Tootfinder

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…

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…

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…

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…

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…

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 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…

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 …

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…

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…