Tootfinder

Improving Merge Pipeline Throughput in Continuous Integration via Pull Request Prioritization
Maximilian Jungwirth, Martin Gruber, Gordon Fraser
https://arxiv.org/abs/2508.08342

Improving Merge Pipeline Throughput in Continuous Integration via Pull Request Prioritization
Integrating changes into large monolithic software repositories is a critical step in modern software development that substantially impacts the speed of feature delivery, the stability of the codebase, and the overall productivity of development teams. To ensure the stability of the main branch, many organizations use merge pipelines that test software versions before the changes are permanently integrated. However, the load on merge pipelines is often so high that they become bottlenecks, des…

Information in quantum field theory simulators: Thin-film superfluid helium
Maciej T. Jarema, Cameron R. D. Bunney, Vitor S. Barroso, Mohammadamin Tajik, Chris Goodwin, Silke Weinfurtner
https://arxiv.org/abs/2508.07247

Information in quantum field theory simulators: Thin-film superfluid helium
Understanding quantum correlations through information-theoretic measures is fundamental to developments in quantum field theory, quantum information, and quantum many-body physics. A central feature in a plethora of systems is the area law, under which information scales with the size of the boundary of the system, rather than volume. Whilst many systems and regimes exhibiting an area law have been identified theoretically, experimental verification remains limited, particularly in continuous …

The Linear Reliability Channel
Alexander Mariona, Ken R. Duffy, Muriel M\'edard
https://arxiv.org/abs/2509.08079 https://arxiv.org/pdf/2509.08079

The Linear Reliability Channel
We introduce and analyze a discrete soft-decision channel called the linear reliability channel (LRC) in which the soft information is the rank ordering of the received symbol reliabilities. We prove that the LRC is an appropriate approximation to a general class of discrete modulation, continuous noise channels when the noise variance is high. The central feature of the LRC is that its combinatorial nature allows for an extensive mathematical analysis of the channel and its corresponding hard-…

Sparse minimum Redundancy Maximum Relevance for feature selection
Peter Naylor, Benjamin Poignard, H\'ector Climente-Gonz\'alez, Makoto Yamada
https://arxiv.org/abs/2508.18901

Sparse minimum Redundancy Maximum Relevance for feature selection
We propose a feature screening method that integrates both feature-feature and feature-target relationships. Inactive features are identified via a penalized minimum Redundancy Maximum Relevance (mRMR) procedure, which is the continuous version of the classic mRMR penalized by a non-convex regularizer, and where the parameters estimated as zero coefficients represent the set of inactive features. We establish the conditions under which zero coefficients are correctly identified to guarantee acc…

Quantized Thouless Pumping of Dark Solitons
Yu-Liang Tao, Huaxin He, Hao Lyu, Yongping Zhang, Yong Xu
https://arxiv.org/abs/2508.06881 https://arxiv.org/pd…

Quantized Thouless Pumping of Dark Solitons
Nonlinearity enables the emergence of localized waves such as solitons that maintain their shapes during propagation. Solitons are broadly classified into bright and dark solitons. While a bright soliton exhibits a density peak, a dark soliton presents as a defect on a continuous wave background. A distinctive feature of dark solitons is the abrupt phase change in their wave function, which can host Majorana zero modes in topological fermionic superfluids. Recent studies have shown that bright …

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding
Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka
https://arxiv.org/abs/2507.03836

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding
Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. …

Automated Feature Tracking for Real-Time Kinematic Analysis and Shape Estimation of Carbon Nanotube Growth
Kaveh Safavigerdini, Ramakrishna Surya, Jaired Collins, Prasad Calyam, Filiz Bunyak, Matthew R. Maschmann, Kannappan Palaniappan
https://arxiv.org/abs/2508.19232

Automated Feature Tracking for Real-Time Kinematic Analysis and Shape Estimation of Carbon Nanotube Growth
Carbon nanotubes (CNTs) are critical building blocks in nanotechnology, yet the characterization of their dynamic growth is limited by the experimental challenges in nanoscale motion measurement using scanning electron microscopy (SEM) imaging. Existing ex situ methods offer only static analysis, while in situ techniques often require manual initialization and lack continuous per-particle trajectory decomposition. We present Visual Feature Tracking (VFTrack) an in-situ real-time particle tracki…

iLearnRobot: An Interactive Learning-Based Multi-Modal Robot with Continuous Improvement
Kohou Wang, ZhaoXiang Liu, Lin Bai, Kun Fan, Xiang Liu, Huan Hu, Kai Wang, Shiguo Lian
https://arxiv.org/abs/2507.22896

iLearnRobot: An Interactive Learning-Based Multi-Modal Robot with Continuous Improvement
It is crucial that robots' performance can be improved after deployment, as they are inherently likely to encounter novel scenarios never seen before. This paper presents an innovative solution: an interactive learning-based robot system powered by a Multi-modal Large Language Model(MLLM). A key feature of our system is its ability to learn from natural dialogues with non-expert users. We also propose chain of question to clarify the exact intent of the question before providing an answer and d…

A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction
Yijun Geng, Jianzhou Wang, Jinze Li, Zhiwu Li
https://arxiv.org/abs/2507.05761

A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction
Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This sys…

Wavelet-Based Time-Frequency Fingerprinting for Feature Extraction of Traditional Irish Music
Noah Shore
https://arxiv.org/abs/2508.00479 https://arxiv.org…

Wavelet-Based Time-Frequency Fingerprinting for Feature Extraction of Traditional Irish Music
This work presents a wavelet-based approach to time-frequency fingerprinting for time series feature extraction, with a focus on audio identification from live recordings of traditional Irish tunes. The challenges of identifying features in time-series data are addressed by employing a continuous wavelet transform to extract spectral features and wavelet coherence analysis is used to compare recorded audio spectrograms to synthetically generated tunes. The synthetic tunes are derived from ABC n…

Enhancing Self-Supervised Speaker Verification Using Similarity-Connected Graphs and GCN
Zhaorui Sun, Yihao Chen, Jialong Wang, Minqiang Xu, Lei Fang, Sian Fang, Lin Liu
https://arxiv.org/abs/2509.04147

Enhancing Self-Supervised Speaker Verification Using Similarity-Connected Graphs and GCN
With the continuous development of speech recognition technology, speaker verification (SV) has become an important method for identity authentication. Traditional SV methods rely on handcrafted feature extraction, while deep learning has significantly improved system performance. However, the scarcity of labeled data still limits the widespread application of deep learning in SV. Self-supervised learning, by mining latent information in large unlabeled datasets, enhances model generalization a…

Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings
Soumen Sinha, Shahryar Rahnamayan, Azam Asilian Bidgoli
https://arxiv.org/abs/2507.17025

Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings
Efficient text embedding is crucial for large-scale natural language processing (NLP) applications, where storage and computational efficiency are key concerns. In this paper, we explore how using binary representations (barcodes) instead of real-valued features can be used for NLP embeddings derived from machine learning models such as BERT. Thresholding is a common method for converting continuous embeddings into binary representations, often using a fixed threshold across all features. We pr…

Good Morning #Canada
Before our move to #Innisfil, we spent 16 years in Caledon living on top of the Niagara Escarpment. We were fortunate to take advantage of the hiking trails and scenic beauty of the area with the Bruce Trail, Forks of The Credit Road, and numerous parks within a few kilometers. More Canadians should be aware of this approximately 1,050-kilometre geological feature that today is protected in Ontario as a continuous corridor.
#CanadaIsAwesome
https://youtu.be/5V5DIgF2yag?si=Z4tTs4k-3htyv6V8

Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
Masaki Murooka, Iori Kumagai, Mitsuharu Morisawa, Fumio Kanehiro
https://arxiv.org/abs/2508.11275

Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
To reduce the computational cost of humanoid motion generation, we introduce a new approach to representing robot kinematic reachability: the differentiable reachability map. This map is a scalar-valued function defined in the task space that takes positive values only in regions reachable by the robot's end-effector. A key feature of this representation is that it is continuous and differentiable with respect to task-space coordinates, enabling its direct use as constraints in continuous optim…