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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…

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…

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…

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…

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 …

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…

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…

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 …

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…

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…

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 …

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…

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…

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity
Xuejun Sun, Yiran Song, Xiaochen Zhou, Ruilie Cai, Yu Zhang, Xinyi Li, Rui Peng, Jialiu Xie, Yuanyuan Yan, Muyao Tang, Prem Lakshmanane, Baiming Zou, James S. Hagood, Raymond J. Pickles, Didong Li, Fei Zou, Xiaojing Zheng
https://

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity
Respiratory viral infections pose a global health burden, yet the cellular immune responses driving protection or pathology remain unclear. Natural infection cohorts often lack pre-exposure baseline data and structured temporal sampling. In contrast, inoculation and vaccination trials generate insightful longitudinal transcriptomic data. However, the scattering of these datasets across platforms, along with inconsistent metadata and preprocessing procedure, hinders AI-driven discovery. To addre…

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 …

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…

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…

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…

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers
Kartavya Mathur (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, School of Biotechnology, Gautam Buddha University, Uttar Pradesh), Shipra Jain (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi), Nisha Bajiya (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi), Nishant Ku…

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers
Colorectal cancer remains a major global health concern, with early detection being pivotal for improving patient outcomes. In this study, we leveraged high throughput methylation profiling of cellfree DNA to identify and validate diagnostic biomarkers for CRC. The GSE124600 study data were downloaded from the Gene Expression Omnibus, as the discovery cohort, comprising 142 CRC and 132 normal cfDNA methylation profiles obtained via MCTA seq. After preprocessing and filtering, 97,863 CpG sites w…

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…

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…

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…