Tootfinder

This https://arxiv.org/abs/2410.00023 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Self-Tuning Spectral Clustering for Speaker Diarization
Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the need for careful tuning before constructing the Laplacian. In this study, we present a novel pruning algorithm to create a sparse affinity matrix called spectral clustering on p-neighborhood retained affinity matrix (SC-pNA). Our method improves on node-specific fixed neighbor selection by allowing a variable number …

This https://arxiv.org/abs/2506.01222 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Learning collective variables that preserve transition rates
Collective variables (CVs) play a crucial role in capturing rare events in high-dimensional systems, motivating the continual search for principled approaches to their design. In this work, we revisit the framework of quantitative coarse graining and identify the orthogonality condition from Legoll and Lelievre (2010) as a key criterion for constructing CVs that accurately preserve the statistical properties of the original process. We establish that satisfaction of the orthogonality condition …

This https://arxiv.org/abs/2505.22984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

A Computational Approach to Improving Fairness in K-means Clustering
The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster …

hiv_transmission: HIV transmission network (1988-2001)
A set of networks of HIV transmissions between people through sexual, needle-sharing, or social connections, based on combining 8 datasets collected from 1988 to 2001. Metadata includes test results of several diseases, as well as demographic variables such as age, ethnicity, and gender. Networks come in two flavors: egodyads and altdyads. Egodyads are the network among study-participants and their direct partners. Altdyads are the…

This https://arxiv.org/abs/2501.00394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Cluster algebras and quantum cohomology rings: A-type
We construct a cluster algebra structure within the quantum cohomology ring of a quiver variety associated with an $A$-type quiver. Specifically, let $Fl:=Fl(N_1,\ldots,N_{n+1})$ denote a partial flag variety of length $n$, and $QH_S^*(Fl)[t]:=QH_S^*(Fl)\otimes \mathbb C[t]$ be its equivariant quantum cohomology ring extended by a formal variable $t$, regarded as a $\mathbb Q$-algebra. We establish an injective $\mathbb Q$-algebra homomorphism from the $A_n$-type cluster algebra to the algebra …

Fast Sampling for System Identification: Overcoming Noise, Offsets, and Closed-Loop Challenges with State Variable Filter
Ichiro Maruta, Toshiharu Sugie
https://arxiv.org/abs/2506.03650

Fast Sampling for System Identification: Overcoming Noise, Offsets, and Closed-Loop Challenges with State Variable Filter
This paper investigates the effects of setting the sampling frequency significantly higher than conventional guidelines in system identification. Although continuous-time identification methods resolve the numerical difficulties encountered in discrete-time approaches when employing fast sampling (e.g., the problems caused by all poles approaching unity), the potential benefits of using sampling frequencies that far exceed traditional rules like the "ten times the bandwidth" guideline remained …

Variable Selection in Functional Linear Cox Model
Yuanzhen Yue, Stella Self, Yichao Wu, Jiajia Zhang, Rahul Ghosal
https://arxiv.org/abs/2506.02524 https:/…

Variable Selection in Functional Linear Cox Model
Modern biomedical studies frequently collect complex, high-dimensional physiological signals using wearables and sensors along with time-to-event outcomes, making efficient variable selection methods crucial for interpretation and improving the accuracy of survival models. We propose a novel variable selection method for a functional linear Cox model with multiple functional and scalar covariates measured at baseline. We utilize a spline-based semiparametric estimation approach for the function…

This https://arxiv.org/abs/2503.16512 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Multimodal Sensing and Machine Learning to Compare Printed and Verbal Assembly Instructions Delivered by a Social Robot
In this paper, we compare a manual assembly task communicated to workers using both printed and robot-delivered instructions. The comparison was made using physiological signals (blood volume pulse (BVP) and electrodermal activity (EDA)) collected from individuals during an experimental study. In addition, we also collected responses of individuals using the NASA Task Load Index (TLX) survey. Furthermore, we mapped the collected physiological signals to the responses of participants for NASA TL…

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Nitesh Kumar (Department of Physics, Applied Science Cluster, University of Petroleum and Energy Studies), Harinder P. Singh (Department of Physics and Astrophysics, University of Delhi, Delhi, India), Oleg Malkov (Institute of Astronomy of the Russian Academy of Sciences), Santosh Joshi (Aryabhatta Research Institute of Observational Sciences), Kefeng Tan (National Astronomical Observatories, C…

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters-mass ($M$), luminosity (log($L/L_\odot$)), effective temperature (log($T_{\rm eff}$)), and metallicity ($Z$)-directly from Transiting Exoplanet Survey Satellite (TESS) light cu…

Tensor-based multivariate function approximation: methods benchmarking and comparison
Athanasios C. Antoulas, Ion Victor Gosea, Charles Poussot-Vassal, Pierre Vuillemin
https://arxiv.org/abs/2506.04791

Tensor-based multivariate function approximation: methods benchmarking and comparison
In this note, we evaluate the performances, the features and the user-experience of some methods (and their implementations) designed for tensor- (or data-) based multivariate function construction and approximation. To this aim, a collection of multivariate functions extracted from contributive works coming from different communities, is suggested. First, these functions with varying complexity (e.g. number and degree of the variables) and nature (e.g. rational, irrational, differentiable or n…