Tootfinder

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
Mahin Montasir Afif, Abdullah Al Noman, K. M. Tahsin Kabir, Md. Mortuza Ahmmed, Md. Mostafizur Rahman, Mufti Mahmud, Md. Ashraful Babu
https://arxiv.org/abs/2506.17165

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains hig…

A Deep Convolutional Neural Network-Based Novel Class Balancing for Imbalance Data Segmentation
Atifa Kalsoom, M. A. Iftikhar, Amjad Ali, Zubair Shah, Shidin Balakrishnan, Hazrat Ali
https://arxiv.org/abs/2506.18474

A Deep Convolutional Neural Network-Based Novel Class Balancing for Imbalance Data Segmentation
Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macula, and fovea. However, accurate segmentation of retinal blood vessels can be challenging due to imbalanced data distribution and varying vessel thickness. In this paper, we propose BLCB-CNN, a novel pipeline based on deep learning and bi-level class balancing scheme to achieve vessel segmentation in retinal fundus images. The BLCB-CNN scheme uses …

Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs
H M Mohaimanul Islam, Huynh Q. N. Vo, Aditya Rane
https://arxiv.org/abs/2507.17010 https:/…

Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs
In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework comprising (i) a lightweight convolutional neural network (CNN) that detects deepfake imagery in real-time extended reality (XR) streams, and (ii) an integrated succinct zero-knowledge proof (ZKP) protocol that validates detection results without disclosing raw user data. Our design addresses both the computational c…

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation
Muhammad Daud, Philippe Charton, Cedric Damour, Jingbo Wang, Frederic Cadet
https://arxiv.org/abs/2506.16921

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation
Understanding the relationship between protein sequences and their functions is fundamental to protein engineering, but this task is hindered by the combinatorially vast sequence space and the experimental noise inherent in fitness measurements. In this study, we present a novel framework that models the sequence landscape as a hypercube $H(k,2)$ and integrates wavelet-based signal denoising with a graph convolutional neural network (GCN) to predict protein fitness across rugged fitness landsca…

Geophysics-informed neural network for model-based seismic inversion using surrogate point spread functions
Marcus Saraiva, Ana Muller, Alexandre Maul
https://arxiv.org/abs/2507.14140

Geophysics-informed neural network for model-based seismic inversion using surrogate point spread functions
Model-based seismic inversion is a key technique in reservoir characterization, but traditional methods face significant limitations, such as relying on 1D average stationary wavelets and assuming an unrealistic lateral resolution. To address these challenges, we propose a Geophysics-Informed Neural Network (GINN) that integrates deep learning with seismic modeling. This novel approach employs a Deep Convolutional Neural Network (DCNN) to simultaneously estimate Point Spread Functions (PSFs) an…

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
Osman Tokluoglu, Enver Cavus, Ebrahim Bedeer, Halim Yanikomeroglu
https://arxiv.org/abs/2507.15291

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier l…

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Ahmad Mustafa, Reza Rastegar, Ghassan AlRegib
https://arxiv.org/abs/2506.19687 …

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Prostate gland segmentation from T2-weighted MRI is a critical yet challenging task in clinical prostate cancer assessment. While deep learning-based methods have significantly advanced automated segmentation, most conventional approaches-particularly 2D convolutional neural networks (CNNs)-fail to leverage inter-slice anatomical continuity, limiting their accuracy and robustness. Fully 3D models offer improved spatial coherence but require large amounts of annotated data, which is often imprac…

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
Cameron T. Pratt, Zhijie Qu, Joel N. Bregman
https://arxiv.org/abs/2507.13400

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
The thermal Sunyaev$-$Zel'dovich (SZ) effect offers a unique probe of the hot and diffuse universe that could help close the missing baryon problem. Traditional extractions of the SZ effect, however, exhibit systematic noise that may lead to unreliable results. In this work, we provide an alternative solution using a three-dimensional Attention Nested U-Net trained end-to-end with supervised learning. Our labeled data consists of simulated SZ signals injected into $\textit{Planck}$ frequency ma…

Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
Alexander D. Kalian, Lennart Otte, Jaewook Lee, Emilio Benfenati, Jean-Lou C. M. Dorne, Claire Potter, Olivia J. Osborne, Miao Guo, Christer Hogstrand
https://arxiv.org/abs/2507.17775

Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
Geometric deep learning is an emerging technique in Artificial Intelligence (AI) driven cheminformatics, however the unique implications of different Graph Neural Network (GNN) architectures are poorly explored, for this space. This study compared performances of Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs) and Graph Isomorphism Networks (GINs), applied to 7 different toxicological assay datasets of varying data abundance and endpoint, to perform binary classification of…

SCRNet: Spatial-Channel Regulation Network for Medical Ultrasound Image Segmentation
Weixin Xu, Ziliang Wang
https://arxiv.org/abs/2508.13899 https://arxiv…

SCRNet: Spatial-Channel Regulation Network for Medical Ultrasound Image Segmentation
Medical ultrasound image segmentation presents a formidable challenge in the realm of computer vision. Traditional approaches rely on Convolutional Neural Networks (CNNs) and Transformer-based methods to address the intricacies of medical image segmentation. Nevertheless, inherent limitations persist, as CNN-based methods tend to disregard long-range dependencies, while Transformer-based methods may overlook local contextual information. To address these deficiencies, we propose a novel Feature…

CNN-Derived Elemental Abundances of LAMOST DR10 Giants: Implications for Galactic Substructures
Haoyang Liu, Cuihua Du, Mingji Deng, Jian Zhang
https://arxiv.org/abs/2506.16135

CNN-Derived Elemental Abundances of LAMOST DR10 Giants: Implications for Galactic Substructures
Stellar parameters and abundances provide crucial insights into stellar and Galactic evolution studies. In this work, we developed a convolutional neural network (CNN) to estimate stellar parameters: effective temperature ($T_{\text{eff}}$), surface gravity (log $g$) and metallicity (both [Fe/H] and [M/H]) as well as six $α$-elements (C, N, O, Mg, Si, Ca) and [$α$/M]. We selected giant stars with \( 3500 \, \text{K} < T_{\text{eff}} < 5500 \, \text{K} \) and \( 0 \, \text{dex} < \log g < 3.6 …

Leveraging Quantum Layers in Classical Neural Networks
Silvie Ill\'esov\'a
https://arxiv.org/abs/2507.12505 https://arxiv.org…

Leveraging Quantum Layers in Classical Neural Networks
Hybrid quantum-classical neural networks represent a promising frontier in the search for improved machine learning models. This thesis explores the integration of quantum layers within classical convolutional neural network architectures, aiming to leverage quantum entanglement and feature mapping to enhance learning capabilities. A detailed methodology for constructing and training such hybrid models is presented, using PyTorch and Qiskit Machine Learning frameworks. Experiments investigate t…

Single-shot thermometry of simulated Bose--Einstein condensates using artificial intelligence
Jack Griffiths, Steven A. Wrathmall, Simon A. Gardiner
https://arxiv.org/abs/2506.16925

Single-shot thermometry of simulated Bose--Einstein condensates using artificial intelligence
Precise determination of thermodynamic parameters in ultracold Bose gases remains challenging due to the destructive nature of conventional measurement techniques and inherent experimental uncertainties. We demonstrate an artificial intelligence approach for rapid, non-destructive estimation of the chemical potential and temperature from single-shot, in situ imaged density profiles of finite-temperature Bose gases. Our convolutional neural network is trained exclusively on quasi-2D `pancake' co…

XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization
Patricia Amado-Caballero, Luis Miguel San-Jos\'e-Revuelta, Mar\'ia Dolores Aguilar-Garc\'ia, Jos\'e Ram\'on Garmendia-Leiza, Carlos Alberola-L\'opez, Pablo Casaseca-de-la-Higuera
https://arxiv.org/abs/2508.14949

XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization
This paper proposes an eXplainable Artificial Intelligence (XAI)-driven methodology to enhance the understanding of cough sound analysis for respiratory disease management. We employ occlusion maps to highlight relevant spectral regions in cough spectrograms processed by a Convolutional Neural Network (CNN). Subsequently, spectral analysis of spectrograms weighted by these occlusion maps reveals significant differences between disease groups, particularly in patients with COPD, where cough patt…

i am determined to read the attention/transformer paper
i even printed it out

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 …

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…

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
Deqing Song, Weimin Yang, Maryam Rezayati, Hans Wernher van de Venn
https://arxiv.org/abs/2508.12435

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors. We evaluated various convolutional neural network (CNN) architectures and collected two datasets to study the impact of data representation and model architecture on the recognition accuracy. Our results show that spectrogram-based representatio…

Semi-Tensor-Product Based Convolutional Neural Networks
Daizhan Cheng
https://arxiv.org/abs/2506.10407 https://arxiv.org/pdf/2506.104…

Semi-Tensor-Product Based Convolutional Neural Networks
The semi-tensor product (STP) of vectors is a generalization of conventional inner product of vectors, which allows the factor vectors to of different dimensions. This paper proposes a domain-based convolutional product (CP). Combining domain-based CP with STP of vectors, a new CP is proposed. Since there is no zero or any other padding, it can avoid the junk information caused by padding. Using it, the STP-based convolutional neural network (CNN) is developed. Its application to image and thir…

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. …

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…

Offline Neutrino Filtering using a Convolutional Neural Network-Based Algorithm at the Radio Neutrino Observatory Greenland
Ruben Camphyn (for the RNO-G collaboration)
https://arxiv.org/abs/2508.13860 …

Offline Neutrino Filtering using a Convolutional Neural Network-Based Algorithm at the Radio Neutrino Observatory Greenland
Neutrino astronomy is a vibrant field of study in astrophysics, offering unique insights into the Universe's most energetic phenomena. The combination of a low cross section and zero electromagnetic charge ensure that a neutrino retains most information about its original source while traversing the universe. On the other hand, these low cross sections, combined with a reduced flux at higher energies, make the neutrino one of the most elusive particles to detect in the standard model. The Radio…

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…

Phase Retrieval of Highly Strained Bragg Coherent Diffraction Patterns using Supervised Convolutional Neural Network
Matteo Masto, Vincent Favre-Nicolin, Steven Leake, Clement Atlan, Marie-Ingrid Richard, Tobias Sch\"ulli, Ewen Bellec
https://arxiv.org/abs/2507.06644

Phase Retrieval of Highly Strained Bragg Coherent Diffraction Patterns using Supervised Convolutional Neural Network
In Bragg Coherent Diffraction Imaging (BCDI), Phase Retrieval of highly strained crystals is often challenging with standard iterative algorithms. This computational obstacle limits the potential of the technique as it precludes the reconstruction of physically interesting highly-strained particles. Here, we propose a novel approach to this problem using a supervised Convolutional Neural Network (CNN) trained on 3D simulated diffraction data to predict the corresponding reciprocal space phase. …

A Time- and Energy-Efficient CNN with Dense Connections on Memristor-Based Chips
Wenyong Zhou, Yuan Ren, Jiajun Zhou, Tianshu Hou, Ngai Wong
https://arxiv.org/abs/2508.12251 htt…

A Time- and Energy-Efficient CNN with Dense Connections on Memristor-Based Chips
Designing lightweight convolutional neural network (CNN) models is an active research area in edge AI. Compute-in-memory (CIM) provides a new computing paradigm to alleviate time and energy consumption caused by data transfer in von Neumann architecture. Among competing alternatives, resistive random-access memory (RRAM) is a promising CIM device owing to its reliability and multi-bit programmability. However, classical lightweight designs such as depthwise convolution incurs under-utilization …

Sensitivity toward dark matter annihilation imprints on 21-cm signal with SKA-Low: A convolutional neural network approach
Pravin Kumar Natwariya, Kenji Kadota, Atsushi J. Nishizawa
https://arxiv.org/abs/2508.08251

Sensitivity toward dark matter annihilation imprints on 21-cm signal with SKA-Low: A convolutional neural network approach
This study investigates the sensitivity of the radio interferometers to identify imprints of spatially inhomogeneous dark matter annihilation signatures in the 21-cm signal during the pre-reionization era. We focus on the upcoming low-mode survey of the Square Kilometre Array (SKA-Low) telescope. Using CNNs, we analyze simulated 3D 21-cm differential brightness temperature maps generated via the DM21cm code, which is based on 21cmFAST and DarkHistory, to distinguish between spatially homogeneou…

Machine learning-based classification of variable stars using phase-folded light curves
Almat Akhmetali, Alisher Zhunuskanov, Timur Namazbayev, Marat Zaidyn, Aknur Sakan, Dana Turlykozhayeva, Nurzhan Ussipov
https://arxiv.org/abs/2508.11964

Machine learning-based classification of variable stars using phase-folded light curves
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques continues to grow. Traditional methods often rely on manual feature extraction and selection, which can be labor-intensive and less effective for managing extensive datasets. In this study, we present a convolutional neural network (CNN)-based method for cla…

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 …

Advanced Northern Tracks Selection using a Graph Convolutional Neural Network for the IceCube Neutrino Observatory
Philipp Soldin (for the IceCube Collaboration), Shuyang Deng (for the IceCube Collaboration), Lasse D\"user (for the IceCube Collaboration), Philipp F\"urst (for the IceCube Collaboration)
https://arxiv.or…

Advanced Northern Tracks Selection using a Graph Convolutional Neural Network for the IceCube Neutrino Observatory
The IceCube Neutrino Observatory is a cubic-kilometer detector located in the Antarctic ice at the geographic South Pole. It reads out over 5,000 photomultiplier tubes (PMTs) to detect Cherenkov light produced by secondary particles, enabling IceCube to identify both atmospheric and astrophysical neutrinos. One of the main challenges in this effort is effectively distinguishing between muons induced by neutrinos and those generated by cosmic-ray air showers. To address this challenge, the Advan…

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…

Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
Godwin Badu-Marfo, Bilal Farooq
https://arxiv.org/abs/2507.05507

Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
We present an integrated graph-based neural networks architecture for predicting campus buildings occupancy and inter-buildings movement at dynamic temporal resolution that learns traffic flow patterns from Wi-Fi logs combined with the usage schedules within the buildings. The relative traffic flows are directly estimated from the WiFi data without assuming the occupant behaviour or preferences while maintaining individual privacy. We formulate the problem as a data-driven graph structure repre…

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
Mana Mohammadi, Amirhesam Jafari Rad, Ashkan Behrouzi
https://arxiv.org/abs/2506.15364

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature extraction. Accurate and timely stroke detection is critical for effective treatment and improved patient outcomes in neuroimaging. While Convolutional Neural Networks (CNNs) are widely used for medical image analysis, their computational complexity often hinders deployment in re…

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

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

Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshes
Saqib Nazir (UNICAEN), Olivier L\'ezoray (UNICAEN), S\'ebastien Bougleux (UNICAEN)
https://arxiv.org/abs/2507.05304

Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshes
3D meshes are fundamental data representations for capturing complex geometric shapes in computer vision and graphics applications. While Convolutional Neural Networks (CNNs) have excelled in structured data like images, extending them to irregular 3D meshes is challenging due to the non-Euclidean nature of the data. Graph Convolutional Networks (GCNs) offer a solution by applying convolutions to graph-structured data, but many existing methods rely on isotropic filters or spectral decompositio…

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…

Quantum Convolutional Neural Network with Nonlinear Effects and Barren Plateau Mitigation
Pei-Kun Yang
https://arxiv.org/abs/2508.02459 https://arxiv.org/p…

Quantum Convolutional Neural Network with Nonlinear Effects and Barren Plateau Mitigation
Quantum neural networks (QNNs) leverage quantum entanglement and superposition to enable large-scale parallel linear computation, offering a potential solution to the scalability limits of classical deep learning. However, their practical deployment is hampered by two key challenges: the lack of intrinsic nonlinear operations and the barren plateau phenomenon. We propose a quantum convolutional neural network (QCNN) architecture that simultaneously addresses both issues. Nonlinear effects are i…

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

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

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…

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…

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
Eman Alqudah, Ashfaq Khokhar
https://arxiv.org/abs/2506.14987 https://

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a CNN-based dynamic priority prediction mechanism for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach uses a Convolutional Neural Network and graph coloring to adaptively assign link priorities based on r…

Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations
Sebastian Murgul, Moritz Reiser, Michael Heizmann, Christoph Seibert
https://arxiv.org/abs/2506.22237

Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations
In this paper, we present a neural network approach for synchronizing audio recordings of human piano performances with their corresponding loosely aligned MIDI files. The task is addressed using a Convolutional Recurrent Neural Network (CRNN) architecture, which effectively captures spectral and temporal features by processing an unaligned piano roll and a spectrogram as inputs to estimate the aligned piano roll. To train the network, we create a dataset of piano pieces with augmented MIDI fil…

Improving spliced alignment by modeling splice sites with deep learning
Siying Yang, Neng Huang, Heng Li
https://arxiv.org/abs/2506.12986 https://

Improving spliced alignment by modeling splice sites with deep learning
Motivation: Spliced alignment refers to the alignment of messenger RNA (mRNA) or protein sequences to eukaryotic genomes. It plays a critical role in gene annotation and the study of gene functions. Accurate spliced alignment demands sophisticated modeling of splice sites, but current aligners use simple models, which may affect their accuracy given dissimilar sequences. Results: We implemented minisplice to learn splice signals with a one-dimensional convolutional neural network (1D-CNN) and…

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…

Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime
Alberto Aziani, Michael V. Lo Giudice, Ali Shadman Yazdi
https://arxiv.org/abs/2507.07884

Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime
Is demand for conspiracy theories online linked to real-world hate crimes? By analyzing online search trends for 36 racially and politically-charged conspiracy theories in Michigan (2015-2019), we employ a one-dimensional convolutional neural network (1D-CNN) to predict hate crime occurrences offline. A subset of theories including the Rothschilds family, Q-Anon, and The Great Replacement improves prediction accuracy, with effects emerging two to three weeks after fluctuations in searches. Howe…

HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales
Daniel Abdi, Isidora Jankov, Paul Madden, Vanderlei Vargas, Timothy A. Smith, Sergey Frolov, Montgomery Flora, Corey Potvin
https://arxiv.org/abs/2507.05658

HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales
The High-Resolution Rapid Refresh (HRRR) model is a convection-allowing model used in operational weather forecasting across the contiguous United States (CONUS). To provide a computationally efficient alternative, we introduce HRRRCast, a data-driven emulator built with advanced machine learning techniques. HRRRCast includes two architectures: a ResNet-based model (ResHRRR) and a Graph Neural Network-based model (GraphHRRR). ResHRRR uses convolutional neural networks enhanced with squeeze-and-…

Physics-Informed Transfer Learning for Data-Driven Sound Source Reconstruction in Near-Field Acoustic Holography
Xinmeng Luan, Mirco Pezzoli, Fabio Antonacci, Augusto Sarti
https://arxiv.org/abs/2507.11070

Physics-Informed Transfer Learning for Data-Driven Sound Source Reconstruction in Near-Field Acoustic Holography
We propose a transfer learning framework for sound source reconstruction in Near-field Acoustic Holography (NAH), which adapts a well-trained data-driven model from one type of sound source to another using a physics-informed procedure. The framework comprises two stages: (1) supervised pre-training of a complex-valued convolutional neural network (CV-CNN) on a large dataset, and (2) purely physics-informed fine-tuning on a single data sample based on the Kirchhoff-Helmholtz integral. This meth…

AirCANS: CFD 2D Mesh Optimisation-based Airfoil Classification and Assessment using Neural Networks
Lushun Fan, Yuqin Xia, Jun Li, Karl Jenkins
https://arxiv.org/abs/2506.22662

AirCANS: CFD 2D Mesh Optimisation-based Airfoil Classification and Assessment using Neural Networks
This study explores the possibilities of automating the loading, classification and assessment of Computational Fluid Dynamics (CFD) mesh data by Convolutional Neural Networks (CNNs). The research aim is finding a feasible way to quickly make classification and assessment on airfoil mesh data. For this purpose, this study designed a new framework named CFD-based airfoil Classification and Assessment Network (AirCANS) for CFD mesh data which including the data loader and improved the CNN structu…

VCNet: Recreating High-Level Visual Cortex Principles for Robust Artificial Vision
Brennen A. Hill, Zhang Xinyu, Timothy Putra Prasetio
https://arxiv.org/abs/2508.02995 https://…

VCNet: Recreating High-Level Visual Cortex Principles for Robust Artificial Vision
Despite their success in image classification, modern convolutional neural networks (CNNs) exhibit fundamental limitations, including data inefficiency, poor out-of-distribution generalization, and vulnerability to adversarial perturbations. The primate visual system, in contrast, demonstrates superior efficiency and robustness, suggesting that its architectural principles may offer a blueprint for more capable artificial vision systems. This paper introduces Visual Cortex Network (VCNet), a no…

This https://arxiv.org/abs/2505.22238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval
We present Yambda-5B, a large-scale open dataset sourced from the Yandex.Music streaming platform. Yambda-5B contains 4.79 billion user-item interactions from 1 million users across 9.39 million tracks. The dataset includes two primary types of interactions: implicit feedback (listening events) and explicit feedback (likes, dislikes, unlikes and undislikes). In addition, we provide audio embeddings for most tracks, generated by a convolutional neural network trained on audio spectrograms. A key…

Disentangling coincident cell events using deep transfer learning and compressive sensing
Moritz Leuthner, Rafael Vorl\"ander, Oliver Hayden
https://arxiv.org/abs/2507.13176 …

Disentangling coincident cell events using deep transfer learning and compressive sensing
Accurate single-cell analysis is critical for diagnostics, immunomonitoring, and cell therapy, but coincident events - where multiple cells overlap in a sensing zone - can severely compromise signal fidelity. We present a hybrid framework combining a fully convolutional neural network (FCN) with compressive sensing (CS) to disentangle such overlapping events in one-dimensional sensor data. The FCN, trained on bead-derived datasets, accurately estimates coincident event counts and generalizes to…

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…

Starobinsky in Stereo: SKA-CMB Synergy in SBI
Benedikt Schosser, Caroline Heneka, Bj\"orn Malte Sch\"afer
https://arxiv.org/abs/2508.10094 https://

Starobinsky in Stereo: SKA-CMB Synergy in SBI
Modern machine learning techniques can unlock the vast cosmological information encoded in forthcoming Square Kilometre Array (SKA) observations. We show that tomographic 21 cm data from the reionisation era can yield stringent tests of inflationary models - here illustrated with Starobinsky $R+R^2$ inflation. Using a simulation-based inference (SBI) framework, we compare neural summaries (convolutional network and vision transformer) with a traditional power spectrum summary and perform a full…

EyeAI: AI-Assisted Ocular Disease Detection for Equitable Healthcare Access
Shiv Garg, Ginny Berkemeier
https://arxiv.org/abs/2507.20346 https://arxiv.org/…

EyeAI: AI-Assisted Ocular Disease Detection for Equitable Healthcare Access
Ocular disease affects billions of individuals unevenly worldwide. It continues to increase in prevalence with trends of growing populations of diabetic people, increasing life expectancies, decreasing ophthalmologist availability, and rising costs of care. We present EyeAI, a system designed to provide artificial intelligence-assisted detection of ocular diseases, thereby enhancing global health. EyeAI utilizes a convolutional neural network model trained on 1,920 retinal fundus images to auto…

Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Ziheng Chen, Po T. Wang, Mina Ibrahim, Shivali Baveja, Rong Mu, An H. Do, Zoran Nenadic
https://arxiv.org/abs/2506.14120

Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Lengthy subject- or session-specific data acquisition and calibration remain a key barrier to deploying electroencephalography (EEG)-based brain-computer interfaces (BCIs) outside the laboratory. Previous work has shown that cross subject, cross-session invariant features exist in EEG. We propose a transfer learning pipeline based on a two-layer convolutional neural network (CNN) that leverages these invariants to reduce the burden of data acquisition and calibration. A baseline model is traine…

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman
https://arxiv.org/abs/2508.07536

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Accurate and interpretable bearing fault classification is critical for ensuring the reliability of rotating machinery, particularly under variable operating conditions where domain shifts can significantly degrade model performance. This study proposes a physics-informed multimodal convolutional neural network (CNN) with a late fusion architecture, integrating vibration and motor current signals alongside a dedicated physics-based feature extraction branch. The model incorporates a novel physi…

Towards the Habitable Worlds Observatory: 1D CNN Retrieval of Reflection Spectra from Evolving Earth Analogs
Sarah G. A. Barbosa, Raissa Estrela, Paulo C. F. da Silva Filho, Daniel B. de Freitas
https://arxiv.org/abs/2508.00076

Towards the Habitable Worlds Observatory: 1D CNN Retrieval of Reflection Spectra from Evolving Earth Analogs
Upcoming direct-imaging missions like the Habitable Worlds Observatory (HWO) aim to characterize dozens of Earth-like exoplanets by capturing their reflected-light spectra. However, traditional atmospheric retrieval frameworks are too computationally intensive to explore the high-dimensional parameter spaces such missions will generate. Here, we present a one-dimensional convolutional neural network (1D CNN), trained on over one million synthetic, noise-injected spectra simulating Archean, Prot…

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…

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer, Chetra Mang
https://arxiv.org/abs/2506.08033

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases. The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture, more commonly used for image processing. Two precision datasets have be…

LKFMixer: Exploring Large Kernel Feature For Efficient Image Super-Resolution
Yinggan Tang, Quanwei Hu
https://arxiv.org/abs/2508.11391 https://arxiv.org/p…

LKFMixer: Exploring Large Kernel Feature For Efficient Image Super-Resolution
The success of self-attention (SA) in Transformer demonstrates the importance of non-local information to image super-resolution (SR), but the huge computing power required makes it difficult to implement lightweight models. To solve this problem, we propose a pure convolutional neural network (CNN) model, LKFMixer, which utilizes large convolutional kernel to simulate the ability of self-attention to capture non-local features. Specifically, we increase the kernel size to 31 to obtain the larg…

This https://arxiv.org/abs/2309.14546 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Fall Prediction for Bipedal Robots: The Standing Phase
This paper presents a novel approach to fall prediction for bipedal robots, specifically targeting the detection of potential falls while standing caused by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional neural network (CNN), our method aims to maximize lead time for fall prediction while minimizing false positive rates. The proposed algorithm uniquely integrates the detection of various fault types and estimates the lead time for potential falls. Our contributions in…

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 …

Leveraging GNN to Enhance MEF Method in Predicting ENSO
Saghar Ganji, Mohammad Naisipour
https://arxiv.org/abs/2508.07410 https://arxiv.org/pdf/2508.07410

Leveraging GNN to Enhance MEF Method in Predicting ENSO
Reliable long-lead forecasting of the El Nino Southern Oscillation (ENSO) remains a long-standing challenge in climate science. The previously developed Multimodal ENSO Forecast (MEF) model uses 80 ensemble predictions by two independent deep learning modules: a 3D Convolutional Neural Network (3D-CNN) and a time-series module. In their approach, outputs of the two modules are combined using a weighting strategy wherein one is prioritized over the other as a function of global performance. Sepa…

Practical insights on the effect of different encodings, ans\"atze and measurements in quantum and hybrid convolutional neural networks
Jes\'us Lozano-Cruz, Albert Nieto-Morales, Oriol Ball\'o-Gimbernat, Adan Garriga, Ant\'on Rodr\'iguez-Otero, Alejandro Borrallo-Rentero
https://arxiv.org/abs/2506.20355

Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to the task of satellite image classification using the EuroSAT dataset. We systematically evaluate the performance implications of data encoding techniques, variational ansätze, and measurement in approx. 500 distinct model configurations. Our analysis reveals a clear hierarchy of influence on model performance. For h…

A Real-time 3D Desktop Display
Livio Tenze, Enrique Canessa
https://arxiv.org/abs/2506.08064 https://arxiv.org/pdf/2506.08064

A Real-time 3D Desktop Display
A new extended version of the altiro3D C++ Library -- initially developed to get glass-free holographic displays starting from 2D images -- is here introduced aiming to deal with 3D video streams from either 2D webcam images or flat video files. These streams are processed in real-time to synthesize light-fields (in Native format) and feed realistic 3D experiences. The core function needed to recreate multiviews consists on the use of MiDaS Convolutional Neural Network (CNN), which allows to ex…

Optimizing SPION Labeling for Single-Cell Magnetic Microscopy
A. Pointner, D. Thalheim, S. Belasi, L. Heinen, C. Bonato, T. Luehmann, J. Meijer, R. Tietze, C. Alexiou, R. Schneider-Stock, R. Nagy
https://arxiv.org/abs/2505.20373

Optimizing SPION Labeling for Single-Cell Magnetic Microscopy
This study explores the correlation between iron mass on cell surfaces and the resultant magnetic field. Human colorectal cancer cells (HT29 line) were labeled with varying concentrations of SPIONs and imaged via a NV center widefield magnetic microscope. To assess the labeling efficacy, a convolutional neural network trained on simulated magnetic dipole data was utilized to reconstruct key labeling parameters on a cell-by-cell basis, including cell diameter, sensor proximity, and the iron mass…

This https://arxiv.org/abs/2412.18872 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Proton Flux Measurement from Neutron Monitor Data Using Neural Networks
Accurate measurements of cosmic ray proton flux are essential for studying the modulation processes of cosmic rays during the solar activity cycle. A proton flux measurement method, based on ground-based neutron monitor (NM) data and deep learning techniques, is presented. After the necessary pre-processing of ground-based NM data using a convolutional neural network (CNN) model, we simulate the relationship between NM observations and proton flux measured by the Alpha Magnetic Spectrometer (AM…

Ambisonics Super-Resolution Using A Waveform-Domain Neural Network
Ismael Nawfal, Symeon Delikaris Manias, Mehrez Souden, Juha Merimaa, Joshua Atkins, Elisabeth McMullin, Shadi Pirhosseinloo, Daniel Phillips
https://arxiv.org/abs/2508.00240

Ambisonics Super-Resolution Using A Waveform-Domain Neural Network
Ambisonics is a spatial audio format describing a sound field. First-order Ambisonics (FOA) is a popular format comprising only four channels. This limited channel count comes at the expense of spatial accuracy. Ideally one would be able to take the efficiency of a FOA format without its limitations. We have devised a data-driven spatial audio solution that retains the efficiency of the FOA format but achieves quality that surpasses conventional renderers. Utilizing a fully convolutional time-d…

DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
Usman Muhammad, Jorma Laaksonen
https://arxiv.org/abs/2506.05041 https:…

DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
2D convolutional neural networks (CNNs) have attracted significant attention for hyperspectral image super-resolution tasks. However, a key limitation is their reliance on local neighborhoods, which leads to a lack of global contextual understanding. Moreover, band correlation and data scarcity continue to limit their performance. To mitigate these issues, we introduce DACN, a dual-attention convolutional network for hyperspectral image super-resolution. Specifically, the model first employs au…

Real-World Modeling of Computation Offloading for Neural Networks with Early Exits and Splits
Jan Danek, Zdenek Becvar, Adam Janes
https://arxiv.org/abs/2505.22149

Real-World Modeling of Computation Offloading for Neural Networks with Early Exits and Splits
We focus on computation offloading of applications based on convolutional neural network (CNN) from moving devices, such as mobile robots or autonomous vehicles, to MultiAccess Edge Computing (MEC) servers via a mobile network. In order to reduce overall CNN inference time, we design and implement CNN with early exits and splits, allowing a flexible partial or full offloading of CNN inference. Through real-world experiments, we analyze an impact of the CNN inference offloading on the total CNN …

An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan
Mengyuan Liu, Jeongkyu Lee
https://arxiv.org/abs/2507.08690

An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan
Magnetic resonance imaging (MRI) enables non-invasive, high-resolution analysis of muscle structures. However, automated segmentation remains limited by high computational costs, reliance on large training datasets, and reduced accuracy in segmenting smaller muscles. Convolutional neural network (CNN)-based methods, while powerful, often suffer from substantial computational overhead, limited generalizability, and poor interpretability across diverse populations. This study proposes a training-…

This https://arxiv.org/abs/2307.07618 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility
The Bright Transient Survey (BTS) relies on visual inspection ("scanning") to select sources for accomplishing its mission of spectroscopically classifying all bright extragalactic transients found by the Zwicky Transient Facility (ZTF). We present $\texttt{BTSbot}$, a multi-input convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 14 extracted features. $\texttt{BTSbot}$ eliminates the need for scanning by automatically …

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…

Fast-VGAN: Lightweight Voice Conversion with Explicit Control of F0 and Duration Parameters
Mathilde Abrassart, Nicolas Obin, Axel Roebel
https://arxiv.org/abs/2507.04817

Fast-VGAN: Lightweight Voice Conversion with Explicit Control of F0 and Duration Parameters
Precise control over speech characteristics, such as pitch, duration, and speech rate, remains a significant challenge in the field of voice conversion. The ability to manipulate parameters like pitch and syllable rate is an important element for effective identity conversion, but can also be used independently for voice transformation, achieving goals that were historically addressed by vocoder-based methods. In this work, we explore a convolutional neural network-based approach that aims to…

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Zhaoyang Xu, Yunbo Liu
https://arxiv.org/abs/2506.19877 https…

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models - Multi-Layer Perceptron (MLP), 1D Convolutional Neural Network (CNN), One-Class Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) - on the CICIDS2017 dataset under two scenarios: detecting known attack types and generalizing to previously unseen threats. O…

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Christian Tinauer, Maximilian Sackl, Stefan Ropele, Christian Langkammer
https://arxiv.org/abs/2506.03890

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, raising concerns about interpretability. Previous studies suggest biases in model decisions, necessitating further analysis. This study uses Layer-wise Relevance Propagation (LRP) and spectral clustering to explore classifier decision strategies across preprocessing and training configurations using R2* maps. We trained a 3D convolutional neural net…

Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
An Le, Nehal Mehta, William Freeman, Ines Nagel, Melanie Tran, Anna Heinke, Akshay Agnihotri, Lingyun Cheng, Dirk-Uwe Bartsch, Hung Nguyen, Truong Nguyen, Cheolhong An
https://

Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
In this study, we developed deep learning-based method to classify the type of surgery performed for epiretinal membrane (ERM) removal, either internal limiting membrane (ILM) removal or ERM-alone removal. Our model, based on the ResNet18 convolutional neural network (CNN) architecture, utilizes postoperative optical coherence tomography (OCT) center scans as inputs. We evaluated the model using both original scans and scans preprocessed with energy crop and wavelet denoising, achieving 72% acc…

Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers
Mohanad Obeed, Ming Jian
https://arxiv.org/abs/2506.20084 https://…

Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers
Towards fast, hardware-efficient, and low-complexity receivers, we propose a compression-aware learning approach and examine it on free-space optical (FSO) receivers for turbulence mitigation. The learning approach jointly quantize, prune, and train a convolutional neural network (CNN). In addition, we propose to have the CNN weights of power of two values so we replace the multiplication operations bit-shifting operations in every layer that has significant lower computational cost. The compre…

A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers
Tao Hong, Umberto Villa, Jeffrey A. Fessler
https://arxiv.org/abs/2508.11219

A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers
Model-based reconstruction plays a key role in compressed sensing (CS) MRI, as it incorporates effective image regularizers to improve the quality of reconstruction. The Plug-and-Play and Regularization-by-Denoising frameworks leverage advanced denoisers (e.g., convolutional neural network (CNN)-based denoisers) and have demonstrated strong empirical performance. However, their theoretical guarantees remain limited, as practical CNNs often violate key assumptions. In contrast, gradient-driven d…

Segmenting proto-halos with vision transformers
Toka Alokda, Cristiano Porciani
https://arxiv.org/abs/2508.00049 https://arxiv.org/pdf/2508.00049

Segmenting proto-halos with vision transformers
The formation of dark-matter halos from small cosmological perturbations generated in the early universe is a highly non-linear process typically modeled through N-body simulations. In this work, we explore the use of deep learning to segment and classify proto-halo regions in the initial density field according to their final halo mass at redshift $z=0$. We compare two architectures: a fully convolutional neural network (CNN) based on the V-Net design and a U-Net transformer. We find that the …

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices
Hailong Yan, Ao Li, Xiangtao Zhang, Zhe Liu, Zenglin Shi, Ce Zhu, Le Zhang
https://arxiv.org/abs/2507.01838

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices
Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, such as mobile devices, remains challenging due to high computation and memory demands. To address these challenges and facilitate real-time IE on mobile, we introduce an extremely lightweight Convolutional Neural Network (CNN) framework with around 4K parameters. Our approach integrates reparameterization with an Incr…

From Large-scale Audio Tagging to Real-Time Explainable Emergency Vehicle Sirens Detection
Stefano Giacomelli, Marco Giordano, Claudia Rinaldi, Fabio Graziosi
https://arxiv.org/abs/2506.23437

From Large-scale Audio Tagging to Real-Time Explainable Emergency Vehicle Sirens Detection
Accurate recognition of Emergency Vehicle (EV) sirens is critical for the integration of intelligent transportation systems, smart city monitoring systems, and autonomous driving technologies. Modern automatic solutions are limited by the lack of large scale, curated datasets and by the computational demands of state of the art sound event detection models. This work introduces E2PANNs (Efficient Emergency Pre trained Audio Neural Networks), a lightweight Convolutional Neural Network architectu…

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Qibiao Wu, Yagang Wang, Qian Zhang
https://arxiv.org/abs/2507.06581 https://

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is therefore a prerequisite for enabling rapid bronchoscopic navigation and the clinical deployment of bronchoscopic robotic systems. Although convolutional neural network methods have gained considerable attention in airway segmentation, the unique tree-like structure of airways poses challenges for conventional and deformable convolutions, which oft…

Estimating cluster masses: a comparative study between machine learning and maximum likelihood
Raeed Mundow, Adi Nusser
https://arxiv.org/abs/2507.21876 https://

Estimating cluster masses: a comparative study between machine learning and maximum likelihood
We compare an autoencoder convolutional neural network (AE-CNN) with a conventional maximum-likelihood estimator (MLE) for inferring cluster virial masses, $M_v$, directly from the galaxy distribution around clusters, without identifying members or interlopers. The AE-CNN is trained on mock galaxy catalogues, whereas the MLE assumes that clusters of similar mass share the same phase-space galaxy profile. Conceptually, the MLE returns an unbiased estimate of $\log M_v$ at fixed true mass, wherea…

Estimating Head Motion in Structural MRI Using a Deep Neural Network Trained on Synthetic Artifacts
Charles Bricout, Samira Ebrahimi Kahou, Sylvain Bouix
https://arxiv.org/abs/2505.23916

Estimating Head Motion in Structural MRI Using a Deep Neural Network Trained on Synthetic Artifacts
Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical thickness. Manual review cannot objectively quantify motion in anatomical scans, and existing automated approaches often require specialized hardware or rely on unbalanced noisy training data. Here, we train a 3D convolutional neural network to estimate motion severity using only synthetically corrupted volumes. We validate our method with one held-out site…

Real-Time Emergency Vehicle Siren Detection with Efficient CNNs on Embedded Hardware
Marco Giordano, Stefano Giacomelli, Claudia Rinaldi, Fabio Graziosi
https://arxiv.org/abs/2507.01563

Real-Time Emergency Vehicle Siren Detection with Efficient CNNs on Embedded Hardware
We present a full-stack emergency vehicle (EV) siren detection system designed for real-time deployment on embedded hardware. The proposed approach is based on E2PANNs, a fine-tuned convolutional neural network derived from EPANNs, and optimized for binary sound event detection under urban acoustic conditions. A key contribution is the creation of curated and semantically structured datasets - AudioSet-EV, AudioSet-EV Augmented, and Unified-EV - developed using a custom AudioSet-Tools framework…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Fully Differentiable Lagrangian Convolutional Neural Network for Physics-Informed Precipitation N...
Peter Pavl\'ik, Martin V\'yboh, Anna Bou Ezzeddine, Viera Rozinajov\'a

This https://arxiv.org/abs/2504.11918 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Deep learning to improve the discovery of near-Earth asteroids in the Zwicky Transient Facility
We present a novel pipeline that uses a convolutional neural network (CNN) to improve the detection capability of near-Earth asteroids (NEAs) in the context of planetary defense. Our work aims to minimize the dependency on human intervention of the current approach adopted by the Zwicky Transient Facility (ZTF). The target NEAs have a high proper motion of up to tens of degrees per day and thus appear as streaks of light in the images. We trained our CNNs to detect these streaks using three dat…

Med-URWKV: Pure RWKV With ImageNet Pre-training For Medical Image Segmentation
Zhenhuan Zhou
https://arxiv.org/abs/2506.10858 https://

Med-URWKV: Pure RWKV With ImageNet Pre-training For Medical Image Segmentation
Medical image segmentation is a fundamental and key technology in computer-aided diagnosis and treatment. Previous methods can be broadly classified into three categories: convolutional neural network (CNN) based, Transformer based, and hybrid architectures that combine both. However, each of them has its own limitations, such as restricted receptive fields in CNNs or the computational overhead caused by the quadratic complexity of Transformers. Recently, the Receptance Weighted Key Value (RWKV…

Residual 1D CNN for Low SFR Surface Density Regression: A Design Note
Po-Chieh Yu
#toXiv_bot_toot

Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou
Pengfei Fan, Yuli Zhang, Xinheng Wang, Ruiyuan Jiang, Hankang Gu, Dongyao Jia, Shangbo Wang
https://arxiv.org/abs/2506.21269

Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou
This study presents and publicly releases the Suzhou Urban Road Acoustic Dataset (SZUR-Acoustic Dataset), which is accompanied by comprehensive data-acquisition protocols and annotation guidelines to ensure transparency and reproducibility of the experimental workflow. To model the coupling between vehicular noise and driving speed, we propose a bimodal-feature-fusion deep convolutional neural network (BMCNN). During preprocessing, an adaptive denoising and normalization strategy is applied to …

Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization
Pol Francesch Huc, Emily Bates, Simone D'Amico
https://arxiv.org/abs/2507.19459 https:…

Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization
The advent of novel view synthesis techniques such as NeRF and 3D Gaussian Splatting (3DGS) has enabled learning precise 3D models only from posed monocular images. Although these methods are attractive, they hold two major limitations that prevent their use in space applications: they require poses during training, and have high computational cost at training and inference. To address these limitations, this work contributes: (1) a Convolutional Neural Network (CNN) based primitive initializer…

Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction
Anam Hashmi, Julia Dietlmeier, Kathleen M. Curran, Noel E. O'Connor
https://arxiv.org/abs/2505.23872

Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction
Attention is a fundamental component of the human visual recognition system. The inclusion of attention in a convolutional neural network amplifies relevant visual features and suppresses the less important ones. Integrating attention mechanisms into convolutional neural networks enhances model performance and interpretability. Spatial and channel attention mechanisms have shown significant advantages across many downstream tasks in medical imaging. While existing attention modules have proven …

A Tree-guided CNN for image super-resolution
Chunwei Tian, Mingjian Song, Xiaopeng Fan, Xiangtao Zheng, Bob Zhang, David Zhang
https://arxiv.org/abs/2506.02585

A Tree-guided CNN for image super-resolution
Deep convolutional neural networks can extract more accurate structural information via deep architectures to obtain good performance in image super-resolution. However, it is not easy to find effect of important layers in a single network architecture to decrease performance of super-resolution. In this paper, we design a tree-guided CNN for image super-resolution (TSRNet). It uses a tree architecture to guide a deep network to enhance effect of key nodes to amplify the relation of hierarchica…

Multi-modal brain MRI synthesis based on SwinUNETR
Haowen Pang, Weiyan Guo, Chuyang Ye
https://arxiv.org/abs/2506.02467 https://arxiv…

Multi-modal brain MRI synthesis based on SwinUNETR
Multi-modal brain magnetic resonance imaging (MRI) plays a crucial role in clinical diagnostics by providing complementary information across different imaging modalities. However, a common challenge in clinical practice is missing MRI modalities. In this paper, we apply SwinUNETR to the synthesize of missing modalities in brain MRI. SwinUNETR is a novel neural network architecture designed for medical image analysis, integrating the strengths of Swin Transformer and convolutional neural networ…

This https://arxiv.org/abs/2506.00605 has been replaced.
link: https://scholar.google.com/scholar?q=a

ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education
With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, partic…

Classification based deep learning models for lung cancer and disease using medical images
Ahmad Chaddad, Jihao Peng, Yihang Wu
https://arxiv.org/abs/2507.01279

Classification based deep learning models for lung cancer and disease using medical images
The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the established ResNet framework. This model is specifically designed to improve the prediction of lung cancer and diseases using the images. To address the challenge of missing feature information that occurs during the downsampling process in CNNs, we integrate …

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Jisoo Kim, Chu-Hsuan Lin, Alberto Ceballos-Arroyo, Ping Liu, Huaizu Jiang, Shrikanth Yadav, Qi Wan, Lei Qin, Geoffrey S Young
https://arxiv.org/abs/2507.00832

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D convolutional neural network-transformer hybrid (3D-CN…