang
https://arxiv.org/abs/2506.01686

Graph neural network model for the era of large atomistic models
Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the development of large models, suggesting that their generalizability in downstream tasks consistently improves with increased model size, expanded training datasets, and larger computational budgets. In this study, we present DPA3, a multi-layer graph neural network f…

Safe Domains of Attraction for Discrete-Time Nonlinear Systems: Characterization and Verifiable Neural Network Estimation
Mohamed Serry, Haoyu Li, Ruikun Zhou, Huan Zhang, Jun Liu
https://arxiv.org/abs/2506.13961

Safe Domains of Attraction for Discrete-Time Nonlinear Systems: Characterization and Verifiable Neural Network Estimation
Analysis of nonlinear autonomous systems typically involves estimating domains of attraction, which have been a topic of extensive research interest for decades. Despite that, accurately estimating domains of attraction for nonlinear systems remains a challenging task, where existing methods are conservative or limited to low-dimensional systems. The estimation becomes even more challenging when accounting for state constraints. In this work, we propose a framework to accurately estimate safe (…

This https://arxiv.org/abs/2505.18300 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on pa…

Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
Hassan Mortada, Cyril Falcon, Yanis Kahil, Math\'eo Clavaud, Jean-Philippe Michel
https://arxiv.org/abs/2506.11639

Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
State estimation in stochastic dynamical systems with noisy measurements is a challenge. While the Kalman filter is optimal for linear systems with independent Gaussian white noise, real-world conditions often deviate from these assumptions, prompting the rise of data-driven filtering techniques. This paper introduces Recursive KalmanNet, a Kalman-filter-informed recurrent neural network designed for accurate state estimation with consistent error covariance quantification. Our approach propaga…

Mazzaroth: A High-Throughput DAG Consensus with State Root
Haohan Li
https://arxiv.org/abs/2506.01960 https://arxiv.org/pdf/2506.0196…

Mazzaroth: A High-Throughput DAG Consensus with State Root
Nakamoto Consensus achieves a decentralized ledger through a single-chain blockchain, assuming a maximum network delay, which limits block generation speed, resulting in low throughput. \cite{pg2018} (PG) enhances throughput using a blockDAG structure, but its probabilistic confirmation restricts smart contract applications. To address this, Mazzaroth proposes a Pow-based blockDAG consensus, employing a linear ordering algorithm to compute the \cite{eth} and achieve state finality, thereby supp…

Group Convolutional Neural Network Ground State of the Quantum Dimer Model
Ojasvi Sharma, Sandipan Manna, Prashant Shekhar Rao, G J Sreejith
https://arxiv.org/abs/2505.23728

Group Convolutional Neural Network Ground State of the Quantum Dimer Model
We estimate the ground state of the square lattice Quantum Dimer Model in a $\rm{p4m}$-symmetric Group Convolutional Neural Network (GCNN) representation and show that results in agreement with exact diagonalization (ED) and quantum Monte Carlo (QMC) can be obtained with a $\mathcal{L}=2$ layer network. In systems of linear size $L=8$ with Hilbert space dimension $3.1\times 10^8$, GCNN shows fidelity as high as $0.99999$ with ED. For $12\leq L\leq 32$, we find excellent agreement with QMC estim…

Diffusive Spreading Across Dynamic Mitochondrial Network Architectures
Keaton B. Holt, Lizzy Teryoshin, Elena F. Koslover
https://arxiv.org/abs/2506.05643 …

Diffusive Spreading Across Dynamic Mitochondrial Network Architectures
Networks of physical units can vary from a stationary set of spatially-embedded links to a collection of mobile agents that undergo transient social interactions. In living cells, mitochondria form architectures that span across these regimes, transitioning between fragmented, partly connected, and highly fused structures depending on cell type and state. Diffusive transport of biomolecular components through these networks helps to homogenize the mitochondrial population. Here we address the c…

Quantum secret sharing in a triangular superconducting quantum network
Haoxiong Yan, Allen Zang, Joel Grebel, Xuntao Wu, Ming-Han Chou, Gustav Andersson, Christopher R. Conner, Yash J. Joshi, Shiheng Li, Jacob M. Miller, Rhys G. Povey, Hong Qiao, Eric Chitambar, Andrew N. Cleland
https://arxiv.org/abs/2506.10878

Quantum secret sharing in a triangular superconducting quantum network
We present a three-node quantum communication testbed with a triangular topology, each side of the triangle formed by a 1.3-meter-long transmission line. We demonstrate state transfer and entanglement generation between any two nodes, generate genuine multipartite entangled GHZ states, and implement quantum secret sharing (QSS) of classical information. Our experiments show that the QSS protocol can effectively detect eavesdropping, ensuring the secure sharing of secrets. This device forms a te…

Deep Potential-Driven Molecular Dynamics of CO Ice Analogues: Investigating Desorption Following Vibrational Excitation
Maxime Infuso, Samuel Del Fr\'e, Gilberto A. Alou, Mathieu Bertin, Jean-Hugues Fillion, Alejandro Rivero Santamar\'ia, Maurice Monnerville
https://arxiv.org/abs/2506.10882…

Deep Potential-Driven Molecular Dynamics of CO Ice Analogues: Investigating Desorption Following Vibrational Excitation
We present a new deep learning-based machine learning potential (MLP) for molecular dynamics simulations of solid carbon monoxide (CO), capable of accurately describing CO vibrations both in the fundamental state and in highly excited vibrational states, up to approximately v = 40. The MLP is based on the combination of high-dimensional neural network atomic potentials using the DeePMD-kit package, trained on prior ab initio molecular dynamics (AIMD) data, with selective treatment of the excite…

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Yefeng Fan, Simon Richard White
https://arxiv.org/abs/2506.04558

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Exponential Random Graph Models (ERGMs) are an inferential model for analysing statistical networks. Recent development in ERGMs uses hierarchical Bayesian setup to jointly model a group of networks, which is called a multiple-network Exponential Random Graph Model (MN-ERGMs). MN-ERGM has been successfully applied on real-world resting-state fMRI data from the Cam-CAN project to infer the brain connectivity on aging. However, conventional Bayesian ERGM estimation approach is computationally int…

This https://arxiv.org/abs/2504.07932 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Fractional Chern Insulator and Quantum Anomalous Hall Crystal in Twisted MoTe$_2$
Recent experimental advances have uncovered fractional Chern insulators in twisted MoTe$_2$ (tMoTe$_2$) systems, posing significant theoretical challenges in understanding the interaction effects and correlated topological phases. Here, we construct a realistic moiré lattice model tailored for tMoTe$_2$ and conduct investigations using state-of-the-art tensor-network methods. Our ground-state calculations reveal a rich array of interaction- and filling-dependent phases, including the FCI, Cher…

This https://arxiv.org/abs/2503.07792 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Efficient Neural Clause-Selection Reinforcement
Clause selection is arguably the most important choice point in saturation-based theorem proving. Framing it as a reinforcement learning (RL) task is a way to challenge the human-designed heuristics of state-of-the-art provers and to instead automatically evolve -- just from prover experiences -- their potentially optimal replacement. In this work, we present a neural network architecture for scoring clauses for clause selection that is powerful yet efficient to evaluate. Following RL principle…

Solving excited states for long-range interacting trapped ions with neural networks
Yixuan Ma, Chang Liu, Weikang Li, Shun-Yao Zhang, L. -M. Duan, Yukai Wu, Dong-Ling Deng
https://arxiv.org/abs/2506.08594

Solving excited states for long-range interacting trapped ions with neural networks
The computation of excited states in strongly interacting quantum many-body systems is of fundamental importance. Yet, it is notoriously challenging due to the exponential scaling of the Hilbert space dimension with the system size. Here, we introduce a neural network-based algorithm that can simultaneously output multiple low-lying excited states of a quantum many-body spin system in an accurate and efficient fashion. This algorithm, dubbed the neural quantum excited-state (NQES) algorithm, re…

Efficient Optimization Accelerator Framework for Multistate Ising Problems
Chirag Garg, Sayeef Salahuddin
https://arxiv.org/abs/2505.20250 https://

Efficient Optimization Accelerator Framework for Multistate Ising Problems
Ising Machines are a prominent class of hardware architectures that aim to solve NP-hard combinatorial optimization problems. These machines consist of a network of interacting binary spins/neurons that evolve to represent the optimum ground state energy solution. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form to harness the computational efficiency of these Ising machines. However, this transformation, especially for multi-state p…

Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model
Weiqiang Zhu, Junhao Song, Haoyu Wang, Jannes M\"unchmeyer
https://arxiv.org/abs/2506.06939

Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model
Seismic waveforms contain rich information about earthquake processes, making effective data analysis crucial for earthquake monitoring, source characterization, and seismic hazard assessment. With rapid developments in deep learning, the state-of-the-art approach in artificial intelligence, many neural network models have been developed to enhance earthquake monitoring tasks, such as earthquake detection, phase picking, and phase association. However, most current efforts focus on developing s…

This https://arxiv.org/abs/2505.19953 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Interpretable Augmented Physics-Based Model for Estimation and Tracking
State-space estimation and tracking rely on accurate dynamical models to perform well. However, obtaining an vaccurate dynamical model for complex scenarios or adapting to changes in the system poses challenges to the estimation process. Recently, augmented physics-based models (APBMs) appear as an appealing strategy to cope with these challenges where the composition of a small and adaptive neural network with known physics-based models (PBM) is learned on the fly following an augmented state-…

This https://arxiv.org/abs/2503.22733 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
Neural Architecture Search (NAS) is an automated technique to design optimal neural network architectures for a specific workload. Conventionally, evaluating candidate networks in NAS involves extensive training, which requires significant time and computational resources. To address this, training-free NAS has been proposed to expedite network evaluation with minimal search time. However, state-of-the-art training-free NAS algorithms struggle to precisely distinguish well-performing networks f…

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

This https://arxiv.org/abs/2307.14984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents
Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$y…

Simplifying Root Cause Analysis in Kubernetes with StateGraph and LLM
Yong Xiang (Tsinghua University), Charley Peter Chen (Harmonic Inc), Liyi Zeng (Peng Cheng Laboratory), Wei Yin (Tsinghua University), Xin Liu (Tsinghua University), Hu Li (Unaffiliated), Wei Xu (Tsinghua University)
https://arxiv.org/abs/2506.02490

Simplifying Root Cause Analysis in Kubernetes with StateGraph and LLM
Kubernetes, a notably complex and distributed system, utilizes an array of controllers to uphold cluster management logic through state reconciliation. Nevertheless, maintaining state consistency presents significant challenges due to unexpected failures, network disruptions, and asynchronous issues, especially within dynamic cloud environments. These challenges result in operational disruptions and economic losses, underscoring the necessity for robust root cause analysis (RCA) to enhance Kube…

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

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

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

Experimental realization of all logic elements and memory latch in SC-CNN Chua's circuit
Ashokkumar P, Sathish Aravindh M, Venkatesan A, Lakshmanan M
https://arxiv.org/abs/2505.23303

Experimental realization of all logic elements and memory latch in SC-CNN Chua's circuit
The Chua's circuit is examined using a State Controlled-Cellular Neural Network (SC-CNN) framework with two logical square wave input signals. We illustrate, in particular, that this nonlinear circuit can generate all the basic logic operations, including OR/NOR, AND/NAND, and XOR/XNOR gates, by making use of the hopping of attractors which this circuit produces in different phase space regimes. Further, it is shown that besides two-inputs, the circuit emulates multi-input logic elements. Moreo…

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric Reinhardt, Talal Ahmed Chowdhury, Sergei Gleyzer, Paulo T Araujo
https://arxiv.org/abs/2506.01891

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Neural Quantum States (NQS) are a class of variational wave functions parametrized by neural networks (NNs) to study quantum many-body systems. In this work, we propose SineKAN, the NQS ansatz based on Kolmogorov-Arnold Networks (KANs), to represent quantum mechanical wave functions as nested univariate functions. We show that \sk wavefunction with learnable sinusoidal activation functions can capture the ground state energies, fidelities and various correlation functions of the 1D Transverse-F…

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

This https://arxiv.org/abs/2502.17826 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Feedback-Free Resource Scheduling for Flexible Multi-BS Cooperation in FD-RAN
Flexible cooperation among base stations (BSs) is critical to improve resource utilization efficiency and meet personalized user demands. However, its practical implementation is hindered by the current radio access network (RAN), which relies on the coupling of uplink and downlink transmissions and channel state information feedback with inherent issues such as overheads and delays. To overcome these limitations, we consider the fully-decoupled RAN (FD-RAN), in which uplink and downlink functi…

Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems
Yi-Qiang Wu, Xuan Liu, Hanlin Li, Fuqiang Wang
https://arxiv.org/abs/2505.24194 https://…

Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems
Physics-informed neural networks (PINN) have been widely used in computational physics to solve partial differential equations (PDEs). In this study, we propose an energy-embedding-based physics-informed neural network method for solving the one-dimensional time-independent Schrödinger equation to obtain ground- and excited-state wave functions, as well as energy eigenvalues by incorporating an embedding layer to generate process-driven data. The method demonstrates high accuracy for several w…

A double selection entanglement distillation-based state estimator
Joshua Carlo A. Casapao, Ananda G. Maity, Naphan Benchasattabuse, Michal Hajdu\v{s}ek, Akihito Soeda, Rodney Van Meter, David Elkouss
https://arxiv.org/abs/2505.24280

A double selection entanglement distillation-based state estimator
With the advent of practical quantum communication networks drawing closer, there is a growing need for reliable estimation protocols that can efficiently characterize quantum resources with minimum resource overhead requirement. A novel approach to this problem is to integrate an estimator into an existing network task, thereby removing the need for an additional characterization protocol. In this work, we show that the measurement statistics of a double selection distillation protocol alone c…

Coordinated Multi-BS SSB Beam Design for Enhanced Initial Access Coverage
Hakime BarghiZanjani, Bikshapathi Gouda, Antti T\"olli
https://arxiv.org/abs/2506.02760

Coordinated Multi-BS SSB Beam Design for Enhanced Initial Access Coverage
Ensuring strong synchronization signal block (SSB) coverage is essential for reliable user equipment (UE) connection during initial access. While techniques such as power boosting and network densification are commonly used, this work explores joint transmission (JT) of SSBs as an alternative to enhance coverage. Although JT is widely applied in data transmission, its use for SSBs has not been explored due to the lack of channel state information, which prevents coherent signal alignment across…