Tootfinder

Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering
Chen Liu, Mingchen Li, Yang Tan, Wenrui Gou, Guisheng Fan, Bingxin Zhou
https://arxiv.org/abs/2505.20301

Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering
A pivotal area of research in antibody engineering is to find effective modifications that enhance antibody-antigen binding affinity. Traditional wet-lab experiments assess mutants in a costly and time-consuming manner. Emerging deep learning solutions offer an alternative by modeling antibody structures to predict binding affinity changes. However, they heavily depend on high-quality complex structures, which are frequently unavailable in practice. Therefore, we propose ProtAttBA, a deep learn…

U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs
Racheal Mukisa, Arvind K. Bansal
https://arxiv.org/abs/2506.20689

U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs
Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links…

Deep Learning Framework Testing via Model Mutation: How Far Are We?
Yanzhou Mu, Rong Wang, Juan Zhai, Chunrong Fang, Xiang Chen, Zhiyuan Peng, Peiran Yang, Ruixiang Qian, Shaoyu Yang, Zhenyu Chen
https://arxiv.org/abs/2506.17638

Deep Learning Framework Testing via Model Mutation: How Far Are We?
Deep Learning (DL) frameworks are a fundamental component of DL development. Therefore, the detection of DL framework defects is important and challenging. As one of the most widely adopted DL testing techniques, model mutation has recently gained significant attention. In this study, we revisit the defect detection ability of existing mutation-based testing methods and investigate the factors that influence their effectiveness. To begin with, we reviewed existing methods and observed that many…

Testing a 95 GeV Scalar at the CEPC with Machine Learning
Yabo Dong, Manqi Ruan, Kun Wang, Haijun Yang, Jingya Zhu
https://arxiv.org/abs/2506.21454 https:/…

Testing a 95 GeV Scalar at the CEPC with Machine Learning
Several possible excesses around $95\,$GeV hint at an additional light scalar beyond the Standard Model. We examine the capability of the CEPC to test this hypothesis in the Higgsstrahlung channel $e^{+}e^{-}\!\to ZS$ with $Z\!\toμ^{+}μ^{-}$ and $S\!\toτ^{+}τ^{-}$. Full detector simulation shows that the optimal center-of-mass energy to study the 95 GeV light scalar is 210 GeV. A deep neural network classifier reduces the luminosity required for discovery by half. At $L=500$ fb$^{-1}$, the …

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…

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 …

SeisCoDE: 3D Seismic Interpretation Foundation Model with Contrastive Self-Distillation Learning
Goodluck Archibong, Ardiansyah Koeshidayatullah, Umair Waheed, Weichang Li, Dicky Harishidayat, Motaz Alfarraj
https://arxiv.org/abs/2505.20518

SeisCoDE: 3D Seismic Interpretation Foundation Model with Contrastive Self-Distillation Learning
Seismic interpretation is vital for understanding subsurface structures but remains labor-intensive, subjective, and computationally demanding. While deep learning (DL) offers promise, its success hinges on large, high-quality datasets, often scarce in geophysics. Foundation Models (FMs), which have shown significant success in fields like natural language processing and computer vision, offer a transformative opportunity for seismic interpretation by enabling knowledge transfer and generalizat…

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Adam Foster, Zeno Sch\"atzle, P. Bern\'at Szab\'o, Lixue Cheng, Jonas K\"ohler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank No\'e, Jan Hermann
https://arxiv.org/abs/2506.19960

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireferential character of the electronic structure in dissociating species. Multireferential methods in particular suffer from large computational cost, which under the normal paradigm has to be paid anew for each system at a full price, ignoring commonalities in electronic structure across molecules. Quantum Monte Carlo with deep neural networks (deep QMC) uniquely offers to exploit such common…

A deep-learning model for predicting daily PM2.5 concentration in response to emission reduction
Shigan Liu, Guannan Geng, Yanfei Xiang, Hejun Hu, Xiaodong Liu, Xiaomeng Huang, Qiang Zhang
https://arxiv.org/abs/2506.18018

A deep-learning model for predicting daily PM2.5 concentration in response to emission reduction
Air pollution remains a leading global health threat, with fine particulate matter (PM2.5) contributing to millions of premature deaths annually. Chemical transport models (CTMs) are essential tools for evaluating how emission controls improve air quality and save lives, but they are computationally intensive. Reduced form models accelerate simulations but sacrifice spatial-temporal granularity, accuracy, and flexibility. Here we present CleanAir, a deep-learning-based model developed as an eff…

Measurements, simulations, and models of the point-spread function of electron-beam lithography
Nikolaj B. Hougs, Kristian S. Knudsen, Marcus Albrechtsen, Taichi Suhara, Christian A. Rosiek, S{\o}ren Stobbe
https://arxiv.org/abs/2506.21236

Measurements, simulations, and models of the point-spread function of electron-beam lithography
When a sample is exposed using electron-beam lithography, the electrons scatter deep and far in the substrate, resulting in unwanted deposition of dose at both the nano- and the microscale. This proximity effect can be mitigated by proximity effect correction provided that accurate and validated models of the point-spread function of the electron scattering are available. Most works so far considered a double-Gaussian model of the electron point-spread function, which is very inaccurate for mod…

Development of MR spectral analysis method robust against static magnetic field inhomogeneity
Shuki Maruyama, Hidenori Takeshima
https://arxiv.org/abs/2506.20897

Development of MR spectral analysis method robust against static magnetic field inhomogeneity
Purpose:To develop a method that enhances the accuracy of spectral analysis in the presence of static magnetic field B0 inhomogeneity. Methods:The authors proposed a new spectral analysis method utilizing a deep learning model trained on modeled spectra that consistently represent the spectral variations induced by B0 inhomogeneity. These modeled spectra were generated from the B0 map and metabolite ratios of the healthy human brain. The B0 map was divided into a patch size of subregions, and t…

A Comparative Study of NAFNet Baselines for Image Restoration
Vladislav Esaulov, M. Moein Esfahani
https://arxiv.org/abs/2506.19845 https://

A Comparative Study of NAFNet Baselines for Image Restoration
We study NAFNet (Nonlinear Activation Free Network), a simple and efficient deep learning baseline for image restoration. By using CIFAR10 images corrupted with noise and blur, we conduct an ablation study of NAFNet's core components. Our baseline model implements SimpleGate activation, Simplified Channel Activation (SCA), and LayerNormalization. We compare this baseline to different variants that replace or remove components. Quantitative results (PSNR, SSIM) and examples illustrate how each m…

PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching
Guillem Cort\`es-Sebasti\`a, Benjamin Martin, Emilio Molina, Xavier Serra, Romain Hennequin
https://arxiv.org/abs/2506.21086

PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching
This work introduces PeakNetFP, the first neural audio fingerprinting (AFP) system designed specifically around spectral peaks. This novel system is designed to leverage the sparse spectral coordinates typically computed by traditional peak-based AFP methods. PeakNetFP performs hierarchical point feature extraction techniques similar to the computer vision model PointNet++, and is trained using contrastive learning like in the state-of-the-art deep learning AFP, NeuralFP. This combination allow…

Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning
Xiaodan Shao, Limei Hu, Yulong Sun, Xing Li, Yixiao Zhang, Jingze Ding, Xiaoming Shi, Feng Chen, Derrick Wing Kwan Ng, Robert Schober
https://arxiv.org/abs/2506.15808

Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning
Six-dimensional movable antenna (6DMA) has been identified as a new disruptive technology for future wireless systems to support a large number of users with only a few antennas. However, the intricate relationships between the signal carrier wavelength and the transceiver region size lead to inaccuracies in traditional far-field 6DMA channel model, causing discrepancies between the model predictions and the hybrid-field channel characteristics in practical 6DMA systems, where users might be in…

Aptamer-protein interaction prediction model based on transformer
Zhichao Yan, Yue Kang, Buyong Ma
https://arxiv.org/abs/2506.16084 https://

Aptamer-protein interaction prediction model based on transformer
Aptamers are single-stranded DNA/RNAs or short peptides with unique tertiary structures that selectively bind to specific targets. They have great potential in the detection and medical fields. Here, we present SelfTrans-Ensemble, a deep learning model that integrates sequence information models and structural information models to extract multi-scale features for predicting aptamer-protein interactions (APIs). The model employs two pre-trained models, ProtBert and RNA-FM, to encode protein and…

A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
Yiyu Ni, Marine A. Denolle, Amanda M. Thomas, Alex Hamilton, Jannes M\"unchmeyer, Yinzhi Wang, Lo\"ic Bachelot, Chad Trabant, David Mencin
https://arxiv.org/abs/2505.18874

A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing services on Amazon Web Services, we launched ~145,000 containerized jobs on continuous records from 47,354 stations spanning 2002-2025, completing in under three days. Phase arrivals were identified with a deep learning model, PhaseNet, through an open-source Python ecosystem for deep learning, SeisBench. To visualize and …

An analytical model of depth-dose distributions for carbon-ion beams
Fulya Hal{\i}c{\i}lar, Metin Ar{\i}k
https://arxiv.org/abs/2506.19479 https://

An analytical model of depth-dose distributions for carbon-ion beams
Improving effective treatment plans in carbon ion therapy, especially for targeting radioresistant tumors located in deep seated regions while sparing normal tissues, depends on a precise and computationally efficient dose calculation model. Although dose calculations are mostly performed using Monte Carlo simulations, the large amount of computational effort required for these simulations hinders their use in clinical practice. To address this gap, we propose, for the first time in the literat…

Google DeepMind Just Changed Hurricane Forecasting Forever With New AI Model (VentureBeat, 11 June 2025)
https://venturebeat.com/ai/google-deepmind-just-changed-hurricane-forecasting-forever-with-new-ai-model/

Google DeepMind just changed hurricane forecasting forever with new AI model
Google DeepMind launches Weather Lab platform for AI hurricane forecasting, showing improved accuracy in early tests with U.S. National Hurricane Center partnership.

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Pengyu Han, Zeyi Liu, Shijin Chen, Dongliang Zou, Xiao He
https://arxiv.org/abs/2506.17740

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions across different operating conditions degrades model performance when a model trained under one condition is applied to others. With the recent advancements in deep learning, transfer learning has been introduced to the fault diagnosis field as a paradigm for addressing multi-condition fault diagnosis. Among these me…

Some researchers say it looks like DeepSeek may have used Google Gemini to help train its R1-0528 reasoning model (Kyle Wiggers/TechCrunch)
https://techcrunch.com/2025/06/03/deepseek-may-have-used-googles-gemini-to-train-its-lates…

DeepSeek may have used Google's Gemini to train its latest model | TechCrunch
Chinese AI lab DeepSeek released an updated version of its R1 reasoning model that performs well on a number of math and coding benchmarks. Some AI researchers speculate that at least a portion came from Google's Gemini family of AI.

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
Francesco Marchiori, Marco Alecci, Luca Pajola, Mauro Conti
https://arxiv.org/abs/2506.18516

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
Adversarial examples are small and often imperceptible perturbations crafted to fool machine learning models. These attacks seriously threaten the reliability of deep neural networks, especially in security-sensitive domains. Evasion attacks, a form of adversarial attack where input is modified at test time to cause misclassification, are particularly insidious due to their transferability: adversarial examples crafted against one model often fool other models as well. This property, known as a…

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang, Kowei Shih, Dannier Li, Chengrui Zhou
https://arxiv.org/abs/2506.11421

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined s…

Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso, Simone Rossi, Giulio Franzese, Markus Heinonen, Maurizio Filippone
https://arxiv.org/abs/2506.09648

Scaling Laws for Uncertainty in Deep Learning
Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by these findings and fascinating phenomena emerging in the over-parameterized regime, we examine a parallel direction: do similar scaling laws govern predictive uncertainties in deep learning? In identifiable parametric models, such scaling laws can be derived in a straightforward manner by treating model parameters in a B…

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Ne\c{s}et \"Unver Akmandor, Sarvesh Prajapati, Mark Zolotas, Ta\c{s}k{\i}n Pad{\i}r
https://arxiv.org/abs/2506.08344

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Traditional motion planning methods for robots with many degrees-of-freedom, such as mobile manipulators, are often computationally prohibitive for real-world settings. In this paper, we propose a novel multi-model motion planning pipeline, termed Re4MPC, which computes trajectories using Nonlinear Model Predictive Control (NMPC). Re4MPC generates trajectories in a computationally efficient manner by reactively selecting the model, cost, and constraints of the NMPC problem depending on the comp…

The time course of visuo-semantic representations in the human brain is captured by combining vision and language models
Boyan Rong, Alessandro Thomas Gifford, Emrah D\"uzel, Radoslaw Martin Cichy
https://arxiv.org/abs/2506.19497

The time course of visuo-semantic representations in the human brain is captured by combining vision and language models
The human visual system provides us with a rich and meaningful percept of the world, transforming retinal signals into visuo-semantic representations. For a model of these representations, here we leveraged a combination of two currently dominating approaches: vision deep neural networks (DNNs) and large language models (LLMs). Using large-scale human electroencephalography (EEG) data recorded during object image viewing, we built encoding models to predict EEG responses using representations f…

Emergent collective dynamics from motile photokinetic organisms
J. Morales, P. Munoz, D. Noto, H. N Ulloa, F. Guzman-Lastra
https://arxiv.org/abs/2506.19081

Emergent collective dynamics from motile photokinetic organisms
The day-night cycle drives the largest biomass migration on Earth: the diel vertical migration (DVM) of aquatic organisms. Here, we present a three-dimensional agent-based model that incorporates photokinesis, gyrotaxis, and stochastic reorientation to explore how individual-level swimming behaviors give rise to population-scale DVM patterns. By solving Langevin equations for swarms of swimmers, we identify four distinct regimes -- Surface Accumulation, Shallow DVM, Deep DVM, and Sinking -- gov…

MUSE-DARK-I: Dark matter halo properties of intermediate-z star-forming galaxies
B. I. Ciocan, N. F. Bouch\'e, J. Fensch, W. Mercier, D. Krajnovi\'c, J. Richard, T. Contini, A. Jeanneau
https://arxiv.org/abs/2506.19721

MUSE-DARK-I: Dark matter halo properties of intermediate-z star-forming galaxies
[Abridged] We analyse the dark matter (DM) halo properties of 127 0.3

The Blind Spot of BGP Anomaly Detection: Why LSTM Autoencoders Fail on Real-World Outages
Samuel Oluwafemi Adebayo
https://arxiv.org/abs/2506.17821 https:/…

The Blind Spot of BGP Anomaly Detection: Why LSTM Autoencoders Fail on Real-World Outages
Deep learning has significant potential to make the Internet's Border Gateway Protocol (BGP) secure by detecting anomalous routing activity. However, all but a few of these approaches rely on the implicit assumption that anomalies manifest as noisy, high-complexity outliers from some normal baseline. This work challenges this assumption by investigating if a best-in-class detection model built on this assumption can effectively deal with real-world security events' diverse signatures. We employ…

RCNet: $\Delta\Sigma$ IADCs as Recurrent AutoEncoders
Arnaud Verdant, William Guicquero, J\'er\^ome Chossat
https://arxiv.org/abs/2506.16903 https://…

RCNet: $ΔΣ$ IADCs as Recurrent AutoEncoders
This paper proposes a deep learning model (RCNet) for Delta-Sigma ($ΔΣ$) ADCs. Recurrent Neural Networks (RNNs) allow to describe both modulators and filters. This analogy is applied to Incremental ADCs (IADC). High-end optimizers combined with full-custom losses are used to define additional hardware design constraints: quantized weights, signal saturation, temporal noise injection, devices area. Focusing on DC conversion, our early results demonstrate that $SNR$ defined as an Effective Numb…

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu
https://arxiv.org/abs/2506.06001

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
Scientific computing for large deformation of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximation and are constrained by an inherent trade-off between accuracy and efficiency. Recently, deep learning models have achieved impressive progress in solving the continuum mechanism. While previous models have explored various architectures and constructed coefficient-solution mappings, they are desig…

TrainVerify: Equivalence-Based Verification for Distributed LLM Training
Yunchi Lu, Youshan Miao, Cheng Tan, Peng Huang, Yi Zhu, Xian Zhang, Fan Yang
https://arxiv.org/abs/2506.15961

TrainVerify: Equivalence-Based Verification for Distributed LLM Training
Training large language models (LLMs) at scale requires parallel execution across thousands of devices, incurring enormous computational costs. Yet, these costly distributed trainings are rarely verified, leaving them prone to silent errors and potentially wasting millions of GPU hours. We introduce TrainVerify, a system for verifiable distributed training of LLMs. Given a deep learning model's logical specification as the ground truth, TrainVerify formally verifies that a distributed parallel …

Multi-partonic interactions, iterated discontinuities and the virtuality expansion in deep inelastic scattering
Zeno Capatti, Lucien Huber, Michael Ruf
https://arxiv.org/abs/2506.16489

Multi-partonic interactions, iterated discontinuities and the virtuality expansion in deep inelastic scattering
We introduce a perturbative model that accounts for the contribution of multi-partonic interactions to collider observables. A key feature of this multi-parton model is that cross sections are organised in terms of building blocks that are separately infrared-finite in virtue of the KLN theorem. We find compact expressions for these building blocks in terms of double discontinuities of Feynman-type integrals obtained by endowing a vacuum graph with additional topological information. The framew…

Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation
Zhifeng Wang, Renjiao Yi, Xin Wen, Chenyang Zhu, Kai Xu, Kunlun He
https://arxiv.org/abs/2506.19455

Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation
Vascular diseases pose a significant threat to human health, with X-ray angiography established as the gold standard for diagnosis, allowing for detailed observation of blood vessels. However, angiographic X-rays expose personnel and patients to higher radiation levels than non-angiographic X-rays, which are unwanted. Thus, modality translation from non-angiographic to angiographic X-rays is desirable. Data-driven deep approaches are hindered by the lack of paired large-scale X-ray angiography …

FerroAI: A Deep Learning Model for Predicting Phase Diagrams of Ferroelectric Materials
Chenbo Zhang, Xian Chen
https://arxiv.org/abs/2506.10970 https://…

FerroAI: A Deep Learning Model for Predicting Phase Diagrams of Ferroelectric Materials
Composition-temperature phase diagrams are crucial for designing ferroelectric materials, however predicting them accurately remains challenging due to limited phase transformation data and the constraints of conventional methods. Here, we utilize natural language processing (NLP) to text-mine 41,597 research articles, compiling a dataset of 2,838 phase transformations across 846 ferroelectric materials. Leveraging this dataset, we develop FerroAI, a deep learning model for phase diagram predic…

Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
M. Janssen, C. -k. Chan, J. Davelaar, M. Wielgus
https://arxiv.org/abs/2506.13875

Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
(abridged) In this second paper in our publication series, we present the open-source Zingularity framework for parameter inference with deep Bayesian artificial neural networks. We carried out out supervised learning with synthetic millimeter very long baseline interferometry observations of the EHT. Our ground-truth models are based on GRMHD simulations of Sgr A* and M87* on horizon scales. We investigated how well Zingularity neural networks are able to infer key model parameters from EHT ob…

Partially-Supervised Neural Network Model For Quadratic Multiparametric Programming
Fuat Can Beylunioglu, Mehrdad Pirnia, P. Robert Duimering
https://arxiv.org/abs/2506.05567

Partially-Supervised Neural Network Model For Quadratic Multiparametric Programming
Neural Networks (NN) with ReLU activation functions are used to model multiparametric quadratic optimization problems (mp-QP) in diverse engineering applications. Researchers have suggested leveraging the piecewise affine property of deep NN models to solve mp-QP with linear constraints, which also exhibit piecewise affine behaviour. However, traditional deep NN applications to mp-QP fall short of providing optimal and feasible predictions, even when trained on large datasets. This study propos…

Unsupervised deep learning model for fast energy layer pre-selection of delivery-efficient proton arc therapy plan optimization of nasopharyngeal carcinoma
Bohan Yang, Gang Liu, Rirao Dao, Yujia Qian, Ke Shi, Anke Tang, Yong Luo, Jingnan Liu
https://arxiv.org/abs/2506.15803

Unsupervised deep learning model for fast energy layer pre-selection of delivery-efficient proton arc therapy plan optimization of nasopharyngeal carcinoma
Objective. Proton arc therapy (PAT) is an emerging and promising modality in radiotherapy, offering several advantages over conventional intensitymodulated proton therapy (IMPT). However, identifying the optimal energy layer (EL) sequence remains computationally intensive due to the large number of possible energy layer transitions. This study proposes an unsupervised deep learning framework for fast and effective EL pre-selection, aiming to minimize energy layer switch time while preserving hi…

Overfitting in Histopathology Model Training: The Need for Customized Architectures
Saghir Alfasly, Ghazal Alabtah, H. R. Tizhoosh
https://arxiv.org/abs/2506.16631

Overfitting in Histopathology Model Training: The Need for Customized Architectures
This study investigates the critical problem of overfitting in deep learning models applied to histopathology image analysis. We show that simply adopting and fine-tuning large-scale models designed for natural image analysis often leads to suboptimal performance and significant overfitting when applied to histopathology tasks. Through extensive experiments with various model architectures, including ResNet variants and Vision Transformers (ViT), we show that increasing model capacity does not …

CLIP-HandID: Vision-Language Model for Hand-Based Person Identification
Nathanael L. Baisa, Babu Pallam, Amudhavel Jayavel
https://arxiv.org/abs/2506.12447

CLIP-HandID: Vision-Language Model for Hand-Based Person Identification
This paper introduces a new approach to person identification based on hand images, designed specifically for criminal investigations. The method is particularly valuable in serious crimes like sexual abuse, where hand images are often the sole identifiable evidence available. Our proposed method, CLIP-HandID, leverages pre-trained foundational vision-language model, particularly CLIP, to efficiently learn discriminative deep feature representations from hand images given as input to the image …

Attention Head Embeddings with Trainable Deep Kernels for Hallucination Detection in LLMs
Rodion Oblovatny, Alexandra Bazarova, Alexey Zaytsev
https://arxiv.org/abs/2506.09886

Attention Head Embeddings with Trainable Deep Kernels for Hallucination Detection in LLMs
We present a novel approach for detecting hallucinations in large language models (LLMs) by analyzing the probabilistic divergence between prompt and response hidden-state distributions. Counterintuitively, we find that hallucinated responses exhibit smaller deviations from their prompts compared to grounded responses, suggesting that hallucinations often arise from superficial rephrasing rather than substantive reasoning. Leveraging this insight, we propose a model-intrinsic detection method t…

Sparse Autoencoders Bridge The Deep Learning Model and The Brain
Ziming Mao, Jia Xu, Zeqi Zheng, Haofang Zheng, Dabing Sheng, Yaochu Jin, Guoyuan Yang
https://arxiv.org/abs/2506.11123

Sparse Autoencoders Bridge The Deep Learning Model and The Brain
We present SAE-BrainMap, a novel framework that directly aligns deep learning visual model representations with voxel-level fMRI responses using sparse autoencoders (SAEs). First, we train layer-wise SAEs on model activations and compute the correlations between SAE unit activations and cortical fMRI signals elicited by the same natural image stimuli with cosine similarity, revealing strong activation correspondence (maximum similarity up to 0.76). Depending on this alignment, we construct a vo…

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models
Zhenyu Liao, Michael W. Mahoney
https://arxiv.org/abs/2506.13139 https://

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models
Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, where classical low-dimensional intuitions break down. In particular, the proportional regime where the data dimension, sample size, and number of model parameters are all large and comparable, gives rise to novel and sometimes counterintuitive behaviors. This paper extends traditional Random Matrix Theory (RMT) beyond eigenvalue-based analysis of linear mode…

This https://arxiv.org/abs/2505.16223 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network
In this paper, we propose MADCluster, a novel model-agnostic anomaly detection framework utilizing self-supervised clustering. MADCluster is applicable to various deep learning architectures and addresses the 'hypersphere collapse' problem inherent in existing deep learning-based anomaly detection methods. The core idea is to cluster normal pattern data into a 'single cluster' while simultaneously learning the cluster center and mapping data close to this center. Also, to improve expressiveness…

These pieces are much harsher than Fred’s, much more in the LLM-bashing camp, but feel relevant here:
https://softwarecrisis.dev/letters/llmentalist/
https://pivot-to-ai.com/2025/06/05/generative-ai-runs-on-gambling-addiction-just-one-more-prompt-bro/
Fred’s point is that a bad interaction model creates hidden work, then humans do that work and “the machine claims the praise.” These other two pieces make the point that this phenomenon of giving the machine credit for unrecognized human work is age-old, taps into some deep trapdoors in human cognition.
10/

This https://arxiv.org/abs/2505.06732 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Particle Identification with Deep Neural Networks Across Collision Energies in Simulated Proton-Proton Collisions
This study demonstrates a proof-of-concept application of a deep neural network for particle identification in simulated high transverse momentum proton-proton collisions, with a focus on evaluating model performance under controlled conditions. A model trained on simulated Large Hadron Collider (LHC) proton-proton collisions at $\sqrt{s} = 13\,\mathrm{TeV}$ is used to classify nine particle species based on seven kinematic-level features. The model is then tested on simulated high transverse m…

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
Jihao Li, Keqi Zhu, Guodong Lu, I-Ming Chen, Huixu Dong
https://arxiv.org/abs/2506.11570

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers…

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Ramneet Singh, Sathvik Joel, Abhav Mehrotra, Nalin Wadhwa, Ramakrishna B Bairi, Aditya Kanade, Nagarajan Natarajan
https://arxiv.org/abs/2506.11060

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and complexities of systems code, making changes to a systems codebase is a daunting task, even for humans. It requires researching about many pieces of context, derived from the large codebase and its massive commit history, before making changes. Inspired by the recent progress on deep research agents, we design the fi…

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation
Shunxin Yao
https://arxiv.org/abs/2506.02790 https://

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation
OC-DeepIV is a neural network model designed for estimating causal effects. It characterizes heterogeneity by adding interaction features and reduces redundancy through orthogonal constraints. The model includes two feature extractors, one for the instrumental variable Z and the other for the covariate X*. The training process is divided into two stages: the first stage uses the mean squared error (MSE) loss function, and the second stage incorporates orthogonal regularization. Experimental res…

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Chi Liu, Zhezhuang Xu, Jiawei Zhou, Yazhou Yuan, Kai Ma, Meng Yuan
https://arxiv.org/abs/2506.06824

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance system flexibility when integrated with stationary energy storage systems (ESS) for real-time scheduling. However, differing degradation and operational characteristics of ESS and EVs complicate scheduling strategies. This paper proposes a model-free deep reinforcement learning (…

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
Jiahao Yang, Ran Fang, Ming Zhang, Jun Zhou
https://arxiv.org/abs/2505.22678

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes. However, this task is challenging due to the high-dimensional and volatile nature of the original data. Even recent deep learning models often struggle to capture price movement patterns effectively, particularly without well-designed features. We observed that raw LOB data exhibits inherent symmetry between the ask and bid sides, and the bid-ask diffe…

This https://arxiv.org/abs/2501.13255 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Stochastic Deep Learning Surrogate Models for Uncertainty Propagation in Microstructure-Properties of Ceramic Aerogels
This study presents an integrated computational framework that, given synthesis parameters, predicts the resulting microstructural morphology and mechanical response of ceramic aerogel porous materials by combining physics-based simulations with deep learning surrogate models. Lattice Boltzmann simulations are employed to model microstructure formation during material synthesis process, while a finite element model is used to compute the corresponding mechanical properties. To overcome the proh…

AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis
Jiale Liu, Dandan Peng, Huan Wang, Chenyu Liu, Yan-Fu Li, Min Xie
https://arxiv.org/abs/2506.16225

AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis
Aerospace engines, as critical components in aviation and aerospace industries, require continuous and accurate fault diagnosis to ensure operational safety and prevent catastrophic failures. While deep learning techniques have been extensively studied in this context, they output logits or confidence scores, necessitating post-processing to derive actionable insights. Furthermore, the potential of large-scale audio models in this domain remains largely untapped. To address these limitations, t…

Gradient Similarity Surgery in Multi-Task Deep Learning
Thomas Borsani, Andrea Rosani, Giuseppe Nicosia, Giuseppe Di Fatta
https://arxiv.org/abs/2506.06130

Gradient Similarity Surgery in Multi-Task Deep Learning
The multi-task learning ($MTL$) paradigm aims to simultaneously learn multiple tasks within a single model capturing higher-level, more general hidden patterns that are shared by the tasks. In deep learning, a significant challenge in the backpropagation training process is the design of advanced optimisers to improve the convergence speed and stability of the gradient descent learning rule. In particular, in multi-task deep learning ($MTDL$) the multitude of tasks may generate potentially conf…

This https://arxiv.org/abs/2504.13883 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signals in Educational Game Playing
This study estimates cognitive effort (CE) based on functional near-infrared spectroscopy (fNIRS) data and performance scores using a hybrid deep learning model. The estimation of CE enables educators to modify material to enhance learning effectiveness and student engagement. Relative neural efficiency (RNE) and relative neural involvement (RNI) are two metrics that have been used to represent CE. To estimate RNE and RNI we need hemodynamic response in the brain and the performance score of a …

Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
Mahmoud Mbarak, Manmeet Singh, Naveen Sudharsan, Zong-Liang Yang
https://arxiv.org/abs/2506.12919

Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
Accurate soil moisture prediction during extreme events remains a critical challenge for Earth system modeling, with profound implications for drought monitoring, flood forecasting, and climate adaptation strategies. While land surface models (LSMs) provide physically-based predictions, they exhibit systematic biases during extreme conditions when their parameterizations operate outside calibrated ranges. Here we present NoahMP-AI, a physics-guided deep learning framework that addresses this ch…

Deep Learning and Explainable AI: New Pathways to Genetic Insights
Chenyu Wang, Chaoying Zuo, Zihan Su, Yuhang Xing, Lu Li, Maojun Wang, Zeyu Zhang
https://arxiv.org/abs/2505.09873

Deep Learning and Explainable AI: New Pathways to Genetic Insights
Deep learning-based AI models have been extensively applied in genomics, achieving remarkable success across diverse applications. As these models gain prominence, there exists an urgent need for interpretability methods to establish trustworthiness in model-driven decisions. For genetic researchers, interpretable insights derived from these models hold significant value in providing novel perspectives for understanding biological processes. Current interpretability analyses in genomics predomi…

Quantifying Flow State Dynamics: A Prefrontal Cortex EEG-Based Model Validation Study. Unveiling the Prefrontal Cortex's Role in Flow State Experience: An Empirical EEG Analysis
Gianluca Rosso, Raffaella Ricci, Lorenzo Pia, Giovanni Rebaudo, Michele Guindani, Alberto Marocchino, Giorgio De Pieri, Andrea Filippo Rosso
https://

Quantifying Flow State Dynamics: A Prefrontal Cortex EEG-Based Model Validation Study. Unveiling the Prefrontal Cortex's Role in Flow State Experience: An Empirical EEG Analysis
This article aims to explore the optimization of mental performance through the analysis of metrics associated with the psychological state known as flow. Several clinical studies have shown a correlation between the mental state of flow (characterized by deep and relaxed concentration and high psychophysical efficiency) and brain activity measured through electroencephalography (EEG). This study confirms such a correlation, focusing in particular on the sports field, where the flow state tends…

This https://arxiv.org/abs/2407.03883 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual property rights. Existing white-box testing-based approaches cannot address the common…

This https://arxiv.org/abs/2412.08701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference
We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizations of galaxy luminosity functions, morphologies and spectral energy distributions. Model constraints are derived through iterative Approximate Bayesian Computation, by comparing Hyper Suprime-Cam deep field images with simulations which include a forward model of instrumental, observational and source extraction effec…

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
Matteo Zecchin, Tomer Raviv, Dileep Kalathil, Krishna Narayanan, Nir Shlezinger, Osvaldo Simeone
https://arxiv.org/abs/2506.15176

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. Leveraging programmable hardware architectures, deep learning-based receivers offer the potential to dynamically adapt to varying channel environments. However, current adaptation strategies, including joint training, hypernetwork-based methods, and meta-learning, either demonstrate limited flexibility or necessitate e…

A deep learning model for chemical shieldings in molecular organic solids including anisotropy
Matthias Kellner, Jacob B. Holmes, Ruben Rodriguez-Madrid, Florian Viscosi, Yuxuan Zhang, Lyndon Emsley, Michele Ceriotti
https://arxiv.org/abs/2506.13146

A deep learning model for chemical shieldings in molecular organic solids including anisotropy
Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction acc…

A Deep Generative Model for the Simulation of Discrete Karst Networks
Dany Lauzon, Julien Straubhaar, Philippe Renard
https://arxiv.org/abs/2506.09832 http…

A Deep Generative Model for the Simulation of Discrete Karst Networks
The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture …

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
Mohammad Ghassemi, Sara Farrag Mobarak, Han Zhang, Ali Afana, Akram Bin Sediq, Melike Erol-Kantarci
https://arxiv.org/abs/2506.09855

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling signal propagation in the environment. However, their efficient deployment relies on accurate channel state information (CSI), which leads to high channel estimation overhead due to their passive nature and the large number of reflective elements. In this work, we solve this challenge by proposing a novel framework that leverages a pre-trained open-sou…

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman, Jean Lahoud, Mustansar Fiaz, Hisham Cholakkal
https://arxiv.org/abs/2506.12208

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreove…

Deep-Inelastic Scattering at TeV Energies with LHC Muons
Reinaldo Francener, Victor P. Goncalves, Felix Kling, Peter Krack, Juan Rojo
https://arxiv.org/abs/2506.13889

Deep-Inelastic Scattering at TeV Energies with LHC Muons
The LHC far-forward experiments FASER and SND@LHC have pioneered the detection of TeV-energy neutrinos produced in hard-scattering proton-proton collisions at the LHC. In addition to neutrinos, an intense flux of TeV-energy muons reaches these detectors, representing a dominant background for both neutrino studies and beyond the Standard Model searches. Here we demonstrate that this forward muon flux enables a comprehensive neutral-current deep-inelastic scattering (DIS) program at FASER with a…

Automatic Cannulation of Femoral Vessels in a Porcine Shock Model
Nico Zevallos, Cecilia G. Morales, Andrew Orekhov, Tejas Rane, Hernando Gomez, Francis X. Guyette, Michael R. Pinsky, John Galeotti, Artur Dubrawski, Howie Choset
https://arxiv.org/abs/2506.14467

Automatic Cannulation of Femoral Vessels in a Porcine Shock Model
Rapid and reliable vascular access is critical in trauma and critical care. Central vascular catheterization enables high-volume resuscitation, hemodynamic monitoring, and advanced interventions like ECMO and REBOA. While peripheral access is common, central access is often necessary but requires specialized ultrasound-guided skills, posing challenges in prehospital settings. The complexity arises from deep target vessels and the precision needed for needle placement. Traditional techniques, li…

Designing Deep Learning Frameworks for LLMs:Challenges, Expectations, and Opportunities
Yanzhou Mu, Rong Wang, Juan Zhai, Chunrong Fang, Xiang Chen, Jiacong Wu, An Guo, Jiawei Shen, Bingzhuo Li, Zhenyu Chen
https://arxiv.org/abs/2506.13114

Designing Deep Learning Frameworks for LLMs:Challenges, Expectations, and Opportunities
Large language models (LLMs) drive significant advancements in real industry applications. LLMs rely on DL frameworks for efficient model construction, distributed execution, and optimized deployment. Their large parameter scale and long execution cycles place extreme demands on DL frameworks in terms of scalability, stability, and efficiency. Therefore, poor usability, limited functionality, and subtle bugs in DL frameworks may hinder development efficiency and cause severe failures or resourc…

Classification of Multi-Parametric Body MRI Series Using Deep Learning
Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm, Peter A. Pinto, Ronald M. Summers
https://arxiv.org/abs/2506.15182

Classification of Multi-Parametric Body MRI Series Using Deep Learning
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional technologist errors. To address this, we present a deep learning-based classification model to classify 8 different body mpMRI series types so that radiologists read the exams efficiently. Using mpMRI data from various institutions, multiple deep lea…

Understanding Stability Mechanisms in Single-Atom Alloys with Theory-infused Deep Learning
Yang Huang, Shih-Han Wang, Shuyi Cao, Luke E. K. Achenie, Hongliang Xin
https://arxiv.org/abs/2506.03031

Understanding Stability Mechanisms in Single-Atom Alloys with Theory-infused Deep Learning
We present an interpretable deep learning model that enhances the prediction of cohesive energy in transition metal alloys (TMAs) by incorporating cohesion theory into a graph neural network (GNN) framework. The model not only predicts the total cohesive energy-an indicator of crystal stability-but also disentangles its various contributing factors and underlying physical parameters. The physics insights extracted from the model clarify the stability trends of transition metal surfaces across t…

Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
Boshra Khajehpiri, Eric Granger, Massimiliano de Zambotti, Fiona C. Baker, Mohamad Forouzanfar
https://arxiv.org/abs/2506.00279

Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
Despite extensive research on the relationship between sleep and cognition, the connection between sleep microstructure and human performance across specific cognitive domains remains underexplored. This study investigates whether deep learning models can predict executive functions, particularly cognitive adaptability and conceptual reasoning from physiological processes during a night's sleep. To address this, we introduce CogPSGFormer, a multi-scale convolutional-transformer model designed t…

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks
Xiaobei Yan, Han Qiu, Tianwei Zhang
https://arxiv.org/abs/2506.10744

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks
Bit-flip attacks (BFAs) represent a serious threat to Deep Neural Networks (DNNs), where flipping a small number of bits in the model parameters or binary code can significantly degrade the model accuracy or mislead the model prediction in a desired way. Existing defenses exclusively focus on protecting models for specific attacks and platforms, while lacking effectiveness for other scenarios. We propose ObfusBFA, an efficient and holistic methodology to mitigate BFAs targeting both the high-le…

Projecting U.S. coastal storm surge risks and impacts with deep learning
Julian R. Rice, Karthik Balaguru, Fadia Ticona Rollano, John Wilson, Brent Daniel, David Judi, Ning Sun, L. Ruby Leung
https://arxiv.org/abs/2506.13963

Projecting U.S. coastal storm surge risks and impacts with deep learning
Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes i…

Investigating DUNE oscillations sensitivity to Pseudo-Dirac Neutrinos
Asmaa Abada, Joao Paulo Pinheiro, Salvador Urrea
https://arxiv.org/abs/2506.16390 htt…

Investigating DUNE oscillations sensitivity to Pseudo-Dirac Neutrinos
We explore the sensitivity of the Deep Underground Neutrino Experiment (DUNE) to sterile neutrino oscillations within a $3+$(pseudo-Dirac pair) framework. We first consider a pair of two sterile neutrinos forming a pseudo-Dirac pair, then we consider a low-scale seesaw realization, that we name ``Linear-Inverse Seesaw" model. This scenario features two nearly degenerate sterile neutrino states at the keV scale, characterized by a small mass splitting arising from a small amount of lepton number…

Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das
https://arxiv.org/abs/2506.09234

Transaction Categorization with Relational Deep Learning in QuickBooks
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in this domain stem from the unique formatting of transaction descriptions, the wide variety of transaction categories, and the vast scale of the data involved. Furthermore, organizing transaction data in a relational database creates difficulties in developing a unified model that covers the entire database. In this work, …

Forecasting the spatiotemporal evolution of fluid-induced microearthquakes with deep learning
Jaehong Chung, Michael Manga, Timothy Kneafsey, Tapan Mukerji, Mengsu Hu
https://arxiv.org/abs/2506.14923

Forecasting the spatiotemporal evolution of fluid-induced microearthquakes with deep learning
Microearthquakes (MEQs) generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their full spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems (EGS), CO$_2$ sequestration and other geo-engineering applications. We present a transformer-based deep learning model that ingests hydraulic stimulation history and prior MEQ observations to forecast four key quantities: cumulative MEQ count, cum…

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory
Yi-Chun Liao, Chieh-Lin Tsai, Yuan-Hao Chang, Cam\'elia Slimani, Jalil Boukhobza, Tei-Wei Kuo
https://arxiv.org/abs/2506.05994

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory
Although deep learning has demonstrated remarkable capabilities in learning from unstructured data, modern tree-based ensemble models remain superior in extracting relevant information and learning from structured datasets. While several efforts have been made to accelerate tree-based models, the inherent characteristics of the models pose significant challenges for conventional accelerators. Recent research leveraging content-addressable memory (CAM) offers a promising solution for acceleratin…

MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Bi Yuda, Jia Sihan, Gao Yutong, Abrol Anees, Fu Zening, Calhoun Vince
https://arxiv.org/abs/2506.13667

MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions and improve outcomes. This study focuses on a neuroimaging prediction framework based on both structural and functional neuroimaging data. We propose a next-generation prediction model, \textbf{MultiViT2}, which combines a pretrained representative learning base model with a vision transformer backbone for prediction o…

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Muhammad Haris Khan, Rao Muhammad Anwer, Jorma Laaksonen, Fahad Shahbaz Khan, Salman Khan
https://arxiv.org/abs/2506.06281

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Modern Earth observation (EO) increasingly leverages deep learning to harness the scale and diversity of satellite imagery across sensors and regions. While recent foundation models have demonstrated promising generalization across EO tasks, many remain limited by the scale, geographical coverage, and spectral diversity of their training data, factors critical for learning globally transferable representations. In this work, we introduce TerraFM, a scalable self-supervised learning model that l…

A Two-Stage Hierarchical Deep Filtering Framework for Real-Time Speech Enhancement
Shenghui Lu, Hukai Huang, Jinanglong Yao, Kaidi Wang, Qingyang Hong, Lin Li
https://arxiv.org/abs/2506.01023

A Two-Stage Hierarchical Deep Filtering Framework for Real-Time Speech Enhancement
This paper proposes a model that integrates sub-band processing and deep filtering to fully exploit information from the target time-frequency (TF) bin and its surrounding TF bins for single-channel speech enhancement. The sub-band module captures surrounding frequency bin information at the input, while the deep filtering module applies filtering at the output to both the target TF bin and its surrounding TF bins. To further improve the model performance, we decouple deep filtering into tempor…

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
Chaohui Xu, Qi Cui, Chip-Hong Chang
https://arxiv.org/abs/2505.24536

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
The pervasion of large-scale Deep Neural Networks (DNNs) and their enormous training costs make their intellectual property (IP) protection of paramount importance. Recently introduced passport-based methods attempt to steer DNN watermarking towards strengthening ownership verification against ambiguity attacks by modulating the affine parameters of normalization layers. Unfortunately, neither watermarking nor passport-based methods provide a holistic protection with robust ownership proof, hig…

Diffusion-based Counterfactual Augmentation: Towards Robust and Interpretable Knee Osteoarthritis Grading
Zhe Wang, Yuhua Ru, Aladine Chetouani, Tina Shiang, Fang Chen, Fabian Bauer, Liping Zhang, Didier Hans, Rachid Jennane, William Ewing Palmer, Mohamed Jarraya, Yung Hsin Chen
https://arxiv.org/abs/2506.15748

Diffusion-based Counterfactual Augmentation: Towards Robust and Interpretable Knee Osteoarthritis Grading
Automated grading of Knee Osteoarthritis (KOA) from radiographs is challenged by significant inter-observer variability and the limited robustness of deep learning models, particularly near critical decision boundaries. To address these limitations, this paper proposes a novel framework, Diffusion-based Counterfactual Augmentation (DCA), which enhances model robustness and interpretability by generating targeted counterfactual examples. The method navigates the latent space of a diffusion model…

Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
Aurore Bussalb, Fran\c{c}ois Le Gac, Guillaume Jubien, Mohamed Rahmouni, Ruggero G. Bettinardi, Pedro Marinho R. de Oliveira, Phillipe Derambure, Nicolas Gaspard, Jacques Jonas, Louis Maillard, Laurent Vercueil, Herv\'e Vespignani, Philippe Laval, Laurent Koessler, Ulysse Gimenez

Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
Electroencephalography (EEG) is commonly used by physicians for the diagnosis of numerous neurological disorders. Due to the large volume of EEGs requiring interpretation and the specific expertise involved, artificial intelligence-based tools are being developed to assist in their visual analysis. In this paper, we compare two deep learning models (CNN-LSTM and Transformer-based) with BioSerenity-E1, a recently proposed foundation model, in the task of classifying entire EEG recordings as norm…

This https://arxiv.org/abs/2505.01199 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning
Medical audio signals, such as heart and lung sounds, play a crucial role in clinical diagnosis. However, analyzing these signals remains challenging: traditional methods rely on handcrafted features or supervised deep learning models that demand extensive labeled datasets, limiting their scalability and applicability. To address these issues, we propose CaReAQA, an audio-language model that integrates a foundation audio model with the reasoning capabilities of large language models, enabling c…

This https://arxiv.org/abs/2412.06430 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Subgraph-Oriented Testing for Deep Learning Libraries
Deep Learning (DL) libraries, such as PyTorch, are widely used for building and deploying DL models on various hardware platforms. Meanwhile, they are found to contain bugs that lead to incorrect calculation results and cause issues like non-convergence training and inaccurate prediction of DL models. Thus, many efforts have been made to test DL libraries and reveal bugs. However, existing DL library testing methods manifest limitations: model-level testing methods cause complexity in fault loc…

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

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

Fusion of multi-source precipitation records via coordinate-based generative model
Sencan Sun, Congyi Nai, Baoxiang Pan, Wentao Li, Xin Li, Efi Foufoula-Georgiou, Yanluan Lin
https://arxiv.org/abs/2506.11698

Fusion of multi-source precipitation records via coordinate-based generative model
Precipitation remains one of the most challenging climate variables to observe and predict accurately. Existing datasets face intricate trade-offs: gauge observations are relatively trustworthy but sparse, satellites provide global coverage with retrieval uncertainties, and numerical models offer physical consistency but are biased and computationally intensive. Here we introduce PRIMER (Precipitation Record Infinite MERging), a deep generative framework that fuses these complementary sources t…

MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion
Franck Meyer, Kyunghoon Hur, Edward Choi
https://arxiv.org/abs/2506.08357 ht…

MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion
Despite the remarkable progress of deep-learning methods generating a target vital sign waveform from a source vital sign waveform, most existing models are designed exclusively for a specific source-to-target pair. This requires distinct model architectures, optimization procedures, and pre-processing pipelines, resulting in multiple models that hinder usability in clinical settings. To address this limitation, we propose the Multi-Directional Vital-Sign Converter (MD-ViSCo), a unified framewo…

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Yifei Tang, Hongjie Jiang, Changhong Jing, Hieu Pham, Shuqiang Wang
https://arxiv.org/abs/2506.11671

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Functional brain network analysis has become an indispensable tool for brain disease analysis. It is profoundly impacted by deep learning methods, which can characterize complex connections between ROIs. However, the research on foundation models of brain network is limited and constrained to a single dimension, which restricts their extensive application in neuroscience. In this study, we propose a fine-tuned brain network model for brain disease diagnosis. It expands brain region representati…

Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model
Weiqiang Zhu, Junhao Song, Haoyu Wang, Jannes M\"unchmeyer
https://arxiv.org/abs/2506.06939

Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model
Seismic waveforms contain rich information about earthquake processes, making effective data analysis crucial for earthquake monitoring, source characterization, and seismic hazard assessment. With rapid developments in deep learning, the state-of-the-art approach in artificial intelligence, many neural network models have been developed to enhance earthquake monitoring tasks, such as earthquake detection, phase picking, and phase association. However, most current efforts focus on developing s…

$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing
Yifan Zeng
https://arxiv.org/abs/2506.05844

$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing
Network Intrusion Detection Systems (NIDS) face challenges due to class imbalance, affecting their ability to detect novel and rare attacks. This paper proposes a Dual-Conditional Batch Normalization Variational Autoencoder ($\text{C}^{2}\text{BNVAE}$) for generating balanced and labeled network traffic data. $\text{C}^{2}\text{BNVAE}$ improves the model's adaptability to different data categories and generates realistic category-specific data by incorporating Conditional Batch Normalization (C…

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu, Nicholas Konz, Yaqian Chen, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2506.12186

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or object segmentation. However, training models for specific new tasks often requires large amounts of labeled data, which is difficult to obtain due to high annotation costs and data privacy concerns. To circumvent this issue, we introduce MRI-CORE (MRI COmprehensive Representat…

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Dingyi Rong, Haotian Lu, Wenzhuo Zheng, Fan Zhang, Shuangjia Zheng, Ning Liu
https://arxiv.org/abs/2506.09496

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Designing protein sequences with optimal energetic stability is a key challenge in protein inverse folding, as current deep learning methods are primarily trained by maximizing sequence recovery rates, often neglecting the energy of the generated sequences. This work aims to overcome this limitation by developing a model that directly generates low-energy, stable protein sequences. We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability prot…

SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models
Wenhan Yao, Fen Xiao, Xiarun Chen, Jia Liu, YongQiang He, Weiping Wen
https://arxiv.org/abs/2506.08346

SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models
Deep speech classification tasks, including keyword spotting and speaker verification, are vital in speech-based human-computer interaction. Recently, the security of these technologies has been revealed to be susceptible to backdoor attacks. Specifically, attackers use noisy disruption triggers and speech element triggers to produce poisoned speech samples that train models to become vulnerable. However, these methods typically create only a limited number of backdoors due to the inherent cons…

Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification
Rinat Prochii, Elizaveta Dakhova, Pavel Birulin, Maxim Sharaev
https://arxiv.org/abs/2506.08623

Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification
Accurate classification of second-trimester fetal ultrasound images remains challenging due to low image quality, high intra-class variability, and significant class imbalance. In this work, we introduce a simple yet powerful, biologically inspired deep learning ensemble framework that-unlike prior studies focused on only a handful of anatomical targets-simultaneously distinguishes 16 fetal structures. Drawing on the hierarchical, modular organization of biological vision systems, our model sta…

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initial toot: https://mastoxiv.page/@arXiv_ees…

An Optimized Ensemble Deep Learning Model For Brain Tumor Classification
Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and…

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…

DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
Donglian Li, Hui Guo, Minglang Chen, Huizhen Chen, Jialing Chen, Bocheng Liang, Pengchen Liang, Ying Tan
https://arxiv.org/abs/2506.08534

DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
Accurate segmentation of anatomical structures in the apical four-chamber (A4C) view of fetal echocardiography is essential for early diagnosis and prenatal evaluation of congenital heart disease (CHD). However, precise segmentation remains challenging due to ultrasound artifacts, speckle noise, anatomical variability, and boundary ambiguity across different gestational stages. To reduce the workload of sonographers and enhance segmentation accuracy, we propose DCD, an advanced deep learning-ba…

Deep histological synthesis from mass spectrometry imaging for multimodal registration
Kimberley M. Bird, Xujiong Ye, Alan M. Race, James M. Brown
https://arxiv.org/abs/2506.05441

Deep histological synthesis from mass spectrometry imaging for multimodal registration
Registration of histological and mass spectrometry imaging (MSI) allows for more precise identification of structural changes and chemical interactions in tissue. With histology and MSI having entirely different image formation processes and dimensionalities, registration of the two modalities remains an ongoing challenge. This work proposes a solution that synthesises histological images from MSI, using a pix2pix model, to effectively enable unimodal registration. Preliminary results show prom…

MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Armina Fani (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Mike Doan (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Isabelle Le (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Alex Fedorov (Emory University), Malte Hoffmann (Harvard University), Chris Rorden (University of South C…

MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
We developed MindGrab, a parameter- and memory-efficient deep fully-convolutional model for volumetric skull-stripping in head images of any modality. Its architecture, informed by a spectral interpretation of dilated convolutions, was trained exclusively on modality-agnostic synthetic data. MindGrab was evaluated on a retrospective dataset of 606 multimodal adult-brain scans (T1, T2, DWI, MRA, PDw MRI, EPI, CT, PET) sourced from the SynthStrip dataset. Performance was benchmarked against Synth…

Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine
Wenjun Xia, Chuang Niu, Ge Wang
https://arxiv.org/abs/2506.02149 ht…

Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine
Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal implants, often lead to severe noise and artifacts in reconstructed images, requiring improved reconstruction techniques. The introduction of deep learning has significantly advanced CT image reconstruction. However, obtaining paired training data remains rather challenging due to patient motion and other constraints. Although deep learning methods can…