Tootfinder

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Zachary James, Joseph Guinness
https://arxiv.org/abs/2507.12262 https://

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Gaussian processes have become a popular tool for nonparametric regression because of their flexibility and uncertainty quantification. However, they often use stationary kernels, which limit the expressiveness of the model and may be unsuitable for many datasets. We propose a framework that uses nonstationary kernels whose parameters vary across the feature space, modeling these parameters as the output of a neural network that takes the features as input. The neural network and Gaussian proce…

SecONNds: Secure Outsourced Neural Network Inference on ImageNet
Shashank Balla
https://arxiv.org/abs/2506.11586 https://arxiv.org/pd…

SecONNds: Secure Outsourced Neural Network Inference on ImageNet
The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and communication costs, rendering them impractical for real-world deployment. We introduce SecONNds, a non-intrusive secure inference framework optimized for large ImageNet-scale Convolutional Neural Networks. …

ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications
Valentin Ackva, Fares Schulz
https://arxiv.org/abs/2506.12665 https://

ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications
Numerous tools for neural network inference are currently available, yet many do not meet the requirements of real-time audio applications. In response, we introduce anira, an efficient cross-platform library. To ensure compatibility with a broad range of neural network architectures and frameworks, anira supports ONNX Runtime, LibTorch, and TensorFlow Lite as backends. Each inference engine exhibits real-time violations, which anira mitigates by decoupling the inference from the audio callback…

Online-Optimized Gated Radial Basis Function Neural Network-Based Adaptive Control
Mingcong Li
https://arxiv.org/abs/2506.13168 https://

Online-Optimized Gated Radial Basis Function Neural Network-Based Adaptive Control
Real-time adaptive control of nonlinear systems with unknown dynamics and time-varying disturbances demands precise modeling and robust parameter adaptation. While existing neural network-based strategies struggle with computational inefficiency or inadequate temporal dependencies, this study proposes a hybrid control framework integrating a Temporal-Gated Radial Basis Function (TGRBF) network with a nonlinear robust controller. The TGRBF synergizes radial basis function neural networks (RBFNNs…

A Synthetic Pseudo-Autoencoder Invites Examination of Tacit Assumptions in Neural Network Design
Assaf Marron
https://arxiv.org/abs/2506.12076 https://

A Synthetic Pseudo-Autoencoder Invites Examination of Tacit Assumptions in Neural Network Design
We present a handcrafted neural network that, without training, solves the seemingly difficult problem of encoding an arbitrary set of integers into a single numerical variable, and then recovering the original elements. While using only standard neural network operations -- weighted sums with biases and identity activation -- we make design choices that challenge common notions in this area around representation, continuity of domains, computation, learnability and more. For example, our const…

"Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity."
"LLM users also struggled to accurately quote their own work."
"Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels."

Towards Unified Neural Decoding with Brain Functional Network Modeling
Di Wu, Linghao Bu, Yifei Jia, Lu Cao, Siyuan Li, Siyu Chen, Yueqian Zhou, Sheng Fan, Wenjie Ren, Dengchang Wu, Kang Wang, Yue Zhang, Yuehui Ma, Jie Yang, Mohamad Sawan
https://arxiv.org/abs/2506.12055

Towards Unified Neural Decoding with Brain Functional Network Modeling
Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that co…

macaque_neural: Macaque cortical connectivity (Young)
A network of cortical regions in the Macaque cortex.
This network has 47 nodes and 505 edges.
Tags: Biological, Connectome, Unweighted
https://networks.skewed.de/net/macaque_neural
Ridiculogram:

Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
Eugene Kofi Okrah Denteh, Andrews Danyo, Joshua Kofi Asamoah, Blessing Agyei Kyem, Armstrong Aboah
https://arxiv.org/abs/2506.12456

Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio-demographics, and travel behavior dynamics. The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data to accurately forecast future spatial transformations. The study also introduces a demographics prediction component which…

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta, Jovin D'sa, Faizan M. Tariq, David Isele, Sangjae Bae
https://arxiv.org/abs/2507.11940

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems from the AVs' limited ability to anticipate and respond to the interactive behavior of surrounding agents. Traditional decoupled prediction and planning pipelines rely on non-interactive predictions that overlook the fact that agents often adapt their behavior in response to the AV's actions. To address this, we propose …

Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention
Yu-Chao Hsu, Kuan-Cheng Chen, Tai-Yue Li, Nan-Yow Chen
https://arxiv.org/abs/2507.11217

Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention
In this work, we introduce the Quantum Adaptive Excitation Network (QAE-Net), a hybrid quantum-classical framework designed to enhance channel attention mechanisms in Convolutional Neural Networks (CNNs). QAE-Net replaces the classical excitation block of Squeeze-and-Excitation modules with a shallow Variational Quantum Circuit (VQC), leveraging quantum superposition and entanglement to capture higher-order inter-channel dependencies that are challenging to model with purely classical approache…

Neural Network-Augmented Pfaffian Wave-functions for Scalable Simulations of Interacting Fermions
Ao Chen, Zhou-Quan Wan, Anirvan Sengupta, Antoine Georges, Christopher Roth
https://arxiv.org/abs/2507.10705

Neural Network-Augmented Pfaffian Wave-functions for Scalable Simulations of Interacting Fermions
Developing accurate numerical methods for strongly interacting fermions is crucial for improving our understanding of various quantum many-body phenomena, especially unconventional superconductivity. Recently, neural quantum states have emerged as a promising approach for studying correlated fermions, highlighted by the hidden fermion and backflow methods, which use neural networks to model corrections to fermionic quasiparticle orbitals. In this work, we expand these ideas to the space of Pfaf…

Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models
Eunchi Kim, Paula Hartnagel, Barbara Urbano, Leonard Christen, Thomas Kirchartz
https://arxiv.org/abs/2506.13308

Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models
Machine learning has emerged as a promising approach for estimating material parameters in solar cells. Traditional methods for parameter extraction often rely on time-consuming numerical simulations that fail to capture the full complexity of the parameter space and discard valuable information from suboptimal simulations. In this study, we introduce a novel workflow for parameter estimation in organic solar cells based on a combination of numerical simulations and neural networks. The workflo…

Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking
Yuan Yao, Jin Song, Jian Jin
https://arxiv.org/abs/2507.11137

Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking
As valuable digital assets, deep neural networks necessitate robust ownership protection, positioning neural network watermarking (NNW) as a promising solution. Among various NNW approaches, weight-based methods are favored for their simplicity and practicality; however, they remain vulnerable to forging and overwriting attacks. To address those challenges, we propose NeuralMark, a robust method built around a hashed watermark filter. Specifically, we utilize a hash function to generate an irre…

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network
Shin-ichi Ito
https://arxiv.org/abs/2507.11799 ht…

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network
This paper presents a neural network (NN)-based solver for an integro-differential equation that models shrinkage-induced fragmentation. The proposed method directly maps input parameters to the corresponding probability density function without numerically solving the governing equation, thereby significantly reducing computational costs. Specifically, it enables efficient evaluation of the density function in Monte Carlo simulations while maintaining accuracy comparable to or even exceeding t…

Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino
https://arxiv.org/abs/2506.11319

Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices
This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained Internet of Things (IoT) and edge devices. Thanks to HW-NAS, a 1D convolutional neural network (CNN) is tailored on the ISCX VPN-nonVPN dataset to meet strict memory and computational limits while achieving robust performance. The optimized model attains an accuracy…

Directed Acyclic Graph Convolutional Networks
Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
https://arxiv.org/abs/2506.12218 https://arxi…

Directed Acyclic Graph Convolutional Networks
Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architecture search. In this work, we introduce the DAG Convolutional Network (DCN), a novel graph neural network (GNN) architecture designed specifically for convolutional learning from signals supported on DAGs. The DCN leverages causal graph filters to learn nodal representations that account for the partial ordering inherent to DAGs, a strong inductive bias d…

Enhancements to the IceCube Extremely High Energy Neutrino Selection using Graph & Transformer Based Neural Networks
Maxwell Nakos (for the IceCube Collaboration), Aske Rosted (for the IceCube Collaboration), Lu Lu (for the IceCube Collaboration)
https://arxiv.org/abs/2507.11774

Enhancements to the IceCube Extremely High Energy Neutrino Selection using Graph & Transformer Based Neural Networks
KM3NeT has recently reported the detection of a very high-energy neutrino event, while IceCube has previously set upper limits on the differential neutrino flux above 100 PeV but has yet to observe a neutrino event with an energy comparable to that of the KM3NeT detection. To improve diffuse measurements above 10 PeV, we apply machine learning techniques to enhance atmospheric muon background rejection and directional reconstruction. We utilize a Graph Neural Network (GNN) to perform a classifi…

From Redshift to Real Space: Combining Linear Theory With Neural Networks
Edoardo Maragliano, Punyakoti Ganeshaiah Veena, Giulia Degni, Enzo Franco Branchini
https://arxiv.org/abs/2507.11462

From Redshift to Real Space: Combining Linear Theory With Neural Networks
Spectroscopic redshift surveys are key tools to trace the large-scale structure (LSS) of the Universe and test the $Λ$CDM model. However, using redshifts as distance proxies introduces distortions in the 3D galaxy distribution. If uncorrected, these distortions lead to systematic errors in LSS analyses and cosmological parameter estimation. We present a new method that combines linear theory (LT) and a neural network (NN) to mitigate redshift space distortions (RSDs). The hybrid LT+NN approach…

How does the #brain transfer #MotorSkills between hands?
This study reveals that transfer relies on re-expressing the neural patterns established during initial learning in distributed higher-order brain areas,
offering new insights into learning

Transfer of motor learning is associated with patterns of activity in the default mode network
How does the brain transfer motor skills between hands? This study reveals that transfer relies on re-expressing the neural patterns established during initial learning in distributed higher-order brain areas, offering new insights into learning generalization.

Catching Bid-rigging Cartels with Graph Attention Neural Networks
David Imhof, Emanuel W Viklund, Martin Huber
https://arxiv.org/abs/2507.12369 https://arx…

Catching Bid-rigging Cartels with Graph Attention Neural Networks
We propose a novel application of graph attention networks (GATs), a type of graph neural network enhanced with attention mechanisms, to develop a deep learning algorithm for detecting collusive behavior, leveraging predictive features suggested in prior research. We test our approach on a large dataset covering 13 markets across seven countries. Our results show that predictive models based on GATs, trained on a subset of the markets, can be effectively transferred to other markets, achieving …

Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts
Yeming Xian, Xiaoming Wang, Yanfa Yan
https://arxiv.org/abs/2507.12404

Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts
Understanding and predicting the activity of oxide perovskite catalysts for the oxygen evolution reaction (OER) requires descriptors that are both accurate and physically interpretable. While symbolic regression (SR) offers a path to discover such formulas, its performance degrades with high-dimensional inputs and small datasets. We present a two-phase framework that combines neural networks (NN), feature importance analysis, and symbolic regression (SR) to discover interpretable descriptors fo…

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach
Thomas Feys, Liesbet Van der Perre, Fran\c{c}ois Rottenberg
https://arxiv.org/abs/2507.10634

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach
Massive MIMO systems are moving toward increased numbers of radio frequency chains, higher carrier frequencies and larger bandwidths. As such, digital-to-analog converters (DACs) are becoming a bottleneck in terms of hardware complexity and power consumption. In this work, non-linear precoding for coarsely quantized downlink massive MIMO is studied. Given the NP-hard nature of this problem, a graph neural network (GNN) is proposed that directly outputs the precoded quantized vector based on the…

Researchers detail All-Topographic Neural Networks, claiming they better mimic human spatial biases and consume less energy than other machine vision networks (Matthew S. Smith/IEEE Spectrum)
https://spectrum.ieee.org/topographic-neural-network

Topographic Neural Networks Help AI See Like a Human
All-TNN networks mimic human vision and consume less energy than CNNs. Curious how they achieve this? Dive into the details.

Improving Neural Pitch Estimation with SWIPE Kernels
David Marttila, Joshua D. Reiss
https://arxiv.org/abs/2507.11233 https://arxiv.o…

Improving Neural Pitch Estimation with SWIPE Kernels
Neural networks have become the dominant technique for accurate pitch and periodicity estimation. Although a lot of research has gone into improving network architectures and training paradigms, most approaches operate directly on the raw audio waveform or on general-purpose time-frequency representations. We investigate the use of Sawtooth-Inspired Pitch Estimation (SWIPE) kernels as an audio frontend and find that these hand-crafted, task-specific features can make neural pitch estimators mor…

Multiscale transform based seismic reflectivity inversion using convolutional neural network
John Castagna (University of Houston), Oleg Portniaguine (Lumina Geophysical, Houston, Tx), Gabriel Gil (Lumina Geophysical, Houston, Tx), Arnold Oyem (Lumina Geophysical, Houston, Tx), Chen Liang (Lumina Geophysical, Houston, Tx)
https://

Multiscale transform based seismic reflectivity inversion using convolutional neural network
The Multiscale Fourier Transform of a seismic trace performs time-frequency analyses over a range of window lengths. The variation in window length captures local and global relative amplitudes between events, thereby allowing reflectivity inversion that is independent of the amplitude spectrum of the seismic wavelet. As the temporal and spatial variation of the actual seismic wavelet in seismic reflection data is poorly known, this approach has many advantages over conventional seismic reflect…

HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD
Hanwen Liu, Yuhe Huang, Yifeng Gong, Yanjie Zhai, Jiaxuan Lu
https://arxiv.org/abs/2507.12133

HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD
Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fingerprint Identification (RFFI) offers a non-cryptographic solution by exploiting hardware-induced signal distortions. This paper proposes HyDRA, a Hybrid Dual-mode RF Architecture that integrates an optimized Variational Mode Decomposition (VMD) with a novel architecture based on the fusion of Convolutional Neural Networks (CNNs), Transformers, and Ma…

DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
Hayat Ullah, Muhammad Ali Shafique, Abbas Khan, Arslan Munir
https://arxiv.org/abs/2507.12426

DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
The landscape of video recognition has evolved significantly, shifting from traditional Convolutional Neural Networks (CNNs) to Transformer-based architectures for improved accuracy. While 3D CNNs have been effective at capturing spatiotemporal dynamics, recent Transformer models leverage self-attention to model long-range spatial and temporal dependencies. Despite achieving state-of-the-art performance on major benchmarks, Transformers remain computationally expensive, particularly with dense …

Viscosity, breakdown of Stokes-Einstein relation and dynamical heterogeneity in supercooled liquid Ge 2 Sb 2 Te 5 from simulations with a neural network potential
Simone Marcorini, Rocco Pomodoro, Omar Abou El Kheir, Marco Bernasconi
https://arxiv.org/abs/2506.13668

Viscosity, breakdown of Stokes-Einstein relation and dynamical heterogeneity in supercooled liquid Ge 2 Sb 2 Te 5 from simulations with a neural network potential
Phase change materials are exploited in non-volatile electronic memories and photonic devices that rely on a fast and reversible transformation between the amorphous and crystalline phase upon heating. The recrystallization of the amorphous phase at the operation conditions of the memories occurs in the supercooled liquid phase above the glass transition temperature $T_g$. The dynamics of the supercooled liquid is thus of great relevance for the operation of the devices and, close to $T_g$, als…

Memorisation and forgetting in a learning Hopfield neural network: bifurcation mechanisms, attractors and basins
Adam E. Essex (Loughborough University, England), Natalia B. Janson (Loughborough University, England), Rachel A. Norris (Loughborough University, England), Alexander G. Balanov (Loughborough University, England)
https://arxiv.o…

Memorisation and forgetting in a learning Hopfield neural network: bifurcation mechanisms, attractors and basins
Despite explosive expansion of artificial intelligence based on artificial neural networks (ANNs), these are employed as "black boxes'', as it is unclear how, during learning, they form memories or develop unwanted features, including spurious memories and catastrophic forgetting. Much research is available on isolated aspects of learning ANNs, but due to their high dimensionality and non-linearity, their comprehensive analysis remains a challenge. In ANNs, knowledge is thought to reside in con…

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
https://arxiv.org/abs/2506.11753

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also…

Genericity of Polyak-Lojasiewicz Inequalities for Entropic Mean-Field Neural ODEs
Samuel Daudin, Fran\c{c}ois Delarue
https://arxiv.org/abs/2507.08486 http…

Genericity of Polyak-Lojasiewicz Inequalities for Entropic Mean-Field Neural ODEs
We address the behavior of idealized deep residual neural networks (ResNets), modeled via an optimal control problem set over continuity (or adjoint transport) equations. The continuity equations describe the statistical evolution of the features in the asymptotic regime where the layers of the network form a continuum. The velocity field is expressed through the network activation function, which is itself viewed as a function of the statistical distribution of the network parameters (weights …

EP-GAT: Energy-based Parallel Graph Attention Neural Network for Stock Trend Classification
Zhuodong Jiang, Pengju Zhang, Peter Martin
https://arxiv.org/abs/2507.08184

EP-GAT: Energy-based Parallel Graph Attention Neural Network for Stock Trend Classification
Graph neural networks have shown remarkable performance in forecasting stock movements, which arises from learning complex inter-dependencies between stocks and intra-dynamics of stocks. Existing approaches based on graph neural networks typically rely on static or manually defined factors to model changing inter-dependencies between stocks. Furthermore, these works often struggle to preserve hierarchical features within stocks. To bridge these gaps, this work presents the Energy-based Parallel…

Device-Cloud Collaborative Correction for On-Device Recommendation
Tianyu Zhan, Shengyu Zhang, Zheqi Lv, Jieming Zhu, Jiwei Li, Fan Wu, Fei Wu
https://arxiv.org/abs/2506.12687

Device-Cloud Collaborative Correction for On-Device Recommendation
With the rapid development of recommendation models and device computing power, device-based recommendation has become an important research area due to its better real-time performance and privacy protection. Previously, Transformer-based sequential recommendation models have been widely applied in this field because they outperform Recurrent Neural Network (RNN)-based recommendation models in terms of performance. However, as the length of interaction sequences increases, Transformer-based mo…

Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity
Wanjin Feng, Xingyu Gao, Wenqian Du, Hailong Shi, Peilin Zhao, Pengcheng Wu, Chunyan Miao
https://arxiv.org/abs/2506.12087

Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity
Spiking Neural Networks (SNNs) often suffer from high time complexity $O(T)$ due to the sequential processing of $T$ spikes, making training computationally expensive. In this paper, we propose a novel Fixed-point Parallel Training (FPT) method to accelerate SNN training without modifying the network architecture or introducing additional assumptions. FPT reduces the time complexity to $O(K)$, where $K$ is a small constant (usually $K=3$), by using a fixed-point iteration form of Leaky Inte…

Symmetry-preserving neural networks in lattice field theories
Matteo Favoni
https://arxiv.org/abs/2506.12493 https://arxiv.org/pdf/25…

Symmetry-preserving neural networks in lattice field theories
This thesis deals with neural networks that respect symmetries and presents the advantages in applying them to lattice field theory problems. The concept of equivariance is explained, together with the reason why such a property is crucial for the network to preserve the desired symmetry. The benefits of choosing equivariant networks are first illustrated for translational symmetry on a complex scalar field toy model. The discussion is then extended to gauge theories, for which Lattice Gauge Eq…

Bio-Inspired Artificial Neural Networks based on Predictive Coding
Davide Casnici, Charlotte Frenkel, Justin Dauwels
https://arxiv.org/abs/2508.08762 https://

Bio-Inspired Artificial Neural Networks based on Predictive Coding
Backpropagation (BP) of errors is the backbone training algorithm for artificial neural networks (ANNs). It updates network weights through gradient descent to minimize a loss function representing the mismatch between predictions and desired outputs. BP uses the chain rule to propagate the loss gradient backward through the network hierarchy, allowing efficient weight updates. However, this process requires weight updates at every layer to rely on a global error signal generated at the network…

Emulating CO Line Radiative Transfer with Deep Learning
Shiqi Su, Frederik De Ceuster, Jaehoon Cha, Mark I. Wilkinson, Jeyan Thiyagalingam, Jeremy Yates, Yi-Hang Zhu, Jan Bolte
https://arxiv.org/abs/2507.11398

Emulating CO Line Radiative Transfer with Deep Learning
Modelling carbon monoxide (CO) line radiation is computationally expensive for traditional numerical solvers, especially when applied to complex, three-dimensional stellar atmospheres. We present COEmuNet, a 3D convolutional neural network (CNN)-based surrogate model that emulates CO line radiation transport with high accuracy and efficiency. It consists of an asymmetric encoder-decoder design that takes 3D hydrodynamical models as inputs and generates synthetic observations of evolved stellar …

Machine Intelligence on Wireless Edge Networks
Sri Krishna Vadlamani, Kfir Sulimany, Zhihui Gao, Tingjun Chen, Dirk Englund
https://arxiv.org/abs/2506.12210

Machine Intelligence on Wireless Edge Networks
Deep neural network (DNN) inference on power-constrained edge devices is bottlenecked by costly weight storage and data movement. We introduce MIWEN, a radio-frequency (RF) analog architecture that ``disaggregates'' memory by streaming weights wirelessly and performing classification in the analog front end of standard transceivers. By encoding weights and activations onto RF carriers and using native mixers as computation units, MIWEN eliminates local weight memory and the overhead of analog-t…

Physics-informed Multiresolution Wavelet Neural Network Method for Solving Partial Differential Equations
Feng Han, Jianguo Wang, Guoliang Peng, Xueting Shi
https://arxiv.org/abs/2508.07546

Physics-informed Multiresolution Wavelet Neural Network Method for Solving Partial Differential Equations
In this paper, a physics-informed multiresolution wavelet neural network (PIMWNN) method is proposed for solving partial differential equations (PDEs). This method uses the multiresolution wavelet neural network (MWNN) to approximate unknown functions, then substituting the MWNN into PDEs and training the MWNN by least-squares algorithm. We apply the proposed method to various problems, including stationary/nonstationary advection, diffusion and advection-diffusion problems, and linear/nonlinea…

Continuous-time parametrization of neural quantum states for quantum dynamics
Dingzu Wang, Wenxuan Zhang, Xiansong Xu, Dario Poletti
https://arxiv.org/abs/2507.08418

Continuous-time parametrization of neural quantum states for quantum dynamics
Neural quantum states are a promising framework for simulating many-body quantum dynamics, as they can represent states with volume-law entanglement. As time evolves, the neural network parameters are typically optimized at discrete time steps to approximate the wave function at each point in time. Given the differentiability of the wave function stemming from the Schrödinger equation, here we impose a time-continuous and differentiable parameterization of the neural network by expressing its …

New machine vision is more energy efficient - and more human #AI vision

Topographic Neural Networks Help AI See Like a Human
All-TNN networks mimic human vision and consume less energy than CNNs. Curious how they achieve this? Dive into the details.

Online Training and Pruning of Deep Reinforcement Learning Networks
Valentin Frank Ingmar Guenter, Athanasios Sideris
https://arxiv.org/abs/2507.11975 http…

Online Training and Pruning of Deep Reinforcement Learning Networks
Scaling deep neural networks (NN) of reinforcement learning (RL) algorithms has been shown to enhance performance when feature extraction networks are used but the gained performance comes at the significant expense of increased computational and memory complexity. Neural network pruning methods have successfully addressed this challenge in supervised learning. However, their application to RL is underexplored. We propose an approach to integrate simultaneous training and pruning within advance…

A Multimodal Neural Network for Recognizing Subjective Self-Disclosure Towards Social Robots
Henry Powell, Guy Laban, Emily S. Cross
https://arxiv.org/abs/2508.10828 https://

A Multimodal Neural Network for Recognizing Subjective Self-Disclosure Towards Social Robots
Subjective self-disclosure is an important feature of human social interaction. While much has been done in the social and behavioural literature to characterise the features and consequences of subjective self-disclosure, little work has been done thus far to develop computational systems that are able to accurately model it. Even less work has been done that attempts to model specifically how human interactants self-disclose with robotic partners. It is becoming more pressing as we require so…

Neural Co-state Regulator: A Data-Driven Paradigm for Real-time Optimal Control with Input Constraints
Lihan Lian, Yuxin Tong, Uduak Inyang-Udoh
https://arxiv.org/abs/2507.12259

Neural Co-state Regulator: A Data-Driven Paradigm for Real-time Optimal Control with Input Constraints
We propose a novel unsupervised learning framework for solving nonlinear optimal control problems (OCPs) with input constraints in real-time. In this framework, a neural network (NN) learns to predict the optimal co-state trajectory that minimizes the control Hamiltonian for a given system, at any system's state, based on the Pontryagin's Minimum Principle (PMP). Specifically, the NN is trained to find the norm-optimal co-state solution that simultaneously satisfies the nonlinear system dynamic…

Dynamical Alignment: A Principle for Adaptive Neural Computation
Xia Chen
https://arxiv.org/abs/2508.10064 https://arxiv.org/pdf/2508.10064

Dynamical Alignment: A Principle for Adaptive Neural Computation
The computational capabilities of a neural network are widely assumed to be determined by its static architecture. Here we challenge this view by establishing that a fixed neural structure can operate in fundamentally different computational modes, driven not by its structure but by the temporal dynamics of its input signals. We term this principle 'Dynamical Alignment'. Applying this principle offers a novel resolution to the long-standing paradox of why brain-inspired spiking neural network…

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
Zhonghao Zhan, Huichi Zhou, Hamed Haddadi
https://arxiv.org/abs/2507.10836

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
Graph Neural Network (GNN)-based network intrusion detection systems (NIDS) are often evaluated on single datasets, limiting their ability to generalize under distribution drift. Furthermore, their adversarial robustness is typically assessed using synthetic perturbations that lack realism. This measurement gap leads to an overestimation of GNN-based NIDS resilience. To address the limitations, we propose \textbf{REAL-IoT}, a comprehensive framework for robustness evaluation of GNN-based NIDS i…

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- A deep neural network approach to solve the Dirac equation
Chuanxin Wang, Tomoya Naito, Jian Li, Haozhao Liang

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology
Gibaek Kim, Jungho Kim
https://arxiv.org/abs/2506.10418

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology
We propose a neural network(NN)-based surrogate modeling framework for photonic device optimization, especially in domains with imbalanced feature importance and high data generation costs. Our framework, which comprises physics-based transfer learning (PBTL)-enhanced surrogate modeling and scalarized multi-objective genetic algorithms (GAs), offers a generalizable solution for photonic design automation with minimal data resources.To validate the framework, we optimize mid-infrared quantum cas…

Deep Spatial Neural Net Models with Functional Predictors: Application in Large-Scale Crop Yield Prediction
Yeonjoo Park, Bo Li, Yehua Li
https://arxiv.org/abs/2506.13017

Deep Spatial Neural Net Models with Functional Predictors: Application in Large-Scale Crop Yield Prediction
Accurate prediction of crop yield is critical for supporting food security, agricultural planning, and economic decision-making. However, yield forecasting remains a significant challenge due to the complex and nonlinear relationships between weather variables and crop production, as well as spatial heterogeneity across agricultural regions. We propose DSNet, a deep neural network architecture that integrates functional and scalar predictors with spatially varying coefficients and spatial rando…

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

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

macaque_neural: Macaque cortical connectivity (Young)
A network of cortical regions in the Macaque cortex.
This network has 47 nodes and 505 edges.
Tags: Biological, Connectome, Unweighted
https://networks.skewed.de/net/macaque_neural
Ridiculogram:

Deep learning inference with the #EventHorizonTelescope I. Calibration improvements and a comprehensive synthetic data library / II. The ZINGULARITY framework for Bayesian artificial neural networks / III. ZINGULARITY results from the 2017 observations and predictions for future array expansions: https://www.aanda.org/articles/aa/full_html/2025/06/aa53784-25/aa53784-25.html / https://www.aanda.org/articles/aa/full_html/2025/06/aa53785-25/aa53785-25.html / https://www.aanda.org/articles/aa/full_html/2025/06/aa53786-25/aa53786-25.html -> Self-learning neural network cracks iconic black holes: https://www.astronomie.nl/nieuws/en/self-learning-neural-network-cracks-iconic-black-holes-4528

Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems
Doan Hieu Nguyen, Xuan Tung Nguyen, Seon-Geun Jeong, Trinh Van Chien, Lajos Hanzo, Won Joo Hwang
https://arxiv.org/abs/2507.06585

Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems
A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous …

Explainable AI Technique in Lung Cancer Detection Using Convolutional Neural Networks
Nishan Rai, Sujan Khatri, Devendra Risal
https://arxiv.org/abs/2508.10196 https://

Explainable AI Technique in Lung Cancer Detection Using Convolutional Neural Networks
Early detection of lung cancer is critical to improving survival outcomes. We present a deep learning framework for automated lung cancer screening from chest computed tomography (CT) images with integrated explainability. Using the IQ-OTH/NCCD dataset (1,197 scans across Normal, Benign, and Malignant classes), we evaluate a custom convolutional neural network (CNN) and three fine-tuned transfer learning backbones: DenseNet121, ResNet152, and VGG19. Models are trained with cost-sensitive learni…

A Framework to Pinpoint Bottlenecks in Emerging Solar Cells and Disordered Devices via Differential Machine Learning
Cai Williams, Chen Wang, Alexander Ehm, Dietrich R. T. Zahn, Maria Saladina, Carsten Deibel, Roderick C. I. Mackenzie
https://arxiv.org/abs/2507.11740

A Framework to Pinpoint Bottlenecks in Emerging Solar Cells and Disordered Devices via Differential Machine Learning
A key challenge in the development of materials for the next generation of solar cells, sensors and transistors is linking macroscopic device performance to underlying microscopic properties. For years, fabrication of devices has been faster than our ability to characterize them. This has led to a random walk of material development, with new materials being proposed faster than our understanding. We present two neural network-based methods for extracting key material parameters, including char…

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Morteza Ziabakhsh, Kiyan Rezaee, Sadegh Eskandari, Seyed Amir Hossein Tabatabaei, Mohammad M. Ghassemi
https://arxiv.org/abs/2508.07486

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Modern software systems are increasingly shifting from monolithic architectures to microservices to enhance scalability, maintainability, and deployment flexibility. Existing microservice extraction methods typically rely on hard clustering, assigning each software component to a single microservice. This approach often increases inter-service coupling and reduces intra-service cohesion. We propose Mo2oM (Monolithic to Overlapping Microservices), a framework that formulates microservice extract…

Physics-Informed Linear Model (PILM): Analytical Representations and Application to Crustal Strain Rate Estimation
Tomohisa Okazaki
https://arxiv.org/abs/2507.12218

Physics-Informed Linear Model (PILM): Analytical Representations and Application to Crustal Strain Rate Estimation
Many physical systems are described by partial differential equations (PDEs), and solving these equations and estimating their coefficients or boundary conditions (BCs) from observational data play a crucial role in understanding the associated phenomena. Recently, a machine learning approach known as physics-informed neural network, which solves PDEs using neural networks by minimizing the sum of residuals from the PDEs, BCs, and data, has gained significant attention in the scientific communi…

GroupNL: Low-Resource and Robust CNN Design over Cloud and Device
Chuntao Ding, Jianhang Xie, Junna Zhang, Salman Raza, Shangguang Wang, Jiannong Cao
https://arxiv.org/abs/2506.12335

GroupNL: Low-Resource and Robust CNN Design over Cloud and Device
It has become mainstream to deploy Convolutional Neural Network (CNN) models on ubiquitous Internet of Things (IoT) devices with the help of the cloud to provide users with a variety of high-quality services. Most existing methods have two limitations: (i) low robustness in handling corrupted image data collected by IoT devices; and (ii) high consumption of computational and transmission resources. To this end, we propose the Grouped NonLinear transformation generation method (GroupNL), which g…

Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
Siyu Yu, Zihan Qin, Tingshan Liu, Beiya Xu, R. Jacob Vogelstein, Jason Brown, Joshua T. Vogelstein
https://arxiv.org/abs/2507.10951

Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
The complete connectome of the Drosophila larva brain offers a unique opportunity to investigate whether biologically evolved circuits can support artificial intelligence. We convert this wiring diagram into a Biological Processing Unit (BPU), a fixed recurrent network derived directly from synaptic connectivity. Despite its modest size 3,000 neurons and 65,000 weights between them), the unmodified BPU achieves 98% accuracy on MNIST and 58% on CIFAR-10, surpassing size-matched MLPs. Scaling the…

CKFNet: Neural Network Aided Cubature Kalman filtering
Jinhui Hu, Haiquan Zhao, Yi Peng
https://arxiv.org/abs/2508.09727 https://arxiv.org/pdf/2508.09727…

CKFNet: Neural Network Aided Cubature Kalman filtering
The cubature Kalman filter (CKF), while theoretically rigorous for nonlinear estimation, often suffers performance degradation due to model-environment mismatches in practice. To address this limitation, we propose CKFNet-a hybrid architecture that synergistically integrates recurrent neural networks (RNN) with the CKF framework while preserving its cubature principles. Unlike conventional model-driven approaches, CKFNet embeds RNN modules in the prediction phase to dynamically adapt to unmodel…

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
Wei Shan Lee, I Hang Kwok, Kam Ian Leong, Chi Kiu Althina Chau, Kei Chon Sio
https://arxiv.org/abs/2508.10718

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
Accurate prediction of electronic band structures in two-dimensional materials remains a fundamental challenge, with existing methods struggling to balance computational efficiency and physical accuracy. We present the Symmetry-Constrained Multi-Scale Physics-Informed Neural Network (SCMS-PINN) v35, which directly learns graphene band structures while rigorously enforcing crystallographic symmetries through a multi-head architecture. Our approach introduces three specialized ResNet-6 pathways -…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Preston G. Waldrop, Dimitrios Psaltis, Tong Zhao
https://arxiv.org/abs/2506.11194 http…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Ray tracing algorithms that compute pulse profiles from rotating neutron stars are essential tools for constraining neutron-star properties with data from missions such as NICER. However, the high computational cost of these simulations presents a significant bottleneck for inference algorithms that require millions of evaluations, such as Markov Chain Monte Carlo methods. In this work, we develop a residual neural network model that accelerates this calculation by predicting the observed flux …

Generalised Rate Control Approach For Stream Processing Applications
Ziren Xiao
https://arxiv.org/abs/2506.11710 https://arxiv.org/pd…

Generalised Rate Control Approach For Stream Processing Applications
Distributed stream processing systems are widely deployed to process real-time data generated by various devices, such as sensors and software systems. A key challenge in the system is overloading, which leads to an unstable system status and consumes additional system resources. In this paper, we use a graph neural network-based deep reinforcement learning to collaboratively control the data emission rate at which the data is generated in the stream source to proactively avoid overloading scen…

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…

Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems
Varsha Sen, Biswash Basnet
https://arxiv.org/abs/2508.10035 https:…

Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems
False Data Injection Attacks (FDIAs) pose a significant threat to smart grid infrastructures, particularly Home Area Networks (HANs), where real-time monitoring and control are highly adopted. Owing to the comparatively less stringent security controls and widespread availability of HANs, attackers view them as an attractive entry point to manipulate aggregated demand patterns, which can ultimately propagate and disrupt broader grid operations. These attacks undermine the integrity of smart met…

Sum-of-Gaussians tensor neural networks for high-dimensional Schr\"odinger equation
Qi Zhou, Teng Wu, Jianghao Liu, Qingyuan Sun, Hehu Xie, Zhenli Xu
https://arxiv.org/abs/2508.10454

Sum-of-Gaussians tensor neural networks for high-dimensional Schrödinger equation
We propose an accurate, efficient, and low-memory sum-of-Gaussians tensor neural network (SOG-TNN) algorithm for solving the high-dimensional Schrödinger equation. The SOG-TNN utilizes a low-rank tensor product representation of the solution to overcome the curse of dimensionality associated with high-dimensional integration. To handle the Coulomb interaction, we introduce an SOG decomposition to approximate the interaction kernel such that it is dimensionally separable, leading to a tensor re…

A discontinuous Galerkin plane wave neural network method for Helmholtz equation and Maxwell's equations
Long Yuan, Menghui Wu, Qiya Hu
https://arxiv.org/abs/2506.09309

A discontinuous Galerkin plane wave neural network method for Helmholtz equation and Maxwell's equations
In this paper we propose a discontinuous Galerkin plane wave neural network (DGPWNN) method for approximately solving Helmholtz equation and Maxwell's equations. In this method, we define an elliptic-type variational problem as in the plane wave least square method with $h-$refinement and introduce the adaptive construction of recursively augmented discontinuous Galerkin subspaces whose basis functions are realizations of element-wise neural network functions with $hp-$refinement, where the act…

I$^2$S-TFCKD: Intra-Inter Set Knowledge Distillation with Time-Frequency Calibration for Speech Enhancement
Jiaming Cheng, Ruiyu Liang, Chao Xu, Ye Ni, Wei Zhou, Bj\"orn W. Schuller, Xiaoshuai Hao
https://arxiv.org/abs/2506.13127

I$^2$S-TFCKD: Intra-Inter Set Knowledge Distillation with Time-Frequency Calibration for Speech Enhancement
In recent years, complexity compression of neural network (NN)-based speech enhancement (SE) models has gradually attracted the attention of researchers, especially in scenarios with limited hardware resources or strict latency requirements. The main difficulties and challenges lie in achieving a balance between complexity and performance according to the characteristics of the task. In this paper, we propose an intra-inter set knowledge distillation (KD) framework with time-frequency calibrati…

Selective Quantization Tuning for ONNX Models
Nikolaos Louloudakis, Ajitha Rajan
https://arxiv.org/abs/2507.12196 https://arxiv.org/p…

Selective Quantization Tuning for ONNX Models
Quantization is a process that reduces the precision of deep neural network models to lower model size and computational demands, often at the cost of accuracy. However, fully quantized models may exhibit sub-optimal performance below acceptable levels and face deployment challenges on low-end hardware accelerators due to practical constraints. To address these issues, quantization can be selectively applied to only a subset of layers, but selecting which layers to exclude is non-trivial. To th…

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…

HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
Matteo Bagagli, Francesco Grigoli, Davide Bacciu
https://arxiv.org/abs/2507.10850 …

HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
In this work, we present a new deep-learning model for microseismicity monitoring that utilizes continuous spatiotemporal relationships between seismic station recordings, forming an end-to-end pipeline for seismic catalog creation. It employs graph theory and state-of-the-art graph neural network architectures to perform phase picking, association, and event location simultaneously over rolling windows, making it suitable for both playback and near-real-time monitoring. As part of the global s…

COLI: A Hierarchical Efficient Compressor for Large Images
Haoran Wang, Hanyu Pei, Yang Lyu, Kai Zhang, Li Li, Feng-Lei Fan
https://arxiv.org/abs/2507.11443

COLI: A Hierarchical Efficient Compressor for Large Images
The escalating adoption of high-resolution, large-field-of-view imagery amplifies the need for efficient compression methodologies. Conventional techniques frequently fail to preserve critical image details, while data-driven approaches exhibit limited generalizability. Implicit Neural Representations (INRs) present a promising alternative by learning continuous mappings from spatial coordinates to pixel intensities for individual images, thereby storing network weights rather than raw pixels a…

Capsule-ConvKAN: A Hybrid Neural Approach to Medical Image Classification
Laura Pitukov\'a, Peter Sin\v{c}\'ak, L\'aszl\'o J\'ozsef Kov\'acs
https://arxiv.org/abs/2507.06417

Capsule-ConvKAN: A Hybrid Neural Approach to Medical Image Classification
This study conducts a comprehensive comparison of four neural network architectures: Convolutional Neural Network, Capsule Network, Convolutional Kolmogorov--Arnold Network, and the newly proposed Capsule--Convolutional Kolmogorov--Arnold Network. The proposed Capsule-ConvKAN architecture combines the dynamic routing and spatial hierarchy capabilities of Capsule Network with the flexible and interpretable function approximation of Convolutional Kolmogorov--Arnold Networks. This novel hybrid mod…

An In-situ Solid Fuel Ramjet Thrust Monitoring and Regulation Framework Using Neural Networks and Adaptive Control
Ryan DeBoskey, Parham Oveissi, Venkat Narayanaswamy, Ankit Goel
https://arxiv.org/abs/2506.08157

An In-situ Solid Fuel Ramjet Thrust Monitoring and Regulation Framework Using Neural Networks and Adaptive Control
Controlling the complex combustion dynamics within solid fuel ramjets (SFRJs) remains a critical challenge limiting deployment at scale. This paper proposes the use of a neural network model to process in-situ measurements for monitoring and regulating SFRJ thrust with a learning-based adaptive controller. A neural network is trained to estimate thrust from synthetic data generated by a feed-forward quasi-one-dimensional SFRJ model with variable inlet control. An online learning controller base…

Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
Filippo Marostica, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
https://arxiv.org/abs/2506.13268

Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. We begin our investigation in software, rapidly prototyping and testing various SNN models based on different variants of the Leaky Integrate and Fire (LIF) neuron across multiple datasets. This phase enables controlled performance assessment and informs design refinement. Our subsequent hardware phase, implemented on …

3D Magnetic Inverse Routine for Single-Segment Magnetic Field Images
J. Senthilnath, Chen Hao, F. C. Wellstood
https://arxiv.org/abs/2507.11293 https://

3D Magnetic Inverse Routine for Single-Segment Magnetic Field Images
In semiconductor packaging, accurately recovering 3D information is crucial for non-destructive testing (NDT) to localize circuit defects. This paper presents a novel approach called the 3D Magnetic Inverse Routine (3D MIR), which leverages Magnetic Field Images (MFI) to retrieve the parameters for the 3D current flow of a single-segment. The 3D MIR integrates a deep learning (DL)-based Convolutional Neural Network (CNN), spatial-physics-based constraints, and optimization techniques. The metho…

FieldFormer: Self-supervised Reconstruction of Physical Fields via Tensor Attention Prior
Panqi Chen, Siyuan Li, Lei Cheng, Xiao Fu, Yik-Chung Wu, Sergios Theodoridis
https://arxiv.org/abs/2506.11629

FieldFormer: Self-supervised Reconstruction of Physical Fields via Tensor Attention Prior
Reconstructing physical field tensors from \textit{in situ} observations, such as radio maps and ocean sound speed fields, is crucial for enabling environment-aware decision making in various applications, e.g., wireless communications and underwater acoustics. Field data reconstruction is often challenging, due to the limited and noisy nature of the observations, necessitating the incorporation of prior information to aid the reconstruction process. Deep neural network-based data-driven struct…

Standards-Compliant DM-RS Allocation via Temporal Channel Prediction for Massive MIMO Systems
Sehyun Ryu, Hyun Jong Yang
https://arxiv.org/abs/2507.11064 h…

Standards-Compliant DM-RS Allocation via Temporal Channel Prediction for Massive MIMO Systems
Reducing feedback overhead in beyond 5G networks is a critical challenge, as the growing number of antennas in modern massive MIMO systems substantially increases the channel state information (CSI) feedback demand in frequency division duplex (FDD) systems. To address this, extensive research has focused on CSI compression and prediction, with neural network-based approaches gaining momentum and being considered for integration into the 3GPP 5G-Advanced standards. While deep learning has been …

Generative Neural Network for Simulating Radio Emission from Extensive Air Showers
Pranav Sampathkumar, Tim Huege, Andreas Haungs, Ralph Engel
https://arxiv.org/abs/2507.07713

Generative Neural Network for Simulating Radio Emission from Extensive Air Showers
Cosmic ray shower detection using large radio arrays has gained significant traction in recent years. With massive improvements in signal modelling and microscopic simulations, the analysis of incoming events is still severely limited by the simulation cost of radio emission to interpret the data. In this work, we show that a neural network can be used for simulating such radio pulses. We also demonstrate how such a neural network can be used for $X_\mathrm{max}$ reconstruction, while retaining…

macaque_neural: Macaque cortical connectivity (Young)
A network of cortical regions in the Macaque cortex.
This network has 47 nodes and 505 edges.
Tags: Biological, Connectome, Unweighted
https://networks.skewed.de/net/macaque_neural
Ridiculogram:

Developing a Transferable Federated Network Intrusion Detection System
Abu Shafin Mohammad Mahdee Jameel, Shreya Ghosh, Aly El Gamal
https://arxiv.org/abs/2508.09060 https://

Developing a Transferable Federated Network Intrusion Detection System
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown attacks using knowledge from known attacks. To this end, we develop algorithms to maximize the number of transferability relationships. We propose a Convolutional Neural Network (CNN) model, along wi…

HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Arefin Ittesafun Abian, Ripon Kumar Debnath, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Md Rafiqul Islam, Asif Karim, Reem E. Mohamed, Sami Azam
https://arxiv.org/abs/2507.11325

HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Accurate liver and tumor segmentation on abdominal CT images is critical for reliable diagnosis and treatment planning, but remains challenging due to complex anatomical structures, variability in tumor appearance, and limited annotated data. To address these issues, we introduce Hyperbolic-convolutions Adaptive-temporal-attention with Neural-representation and Synaptic-plasticity Network (HANS-Net), a novel segmentation framework that synergistically combines hyperbolic convolutions for hierar…

Neutone SDK: An Open Source Framework for Neural Audio Processing
Christopher Mitcheltree, Bogdan Teleaga, Andrew Fyfe, Naotake Masuda, Matthias Sch\"afer, Alfie Bradic, Nao Tokui
https://arxiv.org/abs/2508.09126

Neutone SDK: An Open Source Framework for Neural Audio Processing
Neural audio processing has unlocked novel methods of sound transformation and synthesis, yet integrating deep learning models into digital audio workstations (DAWs) remains challenging due to real-time / neural network inference constraints and the complexities of plugin development. In this paper, we introduce the Neutone SDK: an open source framework that streamlines the deployment of PyTorch-based neural audio models for both real-time and offline applications. By encapsulating common chall…

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials
Kazutaka Nishiguchi, Ryota Yamamoto, Meguru Yamazaki, Naoki Matsumura, Yuta Yoshimoto, Seiichiro L. Ten-no, Yasufumi Sakai
https://arxiv.org/abs/2506.09372

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials
A precise analysis of point defects in solids requires accurate molecular dynamics (MD) simulations of large-scale systems. However, ab initio MD simulations based on density functional theory (DFT) incur high computational cost, while classical MD simulations lack accuracy. We perform MD simulations using a neural network potential (NNP) model (NNP-MD) to predict the physical quantities of both pristine SrTiO$_3$ and SrTiO$_3$ in the presence of oxygen vacancies (V$_{\text{O}}$). To verify the…

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…

Alternating Approach-Putt Models for Multi-Stage Speech Enhancement
Iksoon Jeong, Kyung-Joong Kim, Kang-Hun Ahn
https://arxiv.org/abs/2508.10436 https://ar…

Alternating Approach-Putt Models for Multi-Stage Speech Enhancement
Speech enhancement using artificial neural networks aims to remove noise from noisy speech signals while preserving the speech content. However, speech enhancement networks often introduce distortions to the speech signal, referred to as artifacts, which can degrade audio quality. In this work, we propose a post-processing neural network designed to mitigate artifacts introduced by speech enhancement models. Inspired by the analogy of making a `Putt' after an `Approach' in golf, we name our mod…

celegansneural: C. elegans neurons (1986)
A network representing the neural connections of the Caenorhabditis elegans nematode.
This network has 297 nodes and 2359 edges.
Tags: Biological, Connectome, Weighted
https://networks.skewed.de/net/celegansneural
Rid…

Sampling Theory for Super-Resolution with Implicit Neural Representations
Mahrokh Najaf, Gregory Ongie
https://arxiv.org/abs/2506.09949 https://

Sampling Theory for Super-Resolution with Implicit Neural Representations
Implicit neural representations (INRs) have emerged as a powerful tool for solving inverse problems in computer vision and computational imaging. INRs represent images as continuous domain functions realized by a neural network taking spatial coordinates as inputs. However, unlike traditional pixel representations, little is known about the sample complexity of estimating images using INRs in the context of linear inverse problems. Towards this end, we study the sampling requirements for recove…

A Context-aware Attention and Graph Neural Network-based Multimodal Framework for Misogyny Detection
Mohammad Zia Ur Rehman, Sufyaan Zahoor, Areeb Manzoor, Musharaf Maqbool, Nagendra Kumar
https://arxiv.org/abs/2508.09175

A Context-aware Attention and Graph Neural Network-based Multimodal Framework for Misogyny Detection
A substantial portion of offensive content on social media is directed towards women. Since the approaches for general offensive content detection face a challenge in detecting misogynistic content, it requires solutions tailored to address offensive content against women. To this end, we propose a novel multimodal framework for the detection of misogynistic and sexist content. The framework comprises three modules: the Multimodal Attention module (MANM), the Graph-based Feature Reconstruction …

Geometry-Aware Spiking Graph Neural Network
Bowen Zhang, Genan Dai, Hu Huang, Long Lan
https://arxiv.org/abs/2508.06793 https://arxiv.org/pdf/2508.06793

Geometry-Aware Spiking Graph Neural Network
Graph Neural Networks (GNNs) have demonstrated impressive capabilities in modeling graph-structured data, while Spiking Neural Networks (SNNs) offer high energy efficiency through sparse, event-driven computation. However, existing spiking GNNs predominantly operate in Euclidean space and rely on fixed geometric assumptions, limiting their capacity to model complex graph structures such as hierarchies and cycles. To overcome these limitations, we propose \method{}, a novel Geometry-Aware Spikin…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Enrico Testi, Enrico Paolini
https://arxiv.org/abs/2506.10513 https://a…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Ultra-reliable low-latency communications (URLLC) demand decoding algorithms that simultaneously offer high reliability and low complexity under stringent latency constraints. While iterative decoding schemes for LDPC and Polar codes offer a good compromise between performance and complexity, they fall short in approaching the theoretical performance limits in the typical URLLC short block length regime. Conversely, quasi-ML decoding schemes for algebraic codes, like Chase-II decoding, exhibit …

DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
Yuliang Zhu, Jing Cheng, Qi Xie, Zhuo-Xu Cui, Qingyong Zhu, Yuanyuan Liu, Xin Liu, Jianfeng Ren, Chengbo Wang, Dong Liang
https://arxiv.org/abs/2506.10309

DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
Dynamic Magnetic Resonance Imaging (MRI) exhibits transformation symmetries, including spatial rotation symmetry within individual frames and temporal symmetry along the time dimension. Explicit incorporation of these symmetry priors in the reconstruction model can significantly improve image quality, especially under aggressive undersampling scenarios. Recently, Equivariant convolutional neural network (ECNN) has shown great promise in exploiting spatial symmetry priors. However, existing ECNN…

CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
Zhengqing Wang, Yuefan Wu, Jiacheng Chen, Fuyang Zhang, Yasutaka Furukawa
https://arxiv.org/abs/2507.08776

CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
This paper proposes a neural rendering approach that represents a scene as "compressed light-field tokens (CLiFTs)", retaining rich appearance and geometric information of a scene. CLiFT enables compute-efficient rendering by compressed tokens, while being capable of changing the number of tokens to represent a scene or render a novel view with one trained network. Concretely, given a set of images, multi-view encoder tokenizes the images with the camera poses. Latent-space K-means selects a re…

Quasi-Random Physics-informed Neural Networks
Tianchi Yu, Ivan Oseledets
https://arxiv.org/abs/2507.08121 https://arxiv.org/pdf/2507.08121 https://arxiv.org/html/2507.08121
arXiv:2507.08121v1 Announce Type: new
Abstract: Physics-informed neural networks have shown promise in solving partial differential equations (PDEs) by integrating physical constraints into neural network training, but their performance is sensitive to the sampling of points. Based on the impressive performance of quasi Monte-Carlo methods in high dimensional problems, this paper proposes Quasi-Random Physics-Informed Neural Networks (QRPINNs), which use low-discrepancy sequences for sampling instead of random points directly from the domain. Theoretically, QRPINNs have been proven to have a better convergence rate than PINNs. Empirically, experiments demonstrate that QRPINNs significantly outperform PINNs and some representative adaptive sampling methods, especially in high-dimensional PDEs. Furthermore, combining QRPINNs with adaptive sampling can further improve the performance.
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Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods
We introduce neural network architectures that combine physics-informed neural networks (PINNs) with the immersed boundary method (IBM) to solve fluid-structure interaction (FSI) problems. Our approach features two distinct architectures: a Single-FSI network with a unified parameter space, and an innovative Eulerian-Lagrangian network that maintains separate parameter spaces for fluid and structure domains. We study each architecture using standard Tanh and adaptive B-spline activation functio…

SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classification
Ali Waqas, Sinem Coleri
https://arxiv.org/abs/2506.10699 https://

SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classification
Sensor-based local inference at IoT devices faces severe computational limitations, often requiring data transmission over noisy wireless channels for server-side processing. To address this, split-network Deep Neural Network (DNN) based Joint Source-Channel Coding (JSCC) schemes are used to extract and transmit relevant features instead of raw data. However, most existing methods rely on fixed network splits and static configurations, lacking adaptability to varying computational budgets and c…

Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks
Zhihao Xue, Yun Zi, Nia Qi, Ming Gong, Yujun Zou
https://arxiv.org/abs/2508.07122 https://…

Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks
This paper proposes a spatiotemporal graph neural network-based performance prediction algorithm to address the challenge of forecasting performance fluctuations in distributed backend systems with multi-level service call structures. The method abstracts system states at different time slices into a sequence of graph structures. It integrates the runtime features of service nodes with the invocation relationships among services to construct a unified spatiotemporal modeling framework. The mode…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
Ahmed Farooq
https://arxiv.org/abs/2507.08766 https://…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that b…

MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification
Tiantian Yang, Zhiqian Chen
https://arxiv.org/abs/2508.07465 https://arx…

MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification
Integrating multi-omics data, such as DNA methylation, mRNA expression, and microRNA (miRNA) expression, offers a comprehensive view of the biological mechanisms underlying disease. However, the high dimensionality and complex interactions among omics layers present major challenges for predictive modeling. We propose Multi-Omics integration with Tree-generated Graph Neural Network (MOTGNN), a novel and interpretable framework for binary disease classification. MOTGNN employs eXtreme Gradient B…