Distributed Sensor Network Localization from Local Connectivity: Performance Analysis for the HOP-TERRAIN Algorithm

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Cellular Fronthauling for Data Capacity Increase in Underserved Spaces

The increase in wireless data traffic continues and is a product of several factors. First, new technologies and capabilities enable new use cases for which new products emerge. Then, with the growing user adoption over time, the data traffic is further in ...

EPFL2022

Blind as a bat: spatial perception without sight

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Among our five senses, we rely mostly on audition and vision to perceive an environment. Our ears are able to detect stimuli from all directions, especially from obstructed and far-away objects. Even in smoke, harsh weather conditions, or at night â situ ...

EPFL2021

Realizing doubles: a conjugation zoo

Jérôme Scherer

Conjugation spaces are topological spaces equipped with an involution such that their fixed points have the same mod 2 cohomology (as a graded vector space, a ring and even an unstable algebra) but with all degrees divided by two, generalizing the classica ...

CAMBRIDGE UNIV PRESS2021

Exploring the foundations of tonality: statistical cognitive modeling of modes in the history of Western classical music

Martin Alois Rohrmeier, Daniel Harasim, Fabian Claude Moss

Tonality is one of the most central theoretical concepts for the analysis of Western classical music. This study presents a novel approach for the study of its historical development, exploring in particular the concept of mode. Based on a large dataset of ...

2021

Coordinate Difference Matrices

Martin Vetterli, Gilles Baechler, Golnooshsadat Elhami, Miranda Krekovic

In many problems such as phase retrieval, molecular biology, source localization, and sensor array calibration, one can measure vector differences between pairs of points and attempt to recover the position of these points; this class of problems is called ...

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UCLID-Net: Single View Reconstruction in Object Space

Pascal Fua, Edoardo Remelli, Benoît Alain René Guillard

Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations. However, these representations rarely preserve the Euclidean st ...

2020

On Perfect Clustering of High Dimension, Low Sample Size Data

Soham Sarkar

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...

IEEE COMPUTER SOC2020

From Trees to Barcodes and Back Again: Theoretical and Statistical Perspectives

Kathryn Hess Bellwald, Lida Kanari, Adélie Eliane Garin

Methods of topological data analysis have been successfully applied in a wide range of fields to provide useful summaries of the structure of complex data sets in terms of topological descriptors, such as persistence diagrams. While there are many powerful ...

2020

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Ola Nils Anders Svensson, Ashkan Norouzi Fard, Justin Dean Ward

Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best-known algorithm for k-means in Euclidean space with a provable guarantee is a simple local ...

SIAM PUBLICATIONS2020

Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case

Valentin Hartmann

We consider the problem of finding an optimal transport plan between an absolutely continuous measure and a finitely supported measure of the same total mass when the transport cost is the unsquared Euclidean distance. We may think of this problem as close ...

SPRINGER HEIDELBERG2020

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