Graph Laplacian Mixture Model

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Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabling them to build a hierarchy of abstract, low-dimensional data representatio ...

Despite the widespread use of self-tracking technologies for promoting personal wellbeing, there is limited research on the monitoring of intimate data, particularly urine. To shed light on the design possibilities within this unexplored domain we designed ...

Urban proximity planning is foreseen as a solution to foster a “sustainable” city, including economic viability, environmental soundness and social inclusivity. This paper focuses on the inclusivity aspects by questioning the adoption of urban proximities: ...

This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...

Graph Laplacian Mixture Model

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Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Designing for Intimate Wellbeing: Aidee, a Qualitative Approach To Urine Home Monitoring

Planning urban proximities: An empirical analysis of how residential preferences conflict with the urban morphologies and residential practices

Recovering Static and Time-Varying Communities Using Persistent Edges