Tootfinder

A novel fast short-time root music method for vibration monitoring of high-speed spindles
Huiguang Zhang, Baoguo Liu, Wei Feng, Zongtang Li
https://arxiv.org/abs/2506.17600

A novel fast short-time root music method for vibration monitoring of high-speed spindles
Ultra-high-speed spindle bearings challenge traditional vibration monitoring due to broadband noise, non-stationarity, and limited time-frequency resolution. We present a fast Short-Time Root-MUSIC (fSTrM) algorithm that exploits FFT-accelerated Lanczos bidiagonalization to reduce computational complexity from $\mathcal{O}(N^3)$ to $SN\log_2N+S^2(N+S)+M^2(N+M)$ while preserving parametric super-resolution. The method constructs Hankel matrices from 16 ms signal frames and extracts fault fre…

Edge Universality for Inhomogeneous Random Matrices
Dang-Zheng Liu, Guangyi Zou
https://arxiv.org/abs/2508.17838 https://arxiv.org/pdf/2508.17838

Edge Universality for Inhomogeneous Random Matrices
We consider symmetric and Hermitian random matrices whose entries are independent and symmetric random variables with an arbitrary variance pattern. Under a novel Short-to-Long Mixing condition, which is sharp in the sense that it precludes a corrected shift at the spectral edge, we establish GOE/GUE edge universality for such inhomogeneous random matrices. This condition effectively reduces the universality problem to verifying the mixing properties of a random walk governed by the variance pr…

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Arif Pathan
https://arxiv.org/abs/2506.17244

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Short-term sentiment forecasting in financial markets (e.g., stocks, indices) is challenging due to volatility, non-linearity, and noise in OHLC (Open, High, Low, Close) data. This paper introduces a novel CMG (Chaos-Markov-Gaussian) framework that integrates chaos theory, Markov property, and Gaussian processes to improve prediction accuracy. Chaos theory captures nonlinear dynamics; the Markov chain models regime shifts; Gaussian processes add probabilistic reasoning. We enhance the framework…

A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion
Yalda Zafari, Roaa Elalfy, Mohamed Mabrok, Somaya Al-Maadeed, Tamer Khattab, Essam A. Rashed
https://arxiv.org/abs/2507.16955

A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion
Early and accurate interpretation of screening mammograms is essential for effective breast cancer detection, yet it remains a complex challenge due to subtle imaging findings and diagnostic ambiguity. Many existing AI approaches fall short by focusing on single view inputs or single-task outputs, limiting their clinical utility. To address these limitations, we propose a novel multi-view, multitask hybrid deep learning framework that processes all four standard mammography views and jointly pr…

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime
Hongzhi Zhu, Wei Xu, Xiaohu You
https://arxiv.org/abs/2507.14951 https:/…

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime
Transformer architectures have emerged as promising deep learning (DL) tools for modeling complex sequence-to-sequence interactions in channel decoding. However, current transformer-based decoders for error correction codes (ECCs) demonstrate inferior performance and generalization capabilities compared to conventional algebraic decoders, especially in short-code regimes. In this work, we propose a novel latent-attention based transformer (LAT) decoder for polar codes that addresses the limitat…

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Minbok Wi, Seungmin Baek, Seonyong Park, Mattan Erez, Jung Ho Ahn
https://arxiv.org/abs/2506.15918

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Decomposing DRAM address mappings into component-level functions is critical for understanding memory behavior and enabling precise RowHammer attacks, yet existing reverse-engineering methods fall short. We introduce novel timing-based techniques leveraging DRAM refresh intervals and consecutive access latencies to infer component-specific functions. Based on this, we present Sudoku, the first software-based tool to automatically decompose full DRAM address mappings into channel, rank, bank gro…

Network Modelling of Asynchronous Change-Points in Multivariate Time Series
Carson McKee, Maria Kalli
https://arxiv.org/abs/2506.15801 https://

Network Modelling of Asynchronous Change-Points in Multivariate Time Series
This article introduces a novel Bayesian method for asynchronous change-point detection in multivariate time series. This method allows for change-points to occur earlier in some series (leading series) followed, after a short delay, by change-points in some other series (lagging series). Such dynamic dependence structure is common in fields such as seismology and neurology where a latent event such as an earthquake or seizure causes certain sensors to register change-points before others. We m…

The short version of my larger soapbox here is that a lot of the hand-wringing and hair-pulling about e-bikes, novel UPS delivery thingers, Those Darned Kids tearing down the sidewalk on scooters, etc. is at its root not about the vehicles, but about infrastructure.
We need vastly more and better infrastructure — physical, legal, and social — for vehicles that are:
- zero-emission (which currently means human- and electric-powered)
- of limited mass (just how much could it harm a pedestrian?)
- traveling in the 10-20mph range

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Mehdi Khalaj, Shahrzad Golestani Najafabadi, Julita Vassileva
https://arxiv.org/abs/2507.15460

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Personalized News Recommendation systems (PNR) have emerged as a solution to information overload by predicting and suggesting news items tailored to individual user interests. However, traditional PNR systems face several challenges, including an overreliance on textual content, common neglect of short-term user interests, and significant privacy concerns due to centralized data storage. This paper addresses these issues by introducing a novel multimodal federated learning-based approach for n…

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Hans-Arno Jacobsen
https://arxiv.org/abs/2508.12472

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasonin…

Cyclic Data Streaming on GPUs for Short Range Stencils Applied to Molecular Dynamics
Martin Rose, Simon Homes, Lukas Ramsperger, Jose Gracia, Christoph Niethammer, Jadran Vrabec
https://arxiv.org/abs/2507.11289

Cyclic Data Streaming on GPUs for Short Range Stencils Applied to Molecular Dynamics
In the quest for highest performance in scientific computing, we present a novel framework that relies on high-bandwidth communication between GPUs in a compute cluster. The framework offers linear scaling of performance for explicit algorithms that is only limited by the size of the dataset and the number of GPUs. Slices of the dataset propagate in a ring of processes (GPUs) from one GPU, where they are processed, to the next, which results in a parallel-in-time parallelization. The user of th…

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
Maciej Wysocki, Pawe{\l} Sakowski
https://arxiv.org/abs/2508.14999 https://

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use …

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
Jihao Li, Keqi Zhu, Guodong Lu, I-Ming Chen, Huixu Dong
https://arxiv.org/abs/2506.11570

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers…

Online Convex Optimization for Coordinated Long-Term and Short-Term Isolated Microgrid Dispatch
Ning Qi, Yousuf Baker, Bolun Xu
https://arxiv.org/abs/2507.02636

Online Convex Optimization for Coordinated Long-Term and Short-Term Isolated Microgrid Dispatch
This paper proposes a novel non-anticipatory long-short-term coordinated dispatch framework for isolated microgrid with hybrid short-long-duration energy storages (LDES). We introduce a convex hull approximation model for nonconvex LDES electrochemical dynamics, facilitating computational tractability and accuracy. To address temporal coupling in SoC dynamics and long-term contracts, we generate hindsight-optimal state-of-charge (SoC) trajectories of LDES and netloads for offline training. In t…

PreSem-Surf: RGB-D Surface Reconstruction with Progressive Semantic Modeling and SG-MLP Pre-Rendering Mechanism
Yuyan Ye, Hang Xu, Yanghang Huang, Jiali Huang, Qian Weng
https://arxiv.org/abs/2508.13228

PreSem-Surf: RGB-D Surface Reconstruction with Progressive Semantic Modeling and SG-MLP Pre-Rendering Mechanism
This paper proposes PreSem-Surf, an optimized method based on the Neural Radiance Field (NeRF) framework, capable of reconstructing high-quality scene surfaces from RGB-D sequences in a short time. The method integrates RGB, depth, and semantic information to improve reconstruction performance. Specifically, a novel SG-MLP sampling structure combined with PR-MLP (Preconditioning Multilayer Perceptron) is introduced for voxel pre-rendering, allowing the model to capture scene-related information…

PermRust: A Token-based Permission System for Rust
Lukas Gehring, Sebastian Rehms, Florian Tschorsch
https://arxiv.org/abs/2506.11701 https://

PermRust: A Token-based Permission System for Rust
Permission systems which restrict access to system resources are a well-established technology in operating systems, especially for smartphones. However, as such systems are implemented in the operating system they can at most manage access on the process-level. Since moderns software often (re)uses code from third-parties libraries, a permission system for libraries can be desirable to enhance security. In this short-paper, we adapt concepts from capability systems building a novel theoretical…

A Novel Signal Processing Strategy for Short-Range Laser Feedback Interferometry Sensors
Alexander Zimmer, Johannes Meyer, Enkelejda Kasneci
https://arxiv.org/abs/2506.10705

A Novel Signal Processing Strategy for Short-Range Laser Feedback Interferometry Sensors
The rapid evolution of wearable technologies, such as AR glasses, demands compact, energy-efficient sensors capable of high-precision measurements in dynamic environments. Traditional Frequency-Modulated Continuous Wave (FMCW) Laser Feedback Interferometry (LFI) sensors, while promising, falter in applications that feature small distances, high velocities, shallow modulation, and low-power constraints. We propose a novel sensor-processing pipeline that reliably extracts distance and velocity me…

Large Language Model Data Generation for Enhanced Intent Recognition in German Speech
Theresa Pekarek Rosin, Burak Can Kaplan, Stefan Wermter
https://arxiv.org/abs/2508.06277 ht…

Large Language Model Data Generation for Enhanced Intent Recognition in German Speech
Intent recognition (IR) for speech commands is essential for artificial intelligence (AI) assistant systems; however, most existing approaches are limited to short commands and are predominantly developed for English. This paper addresses these limitations by focusing on IR from speech by elderly German speakers. We propose a novel approach that combines an adapted Whisper ASR model, fine-tuned on elderly German speech (SVC-de), with Transformer-based language models trained on synthetic text d…

A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari, Mohamed Chahine Ghanem
https://arxiv.org/abs/2508.12470 …

A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
The increased Internet of Medical Things IoMT and the Industrial Internet of Things IIoT interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces a novel transformer-based intrusion detection system IDS, termed BiGAT-ID a hybrid model that combines bidirectional gated recurrent units BiGRU, long short-term memory LSTM networks, and multi-head att…

Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
https://arxiv.org/abs/2508.08454

Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Accurately modeling user preferences is crucial for improving the performance of content-based recommender systems. Existing approaches often rely on simplistic user profiling methods, such as averaging or concatenating item embeddings, which fail to capture the nuanced nature of user preference dynamics, particularly the interactions between long-term and short-term preferences. In this work, we propose LLM-driven Temporal User Profiling (LLM-TUP), a novel method for user profiling that explic…

LiDO: Discovery of a 10:1 Resonator with a Novel Libration State
Rosemary E. Pike, Ruth Murray-Clay, Kathryn Volk, Mike Alexandersen, Mark Comte, Samantha M. Lawler, Ying-Tung Chen, Arcelia Hermosillo Ruiz, Cameron Semenchuck, Cameron Collyer, J. J. Kavelaars, Lowell Peltier
https://arxiv.org/abs/2508.05627…

LiDO: Discovery of a 10:1 Resonator with a Novel Libration State
The Large inclination Distant Objects LiDO survey has discovered the first securely classified object in the 10:1 mean motion resonance of Neptune. This object, 2020 VN40, is short-term stable in the 10:1 resonance, but not stable on Gyr timescales. 2020 VN40 is likely part of the scattering sticking population, and temporarily resides in the 10:1 resonance at ~139.5 au. This discovery confirms that this distant resonance is populated, as a single detection is likely to be indicative of a large…

Audio Inpanting using Discrete Diffusion Model
Tali Dror, Iftach Shoham, Moshe Buchris, Oren Gal, Haim Permuter, Gilad Katz, Eliya Nachmani
https://arxiv.org/abs/2507.08333

Audio Inpanting using Discrete Diffusion Model
Audio inpainting refers to the task of reconstructing missing segments in corrupted audio recordings. While prior approaches-including waveform and spectrogram-based diffusion models-have shown promising results for short gaps, they often degrade in quality when gaps exceed 100 milliseconds (ms). In this work, we introduce a novel inpainting method based on discrete diffusion modeling, which operates over tokenized audio representations produced by a pre-trained audio tokenizer. Our approach mo…

Low-Latency Software Polar Encoders and Decoders for Short Blocklengths
Mathieu Leonardon (IMT Atlantique - MEE, Lab\_STICC\_BRAIn), Mohammed El Houcine Ayoubi (IMT Atlantique), Adrien Cassagne (ALSOC), Romain Tajan (Bordeaux INP, UB), Camille Leroux (Bordeaux INP, UB)
https://arxiv.org/abs/2507.04734

Low-Latency Software Polar Encoders and Decoders for Short Blocklengths
This paper presents our low-latency Polar code encoders and decoders developed for the 2025 International Symposium on Topics in Coding (ISTC 2025) contest, which challenges participants to implement the fastest possible channel code encoders and decoders in terms of average and maximum latency on a CPU target. Our solution is based on Polar codes with an Adaptive Successive Cancellation List (ASCL) decoder. We introduce a novel ASCL unrolled decoder generator. We conduct an extensive explorati…

Exact Renormalization Relation and Binding Energies for Three Identical Bosons
Langxuan Chen, Pengfei Zhang
https://arxiv.org/abs/2506.12531 https://

Exact Renormalization Relation and Binding Energies for Three Identical Bosons
In the low-energy limit, non-relativistic particles with short-range interactions exhibit universal behavior that is largely independent of microscopic details. This universality is typically described by effective field theory, in which the two-body interaction is renormalized to a single parameter-the scattering length. For systems of identical bosons, the three-body problem reveals the Efimov effect, a novel phenomenon proposed that necessitates the introduction of an additional three-body p…

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Xunzhi Xiang, Yabo Chen, Guiyu Zhang, Zhongyu Wang, Zhe Gao, Quanming Xiang, Gonghu Shang, Junqi Liu, Haibin Huang, Yang Gao, Chi Zhang, Qi Fan, Xuelong Li
https://arxiv.org/abs/2508.03334

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Current autoregressive diffusion models excel at video generation but are generally limited to short temporal durations. Our theoretical analysis indicates that the autoregressive modeling typically suffers from temporal drift caused by error accumulation and hinders parallelization in long video synthesis. To address these limitations, we propose a novel planning-then-populating framework centered on Macro-from-Micro Planning (MMPL) for long video generation. MMPL sketches a global storyline f…

IOCC: Aligning Semantic and Cluster Centers for Few-shot Short Text Clustering
Jixuan Yin, Zhihao Yao, Wenshuai Huo, Xinmiao Yu, Xiaocheng Feng, Bo Li
https://arxiv.org/abs/2508.06126

IOCC: Aligning Semantic and Cluster Centers for Few-shot Short Text Clustering
In clustering tasks, it is essential to structure the feature space into clear, well-separated distributions. However, because short text representations have limited expressiveness, conventional methods struggle to identify cluster centers that truly capture each category's underlying semantics, causing the representations to be optimized in suboptimal directions. To address this issue, we propose IOCC, a novel few-shot contrastive learning method that achieves alignment between the cluster ce…

Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Kevin Bradley Dsouza, Graham Alexander Watt, Yuri Leonenko, Juan Moreno-Cruz
https://arxiv.org/abs/2508.06386

Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically opt…

This https://arxiv.org/abs/2409.13414 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An Eulerian-Lagrangian formulation for compressible Euler Equations
In this paper, we present a novel Eulerian-Lagrangian formulation for the compressible isentropic Euler equations with a general pressure law. Using the developed Lagrangian formulation, we show a short-time solution which is unique in the framework of Bessel space $H_{p}^β(\mathbb{T}^{d})$ for $β>\frac{d}{p}+1$. The proof is precise and relatively simple, relying on the application of the Implicit Function Theorem in Banach spaces combined with a carefully designed regularization procedure. …

Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves
Taiki Kawamuro, Shinya Yamada, Shigehiro Nagataki, Shunji Matsuura, Yusuke Sakai, Satoshi Yamada
https://arxiv.org/abs/2507.05589

Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves
We investigate whether a novel method of quantum machine learning (QML) can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we adopt is a quantum version of the long-short term memory (LSTM) where some fully connected layers are replaced with quantum circuits. The LSTM, making predictions based on preceding data, allows identification of anomalies by comparing predicted and actual time…

This https://arxiv.org/abs/2505.23387 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration ...
Minwoo Oh, Minsu Park, Eunil Park

AZRA: Extending the Affective Capabilities of Zoomorphic Robots using Augmented Reality
Shaun Macdonald, Salma ElSayed, Mark McGill
https://arxiv.org/abs/2508.08507 https://

AZRA: Extending the Affective Capabilities of Zoomorphic Robots using Augmented Reality
Zoomorphic robots could serve as accessible and practical alternatives for users unable or unwilling to keep pets. However, their affective interactions are often simplistic and short-lived, limiting their potential for domestic adoption. In order to facilitate more dynamic and nuanced affective interactions and relationships between users and zoomorphic robots we present AZRA, a novel augmented reality (AR) framework that extends the affective capabilities of these robots without physical modi…

An Improved Solution to the Two Normal Means Problem via Regularization
Yang Liu, Jonathan P. Williams
https://arxiv.org/abs/2508.13012 https://arxiv.org/p…

An Improved Solution to the Two Normal Means Problem via Regularization
The many-normal-means problem is a classic example that motivates the development of many important inferential procedures in the history of statistics. In this short note, we consider a further special case of the problem, which involves only two normally distributed data points with a constraint that the pair of means are not too far apart from one another. Starting with a regularized ML estimator, we construct a novel possibilistic IM for marginal inference on one of the two means. Not only …

A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features
Korkut Anapa, \.Ismail G\"uzel, Ceylan Yozgatl{\i}gil
https://arxiv.org/abs/2507.02890

A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features
Purpose: Overall equipment efficiency (OEE) is a key manufacturing KPI, but its volatile nature complicates short-term forecasting. This study presents a novel framework combining time series decomposition and topological data analysis to improve OEE prediction across various equipment, such as hydraulic press systems. Methods: The approach begins by decomposing hourly OEE data into trend, seasonal, and residual components. The residual, capturing short-term variability, is modeled using a se…

Asterinas: A Linux ABI-Compatible, Rust-Based Framekernel OS with a Small and Sound TCB
Yuke Peng, Hongliang Tian, Zhang Junyang, Ruihan Li, Chengjun Chen, Jianfeng Jiang, Jinyi Xian, Xiaolin Wang, Chenren Xu, Diyu Zhou, Yingwei Luo, Shoumeng Yan, Yinqian Zhang
https://arxiv.org/abs/2506.03876

Asterinas: A Linux ABI-Compatible, Rust-Based Framekernel OS with a Small and Sound TCB
How can one build a feature-rich, general-purpose, Rust-based operating system (OS) with a minimal and sound Trusted Computing Base (TCB) for memory safety? Existing Rust-based OSes fall short due to their improper use of unsafe Rust in kernel development. To address this challenge, we propose a novel OS architecture called framekernel that realizes Rust's full potential to achieve intra-kernel privilege separation, ensuring TCB minimality and soundness. We present OSTD, a streamlined framework…

This https://arxiv.org/abs/2409.16548 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Optimised neural network predictions of galaxy formation histories using semi-stochastic corrections
We present a novel methodology to improve predictions of galaxy formation histories by incorporating semi-stochastic corrections to account for short-timescale variability. Our paper addresses limitations in existing models that capture broad trends in galaxy evolution, but fail to reproduce the bursty nature of star formation and chemical enrichment, resulting in inaccurate predictions of key observables such as stellar masses, optical spectra, and colour distributions. We introduce a simple t…

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Gjorgjina Cenikj, Ga\v{s}per Petelin, Tome Eftimov
https://arxiv.org/abs/2507.02337

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Understanding the behavior of numerical metaheuristic optimization algorithms is critical for advancing their development and application. Traditional visualization techniques, such as convergence plots, trajectory mapping, and fitness landscape analysis, often fall short in illustrating the structural dynamics of the search process, especially in high-dimensional or complex solution spaces. To address this, we propose a novel representation and visualization methodology that clusters solution …

Revisiting Graph Analytics Benchmark
Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai, Peng Cheng, Xue Li, Wenyuan Yu, Wenjie Zhang, Xuemin Lin, Jingren Zhou
https://arxiv.org/abs/2506.21811

Revisiting Graph Analytics Benchmark
The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of API usability evaluation. To address these shortcomings, we propose a novel graph analytics benchmark. First, we select eight core algorithm…

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Qi Peng, Jialin Cui, Jiayuan Xie, Yi Cai, Qing Li
https://arxiv.org/abs/2508.03038

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Large language models (LLMs) have shown great potential in the medical domain. However, existing models still fall short when faced with complex medical diagnosis task in the real world. This is mainly because they lack sufficient reasoning depth, which leads to information loss or logical jumps when processing a large amount of specialized medical data, leading to diagnostic errors. To address these challenges, we propose Tree-of-Reasoning (ToR), a novel multi-agent framework designed to handl…

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Xinzhu Li, Juepeng Zheng, Yikun Chen, Xudong Mao, Guanghui Yue, Wei Zhou, Chenlei Lv, Ruomei Wang, Fan Zhou, Baoquan Zhao
https://arxiv.org/abs/2508.03397

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait r…

Deep Learning Enhanced Multivariate GARCH
Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, Chao Wang
https://arxiv.org/abs/2506.02796 https://…

Deep Learning Enhanced Multivariate GARCH
This paper introduces a novel multivariate volatility modeling framework, named Long Short-Term Memory enhanced BEKK (LSTM-BEKK), that integrates deep learning into multivariate GARCH processes. By combining the flexibility of recurrent neural networks with the econometric structure of BEKK models, our approach is designed to better capture nonlinear, dynamic, and high-dimensional dependence structures in financial return data. The proposed model addresses key limitations of traditional multiva…

Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
Chengxu Zuo, Jiawei Huang, Xiao Jiang, Yuan Yao, Xiangren Shi, Rui Cao, Xinyu Yi, Feng Xu, Shihui Guo, Yipeng Qin
https://arxiv.org/abs/2506.10580

Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
In this paper, we propose a novel dynamic calibration method for sparse inertial motion capture systems, which is the first to break the restrictive absolute static assumption in IMU calibration, i.e., the coordinate drift RG'G and measurement offset RBS remain constant during the entire motion, thereby significantly expanding their application scenarios. Specifically, we achieve real-time estimation of RG'G and RBS under two relaxed assumptions: i) the matrices change negligibly in a short tim…

This https://arxiv.org/abs/2501.00296 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
Our aim is to learn to solve long-horizon decision-making problems in complex robotics domains given low-level skills and a handful of short-horizon demonstrations containing sequences of images. To this end, we focus on learning abstract symbolic world models that facilitate zero-shot generalization to novel goals via planning. A critical component of such models is the set of symbolic predicates that define properties of and relationships between objects. In this work, we leverage pretrained …

Using LLMs to Capture Users' Temporal Context for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
https://arxiv.org/abs/2508.08512 https://

Using LLMs to Capture Users' Temporal Context for Recommendation
Effective recommender systems demand dynamic user understanding, especially in complex, evolving environments. Traditional user profiling often fails to capture the nuanced, temporal contextual factors of user preferences, such as transient short-term interests and enduring long-term tastes. This paper presents an assessment of Large Language Models (LLMs) for generating semantically rich, time-aware user profiles. We do not propose a novel end-to-end recommendation architecture; instead, the c…

An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement
Chun-Wei Ho, Pin-Jui Ku, Hao Yen, Sabato Marco Siniscalchi, Yu Tsao, Chin-Hui Lee
https://arxiv.org/abs/2507.02192

An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement
We propose a novel iterative phase estimation framework, termed multi-source Griffin-Lim algorithm (MSGLA), for speech enhancement (SE) under additive noise conditions. The core idea is to leverage the ad-hoc consistency constraint of complex-valued short-time Fourier transform (STFT) spectrograms to address the sign ambiguity challenge commonly encountered in geometry-based phase estimation. Furthermore, we introduce a variant of the geometric constraint framework based on the law of sines and…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Mingzhe Du, Luu Tuan Tuan, Yue Liu, Yuhao Qing, Dong Huang, Xinyi He, Qian Liu, Zejun Ma, See-kiong Ng
https://arxiv.org/abs/2505.23387

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

Fx-Encoder : Extracting Instrument-Wise Audio Effects Representations from Mixtures
Yen-Tung Yeh, Junghyun Koo, Marco A. Mart\'inez-Ram\'irez, Wei-Hsiang Liao, Yi-Hsuan Yang, Yuki Mitsufuji
https://arxiv.org/abs/2507.02273

Fx-Encoder++: Extracting Instrument-Wise Audio Effects Representations from Mixtures
General-purpose audio representations have proven effective across diverse music information retrieval applications, yet their utility in intelligent music production remains limited by insufficient understanding of audio effects (Fx). Although previous approaches have emphasized audio effects analysis at the mixture level, this focus falls short for tasks demanding instrument-wise audio effects understanding, such as automatic mixing. In this work, we present Fx-Encoder++, a novel model design…

SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting
Di Li, Jie Feng, Jiahao Chen, Weisheng Dong, Guanbin Li, Yuhui Zheng, Mingtao Feng, Guangming Shi
https://arxiv.org/abs/2507.23772

SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting
3D affordance reasoning, the task of associating human instructions with the functional regions of 3D objects, is a critical capability for embodied agents. Current methods based on 3D Gaussian Splatting (3DGS) are fundamentally limited to single-object, single-step interactions, a paradigm that falls short of addressing the long-horizon, multi-object tasks required for complex real-world applications. To bridge this gap, we introduce the novel task of Sequential 3D Gaussian Affordance Reasonin…

Stealtooth: Breaking Bluetooth Security Abusing Silent Automatic Pairing
Keiichiro Kimura, Hiroki Kuzuno, Yoshiaki Shiraishi, Masakatu Morii
https://arxiv.org/abs/2507.00847

Stealtooth: Breaking Bluetooth Security Abusing Silent Automatic Pairing
Bluetooth is a pervasive wireless communication technology used by billions of devices for short-range connectivity. The security of Bluetooth relies on the pairing process, where devices establish shared long-term keys for secure communications. However, many commercial Bluetooth devices implement automatic pairing functions to improve user convenience, creating a previously unexplored attack surface. We present Stealtooth, a novel attack that abuses unknown vulnerabilities in the automatic …

Scenario-Agnostic Deep-Learning-Based Localization with Contrastive Self-Supervised Pre-training
Lingyan Zhang, Yuanfeng Qiu, Dachuan Li, Shaohua Wu, Tingting Zhang, Qinyu Zhang
https://arxiv.org/abs/2508.03084

Scenario-Agnostic Deep-Learning-Based Localization with Contrastive Self-Supervised Pre-training
Wireless localization has become a promising technology for offering intelligent location-based services. Although its localization accuracy is improved under specific scenarios, the short of environmental dynamic vulnerability still hinders this approach from being fully practical applications. In this paper, we propose CSSLoc, a novel framework on contrastive self-supervised pre-training to learn generic representations for accurate localization in various scenarios. Without the location info…

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li, Xiaoxiao Xu, Jiaqi Yu, Zixiu Wang, Lantao Hu, Han Li, Kun Gai
https://arxiv.org/abs/2508.05093

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-tru…

This https://arxiv.org/abs/2505.16196 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SEM: Enhancing Spatial Understanding for Robust Robot Manipulation
A key challenge in robot manipulation lies in developing policy models with strong spatial understanding, the ability to reason about 3D geometry, object relations, and robot embodiment. Existing methods often fall short: 3D point cloud models lack semantic abstraction, while 2D image encoders struggle with spatial reasoning. To address this, we propose SEM (Spatial Enhanced Manipulation model), a novel diffusion-based policy framework that explicitly enhances spatial understanding from two com…

This https://arxiv.org/abs/2308.13614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The role of interior dynamics and differentiation on the surface and atmosphere of lava planets
Lava planets are rocky exoplanets that orbit so close to their host star that their day-side is hot enough to melt silicate rock. Their short orbital periods ensure that lava planets are tidally locked into synchronous rotation, with permanent day and night hemispheres. Such asymmetric magma oceans have no analogs in the Solar System and will exhibit novel fluid dynamics. Here we report numerical simulations of lava planet interiors showing that solid-liquid fractionation in the planetary int…

This https://arxiv.org/abs/2312.09720 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

RIS-Enabled NLoS Near-Field Joint Position and Velocity Estimation under User Mobility
In the context of single-base station (BS) non-line-of-sight (NLoS) single-epoch localization with the aid of a reflective reconfigurable intelligent surface (RIS), this paper introduces a novel three-step algorithm that jointly estimates the position and velocity of a mobile user equipment (UE), while compensating for the Doppler effects observed in near-field (NF) at the RIS elements over the short transmission duration of a sequence of downlink (DL) pilot symbols. First, a low-complexity ini…

DiG-Net: Enhancing Quality of Life through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics
Eran Bamani Beeri, Eden Nissinman, Avishai Sintov
https://arxiv.org/abs/2505.24786

DiG-Net: Enhancing Quality of Life through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics
Dynamic hand gestures play a pivotal role in assistive human-robot interaction (HRI), facilitating intuitive, non-verbal communication, particularly for individuals with mobility constraints or those operating robots remotely. Current gesture recognition methods are mostly limited to short-range interactions, reducing their utility in scenarios demanding robust assistive communication from afar. In this paper, we introduce a novel approach designed specifically for assistive robotics, enabling …

MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments
Sai Krishna Ghanta, Ramviyas Parasuraman
https://arxiv.org/abs/2506.23514

MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments
Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments. Existing approaches for multi-robot relative localization often depend on costly or short-range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi-robot relative loca…