Tootfinder

Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
Saeid Mashhadi, Amirhossein Saghezchi, Vesal Ghassemzadeh Kashani
https://arxiv.org/abs/2510.09465

Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated: next funding within 12 months, patent-stock growth within 24 months, and exit through an initial public offering (IPO) or acquisition within 36 months. Preprocessing is fit on a development window (2010-2019…

Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models
Marco Siino, Giuseppe Bonomo, Rosario Sorbello, Ilenia Tinnirello
https://arxiv.org/abs/2510.09242

Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models
The present study investigates the impact of the Rational Discrete Wavelet Transform (RDWT), used as a plug-in preprocessing step for motor imagery electroencephalographic (EEG) decoding prior to applying deep learning classifiers. A systematic paired evaluation (with/without RDWT) is conducted on four state-of-the-art deep learning architectures: EEGNet, ShallowConvNet, MBEEG\_SENet, and EEGTCNet. This evaluation was carried out across three benchmark datasets: High Gamma, BCI-IV-2a, and BCI-I…

PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Jianhan Zhang, Jitao Wang, Chengchun Shi, John D. Piette, Donglin Zeng, Zhenke Wu
https://arxiv.org/abs/2510.06935

PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment across possibly infinitely many time steps. However, the sequential decisions made by an RL algorithm, while optimized to maximize overall population benefits, may disadvantage certain individuals who are in minority or socioeconomically disadvantaged groups. To address this problem, we introduce PyCFRL, a Python librar…

Beyond Motion Artifacts: Optimizing PPG Preprocessing for Accurate Pulse Rate Variability Estimation
Yuna Watanabe (Northeastern University), Natasha Yamane (Northeastern University), Aarti Sathyanarayana (Northeastern University), Varun Mishra (Northeastern University), Matthew S. Goodwin (Northeastern University)
https://arxiv.org/abs/25…

A Novel Preprocessing Unit for Effective Deep Learning based Classification and Grading of Diabetic Retinopathy
Pranoti Nage, Sanjay Shitole
https://arxiv.org/abs/2509.24497 htt…

A Novel Preprocessing Unit for Effective Deep Learning based Classification and Grading of Diabetic Retinopathy
Early detection of diabetic retinopathy (DR) is crucial as it allows for timely intervention, preventing vision loss and enabling effective management of diabetic complications. This research performs detection of DR and DME at an early stage through the proposed framework which includes three stages: preprocessing, segmentation, feature extraction, and classification. In the preprocessing stage, noise filtering is performed by fuzzy filtering, artefact removal is performed by non-linear diffus…

Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving
Dmytro Zakharov, Oleksandr Kurbatov, Artem Sdobnov, Lev Soukhanov, Yevhenii Sekhin, Vitalii Volovyk, Mykhailo Velykodnyi, Mark Cherepovskyi, Kyrylo Baibula, Lasha Antadze, Pavlo Kravchenko, Volodymyr Dubinin, Yaroslav Panasenko
https://arxiv.org/abs/2510.06784

Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving
In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating t…

Evaluation of preprocessing pipelines in the creation of in-the-wild TTS datasets
Mat\'ias Di Bernardo, Emmanuel Misley, Ignacio Correa, Mateo Garc\'ia Iacovelli, Sim\'on Mellino, Gala Luc\'ia Gonzalez Barrios
https://arxiv.org/abs/2510.03111

Evaluation of preprocessing pipelines in the creation of in-the-wild TTS datasets
This work introduces a reproducible, metric-driven methodology to evaluate preprocessing pipelines for in-the-wild TTS corpora generation. We apply a custom low-cost pipeline to the first in-the-wild Argentine Spanish collection and compare 24 pipeline configurations combining different denoising and quality filtering variants. Evaluation relies on complementary objective measures (PESQ, SI-SDR, SNR), acoustic descriptors (T30, C50), and speech-preservation metrics (F0-STD, MCD). Results expose…

Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform
Giacomo Vitali, Chiara Vercellino, Paolo Viviani, Olivier Terzo, Bartolomeo Montrucchio, Valeria Zaffaroni, Francesca Cibrario, Christian Mattia, Giacomo Ranieri, Alessandro Sabatino, Francesco Bonazzi, Davide Corbelletto
https://arxiv.org/abs/2510.04…

Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform
In this paper, we define and benchmark a hybrid quantum-classical machine learning pipeline by performing a binary classification task applied to a real-world financial use case. Specifically, we implement a Quantum Reservoir Computing (QRC) layer within a classical routine that includes data preprocessing and binary classification. The reservoir layer has been executed on QuEra's Aquila, a 256-qubit neutral atom simulator, using two different types of encoding: position and local detuning. In …

Zenbo Patrol: A Social Assistive Robot Based on Multimodal Deep Learning for Real-time Illegal Parking Recognition and Notification
Jian-jie Zheng, Chih-kai Yang, Po-han Chen, Lyn Chao-ling Chen
https://arxiv.org/abs/2510.04190

Zenbo Patrol: A Social Assistive Robot Based on Multimodal Deep Learning for Real-time Illegal Parking Recognition and Notification
In the study, the social robot act as a patrol to recognize and notify illegal parking in real-time. Dual-model pipeline method and large multimodal model were compared, and the GPT-4o multimodal model was adopted in license plate recognition without preprocessing. For moving smoothly on a flat ground, the robot navigated in a simulated parking lot in the experiments. The robot changes angle view of the camera automatically to capture the images around with the format of license plate number. F…

Investigating Large Language Models' Linguistic Abilities for Text Preprocessing
Marco Braga, Gian Carlo Milanese, Gabriella Pasi
https://arxiv.org/abs/2510.11482 https://…

Investigating Large Language Models' Linguistic Abilities for Text Preprocessing
Text preprocessing is a fundamental component of Natural Language Processing, involving techniques such as stopword removal, stemming, and lemmatization to prepare text as input for further processing and analysis. Despite the context-dependent nature of the above techniques, traditional methods usually ignore contextual information. In this paper, we investigate the idea of using Large Language Models (LLMs) to perform various preprocessing tasks, due to their ability to take context into acco…

MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing
Rahma Nouaji, Stella Bitchebe, Ricardo Macedo, Oana Balmau
https://arxiv.org/abs/2509.10712

MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing
Data loaders are used by Machine Learning (ML) frameworks like PyTorch and TensorFlow to apply transformations to data before feeding it into the accelerator. This operation is called data preprocessing. Data preprocessing plays an important role in the ML training workflow because if it is inefficiently pipelined with the training, it can yield high GPU idleness, resulting in important training delays. Unfortunately, existing data loaders turn out to waste GPU resources, with $76\%$ GPU idlene…

Designing Compact ILPs via Fast Witness Verification
Micha{\l} W{\l}odarczyk
https://arxiv.org/abs/2509.25445 https://arxiv.org/pdf/2509.25445

Designing Compact ILPs via Fast Witness Verification
The standard formalization of preprocessing in parameterized complexity is given by kernelization. In this work, we depart from this paradigm and study a different type of preprocessing for problems without polynomial kernels, still aiming at producing instances that are easily solvable in practice. Specifically, we ask for which parameterized problems an instance (I,k) can be reduced in polynomial time to an integer linear program (ILP) with poly(k) constraints. We show that this property co…

Higher-Order Network Structure Inference: A Topological Approach to Network Selection
Adam Schroeder, Russell Funk, Jingyi Guan, Taylor Okonek, Lori Ziegelmeier
https://arxiv.org/abs/2510.04884

Higher-Order Network Structure Inference: A Topological Approach to Network Selection
Thresholding--the pruning of nodes or edges based on their properties or weights--is an essential preprocessing tool for extracting interpretable structure from complex network data, yet existing methods face several key limitations. Threshold selection often relies on heuristic methods or trial and error due to large parameter spaces and unclear optimization criteria, leading to sensitivity where small parameter variations produce significant changes in network structure. Moreover, most approa…

Power Transform Revisited: Numerically Stable, and Federated
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04995 https://arxiv.org/pdf/2510.04995…

Power Transform Revisited: Numerically Stable, and Federated
Power transforms are popular parametric techniques for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide a comprehensive analysis of the sources of these instabilities and propose effective remedies. We further extend power transforms to th…

Enhancing TreePIR for a Single-Server Setting via Resampling
Elian Morel
https://arxiv.org/abs/2510.04882 https://arxiv.org/pdf/2510.04882

Enhancing TreePIR for a Single-Server Setting via Resampling
Private Information Retrieval (PIR) allows a client to retrieve an entry $\text{DB}[i]$ from a public database $\text{DB}$ held by one or more servers, without revealing the queried index $i$. Traditional PIR schemes achieve sublinear server computation only under strong assumptions, such as the presence of multiple non-colluding servers or the use of public-key cryptography. To overcome these limitations, \textit{preprocessing PIR} schemes introduce a query-independent offline phase where the …

Fast-SEnSeI: Lightweight Sensor-Independent Cloud Masking for On-board Multispectral Sensors
Jan Kn\v{e}\v{z}\'ik, Jon\'a\v{s} Herec, Rado Pito\v{n}\'ak
https://arxiv.org/abs/2509.20991

Fast-SEnSeI: Lightweight Sensor-Independent Cloud Masking for On-board Multispectral Sensors
Cloud segmentation is a critical preprocessing step for many Earth observation tasks, yet most models are tightly coupled to specific sensor configurations and rely on ground-based processing. In this work, we propose Fast-SEnSeI, a lightweight, sensor-independent encoder module that enables flexible, on-board cloud segmentation across multispectral sensors with varying band configurations. Building upon SEnSeI-v2, Fast-SEnSeI integrates an improved spectral descriptor, lightweight architecture…

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📊 Structured response format eliminates need for complex data parsing and preprocessing steps
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Static or temporal? Semantic scene simplification to aid wayfinding in immersive simulations of bionic vision https://arxiv.org/abs/2507.10813

Comparative study of Wavelet transform and Fourier domain filtering for medical image denoising
M. Ali Saif, Bassam M. Mughalles, Ibrahim G. H. Loqman
https://arxiv.org/abs/2509.26608

Comparative study of Wavelet transform and Fourier domain filtering for medical image denoising
Denoising of images is a crucial preprocessing step in medical imaging, essential for improving diagnostic clarity. While deep learning methods offer state-of-the-art performance, their computational complexity and data requirements can be prohibitive. In this study we present a comprehensive comparative analysis of two classical, computationally efficient transform-domain techniques: Discrete Wavelet Transform (DWT) and Discrete Fourier Cosine Transform (DFCT) filtering. We evaluated their eff…

PAT: Pattern-Perceptive Transformer for Error Detection in Relational Databases
Jian Fu, Xixian Han, Xiaolong Wan, Wenjian Wang
https://arxiv.org/abs/2509.25907 https://

PAT: Pattern-Perceptive Transformer for Error Detection in Relational Databases
Error detection in relational databases is critical for maintaining data quality and is fundamental to tasks such as data cleaning and assessment. Current error detection studies mostly employ the multi-detector approach to handle heterogeneous attributes in databases, incurring high costs. Additionally, their data preprocessing strategies fail to leverage the variable-length characteristic of data sequences, resulting in reduced accuracy. In this paper, we propose an attribute-wise PAttern-per…

Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning
Siyi Zhao, Wei Wang, Yanmin Qian
https://arxiv.org/abs/2509.21833 https://

Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning
Recent advancements in automatic speech recognition (ASR) have achieved notable progress, whereas robustness in noisy environments remains challenging. While speech enhancement (SE) front-ends are widely used to mitigate noise as a preprocessing step for ASR, they often introduce computational non-negligible overhead. This paper proposes optimizations to reduce SE computational costs without compromising ASR performance. Our approach integrates layer-wise frame resampling and progressive sub-ba…

When marine radar target detection meets pretrained large language models
Qiying Hu, Linping Zhang, Xueqian Wang, Gang Li, Yu Liu, Xiao-Ping Zhang
https://arxiv.org/abs/2509.12110

When marine radar target detection meets pretrained large language models
Deep learning (DL) methods are widely used to extract high-dimensional patterns from the sequence features of radar echo signals. However, conventional DL algorithms face challenges such as redundant feature segments, and constraints from restricted model sizes. To address these issues, we propose a framework that integrates feature preprocessing with large language models (LLMs). Our preprocessing module tokenizes radar sequence features, applies a patch selection algorithm to filter out uninf…

Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning
Richa Rawat, Faisal Ahmed
https://arxiv.org/abs/2509.18553 https://

Automated Machine Learning Pipeline for Training and Analysis Using Large Language Models
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://…

Automated Machine Learning Pipeline for Training and Analysis Using Large Language Models
Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost. Yet, developing reliable MLIPs remains difficult because it requires generating high-quality datasets, preprocessing atomic structures, and carefully training and validating models. In this work, we introduce an Automated Machine Learning Pipeline (AMLP) that unifies the entire workflow f…

QUBOLite: A lightweigth Python toolkit for QUBO
Sascha M\"ucke, Thore Gerlach, Nico Piatkowski, Lukas Thei{\ss}inger
https://arxiv.org/abs/2509.21321 https://

QUBOLite: A lightweigth Python toolkit for QUBO
We present QUBOLite, a Python package for the creation, manipulation, analysis, and solution of Quadratic Unconstrained Binary Optimization (QUBO) instances. Built as a thin wrapper around NumPy arrays, QUBOLite combines efficient numerical operations with high-level abstractions for tasks ranging from instance generation to preprocessing, analysis, and solving strategies, both exact and approximate. The package includes implementations of the QPRO+ algorithm by Glover et al. for identifying st…

RELATE: Relation Extraction in Biomedical Abstracts with LLMs and Ontology Constraints
Olawumi Olasunkanmi, Mathew Satursky, Hong Yi, Chris Bizon, Harlin Lee, Stanley Ahalt
https://arxiv.org/abs/2509.19057

SongPrep: A Preprocessing Framework and End-to-end Model for Full-song Structure Parsing and Lyrics Transcription
Wei Tan, Shun Lei, Huaicheng Zhang, Guangzheng Li, Yixuan Zhang, Hangting Chen, Jianwei Yu, Rongzhi Gu, Dong Yu
https://arxiv.org/abs/2509.17404

SongPrep: A Preprocessing Framework and End-to-end Model for Full-song Structure Parsing and Lyrics Transcription
Artificial Intelligence Generated Content (AIGC) is currently a popular research area. Among its various branches, song generation has attracted growing interest. Despite the abundance of available songs, effective data preparation remains a significant challenge. Converting these songs into training-ready datasets typically requires extensive manual labeling, which is both time consuming and costly. To address this issue, we propose SongPrep, an automated preprocessing pipeline designed specif…

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning
Roberto Morabito, Guanghan Wu
https://arxiv.org/abs/2509.10946 https://arxiv.o…

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning
Large Language Models (LLMs) are increasingly used to automate software generation in embedded machine learning workflows, yet their outputs often fail silently or behave unpredictably. This article presents an empirical investigation of failure modes in LLM-powered ML pipelines, based on an autopilot framework that orchestrates data preprocessing, model conversion, and on-device inference code generation. We show how prompt format, model behavior, and structural assumptions influence both succ…

Constant Time with Minimal Preprocessing, a Robust and Extensive Complexity Class
\'Etienne Grandjean, Louis Jachiet
https://arxiv.org/abs/2509.10188 https://

Constant Time with Minimal Preprocessing, a Robust and Extensive Complexity Class
In this paper, we study the class $\mathtt{cstPP}$ of operations $\mathtt{op}: \mathbb{N}^k\to\mathbb{N}$, of any fixed arity $k\ge 1$, satisfying the following property: for each fixed integer $d\ge 1$, there exists an algorithm for a RAM machine which, for any input integer $N\ge 2$, - pre-computes some tables in $O(N)$ time, - then reads $k$ operands $x_1,\ldots,x_k

NoMod: A Non-modular Attack on Module Learning With Errors
Cristian Bassotto, Ermes Franch, Marina Kr\v{c}ek, Stjepan Picek
https://arxiv.org/abs/2510.02162 https://

NoMod: A Non-modular Attack on Module Learning With Errors
The advent of quantum computing threatens classical public-key cryptography, motivating NIST's adoption of post-quantum schemes such as those based on the Module Learning With Errors (Module-LWE) problem. We present NoMod ML-Attack, a hybrid white-box cryptanalytic method that circumvents the challenge of modeling modular reduction by treating wrap-arounds as statistical corruption and casting secret recovery as robust linear estimation. Our approach combines optimized lattice preprocessing--in…

Evaluation of Real-Time Preprocessing Methods in AI-Based ECG Signal Analysis
Jasmin Freudenberg, Kai Hahn, Christian Weber, Madjid Fathi
https://arxiv.org/abs/2510.12541 https:…

Evaluation of Real-Time Preprocessing Methods in AI-Based ECG Signal Analysis
The increasing popularity of portable ECG systems and the growing demand for privacy-compliant, energy-efficient real-time analysis require new approaches to signal processing at the point of data acquisition. In this context, the edge domain is acquiring increasing importance, as it not only reduces latency times, but also enables an increased level of data security. The FACE project aims to develop an innovative machine learning solution for analysing long-term electrocardiograms that synergi…

OLaPh: Optimal Language Phonemizer
Johannes Wirth
https://arxiv.org/abs/2509.20086 https://arxiv.org/pdf/2509.20086…

OLaPh: Optimal Language Phonemizer
Phonemization, the conversion of text into phonemes, is a key step in text-to-speech. Traditional approaches use rule-based transformations and lexicon lookups, while more advanced methods apply preprocessing techniques or neural networks for improved accuracy on out-of-domain vocabulary. However, all systems struggle with names, loanwords, abbreviations, and homographs. This work presents OLaPh (Optimal Language Phonemizer), a framework that combines large lexica, multiple NLP techniques, and …

CWT-LSTM Autoencoder: A Novel Approach for Gravitational Wave Detection in Synthetic Data
Jericho Cain
https://arxiv.org/abs/2509.10505 https://arxiv.org/p…

CWT-LSTM Autoencoder: A Novel Approach for Gravitational Wave Detection in Synthetic Data
Gravitational wave detection requires sophisticated signal processing to identify weak astrophysical signals buried in instrumental noise. Traditional matched filtering approaches face computational challenges with diverse signal morphologies and non-stationary noise. This work presents a deep learning approach combining Continuous Wavelet Transform (CWT) preprocessing with Long Short-Term Memory (LSTM) autoencoder architecture for gravitational wave detection in synthetic data. The CWT provide…

Does Re-referencing Matter? Large Laplacian Filter Optimizes Single-Trial P300 BCI Performance
Eva Guttmann-Flury, Jian Zhao, Mohamad Sawan
https://arxiv.org/abs/2510.10733 http…

Does Re-referencing Matter? Large Laplacian Filter Optimizes Single-Trial P300 BCI Performance
Electroencephalography (EEG) provides a non-invasive window into brain activity, enabling Brain-Computer Interfaces (BCIs) for communication and control. However, their performance is limited by signal fidelity issues, among which the choice of re-referencing strategy is a pervasive but often overlooked preprocessing bias. Addressing controversies about its necessity and optimal choice, we adopted a quantified approach to evaluate four strategies - no re-referencing, Common Average Reference (C…

A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
Samuele Punzo, Silvia Giulia Galfr\`e, Francesco Massafra, Alessandro Maglione, Corrado Priami, Alina S\^irbu
https://arxiv.org/abs/2509.22484

A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
We present a machine learning pipeline for biomarker discovery in Multiple Sclerosis (MS), integrating eight publicly available microarray datasets from Peripheral Blood Mononuclear Cells (PBMC). After robust preprocessing we trained an XGBoost classifier optimized via Bayesian search. SHapley Additive exPlanations (SHAP) were used to identify key features for model prediction, indicating thus possible biomarkers. These were compared with genes identified through classical Differential Expressi…

SINAI at eRisk@CLEF 2025: Transformer-Based and Conversational Strategies for Depression Detection
Alba Maria Marmol-Romero, Manuel Garcia-Vega, Miguel Angel Garcia-Cumbreras, Arturo Montejo-Raez
https://arxiv.org/abs/2509.19861

SINAI at eRisk@CLEF 2025: Transformer-Based and Conversational Strategies for Depression Detection
This paper describes the participation of the SINAI-UJA team in the eRisk@CLEF 2025 lab. Specifically, we addressed two of the proposed tasks: (i) Task 2: Contextualized Early Detection of Depression, and (ii) Pilot Task: Conversational Depression Detection via LLMs. Our approach for Task 2 combines an extensive preprocessing pipeline with the use of several transformer-based models, such as RoBERTa Base or MentalRoBERTA Large, to capture the contextual and sequential nature of multi-user conve…

Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing
Deeksha Hareesha Kulal, Chidozie Princewill Arannonu, Afsah Anwar, Nidhi Rastogi, Quamar Niyaz
https://arxiv.org/abs/2510.11915

Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing
Phishing remains a critical cybersecurity threat, especially with the advent of large language models (LLMs) capable of generating highly convincing malicious content. Unlike earlier phishing attempts which are identifiable by grammatical errors, misspellings, incorrect phrasing, and inconsistent formatting, LLM generated emails are grammatically sound, contextually relevant, and linguistically natural. These advancements make phishing emails increasingly difficult to distinguish from legitimat…

Chunk Knowledge Generation Model for Enhanced Information Retrieval: A Multi-task Learning Approach
Jisu Kim, Jinhee Park, Changhyun Jeon, Jungwoo Choi, Keonwoo Kim, Minji Hong, Sehyun Kim
https://arxiv.org/abs/2509.15658

Chunk Knowledge Generation Model for Enhanced Information Retrieval: A Multi-task Learning Approach
Traditional query expansion techniques for addressing vocabulary mismatch problems in information retrieval are context-sensitive and may lead to performance degradation. As an alternative, document expansion research has gained attention, but existing methods such as Doc2Query have limitations including excessive preprocessing costs, increased index size, and reliability issues with generated content. To mitigate these problems and seek more structured and efficient alternatives, this study pr…

Benders Decomposition for Passenger-Oriented Train Timetabling with Hybrid Periodicity
Zhiyuan Yao, Anita Sch\"obel, Lei Nie, Sven J\"ager
https://arxiv.org/abs/2511.09892 https://arxiv.org/pdf/2511.09892 https://arxiv.org/html/2511.09892
arXiv:2511.09892v1 Announce Type: new
Abstract: Periodic timetables are widely adopted in passenger railway operations due to their regular service patterns and well-coordinated train connections. However, fluctuations in passenger demand require varying train services across different periods, necessitating adjustments to the periodic timetable. This study addresses a hybrid periodic train timetabling problem, which enhances the flexibility and demand responsiveness of a given periodic timetable through schedule adjustments and aperiodic train insertions, taking into account the rolling stock circulation. Since timetable modifications may affect initial passenger routes, passenger routing is incorporated into the problem to guide planning decisions towards a passenger-oriented objective. Using a time-space network representation, the problem is formulated as a dynamic railway service network design model with resource constraints. To handle the complexity of real-world instances, we propose a decomposition-based algorithm integrating Benders decomposition and column generation, enhanced with multiple preprocessing and accelerating techniques. Numerical experiments demonstrate the effectiveness of the algorithm and highlight the advantage of hybrid periodic timetables in reducing passenger travel costs.
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Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification
Mingzhu Wang, Yun Shang
https://arxiv.org/abs/2510.12291 https://

Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification
Quantum machine learning (QML) holds promise for computational advantage, yet progress on real-world tasks is hindered by classical preprocessing and noisy devices. We introduce ViT-QCNN-FT, a hybrid framework that integrates a fine-tuned Vision Transformer with a quantum convolutional neural network (QCNN) to compress high-dimensional images into features suited for noisy intermediate-scale quantum (NISQ) devices. By systematically probing entanglement, we show that ansatzes with uniformly dis…

Lightweight MobileNetV1 GRU for ECG Biometric Authentication: Federated and Adversarial Evaluation
Dilli Hang Rai, Sabin Kafley
https://arxiv.org/abs/2509.20382 https://

Lightweight MobileNetV1+GRU for ECG Biometric Authentication: Federated and Adversarial Evaluation
ECG biometrics offer a unique, secure authentication method, yet their deployment on wearable devices faces real-time processing, privacy, and spoofing vulnerability challenges. This paper proposes a lightweight deep learning model (MobileNetV1+GRU) for ECG-based authentication, injection of 20dB Gaussian noise & custom preprocessing. We simulate wearable conditions and edge deployment using the ECGID, MIT-BIH, CYBHi, and PTB datasets, achieving accuracies of 99.34%, 99.31%, 91.74%, and 98.49%,…

SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
Alba Maria Marmol-Romero, Flor Miriam Plaza-del-Arco, Arturo Montejo-Raez
https://arxiv.org/abs/2509.14797

SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, one of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling. The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques. Moreover, we integrate Long-short Term Memory (LSTM) architecture with automodels from Transformers. In this Task, our team has be…

Soft Tissue Simulation and Force Estimation from Heterogeneous Structures using Equivariant Graph Neural Networks
Madina Kojanazarova, Sidady El Hadramy, Jack Wilkie, Georg Rauter, Philippe C. Cattin
https://arxiv.org/abs/2509.10125

Soft Tissue Simulation and Force Estimation from Heterogeneous Structures using Equivariant Graph Neural Networks
Accurately simulating soft tissue deformation is crucial for surgical training, pre-operative planning, and real-time haptic feedback systems. While physics-based models such as the finite element method (FEM) provide high-fidelity results, they are often computationally expensive and require extensive preprocessing. We propose a graph neural network (GNN) architecture that predicts both tissue surface deformation and applied force from sparse point clouds. The model incorporates internal anato…

Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
Chiara De Luca, Elisa Donati
https://arxiv.org/abs/2509.14061 https://

Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
Queen bee presence is essential for the health and stability of honeybee colonies, yet current monitoring methods rely on manual inspections that are labor-intensive, disruptive, and impractical for large-scale beekeeping. While recent audio-based approaches have shown promise, they often require high power consumption, complex preprocessing, and are susceptible to ambient noise. To overcome these limitations, we propose a lightweight, multimodal system for queen detection based on environmenta…

Replaced article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- Restoring the Forecasting Power of Google Trends with Statistical Preprocessing
Candice Djorno, Mauricio Santillana, Shihao Yang

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
Mohamed Basem, Mohamed Younes, Seif Ahmed, Abdelrahman Moustafa
https://arxiv.org/abs/2509.10040

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
We present MSAs winning system for the BAREC 2025 Shared Task on fine-grained Arabic readability assessment, achieving first place in six of six tracks. Our approach is a confidence-weighted ensemble of four complementary transformer models (AraBERTv2, AraELECTRA, MARBERT, and CAMeLBERT) each fine-tuned with distinct loss functions to capture diverse readability signals. To tackle severe class imbalance and data scarcity, we applied weighted training, advanced preprocessing, SAMER corpus relabe…

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Kaiwen He, Petros Drineas, Rajiv Khanna
https://arxiv.org/abs/2510.12669 https://arxiv.or…

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Spectral clustering is a fundamental method for graph partitioning, but its reliance on eigenvector computation limits scalability to massive graphs. Classical sparsification methods preserve spectral properties by sampling edges proportionally to their effective resistances, but require expensive preprocessing to estimate these resistances. We study whether uniform edge sampling-a simple, structure-agnostic strategy-can suffice for spectral clustering. Our main result shows that for graphs adm…

Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
Hailay Kidu Teklehaymanot, Wolfgang Nejdl
https://arxiv.org/abs/2510.12389

Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
Tokenization disparities pose a significant barrier to achieving equitable access to artificial intelligence across linguistically diverse populations. This study conducts a large-scale cross-linguistic evaluation of tokenization efficiency in over 200 languages to systematically quantify computational inequities in large language models (LLMs). Using a standardized experimental framework, we applied consistent preprocessing and normalization protocols, followed by uniform tokenization through …