Tootfinder

How many samples to label for an application given a foundation model? Chest X-ray classification study
Nikolay Nechaev, Evgenia Przhezdzetskaya, Viktor Gombolevskiy, Dmitry Umerenkov, Dmitry Dylov
https://arxiv.org/abs/2510.11553

How many samples to label for an application given a foundation model? Chest X-ray classification study
Chest X-ray classification is vital yet resource-intensive, typically demanding extensive annotated data for accurate diagnosis. Foundation models mitigate this reliance, but how many labeled samples are required remains unclear. We systematically evaluate the use of power-law fits to predict the training size necessary for specific ROC-AUC thresholds. Testing multiple pathologies and foundation models, we find XrayCLIP and XraySigLIP achieve strong performance with significantly fewer labeled …

Beyond Token Limits: Assessing Language Model Performance on Long Text Classification
Mikl\'os Seb\H{o}k, Viktor Kov\'acs, Martin B\'an\'oczy, Daniel M{\o}ller Eriksen, Nathalie Neptune, Philippe Roussille
https://arxiv.org/abs/2509.10199

Beyond Token Limits: Assessing Language Model Performance on Long Text Classification
The most widely used large language models in the social sciences (such as BERT, and its derivatives, e.g. RoBERTa) have a limitation on the input text length that they can process to produce predictions. This is a particularly pressing issue for some classification tasks, where the aim is to handle long input texts. One such area deals with laws and draft laws (bills), which can have a length of multiple hundred pages and, therefore, are not particularly amenable for processing with models tha…

Multi-pathology Chest X-ray Classification with Rejection Mechanisms
Yehudit Aperstein, Amit Tzahar, Alon Gottlib, Tal Verber, Ravit Shagan Damti, Alexander Apartsin
https://arxiv.org/abs/2509.10348

Multi-pathology Chest X-ray Classification with Rejection Mechanisms
Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-occurring pathologies must be detected simultaneously. This study introduces an uncertainty-aware framework for chest X-ray diagnosis based on a DenseNet-121 backbone, enhanced with two selective prediction mechanisms: entropy-based rejection and confidence interval-based rejection. Both methods enable the model to ab…

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
Daniel Pulido-Cort\'azar, Daniel Gibert, Felip Many\`a
https://arxiv.org/abs/2510.12310

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a machine learning model into making incorrect predictions. This research presents DeepTrust, a novel metaheuristic that arranges flexible classifiers, like deep neural networks, into an ordered sequence where the final decision is made by a single internal model…

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
Ummy Maria Muna, Md Mehedi Hasan Shawon, Md Jobayer, Sumaiya Akter, Md Rakibul Hasan, Md. Golam Rabiul Alam
https://arxiv.org/abs/2510.10719

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-annotated data. In this paper, we propose the Self-Supervised Dual-Path Prototypical Network (SS-DPPN), a foundation model for cardiac audio representation and classification from unlabeled data. The framework introduces a dual-path contrastive learning based architecture that simultaneously processes 1D waveforms and …

On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification
Faried Abu Zaid, Tim Katzke, Emmanuel M\"uller, Daniel Neider
https://arxiv.org/abs/2510.09452

On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification
Unsupervised anomaly detection is often framed around two widely studied paradigms. Deep one-class classification, exemplified by Deep SVDD, learns compact latent representations of normality, while density estimators realized by normalizing flows directly model the likelihood of nominal data. In this work, we show that uniformly scaling flows (USFs), normalizing flows with a constant Jacobian determinant, precisely connect these approaches. Specifically, we prove how training a USF via maximum…

An Encoder-Integrated PhoBERT with Graph Attention for Vietnamese Token-Level Classification
Ba-Quang Nguyen
https://arxiv.org/abs/2510.11537 https://arxiv…

An Encoder-Integrated PhoBERT with Graph Attention for Vietnamese Token-Level Classification
We propose a novel neural architecture named TextGraphFuseGAT, which integrates a pretrained transformer encoder (PhoBERT) with Graph Attention Networks for token-level classification tasks. The proposed model constructs a fully connected graph over the token embeddings produced by PhoBERT, enabling the GAT layer to capture rich inter-token dependencies beyond those modeled by sequential context alone. To further enhance contextualization, a Transformer-style self-attention layer is applied on …

Conformal prediction without knowledge of labeled calibration data
Jonas Flechsig, Maximilian Pilz
https://arxiv.org/abs/2509.10321 https://arxiv.org/pdf/2…

Conformal prediction without knowledge of labeled calibration data
We extend the method of conformal prediction beyond the case relying on labeled calibration data. Replacing the calibration scores by suitable estimates, we identify conformity sets $C$ for classification and regression models that rely on unlabeled calibration data. Given a classification model with accuracy $1-β$, we prove that the conformity sets guarantee a coverage of $P(Y \in C) \geq 1-α-β$ for an arbitrary parameter $α\in (0,1)$. The same coverage guarantee also holds for regression …

Taming Volatility: Stable and Private QUIC Classification with Federated Learning
Richard Jozsa, Karel Hynek, Adrian Pekar
https://arxiv.org/abs/2509.09997 https://

Taming Volatility: Stable and Private QUIC Classification with Federated Learning
Federated Learning (FL) is a promising approach for privacy-preserving network traffic analysis, but its practical deployment is challenged by the non-IID nature of real-world data. While prior work has addressed statistical heterogeneity, the impact of temporal traffic volatility-the natural daily ebb and flow of network activity-on model stability remains largely unexplored. This volatility can lead to inconsistent data availability at clients, destabilizing the entire training process. In th…

Unlocking Zero-Shot Plant Segmentation with Pl@ntNet Intelligence
Simon Rav\'e, Jean-Christophe Lombardo, Pejman Rasti, Alexis Joly, David Rousseau
https://arxiv.org/abs/2510.12579

Unlocking Zero-Shot Plant Segmentation with Pl@ntNet Intelligence
We present a zero-shot segmentation approach for agricultural imagery that leverages Plantnet, a large-scale plant classification model, in conjunction with its DinoV2 backbone and the Segment Anything Model (SAM). Rather than collecting and annotating new datasets, our method exploits Plantnet's specialized plant representations to identify plant regions and produce coarse segmentation masks. These masks are then refined by SAM to yield detailed segmentations. We evaluate on four publicly avai…

Enhanced Angle-Range Cluster Parameter Estimation in Full-Duplex ISAC Systems
Muhammad Talha, Besma Smida, David Gonz\'alez G
https://arxiv.org/abs/2510.12711 https://

Enhanced Angle-Range Cluster Parameter Estimation in Full-Duplex ISAC Systems
This work studies an integrated sensing and communication (ISAC) framework for targets that are spread both in the angle and range domains. We model each target using a cluster of rays parameterized by a specific density function, and propose a truncated Multiple Signal Classification (MUSIC) spread (TMS) algorithm to accurately estimate the parameters of the density function. Unlike the conventional MUSIC spread (CMS), TMS restricts the signal subspace rank based on the eigen decomposition of …

rCamInspector: Building Reliability and Trust on IoT (Spy) Camera Detection using XAI
Priyanka Rushikesh Chaudhary, Manan Gupta, Jabez Christopher, Putrevu Venkata Sai Charan, Rajib Ranjan Maiti
https://arxiv.org/abs/2509.09989

rCamInspector: Building Reliability and Trust on IoT (Spy) Camera Detection using XAI
The classification of network traffic using machine learning (ML) models is one of the primary mechanisms to address the security issues in IoT networks and/or IoT devices. However, the ML models often act as black-boxes that create a roadblock to take critical decision based on the model output. To address this problem, we design and develop a system, called rCamInspector, that employs Explainable AI (XAI) to provide reliable and trustworthy explanations to model output. rCamInspector adopts t…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[10/14]:
- MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification
Nguyen, Nguyen, Diep, Nguyen, Ho, Metsch, Maurer, Sonntag, Bohnenberger, Hauschild

Transport properties of stochastic fluids
Chandrodoy Chattopadhyay, Josh Ott, Thomas Schaefer, Vladimir V. Skokov
https://arxiv.org/abs/2510.12557 https://…

Transport properties of stochastic fluids
We study heat conduction and momentum transport in the context of stochastic fluid dynamics. We consider a fluid described by model H in the classification of Hohenberg and Halperin. We study both non-critical and critical fluids, and we investigate transport properties in two as well as three dimensions. Our results are based on numerical simulations of model H using a Metropolis algorithm, and we employ Kubo relations to extract transport coefficients. We observe the expected logarithmic dive…

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos, Christiane Kuhl, Daniel Truhn, Sven Nebelung
https://arxiv.org/abs/2510.05664 …

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To …

Unitary Quantum Cellular Automata for Density Classification
Pedro C. S. Costa, Yuval R. Sanders, Pedro Paulo Balbi, Gavin K. Brennen
https://arxiv.org/abs/2510.06947 https://…

Unitary Quantum Cellular Automata for Density Classification
We investigate the density classification task (DCT) -- determining the majority bit in a one-dimensional binary lattice -- within a quantum cellular automaton (CA) framework. While there is no one-dimensional two-state, radius $r \geq 1$, deterministic CA with periodic boundary conditions that solves the DCT perfectly, we explore whether a unitary quantum model can succeed. We employ the Partitioned Unitary Quantum Cellular Automaton (PUQCA), a number-conserving model, and, via evolutionary se…

Train Stochastic Non Linear Coupled ODEs to Classify and Generate
Stefano Gagliani, Feliciano Giuseppe Pacifico, Lorenzo Chicchi, Duccio Fanelli, Diego Febbe, Lorenzo Buffoni, Raffaele Marino
https://arxiv.org/abs/2510.12286

Train Stochastic Non Linear Coupled ODEs to Classify and Generate
A general class of dynamical systems which can be trained to operate in classification and generation modes are introduced. A procedure is proposed to plant asymptotic stationary attractors of the deterministic model. Optimizing the dynamical system amounts to shaping the architecture of inter-nodes connection to steer the evolution towards the assigned equilibrium, as a function of the class to which the item - supplied as an initial condition - belongs to. Under the stochastic perspective, po…

Robust Functional Logistic Regression
Berkay Akturk, Ufuk Beyaztas, Han Lin Shang
https://arxiv.org/abs/2510.12048 https://arxiv.org/pdf/2510.12048

Robust Functional Logistic Regression
Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parameter estimation in functional logistic regression are sensitive to outliers, which may lead to inaccurate parameter estimates and inferior classification accuracy. We propose a robust estimation procedure for functional logistic regression, in which the observations of the functional predictor are projected onto a set …

Engineering Spatial and Molecular Features from Cellular Niches to Inform Predictions of Inflammatory Bowel Disease
Myles Joshua Toledo Tan, Maria Kapetanaki, Panayiotis V. Benos
https://arxiv.org/abs/2509.09923

Engineering Spatial and Molecular Features from Cellular Niches to Inform Predictions of Inflammatory Bowel Disease
Differentiating between the two main subtypes of Inflammatory Bowel Disease (IBD): Crohns disease (CD) and ulcerative colitis (UC) is a persistent clinical challenge due to overlapping presentations. This study introduces a novel computational framework that employs spatial transcriptomics (ST) to create an explainable machine learning model for IBD classification. We analyzed ST data from the colonic mucosa of healthy controls (HC), UC, and CD patients. Using Non-negative Matrix Factorization …

Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective
Harry Caufield, Satrajit Ghosh, Sek Wong Kong, Jillian Parker, Nathan Sheffield, Bhavesh Patel, Andrew Williams, Timothy Clark, Monica C. Munoz-Torres
https://arxiv.org/abs/2509.10432

Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective
AI-readiness describes the degree to which data may be optimally and ethically used for subsequent AI and Machine Learning (AI/ML) methods, where those methods may involve some combination of model training, data classification, and ethical, explainable prediction. The Bridge2AI consortium has defined the particular criteria a biomedical dataset may possess to render it AI-ready: in brief, a dataset's readiness is related to its FAIRness, provenance, degree of characterization, explainability, …

A Unified Optimization Framework for Multiclass Classification with Structured Hyperplane Arrangements
V\'ictor Blanco, Harshit Kothari, James Luedtke
https://arxiv.org/abs/2510.05047

A Unified Optimization Framework for Multiclass Classification with Structured Hyperplane Arrangements
In this paper, we propose a new mathematical optimization model for multiclass classification based on arrangements of hyperplanes. Our approach preserves the core support vector machine (SVM) paradigm of maximizing class separation while minimizing misclassification errors, and it is computationally more efficient than a previous formulation. We present a kernel-based extension that allows it to construct nonlinear decision boundaries. Furthermore, we show how the framework can naturally incor…

NeuroSketch: An Effective Framework for Neural Decoding via Systematic Architectural Optimization
Gaorui Zhang, Zhizhang Yuan, Jialan Yang, Junru Chen, Li Meng, Yang Yang
https://arxiv.org/abs/2512.09524 https://arxiv.org/pdf/2512.09524 https://arxiv.org/html/2512.09524
arXiv:2512.09524v1 Announce Type: new
Abstract: Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at https://github.com/Galaxy-Dawn/NeuroSketch.
toXiv_bot_toot

PAC Learnability in the Presence of Performativity
Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
https://arxiv.org/abs/2510.08335 https://arxiv.org/p…

PAC Learnability in the Presence of Performativity
Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead to decreased test-time performance. In this paper, we study the question of whether and when performative binary classification problems are…

Synchronization of coupled Stuart-Landau oscillators: How heterogeneity can facilitate synchronization
Ana P Mill\'an, David Poyato, David N Reynolds, Francesco Tudisco
https://arxiv.org/abs/2510.05243

Synchronization of coupled Stuart-Landau oscillators: How heterogeneity can facilitate synchronization
We study the collective dynamics of coupled Stuart--Landau oscillators, which model limit-cycle behavior near a Hopf bifurcation and serve as the amplitude-phase analogue of the Kuramoto model. Unlike the well-studied phase-reduced systems, the full Stuart--Landau model retains amplitude dynamics, enabling the emergence of rich phenomena such as amplitude death, quenching, and multistable synchronization. We provide a complete analytical classification of asymptotic behaviors for identical natu…

Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator
Hyunji Lee, Kevin Chenhao Li, Matthias Grabmair, Shanshan Xu
https://arxiv.org/abs/2510.08524

Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator
Prompt optimization aims to systematically refine prompts to enhance a language model's performance on specific tasks. Fairness detection in Terms of Service (ToS) clauses is a challenging legal NLP task that demands carefully crafted prompts to ensure reliable results. However, existing prompt optimization methods are often computationally expensive due to inefficient search strategies and costly prompt candidate scoring. In this paper, we propose a framework that combines Monte Carlo Tree Sea…

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Debojyoti Ghosh, Soumya K Ghosh, Adrijit Goswami
https://arxiv.org/abs/2510.05071

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Efficient and accurate classification of waste and industrial surface defects is essential for ensuring sustainable waste management and maintaining high standards in quality control. This paper introduces the Neuroplastic Modular Classifier, a novel hybrid architecture designed for robust and adaptive image classification in dynamic environments. The model combines a ResNet-50 backbone for localized feature extraction with a Vision Transformer (ViT) to capture global semantic context. Addition…

EEG-Based Acute Pain Classification: Machine Learning Model Comparison and Real-Time Clinical Feasibility
Aavid Mathrawala, Dhruv Kurup, Josie Lau
https://arxiv.org/abs/2510.05511

EEG-Based Acute Pain Classification: Machine Learning Model Comparison and Real-Time Clinical Feasibility
Current pain assessment within hospitals often relies on self-reporting or non-specific EKG vital signs. This system leaves critically ill, sedated, and cognitively impaired patients vulnerable to undertreated pain and opioid overuse. Electroencephalography (EEG) offers a noninvasive method of measuring brain activity. This technology could potentially be applied as an assistive tool to highlight nociceptive processing in order to mitigate this issue. In this study, we compared machine learning…

Bi-Hamiltonian Structures and Equivalent Representations of the Pais-Uhlenbeck Model
Bethan Turner
https://arxiv.org/abs/2509.26477 https://arxiv.org/pdf/2…

Bi-Hamiltonian Structures and Equivalent Representations of the Pais-Uhlenbeck Model
We provide a complete classification of all the ways the Pais-Uhlenbeck osicllator might be embedded in two dimensional space. We discuss the Bi-Hamiltonian structures of this model, and examine how alternative Hamiltonian structures might be generated from the dynamical Lie symmetries of the theory. We then examine how the Bi-Hamiltonian strucutre may be exploited to evade the problem of unbounded Hamiltonians that is usually associated with Higher Time Derivative Theories. The effect of inter…

Complete Classification of Domain Wall Solutions in the $\mathbb{Z}_2$-symmetric 2HDM
Richard A. Battye, Steven J. Cotterill, Adam K. Thomasson
https://arxiv.org/abs/2509.23332 …

Complete Classification of Domain Wall Solutions in the $\mathbb{Z}_2$-symmetric 2HDM
We present a complete classification of domain wall solutions in the two-Higgs Doublet Model (2HDM) with a global $\mathbb{Z}_2$ symmetry, categorised as superconducting, CP-violating, or neither, depending on the scalar particle masses and the ratio of the two Higgs doublets' vacuum expectation values. We demonstrate that any domain wall solution can be reduced to depend on only six of the eight general field components, with further field reductions possible within different regions of the pa…

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Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models
Gianmarco Perantoni, Giulio Weikmann, Lorenzo Bruzzone
https://arxiv.org/abs/2510.07008

Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models
The temporal consistency of yearly land-cover maps is of great importance to model the evolution and change of the land cover over the years. In this paper, we focus the attention on a novel approach to classification of yearly satellite image time series (SITS) that combines deep learning with Bayesian modelling, using Hidden Markov Models (HMMs) integrated with Transformer Encoder (TE) based DNNs. The proposed approach aims to capture both i) intricate temporal correlations in yearly SITS and…

Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
Jiahui An, Chonghao Cai, Olympia Gallou, Sara Irina Fabrikant, Giacomo Indiveri, Elisa Donati
https://arxiv.org/abs/2509.21345

Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
This paper presents a neuromorphic system for cognitive load classification in a real-world setting, an Air Traffic Control (ATC) task, using a hardware implementation of Spiking Neural Networks (SNNs). Electroencephalogram (EEG) and eye-tracking features, extracted from an open-source dataset, were used to train and evaluate both conventional machine learning models and SNNs. Among the SNN architectures explored, a minimalistic, single-layer model trained with a biologically inspired delta-rul…

Behavioural Classification in C. elegans: a Spatio-Temporal Analysis of Locomotion
Nemanja Antonic, Monika Scholz, Aymeric Vellinger, Euphrasie Ramahefarivo, Elio Tuci
https://arxiv.org/abs/2510.00086 …

Behavioural Classification in C. elegans: a Spatio-Temporal Analysis of Locomotion
The 1mm roundworm C. elegans is a model organism used in many sub-areas of biology to investigate different types of biological processes. In order to complement the n-vivo analysis with computer-based investigations, several methods have been proposed to simulate the worm behaviour. These methods extract discrete behavioural units from the flow of the worm movements using different types of tracking techniques. Nevertheless, these techniques require a clear view of the entire worm body, which …

WARBERT: A Hierarchical BERT-based Model for Web API Recommendation
Zishuo Xu, Yuhong Gu, Dezhong Yao
https://arxiv.org/abs/2509.23175 https://arxiv.org/pd…

WARBERT: A Hierarchical BERT-based Model for Web API Recommendation
With the emergence of Web 2.0 and microservices architecture, the number of Web APIs has increased dramatically, further intensifying the demand for efficient Web API recommendation. Existing solutions typically fall into two categories: recommendation-type methods, which treat each API as a label for classification, and match-type methods, which focus on matching mashups through API retrieval. However, three critical challenges persist: 1) the semantic ambiguities in comparing API and mashup d…

The Varieties of Schelling Model Experience
Marlyn Boke, Timothy Sorochkin, Jesse Anttila-Hughes, Alan O. Jamison
https://arxiv.org/abs/2509.14462 https://…

The Varieties of Schelling Model Experience
The Schelling model is a prototype for agent-based modeling in social systems. A comprehensive analysis of Schelling model rule variants is achieved by classification of the space of macroscopic outcomes via phase diagrams. Among 54 rule variants, only 3 phase diagram classes are found, characterized by the number of phase transitions. This classification scheme is found to be robust to the use of sociological and percolation-inspired measures of segregation. The statistical and dynamic drivers…

M3DIS - A grid of 3D radiation-hydrodynamics stellar atmosphere models for stellar surveys. II. Carbon-enhanced metal-poor stars
Philipp Eitner, Maria Bergemann, Richard Hoppe, Nicholas Storm, Veronika Lipatova, Simon C. O. Glover, Ralf S. Klessen, {\AA}ke Nordlund, Andrius Popovas
https://arxiv.org/abs/2509.24555

M3DIS - A grid of 3D radiation-hydrodynamics stellar atmosphere models for stellar surveys. II. Carbon-enhanced metal-poor stars
Understanding the origin and evolution of carbon-enhanced metal-poor (CEMP) stars is key to tracing the Galaxy's early chemical enrichment. We investigate how realistic 3D radiation-hydrodynamics (RHD) model atmospheres affect carbon abundances in CEMP stars and implications for their classification and Galactic chemical evolution (GCE). We focus on biases from traditional 1D hydrostatic models. We use the M3DIS code to compute 3D RHD model atmospheres for main-sequence and sub-giant stars over…

To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera, Roberto Pellungrini, Bruno Lepri, Andrea Passerini
https://arxiv.org/abs/2510.08314

To Ask or Not to Ask: Learning to Require Human Feedback
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machine Learning (ML) model to pass difficult cases to a human expert. However, LtD treats humans and ML models as mutually exclusive decision-makers, restricting the expert contribution to mere predictions. To address this limitation, we propose Learning to Ask (LtA), a new framework that handles both when and how to inco…

Development and Evaluation of an AI-Driven Telemedicine System for Prenatal Healthcare
Juan Barrientos, Michaelle P\'erez, Douglas Gonz\'alez, Favio Reyna, Julio Fajardo, Andrea Lara
https://arxiv.org/abs/2510.01194

Development and Evaluation of an AI-Driven Telemedicine System for Prenatal Healthcare
Access to obstetric ultrasound is often limited in low-resource settings, particularly in rural areas of low- and middle-income countries. This work proposes a human-in-the-loop artificial intelligence (AI) system designed to assist midwives in acquiring diagnostically relevant fetal images using blind sweep protocols. The system incorporates a classification model along with a web-based platform for asynchronous specialist reviews. By identifying key frames in blind sweep studies, the AI syste…

On the Relationship Between the Choice of Representation and In-Context Learning
Ioana Marinescu, Kyunghyun Cho, Eric Karl Oermann
https://arxiv.org/abs/2510.08372 https://

On the Relationship Between the Choice of Representation and In-Context Learning
In-context learning (ICL) is the ability of a large language model (LLM) to learn a new task from a few demonstrations presented as part of the context. Past studies have attributed a large portion of the success of ICL to the way these in-context demonstrations are represented, particularly to how labels are represented in classification tasks. On the other hand, observations of the learning capacity of ICL (i.e., the extent to which more in-context demonstrations can lead to higher performanc…

Repro Samples Method for Model-Free Inference in High-Dimensional Binary Classification
Xiaotian Hou, Peng Wang, Minge Xie, Linjun Zhang
https://arxiv.org/abs/2510.01468 https:/…

Repro Samples Method for Model-Free Inference in High-Dimensional Binary Classification
This paper presents a novel method for statistical inference in high-dimensional binary models with unspecified structure, where we leverage a (potentially misspecified) sparsity-constrained working generalized linear model (GLM) to facilitate the inference process. Our method is based on the repro samples framework, which generates artificial samples that mimic the actual data-generating process. Our inference targets include the model support, case probabilities, and the oracle regression coe…

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Nishant Doshi
https://arxiv.org/abs/2509.21873 https://arxiv.org/pdf/2509.21873

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Conventional maneuver prediction methods use some sort of classification model on temporal trajectory data to predict behavior of agents over a set time horizon. Despite of having the best precision and recall, these models cannot predict a lane change accurately unless they incorporate information about the entire scene. Level-k game theory can leverage the human-like hierarchical reasoning to come up with the most rational decisions each agent can make in a group. This can be leveraged to mod…

Neu-RadBERT for Enhanced Diagnosis of Brain Injuries and Conditions
Manpreet Singh (\'Equipe de Recherche en Soins Intensifs, Centre de recherche du Centre int\'egr\'e universitaire de sant\'e et de services sociaux du Nord-de-l'\^Ile-de-Montr\'eal), Sean Macrae (Facult\'e de M\'edecine, Universit\'e de Montr\'eal), Pierre-Marc Williams (Facult\'e de M\'edecine, Universit\'e de Montr\'eal), Nicole Hung (Facult\'e de M\'ede…

Neu-RadBERT for Enhanced Diagnosis of Brain Injuries and Conditions
Objective: We sought to develop a classification algorithm to extract diagnoses from free-text radiology reports of brain imaging performed in patients with acute respiratory failure (ARF) undergoing invasive mechanical ventilation. Methods: We developed and fine-tuned Neu-RadBERT, a BERT-based model, to classify unstructured radiology reports. We extracted all the brain imaging reports (computed tomography and magnetic resonance imaging) from MIMIC-IV database, performed in patients with ARF. …

Sound Separation and Classification with Object and Semantic Guidance
Younghoo Kwon, Jung-Woo Choi
https://arxiv.org/abs/2509.15899 https://arxiv.org/pdf/2…

Sound Separation and Classification with Object and Semantic Guidance
The spatial semantic segmentation task focuses on separating and classifying sound objects from multichannel signals. To achieve two different goals, conventional methods fine-tune a large classification model cascaded with the separation model and inject classified labels as separation clues for the next iteration step. However, such integration is not ideal, in that fine-tuning over a smaller dataset loses the diversity of large classification models, features from the source separation model…

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Baptiste Hilaire, Emmanouil Karystinaios, Gerhard Widmer
https://arxiv.org/abs/2509.26521

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we introduce MUSE-Explainer, a new method that helps reveal how music Graph Neural Network models make decisions by providing clear, human-friendly explanations. Our approach generates counterfactual explanations by making small, meaningful changes to musical score graphs that alter a model's prediction while ensuring the …

Sample completion, structured correlation, and Netflix problems
Leonardo N. Coregliano, Maryanthe Malliaris
https://arxiv.org/abs/2509.20404 https://arxiv.…

Sample completion, structured correlation, and Netflix problems
We develop a new high-dimensional statistical learning model which can take advantage of structured correlation in data even in the presence of randomness. We completely characterize learnability in this model in terms of VCN${}_{k,k}$-dimension (essentially $k$-dependence from Shelah's classification theory). This model suggests a theoretical explanation for the success of certain algorithms in the 2006~Netflix Prize competition.

microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Sathira Silva, Eman Ali, Chetan Arora, Muhammad Haris Khan
https://arxiv.org/abs/2510.02270

microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Unsupervised adaptation of CLIP-based vision-language models (VLMs) for fine-grained image classification requires sensitivity to microscopic local cues. While CLIP exhibits strong zero-shot transfer, its reliance on coarse global features restricts its performance on fine-grained classification tasks. Prior efforts inject fine-grained knowledge by aligning large language model (LLM) descriptions with the CLIP $\texttt{[CLS]}$ token; however, this approach overlooks spatial precision. We propos…

MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances
Sujatha Kotte, Vangala Govindakrishnan Saipradeep, Vidushi Walia, Dhandapani Nandagopal, Thomas Joseph, Naveen Sivadasan, Bhagat Singh Lali
https://arxiv.org/abs/2509.10502…

MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances
Motivation: Accurate classification of mitotic figures into normal and atypical types is crucial for tumor prognostication in digital pathology. However, developing robust deep learning models for this task is challenging due to the subtle morphological differences, as well as significant class and hardness imbalances in real-world histopathology datasets. Methods: We propose a novel deep learning approach based on a ResNet backbone with specialized classification heads. Our architecture unique…

Perturbative unitarity for models with singlet and doublet scalars
Carolina T. Lopes, Andr\'e Milagre, Jo\~ao P. Silva
https://arxiv.org/abs/2510.02434 https://

Perturbative unitarity for models with singlet and doublet scalars
We provide a complete description of unitarity bounds on the gauge-scalar sectors of models with extra $SU(2)$ doublet, neutral singlet, and charged singlet scalars. Such additions are very frequent in models beyond the Standard Model, and, in particular, they are almost universal in models explaining the dark matter problem. We propose a specific classification and minimal set of scattering matrices containing all the relevant information. We also developed a Mathematica implementation of our …

From Quasiperiodicity to a Complete Coloring of the Kohmoto Butterfly
Ram Band, Siegfried Beckus
https://arxiv.org/abs/2509.24025 https://arxiv.org/pdf/250…

From Quasiperiodicity to a Complete Coloring of the Kohmoto Butterfly
The spectra of the Kohmoto model give rise to a fractal phase diagram, known as the Kohmoto butterfly. The butterfly encapsulates the spectra of all periodic Kohmoto Hamiltonians, whose index invariants are sought after. Topological methods - such as Chern numbers - are ill defined due to the discontinuous potential, and hence fail to provide index invariants. This Letter overcomes that obstacle and provides a complete classification of the Kohmoto model indices. Our approach encodes the Kohmot…

Simultaneous probe of the charm and bottom quark Yukawa couplings using ttH events
CMS Collaboration
https://arxiv.org/abs/2509.22535 https://arxiv.org/pdf…

Simultaneous probe of the charm and bottom quark Yukawa couplings using ttH events
A search for the standard model Higgs boson decaying to a charm quark-antiquark pair, H $\to$ $\mathrm{c\bar{c}}$, produced in association with a top quark-antiquark pair ($\mathrm{t\bar{t}}$H) is presented. The search is performed with data from proton-proton collisions at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Advanced machine learning techniques are employed for jet flavor identification and event classification. The Higgs boson decay to a bottom qua…

DNABERT-2: Fine-Tuning a Genomic Language Model for Colorectal Gene Enhancer Classification
Darren King, Yaser Atlasi, Gholamreza Rafiee
https://arxiv.org/abs/2509.25274 https:/…

DNABERT-2: Fine-Tuning a Genomic Language Model for Colorectal Gene Enhancer Classification
Gene enhancers control when and where genes switch on, yet their sequence diversity and tissue specificity make them hard to pinpoint in colorectal cancer. We take a sequence-only route and fine-tune DNABERT-2, a transformer genomic language model that uses byte-pair encoding to learn variable-length tokens from DNA. Using assays curated via the Johnston Cancer Research Centre at Queen's University Belfast, we assembled a balanced corpus of 2.34 million 1 kb enhancer sequences, applied summit-c…

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Xin An, Ruijie Li, Qiao Ning, Hui Li, Qian Ma, Shikai Guo
https://arxiv.org/abs/2509.20240 https://

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction depth and multimodal fusion, we propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and optionally available expression features of ncRNAs to enhance classificat…

BladderFormer: A Streaming Transformer for Real-Time Urological State Monitoring
Chengwei Zhou, Steve Majerus, Gourav Datta
https://arxiv.org/abs/2509.24178 https://

BladderFormer: A Streaming Transformer for Real-Time Urological State Monitoring
Bladder pressure monitoring systems are increasingly vital in diagnosing and managing urinary tract dysfunction. Existing solutions rely heavily on hand-crafted features and shallow classifiers, limiting their adaptability to complex signal dynamics. We propose a one-layer streaming transformer model for real-time classification of bladder pressure states, operating on wavelet-transformed representations of raw time-series data. Our model incorporates temporal multi-head self-attention and stat…

AutoIntent: AutoML for Text Classification
Ilya Alekseev, Roman Solomatin, Darina Rustamova, Denis Kuznetsov
https://arxiv.org/abs/2509.21138 https://arxiv…

AutoIntent: AutoML for Text Classification
AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and …

Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training
Nutthawara Buatthaisong, Inigo Val Slijepcevic, Anna M. M. Scaife, Micah Bowles, Andrew Hopkins, Devina Mohan, Stanislav S Shabala, O. Ivy Wong
https://arxiv.org/abs/2509.11988

Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training
In this study, we examine over 14,000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5,900 FRIs and 8,100 FRIIs. We present an analysis of these predicted radio galaxy morphologies for the RGZ catalogue, classified using a pre-trained radio galaxy foundation model that has been fine-tuned to predict Fanaroff-Riley (FR) morphology. As seen in previous studies, our results show overlap between morphologically classified FRI and FRI…

Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 data
Abdulhakim M. Abdi, Fan Wang
https://arxiv.org/abs/2509.18228

Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 data
We present a new 10-meter map of dominant tree species in Swedish forests accompanied by pixel-level uncertainty estimates. The tree species classification is based on spatiotemporal metrics derived from Sentinel-1 and Sentinel-2 satellite data, combined with field observations from the Swedish National Forest Inventory. We apply an extreme gradient boosting model with Bayesian optimization to relate field observations to satellite-derived features and generate the final species map. Classifica…

CBAM Integrated Attention Driven Model For Betel Leaf Diseases Classification With Explainable AI
Sumaiya Tabassum, Md. Faysal Ahamed
https://arxiv.org/abs/2509.26484 https://…

CBAM Integrated Attention Driven Model For Betel Leaf Diseases Classification With Explainable AI
Betel leaf is an important crop because of its economic advantages and widespread use. Its betel vines are susceptible to a number of illnesses that are commonly referred to as betel leaf disease. Plant diseases are the largest threat to the food supply's security, and they are challenging to identify in time to stop possible financial damage. Interestingly, artificial intelligence can leave a big mark on the betel leaf industry since it helps with output growth by forecasting sickness. This pa…

Building Transparency in Deep Learning-Powered Network Traffic Classification: A Traffic-Explainer Framework
Riya Ponraj, Ram Durairajan, Yu Wang
https://arxiv.org/abs/2509.18007

Building Transparency in Deep Learning-Powered Network Traffic Classification: A Traffic-Explainer Framework
Recent advancements in deep learning have significantly enhanced the performance and efficiency of traffic classification in networking systems. However, the lack of transparency in their predictions and decision-making has made network operators reluctant to deploy DL-based solutions in production networks. To tackle this challenge, we propose Traffic-Explainer, a model-agnostic and input-perturbation-based traffic explanation framework. By maximizing the mutual information between predictions…

Recidivism and Peer Influence with LLM Text Embeddings in Low Security Correctional Facilities
Shanjukta Nath, Jiwon Hong, Jae Ho Chang, Keith Warren, Subhadeep Paul
https://arxiv.org/abs/2509.20634

Recidivism and Peer Influence with LLM Text Embeddings in Low Security Correctional Facilities
We find AI embeddings obtained using a pre-trained transformer-based Large Language Model (LLM) of 80,000-120,000 written affirmations and correction exchanges among residents in low-security correctional facilities to be highly predictive of recidivism. The prediction accuracy is 30\% higher with embedding vectors than with only pre-entry covariates. However, since the text embedding vectors are high-dimensional, we perform Zero-Shot classification of these texts to a low-dimensional vector of…

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

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

High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification
Nicholas Barnfield, Hugo Cui, Yue M. Lu
https://arxiv.org/abs/2509.25153 https://

High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification
When and how can an attention mechanism learn to selectively attend to informative tokens, thereby enabling detection of weak, rare, and sparsely located features? We address these questions theoretically in a sparse-token classification model in which positive samples embed a weak signal vector in a randomly chosen subset of tokens, whereas negative samples are pure noise. In the long-sequence limit, we show that a simple single-layer attention classifier can in principle achieve vanishing tes…

Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy
Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Cl\'ement Larose, Christian Daul, Andres Mendez-Vazquez, Gilberto Ochoa-Ruiz
https://arxiv.org/abs/2510.06230

Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy
This work introduces Federated Adaptive Gain via Dual Signal Trust (FedAgain), a novel federated learning algorithm designed to enhance anomaly detection in medical imaging under decentralized and heterogeneous conditions. Focusing on the task of kidney stone classification, FedAgain addresses the common challenge of corrupted or low-quality client data in real-world clinical environments by implementing a dual-signal trust mechanism based on reconstruction error and model divergence. This mech…

Probing Whisper for Dysarthric Speech in Detection and Assessment
Zhengjun Yue, Devendra Kayande, Zoran Cvetkovic, Erfan Loweimi
https://arxiv.org/abs/2510.04219 https://…

Probing Whisper for Dysarthric Speech in Detection and Assessment
Large-scale end-to-end models such as Whisper have shown strong performance on diverse speech tasks, but their internal behavior on pathological speech remains poorly understood. Understanding how dysarthric speech is represented across layers is critical for building reliable and explainable clinical assessment tools. This study probes the Whisper-Medium model encoder for dysarthric speech for detection and assessment (i.e., severity classification). We evaluate layer-wise embeddings with a li…

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
Suman Cha, Hyunjoong Kim
https://arxiv.org/abs/2509.11511 https://

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medical diagnosis and fraud detection. Traditional oversampling techniques, including SMOTE and its variants, generate synthetic minority samples via local interpolation but fail to capture global data distributions in high-dimensional spaces. Deep generative models based on GANs offer richer distribution modeling yet suffe…

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
Tavor Z. Baharav, Spyros Dragazis, Aldo Pacchiano
https://arxiv.org/abs/2510.01020 https:/…

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
We study the problem of sequentially testing individuals for a binary disease outcome whose true risk is governed by an unknown logistic model. At each round, a patient arrives with feature vector $x_t$, and the decision maker may either pay to administer a (noiseless) diagnostic test--revealing the true label--or skip testing and predict the patient's disease status based on their feature vector and prior history. Our goal is to minimize the total number of costly tests required while guarante…

Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Riccardo Mazzieri, Eleonora Cicciarella, Jacopo Pegoraro, Federico Corradi, Michele Rossi
https://arxiv.org/abs/2509.23303

Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Radar-based Human Activity Recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first use of Spiking Neural Networks (SNNs) for radar-based HAR on aircraft marshalling signal classification. Our novel hybrid architecture combines convolutional modules for spatial feature extraction with Leaky Integrate-and-Fire (LIF) neurons for temporal processing, inherently capturing gesture dynamics. The model re…

Classification Filtering
Ilker Bayram
https://arxiv.org/abs/2509.13975 https://arxiv.org/pdf/2509.13975

Classification Filtering
We consider a streaming signal in which each sample is linked to a latent class. We assume that multiple classifiers are available, each providing class probabilities with varying degrees of accuracy. These classifiers are employed following a straightforward and fixed policy. In this setting, we consider the problem of fusing the output of the classifiers while incorporating the temporal aspect to improve classification accuracy. We propose a state-space model and develop a filter tailored for…

AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification
Roshan Kenia, Anfei Li, Rishabh Srivastava, Kaveri A. Thakoor
https://arxiv.org/abs/2510.00882

AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification
Glaucoma is a progressive eye disease that leads to optic nerve damage, causing irreversible vision loss if left untreated. Optical coherence tomography (OCT) has become a crucial tool for glaucoma diagnosis, offering high-resolution 3D scans of the retina and optic nerve. However, the conventional practice of condensing information from 3D OCT volumes into 2D reports often results in the loss of key structural details. To address this, we propose a novel hybrid deep learning model that integra…

Feature Identification for Hierarchical Contrastive Learning
Julius Ott, Nastassia Vysotskaya, Huawei Sun, Lorenzo Servadei, Robert Wille
https://arxiv.org/abs/2510.00837 https:…

Feature Identification for Hierarchical Contrastive Learning
Hierarchical classification is a crucial task in many applications, where objects are organized into multiple levels of categories. However, conventional classification approaches often neglect inherent inter-class relationships at different hierarchy levels, thus missing important supervisory signals. Thus, we propose two novel hierarchical contrastive learning (HMLC) methods. The first, leverages a Gaussian Mixture Model (G-HMLC) and the second uses an attention mechanism to capture hierarchy…

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Ahaan Dabholkar, Atul Sharma, Z. Berkay Celik, Saurabh Bagchi
https://arxiv.org/abs/2509.11451 https://…

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Recent works in federated learning (FL) have shown the utility of leveraging transfer learning for balancing the benefits of FL and centralized learning. In this setting, federated training happens after a stable point has been reached through conventional training. Global model weights are first centrally pretrained by the server on a public dataset following which only the last few linear layers (the classification head) of the model are finetuned across clients. In this scenario, existing da…

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Luca Bergamin, Roberto Confalonieri, Fabio Aiolli
https://arxiv.org/abs/2509.22384

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Deep neural networks are widely used in practical applications of AI, however, their inner structure and complexity made them generally not easily interpretable. Model transparency and interpretability are key requirements for multiple scenarios where high performance is not enough to adopt the proposed solution. In this work, a differentiable approximation of $L_0$ regularization is adapted into a logic-based neural network, the Multi-layer Logical Perceptron (MLLP), to study its efficacy in r…

Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
Nick Theisen, Kenny Schlegel, Dietrich Paulus, Peer Neubert
https://arxiv.org/abs/2509.13809

Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
The classification of pixel spectra of hyperspectral images, i.e. spectral classification, is used in many fields ranging from agricultural, over medical to remote sensing applications and is currently also expanding to areas such as autonomous driving. Even though for full hyperspectral images the best-performing methods exploit spatial-spectral information, performing classification solely on spectral information has its own advantages, e.g. smaller model size and thus less data required for …

Self-Guided Target Sound Extraction and Classification Through Universal Sound Separation Model and Multiple Clues
Younghoo Kwon, Dongheon Lee, Dohwan Kim, Jung-Woo Choi
https://arxiv.org/abs/2509.13741

Self-Guided Target Sound Extraction and Classification Through Universal Sound Separation Model and Multiple Clues
This paper introduces a multi-stage self-directed framework designed to address the spatial semantic segmentation of sound scene (S5) task in the DCASE 2025 Task 4 challenge. This framework integrates models focused on three distinct tasks: Universal Sound Separation (USS), Single-label Classification (SC), and Target Sound Extraction (TSE). Initially, USS breaks down a complex audio mixture into separate source waveforms. Each of these separated waveforms is then processed by a SC block, gener…

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Zihan Wei, Tapabrata Chakraborti
https://arxiv.org/abs/2509.22712 https://

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Recent works have shown that deep learning based skin lesion image classification models trained on unbalanced dataset can exhibit bias toward protected demographic attributes such as race, age,and gender. Current bias mitigation methods usually either achieve high level of fairness with the degradation of accuracy, or only improve the model fairness on a single attribute. Additionally usually most bias mitigation strategies are either pre hoc through data processing or post hoc through fairnes…

Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach
Manli Cheng, Subha Maity, Qinglong Tian, Pengfei Li
https://arxiv.org/abs/2509.22268 htt…

Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach
We propose a new framework for binary classification in transfer learning settings where both covariate and label distributions may shift between source and target domains. Unlike traditional covariate shift or label shift assumptions, we introduce a group-label shift assumption that accommodates subpopulation imbalance and mitigates spurious correlations, thereby improving robustness to real-world distributional changes. To model the joint distribution difference, we adopt a flexible exponenti…

A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection
Saifuddin Sagor, Md Taimur Ahad, Faruk Ahmed, Rokonozzaman Ayon, Sanzida Parvin
https://arxiv.org/abs/2509.16250

A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection
Early and accurate detection through Pap smear analysis is critical to improving patient outcomes and reducing mortality of Cervical cancer. State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) require substantial computational resources, extended training time, and large datasets. In this study, a lightweight CNN model, S-Net (Simple Net), is developed specifically for cervical cancer detection and classification using Pap smear images to address these limitations. Alongside S-Net, six…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Training More Robust Classification Model via Discriminative Loss and Gaussian Noise Injection
Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi, Serge Gratton, Thierry Giaccone

Explainable Deep Learning for Cataract Detection in Retinal Images: A Dual-Eye and Knowledge Distillation Approach
MohammadReza Abbaszadeh Bavil Soflaei, Karim SamadZamini
https://arxiv.org/abs/2509.22696

Explainable Deep Learning for Cataract Detection in Retinal Images: A Dual-Eye and Knowledge Distillation Approach
Cataract remains a leading cause of visual impairment worldwide, and early detection from retinal imaging is critical for timely intervention. We present a deep learning pipeline for cataract classification using the Ocular Disease Recognition dataset, containing left and right fundus photographs from 5000 patients. We evaluated CNNs, transformers, lightweight architectures, and knowledge-distilled models. The top-performing model, Swin-Base Transformer, achieved 98.58% accuracy and an F1-score…

Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi, Nikita Kotelevskii, Mohsen Guizani, Maxim Panov
https://arxiv.org/abs/2509.26610

Uncertainty Quantification for Regression using Proper Scoring Rules
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantification (UQ) theory has advanced significantly, building on a firm basis of learning with proper scoring rules. However, these advances were focused on classification, while extending these ideas to regression remains challenging. In this work, we introduce a unified UQ framework for regression based on proper scoring r…

VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL
Kyoungjun Park, Yifan Yang, Juheon Yi, Shicheng Zheng, Yifei Shen, Dongqi Han, Caihua Shan, Muhammad Muaz, Lili Qiu
https://arxiv.org/abs/2510.02282

VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL
With the rapid advancement of AI-generated videos, there is an urgent need for effective detection tools to mitigate societal risks such as misinformation and reputational harm. In addition to accurate classification, it is essential that detection models provide interpretable explanations to ensure transparency for regulators and end users. To address these challenges, we introduce VidGuard-R1, the first video authenticity detector that fine-tunes a multi-modal large language model (MLLM) usin…

Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation
Lekkala Sai Teja, Annepaka Yadagiri, and Partha Pakray, Chukhu Chunka, Mangadoddi Srikar Vardhan
https://arxiv.org/abs/2509.17830

Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation
Generation of Artificial Intelligence (AI) texts in important works has become a common practice that can be used to misuse and abuse AI at various levels. Traditional AI detectors often rely on document-level classification, which struggles to identify AI content in hybrid or slightly edited texts designed to avoid detection, leading to concerns about the model's efficiency, which makes it hard to distinguish between human-written and AI-generated texts. A sentence-level sequence labeling mode…

Crosslisted article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figur...
Kotte, Saipradeep, Walia, Nandagopal, Joseph, Sivadasan, Lali

Adaptive Margin RLHF via Preference over Preferences
Yaswanth Chittepu, Prasann Singhal, Greg Durrett, Scott Niekum
https://arxiv.org/abs/2509.22851 https://

Adaptive Margin RLHF via Preference over Preferences
Margin-based optimization is fundamental to improving generalization and robustness in classification tasks. In the context of reward model learning from preferences within Reinforcement Learning from Human Feedback (RLHF), existing methods typically rely on no margins, fixed margins, or margins that are simplistic functions of preference ratings. However, such formulations often fail to account for the varying strengths of different preferences, for example some preferences are associated with…

Multi Anatomy X-Ray Foundation Model
Nishank Singla, Krisztian Koos, Farzin Haddadpour, Amin Honarmandi Shandiz, Lovish Chum, Xiaojian Xu, Qing Jin, Erhan Bas
https://arxiv.org/abs/2509.12146

Multi Anatomy X-Ray Foundation Model
X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning on a large, private dataset of 1.15 million images spanning diverse anatomical regions and evaluated across 12 datasets and 20 downstream tasks, including classification, retrieval, segmentation, localization, visual grounding, …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/11]:
- DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification
Siyuan Jiang, Yihan Hu, Wenjie Li, Pengcheng Zeng

Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
Luc Boudier, Loris Manganelli, Eleftherios Tsonis, Nicolas Dufour, Vicky Kalogeiton
https://arxiv.org/abs/2509.22635

Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
Few-shot image classification remains challenging due to the limited availability of labeled examples. Recent approaches have explored generating synthetic training data using text-to-image diffusion models, but often require extensive model fine-tuning or external information sources. We present a novel training-free approach, called DIPSY, that leverages IP-Adapter for image-to-image translation to generate highly discriminative synthetic images using only the available few-shot examples. DIP…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Xi He
https://arxiv.org/abs/2509.12057 https://arx…

Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm
Decision trees are a ubiquitous model for classification and regression tasks due to their interpretability and efficiency. However, solving the optimal decision tree (ODT) problem remains a challenging combinatorial optimization task. Even for the simplest splitting rules--axis-parallel hyperplanes--it is NP-hard to optimize. In Part I of this series, we rigorously defined the proper decision tree model through four axioms and, based on these, introduced four formal definitions of the ODT prob…

DeepASA: An Object-Oriented One-for-All Network for Auditory Scene Analysis
Dongheon Lee, Younghoo Kwon, Jung-Woo Choi
https://arxiv.org/abs/2509.17247 https://

DeepASA: An Object-Oriented One-for-All Network for Auditory Scene Analysis
We propose DeepASA, a one-for-all model for auditory scene analysis that performs multi-input multi-output (MIMO) source separation, dereverberation, sound event detection (SED), audio classification, and direction-of-arrival estimation (DoAE) within a unified framework. DeepASA is designed for complex auditory scenes where multiple, often similar, sound sources overlap in time and move dynamically in space. To achieve robust and consistent inference across tasks, we introduce an object-oriente…

CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation
Debopom Sutradhar, Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan, Reem E. Mohamed, Sheikh Izzal Azid, Sami Azam
https://arxiv.org/abs/2509.11952

CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation
Accurate land cover classification from satellite imagery is crucial in environmental monitoring and sustainable resource management. However, it remains challenging due to the complexity of natural landscapes, the visual similarity between classes, and the significant class imbalance in the available datasets. To address these issues, we propose a dual encoder architecture that independently extracts modality-specific features from optical and Synthetic Aperture Radar (SAR) imagery, which are …

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Zhi Chen
https://arxiv.org/abs/2509.11108 https://

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Ultrasound imaging is widely used in clinical practice due to its cost-effectiveness, mobility, and safety. However, current AI research often treats disease prediction and tissue segmentation as two separate tasks and their model requires substantial computational overhead. In such a situation, we introduce UltraUPConvNet, a computationally efficient universal framework designed for both ultrasound image classification and segmentation. Trained on a large-scale dataset containing more than 9,7…

Stratify or Die: Rethinking Data Splits in Image Segmentation
Naga Venkata Sai Jitin Jami, Thomas Altstidl, Jonas Mueller, Jindong Li, Dario Zanca, Bjoern Eskofier, Heike Leutheuser
https://arxiv.org/abs/2509.21056

Stratify or Die: Rethinking Data Splits in Image Segmentation
Random splitting of datasets in image segmentation often leads to unrepresentative test sets, resulting in biased evaluations and poor model generalization. While stratified sampling has proven effective for addressing label distribution imbalance in classification tasks, extending these ideas to segmentation remains challenging due to the multi-label structure and class imbalance typically present in such data. Building on existing stratification concepts, we introduce Iterative Pixel Stratifi…

Enhancing Situational Awareness in Wearable Audio Devices Using a Lightweight Sound Event Localization and Detection System
Jun-Wei Yeow, Ee-Leng Tan, Santi Peksi, Zhen-Ting Ong, Woon-Seng Gan
https://arxiv.org/abs/2509.14650

Enhancing Situational Awareness in Wearable Audio Devices Using a Lightweight Sound Event Localization and Detection System
Wearable audio devices with active noise control (ANC) enhance listening comfort but often at the expense of situational awareness. However, this auditory isolation may mask crucial environmental cues, posing significant safety risks. To address this, we propose an environmental intelligence framework that combines Acoustic Scene Classification (ASC) with Sound Event Localization and Detection (SELD). Our system first employs a lightweight ASC model to infer the current environment. The scene p…

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
Chao Shuai, Gaojian Wang, Kun Pan, Tong Wu, Fanli Jin, Haohan Tan, Mengxiang Li, Zhenguang Liu, Feng Lin, Kui Ren
https://arxiv.org/abs/2509.13776

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
While the pursuit of higher accuracy in deepfake detection remains a central goal, there is an increasing demand for precise localization of manipulated regions. Despite the remarkable progress made in classification-based detection, accurately localizing forged areas remains a significant challenge. A common strategy is to incorporate forged region annotations during model training alongside manipulated images. However, such approaches often neglect the complementary nature of local detail and…