Tootfinder

Optimised neural networks for online processing of ATLAS calorimeter data on FPGAs
Georges Aad, Raphael Bertrand, Lauri Laatu, Emmanuel Monnier, Arno Straessner, Nairit Sur, Johann C. Voigt
https://arxiv.org/abs/2510.11469

Optimised neural networks for online processing of ATLAS calorimeter data on FPGAs
A study of neural network architectures for the reconstruction of the energy deposited in the cells of the ATLAS liquid-argon calorimeters under high pile-up conditions expected at the HL-LHC is presented. These networks are designed to run on the FPGA-based readout hardware of the calorimeters under strict size and latency constraints. Several architectures, including Dense, Recurrent (RNN), and Convolutional (CNN) neural networks, are optimised using a Bayesian procedure that balances energy …

Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training
Haixin Zhao, Kaixuan Yang, Nilesh Madhu
https://arxiv.org/abs/2510.11395 https://

Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training
To further reduce the complexity of lightweight speech enhancement models, we introduce a gating-based Dynamically Slimmable Network (DSN). The DSN comprises static and dynamic components. For architecture-independent applicability, we introduce distinct dynamic structures targeting the commonly used components, namely, grouped recurrent neural network units, multi-head attention, convolutional, and fully connected layers. A policy module adaptively governs the use of dynamic parts at a frame-w…

deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
Sankalp Gilda
https://arxiv.org/abs/2510.09362 https://…

deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning framework that utilizes a regularized, multi-task approach to predict stellar atmospheric parameters from observed spectra. We train a deep convolutional neural network on the PHOENIX synthetic spectral library and use transfer learning to fine-tune the model on a small subset of observed FGK dwarf spectra from the MARV…

GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection (Preprint)
Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Qi Hu, Yan Li, Chang Liu
https://arxiv.org/abs/2510.07285

GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection (Preprint)
The escalating complexity of network threats and the inherent class imbalance in traffic data present formidable challenges for modern Intrusion Detection Systems (IDS). While Graph Neural Networks (GNNs) excel in modeling topological structures and Temporal Convolutional Networks (TCNs) are proficient in capturing time-series dependencies, a framework that synergistically integrates both while explicitly addressing data imbalance remains an open challenge. This paper introduces a novel deep le…

Reconstructing the local density field with combined convolutional and point cloud architecture
Baptiste Barthe-Gold, Nhat-Minh Nguyen, Leander Thiele
https://arxiv.org/abs/2510.08573

Reconstructing the local density field with combined convolutional and point cloud architecture
We construct a neural network to perform regression on the local dark-matter density field given line-of-sight peculiar velocities of dark-matter halos, biased tracers of the dark matter field. Our architecture combines a convolutional U-Net with a point-cloud DeepSets. This combination enables efficient use of small-scale information and improves reconstruction quality relative to a U-Net-only approach. Specifically, our hybrid network recovers both clustering amplitudes and phases better than…

HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
Junggu Choi, Junho Lee, Kyle L. Jung, Jae U. Jung
https://arxiv.org/abs/2510.01993 …

HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
In this study, we propose a quantum convolutional neural network (QCNN)-based framework with the neural quantum embedding (NQE) to predict HIV-1 protease cleavage sites in amino acid sequences from viral and human proteins. To assess the effectiveness and robustness of our framework, we compared the classification performance against classical neural networks under both noiseless and noisy simulations. Among experimental conditions, the QCNN with the angle and amplitude encoding NQE conditions …

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Ahsan Farabi, Muhaiminul Rashid Shad, Israt Khandaker
https://arxiv.org/abs/2510.00572

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD. To address these issues, we propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. The architecture is further optimized using the Squirrel Search Algorithm (SSA), e…

Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
Martino Errico (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Davide Fiacco (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Stefano Giagu (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Giuliano Gustavino (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Valerio Ippolito (INFN Sezione di Roma), Graziella Russo (Sapienza Universit\`a di Roma, INFN Sezione di Roma)

Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
The High-Luminosity LHC (HL-LHC) will reach luminosities up to 7 times higher than the previous run, yielding denser events and larger occupancies. Next generation trigger algorithms must retain reliable selection within a strict latency budget. This work explores machine-learning approaches for future muon triggers, using the ATLAS Muon Spectrometer as a benchmark. A Convolutional Neural Network (CNN) is used as a reference, while a Graph Neural Network (GNN) is introduced as a natural model f…

smallNet: Implementation of a convolutional layer in tiny FPGAs
Fernanda Zapata Bascu\~n\'an, Alan Ezequiel Fuster
https://arxiv.org/abs/2509.25391 https://

smallNet: Implementation of a convolutional layer in tiny FPGAs
Since current neural network development systems in Xilinx and VLSI require codevelopment with Python libraries, the first stage of a convolutional network has been implemented by developing a convolutional layer entirely in Verilog. This handcoded design, free of IP cores and based on a filter polynomial like structure, enables straightforward deployment not only on low cost FPGAs but also on SoMs, SoCs, and ASICs. We analyze the limitations of numerical representations and compare our impleme…

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Huaizhi Liao, Tian Qiu, Guang Chen
https://arxiv.org/abs/2509.18325 https://

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Vital nodes usually play a key role in complex networks. Uncovering these nodes is an important task in protecting the network, especially when the network suffers intentional attack. Many existing methods have not fully integrated the node feature, interaction and state. In this article, we propose a novel method (GNNE) based on graph neural networks and information entropy. The method employs a Graph Convolutional Network (GCN) to learn the nodes' features, which are input into a Graph Attent…

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes
Guangwen Li, Xiao Yu
https://arxiv.org/abs/2509.25580 https://arxiv.org/pdf…

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes
This paper introduces a hybrid decoding architecture that serially couples a normalized min-sum (NMS) decoder with reinforced ordered statistics decoding (OSD) to achieve near-maximum likelihood (ML) performance for short linear block codes. The framework incorporates several key innovations: a decoding information aggregation model that employs a convolutional neural network to refine bit reliability estimates for OSD using the soft-output trajectory of the NMS decoder; an adaptive decoding pa…

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
Shangqing Yuan, Wenshuang Zhai, Shengwen Guo
https://arxiv.org/abs/2509.19334

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
To address the issue of limited channels and insufficient information collection in portable EEG devices, this study explores an EEG virtual channel signal generation network using a novel spatio-temporal feature fusion strategy. Based on the EEG signals from four frontal lobe channels, the network aims to generate virtual channel EEG signals for other 13 important brain regions. The architecture of the network is a two-dimensional convolutional neural network and it includes a parallel module …

S$^3$F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network
Md. Saiful Bari Siddiqui, Mohammed Imamul Hassan Bhuiyan
https://arxiv.org/abs/2509.23442

S$^3$F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network
Convolutional Neural Networks have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial features. However, this focus on a single domain is inefficient at capturing global, holistic patterns and fails to explicitly model an image's frequency-domain characteristics. To address these challenges, we propose the Spatial-Spectral Summarizer Fusion Network (S$^3$F-Net), a dual-branch framework that learns from both spatial and spectral representatio…

Textual interpretation of transient image classifications from large language models
Fiorenzo Stoppa, Turan Bulmus, Steven Bloemen, Stephen J. Smartt, Paul J. Groot, Paul Vreeswijk, Ken W. Smith
https://arxiv.org/abs/2510.06931

Textual interpretation of transient image classifications from large language models
Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders interpretability. Here we show that large language models (LLMs) can approach the performance level of a convolutional neural network on thr…

Detection of kink oscillations in solar coronal loops by a CNN-LSTM neural network
Sergey A. Belov, Yu Zhong, Dmitrii Y. Kolotkov, Valery M. Nakariakov
https://arxiv.org/abs/2509.15041

Detection of kink oscillations in solar coronal loops by a CNN-LSTM neural network
A hybrid machine learning model which combines a shallow convolutional neural network and a long short-term memory network (CNN--LSTM), has been developed to automate the detection of kink oscillations in coronal plasma loops within large volumes of high-cadence sequences of imaging data. The network was trained on a set of 10,000 synthetic data cubes designed to mimic sequences of coronal images, achieving an accuracy greater than 98\% on this synthetic dataset. The model was then applied to d…

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Sankar Vinayak. E. P, Gopalakrishnan Srinivasan
https://arxiv.org/abs/2509.26003 https://

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Equilibrium propagation has been proposed as a biologically plausible alternative to the backpropagation algorithm. The local nature of gradient computations, combined with the use of convergent RNNs to reach equilibrium states, make this approach well-suited for implementation on neuromorphic hardware. However, previous studies on equilibrium propagation have been restricted to networks containing only dense layers or relatively small architectures with a few convolutional layers followed by a…

Convolutional Neural Network Optimization for Beehive Classification Using Bioacoustic Signals
Harshit, Rahul Jana, Ritesh Kumar
https://arxiv.org/abs/2509.17800 https://…

Convolutional Neural Network Optimization for Beehive Classification Using Bioacoustic Signals
The behavior of honeybees is an important ecological phenomenon not only in terms of honey and beeswax production but also due to the proliferation of flora and fauna around it. The best way to study this significant phenomenon is by non-invasive monitoring of beehives using the sounds produced by various body movements that give out audio signals which can be exploited for various predictions related to the objectives mentioned above. This study investigates the application of Convolutional Ne…

Electron neural closure for turbulent magnetosheath simulations: energy channels
George Miloshevich, Luka Vranckx, Felipe Nathan de Oliveira Lopes, Pietro Dazzi, Giuseppe Arr\`o, Giovanni Lapenta
https://arxiv.org/abs/2510.00282

Electron neural closure for turbulent magnetosheath simulations: energy channels
In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a Fully Convolutional Neural Network (FCNN). Electron pressure plays an important role in generalized Ohm's law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit Particle-in-Cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with…

Super-resolution image projection over an extended depth of field using a diffractive decoder
Hanlong Chen, Cagatay Isil, Tianyi Gan, Mona Jarrahi, Aydogan Ozcan
https://arxiv.org/abs/2510.03938

Super-resolution image projection over an extended depth of field using a diffractive decoder
Image projection systems must be efficient in data storage, computation and transmission while maintaining a large space-bandwidth-product (SBP) at their output. Here, we introduce a hybrid image projection system that achieves extended depth-of-field (DOF) with improved resolution, combining a convolutional neural network (CNN)-based digital encoder with an all-optical diffractive decoder. A CNN-based encoder compresses input images into compact phase representations, which are subsequently di…

Deep learning the sources of MJO predictability: a spectral view of learned features
Lin Yao, Da Yang, James P. C. Duncan, Ashesh Chattopadhyay, Pedram Hassanzadeh, Wahid Bhimji, Bin Yu
https://arxiv.org/abs/2510.03582

Deep learning the sources of MJO predictability: a spectral view of learned features
The Madden-Julian oscillation (MJO) is a planetary-scale, intraseasonal tropical rainfall phenomenon crucial for global weather and climate; however, its dynamics and predictability remain poorly understood. Here, we leverage deep learning (DL) to investigate the sources of MJO predictability, motivated by a central difference in MJO theories: which spatial scales are essential for driving the MJO? We first develop a deep convolutional neural network (DCNN) to forecast the MJO indices (RMM and …

Short-Duration Gravitational Wave Burst Detection using Convolutional Neural Network
Matteo Pracchia, Sacha Peters, Maxime Fays
https://arxiv.org/abs/2509.13241 https://

Short-Duration Gravitational Wave Burst Detection using Convolutional Neural Network
Detecting unmodeled gravitational wave (GW) bursts presents significant challenges due to the lack of accurate waveform templates required for matched-filtering techniques. A primary difficulty lies in distinguishing genuine signals from transient noise. Machine learning approaches, particularly convolutional neural networks (CNNs), offer promising alternatives for this classification problem. This paper presents a CNN-based pipeline for detecting short GW bursts (duration $< 10~\mathrm{s}$), a…

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

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

PanoTPS-Net: Panoramic Room Layout Estimation via Thin Plate Spline Transformation
Hatem Ibrahem, Ahmed Salem, Qinmin Vivian Hu, Guanghui Wang
https://arxiv.org/abs/2510.11992 h…

PanoTPS-Net: Panoramic Room Layout Estimation via Thin Plate Spline Transformation
Accurately estimating the 3D layout of rooms is a crucial task in computer vision, with potential applications in robotics, augmented reality, and interior design. This paper proposes a novel model, PanoTPS-Net, to estimate room layout from a single panorama image. Leveraging a Convolutional Neural Network (CNN) and incorporating a Thin Plate Spline (TPS) spatial transformation, the architecture of PanoTPS-Net is divided into two stages: First, a convolutional neural network extracts the high-l…

Interpreting Convolutional Neural Network Activation Maps with Hand-crafted Radiomics Features on Progression of Pediatric Craniopharyngioma after Irradiation Therapy
Wenjun Yang, Chuang Wang, Tina Davis, Jinsoo Uh, Chia-Ho Hua, Thomas E. Merchant
https://arxiv.org/abs/2509.20728

Interpreting Convolutional Neural Network Activation Maps with Hand-crafted Radiomics Features on Progression of Pediatric Craniopharyngioma after Irradiation Therapy
Purpose: Convolutional neural networks (CNNs) are promising in predicting treatment outcome for pediatric craniopharyngioma while the decision mechanisms are difficult to interpret. We compared the activation maps of CNN with hand crafted radiomics features of a densely connected artificial neural network (ANN) to correlate with clinical decisions. Methods: A cohort of 100 pediatric craniopharyngioma patients were included. Binary tumor progression was classified by an ANN and CNN with input of…

Predicting void nucleation in microstructure with convolutional neural networks
Abhijith Thoopul Anantharanga, Jackson Plummer, Saryu Fensin, Brandon Runnels
https://arxiv.org/abs/2509.09938

Predicting void nucleation in microstructure with convolutional neural networks
Void nucleation in ductile materials subjected to high strain-rate loading remains a critical yet elusive phenomenon to understand. Traditional methods to understand void nucleation typically rely on experiments and molecular dynamics and do not capture the underlying factors leading to void nucleation. In this study, a convolutional neural network, specifically a U-Net enhanced with attention gates is developed, to predict void nucleation probability in pristine tantalum microstructures. The a…

Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification
Mingzhu Wang, Yun Shang
https://arxiv.org/abs/2510.12291 https://

Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification
Quantum machine learning (QML) holds promise for computational advantage, yet progress on real-world tasks is hindered by classical preprocessing and noisy devices. We introduce ViT-QCNN-FT, a hybrid framework that integrates a fine-tuned Vision Transformer with a quantum convolutional neural network (QCNN) to compress high-dimensional images into features suited for noisy intermediate-scale quantum (NISQ) devices. By systematically probing entanglement, we show that ansatzes with uniformly dis…

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov
https://arxiv.org/abs/2509.14078

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands,…

A Resource-Driven Approach for Implementing CNNs on FPGAs Using Adaptive IPs
Philippe Magalh\~aes (LabHC), Virginie Fresse (LabHC), Beno\^it Suffran (LabHC), Olivier Alata (LabHC)
https://arxiv.org/abs/2510.02990

A Resource-Driven Approach for Implementing CNNs on FPGAs Using Adaptive IPs
The increasing demand for real-time, low-latency artificial intelligence applications has propelled the use of Field-Programmable Gate Arrays (FPGAs) for Convolutional Neural Network (CNN) implementations. FPGAs offer reconfigurability, energy efficiency, and performance advantages over GPUs, making them suitable for edge devices and embedded systems. This work presents a novel library of resource-efficient convolution IPs designed to automatically adapt to the available FPGA resources. Develop…

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks
Ouassim Karrakchou, Alaa Zniber, Anass Sebbar, Mounir Ghogho
https://arxiv.org/abs/2509.12291 …

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks
Distributed Denial of Service (DDoS) attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDoS detection and response. At the core of our approach is a split early-exit neural network that performs partial inference in the data plane using a quantized Convolutional Neural Network (CNN), while…

Automatic Identification of Magnetospheric Regions using Supervised Machine Learning Models
Narges Ahmadi, Robert Ergun, Xiangning Chu, Alex Chasapis, Victoria Wilder
https://arxiv.org/abs/2510.00190

Automatic Identification of Magnetospheric Regions using Supervised Machine Learning Models
We present an automated approach for identifying magnetospheric regions using supervised machine learning techniques applied to Magnetospheric MultiScale mission data. Our method utilizes ion energy spectra, total magnetic field, total ion temperature, and spacecraft position data to classify five distinct plasma environments: solar wind, magnetosheath, inner magnetosphere, plasma sheet, and lobe regions. The approach combines a convolutional neural network (CNN) for analyzing ion energy spectr…

Hybrid Real- And Complex-Valued Neural Network Concept For Low-Complexity Phase-Aware Speech Enhancement
Luan Vin\'icius Fiorio, Alex Young, Ronald M. Aarts
https://arxiv.org/abs/2509.21185

Hybrid Real- And Complex-Valued Neural Network Concept For Low-Complexity Phase-Aware Speech Enhancement
In this paper, we propose hybrid real- and complex-valued neural networks for speech enhancement. Real- or complex-valued models are either inefficient or present high complexity. We devise a straightforward design method for extending a real-valued network into its hybrid counterpart. Based on speech intelligibility and quality metrics, we compare the real, complex, and hybrid versions of a convolutional and a convolutional-recurrent architecture. The hybrid network consistently outperforms it…

A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
Simon Klaes, Axel Klawonn, Natalie Kubicki, Martin Lanser, Kengo Nakajima, Takashi Shimokawabe, Janine Weber
https://arxiv.org/abs/2509.15900

A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
This work aims to predict blood flow with non-Newtonian viscosity in stenosed arteries using convolutional neural network (CNN) surrogate models. An alternating Schwarz domain decomposition method is proposed which uses CNN-based subdomain solvers. A universal subdomain solver (USDS) is trained on a single, fixed geometry and then applied for each subdomain solve in the Schwarz method. Results for two-dimensional stenotic arteries of varying shape and length for different inflow conditions are …

YOLO-CIANNA: Galaxy detection with deep learning in radio data: II. Winning the SKA SDC2 using a generalized 3D-YOLO network
D. Cornu, B. Semelin, P. Salom\'e, X. Lu, S. Aicardi, J. Freundlich, F. Mertens, A. Marchal, G. Sainton, F. Combes, C. Tasse
https://arxiv.org/abs/2509.12082

YOLO-CIANNA: Galaxy detection with deep learning in radio data: II. Winning the SKA SDC2 using a generalized 3D-YOLO network
As the scientific exploitation of the Square Kilometre Array (SKA) approaches, there is a need for new advanced data analysis and visualization tools capable of processing large high-dimensional datasets. In this study, we aim to generalize the YOLO-CIANNA deep learning source detection and characterization method for 3D hyperspectral HI emission cubes. We present the adaptations we made to the regression-based detection formalism and the construction of an end-to-end 3D convolutional neural ne…

Comprehensive Evaluation of CNN-Based Audio Tagging Models on Resource-Constrained Devices
Jordi Grau-Haro, Ruben Ribes-Serrano, Javier Naranjo-Alcazar, Marta Garcia-Ballesteros, Pedro Zuccarello
https://arxiv.org/abs/2509.14049

Comprehensive Evaluation of CNN-Based Audio Tagging Models on Resource-Constrained Devices
Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in audio tagging tasks. However, deploying these models on resource-constrained devices like the Raspberry Pi poses challenges related to computational efficiency and thermal management. In this paper, a comprehensive evaluation of multiple convolutional neural network (CNN) architectures for audio tagging on the Raspberry Pi is conducted, encompassing all 1D and 2D models from the Pretrained Audio Neural Networks (P…

Crosslisted article(s) found for cs.OH. https://arxiv.org/list/cs.OH/new
[1/1]:
- Convolutional Neural Network Optimization for Beehive Classification Using Bioacoustic Signals
Harshit, Rahul Jana, Ritesh Kumar

Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network
Rasil Baidar, Sasa Maric, Robert Abbas
https://arxiv.org/abs/2509.15555

Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network
The exponential expansion of IoT and 5G-Advanced applications has enlarged the attack surface for DDoS, malware, and zero-day intrusions. We propose an intrusion detection system that fuses a convolutional neural network (CNN), a bidirectional LSTM (BiLSTM), and an autoencoder (AE) bottleneck within a privacy-preserving federated learning (FL) framework. The CNN-BiLSTM branch captures local and gated cross-feature interactions, while the AE emphasizes reconstruction-based anomaly sensitivity. T…

Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Javier Bisbal, Patrick Winter, Sebastian Jofre, Aaron Ponce, Sameer A. Ansari, Ramez Abdalla, Michael Markl, Oliver Welin Odeback, Sergio Uribe, Cristian Tejos, Julio Sotelo, Susanne Schnell, David Marlevi
https://arxiv.org/abs/2509.17726…

Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator variability. We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n=35), incorporating uncertainty quantification to enhance interpretability and reliability. We evaluated three convolutional neural network architecture…

A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction
Md Masud Rana, Farjana Tasnim Mukta, Duc D. Nguyen
https://arxiv.org/abs/2509.13476 https://

A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction
In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge. Recent advances in artificial intelligence, particularly deep learning, have demonstrated superior performance over traditional empirical and physics-based methods for this task, enabled by the growing availability of structural and experimental affinity data. In this work, we introduce DeepGGL, a deep convolutional neural network that integrates…

Learning Scan-Adaptive MRI Undersampling Patterns with Pre-Optimized Mask Supervision
Aryan Dhar, Siddhant Gautam, Saiprasad Ravishankar
https://arxiv.org/abs/2509.16846 https:/…

Learning Scan-Adaptive MRI Undersampling Patterns with Pre-Optimized Mask Supervision
Deep learning techniques have gained considerable attention for their ability to accelerate MRI data acquisition while maintaining scan quality. In this work, we present a convolutional neural network (CNN) based framework for learning undersampling patterns directly from multi-coil MRI data. Unlike prior approaches that rely on in-training mask optimization, our method is trained with precomputed scan-adaptive optimized masks as supervised labels, enabling efficient and robust scan-specific sa…

Attention U-Net for all-sky continuous gravitational wave searches
Damon H. T. Cheung
https://arxiv.org/abs/2509.19838 https://arxiv.org/pdf/2509.19838

Attention U-Net for all-sky continuous gravitational wave searches
Detecting continuous gravitational waves is challenging due to the high computational cost of template-based searches across large parameter spaces, particularly for all-sky searches. Machine learning offers a promising solution to perform these searches with reasonable computational resources. In this study, we trained an attention U-Net, a convolutional neural network, on $\approx$ 10.67 days of simulated data with Gaussian noise for all-sky searches at different frequencies within the 20-100…

Text-Independent Speaker Identification Using Audio Looping With Margin Based Loss Functions
Elliot Q C Garcia, Nic\'eias Silva Vilela, K\'atia Pires Nascimento do Sacramento, Tiago A. E. Ferreira
https://arxiv.org/abs/2509.22838

Text-Independent Speaker Identification Using Audio Looping With Margin Based Loss Functions
Speaker identification has become a crucial component in various applications, including security systems, virtual assistants, and personalized user experiences. In this paper, we investigate the effectiveness of CosFace Loss and ArcFace Loss for text-independent speaker identification using a Convolutional Neural Network architecture based on the VGG16 model, modified to accommodate mel spectrogram inputs of variable sizes generated from the Voxceleb1 dataset. Our approach involves implementin…

R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration
Rokonozzaman Ayon, Md Taimur Ahad, Bo Song, Yan Li
https://arxiv.org/abs/2509.16251

R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration
State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) are criticized for their extensive computational power, long training times, and large datasets. To overcome this limitation, we propose a reasonable network (R-Net), a lightweight CNN only to detect and classify colorectal cancer (CRC) using the Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset (EBHI). Furthermore, six SOTA CNNs, including Multipath-based CNNs (DenseNet121, ResNet50), Depth-based CNNs (Inceptio…

Hierarchical MLANet: Multi-level Attention for 3D Face Reconstruction From Single Images
Danling Cao
https://arxiv.org/abs/2509.10024 https://arxiv.org/pdf…

Hierarchical MLANet: Multi-level Attention for 3D Face Reconstruction From Single Images
Recovering 3D face models from 2D in-the-wild images has gained considerable attention in the computer vision community due to its wide range of potential applications. However, the lack of ground-truth labeled datasets and the complexity of real-world environments remain significant challenges. In this chapter, we propose a convolutional neural network-based approach, the Hierarchical Multi-Level Attention Network (MLANet), for reconstructing 3D face models from single in-the-wild images. Our …

Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
Hua-Lin Wu, Ao Xu, Heng-Dong Xi
https://arxiv.org/abs/2509.17109 https://

Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
We studied the reconstruction of turbulent flow fields from trajectory data recorded by actively migrating Lagrangian agents. We propose a deep learning model, Track-to-Flow (T2F), which employs a Vision Transformer as an encoder to capture the spatiotemporal features of a single agent trajectory, and a convolutional neural network as the decoder to reconstruct the flow field. To enhance the physical consistency of the T2F model, we further incorporate a physics-informed loss function inspired …

Neural networks in the search for fast radio bursts with RATAN-600
D. O. Kudryavtsev, S. A. Trushkin, P. G. Tsybulev, V. A. Stolyarov
https://arxiv.org/abs/2509.11215 https://…

Neural networks in the search for fast radio bursts with RATAN-600
We present a technique to search for fast radio bursts in records obtained with broadband radiometers having few radio channels. The technique is applied to the RATAN-600 surveys carried out at its Western Sector since the year 2017. A 1D convolutional neural network for multichannel time series classification is developed based on the EfficientNet family of models. The procedure to generate synthetic FRB signals needed for the training dataset is described. We implement a two-stage cascade sch…

Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, A. Lee Swindlehurst, Markku Juntti
https://arxiv.org/abs/2509.11419

Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an efficient sensing-assisted long-term beam tracking framework that selects optimal beams from a codebook for current and multiple future time slots. We first design a large attention-enhanced neural network (NN) to fully exploit past visual observations for beam tracking. A convolutional NN extracts compact image features…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Nathan He, Cody Chen
https://arxiv.org/abs/2509.12287 https://arxi…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaChe…

Wavefront Coding for Accommodation-Invariant Near-Eye Displays
Ugur Akpinar, Erdem Sahin, Tina M. Hayward, Apratim Majumder, Rajesh Menon, Atanas Gotchev
https://arxiv.org/abs/2510.12778

Wavefront Coding for Accommodation-Invariant Near-Eye Displays
We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays through accommodation-invariance. Our system integrates a refractive lens eyepiece with a novel wavefront coding diffractive optical element, operating in tandem with a pre-processing convolutional neural network. We employ end-to-end learning to jointly optimize the wavefront-coding optics and the image pre-processing module. To implement this approach, we …

A Deep Learning Model of Lightning Stroke Density
Randall Jones II, Joel A. Thornton, Chris J. Wright, Robert Holzworth
https://arxiv.org/abs/2509.10399 https://

A Deep Learning Model of Lightning Stroke Density
Lightning plays a crucial role in the Earth's climate system, yet existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study develops deep learning-based parameterizations of lightning stroke density using meteorological variables from the ERA and IMERG datasets. Convolutional neural networks (CNNs) with U-Net architectures are trained using World Wide Lightning Location Network (W…

Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion
Farnaz Jazaeri, Homayoun Kamkar-Parsi, Fran\c{c}ois Grondin, Martin Bouchard
https://arxiv.org/abs/2509.21382

Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion
For extracting a target speaker voice, direction-of-arrival (DOA) estimation is crucial for binaural hearing aids operating in noisy, multi-speaker environments. Among the solutions developed for this task, a deep learning convolutional recurrent neural network (CRNN) model leveraging spectral phase differences and magnitude ratios between microphone signals is a popular option. In this paper, we explore adding source-count information for multi-sources DOA estimation. The use of dual-task trai…

A proposal for automated turbulence modelling
Marco Castelletti, Maurizio Quadrio
https://arxiv.org/abs/2509.14140 https://arxiv.org/pdf/2509.14140

A proposal for automated turbulence modelling
Solving the Reynolds-averaged Navier-Stokes equations (RANS) closed with an eddy viscosity computed through a turbulence model is still the leading approach for Computational Fluid Dynamics simulations. Unfortunately, universal models with good predictive capabilities over a wide range of flows are not available. In this work, we propose the use of machine learning to improve existing RANS models. The approach does not require high-fidelity training data. A convolutional neural network is use…

Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation
Arnab Kumar Roy, Hemant Kumar Kathania, Paban Sapkota
https://arxiv.org/abs/2509.13442

Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation
Dysarthric speech severity classification is crucial for objective clinical assessment and progress monitoring in individuals with motor speech disorders. Although prior methods have addressed this task, achieving robust generalization in speaker-independent (SID) scenarios remains challenging. This work introduces DSSCNet, a novel deep neural architecture that combines Convolutional, Squeeze-Excitation (SE), and Residual network, helping it extract discriminative representations of dysarthric …