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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…

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 …

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…

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…

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…

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…

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 …

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…

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…

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…

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…

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

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/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…

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/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…

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 …