Tootfinder

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Kangyu Wu, Jiaqi Qiao, Ya Zhang
https://arxiv.org/abs/2509.15984

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Recently, data-driven trajectory prediction methods have achieved remarkable results, significantly advancing the development of autonomous driving. However, the instability of single-vehicle perception introduces certain limitations to trajectory prediction. In this paper, a novel lightweight framework for cooperative trajectory prediction, CoPAD, is proposed. This framework incorporates a fusion module based on the Hungarian algorithm and Kalman filtering, along with the Past Time Attention (…

Communications to Circulations: 3D Wind Field Retrieval and Real-Time Prediction Using 5G GNSS Signals and Deep Learning
Yuchen Ye, Hong Liang, Chaoxia Yuan, Mingyu Li, Aoqi Zhou, Chunqing Shang, Hua Cai, Peixi Liu, Kezuan Wang, Yifeng Zheng
https://arxiv.org/abs/2509.16068

Communications to Circulations: 3D Wind Field Retrieval and Real-Time Prediction Using 5G GNSS Signals and Deep Learning
Accurate atmospheric wind field information is crucial for various applications, including weather forecasting, aviation safety, and disaster risk reduction. However, obtaining high spatiotemporal resolution wind data remains challenging due to limitations in traditional in-situ observations and remote sensing techniques, as well as the computational expense and biases of numerical weather prediction (NWP) models. This paper introduces G-WindCast, a novel deep learning framework that leverages …

SPH-Net: A Co-Attention Hybrid Model for Accurate Stock Price Prediction
Yiyang Wu, Hanyu Ma, Muxin Ge, Xiaoli Ma, Yadi Liu, Ye Aung Moe, Zeyu Han, Weizheng Xie
https://arxiv.org/abs/2509.15414

SPH-Net: A Co-Attention Hybrid Model for Accurate Stock Price Prediction
Prediction of stock price movements presents a formidable challenge in financial analytics due to the inherent volatility, non-stationarity, and nonlinear characteristics of market data. This paper introduces SPH-Net (Stock Price Prediction Hybrid Neural Network), an innovative deep learning framework designed to enhance the accuracy of time series forecasting in financial markets. The proposed architecture employs a novel co-attention mechanism that initially processes temporal patterns throug…

DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction
Manajit Das, Ajnabiul Hoque, Mayank Baranwal, Raghavan B. Sunoj
https://arxiv.org/abs/2509.15872

DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction
Prediction of complete step-by-step chemical reaction mechanisms (CRMs) remains a major challenge. Whereas the traditional approaches in CRM tasks rely on expert-driven experiments or costly quantum chemical computations, contemporary deep learning (DL) alternatives ignore key intermediates and mechanistic steps and often suffer from hallucinations. We present DeepMech, an interpretable graph-based DL framework employing atom- and bond-level attention, guided by generalized templates of mechani…

Uncovering multi-technology convergence patterns with hypergraphs: Evolution and prediction using patent data
Yiwei Huang, Shuqi Xu, Shimin Cai, Linyuan L\"u
https://arxiv.org/abs/2509.15521

Uncovering multi-technology convergence patterns with hypergraphs: Evolution and prediction using patent data
Technology convergence integrates distinct domains to create novel combinations, driving radical innovation that reshapes markets and industries. However, most approaches rely on pairwise networks that cannot capture multi-technology interactions and suffer scale biases from heterogeneous patenting activity. To overcome these gaps, we propose a hypergraph-based framework that directly models multi-technology convergence and identifies statistically significant convergence via a probabilistic nu…

AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports
Yi Xu, Yun Fu
https://arxiv.org/abs/2509.16095 https://arxi…

AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports
Trajectory prediction in multi-agent sports scenarios is inherently challenging due to the structural heterogeneity across agent roles (e.g., players vs. ball) and dynamic distribution gaps across different sports domains. Existing unified frameworks often fail to capture these structured distributional shifts, resulting in suboptimal generalization across roles and domains. We propose AdaSports-Traj, an adaptive trajectory modeling framework that explicitly addresses both intra-domain and inte…

Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
Chengyang Dong, Nan Guo
https://arxiv.org/abs/2510.12428 https://

Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
Autonomous driving decision-making at unsignalized intersections is highly challenging due to complex dynamic interactions and high conflict risks. To achieve proactive safety control, this paper proposes a deep reinforcement learning (DRL) decision-making framework integrated with a biased attention mechanism. The framework is built upon the Soft Actor-Critic (SAC) algorithm. Its core innovation lies in the use of biased attention to construct a traffic risk predictor. This predictor assesses …

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao, Weian Mao, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.11726

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude…

MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding
Huu-Tai Phung, Zong-Lin Gao, Yi-Chen Yao, Kuan-Wei Ho, Yi-Hsin Chen, Yu-Hsiang Lin, Alessandro Gnutti, Wen-Hsiao Peng
https://arxiv.org/abs/2510.12479

MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding
This work, termed MH-LVC, presents a multi-hypothesis temporal prediction scheme that employs long- and short-term reference frames in a conditional residual video coding framework. Recent temporal context mining approaches to conditional video coding offer superior coding performance. However, the need to store and access a large amount of implicit contextual information extracted from past decoded frames in decoding a video frame poses a challenge due to excessive memory access. Our MH-LVC ov…

Failure Prediction at Runtime for Generative Robot Policies
Ralf R\"omer, Adrian Kobras, Luca Worbis, Angela P. Schoellig
https://arxiv.org/abs/2510.09459 https://

Failure Prediction at Runtime for Generative Robot Policies
Imitation learning (IL) with generative models, such as diffusion and flow matching, has enabled robots to perform complex, long-horizon tasks. However, distribution shifts from unseen environments or compounding action errors can still cause unpredictable and unsafe behavior, leading to task failure. Early failure prediction during runtime is therefore essential for deploying robots in human-centered and safety-critical environments. We propose FIPER, a general framework for Failure Prediction…

From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance
Runze Xia, Yupeng Ji, Yuxi Zhou, Haodong Liu, Teng Zhang, Piji Li
https://arxiv.org/abs/2510.11056

From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance
Query-service relevance prediction in e-commerce search systems faces strict latency requirements that prevent the direct application of Large Language Models (LLMs). To bridge this gap, we propose a two-stage reasoning distillation framework to transfer reasoning capabilities from a powerful teacher LLM to a lightweight, deployment-friendly student model. In the first stage, we address the limitations of general-purpose LLMs by constructing a domain-adapted teacher model. This is achieved thro…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz
https://arxiv.org/abs/2510.05566 http…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under domain shift, often leading to under-coverage and unreliable prediction sets. We propose a new framework called Domain-Shift-Aware Conf…

How Reinforcement Learning After Next-Token Prediction Facilitates Learning
Nikolaos Tsilivis, Eran Malach, Karen Ullrich, Julia Kempe
https://arxiv.org/abs/2510.11495 https://

How Reinforcement Learning After Next-Token Prediction Facilitates Learning
Recent advances in reasoning domains with neural networks have primarily been enabled by a training recipe that optimizes Large Language Models, previously trained to predict the next-token in a sequence, with reinforcement learning algorithms. We introduce a framework to study the success of this paradigm, and we theoretically expose the optimization mechanisms by which reinforcement learning improves over next-token prediction in this setting. We study learning from mixture distributions of s…

A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices
Yuqi Zhang, Yuxin Yang, Feixiong Chen, Cheng-Chang Lu, Nima Saeidi, Samuel L. Volchenboum, Junhan Zhao, Siwei Chen, Weiwen Jiang, Qiang Guan
https://arxiv.org/abs/2510.06413

A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices
Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework that combines quantum computation with deep learning, formulating structure prediction as a problem of energy fusion. Candidate conformations are obtained through the Variational Quantum Eigensolver (VQE) executed on IBM's 127-qubit superconducting…

A Geomechanically-Informed Framework for Wellbore Trajectory Prediction: Integrating First-Principles Kinematics with a Rigorous Derivation of Gated Recurrent Networks
Shubham Kumar, Anshuman Sahoo
https://arxiv.org/abs/2510.07564

A Geomechanically-Informed Framework for Wellbore Trajectory Prediction: Integrating First-Principles Kinematics with a Rigorous Derivation of Gated Recurrent Networks
Accurate wellbore trajectory prediction is a paramount challenge in subsurface engineering, governed by complex interactions between the drilling assembly and heterogeneous geological formations. This research establishes a comprehensive, mathematically rigorous framework for trajectory prediction that moves beyond empirical modeling to a geomechanically-informed, data-driven surrogate approach.The study leverages Log ASCII Standard (LAS) and wellbore deviation (DEV) data from 14 wells in the G…

QSearchNet: A Quantum Walk Search Framework for Link Prediction
Priyank Dubey
https://arxiv.org/abs/2510.00325 https://arxiv.org/pdf/2510.00325

QSearchNet: A Quantum Walk Search Framework for Link Prediction
Link prediction is one of the fundamental problems in graph theory, critical for understanding and forecasting the evolution of complex systems like social and biological networks. While classical heuristics capture certain aspects of graph topology, they often struggle to optimally integrate local and global structural information or adapt to complex dependencies. Quantum computing offers a powerful alternative by leveraging superposition for simultaneous multi-path exploration and interferenc…

LLM-Augmented and Fair Machine Learning Framework for University Admission Prediction
Mohammad Abbadi, Yassine Himeur, Shadi Atalla, Dahlia Mansoor, Wathiq Mansoor
https://arxiv.org/abs/2509.22560

LLM-Augmented and Fair Machine Learning Framework for University Admission Prediction
Universities face surging applications and heightened expectations for fairness, making accurate admission prediction increasingly vital. This work presents a comprehensive framework that fuses machine learning, deep learning, and large language model techniques to combine structured academic and demographic variables with unstructured text signals. Drawing on more than 2,000 student records, the study benchmarks logistic regression, Naive Bayes, random forests, deep neural networks, and a stac…

Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Yifei Xu, Jiaying Wu, Herun Wan, Yang Li, Zhen Hou, Min-Yen Kan
https://arxiv.org/abs/2510.08481

Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Hashtag trends ignite campaigns, shift public opinion, and steer millions of dollars in advertising spend, yet forecasting which tag goes viral is elusive. Classical regressors digest surface features but ignore context, while large language models (LLMs) excel at contextual reasoning but misestimate numbers. We present BuzzProphet, a reasoning-augmented hashtag popularity prediction framework that (1) instructs an LLM to articulate a hashtag's topical virality, audience reach, and timing advan…

Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra
Vasuda Trehan, Kevin H. Knuth, M. J. Way
https://arxiv.org/abs/2510.08766

Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra
The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model…

SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Yeo Jin Jung, Yating Liu, Zixuan Wu, So Won Jeong, Claire Donnat
https://arxiv.org/abs/2509.24100 https://

SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framework of Gibbs et al. (2023), which leverages families of covariate shifts to provide approximate conditional conformal prediction intervals, an approach with strong theoretical promise, but with prohibitive computational cost. To bridge this gap, we develop a stable and efficient algorithm that computes the full solution path of the regularized RKHS confor…

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi
https://arxiv.org/abs/2510.05060 https…

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample size is small and often require expensive retraining when the underlying data distribution changes. To overcome these limitations, we propose Reservoir Conformal Prediction (ResCP), a…

Novel Market Temperature Definition Through Fluctuation Theorem: A Statistical Physics Framework for Financial Crisis Prediction
Masoome Ramezani, Fereydoun Rahnama Roodposhti, Ghanbar Abbaspour Esfeden, Mehdi Ramezani
https://arxiv.org/abs/2509.23692

Novel Market Temperature Definition Through Fluctuation Theorem: A Statistical Physics Framework for Financial Crisis Prediction
This paper introduces a novel approach to financial crisis prediction by establishing a thermodynamic-like framework derived from the fluctuation theorem of statistical physics. We define market temperature through the probability ratio of positive to negative returns and demonstrate its effectiveness in identifying market states and predicting potential crises. Our empirical analysis spans nine major global indices from 2005 to 2025, revealing statistically significant differences in temperatu…

Modular and Adaptive Conformal Prediction for Sequential Models via Residual Decomposition
William Zhang, Saurabh Amin, Georgia Perakis
https://arxiv.org/abs/2510.04406 https://…

Modular and Adaptive Conformal Prediction for Sequential Models via Residual Decomposition
Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling process as a black box, overlooking opportunities to exploit modular structure. We introduce a conformal prediction framework for two-stage sequential models, where an upstream predictor generates intermediate representations for a downstream model. By decomposing the overall prediction residual into stage-specific components, our method enables practition…

Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN
Abdelaziz Salama, Mohammed M. H. Qazzaz, Zeinab Nezami, Maryam Hafeez, Syed Ali Raza Zaidi
https://arxiv.org/abs/2510.00851

Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN
Next-generation wireless networks require intelligent traffic prediction to enable autonomous resource management and handle diverse, dynamic service demands. The Open Radio Access Network (O-RAN) framework provides a promising foundation for embedding machine learning intelligence through its disaggregated architecture and programmable interfaces. This work applies a Neural Architecture Search (NAS)-based framework that dynamically selects and orchestrates efficient Long Short-Term Memory (LST…

MS2toImg: A Framework for Direct Bioactivity Prediction from Raw LC-MS/MS Data
Hansol Hong, Sangwon Lee, Jang-Ho Ha, Sung-June Chu, So-Hee An, Woo-Hyun Paek, Gyuhwa Chung, Kyoung Tai No
https://arxiv.org/abs/2510.09716

MS2toImg: A Framework for Direct Bioactivity Prediction from Raw LC-MS/MS Data
Untargeted metabolomics using LC-MS/MS offers the potential to comprehensively profile the chemical diversity of biological samples. However, the process is fundamentally limited by the "identification bottleneck," where only a small fraction of detected features can be annotated using existing spectral libraries, leaving the majority of data uncharacterized and unused. In addition, the inherently low reproducibility of LC-MS/MS instruments introduces alignment errors between runs, making featu…

NeuroScalar: A Deep Learning Framework for Fast, Accurate, and In-the-Wild Cycle-Level Performance Prediction
Shayne Wadle, Yanxin Zhang, Vikas Singh, Karthikeyan Sankaralingam
https://arxiv.org/abs/2509.22410

NeuroScalar: A Deep Learning Framework for Fast, Accurate, and In-the-Wild Cycle-Level Performance Prediction
The evaluation of new microprocessor designs is constrained by slow, cycle-accurate simulators that rely on unrepresentative benchmark traces. This paper introduces a novel deep learning framework for high-fidelity, ``in-the-wild'' simulation on production hardware. Our core contribution is a DL model trained on microarchitecture-independent features to predict cycle-level performance for hypothetical processor designs. This unique approach allows the model to be deployed on existing silicon to…

LDMD with Temporally Adaptive Segmentation
Qiuqi Li, Chang Liu, Yifei Yang
https://arxiv.org/abs/2510.08065 https://arxiv.org/pdf/2510.08065

LDMD with Temporally Adaptive Segmentation
Dynamic mode decomposition (DMD) is a widely used data-driven algorithm for predicting the future states of dynamical systems. However, its standard formulation often struggles with poor long-term predictive accuracy. To address this limitation, we propose a localized DMD (LDMD) framework that improves prediction performance by integrating DMD's strong linear forecasting capabilities with time-domain segmentation techniques. In this framework, the temporal domain is segmented into multiple subi…

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
Giacomo Rizzieri, Federico Lanteri, Liberato Ferrara, Massimiliano Cremonesi
https://arxiv.org/abs/2510.02009 …

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
This work introduces ShapeGen3DCP, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are refo…

Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs
Lokesh Antony Kadiyala, Amir Mirzaeinia
https://arxiv.org/abs/2510.01203 https://

Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs
The stock market is extremely difficult to predict in the short term due to high market volatility, changes caused by news, and the non-linear nature of the financial time series. This research proposes a novel framework for improving minute-level prediction accuracy using semantic sentiment scores from top ten different large language models (LLMs) combined with minute interval intraday stock price data. We systematically constructed a time-aligned dataset of AAPL news articles and 1-minute Ap…

Probabilistically-Safe Bipedal Navigation over Uncertain Terrain via Conformal Prediction and Contraction Analysis
Kasidit Muenprasitivej, Ye Zhao, Glen Chou
https://arxiv.org/abs/2510.07725

Probabilistically-Safe Bipedal Navigation over Uncertain Terrain via Conformal Prediction and Contraction Analysis
We address the challenge of enabling bipedal robots to traverse rough terrain by developing probabilistically safe planning and control strategies that ensure dynamic feasibility and centroidal robustness under terrain uncertainty. Specifically, we propose a high-level Model Predictive Control (MPC) navigation framework for a bipedal robot with a specified confidence level of safety that (i) enables safe traversal toward a desired goal location across a terrain map with uncertain elevations, an…

A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
Saptarshi Banerjee, Himadri Nath Saha, Utsho Banerjee, Rajarshi Karmakar, Jon Turdiev
https://arxiv.org/abs/2510.05123

A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
Neuro-oncological prognostics are now vital in modern clinical neuroscience because brain tumors pose significant challenges in detection and management. To tackle this issue, we propose a cognitive digital twin framework that combines real-time EEG signals from a wearable skullcap with structural MRI data for dynamic and personalized tumor monitoring. At the heart of this framework is an Enhanced Vision Transformer (ViT++) that includes innovative components like Patch-Level Attention Regulari…

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
Jianhui Yang, Yiming Jin, Pengkun Jiao, Chenhe Dong, Zerui Huang, Shaowei Yao, Xiaojiang Zhou, Dan Ou, Haihong Tang
https://arxiv.org/abs/2510.08048

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and business conversion. Large Language Models (LLMs) enable generative, reasoning-based approaches, typically aligned via supervised fine-tuning (SFT) or preference optimization methods like Direct Preference Optimization (DPO). However, the increasing complexity of busin…

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
Shin Kiyohara, Chisa Shibui, Soungmin Bae, Yu Kumagai
https://arxiv.org/abs/2510.00513 h…

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
Recent advances in materials informatics have expanded the number of synthesizable materials. However, screening promising candidates, such as semiconductors, based on defect properties remains challenging. This is primarily due to the lack of a general framework for predicting defect formation energies in multiple charge states from structural data. In this Letter, we present a protocol, namely data normalization, Fermi level alignment, and treatment of perturbed host states, and validate it b…

Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners
Boxuan Cao, Linkai Li, Hanlin Yu, Changgeng Mo, Haoshuai Zhou, Shan Xiang Wang
https://arxiv.org/abs/2509.16979

Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners
Speech intelligibility evaluation for hearing-impaired (HI) listeners is essential for assessing hearing aid performance, traditionally relying on listening tests or intrusive methods like HASPI. However, these methods require clean reference signals, which are often unavailable in real-world conditions, creating a gap between lab-based and real-world assessments. To address this, we propose a non-intrusive intelligibility prediction framework that leverages speech enhancers to provide a parall…

Learning from Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC
Hsin-Pei Yu, Si-Qin Lyu, Yi-Hsien Hsieh, Weichung Wang, Tung-Hung Su, Jia-Horng Kao, Che Lin
https://arxiv.org/abs/2510.07347

Learning from Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC
Early recurrence (ER) prediction after curative-intent resection remains a critical challenge in the clinical management of hepatocellular carcinoma (HCC). Although contrast-enhanced computed tomography (CT) with full multi-phase acquisition is recommended in clinical guidelines and routinely performed in many tertiary centers, complete phase coverage is not consistently available across all institutions. In practice, single-phase portal venous (PV) scans are often used alone, particularly in s…

Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
Mingyi Zheng, Hongyu Jiang, Yizhou Lu, Jiaye Teng
https://arxiv.org/abs/2509.22529 https://

Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
Conformal Prediction (CP) is a distribution-free framework for constructing statistically rigorous prediction sets. While popular variants such as CD-split improve CP's efficiency, they often yield prediction sets composed of multiple disconnected subintervals, which are difficult to interpret. In this paper, we propose SCD-split, which incorporates smoothing operations into the CP framework. Such smoothing operations potentially help merge the subintervals, thus leading to interpretable predic…

Physics-Informed Machine Learning Approach in Augmenting RANS Models Using DNS Data and DeepInsight Method on FDA Nozzle
Hossein Geshani, Mehrdad Raisee Dehkordi, Masoud Shariat Panahi
https://arxiv.org/abs/2510.01091

Physics-Informed Machine Learning Approach in Augmenting RANS Models Using DNS Data and DeepInsight Method on FDA Nozzle
We present a data-driven framework for turbulence modeling, applied to flow prediction in the FDA nozzle. In this study, the standard RANS equations have been modified using an implicit-explicit hybrid approach. New variables were introduced, and a solver was developed within the OpenFOAM framework, integrating a machine learning module to estimate these variables. The invariant input features were derived based on Hilbert's basis theorem, and the outputs of the machine learning model were obta…

A general optimization framework for mapping local transition-state networks
Qichen Xu, Anna Delin
https://arxiv.org/abs/2509.26269 https://arxiv.org/pdf/2…

A general optimization framework for mapping local transition-state networks
Understanding how complex systems transition between states requires mapping the energy landscape that governs these changes. Local transition-state networks reveal the barrier architecture that explains observed behaviour and enables mechanism-based prediction across computational chemistry, biology, and physics, yet current practice either prescribes endpoints or randomly samples only a few saddles around an initial guess. We present a general optimization framework that systematically expand…

Articulation-Informed ASR: Integrating Articulatory Features into ASR via Auxiliary Speech Inversion and Cross-Attention Fusion
Ahmed Adel Attia, Jing Liu, Carol Espy Wilson
https://arxiv.org/abs/2510.08585

Articulation-Informed ASR: Integrating Articulatory Features into ASR via Auxiliary Speech Inversion and Cross-Attention Fusion
Prior works have investigated the use of articulatory features as complementary representations for automatic speech recognition (ASR), but their use was largely confined to shallow acoustic models. In this work, we revisit articulatory information in the era of deep learning and propose a framework that leverages articulatory representations both as an auxiliary task and as a pseudo-input to the recognition model. Specifically, we employ speech inversion as an auxiliary prediction task, and th…

Out-of-bag prediction balls for random forests in metric spaces
Diego Serrano, Eduardo Garc\'ia-Portugu\'es
https://arxiv.org/abs/2510.04299 https://

Out-of-bag prediction balls for random forests in metric spaces
Statistical methods for metric spaces provide a general and versatile framework for analyzing complex data types. We introduce a novel approach for constructing confidence regions around new predictions from any bagged regression algorithm with metric-space-valued responses. This includes the recent extensions of random forests for metric responses: Fréchet random forests (Capitaine et al., 2024), random forest weighted local constant Fréchet regression (Qiu et al., 2024), and metric random f…

Networks Multiscale Entropy Analysis
Sebasti\'an Brzovic, Crist\'obal Rojas, Andr\'es Abeliuk
https://arxiv.org/abs/2510.11524 https://arxiv.or…

Networks Multiscale Entropy Analysis
Understanding the structural complexity and predictability of complex networks is a central challenge in network science. Although recent studies have revealed a relationship between compression-based entropy and link prediction performance, existing methods focus on single-scale representations. This approach often overlooks the rich hierarchical patterns that can exist in real-world networks. In this study, we introduce a multiscale entropy framework that extends previous entropy-based approa…

Enhancing Credit Risk Prediction: A Meta-Learning Framework Integrating Baseline Models, LASSO, and ECOC for Superior Accuracy
Haibo Wang, Lutfu S. Sua, Jun Huang, Figen Balo, Burak Dolar
https://arxiv.org/abs/2509.22381

Enhancing Credit Risk Prediction: A Meta-Learning Framework Integrating Baseline Models, LASSO, and ECOC for Superior Accuracy
Effective credit risk management is fundamental to financial decision-making, necessitating robust models for default probability prediction and financial entity classification. Traditional machine learning approaches face significant challenges when confronted with high-dimensional data, limited interpretability, rare event detection, and multi-class imbalance problems in risk assessment. This research proposes a comprehensive meta-learning framework that synthesizes multiple complementary mod…

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Fan Yang, Zhiyang Chen, Yousong Zhu, Xin Li, Jinqiao Wang
https://arxiv.org/abs/2510.00806

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Current video generation models produce physically inconsistent motion that violates real-world dynamics. We propose TrajVLM-Gen, a two-stage framework for physics-aware image-to-video generation. First, we employ a Vision Language Model to predict coarse-grained motion trajectories that maintain consistency with real-world physics. Second, these trajectories guide video generation through attention-based mechanisms for fine-grained motion refinement. We build a trajectory prediction dataset ba…

EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Kwanhyung Lee, Sungsoo Hong, Joonhyung Park, Jeonghyeop Lim, Juhwan Choi, Donghwee Yoon, Eunho Yang
https://arxiv.org/abs/2510.00549

EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Machine learning models for clinical prediction rely on structured data extracted from Electronic Medical Records (EMRs), yet this process remains dominated by hardcoded, database-specific pipelines for cohort definition, feature selection, and code mapping. These manual efforts limit scalability, reproducibility, and cross-institutional generalization. To address this, we introduce EMR-AGENT (Automated Generalized Extraction and Navigation Tool), an agent-based framework that replaces manual r…

See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation
Chih Yao Hu, Yang-Sen Lin, Yuna Lee, Chih-Hai Su, Jie-Ying Lee, Shr-Ruei Tsai, Chin-Yang Lin, Kuan-Wen Chen, Tsung-Wei Ke, Yu-Lun Liu
https://arxiv.org/abs/2509.22653

See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation
We present See, Point, Fly (SPF), a training-free aerial vision-and-language navigation (AVLN) framework built atop vision-language models (VLMs). SPF is capable of navigating to any goal based on any type of free-form instructions in any kind of environment. In contrast to existing VLM-based approaches that treat action prediction as a text generation task, our key insight is to consider action prediction for AVLN as a 2D spatial grounding task. SPF harnesses VLMs to decompose vague language i…

Shapley Features for Robust Signal Prediction in Tactile Internet
Mohammad Ali Vahedifar, Qi Zhang
https://arxiv.org/abs/2509.21032 https://arxiv.org/pdf/2…

Shapley Features for Robust Signal Prediction in Tactile Internet
The Tactile Internet (TI) requires ultra-low latency and reliable haptic signal transmission, yet packet loss and delay remain unresolved challenges. We present a novel prediction framework that integrates Gaussian Processes (GP) with a ResNet-based Neural Network, where GP acts as an oracle to recover signals lost or heavily delayed. To further optimize performance, we introduce Shapley Feature Values (SFV), a principled feature selection mechanism that isolates the most informative inputs for…

Typhoon Path Prediction Using Functional Data Analysis and Clustering-Based Regression
Jimin Kim
https://arxiv.org/abs/2510.02316 https://arxiv.org/pdf/251…

Typhoon Path Prediction Using Functional Data Analysis and Clustering-Based Regression
Accurate prediction of typhoon trajectories is essential for mitigating the impact of these extreme weather events. This study proposes a functional data analysis (FDA) framework for modeling and forecasting typhoon paths using historical trajectory data. Latitude and longitude sequences are represented as smooth functional observations, and a function-on-function regression model is employed to capture the temporal dynamics of typhoon movement. While the baseline model demonstrates reasonable …

Template-free precision synthesis of biphilic microdome arrays
Haobo Xu, Haonian Shu, Rong Yang
https://arxiv.org/abs/2510.00396 https://arxiv.org/pdf/2510…

Template-free precision synthesis of biphilic microdome arrays
Biphilic microdome arrays are ubiquitous in nature, but their synthetic counterparts have been scarce. To bridge that gap, we leverage condensed droplet polymerization (CDP) to enable their template-free synthesis. During CDP, monomer droplets serve as microreactors for free-radical polymerization. The droplet diameter is monitored in real time. To enable the precise prediction of the convex geometric parameters, we develop a theoretical framework that integrates geometric arguments, scaling an…

Mitigating Surgical Data Imbalance with Dual-Prediction Video Diffusion Model
Danush Kumar Venkatesh, Adam Schmidt, Muhammad Abdullah Jamal, Omid Mohareri
https://arxiv.org/abs/2510.07345

Mitigating Surgical Data Imbalance with Dual-Prediction Video Diffusion Model
Surgical video datasets are essential for scene understanding, enabling procedural modeling and intra-operative support. However, these datasets are often heavily imbalanced, with rare actions and tools under-represented, which limits the robustness of downstream models. We address this challenge with $SurgiFlowVid$, a sparse and controllable video diffusion framework for generating surgical videos of under-represented classes. Our approach introduces a dual-prediction diffusion module that joi…

Text2Move: Text-to-moving sound generation via trajectory prediction and temporal alignment
Yunyi Liu, Shaofan Yang, Kai Li, Xu Li
https://arxiv.org/abs/2509.21919 https://

Text2Move: Text-to-moving sound generation via trajectory prediction and temporal alignment
Human auditory perception is shaped by moving sound sources in 3D space, yet prior work in generative sound modelling has largely been restricted to mono signals or static spatial audio. In this work, we introduce a framework for generating moving sounds given text prompts in a controllable fashion. To enable training, we construct a synthetic dataset that records moving sounds in binaural format, their spatial trajectories, and text captions about the sound event and spatial motion. Using this…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- OneDSE: A Unified Microprocessor Metric Prediction and Design Space Exploration Framework
Ritik Raj, Akshat Ramachandran, Jeff Nye, Shashank Nemawarkar, Tushar Krishna

COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics
Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan
https://arxiv.org/abs/2509.22240 https://

COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics
In clinical applications, the utility of segmentation models is often based on the accuracy of derived downstream metrics such as organ size, rather than by the pixel-level accuracy of the segmentation masks themselves. Thus, uncertainty quantification for such metrics is crucial for decision-making. Conformal prediction (CP) is a popular framework to derive such principled uncertainty guarantees, but applying CP naively to the final scalar metric is inefficient because it treats the complex, n…

Neural Optimal Transport Meets Multivariate Conformal Prediction
Vladimir Kondratyev, Alexander Fishkov, Nikita Kotelevskii, Mahmoud Hegazy, Remi Flamary, Maxim Panov, Eric Moulines
https://arxiv.org/abs/2509.25444

Neural Optimal Transport Meets Multivariate Conformal Prediction
We propose a framework for conditional vector quantile regression (CVQR) that combines neural optimal transport with amortized optimization, and apply it to multivariate conformal prediction. Classical quantile regression does not extend naturally to multivariate responses, while existing approaches often ignore the geometry of joint distributions. Our method parametrizes the conditional vector quantile function as the gradient of a convex potential implemented by an input-convex neural network…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Yuyao Wang, Yu-Hung Cheng, Debarghya Mukherjee, Huimin Cheng
https://arxiv.org/abs/2510.05527 htt…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, accurate graphon estimation typically requires a large graph, whereas in practice, one often only observes a small-sized network. One approach to addressing this issue is to adopt a transfer learning framework, which aims to improve estimation in a small target graph by leveragi…

Near-Optimal Simultaneous Estimation of Quantum State Moments
Xiao Shi, Jiyu Jiang, Xian Wu, Jingu Xie, Hongshun Yao, Xin Wang
https://arxiv.org/abs/2509.24842 https://

Near-Optimal Simultaneous Estimation of Quantum State Moments
Estimating nonlinear properties such as Rényi entropies and observable-weighted moments serves as a central strategy for spectrum spectroscopy, which is fundamental to property prediction and analysis in quantum information science, statistical mechanics, and many-body physics. However, existing approaches are susceptible to noise and require significant resources, making them challenging for near-term quantum hardware. In this work, we introduce a framework for resource-efficient simultaneous…

Deep Learning-Based Prediction of Energy Decay Curves from Room Geometry and Material Properties
Imran Muhammad, Gerald Schuller
https://arxiv.org/abs/2509.24769 https://…

Deep Learning-Based Prediction of Energy Decay Curves from Room Geometry and Material Properties
Accurate prediction of energy decay curves (EDCs) enables robust analysis of room acoustics and reliable estimation of key parameters. We present a deep learning framework that predicts EDCs directly from room geometry and surface absorption. A dataset of 6000 shoebox rooms with realistic dimensions, source-receiver placements, and frequency-dependent wall absorptions was synthesized. For each configuration we simulate room impulse responses (RIRs) using Pyroomacoustics and compute target EDCs.…

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Junyu Lu, Songxin Zhang, Zejian Xie, Zhuoyang Song, Jiaxing Zhang
https://arxiv.org/abs/2509.17917 https://…

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Recent advances in GUI agents have achieved remarkable grounding and action-prediction performance, yet existing models struggle with unreliable reward signals and limited online trajectory generation. In this paper, we introduce Orcust, a framework that integrates Principle-Constrained Reward Modeling (PCRM) and Online VM-Grounded Trajectory Construction (OVTC) to enhance reasoning reliability and data efficiency in interactive GUI tasks. We leverages environment-verifiable and LLM-derived pri…

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
Cheuk Hin Ho, Christoph Ortner, Yangshuai Wang
https://arxiv.org/abs/2510.00721 https://

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) is a statistical framework that constructs prediction intervals with guaranteed coverage under minimal assumptions, making it an attractive tool for UQ. However, existing CP techniques, while offering formal coverage guarantees, often lack accuracy, scalability, and adaptability to the complexity of atomic environments…

Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Erdun Gao, Jake Fawkes, Dino Sejdinovic
https://arxiv.org/abs/2509.21866 https://

Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Estimating the Conditional Average Treatment Effect (CATE) is often constrained by the high cost of obtaining outcome measurements, making active learning essential. However, conventional active learning strategies suffer from a fundamental objective mismatch. They are designed to reduce uncertainty in model parameters or in observable factual outcomes, failing to directly target the unobservable causal quantities that are the true objects of interest. To address this misalignment, we introduce…

Talking Head Generation via AU-Guided Landmark Prediction
Shao-Yu Chang, Jingyi Xu, Hieu Le, Dimitris Samaras
https://arxiv.org/abs/2509.19749 https://arxi…

Talking Head Generation via AU-Guided Landmark Prediction
We propose a two-stage framework for audio-driven talking head generation with fine-grained expression control via facial Action Units (AUs). Unlike prior methods relying on emotion labels or implicit AU conditioning, our model explicitly maps AUs to 2D facial landmarks, enabling physically grounded, per-frame expression control. In the first stage, a variational motion generator predicts temporally coherent landmark sequences from audio and AU intensities. In the second stage, a diffusion-base…

Neural Tangent Kernels for Complex Genetic Risk Prediction: Bridging Deep Learning and Kernel Methods in Genomics
Heng Ge, Qing Lu
https://arxiv.org/abs/2510.01426 https://

Neural Tangent Kernels for Complex Genetic Risk Prediction: Bridging Deep Learning and Kernel Methods in Genomics
Given the complexity of genetic risk prediction, there is a critical need for the development of novel methodologies that can effectively capture intricate genotype--phenotype relationships (e.g., nonlinear) while remaining statistically interpretable and computationally tractable. We develop a Neural Tangent Kernel (NTK) framework to integrate kernel methods into deep neural networks for genetic risk prediction analysis. We consider two approaches: NTK-LMM, which embeds the empirical NTK in a …

Hybrid Predictive Modeling of Malaria Incidence in the Amhara Region, Ethiopia: Integrating Multi-Output Regression and Time-Series Forecasting
Kassahun Azezew, Amsalu Tesema, Bitew Mekuria, Ayenew Kassie, Animut Embiale, Ayodeji Olalekan Salau, Tsega Asresa
https://arxiv.org/abs/2510.01302

Hybrid Predictive Modeling of Malaria Incidence in the Amhara Region, Ethiopia: Integrating Multi-Output Regression and Time-Series Forecasting
Malaria remains a major public health concern in Ethiopia, particularly in the Amhara Region, where seasonal and unpredictable transmission patterns make prevention and control challenging. Accurately forecasting malaria outbreaks is essential for effective resource allocation and timely interventions. This study proposes a hybrid predictive modeling framework that combines time-series forecasting, multi-output regression, and conventional regression-based prediction to forecast the incidence o…

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
Pinhao Song, Yurui Du, Ophelie Saussus, Sofie De Schrijver, Irene Caprara, Peter Janssen, Renaud Detry
https://arxiv.org/abs/2509.19954

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
We propose a probabilistic shared-control solution for navigation, called Robot Trajectron V2 (RT-V2), that enables accurate intent prediction and safe, effective assistance in human-robot interaction. RT-V2 jointly models a user's long-term behavioral patterns and their noisy, low-dimensional control signals by combining a prior intent model with a posterior update that accounts for real-time user input and environmental context. The prior captures the multimodal and history-dependent nature o…

Bi-modal Prediction and Transformation Coding for Compressing Complex Human Dynamics
Huong Hoang, Keito Suzuki, Truong Nguyen, Pamela Cosman
https://arxiv.org/abs/2509.16919 htt…

Bi-modal Prediction and Transformation Coding for Compressing Complex Human Dynamics
For dynamic human motion sequences, the original KeyNode-Driven codec often struggles to retain compression efficiency when confronted with rapid movements or strong non-rigid deformations. This paper proposes a novel Bi-modal coding framework that enhances the flexibility of motion representation by integrating semantic segmentation and region-specific transformation modeling. The rigid transformation model (rotation & translation) is extended with a hybrid scheme that selectively applies affi…

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
Patrick Peixuan Ye, Chen Shani, Ellen Vitercik
https://arxiv.org/abs/2510.07182 https://

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input $X$ and output $Y$ dataset. Our method first clusters $X$ and $Y$ independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples. At inference, a new input $x$ is assigned to its nearest input cluster, and the centroid of the linked output cluster is returned as the prediction $\hat{y}$. Unlike traditional SSL, Bridged Clustering explicitly levera…

Room Impulse Response Prediction with Neural Networks: From Energy Decay Curves to Perceptual Validation
Imran Muhammad, Gerald Schuller
https://arxiv.org/abs/2509.24834 https:/…

Room Impulse Response Prediction with Neural Networks: From Energy Decay Curves to Perceptual Validation
Prediction of room impulse responses (RIRs) is essential for room acoustics, spatial audio, and immersive applications, yet conventional simulations and measurements remain computationally expensive and time-consuming. This work proposes a neural network framework that predicts energy decay curves (EDCs) from room dimensions, material absorption coefficients, and source-receiver positions, and reconstructs corresponding RIRs via reverse-differentiation. A large training dataset was generated us…

LAMP-PRo: Label-aware Attention for Multi-label Prediction of DNA- and RNA-binding Proteins using Protein Language Models
Nimisha Ghosh, Dheeran Sankaran, Rahul Balakrishnan Adhi, Sharath S, Amrut Anand
https://arxiv.org/abs/2509.24262

LAMP-PRo: Label-aware Attention for Multi-label Prediction of DNA- and RNA-binding Proteins using Protein Language Models
Identifying DNA- (DBPs) and RNA-binding proteins (RBPs) is crucial for the understanding of cell function, molecular interactions as well as regulatory functions. Owing to their high similarity, most of the existing approaches face challenges in differentiating between DBPs and RBPs leading to high cross-prediction errors. Moreover, identifying proteins which bind to both DNA and RNA (DRBPs) is also quite a challenging task. In this regard, we propose a novel framework viz. LAMP-PRo which is ba…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/6]:
- DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction
Manajit Das, Ajnabiul Hoque, Mayank Baranwal, Raghavan B. Sunoj

Replaced article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- RouteKG: A knowledge graph-based framework for route prediction on road networks
Yihong Tang, Zhan Zhao, Weipeng Deng, Shuyu Lei, Yuebing Liang, Zhenliang Ma

Dereverberation Using Binary Residual Masking with Time-Domain Consistency
Daniel G. Williams
https://arxiv.org/abs/2510.00356 https://arxiv.org/pdf/2510.0…

Dereverberation Using Binary Residual Masking with Time-Domain Consistency
Vocal dereverberation remains a challenging task in audio processing, particularly for real-time applications where both accuracy and efficiency are crucial. Traditional deep learning approaches often struggle to suppress reverberation without degrading vocal clarity, while recent methods that jointly predict magnitude and phase have significant computational cost. We propose a real-time dereverberation framework based on residual mask prediction in the short-time Fourier transform (STFT) domai…

Towards a unified turbulence model through multi-objective learning
Zhuo-Ran Liu, Hao-Chen Wang, Zhuo-Lin Zhao, Heng Xiao
https://arxiv.org/abs/2509.17189 https://

Towards a unified turbulence model through multi-objective learning
Turbulence is a central challenge in classical physics and a critical barrier to accurate flow prediction in climate, aerospace, and energy systems. Despite the widespread reliance on Reynolds-averaged Navier-Stokes (RANS) solvers in industrial simulations, existing turbulence models lack the generalizability to handle diverse regimes, such as separation, secondary flows, and free-shear flows, without manual tuning or switching. We propose a unified data-driven turbulence modeling framework bas…

Uncertainty in Machine Learning
Hans Weytjens, Wouter Verbeke
https://arxiv.org/abs/2510.06007 https://arxiv.org/pdf/2510.06007

Uncertainty in Machine Learning
This book chapter introduces the principles and practical applications of uncertainty quantification in machine learning. It explains how to identify and distinguish between different types of uncertainty and presents methods for quantifying uncertainty in predictive models, including linear regression, random forests, and neural networks. The chapter also covers conformal prediction as a framework for generating predictions with predefined confidence intervals. Finally, it explores how uncerta…

A Hierarchical Adaptive Diffusion Model for Flexible Protein-Protein Docking
Rujie Yin, Yang Shen
https://arxiv.org/abs/2509.20542 https://arxiv.org/pdf/25…

A Hierarchical Adaptive Diffusion Model for Flexible Protein-Protein Docking
Structural prediction of protein-protein interactions is important to understand the molecular basis of cellular interactions, but it still faces major challenges when significant conformational changes are present. We propose a generative framework of hierarchical adaptive diffusion to improve accuracy and efficiency in such cases. It is hierarchical in separating global inter-protein rigid-body motions and local intra-protein flexibility in diffusion processes, and the distinct local and glob…

Closing the Safety Gap: Surgical Concept Erasure in Visual Autoregressive Models
Xinhao Zhong, Yimin Zhou, Zhiqi Zhang, Junhao Li, Yi Sun, Bin Chen, Shu-Tao Xia, Ke Xu
https://arxiv.org/abs/2509.22400 …

Closing the Safety Gap: Surgical Concept Erasure in Visual Autoregressive Models
The rapid progress of visual autoregressive (VAR) models has brought new opportunities for text-to-image generation, but also heightened safety concerns. Existing concept erasure techniques, primarily designed for diffusion models, fail to generalize to VARs due to their next-scale token prediction paradigm. In this paper, we first propose a novel VAR Erasure framework VARE that enables stable concept erasure in VAR models by leveraging auxiliary visual tokens to reduce fine-tuning intensity. B…

A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning
Xuhang Chen, Bo Lv, Mengqian Wang, Xunwen Xiang, Shiting Wu, Shenghong Luo, Wenjun Zhang
https://arxiv.org/abs/2509.22654

A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning
Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we present a comprehensive framework for telecommunications churn prediction leveraging deep neural networks. Through systematic problem formulation, rigorous dataset analysis, and careful feature engineering, we develop a model that captures complex patterns in customer behavior indic…

Fast-Forward Lattice Boltzmann: Learning Kinetic Behaviour with Physics-Informed Neural Operators
Xiao Xue, Marco F. P. ten Eikelder, Mingyang Gao, Xiaoyuan Cheng, Yiming Yang, Yi He, Shuo Wang, Sibo Cheng, Yukun Hu, Peter V. Coveney
https://arxiv.org/abs/2509.22411

Fast-Forward Lattice Boltzmann: Learning Kinetic Behaviour with Physics-Informed Neural Operators
The lattice Boltzmann equation (LBE), rooted in kinetic theory, provides a powerful framework for capturing complex flow behaviour by describing the evolution of single-particle distribution functions (PDFs). Despite its success, solving the LBE numerically remains computationally intensive due to strict time-step restrictions imposed by collision kernels. Here, we introduce a physics-informed neural operator framework for the LBE that enables prediction over large time horizons without step-by…

InstructPLM-mu: 1-Hour Fine-Tuning of ESM2 Beats ESM3 in Protein Mutation Predictions
Junde Xu, Yapin Shi, Lijun Lang, Taoyong Cui, Zhiming Zhang, Guangyong Chen, Jiezhong Qiu, Pheng-Ann Heng
https://arxiv.org/abs/2510.03370

InstructPLM-mu: 1-Hour Fine-Tuning of ESM2 Beats ESM3 in Protein Mutation Predictions
Multimodal protein language models deliver strong performance on mutation-effect prediction, but training such models from scratch demands substantial computational resources. In this paper, we propose a fine-tuning framework called InstructPLM-mu and try to answer a question: \textit{Can multimodal fine-tuning of a pretrained, sequence-only protein language model match the performance of models trained end-to-end? } Surprisingly, our experiments show that fine-tuning ESM2 with structural input…

Test time training enhances in-context learning of nonlinear functions
Kento Kuwataka, Taiji Suzuki
https://arxiv.org/abs/2509.25741 https://arxiv.org/pdf/…

Test time training enhances in-context learning of nonlinear functions
Test-time training (TTT) enhances model performance by explicitly updating designated parameters prior to each prediction to adapt to the test data. While TTT has demonstrated considerable empirical success, its theoretical underpinnings remain limited, particularly for nonlinear models. In this paper, we investigate the combination of TTT with in-context learning (ICL), where the model is given a few examples from the target distribution at inference time. We analyze this framework in the sett…

AQA-TTRL: Self-Adaptation in Audio Question Answering with Test-Time Reinforcement Learning
Haoyu Zhang, Jiaxian Guo, Yusuke Iwasawa, Yutaka Matsuo
https://arxiv.org/abs/2510.05478

AQA-TTRL: Self-Adaptation in Audio Question Answering with Test-Time Reinforcement Learning
Large Audio Language Models (LALMs) demonstrate impressive general audio understanding, but once deployed, they are static and fail to improve with new real-world audio data. As traditional supervised fine-tuning is costly, we introduce a novel framework for test-time audio understanding, AQA-TTRL, where an LALM evolves on-the-fly using only unlabeled test data. It first generates pseudo-labels from the prediction via majority voting, then optimizes the model via reinforcement learning. To hand…

Interpretable Audio Editing Evaluation via Chain-of-Thought Difference-Commonality Reasoning with Multimodal LLMs
Yuhang Jia, Xu Zhang, Yang Chen, Hui Wang, Enzhi Wang, Yong Qin
https://arxiv.org/abs/2509.16975

Interpretable Audio Editing Evaluation via Chain-of-Thought Difference-Commonality Reasoning with Multimodal LLMs
Automatic mean opinion score (MOS) prediction provides a more perceptual alternative to objective metrics, offering deeper insights into the evaluated models. With the rapid progress of multimodal large language models (MLLMs), their enhanced perceptual and reasoning abilities enable more comprehensive and interpretable audio quality assessment. In this work, we tackle the challenging task of audio editing evaluation and propose the first natural language-based automated evaluation framework bu…

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems
Jinming Fan, Chao Qian, Shaodong Zhou
https://arxiv.org/abs/2509.17810 https://

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems
Accurate prediction of molecular properties in complex chemical systems is crucial for accelerating material discovery and chemical innovation.However,current computational methods often struggle to capture the intricate compositional interplay across complex chemical systems,from intramolecular bonds to intermolecular forces.In this work,we introduce MesoNet,a novel framework founded on the principle of multi-representation learning and specifically designed for multi-molecule modeling.The cor…

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs
Yidong Zhou, Su I Iao, Hans-Georg M\"uller
https://arxiv.org/abs/2509.23544 https://

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs
Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices. These outputs are naturally modeled as elements of general metric spaces, where classical regression techniques that rely on vector space structure no longer apply. We introduce E2M (End-to-End Metric regression), a deep learning framework for predicting metric space-valued outputs. E2M performs prediction via a weighted Fréchet me…

Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Teodor Chiaburu, Vipin Singh, Frank Hau{\ss}er, Felix Bie{\ss}mann
https://arxiv.org/abs/2509.24873

Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Uncertainty quantification is essential in human-machine collaboration, as human agents tend to adjust their decisions based on the confidence of the machine counterpart. Reliably calibrated model uncertainties, hence, enable more effective collaboration, targeted expert intervention and more responsible usage of Machine Learning (ML) systems. Conformal prediction has become a well established model-agnostic framework for uncertainty calibration of ML models, offering statistically valid confid…

Replaced article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- A weighted-likelihood framework for class imbalance in Bayesian prediction models
Stanley E. Lazic

MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series
Dohyun Ku, Catherine D. Chong, Visar Berisha, Todd J. Schwedt, Jing Li
https://arxiv.org/abs/2509.19577

MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series
Time series analysis has emerged as an important tool for improving patient diagnosis and management in healthcare applications. However, these applications commonly face two critical challenges: time misalignment and data sparsity. Traditional approaches address these issues through a two-step process of imputation followed by prediction. We propose MAGIC (Multi-tAsk Gaussian Process for Imputation and Classification), a novel unified framework that simultaneously performs class-informed missi…

PerformSinger: Multimodal Singing Voice Synthesis Leveraging Synchronized Lip Cues from Singing Performance Videos
Ke Gu, Zhicong Wu, Peng Bai, Sitong Qiao, Zhiqi Jiang, Junchen Lu, Xiaodong Shi, Xinyuan Qian
https://arxiv.org/abs/2509.22718

PerformSinger: Multimodal Singing Voice Synthesis Leveraging Synchronized Lip Cues from Singing Performance Videos
Existing singing voice synthesis (SVS) models largely rely on fine-grained, phoneme-level durations, which limits their practical application. These methods overlook the complementary role of visual information in duration prediction.To address these issues, we propose PerformSinger, a pioneering multimodal SVS framework, which incorporates lip cues from video as a visual modality, enabling high-quality "duration-free" singing voice synthesis. PerformSinger comprises parallel multi-branch multi…

ARTI-6: Towards Six-dimensional Articulatory Speech Encoding
Jihwan Lee, Sean Foley, Thanathai Lertpetchpun, Kevin Huang, Yoonjeong Lee, Tiantian Feng, Louis Goldstein, Dani Byrd, Shrikanth Narayanan
https://arxiv.org/abs/2509.21447

ARTI-6: Towards Six-dimensional Articulatory Speech Encoding
We propose ARTI-6, a compact six-dimensional articulatory speech encoding framework derived from real-time MRI data that captures crucial vocal tract regions including the velum, tongue root, and larynx. ARTI-6 consists of three components: (1) a six-dimensional articulatory feature set representing key regions of the vocal tract; (2) an articulatory inversion model, which predicts articulatory features from speech acoustics leveraging speech foundation models, achieving a prediction correlatio…

Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting
Saranya Ganesh S., Frederick Iat-Hin Tam, Milton S. Gomez, Marie McGraw, Mark DeMaria, Kate Musgrave, Jakob Runge, Tom Beucler
https://arxiv.org/abs/2510.02050

Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting
Improving statistical forecasts of Atlantic hurricane intensity is limited by complex nonlinear interactions and difficulty in identifying relevant predictors. Conventional methods prioritize correlation or fit, often overlooking confounding variables and limiting generalizability to unseen tropical storms. To address this, we leverage a multidata causal discovery framework with a replicated dataset based on Statistical Hurricane Intensity Prediction Scheme (SHIPS) using ERA5 meteorological rea…