Tootfinder

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
Md Min-Ha-Zul Abedin, Tazqia Mehrub
https://arxiv.org/abs/2507.16952

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable (PE) files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet, under three preprocessing settings: original feature space, Principal Component Analysis (PCA), and Linear Discriminant Analysis (L…

PreQRAG -- Classify and Rewrite for Enhanced RAG
Damian Martinez, Catalina Riano, Hui Fang
https://arxiv.org/abs/2506.17493 https://a…

PreQRAG -- Classify and Rewrite for Enhanced RAG
This paper presents the submission of the UDInfo team to the SIGIR 2025 LiveRAG Challenge. We introduce PreQRAG, a Retrieval Augmented Generation (RAG) architecture designed to improve retrieval and generation quality through targeted question preprocessing. PreQRAG incorporates a pipeline that first classifies each input question as either single-document or multi-document type. For single-document questions, we employ question rewriting techniques to improve retrieval precision and generation…

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation
Paul E. Calzada, Zahin Ibnat, Tanvir Rahman, Kamal Kandula, Danyu Lu, Sujan Kumar Saha, Farimah Farahmandi, Mark Tehranipoor
https://arxiv.org/abs/2507.13369

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation
Large Language Models (LLMs) are gaining popularity for hardware design automation, particularly through Register Transfer Level (RTL) code generation. In this work, we examine the current literature on RTL generation using LLMs and identify key requirements for training and fine-tuning datasets. We construct a robust Verilog dataset through an automated three-pronged process involving database (DB) creation and management with PostgreSQL, data collection from code hosting sites like OpenCores …

Stemming -- The Evolution and Current State with a Focus on Bangla
Abhijit Paul, Mashiat Amin Farin, Sharif Md. Abdullah, Ahmedul Kabir, Zarif Masud, Shebuti Rayana
https://arxiv.org/abs/2508.15711

Stemming -- The Evolution and Current State with a Focus on Bangla
Bangla, the seventh most widely spoken language worldwide with 300 million native speakers, faces digital under-representation due to limited resources and lack of annotated datasets. Stemming, a critical preprocessing step in language analysis, is essential for low-resource, highly-inflectional languages like Bangla, because it can reduce the complexity of algorithms and models by significantly reducing the number of words the algorithm needs to consider. This paper conducts a comprehensive su…

Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification
Mengliang Zhang, Jacob M. Luber
https://arxiv.org/abs/2508.14779 https://arxiv…

Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification
Pathology foundation models (PFMs) have demonstrated remarkable potential in whole-slide image (WSI) diagnosis. However, pathology images from different hospitals often vary due to differences in scanning hardware and preprocessing styles, which may lead PFMs to inadvertently learn hospital-specific features, posing risks for clinical deployment. In this work, we present the first systematic study of domain bias in PFMs arising from hospital source characteristics. Specifically, we (1) construc…

Effectiveness of Hybrid Optimization Method for Quantum Annealing Machines
Shuta Kikuchi, Nozomu Togawa, Shu Tanaka
https://arxiv.org/abs/2507.15544 https:…

Effectiveness of Hybrid Optimization Method for Quantum Annealing Machines
To enhance the performance of quantum annealing machines, several methods have been proposed to reduce the number of spins by fixing spin values through preprocessing. We proposed a hybrid optimization method that combines a simulated annealing (SA)-based non-quantum-type Ising machine with a quantum annealing machine. However, its applicability remains unclear. Therefore, we evaluated the performance of the hybrid method on large-size Ising models and analyzed its characteristics. The results …

Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD Filter-CLAHE
Ach Khozaimi, Isnani Darti, Syaiful Anam, Wuryansari Muharini Kusumawinahyu
https://arxiv.org/abs/2506.15489

Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD Filter-CLAHE
Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on Pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluate…

d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement
Hyein Hong, Sangbong Yoo, SeokHwan Choi, Jisue Kim, Seongbum Seo, Haneol Cho, Chansoo Kim, Yun Jang
https://arxiv.org/abs/2507.11960

d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement
Approaches to enhancing data quality (DQ) are classified into two main categories: data- and process-driven. However, prior research has predominantly utilized batch data preprocessing within the data-driven framework, which often proves insufficient for optimizing machine learning (ML) model performance and frequently leads to distortions in data characteristics. Existing studies have primarily focused on data preprocessing rather than genuine data quality improvement (DQI). In this paper, we …

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
Jing Chang, Chang Liu, Jinbin Huang, Rui Mao, Jianbin Qin
https://arxiv.org/abs/2507.13712

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
Automated data preparation is crucial for democratizing machine learning, yet existing reinforcement learning (RL) based approaches suffer from inefficient exploration in the vast space of possible preprocessing pipelines. We present LLaPipe, a novel framework that addresses this exploration bottleneck by integrating Large Language Models (LLMs) as intelligent policy advisors. Unlike traditional methods that rely solely on statistical features and blind trial-and-error, LLaPipe leverages the se…

GalaxyGenius: A Mock Galaxy Image Generator for Various Telescopes from Hydrodynamical Simulations
Xingchen Zhou, Hang Yang, Nan Li, Qi Xiong, Furen Deng, Xian-Min Meng, Renhao Ye, Shiyin Shen, Peng Wei, Qifan Cui, Zizhao He, Ayodeji Ibitoye, Chengliang Wei, Yuedong Fang
https://arxiv.org/abs/2506.15060

GalaxyGenius: A Mock Galaxy Image Generator for Various Telescopes from Hydrodynamical Simulations
To advance research on galaxies in the era of large-scale sky surveys, we introduce GalaxyGenius, a Python package designed to produce synthetic galaxy images tailored to different telescopes based on hydrodynamical simulations. The package comprises three main modules: data preprocessing, ideal data cube generation, and mock observation. Specifically, the preprocessing module extracts necessary properties of star and gas particles for a selected subhalo from hydrodynamical simulations and crea…

Scalable FPGA Framework for Real-Time Denoising in High-Throughput Imaging: A DRAM-Optimized Pipeline using High-Level Synthesis
Weichien Liao
https://arxiv.org/abs/2508.14917 h…

Scalable FPGA Framework for Real-Time Denoising in High-Throughput Imaging: A DRAM-Optimized Pipeline using High-Level Synthesis
High-throughput imaging workflows, such as Parallel Rapid Imaging with Spectroscopic Mapping (PRISM), generate data at rates that exceed conventional real-time processing capabilities. We present a scalable FPGA-based preprocessing pipeline for real-time denoising, implemented via High-Level Synthesis (HLS) and optimized for DRAM-backed buffering. Our architecture performs frame subtraction and averaging directly on streamed image data, minimizing latency through burst-mode AXI4 interfaces. The…

On Kernelization with Access to NP-Oracles
Hendrik Molter, Meirav Zehavi
https://arxiv.org/abs/2508.10550 https://arxiv.org/pdf/2508.10550

On Kernelization with Access to NP-Oracles
Kernelization is the standard framework to analyze preprocessing routines mathematically. Here, in terms of efficiency, we demand the preprocessing routine to run in time polynomial in the input size. However, today, various NP-complete problems are already solved very fast in practice; in particular, SAT-solvers and ILP-solvers have become extremely powerful and used frequently. Still, this fails to capture the wide variety of computational problems that lie at higher levels of the polynomial …

WISE-FUSE: Efficient Whole Slide Image Encoding via Coarse-to-Fine Patch Selection with VLM and LLM Knowledge Fusion
Yonghan Shin, SeungKyu Kim, Won-Ki Jeong
https://arxiv.org/abs/2508.14537

WISE-FUSE: Efficient Whole Slide Image Encoding via Coarse-to-Fine Patch Selection with VLM and LLM Knowledge Fusion
Whole slide images (WSIs) in computational pathology (CPath) pose a major computational challenge due to their gigapixel scale, often requiring the processing of tens to hundreds of thousands of high-resolution patches per slide. This results in prohibitive encoding costs, with preprocessing and training times extending to days or even weeks-making WSI encoding the most significant bottleneck in real-world deployment. In this work, we propose WISE-FUSE, an adaptive WSI encoding framework that l…

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky
https://arxiv.org/abs/2506.05588

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite …

PadAug: Robust Speaker Verification with Simple Waveform-Level Silence Padding
Zijun Huang, Chengdong Liang, Jiadi Yao, Xiao-Lei Zhang
https://arxiv.org/abs/2508.14732 https://

PadAug: Robust Speaker Verification with Simple Waveform-Level Silence Padding
The presence of non-speech segments in utterances often leads to the performance degradation of speaker verification. Existing systems usually use voice activation detection as a preprocessing step to cut off long silence segments. However, short silence segments, particularly those between speech segments, still remain a problem for speaker verification. To address this issue, in this paper, we propose a simple wave-level data augmentation method, \textit{PadAug}, which aims to enhance the sys…

Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
Akhil John Thomas, Christiaan Boerkamp
https://arxiv.org/abs/2507.13394 https://

Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
Nerve segmentation is crucial in medical imaging for precise identification of nerve structures. This study presents an optimized DeepLabV3-based segmentation pipeline that incorporates automated threshold fine-tuning to improve segmentation accuracy. By refining preprocessing steps and implementing parameter optimization, we achieved a Dice Score of 0.78, an IoU of 0.70, and a Pixel Accuracy of 0.95 on ultrasound nerve imaging. The results demonstrate significant improvements over baseline mod…

Detecting AI Assistance in Abstract Complex Tasks
Tyler King, Nikolos Gurney, John H. Miller, Volkan Ustun
https://arxiv.org/abs/2507.10761 https://…

Detecting AI Assistance in Abstract Complex Tasks
Detecting assistance from artificial intelligence is increasingly important as they become ubiquitous across complex tasks such as text generation, medical diagnosis, and autonomous driving. Aid detection is challenging for humans, especially when looking at abstract task data. Artificial neural networks excel at classification thanks to their ability to quickly learn from and process large amounts of data -- assuming appropriate preprocessing. We posit detecting help from AI as a classificatio…

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing
Minnan Pei, Gang Li, Junwen Si, Zeyu Zhu, Zitao Mo, Peisong Wang, Zhuoran Song, Xiaoyao Liang, Jian Cheng
https://arxiv.org/abs/2507.15300

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing
3D Gaussian Splatting (3DGS) has emerged as a leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for mobile applications. Through in-depth analysis, we identify two major limitations in the conventional decoupled preprocessing-rendering dataflow adopted by existing accelerators: 1) a significant portion of preprocessed Gaussians are not used in rendering, and 2) the same Gaussian gets repeatedly loaded across different t…

QUBODock: A Pip-Installable QUBO Tool for Ligand Pose Generation
Pei-Kun Yang
https://arxiv.org/abs/2508.13014 https://arxiv.org/pdf/2508.13014

QUBODock: A Pip-Installable QUBO Tool for Ligand Pose Generation
We present QUBODock, a pip-installable tool that formulates ligand pose generation as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solves it efficiently on CPU or GPU. QUBODock focuses exclusively on pose generation and deliberately excludes any built-in scoring function, allowing researchers to pair its poses with external scorers of their choice. The software provides a minimal, reproducible interface for (i) protein-ligand structure ingestion and preprocessing, (ii) QUBO …

Do\u{g}al Dil \.I\c{s}lemede Tokenizasyon Standartlar{\i} ve \"Ol\c{c}\"um\"u: T\"urk\c{c}e \"Uzerinden B\"uy\"uk Dil Modellerinin Kar\c{s}{\i}la\c{s}t{\i}rmal{\i} Analizi
M. Ali Bayram, Ali Arda Fincan, Ahmet Semih G\"um\"u\c{s}, Sercan Karaka\c{s}, Banu Diri, Sava\c{s} Y{\i}ld{\i}r{\i}m
https://

Doğal Dil İşlemede Tokenizasyon Standartları ve Ölçümü: Türkçe Üzerinden Büyük Dil Modellerinin Karşılaştırmalı Analizi
Tokenization is a fundamental preprocessing step in Natural Language Processing (NLP), significantly impacting the capability of large language models (LLMs) to capture linguistic and semantic nuances. This study introduces a novel evaluation framework addressing tokenization challenges specific to morphologically-rich and low-resource languages such as Turkish. Utilizing the Turkish MMLU (TR-MMLU) dataset, comprising 6,200 multiple-choice questions from the Turkish education system, we assesse…

Leveraging Bi-Directional Channel Reciprocity for Robust Ultra-Low-Rate Implicit CSI Feedback with Deep Learning
Zhenyu Liu, Yi Ma, Rahim Tafazolli, Zhi Ding
https://arxiv.org/abs/2507.12301

Leveraging Bi-Directional Channel Reciprocity for Robust Ultra-Low-Rate Implicit CSI Feedback with Deep Learning
Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high enviro…

Adopting Road-Weather Open Data in Route Recommendation Engine
Henna Tammia, Benjamin K\"am\"a, Ella Peltonen
https://arxiv.org/abs/2508.07881 https://

Adopting Road-Weather Open Data in Route Recommendation Engine
Digitraffic, Finland's open road data interface, provides access to nationwide road sensors with more than 2,300 real-time attributes from 1,814 stations. However, efficiently utilizing such a versatile data API for a practical application requires a deeper understanding of the data qualities, preprocessing phases, and machine learning tools. This paper discusses the challenges of large-scale road weather and traffic data. We go through the road-weather-related attributes from DigiTraffic as a …

Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Erez Meoded
https://arxiv.org/abs/2508.11499 https://arxiv.org/pdf/25…

Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Historical handwritten text recognition (HTR) is essential for unlocking the cultural and scholarly value of archival documents, yet digitization is often hindered by scarce transcriptions, linguistic variation, and highly diverse handwriting styles. In this study, we apply TrOCR, a state-of-the-art transformer-based HTR model, to 16th-century Latin manuscripts authored by Rudolf Gwalther. We investigate targeted image preprocessing and a broad suite of data augmentation techniques, introducing…

This https://arxiv.org/abs/2506.05588 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite …

StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features
Jeremi K. Ochab, Mateusz Matias, Tymoteusz Boba, Tomasz Walkowiak
https://arxiv.org/abs/2507.12064

StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features
This submission to the binary AI detection task is based on a modular stylometric pipeline, where: public spaCy models are used for text preprocessing (including tokenisation, named entity recognition, dependency parsing, part-of-speech tagging, and morphology annotation) and extracting several thousand features (frequencies of n-grams of the above linguistic annotations); light-gradient boosting machines are used as the classifier. We collect a large corpus of more than 500 000 machine-generat…

Beamforming with Random Projections: Upper and Lower Bounds
Manan Mittal, Ryan M. Corey, Andrew C. Singer
https://arxiv.org/abs/2507.05662 https://

Beamforming with Random Projections: Upper and Lower Bounds
Beamformers often trade off white noise gain against the ability to suppress interferers. With distributed microphone arrays, this trade-off becomes crucial as different arrays capture vastly different magnitude and phase differences for each source. We propose the use of multiple random projections as a first-stage preprocessing scheme in a data-driven approach to dimensionality reduction and beamforming. We show that a mixture beamformer derived from the use of multiple such random projection…

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Asbj{\o}rn Munk, Stefano Cerri, Jakob Ambsdorf, Julia Machnio, Sebastian N{\o}rgaard Llambias, Vardan Nersesjan, Christian Hedeager Krag, Peirong Liu, Pablo Rocamora Garc\'ia, Mostafa Mehdipour Ghazi, Mikael Boesen, Michael Eriksen Benros, Juan Eugenio Iglesias, Mads Nielsen

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
We present FOMO60K, a large-scale, heterogeneous dataset of 60,529 brain Magnetic Resonance Imaging (MRI) scans from 13,900 sessions and 11,187 subjects, aggregated from 16 publicly available sources. The dataset includes both clinical- and research-grade images, multiple MRI sequences, and a wide range of anatomical and pathological variability, including scans with large brain anomalies. Minimal preprocessing was applied to preserve the original image characteristics while reducing barriers t…

A Frank-Wolfe-based primal heuristic for quadratic mixed-integer optimization
Gioni Mexi, Deborah Hendrych, S\'ebastien Designolle, Mathieu Besan\c{c}on, Sebastian Pokutta
https://arxiv.org/abs/2508.01299

A Frank-Wolfe-based primal heuristic for quadratic mixed-integer optimization
We propose a primal heuristic for quadratic mixed-integer problems. Our method extends the Boscia framework -- originally a mixed-integer convex solver leveraging a Frank-Wolfe-based branch-and-bound approach -- to address nonconvex quadratic objective and constraints. We reformulate nonlinear constraints, introduce preprocessing steps, and a suite of heuristics including rounding strategies, gradient-guided selection, and large neighborhood search techniques that exploit integer-feasible verti…

Modular and Automated Workflow for Streamlined Raman Signal Analysis
Mykyta Kizilov, Vsevolod Cheburkanov, Joseph Harrington, Vladislav V. Yakovlev
https://arxiv.org/abs/2507.17917

Modular and Automated Workflow for Streamlined Raman Signal Analysis
Raman spectroscopy is a powerful tool for material characterization. However, careful preprocessing is required for the identification and handling of noise, baseline drift, and random spikes. This paper presents a comprehensive approach to generating and preprocessing Raman spectra. Additionally, we describe methods for fitting Voigt peaks to the spectrum to determine peak parameters. The effectiveness of these methods is demonstrated using both synthetic and real Raman spectra, with code prov…

FO-Query Enumeration over SLP-Compressed Structures of Bounded Degree
Markus Lohrey, Sebastian Maneth, Markus L. Schmid
https://arxiv.org/abs/2506.19421 ht…

FO-Query Enumeration over SLP-Compressed Structures of Bounded Degree
Enumerating the result set of a first-order query over a relational structure of bounded degree can be done with linear preprocessing and constant delay. In this work, we extend this result towards the compressed perspective where the structure is given in a potentially highly compressed form by a straight-line program (SLP). Our main result is an algorithm that enumerates the result set of a first-order query over a structure of bounded degree that is represented by an SLP satisfying the so-ca…

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Zahid Ullah, Jihie Kim
https://arxiv.org/abs/2506.12363 …

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling framework that synergistically combines pre-trained deep learning (DL) models for feature extraction with optimized machine learning (ML) classifiers for robust classification. The framework incorporates comprehensive preprocessing and data augmentation of brain magnetic resonance images (MRI), followed by deep feature e…

This https://arxiv.org/abs/2506.04086 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

Optimizing Mesh to Improve the Triangular Expansion Algorithm for Computing Visibility Regions
This paper addresses the problem of improving the query performance of the triangular expansion algorithm (TEA) for computing visibility regions by finding the most advantageous instance of the triangular mesh, the preprocessing structure. The TEA recursively traverses the mesh while keeping track of the visible region, the set of all points visible from a query point in a polygonal world. We show that the measured query time is approximately proportional to the number of triangle edge expansio…

Numerical Uncertainty in Linear Registration: An Experimental Study
Niusha Mirhakimi, Yohan Chatelain, Jean-Baptiste Poline, Tristan Glatard
https://arxiv.org/abs/2508.00781 htt…

Numerical Uncertainty in Linear Registration: An Experimental Study
While linear registration is a critical step in MRI preprocessing pipelines, its numerical uncertainty is understudied. Using Monte-Carlo Arithmetic (MCA) simulations, we assessed the most commonly used linear registration tools within major software packages (SPM, FSL, and ANTs) across multiple image similarity measures, two brain templates, and both healthy control (HC, n=50) and Parkinson's Disease (PD, n=50) cohorts. Our findings highlight the influence of linear registration tools and simi…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
Bujar Raufi
https://arxiv.org/abs/2508.07283 https://arxiv.o…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction pa…

Taking the Garbage Out of Data-Driven Prediction Across Climate Timescales
Jason C. Furtado, Maria J. Molina, Marybeth C. Arcodia, Weston Anderson, Tom Beucler, John A. Callahan, Laura M. Ciasto, Vittorio A. Gensini, Michelle L'Heureux, Kathleen Pegion, Jhayron S. P\'erez-Carrasquilla, Maike Sonnewald, Ken Takahashi, Baoqiang Xiang, Brian G. Zimmerman
h…

Taking the Garbage Out of Data-Driven Prediction Across Climate Timescales
Artificial intelligence (AI) -- and specifically machine learning (ML) -- applications for climate prediction across timescales are proliferating quickly. The emergence of these methods prompts a revisit to the impact of data preprocessing, a topic familiar to the climate community, as more traditional statistical models work with relatively small sample sizes. Indeed, the skill and confidence in the forecasts produced by data-driven models are directly influenced by the quality of the datasets…

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Zhiying Li, Guanggang Geng, Jian Weng
https://arxiv.org/abs/2508.05695

MambaITD: An Efficient Cross-Modal Mamba Network for Insider Threat Detection
Enterprises are facing increasing risks of insider threats, while existing detection methods are unable to effectively address these challenges due to reasons such as insufficient temporal dynamic feature modeling, computational efficiency and real-time bottlenecks and cross-modal information island problem. This paper proposes a new insider threat detection framework MambaITD based on the Mamba state space model and cross-modal adaptive fusion. First, the multi-source log preprocessing module …

CleanCTG: A Deep Learning Model for Multi-Artefact Detection and Reconstruction in Cardiotocography
Sheng Wong, Beth Albert, Gabriel Davis Jones
https://arxiv.org/abs/2508.10928

CleanCTG: A Deep Learning Model for Multi-Artefact Detection and Reconstruction in Cardiotocography
Cardiotocography (CTG) is essential for fetal monitoring but is frequently compromised by diverse artefacts which obscure true fetal heart rate (FHR) patterns and can lead to misdiagnosis or delayed intervention. Current deep-learning approaches typically bypass comprehensive noise handling, applying minimal preprocessing or focusing solely on downstream classification, while traditional methods rely on simple interpolation or rule-based filtering that addresses only missing samples and fail to…

Current State in Privacy-Preserving Text Preprocessing for Domain-Agnostic NLP
Abhirup Sinha, Pritilata Saha, Tithi Saha
https://arxiv.org/abs/2508.03204 https://

Current State in Privacy-Preserving Text Preprocessing for Domain-Agnostic NLP
Privacy is a fundamental human right. Data privacy is protected by different regulations, such as GDPR. However, modern large language models require a huge amount of data to learn linguistic variations, and the data often contains private information. Research has shown that it is possible to extract private information from such language models. Thus, anonymizing such private and sensitive information is of utmost importance. While complete anonymization may not be possible, a number of diffe…

Parallel PLL on DAGs
Patrick Steil
https://arxiv.org/abs/2507.21204 https://arxiv.org/pdf/2507.21204

Parallel PLL on DAGs
We present a parallel variant of Pruned Landmark Labelling (PLL) that is optimised for the preprocessing of hub labels on directed acyclic graphs (DAGs). This method was developed during a seminar at the Karlsruhe Institute of Technology (KIT), focusing on time-expanded graphs that model public transport networks. The approach leverages the topological properties of DAGs to enable a novel parallel construction of hub labels.

CFDagent: A Language-Guided, Zero-Shot Multi-Agent System for Complex Flow Simulation
Zhaoyue Xu, Long Wang, Chunyu Wang, Yixin Chen, Qingyong Luo, Hua-Dong Yao, Shizhao Wang, Guowei He
https://arxiv.org/abs/2507.23693

CFDagent: A Language-Guided, Zero-Shot Multi-Agent System for Complex Flow Simulation
We introduce CFDagent, a zero-shot, multi-agent system that enables fully autonomous computational fluid dynamics (CFD) simulations from natural language prompts. CFDagent integrates three specialized LLM-driven agents: (i) the Preprocessing Agent that generates 3D geometries from textual or visual inputs using a hybrid text-to-3D diffusion model (Point-E) and automatically meshes the geometries; (ii) the Solver Agent that configures and executes an immersed boundary flow solver; and (iii) the …

Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation
Niklas S. Neher, Erik Faulhaber, Sven Berger, Christian Wei{\ss}enfels, Gregor J. Gassner, Michael Schlottke-Lakemper
https://arxiv.org/abs/2506.21206

Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation
Obtaining high-quality particle distributions for stable and accurate particle-based simulations poses significant challenges, especially for complex geometries. We introduce a preprocessing technique for 2D and 3D geometries, optimized for smoothed particle hydrodynamics (SPH) and other particle-based methods. Our pipeline begins with the generation of a resolution-adaptive point cloud near the geometry's surface employing a face-based neighborhood search. This point cloud forms the basis for …

Replaced article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
- Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki, Hendrik M\"oller, Ilhem Isra Mekki, Josef A. Buchner, Anton Schmick, Arianna Pfiffer, Eva Oswald, Lucas Zimmer, Ezequiel de la Rosa, Sarthak Pati, Julian Canisius, Arianna Piffer, Ujjwal Baid, Mahyar Valizadeh, Akis Linardos, Jan C. Peeken, Surprosanna Shit, Felix Steinbauer, Daniel Rueckert, Rolf Heckemann, Spyridon Bakas, Jan Kirschke, …

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python. Following Pythonic principles, BrainLesion Suite is designed to provide a 'brainless' development experience, minimizing cognitive effort and streamlining the creation of complex workflows for clinical and scientific practice. At its core is an adaptable preprocessing module that performs co-registration, atlas registration, and optional skull-stripping and defacing on arbitrary multi-…

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Weiyuan Xu, Juntao Luo, Tao Huang, Kaixin Sui, Jie Geng, Qijun Ma, Isami Akasaka, Xiaoxue Shi, Jing Tang, Peng Cai
https://arxiv.org/abs/2506.03691

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Continuous Integration and Continuous Deployment (CI/CD) pipelines are pivotal to modern software engineering, yet diagnosing and resolving their failures remains a complex and labor-intensive challenge. In this paper, we present LogSage, the first end-to-end LLM-powered framework that performs root cause analysis and solution generation from failed CI/CD pipeline logs. During the root cause analysis stage, LogSage employs a specialized log preprocessing pipeline tailored for LLMs, which extrac…

Agentic AI framework for End-to-End Medical Data Inference
Soorya Ram Shimgekar, Shayan Vassef, Abhay Goyal, Navin Kumar, Koustuv Saha
https://arxiv.org/abs/2507.18115 https://

Agentic AI framework for End-to-End Medical Data Inference
Building and deploying machine learning solutions in healthcare remains expensive and labor-intensive due to fragmented preprocessing workflows, model compatibility issues, and stringent data privacy constraints. In this work, we introduce an Agentic AI framework that automates the entire clinical data pipeline, from ingestion to inference, through a system of modular, task-specific agents. These agents handle both structured and unstructured data, enabling automatic feature selection, model se…

Optimizing Mesh to Improve the Triangular Expansion Algorithm for Computing Visibility Regions
Jan Mikula (Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague), Miroslav Kulich (Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague)

Optimizing Mesh to Improve the Triangular Expansion Algorithm for Computing Visibility Regions
This paper addresses the problem of improving the query performance of the triangular expansion algorithm (TEA) for computing visibility regions by finding the most advantageous instance of the triangular mesh, the preprocessing structure. The TEA recursively traverses the mesh while keeping track of the visible region, the set of all points visible from a query point in a polygonal world. We show that the measured query time is approximately proportional to the number of triangle edge expansio…

This https://arxiv.org/abs/2505.21700 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis
Chunking is a crucial preprocessing step in retrieval-augmented generation (RAG) systems, significantly impacting retrieval effectiveness across diverse datasets. In this study, we systematically evaluate fixed-size chunking strategies and their influence on retrieval performance using multiple embedding models. Our experiments, conducted on both short-form and long-form datasets, reveal that chunk size plays a critical role in retrieval effectiveness -- smaller chunks (64-128 tokens) are optim…

Beat and Downbeat Tracking in Performance MIDI Using an End-to-End Transformer Architecture
Sebastian Murgul, Michael Heizmann
https://arxiv.org/abs/2507.00466

Beat and Downbeat Tracking in Performance MIDI Using an End-to-End Transformer Architecture
Beat tracking in musical performance MIDI is a challenging and important task for notation-level music transcription and rhythmical analysis, yet existing methods primarily focus on audio-based approaches. This paper proposes an end-to-end transformer-based model for beat and downbeat tracking in performance MIDI, leveraging an encoder-decoder architecture for sequence-to-sequence translation of MIDI input to beat annotations. Our approach introduces novel data preprocessing techniques, includi…

Global Predecessor Indexing: Avoiding Binary Search in Weighted Job Scheduling
Amit Joshi
https://arxiv.org/abs/2506.22922 https://ar…

Global Predecessor Indexing: Avoiding Binary Search in Weighted Job Scheduling
We present an improved solution to the Weighted Job Scheduling (WJS) problem. While the classical dynamic programming (DP) solution runs in $O(n \log(n))$ time due to comparison-based sorting and per-job binary search, we eliminate the binary search bottleneck. In its place, we introduce a novel multi-phase preprocessing technique called Global Predecessor Indexing (GPI), which computes the latest non-overlapping job (i.e., the predecessor) for all jobs via a two-pointer linear-time pass. GPI e…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Mask-Guided Attention U-Net for Enhanced Neonatal Brain Extraction and Image Preprocessing
Jafrasteh, Lubian-Lopez, Trimarco, Ruiz, Barrios, Almagro, Benavente-Fernandez

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
Parshva Dhilankumar Patel
https://arxiv.org/abs/2507.07029 https://…

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
This paper presents the design and development of an OCR-powered pipeline for efficient table extraction from invoices. The system leverages Tesseract OCR for text recognition and custom post-processing logic to detect, align, and extract structured tabular data from scanned invoice documents. Our approach includes dynamic preprocessing, table boundary detection, and row-column mapping, optimized for noisy and non-standard invoice formats. The resulting pipeline significantly improves data extr…

Optimized imaging prefiltering for enhanced image segmentation
Ronny Vallejos, Felipe Osorio, Sebastian Vidal, Grisel Britos
https://arxiv.org/abs/2508.03653 https://

Optimized imaging prefiltering for enhanced image segmentation
The Box-Cox transformation, introduced in 1964, is a widely used statistical tool for stabilizing variance and improving normality in data analysis. Its application in image processing, particularly for image enhancement, has gained increasing attention in recent years. This paper investigates the use of the Box-Cox transformation as a preprocessing step for image segmentation, with a focus on the estimation of the transformation parameter. We evaluate the effectiveness of the transformation by…

A Deterministic Partition Tree and Applications
Haitao Wang
https://arxiv.org/abs/2507.01775 https://arxiv.org/pdf/2507.01775

A Deterministic Partition Tree and Applications
In this paper, we present a deterministic variant of Chan's randomized partition tree [Discret. Comput. Geom., 2012]. This result leads to numerous applications. In particular, for $d$-dimensional simplex range counting (for any constant $d \ge 2$), we construct a data structure using $O(n)$ space and $O(n^{1+ε})$ preprocessing time, such that each query can be answered in $o(n^{1-1/d})$ time (specifically, $O(n^{1-1/d} / \log^{Ω(1)} n)$ time), thereby breaking an $Ω(n^{1-1/d})$ lower bound …

scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection
Zhen Yuan, Shaoqing Jiao, Yihang Xiao, Jiajie Peng
https://arxiv.org/abs/2506.20697

scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection
The advent of single-cell multi-omics technologies has enabled the simultaneous profiling of diverse omics layers within individual cells. Integrating such multimodal data provides unprecedented insights into cellular identity, regulatory processes, and disease mechanisms. However, it remains challenging, as current methods often rely on selecting highly variable genes or peaks during preprocessing, which may inadvertently discard crucial biological information. Here, we present scMamba, a foun…

Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations
Caner \"Ozer, Patryk Rygiel, Bram de Wilde, \.Ilkay \"Oks\"uz, Jelmer M. Wolterink
https://arxiv.org/abs/2508.05168

Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations
Artifacts pose a significant challenge in medical imaging, impacting diagnostic accuracy and downstream analysis. While image-based approaches for detecting artifacts can be effective, they often rely on preprocessing methods that can lead to information loss and high-memory-demand medical images, thereby limiting the scalability of classification models. In this work, we propose the use of implicit neural representations (INRs) for image quality assessment. INRs provide a compact and continuou…

Enhancing IoT Intrusion Detection Systems through Adversarial Training
Karma Gurung, Ashutosh Ghimire, Fathi Amsaad
https://arxiv.org/abs/2507.19739 https://

Enhancing IoT Intrusion Detection Systems through Adversarial Training
The augmentation of Internet of Things (IoT) devices transformed both automation and connectivity but revealed major security vulnerabilities in networks. We address these challenges by designing a robust intrusion detection system (IDS) to detect complex attacks by learning patterns from the NF-ToN-IoT v2 dataset. Intrusion detection has a realistic testbed through the dataset's rich and high-dimensional features. We combine distributed preprocessing to manage the dataset size with Fast Gradie…

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky

Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
Gang Yang
https://arxiv.org/abs/2508.03403 https://arxiv.org/…

Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
Hyperspectral unmixing aims at estimating material signatures (known as endmembers) and the corresponding proportions (referred to abundances), which is a critical preprocessing step in various hyperspectral imagery applications. This study develops a novel approach called sparsity and total variation (TV) constrained multilayer linear unmixing (STVMLU) for hyperspectral imagery. Specifically, based on a multilayer matrix factorization model, to improve the accuracy of unmixing, a TV constraint…

Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou
Pengfei Fan, Yuli Zhang, Xinheng Wang, Ruiyuan Jiang, Hankang Gu, Dongyao Jia, Shangbo Wang
https://arxiv.org/abs/2506.21269

Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou
This study presents and publicly releases the Suzhou Urban Road Acoustic Dataset (SZUR-Acoustic Dataset), which is accompanied by comprehensive data-acquisition protocols and annotation guidelines to ensure transparency and reproducibility of the experimental workflow. To model the coupling between vehicular noise and driving speed, we propose a bimodal-feature-fusion deep convolutional neural network (BMCNN). During preprocessing, an adaptive denoising and normalization strategy is applied to …

HiFiTTS-2: A Large-Scale High Bandwidth Speech Dataset
Ryan Langman, Xuesong Yang, Paarth Neekhara, Shehzeen Hussain, Edresson Casanova, Evelina Bakhturina, Jason Li
https://arxiv.org/abs/2506.04152

HiFiTTS-2: A Large-Scale High Bandwidth Speech Dataset
This paper introduces HiFiTTS-2, a large-scale speech dataset designed for high-bandwidth speech synthesis. The dataset is derived from LibriVox audiobooks, and contains approximately 36.7k hours of English speech for 22.05 kHz training, and 31.7k hours for 44.1 kHz training. We present our data processing pipeline, including bandwidth estimation, segmentation, text preprocessing, and multi-speaker detection. The dataset is accompanied by detailed utterance and audiobook metadata generated by o…

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Christian Tinauer, Maximilian Sackl, Stefan Ropele, Christian Langkammer
https://arxiv.org/abs/2506.03890

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, raising concerns about interpretability. Previous studies suggest biases in model decisions, necessitating further analysis. This study uses Layer-wise Relevance Propagation (LRP) and spectral clustering to explore classifier decision strategies across preprocessing and training configurations using R2* maps. We trained a 3D convolutional neural net…

This https://arxiv.org/abs/2410.09939 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

Efficient computations of discrete cubical homology
We present a fast algorithm for computing discrete cubical homology of graphs over finite fields with an appropriate characteristic. This algorithm improves on several computational steps compared to constructions in the existing literature, with the key insights including: a faster way to generate all singular cubes, reducing the dimensions of vector spaces in the chain complex by taking a quotient over automorphisms of the cube, and preprocessing graphs using the axiomatic treatment of discre…

Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, Chih-Chung Hsu
https://arxiv.org/abs/2507.01564

Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
We present our solution for the Multi-Source COVID-19 Detection Challenge, which classifies chest CT scans from four distinct medical centers. To address multi-source variability, we employ the Spatial-Slice Feature Learning (SSFL) framework with Kernel-Density-based Slice Sampling (KDS). Our preprocessing pipeline combines lung region extraction, quality control, and adaptive slice sampling to select eight representative slices per scan. We compare EfficientNet and Swin Transformer architectur…

MID-INFRARED (MIR) OCT-based inspection in industry
N. P. Garc\'ia-de-la-Puente, Roc\'io del Amor, Fernando Garc\'ia-Torres, Niels M{\o}ller Israelsen, Coraline Lapre, Christian Rosenberg Petersen, Ole Bang, Dominik Brouczek, Martin Schwentenwein, Kevin Neumann, Niels Benson, Valery Naranjo
https://arxiv.org/abs/2507…

MID-INFRARED (MIR) OCT-based inspection in industry
This paper aims to evaluate mid-infrared (MIR) Optical Coherence Tomography (OCT) systems as a tool to penetrate different materials and detect sub-surface irregularities. This is useful for monitoring production processes, allowing Non-Destructive Inspection Techniques of great value to the industry. In this exploratory study, several acquisitions are made on composite and ceramics to know the capabilities of the system. In addition, it is assessed which preprocessing and AI-enhanced vision al…

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, Chih-Chung Hsu
https://arxiv.org/abs/2507.19858

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
Multi-source CT-scan classification suffers from domain shifts that impair cross-source generalization. While preprocessing pipelines combining Spatial-Slice Feature Learning (SSFL++) and Kernel-Density-based Slice Sampling (KDS) have shown empirical success, the mechanisms underlying their domain robustness remain underexplored. This study analyzes how this input-space standardization manages the trade-off between local discriminability and cross-source generalization. The SSFL++ and KDS pipel…

Onboard Hyperspectral Super-Resolution with Deep Pushbroom Neural Network
Davide Piccinini, Diego Valsesia, Enrico Magli
https://arxiv.org/abs/2507.20765 https://

Onboard Hyperspectral Super-Resolution with Deep Pushbroom Neural Network
Hyperspectral imagers on satellites obtain the fine spectral signatures essential for distinguishing one material from another at the expense of limited spatial resolution. Enhancing the latter is thus a desirable preprocessing step in order to further improve the detection capabilities offered by hyperspectral images on downstream tasks. At the same time, there is a growing interest towards deploying inference methods directly onboard of satellites, which calls for lightweight image super-reso…

Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency
Dou Hoon Kwark, Shirui Luo, Xiyue Zhu, Yudu Li, Zhi-Pei Liang, Volodymyr Kindratenko
https://arxiv.org/abs/2507.17911

Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency
Pseudo-healthy image inpainting is an essential preprocessing step for analyzing pathological brain MRI scans. Most current inpainting methods favor slice-wise 2D models for their high in-plane fidelity, but their independence across slices produces discontinuities in the volume. Fully 3D models alleviate this issue, but their high model capacity demands extensive training data for reliable, high-fidelity synthesis -- often impractical in medical settings. We address these limitations with a hi…