Tootfinder

Exploring Weighted Property Approaches for RDF Graph Similarity Measure
Ngoc Luyen Le, Marie-H\'el\`ene Abel, Philippe Gouspillou
https://arxiv.org/abs/2404.19052

Exploring Weighted Property Approaches for RDF Graph Similarity Measure
Measuring similarity between RDF graphs is essential for various applications, including knowledge discovery, semantic web analysis, and recommender systems. However, traditional similarity measures often treat all properties equally, potentially overlooking the varying importance of different properties in different contexts. Consequently, exploring weighted property approaches for RDF graph similarity measure presents an intriguing avenue for investigation. Therefore, in this paper, we propos…

Packing sets in Euclidean space by affine transformations
Alex Iosevich, Pertti Mattila, Eyvindur Palsson, Minh-Quy Pham, Thang Pham, Steven Senger, Chun-Yen Shen
https://arxiv.org/abs/2405.03087

Packing sets in Euclidean space by affine transformations
For Borel subsets $Θ\subset O(d)\times \mathbb{R}^d$ (the set of all rigid motions) and $E\subset \mathbb{R}^d$, we define \begin{align*} Θ(E):=\bigcup_{(g,z)\in Θ}(gE+z). \end{align*} In this paper, we investigate the Lebesgue measure and Hausdorff dimension of $Θ(E)$ given the dimensions of the Borel sets $E$ and $Θ$, when $Θ$ has product form. We also study this question by replacing rigid motions with the class of dilations and translations; and similarity transformations. The dimen…

Delaunay Weighted Two-sample Test for High-dimensional Data by Incorporating Geometric Information
Jiaqi Gu, Ruoxu Tan, Guosheng Yin
https://arxiv.org/abs/2404.03198

Delaunay Weighted Two-sample Test for High-dimensional Data by Incorporating Geometric Information
Two-sample hypothesis testing is a fundamental problem with various applications, which faces new challenges in the high-dimensional context. To mitigate the issue of the curse of dimensionality, high-dimensional data are typically assumed to lie on a low-dimensional manifold. To incorporate geometric informtion in the data, we propose to apply the Delaunay triangulation and develop the Delaunay weight to measure the geometric proximity among data points. In contrast to existing similarity meas…

Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives
Jiaxin Liu, Yi Yang, Kar Yan Tam
https://arxiv.org/abs/2403.14341 https…

Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives
In this paper, we introduce the Financial-STS task, a financial domain-specific NLP task designed to measure the nuanced semantic similarity between pairs of financial narratives. These narratives originate from the financial statements of the same company but correspond to different periods, such as year-over-year comparisons. Measuring the subtle semantic differences between these paired narratives enables market stakeholders to gauge changes over time in the company's financial and operation…

This https://arxiv.org/abs/2205.04287 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Regular and Complete Notion of Delay for Streaming String Transducers
The notion of delay between finite transducers is a core element of numerous fundamental results of transducer theory. The goal of this work is to provide a similar notion for more complex abstract machines: we introduce a new notion of delay tailored to measure the similarity between streaming string transducers (SST). We show that our notion is regular: we design a finite automaton that can check whether the delay between any two SSTs executions is smaller than some given bound. As a conseque…

Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
Long Shi, Lei Cao, Yunshan Ye, Yu Zhao, Badong Chen
https://arxiv.org/abs/2403.18393

Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
Graph learning is widely recognized as a crucial technique in multi-view clustering. Existing graph learning methods typically involve constructing an adaptive neighbor graph based on probabilistic neighbors and then learning a consensus graph to for clustering, however, they are confronted with two limitations. Firstly, they often rely on Euclidean distance to measure similarity when constructing the adaptive neighbor graph, which proves inadequate in capturing the intrinsic structure among da…

Assessing the similarity of real matrices with arbitrary shape
Jasper Albers, Anno C. Kurth, Robin Gutzen, Aitor Morales-Gregorio, Michael Denker, Sonja Gr\"un, Sacha J. van Albada, Markus Diesmann
https://arxiv.org/abs/2403.17687

Assessing the similarity of real matrices with arbitrary shape
Assessing the similarity of matrices is valuable for analyzing the extent to which data sets exhibit common features in tasks such as data clustering, dimensionality reduction, pattern recognition, group comparison, and graph analysis. Methods proposed for comparing vectors, such as cosine similarity, can be readily generalized to matrices. However, this approach usually neglects the inherent two-dimensional structure of matrices. Here, we propose singular angle similarity (SAS), a measure for …

This https://arxiv.org/abs/1606.00963 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimal quantization for a probability measure on a nonuniform stretched Sierpiński triangle
Quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with support containing a finite number of elements. In this paper, we have considered a Borel probability measure $P$ on $\mathbb R^2$, which has support a nonuniform stretched Sierpiński triangle generated by a set of three contractive similarity mappings on $\mathbb R^2$. For this probability measure, we investigate the optimal sets of $n$-means and the $n$th quantizat…

This https://arxiv.org/abs/2206.04277 has been replaced.
link: https://scholar.google.com/scholar?q=a

On Hypothesis Transfer Learning of Functional Linear Models
We study the transfer learning (TL) for the functional linear regression (FLR) under the Reproducing Kernel Hilbert Space (RKHS) framework, observing the TL techniques in existing high-dimensional linear regression is not compatible with the truncation-based FLR methods as functional data are intrinsically infinite-dimensional and generated by smooth underlying processes. We measure the similarity across tasks using RKHS distance, allowing the type of information being transferred tied to the p…

Machine learning-based similarity measure to forecast M&A from patent data
Giambattista Albora, Matteo Straccamore, Andrea Zaccaria
https://arxiv.org/abs/2404.07179

Machine learning-based similarity measure to forecast M&A from patent data
Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically designed measure of similarity between companies and we apply it to patenting activity data to forecast M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria fo…

Quantifying Semantic Query Similarity for Automated Linear SQL Grading: A Graph-based Approach
Leo K\"oberlein, Dominik Probst, Richard Lenz
https://arxiv.org/abs/2403.14441 …

Quantifying Semantic Query Similarity for Automated Linear SQL Grading: A Graph-based Approach
Quantifying the semantic similarity between database queries is a critical challenge with broad applications, ranging from query log analysis to automated educational assessment of SQL skills. Traditional methods often rely solely on syntactic comparisons or are limited to checking for semantic equivalence. This paper introduces a novel graph-based approach to measure the semantic dissimilarity between SQL queries. Queries are represented as nodes in an implicit graph, while the transitions b…

This https://arxiv.org/abs/2308.06820 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Divisive Hierarchical Clustering of Variables Identified by Singular Vectors
In this work, we present a novel method for divisive hierarchical variable clustering. A cluster is a group of elements that exhibit higher similarity among themselves than to elements outside this cluster. The correlation coefficient serves as a natural measure to assess the similarity of variables. This means that in a correlation matrix, a cluster is represented by a block of variables with greater internal than external correlation. Our approach provides a nonparametric solution to identify…

Assessing the similarity of real matrices with arbitrary shape
Jasper Albers, Anno C. Kurth, Robin Gutzen, Aitor Morales-Gregorio, Michael Denker, Sonja Gr\"un, Sacha J. van Albada, Markus Diesmann
https://arxiv.org/abs/2403.17687

Assessing the similarity of real matrices with arbitrary shape
Assessing the similarity of matrices is valuable for analyzing the extent to which data sets exhibit common features in tasks such as data clustering, dimensionality reduction, pattern recognition, group comparison, and graph analysis. Methods proposed for comparing vectors, such as cosine similarity, can be readily generalized to matrices. However, this approach usually neglects the inherent two-dimensional structure of matrices. Here, we propose singular angle similarity (SAS), a measure for …

This https://arxiv.org/abs/2307.07289 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics
We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a similarity measure in order to compare our locally constrained graph building approaches with commonly used k-nearest neighbour building approaches. To demonstrate the feasibility of our solution for particle physics applications, we implemented a real-time graph…

This https://arxiv.org/abs/2310.15399 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

GESI: Gammachirp Envelope Similarity Index for Predicting Intelligibility of Simulated Hearing Loss Sounds
We propose an objective intelligibility measure (OIM), called the Gammachirp Envelope Similarity Index (GESI), which can predict the speech intelligibility (SI) of simulated hearing loss (HL) sounds for normal hearing (NH) listeners. GESI is an intrusive method that computes the SI metric using the gammachirp filterbank (GCFB), the modulation filterbank, and the extended cosine similarity measure. The unique features of GESI are that i) it reflects the hearing impaired (HI) listener's HL that a…

This https://arxiv.org/abs/2308.06820 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Divisive Hierarchical Clustering of Variables Identified by Singular Vectors
In this work, we present a novel method for divisive hierarchical variable clustering. A cluster is a group of elements that exhibit higher similarity among themselves than to elements outside this cluster. The correlation coefficient serves as a natural measure to assess the similarity of variables. This means that in a correlation matrix, a cluster is represented by a block of variables with greater internal than external correlation. Our approach provides a nonparametric solution to identify…

Walking Your Frog Fast in 4 LoC
Nis Meinert
https://arxiv.org/abs/2404.05708 https://arxiv.org/pdf/2404.05708

Walking Your Frog Fast in 4 LoC
Given two polygonal curves, there are many ways to define a notion of similarity between them. One popular measure is the Fréchet distance which has many desirable properties but is notoriously expensive to calculate, especially for non-trivial metrics. In 1994, Eiter and Mannila introduced the discrete Fréchet distance which is much easier to implement and approximates the continuous Fréchet distance with a quadratic runtime overhead. However, this algorithm relies on recursions and is not …

A Graph-based Approach to Estimating the Number of Clusters
Yichuan Bai, Lynna Chu
https://arxiv.org/abs/2402.15600 https://arxiv.org…

A Graph-based Approach to Estimating the Number of Clusters
We consider the problem of estimating the number of clusters ($k$) in a dataset. We propose a non-parametric approach to the problem that is based on maximizing a statistic constructed from similarity graphs. This graph-based statistic is a robust summary measure of the similarity information among observations and is applicable even if the number of dimensions or number of clusters is possibly large. The approach is straightforward to implement, computationally fast, and can be paired with any…

This https://arxiv.org/abs/2402.07446 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora
We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil). We ranked each corpus according to a similarity measure and carried out an intrinsic and extrinsic evaluation on different portions of this ranked corpus. We show that there are significant quality differences between different portions of web-mined corpora and that the quality varies across languages and datasets. W…

This https://arxiv.org/abs/2403.14128 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Gen-T: Table Reclamation in Data Lakes
We introduce the problem of Table Reclamation. Given a Source Table and a large table repository, reclamation finds a set of tables that, when integrated, reproduce the source table as closely as possible. Unlike query discovery problems like Query-by-Example or by-Target, Table Reclamation focuses on reclaiming the data in the Source Table as fully as possible using real tables that may be incomplete or inconsistent. To do this, we define a new measure of table similarity, called error-aware i…

Gen-T: Table Reclamation in Data Lakes
Grace Fan, Roee Shraga, Ren\'ee J. Miller
https://arxiv.org/abs/2403.14128 https://arxiv.o…

Gen-T: Table Reclamation in Data Lakes
We introduce the problem of Table Reclamation. Given a Source Table and a large table repository, reclamation finds a set of tables that, when integrated, reproduce the source table as closely as possible. Unlike query discovery problems like Query-by-Example or by-Target, Table Reclamation focuses on reclaiming the data in the Source Table as fully as possible using real tables that may be incomplete or inconsistent. To do this, we define a new measure of table similarity, called error-aware i…

Fine-Grained Complexity of Earth Mover's Distance under Translation
Karl Bringmann, Frank Staals, Karol W\k{e}grzycki, Geert van Wordragen
https://arxiv.org/abs/2403.04356

Fine-Grained Complexity of Earth Mover's Distance under Translation
The Earth Mover's Distance is a popular similarity measure in several branches of computer science. It measures the minimum total edge length of a perfect matching between two point sets. The Earth Mover's Distance under Translation ($\mathrm{EMDuT}$) is a translation-invariant version thereof. It minimizes the Earth Mover's Distance over all translations of one point set. For $\mathrm{EMDuT}$ in $\mathbb{R}^1$, we present an $\widetilde{\mathcal{O}}(n^2)$-time algorithm. We also show that th…