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Publications associées (28)

Page 1 sur 2

Extensions of Peer Prediction Incentive Mechanisms

Adam Julian Richardson

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Verónica del Carmen Estrada Galiñanes

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

2024

Secondary structure of the human mitochondrial genome affects formation of deletions

Jacques Fellay, Konstantin Popadin

BackgroundAging in postmitotic tissues is associated with clonal expansion of somatic mitochondrial deletions, the origin of which is not well understood. Such deletions are often flanked by direct nucleotide repeats, but this alone does not fully explain ...

BMC2023

Sensitivity to user mischaracterizations in electric vehicle charging

Nils Gustav Nilsson

In this paper, we consider electric vehicle charging facilities that offer various levels of service, i.e., charging rates, for varying prices such that rational users choose a level of service that minimizes the total cost to themselves including an oppor ...

ELSEVIER2023

Functional data analysis with rough sample paths?

Victor Panaretos, Neda Mohammadi Jouzdani

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

TAYLOR & FRANCIS LTD2024

A NEW PROOF OF THE ERDOS-KAC CENTRAL LIMIT THEOREM

Thomas Mountford

In this paper we use the Riemann zeta distribution to give a new proof of the Erdos-Kac Central Limit Theorem. That is, if zeta(s) = Sigma(n >= 1) (1)(s)(n) , s > 1, then we consider the random variable X-s with P(X-s = n) = (1) (zeta) ( ...

Amer Mathematical Soc2023

Dated: Guidelines for Creating Synthetic Datasets for Engineering Design Applications

Jürg Alexander Schiffmann, Cyril Picard

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Amer Soc Mechanical Engineers2023

Feature distribution learning by passive exposure

David Pascucci, Gizay Ceylan

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects ...

2022

From Trees to Barcodes and Back Again: A Combinatorial, Probabilistic and Geometric Study of a Topological Inverse Problem

Adélie Eliane Garin

In this thesis, we investigate the inverse problem of trees and barcodes from a combinatorial, geometric, probabilistic and statistical point of view. Computing the persistent homology of a merge tree yields a barcode B. Reconstructing a tree from B invol ...

EPFL2022

Asynchrony Increases Efficiency: Time Encoding of Videos and Low-Rank Signals

Martin Vetterli, Adam James Scholefield, Karen Adam

In event-based sensing, many sensors independently and asynchronously emit events when there is a change in their input. Event-based sensing can present significant improvements in power efficiency when compared to traditional sampling, because (1) the out ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Andrea Zanoni

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Angelos Katharopoulos

Deep neural networks have completely revolutionized the field of machine learning by achieving state-of-the-art results on various tasks ranging from computer vision to protein folding. However, their application is hindered by their large computational an ...

EPFL2022

Localization errors of the stochastic heat equation

David Jean-Michel Candil

In this thesis, we study the stochastic heat equation (SHE) on bounded domains and on the whole Euclidean space

\R^d.

We confirm the intuition that as the bounded domain increases to the whole space, both solutions become arbitrarily close to one another ...

EPFL2022

Iron incorporation in synthetic precipitated calcium silicate hydrates

David Lyndon Emsley, Paul Bowen, Brennan James Walder, Jirawan Siramanont

Effect of Fe3+ on the precipitation of synthetic calcium silicate hydrates (C-S-H) under controlled conditions has been evaluated. Using extremely basic either initial calcium nitrate or sodium silicate solutions (pH > 11), incipient formation of ferrihydr ...

Pergamon-Elsevier Science Ltd2021

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Michael Christoph Gastpar, Adriano Pastore

We consider a setup in which confidential i.i.d. samples X1, . . . , Xn from an unknown finite-support distribution p are passed through n copies of a discrete privatization chan- nel (a.k.a. mechanism) producing outputs Y1, . . . , Yn. The channel law gua ...

2021

Adaptive Approximation of Shapes

Annalisa Buffa

We consider scalar-valued shape functionals on sets of shapes which are small perturbations of a reference shape. The shapes are described by parameterizations and their closeness is induced by a Hilbert space structure on the parameter domain. We justify ...

TAYLOR & FRANCIS INC2021

Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models

Fernando Porté Agel, Tristan Revaz

Large-eddy simulation (LES) with actuator models has become the state-of-the-art numerical tool to study the complex interaction between the atmospheric boundary layer (ABL) and wind turbines. In this paper, a new evaluation of actuator disk models (ADMs) ...

2021

Uncertain Sampling with Certain Priors

Golnooshsadat Elhami

Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...

EPFL2021

Structured light generation by refractive-diffractive micro optical array illuminators

Maryam Yousefi

Structured light generation having broad applications in different optical fields, is the topic of this thesis. Our structured light generation strategy is based on applying periodic microoptical elements at the refraction-diffraction limit, under a focuse ...

EPFL2021

Testing two competing hypotheses for Eurasian jays' caching for the future

Johanni Michael Brea

Previous research reported that corvids preferentially cache food in a location where no food will be available or cache more of a specific food in a location where this food will not be available. Here, we consider possible explanations for these prospect ...

NATURE RESEARCH2021