Tootfinder

KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
Ye Kyaw Thu, Thura Aung, Thazin Myint Oo, Thepchai Supnithi
https://arxiv.org/abs/2507.06753

KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news classification, and imbalanced multiclass ethnic language identification. We investigate various embedding configurations, comparing random to fastText embeddings in both static and fine-tuned settings, with embedding dimensions of 100 and 300 using CBOW and Skip-gram models. Baselin…

A case study of GW190425 for classifying binary neutron star versus binary black hole mergers and constraining asymmetric dark matter with gravitational wave detectors
Sanika Khadkikar, Divya Singh
https://arxiv.org/abs/2507.07895

A case study of GW190425 for classifying binary neutron star versus binary black hole mergers and constraining asymmetric dark matter with gravitational wave detectors
The LIGO Scientific, Virgo, and KAGRA collaboration has identified two binary neutron star merger candidates, GW170817 and GW190425, along with several binary black hole candidates. While GW170817 was confirmed as a BNS merger through its electromagnetic counterparts, GW190425 lacked such observations, leaving its classification uncertain. We examine the possibility that GW190425 originated from black holes that merged after dark matter accretion caused their progenitor neutron stars to implode…

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Peyman Sharifian, Xiaotong Hong, Alireza Karimian, Mehdi Amini, Hossein Arabi
https://arxiv.org/abs/2507.06410

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Breast density assessment is a crucial component of mammographic interpretation, with high breast density (BI-RADS categories C and D) representing both a significant risk factor for developing breast cancer and a technical challenge for tumor detection. This study proposes an automated deep learning system for robust binary classification of breast density (low: A/B vs. high: C/D) using the VinDr-Mammo dataset. We implemented and compared four advanced convolutional neural networks: ResNet18, …

Ensemble-Based Graph Representation of fMRI Data for Cognitive Brain State Classification
Daniil Vlasenko, Vadim Ushakov, Alexey Zaikin, Denis Zakharov
https://arxiv.org/abs/2508.06118

Ensemble-Based Graph Representation of fMRI Data for Cognitive Brain State Classification
Understanding and classifying human cognitive brain states based on neuroimaging data remains one of the foremost and most challenging problems in neuroscience, owing to the high dimensionality and intrinsic noise of the signals. In this work, we propose an ensemble-based graph representation method of functional magnetic resonance imaging (fMRI) data for the task of binary brain-state classification. Our method builds the graph by leveraging multiple base machine-learning models: each edge wei…

This https://arxiv.org/abs/2505.17282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Attention with Trained Embeddings Provably Selects Important Tokens
Token embeddings play a crucial role in language modeling but, despite this practical relevance, their theoretical understanding remains limited. Our paper addresses the gap by characterizing the structure of embeddings obtained via gradient descent. Specifically, we consider a one-layer softmax attention model with a linear head for binary classification, i.e., $\texttt{Softmax}( p^\top E_X^\top ) E_X v = \frac{ \sum_{i=1}^T \exp(p^\top E_{x_i}) E_{x_i}^\top v}{\sum_{j=1}^T \exp(p^\top E_{x_{j…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Mingjie Chen (Zhejiang University), Tiancheng Zhu (Huazhong University of Science,Technology), Mingxue Zhang (The State Key Laboratory of Blockchain,Data Security, Zhejiang University,Hangzhou High-Tech Zone), Yiling He (University College London), Minghao Lin (University of Southern California), Penghui Li (Columbia University), Kui Ren (The State Key Laboratory of Blockchain,Data Security, Z…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such models have therefore drawn extensive attention, aiming at misleading the models to generate erroneous predictions. Prior works have explored various approaches to generating semantic-preserving variants, i.e., adversarial samples, to evaluate the robustness of the models against adversarial attacks. However, the…

L1-Regularized Functional Support Vector Machine
Bingfan Liu, Peijun Sang
https://arxiv.org/abs/2508.05567 https://arxiv.org/pdf/2508.05567

L1-Regularized Functional Support Vector Machine
In functional data analysis, binary classification with one functional covariate has been extensively studied. We aim to fill in the gap of considering multivariate functional covariates in classification. In particular, we propose an $L_1$-regularized functional support vector machine for binary classification. An accompanying algorithm is developed to fit the classifier. By imposing an $L_1$ penalty, the algorithm enables us to identify relevant functional covariates of the binary response. N…

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Binghao Ye, Wenjuan Li, Dong Wang, Man Yao, Bing Li, Weiming Hu, Dong Liang, Kun Shang
https://arxiv.org/abs/2506.05679

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Spiking Neural Networks (SNNs) are noted for their brain-like computation and energy efficiency, but their performance lags behind Artificial Neural Networks (ANNs) in tasks like image classification and object detection due to the limited representational capacity. To address this, we propose a novel spiking neuron, Integer Binary-Range Alignment Leaky Integrate-and-Fire to exponentially expand the information expression capacity of spiking neurons with only a slight energy increase. This is a…

High-Resolution Sustain Pedal Depth Estimation from Piano Audio Across Room Acoustics
Kun Fang, Hanwen Zhang, Ziyu Wang, Ichiro Fujinaga
https://arxiv.org/abs/2507.04230

High-Resolution Sustain Pedal Depth Estimation from Piano Audio Across Room Acoustics
Piano sustain pedal detection has previously been approached as a binary on/off classification task, limiting its application in real-world piano performance scenarios where pedal depth significantly influences musical expression. This paper presents a novel approach for high-resolution estimation that predicts continuous pedal depth values. We introduce a Transformer-based architecture that not only matches state-of-the-art performance on the traditional binary classification task but also ach…

Hybrid quantum tensor networks for aeroelastic applications
M. Lautaro Hickmann (Institute for AI Safety and Security, German Aerospace Center), Pedro Alves (Institute for AI Safety and Security, German Aerospace Center), David Quero (Institute of Aeroelasticity, German Aerospace Center), Friedhelm Schwenker (Institute of Neural Information Processing, University of Ulm, Ulm, Germany), Hans-Martin Rieser (Institute for AI Safety and Security, German Aerospace Center)

Hybrid quantum tensor networks for aeroelastic applications
We investigate the application of hybrid quantum tensor networks to aeroelastic problems, harnessing the power of Quantum Machine Learning (QML). By combining tensor networks with variational quantum circuits, we demonstrate the potential of QML to tackle complex time series classification and regression tasks. Our results showcase the ability of hybrid quantum tensor networks to achieve high accuracy in binary classification. Furthermore, we observe promising performance in regressing discrete…

Binary Classification with the Maximum Score Model and Linear Programming
Joel L. Horowitz, Sokbae Lee
https://arxiv.org/abs/2507.19654 https://arxiv.org/p…

Binary Classification with the Maximum Score Model and Linear Programming
This paper presents a computationally efficient method for binary classification using Manski's (1975,1985) maximum score model when covariates are discretely distributed and parameters are partially but not point identified. We establish conditions under which it is minimax optimal to allow for either non-classification or random classification and derive finite-sample and asymptotic lower bounds on the probability of correct classification. We also describe an extension of our method to conti…

Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction
Junzhe Jiang, Chang Yang, Xinrun Wang, Bo Li
https://arxiv.org/abs/2506.03153

Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction
Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These methods fail to capture indices' inherent nature as aggregations of constituent stocks with complex, time-varying interdependencies. To address these limitations, we propose Cubic, a novel end-to-en…

This https://arxiv.org/abs/2401.03276 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Robust Binary and Multinomial Logit Models for Classification with Data Uncertainties
Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, the collected data for those models are subject to measurement errors. Previous studies on measurement errors mostly focus on "better estimating model parameters" with training data. In this study, we focus on using BNL and MNL for classification problems, that is, to ``better predict the behavior of new samples'' when…

Paper-folding models for the CAR algebra
Grigoris Kopsacheilis, Wilhelm Winter
https://arxiv.org/abs/2508.04837 https://arxiv.org/pdf/2508.04837

Paper-folding models for the CAR algebra
We show that the CAR algebra admits a Cantor spectrum C*-diagonal that is not conjugate to the standard AF diagonal. We obtain this by classification theory of C*-algebras, and the diagonal arises by realising the CAR algebra as the crossed product of a free minimal action on the Cantor space, where the acting group is the product of a locally finite group with the infinite dihedral group. The main ingredient in the construction is a binary subshift associated to the well-known regular paper-fo…

Beyond Binary Moderation: Identifying Fine-Grained Sexist and Misogynistic Behavior on GitHub with Large Language Models
Tanni Dev, Sayma Sultana, Amiangshu Bosu
https://arxiv.org/abs/2507.20358

Beyond Binary Moderation: Identifying Fine-Grained Sexist and Misogynistic Behavior on GitHub with Large Language Models
Background: Sexist and misogynistic behavior significantly hinders inclusion in technical communities like GitHub, causing developers, especially minorities, to leave due to subtle biases and microaggressions. Current moderation tools primarily rely on keyword filtering or binary classifiers, limiting their ability to detect nuanced harm effectively. Aims: This study introduces a fine-grained, multi-class classification framework that leverages instruction-tuned Large Language Models (LLMs) t…

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
MD Shaikh Rahman, Feiroz Humayara, Syed Maudud E Rabbi, Muhammad Mahbubur Rashid
https://arxiv.org/abs/2508.04790

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
Content-based mammographic image retrieval systems require exact BIRADS categorical matching across five distinct classes, presenting significantly greater complexity than binary classification tasks commonly addressed in literature. Current medical image retrieval studies suffer from methodological limitations including inadequate sample sizes, improper data splitting, and insufficient statistical validation that hinder clinical translation. We developed a comprehensive evaluation framework sy…

This https://arxiv.org/abs/2112.13738 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evaluation of binary classifiers for asymptotically dependent and independent extremes
Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this paper, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic…

(2,2)-GB Codes: Classification and Comparison with weight-4 Surface Codes
Fran\c{c}ois Arnault, Philippe Gaborit, Nicolas Saussay
https://arxiv.org/abs/2507.21237 https://

(2,2)-GB Codes: Classification and Comparison with weight-4 Surface Codes
Generalized Bicycle (GB) codes offer a compelling alternative to surface codes for quantum error correction. This paper focuses on (2,2)-Generalized Bicycle codes, constructed from pairs of binary circulant matrices with two non-zero elements per row. Leveraging a lower bound on their minimum distance, we construct three novel infinite families of optimal (2,2)-GB codes with parameters [[ 2n^2, 2, n ]], [[ 4r^2, 2, 2r ]], and [[(2t + 1)^2 + 1, 2, 2t + 1 ]]. These families match the performance …

Preliminary Investigation into Uncertainty-Aware Attack Stage Classification
Alessandro Gaudenzi, Lorenzo Nodari, Lance Kaplan, Alessandra Russo, Murat Sensoy, Federico Cerutti
https://arxiv.org/abs/2508.00368

Preliminary Investigation into Uncertainty-Aware Attack Stage Classification
Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity due to their prolonged, multi-stage nature and the sophistication of their operators. Traditional detection systems typically focus on identifying malicious activity in binary terms (benign or malicious) without accounting for the progression of an attack. However, effective response strategies depend on accurate inference of the attack's current stage, as countermeasures must be tailored to whether an adversa…

This https://arxiv.org/abs/2406.12007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
Here we present the results of benchmarking a quantum processor based on trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning algorithms. Using a quantum-enhanced support vector machine algorithm with up to five qubits we perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology. For the first dataset, images are intentionally selected so that they could be classified with 100% accuracy. This all…

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Johan Hallberg Szabadv\'ary, Tuwe L\"ofstr\"om, Ulf Johansson, Cecilia S\"onstr\"od, Ernst Ahlberg, Lars Carlsson
https://arxiv.org/abs/2506.21802

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a prediction. ML with reject option addresses this issue by abstaining from making a prediction if it is likely to be incorrect. In this work, we formalise the approach to ML with reject option in binary classification, deriving theoretical guarantees on the resulting error rate. This is achieved through conformal pred…

Adversarial Surrogate Risk Bounds for Binary Classification
Natalie S. Frank
https://arxiv.org/abs/2506.09348 https://arxiv.org/pdf/2…

Adversarial Surrogate Risk Bounds for Binary Classification
A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training robust classifiers, which involves minimizing an adversarial surrogate risk. Recent work characterized when a minimizing sequence of an adversarial surrogate risk is also a minimizing sequence of the adversarial classification risk for binary classification -- a property known as adversarial consistency. However, thes…

Adaptive Knowledge Distillation for Device-Directed Speech Detection
Hyung Gun Chi, Florian Pesce, Wonil Chang, Oggi Rudovic, Arturo Argueta, Stefan Braun, Vineet Garg, Ahmed Hussen Abdelaziz
https://arxiv.org/abs/2508.02801

Adaptive Knowledge Distillation for Device-Directed Speech Detection
Device-directed speech detection (DDSD) is a binary classification task that separates the user's queries to a voice assistant (VA) from background speech or side conversations. This is important for achieving naturalistic user experience. To this end, we propose knowledge distillation (KD) to enhance DDSD accuracy while ensuring efficient deployment. Specifically, we introduce a novel adaptive KD method that transfers knowledge from general representations of an ASR large pre-trained acoustic …

This https://arxiv.org/abs/2409.16322 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

On the Within-class Variation Issue in Alzheimer's Disease Detection
Alzheimer's Disease (AD) detection employs machine learning classification models to distinguish between individuals with AD and those without. Different from conventional classification tasks, we identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. Therefore, simplistic binary AD classification may overlook two crucial aspects: within-class heterogeneity and instance-level imbalance. In this work, we found usi…

Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Ming Huang, Zehan Li, Yan Hu, Wanjing Wang, Andrew Wen, Scott Lane, Salih Selek, Lokesh Shahani, Rodrigo Machado-Vieira, Jair Soares, Hua Xu, Hongfang Liu
https://arxiv.org/abs/2507.17009

Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Suicide remains a pressing global health crisis, with over 720,000 deaths annually and millions more affected by suicide ideation (SI) and suicide attempts (SA). Early identification of suicidality-related factors (SrFs), including SI, SA, exposure to suicide (ES), and non-suicidal self-injury (NSSI), is critical for timely intervention. While prior studies have applied AI to detect SrFs in clinical notes, most treat suicidality as a binary classification task, overlooking the complexity of coo…

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Zhuolin Du, Yisheng Song
https://arxiv.org/abs/2507.13012

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support Tensor Machine (LDM-NPSTM). The proposed classifier has the following advantages: First, it utilizes tensor data as training samples, which helps to comprehensively preserve the inherent structural information of high-dimensional data, thereby improving class…

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

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

Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods
Aina Zhang, Xiaoming Kong, Bowen Liu, Nan Li, Yude Bu, Zhenping Yi, Meng Liu
https://arxiv.org/abs/2506.16090

Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods
The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. Thi…

RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David B\'alik, Martin Jure\v{c}ek, Mark Stamp
https://arxiv.org/abs/2506.23909 https://

RawMal-TF: Raw Malware Dataset Labeled by Type and Family
This work addresses the challenge of malware classification using machine learning by developing a novel dataset labeled at both the malware type and family levels. Raw binaries were collected from sources such as VirusShare, VX Underground, and MalwareBazaar, and subsequently labeled with family information parsed from binary names and type-level labels integrated from ClarAVy. The dataset includes 14 malware types and 17 malware families, and was processed using a unified feature extraction p…

GRB 241105A: A test case for GRB classification and rapid r-process nucleosynthesis channels
Dimple, B. P. Gompertz, A. J. Levan, D. B. Malesani, T. Laskar, S. Bala, A. A. Chrimes, K. Heintz, L. Izzo, G. P. Lamb, D. O'Neill, J. T. Palmerio, A. Saccardi, G. E. Anderson, C. De Barra, Y. Huang, A. Kumar, H. Li, S. McBreen, O. Mukherjee, S. R. Oates, U. Pathak, Y. Qiu, O. J. Roberts, R. Sonawane, P. Veres, K. Ackley, X. Han, Y. Julakanti, J. Wang, P. D'Avanzo, A. Martin-Carrillo, M…

GRB 241105A: A test case for GRB classification and rapid r-process nucleosynthesis channels
Gamma-ray bursts (GRBs) offer a powerful window to probe the progenitor systems responsible for the formation of heavy elements through the rapid neutron capture (r-) process, thanks to their exceptional luminosity, which allows them to be observed across vast cosmic distances. GRB 241105A, observed at a redshift of z = 2.681, features a short initial spike (1.5 s) and a prolonged weak emission lasting about 64 s, positioning it as a candidate for a compact binary merger and potentially marking…

Repulsive particle interactions enable selective information processing at cellular interfaces
Jenna Elliott, Hiral Shah, Roman Belousov, Gautam Dey, Anna Erzberger
https://arxiv.org/abs/2506.14739

Repulsive particle interactions enable selective information processing at cellular interfaces
Living systems relay information across membrane interfaces to coordinate compartment functions. We identify a physical mechanism for selective information transmission that arises from the sigmoidal response of surface-bound particle densities to spatial features of adjacent external structures. This mechanism implements a form of spatial thresholding, enabling the binary classification of external cues. Expansion microscopy measurements of nuclear pore complex distributions in S. arctica show…

On the existence of consistent adversarial attacks in high-dimensional linear classification
Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro
https://arxiv.org/abs/2506.12454

On the existence of consistent adversarial attacks in high-dimensional linear classification
What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a central role. We introduce a new error metric that precisely capture this distinction, quantifying model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth l…

Continuity Norm Framework for the Evolution of Nonsingular Matrices
L. Yildiz, D. Kayki, E. Gudekli
https://arxiv.org/abs/2507.20742 https://arxiv.org/pdf/…

Continuity Norm Framework for the Evolution of Nonsingular Matrices
Matrix theory, foundational in diverse fields such as mathematics, physics, and computational sciences, typically categorizes matrices based strictly on their invertibility-determined by a sharply defined singular or nonsingular classification. However, such binary classifications become inadequate in describing matrices whose elements vary continuously over time, thereby transitioning through intermediate states near singular configurations. To address this fundamental limitation, we develop a…

Computer Vision for Real-Time Monkeypox Diagnosis on Embedded Systems
Jacob M. Delgado-L\'opez, Ricardo A. Morell-Rodriguez, Sebasti\'an O. Espinosa-Del Rosario, Wilfredo E. Lugo-Beauchamp
https://arxiv.org/abs/2507.17123

Computer Vision for Real-Time Monkeypox Diagnosis on Embedded Systems
The rapid diagnosis of infectious diseases, such as monkeypox, is crucial for effective containment and treatment, particularly in resource-constrained environments. This study presents an AI-driven diagnostic tool developed for deployment on the NVIDIA Jetson Orin Nano, leveraging the pre-trained MobileNetV2 architecture for binary classification. The model was trained on the open-source Monkeypox Skin Lesion Dataset, achieving a 93.07% F1-Score, which reflects a well-balanced performance in p…

DCE-LLM: Dead Code Elimination with Large Language Models
Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu
https://arxiv.org/abs/2506.11076 https://

DCE-LLM: Dead Code Elimination with Large Language Models
Dead code introduces several challenges in software development, such as increased binary size and maintenance difficulties. It can also obscure logical errors and be exploited for obfuscation in malware. For LLM-based code-related tasks, dead code introduces vulnerabilities that can mislead these models, raising security concerns. Although modern compilers and IDEs offer dead code elimination, sophisticated patterns can bypass these tools. A universal approach that includes classification, loc…

Model Compression Engine for Wearable Devices Skin Cancer Diagnosis
Jacob M. Delgado-L\'opez, Andrea P. Seda-Hernandez, Juan D. Guadalupe-Rosado, Luis E. Fernandez Ramirez, Miguel Giboyeaux-Camilo, Wilfredo E. Lugo-Beauchamp
https://arxiv.org/abs/2507.17125

Model Compression Engine for Wearable Devices Skin Cancer Diagnosis
Skin cancer is one of the most prevalent and preventable types of cancer, yet its early detection remains a challenge, particularly in resource-limited settings where access to specialized healthcare is scarce. This study proposes an AI-driven diagnostic tool optimized for embedded systems to address this gap. Using transfer learning with the MobileNetV2 architecture, the model was adapted for binary classification of skin lesions into "Skin Cancer" and "Other." The TensorRT framework was emplo…

Optimal Convergence Rates of Deep Neural Network Classifiers
Zihan Zhang, Lei Shi, Ding-Xuan Zhou
https://arxiv.org/abs/2506.14899 https://

Optimal Convergence Rates of Deep Neural Network Classifiers
In this paper, we study the binary classification problem on $[0,1]^d$ under the Tsybakov noise condition (with exponent $s \in [0,\infty]$) and the compositional assumption. This assumption requires the conditional class probability function of the data distribution to be the composition of $q+1$ vector-valued multivariate functions, where each component function is either a maximum value function or a Hölder-$β$ smooth function that depends only on $d_*$ of its input variables. Notably, $d_…

Detection of Low-Redshift Excess in Supernova-Linked Gamma-Ray Bursts
Qin-Mei Li, Qi-Bin Sun, Sheng-Bang Qian, Fu-Xing Li
https://arxiv.org/abs/2507.13603 …

Detection of Low-Redshift Excess in Supernova-Linked Gamma-Ray Bursts
Gamma-ray bursts (GRBs) are traditionally classified into long (lGRBs) and short (sGRBs) durations based on their $T_{90}$, with lGRBs widely used as tracers of the cosmic star formation rate (SFR) due to their observed association with core-collapse supernovae. However, recent detections of kilonovae accompanying some lGRBs challenge this assumption, suggesting potential contamination from compact binary mergers. Here, we move beyond the conventional $T_{90}$-based classification and focus exc…

Universal Rates of ERM for Agnostic Learning
Steve Hanneke, Mingyue Xu
https://arxiv.org/abs/2506.14110 https://arxiv.org/pdf/2506.14…

Universal Rates of ERM for Agnostic Learning
The universal learning framework has been developed to obtain guarantees on the learning rates that hold for any fixed distribution, which can be much faster than the ones uniformly hold over all the distributions. Given that the Empirical Risk Minimization (ERM) principle being fundamental in the PAC theory and ubiquitous in practical machine learning, the recent work of arXiv:2412.02810 studied the universal rates of ERM for binary classification under the realizable setting. However, the ass…

Mining the Alerts: A Preliminary Catalog of Compact Binaries from the Fourth Observing Run
Aleyna Aky\"uz, Alex Correia, Jada Garofalo, Keisi Kacanja, Vikas Jadhav Y, Labani Roy, Kanchan Soni, Hung Tan, Collin D. Capano, Alexander H. Nitz
https://arxiv.org/abs/2507.08778

Mining the Alerts: A Preliminary Catalog of Compact Binaries from the Fourth Observing Run
We present a preliminary catalog of compact binary merger candidates from the ongoing fourth observing run (O4) of Advanced LIGO, Virgo, and KAGRA, based on an analysis of public alerts distributed through GraceDB as of May 2025. We developed and applied methods to estimate the source-frame chirp mass for each candidate by utilizing information from public data products, including source classification probabilities, sky localizations, and observatory status. Combining our O4 analysis with prev…