The Imitation Game: Algorithm Selection by Exploiting Black-Box Recommenders

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Reanalysis of mtDNA mutations of human primordial germ cells (PGCs) reveals NUMT contamination and suggests that selection in PGCs may be positive

The resilience of the mitochondrial genome (mtDNA) to a high mutational pressure depends, in part, on negative purifying selection in the germline. A paradigm in the field has been that such selection, at least in part, takes place in primordial germ cells ...

Elsevier Sci Ltd2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Andrei Kucharavy, Matteo Monti

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Assoc Computing Machinery2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Jiancheng Yang, Xiang Li, Amos Sironi, Jian Zhou, Jie Zhou, Siqi Liu

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in m ...

2023

Homogeneous Environmental Selection Structures the Bacterial Communities of Benthic Biofilms in Proglacial Floodplain Streams

Tom Ian Battin, Hannes Markus Peter, Susheel Bhanu Busi, Grégoire Marie Octave Edouard Michoud, Leïla Ezzat, Massimo Bourquin, Jade Brandani, Tyler Joe Kohler, Stylianos Fodelianakis

Streams draining proglacial floodplains harbor benthic biofilms comprised of diverse microbial communities. These high-mountain ecosystems are rapidly changing with climate warming, and it is therefore critical to better understand the mechanisms underlyin ...

AMER SOC MICROBIOLOGY2023

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Paolo Ienne, Kubilay Atasu, Jovan Blanusa

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

arXiv2023

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

Paolo Ienne, Kubilay Atasu, Jovan Blanusa

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in r ...

Assoc Computing Machinery2023

Why do we need so many proteins? A physical insight into the collaboration of Hsp70 and DnaJ

Adélaïde Alice Mohr

The Hsp70 cycle is a key element of protein homeostasis, which is essential to avoid protein aggregation and protein-related diseases. Despite many experimental observations of the interaction between Hsp70, its co-chaperone DnaJ and various substrates, li ...

EPFL2023

A modelling framework for modern heritage buildings energy simulation

Franz Graf, Giulia Marino, Giuseppe Galbiati

The latest European polices highlight the urgent need to rehabilitate the existing building stock, responsible for 40 % of the EU's total energy consumption. In this process, a key role is played by thermal simulations, assessing the effective energy perfo ...

Elsevier2023

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Adrien Michel, Tobias Jonas

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model a ...

COPERNICUS GESELLSCHAFT MBH2023

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...