Tootfinder

A statistical test for network similarity
Pierre Miasnikof, Alexander Y. Shetopaloff
https://arxiv.org/abs/2508.14399 https://arxiv.org/pdf/2508.14399

A statistical test for network similarity
In this article, we revisit and expand our prior work on graph similarity. In this version of our work, we offer an extensive array of empirical tests. We also examine the sensitivity of our test to network variations. Our test performs exactly as expected, on synthetic and real-world graphs. It offers a very accurate measure of graph (dis)similarity.

Extropy-Based Generalized Divergence and Similarity Ratios: Theory and Applications
Saranya P., Sunoj S. M
https://arxiv.org/abs/2508.13696 https://arxiv.o…

Extropy-Based Generalized Divergence and Similarity Ratios: Theory and Applications
In this article, we propose two classes of relative information measures based on extropy, viz., the generalized extropy similarity ratio (GESR) and generalized extropy divergence ratio (GEDR), that measure the similarity and discrepancy between two probability distributions, respectively. Definitions of GESR and GEDR are proposed along with their fundamental axioms, properties, and some measures satisfying those axioms are also introduced. The relationship of GESR with the popular cosine simil…

Assessing Data Replication in Symbolic Music via Adapted Structural Similarity Index Measure
Shulei Ji, Zihao Wang, Le Ma, Jiaxing Yu, Kejun Zhang
https://arxiv.org/abs/2509.13658

Assessing Data Replication in Symbolic Music via Adapted Structural Similarity Index Measure
AI-generated music may inadvertently replicate samples from the training data, raising concerns of plagiarism. Similarity measures can quantify such replication, thereby offering supervision and guidance for music generation models. Existing similarity measure methods for symbolic music mainly target melody repetition, leaving a gap in assessing complex music with rich textures and expressive performance characteristics. To address this gap, we introduce SSIMuse, the first adaptation of the Str…

Measuring Time-Series Dataset Similarity using Wasserstein Distance
Hongjie Chen, Akshay Mehra, Josh Kimball, Ryan A. Rossi
https://arxiv.org/abs/2507.22189 https://

Measuring Time-Series Dataset Similarity using Wasserstein Distance
The emergence of time-series foundation model research elevates the growing need to measure the (dis)similarity of time-series datasets. A time-series dataset similarity measure aids research in multiple ways, including model selection, finetuning, and visualization. In this paper, we propose a distribution-based method to measure time-series dataset similarity by leveraging the Wasserstein distance. We consider a time-series dataset an empirical instantiation of an underlying multivariate norm…

MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval
Yifan Liu, Qianfeng Wen, Mark Zhao, Jiazhou Liang, Scott Sanner
https://arxiv.org/abs/2509.13562 https://

MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval
Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query-passage relevance in embedding space, which is effective when embeddings lie on a linear manifold. However, our experiments across DPR benchmarks suggest that embeddings often lie on lower-dimensional, non-linear manifolds, especially in out-of-distribution (OOD) settings, where cosine and Euclidean distance fail to capture semantic similarity. To address this limitation, we propose a manifold-aware …

Parallel Needleman-Wunsch on CUDA to measure word similarity based on phonetic transcriptions
Dominic Plein
https://arxiv.org/abs/2509.01654 https://arxiv.…

Parallel Needleman-Wunsch on CUDA to measure word similarity based on phonetic transcriptions
We present a method to calculate the similarity between words based on their phonetic transcription (their pronunciation) using the Needleman-Wunsch algorithm. We implement this algorithm in Rust and parallelize it on both CPU and GPU to handle large datasets efficiently. The GPU implementation leverages CUDA and the cudarc Rust library to achieve significant performance improvements. We validate our approach by constructing a fully-connected graph where nodes represent words and edges have wei…

Un cadre paraconsistant pour l'{\'e}valuation de similarit{\'e} dans les bases de connaissances
Jos\'e-Luis Vilchis Medina (ENSTA Bretagne, Lab-STICC, Lab-STICC_ROBEX)
https://arxiv.org/abs/2509.08433

Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances
This article proposes a paraconsistent framework for evaluating similarity in knowledge bases. Unlike classical approaches, this framework explicitly integrates contradictions, enabling a more robust and interpretable similarity measure. A new measure $ S^* $ is introduced, which penalizes inconsistencies while rewarding shared properties. Paraconsistent super-categories $ Ξ_K^* $ are defined to hierarchically organize knowledge entities. The model also includes a contradiction extractor $ E $…

Canonicalization of the E value from BLAST similarity search -- dissimilarity measure and distance function for a metric space of protein sequences
Boryeu Mao
https://arxiv.org/abs/2509.06849

Canonicalization of the E value from BLAST similarity search -- dissimilarity measure and distance function for a metric space of protein sequences
Sequence matching algorithms such as BLAST and FASTA have been widely used in searching for evolutionary origin and biological functions of newly discovered nucleic acid and protein sequences. As parts of these search tools, alignment scores and E values are useful indicators of the quality of search results from querying a database of annotated sequences, whereby a high alignment score (and inversely a low E value) reflects significant similarity between the query and the subject (target) sequ…

A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Chao Xiong, Xianwen Yu, Wei Xu, Lei Cheng, Chuan Yuan, Linjian Mo
https://arxiv.org/abs/2509.12948 https://…

A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Pre-ranking plays a crucial role in large-scale recommender systems by significantly improving the efficiency and scalability within the constraints of providing high-quality candidate sets in real time. The two-tower model is widely used in pre-ranking systems due to a good balance between efficiency and effectiveness with decoupled architecture, which independently processes user and item inputs before calculating their interaction (e.g. dot product or similarity measure). However, this indep…

LaVA-Man: Learning Visual Action Representations for Robot Manipulation
Chaoran Zhu, Hengyi Wang, Yik Lung Pang, Changjae Oh
https://arxiv.org/abs/2508.19391 https://

LaVA-Man: Learning Visual Action Representations for Robot Manipulation
Visual-textual understanding is essential for language-guided robot manipulation. Recent works leverage pre-trained vision-language models to measure the similarity between encoded visual observations and textual instructions, and then train a model to map this similarity to robot actions. However, this two-step approach limits the model to capture the relationship between visual observations and textual instructions, leading to reduced precision in manipulation tasks. We propose to learn visua…

Quantifying Gender Stereotypes in Japan between 1900 and 1999 with Word Embeddings
Shintaro Sakai, Haewoon Kwak, Jisun An, Akira Matsui
https://arxiv.org/abs/2510.03905 https://…

Quantifying Gender Stereotypes in Japan between 1900 and 1999 with Word Embeddings
We quantify the evolution of gender stereotypes in Japan from 1900 to 1999 using a series of 100 word embeddings, each trained on a corpus from a specific year. We define the gender stereotype value to measure the strength of a word's gender association by computing the difference in cosine similarity of the word to female- versus male-related attribute words. We examine trajectories of gender stereotype across three traditionally gendered domains: Home, Work, and Politics, as well as occupatio…

Privacy Disclosure of Similarity in Speech and Language Processing
Tom B\"ackstr\"om, Mohammad Hassan Vali, My Nguyen, Silas Rech
https://arxiv.org/abs/2508.05250 http…

Privacy Disclosure of Similarity in Speech and Language Processing
Speaker, author, and other biometric identification applications often compare a sample's similarity to a database of templates to determine the identity. Given that data may be noisy and similarity measures can be inaccurate, such a comparison may not reliably identify the true identity as the most similar. Still, even the similarity rank based on an inaccurate similarity measure can disclose private information about the true identity. We propose a methodology for quantifying the privacy disc…

Expected Signature Kernels for L\'evy Rough Paths
Peter K. Friz, Paul P. Hager
https://arxiv.org/abs/2509.07893 https://arxiv.org/pdf/2509.07893…

Expected Signature Kernels for Lévy Rough Paths
The expected signature kernel arises in statistical learning tasks as a similarity measure of probability measures on path space. Computing this kernel for known classes of stochastic processes is an important problem that, in particular, can help reduce computational costs. Building on the representation of the expected signature of (inhomogeneous) Lévy processes with absolutely continuous characteristics as the development of an absolutely continuous path in the extended tensor algebra [F.-H…

Some Remarks on the $l_1$-Robust Solution of LexRank Problem
Anna Timonina-Farkas
https://arxiv.org/abs/2509.04131 https://arxiv.org/pdf/2509.04131

Some Remarks on the $l_1$-Robust Solution of LexRank Problem
Graph-based ranking methods, such as LexRank, are fundamental in Natural Language Processing (NLP) applications like text summarization, as they measure the relative importance of textual units. Building on recent advances in ranking methods for growing and dynamic graphs, we develop a robust variant of LexRank that operates on stochastic similarity graphs with uncertain and expanding structure. Our approach introduces a novel $l_1$-based formulation that captures ambiguity in both transition p…

Real-time rail vehicle localisation using spatially resolved magnetic field measurements
Niklas Dieckow, Katharina Ostaszewski, Philip Heinisch, Henriette Struckmann, Hendrik Ranocha
https://arxiv.org/abs/2507.19327

Real-time rail vehicle localisation using spatially resolved magnetic field measurements
This work presents two complementary real-time rail vehicle localization methods based on magnetic field measurements and a pre-recorded magnetic map. The first uses a particle filter reweighted via magnetic similarity, employing a heavy-tailed non-Gaussian kernel for enhanced stability. The second is a stateless sequence alignment technique that transforms real-time magnetic signals into the spatial domain and matches them to the map using a similarity measure. Experiments with operational tra…