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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…

More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations
Miriam J\"ager, Steffen Wolf
https://arxiv.org/abs/2507.01725

More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations
Finding process pathways in molecular simulations such as the unbinding paths of small molecule ligands from their binding sites at protein targets in a set of trajectories via unsupervised learning approaches requires the definition of a suitable similarity measure between trajectories. We here evaluate the performance of four such measures with varying degree of sophistication, i.e., Euclidean and Wasserstein distances, Procrustes analysis and dynamical time warping, when analyzing trajectory…

Evaluating Sound Similarity Metrics for Differentiable, Iterative Sound-Matching
Amir Salimi, Abram Hindle, Osmar R. Zaiane
https://arxiv.org/abs/2506.22628

Evaluating Sound Similarity Metrics for Differentiable, Iterative Sound-Matching
Manual sound design with a synthesizer is inherently iterative: an artist compares the synthesized output to a mental target, adjusts parameters, and repeats until satisfied. Iterative sound-matching automates this workflow by continually programming a synthesizer under the guidance of a loss function (or similarity measure) toward a target sound. Prior comparisons of loss functions have typically favored one metric over another, but only within narrow settings: limited synthesis methods, few l…

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…

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…

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…

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.

Using Neurogram Similarity Index Measure (NSIM) to Model Hearing Loss and Cochlear Neural Degeneration
Ahsan J. Cheema, Sunil Puria
https://arxiv.org/abs/2506.12705

Using Neurogram Similarity Index Measure (NSIM) to Model Hearing Loss and Cochlear Neural Degeneration
Trouble hearing in noisy situations remains a common complaint for both individuals with hearing loss and individuals with normal hearing. This is hypothesized to arise due to condition called: cochlear neural degeneration (CND) which can also result in significant variabilities in hearing aids outcomes. This paper uses computational models of auditory periphery to simulate various hearing tasks. We present an objective method to quantify hearing loss and CND by comparing auditory nerve fiber r…

This https://arxiv.org/abs/2502.10527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Algorithms and Hardness for Estimating Statistical Similarity
We introduce and study the computational problem of determining statistical similarity between probability distributions. For distributions $P$ and $Q$ over a finite sample space, their statistical similarity is defined as $S_{\mathrm{stat}}(P, Q) := \sum_x \min(P(x), Q(x))$. Despite its fundamental nature as a measure of similarity between distributions, capturing essential concepts such as Bayes error in prediction and hypothesis testing, this computational problem has not been previously exp…

Language Models Improve When Pretraining Data Matches Target Tasks
David Mizrahi, Anders Boesen Lindbo Larsen, Jesse Allardice, Suzie Petryk, Yuri Gorokhov, Jeffrey Li, Alex Fang, Josh Gardner, Tom Gunter, Afshin Dehghan
https://arxiv.org/abs/2507.12466

Language Models Improve When Pretraining Data Matches Target Tasks
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies, train models, measure benchmark performance, then refine accordingly. This raises a natural question: what happens when we make this optimization explicit? To explore this, we propose benchmark-targeted ranking (BETR), a simple method that selects pretraining documents based on similarity to benchmark training examp…

Ontological differentiation as a measure of semantic accuracy
Pablo Garcia-Cuadrillero, Fabio Revuelta, Jose Angel Capitan
https://arxiv.org/abs/2507.06208

Ontological differentiation as a measure of semantic accuracy
Understanding semantic relationships within complex networks derived from lexical resources is fundamental for network science and language modeling. While network embedding methods capture contextual similarity, quantifying semantic distance based directly on explicit definitional structure remains challenging. Accurate measures of semantic similarity allow for navigation on lexical networks based on maximizing semantic similarity in each navigation jump (Semantic Navigation, SN). This work in…

A new coefficient to measure agreement between continuous variables
Ronny Vallejos, Felipe Osorio, Clemente Ferrer
https://arxiv.org/abs/2507.07913 https:/…

A new coefficient to measure agreement between continuous variables
Assessing agreement between two instruments is crucial in clinical studies to evaluate the similarity between two methods measuring the same subjects. This paper introduces a novel coefficient, termed rho1, to measure agreement between continuous variables, focusing on scenarios where two instruments measure experimental units in a study. Unlike existing coefficients, rho1 is based on L1 distances, making it robust to outliers and not relying on nuisance parameters. The coefficient is derived f…

Observable Covariance and Principal Observable Analysis for Data on Metric Spaces
Ece Karacam, Washington Mio, Osman Berat Okutan
https://arxiv.org/abs/2506.04003

Observable Covariance and Principal Observable Analysis for Data on Metric Spaces
Datasets consisting of objects such as shapes, networks, images, or signals overlaid on such geometric objects permeate data science. Such datasets are often equipped with metrics that quantify the similarity or divergence between any pair of elements turning them into metric spaces $(X,d)$, or a metric measure space $(X,d,μ)$ if data density is also accounted for through a probability measure $μ$. This paper develops a Lipschitz geometry approach to analysis of metric measure spaces based on…

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…

Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
Tianyu Lin, Xinran Li, Chuntung Zhuang, Qi Chen, Yuanhao Cai, Kai Ding, Alan L. Yuille, Zongwei Zhou
https://arxiv.org/abs/2506.02093

Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
Widely adopted evaluation metrics for sparse-view CT reconstruction--such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio--prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, …