20 papers · ranked by Valyu relevance
Eric W. Hester, Geoffrey M. Vasil
We provide an elementary derivation of an orthogonal coordinate system for boundary layers around evolving smooth surfaces and curves based on the signed-distance function. We go beyond previous works on the signed-distance function and collate useful vector calculus identities for these coordinates. These results and…
Pierre‐Alain Fayolle
We describe in this short note a technique to convert an implicit surface into a Signed Distance Function (SDF) while exactly preserving the zero level-set of the implicit. The proposed approach relies on embedding the input implicit in the final layer of a neural network, which is trained to minimize a loss function…
Dang Van Cuong, Boris S. Mordukhovich, Nguyen Mau Nam, Mike Wells
In this paper we first consider the class of minimal time functions in the general setting of locally convex topological vector (LCTV) spaces. The results obtained in this framework are based on a novel notion of closedness of target sets with respect to constant dynamics. Then we introduce and investigate a new class…
Jiawang Chen, Zhi Qiao, Jun Yan, Zhenqiang Wu + 3 more
'Hao Wang' 'Yixiang Fang'] Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and…
Francesco Monti, David Stewart, Anuradha Surendra, Irina Alecu + 4 more
SiDCo is implemented in Python with a RShiny front-end. It is compatible with all web browsers. Two analytical tabs allow users to perform either distance correlation or partial distance correlation. In both applications, users define their desired threshold values and P values. Data are automatically z-score…
Javier Pardo-Diaz, Lyuba V. Bozhilova, Mariano Beguerisse-Díaz, Philip S. Poole + 2 more
Even within well studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional…
Javier Pardo-Diaz, Philip S. Poole, Mariano Beguerisse-Díaz, Charlotte M. Deane + 1 more
Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful…
Javier Pardo-Diaz, Lyuba V Bozhilova, Mariano Beguerisse-Díaz, Philip S Poole + 3 more
In this work, we have introduced signed distance correlation, and presented a method to construct networks in a self-consistent way exclusively from gene expression data. This method has three main steps: data pre-processing, computing correlations, and thresholding. These steps combine well-established methods such as…
Gustavo Rodrigues Galvão, Orlando Lee, Zanoni Dias
Background During evolution, global mutations may alter the order and the orientation of the genes in a genome. Such mutations are referred to as rearrangement events, or simply operations. In unichromosomal genomes, the most common operations are reversals, which are responsible for reversing the order and orientation…
William Krinsman
We can allow the edges of networks to have both negative and positive weights. For example, signed networks can describe the interactions of microbes. To evaluate the performance of estimators for signed networks, we need quantitative comparison methods for signed networks. Finding such comparison methods is done most…
Andre R. Oliveira, Guillaume Fertin, Ulisses Dias, Zanoni Dias
Background One way to estimate the evolutionary distance between two given genomes is to determine the minimum number of large-scale mutations, or genome rearrangements, that are necessary to transform one into the other. In this context, genomes can be represented as ordered sequences of genes, each gene being…
Karl Kumbier, Sumanta Basu, James B. Brown, Susan Celniker + 1 more
Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically “black-boxes,” learning statistical interactions that are difficult to translate into testable hypotheses. The…
David Huang, Huong Nguyen
We derive a systematic and general method for parametrizing coarse-grained molecular models consisting of anisotropic particles from fine-grained (e.g. all-atom) models for condensed-phase molecular dynamics simulations. The method, which we call anisotropic force-matching coarsegraining (AFM-CG), is based on rigorous…
George Sandler, Stephen I. Wright, Aneil F. Agrawal
Most empirical studies of linkage disequilibrium (LD) study its magnitude, ignoring its sign. Here, we examine patterns of signed LD in two population genomic datasets, one from Capsella grandiflora and one from Drosophila melanogaster. We consider how processes such as drift, admixture, Hill-Robertson interference…
Bryan A. Dawkins, Trang T. Le, Brett A. McKinney
The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. The effects of the distribution and metric, as well as the presence of correlation and interactions, are reflected in the expected…
Andrew R. Tawfeek
We give an accessible introduction and elaboration on the methods used in obtaining a geodesic, which is the curve of shortest length connecting two points lying on the surface of a function. This is found through computing what's known as the variation of a functional, a "function of functions" of sorts. Geodesics are…
Alejandro Cholaquidis, Antonio Cuevas, Ricardo Fraiman
A functional distance H, based on the Hausdorff metric between the function hypographs, is proposed for the space E of non-negative real upper semicontinuous functions on a compact interval. The main goal of the paper is to show that the space (E, H) is particularly suitable in some statistical problems with functional…
Esdras Joseph, Pedro Galeano, Rosa E. Lillo
This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a new semi-distance for functional observations that generalize the…
Stijn van Dongen, Anton J. Enright
We investigate two classes of transformations of cosine similarity and Pearson and Spearman correlations into metric distances, utilising the simple tool of metric-preserving functions. The first class puts anti-correlated objects maximally far apart. Previously known transforms fall within this class. The second class…
Baptiste Py, Francesco Ciucci
The distribution of relaxation times (DRT) has emerged as a promising method for analyzing electrochemical impedance spectroscopy (EIS) data. The standard approach for reconstructing the DRT from measured impedances consists of regularized regression, which usually leverages the Euclidean norm. In this work, we show…