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Concept

Hasard

Publications associées (29)

Social Opinion Formation and Decision Making Under Communication Trends

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Ieee-Inst Electrical Electronics Engineers Inc2024

A First-in-Human Randomized Study to Assess the Safety, Tolerability, Pharmacokinetics, and Neutralization Profile of Two Investigational Long-Acting Anti-SARS-CoV-2 Monoclonal Antibodies

Didier Trono, Norman Lionel Moullan

Introduction: COVID-19 remains a significant risk for the immunocompromised given their lower responsiveness to vaccination or infection. Therefore, passive immunity through long-acting monoclonal antibodies (mAbs) offers a needed approach for pre-exposure ...

2024

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Andreas Fuster

We conduct two survey experiments to study which information people choose to consume and how it affects their beliefs. In the first experiment, respondents choose between optimistic and pessimistic article headlines related to the COVID-19 pandemic and ar ...

2024

Euclid: Improving the efficiency of weak lensing shear bias calibration

Frédéric Courbin, Georges Meylan, Malte Tewes

To obtain an accurate cosmological inference from upcoming weak lensing surveys such as the one conducted by Euclid, the shear measurement requires calibration using galaxy image simulations. As it typically requires millions of simulated galaxy images and ...

Edp Sciences S A2024

Modelling and design of CMOS SPAD sensors for quantum random number generation

Pouyan Keshavarzian

Modern digital connectivity has necessitated the creation of robust methods for securely storing and transferring data. At the heart of all security infrastructure is the random number generator (RNG). While random numbers find use in a variety of applicat ...

EPFL2023

A 3.3-Gb/s SPAD-Based Quantum Random Number Generator

Edoardo Charbon, Francesco Gramuglia, Pouyan Keshavarzian

Quantum random number generators (QRNGs) are a burgeoning technology used for a variety of applications, including modern security and encryption systems. Typical methods exploit an entropy source combined with an extraction or bit generation circuit in or ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Near-Optimal Communication Lower Bounds for Approximate Nash Equilibria

Mika Tapani Göös

We prove an N2-o(1) lower bound on the randomized communication complexity of finding an epsilon-approximate Nash equilibrium (for constant epsilon > 0) in a two-player N x N game. ...

Siam Publications2023

Revisiting the Indifferentiability of the Sum of Permutations

Ritam Bhaumik

The sum of two n-bit pseudorandom permutations is known to behave like a pseudorandom function with n bits of security. A recent line of research has investigated the security of two public n-bit permutations and its degree of indifferentiability. Mandal e ...

Springer2023

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Rachid Guerraoui, Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos, Sadegh Farhadkhani

We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

2023

Exploration-based model learning with self-attention for risk-sensitive robot control

Sudong Lee

Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

2023

STREAMING TENSOR TRAIN APPROXIMATION

Daniel Kressner

Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the streaming tensor train approximation (STTA), a new class of algorithms for approximating a given tensor ' in the tensor train fo ...

Siam Publications2023

Resource aware pricing for electric vehicle charging

Nils Gustav Nilsson

Electric vehicle charging facilities offer their capacity constrained electric charge and parking to users for a fee. As electric vehicle adoption grows, so too does the potential for excessive resource utilization. In this paper, we study how prices set b ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Emergence of Classical Magnetic Order from Anderson Towers: Quantum Darwinism in Action

Frédéric Mila

Environment is assumed to play a negative role in quantum mechanics, destroying the coherence in a quantum system and, thus, randomly changing its state. However, for a quantum system that is initially in a degenerate ground state, the situation could be d ...

American Physical Society2023

The DESI One-Percent Survey: Constructing Galaxy-Halo Connections for ELGs and LRGs Using Auto and Cross Correlations

Jiaxi Yu

In the current Dark Energy Spectroscopic Instrument (DESI) survey, emission line galaxies (ELGs) and luminous red galaxies (LRGs) are essential for mapping the dark matter distribution at < N(M)>. We measure the auto and cross correlation functions of ELGs ...

IOP Publishing Ltd2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

A full characterization of invariant embeddability of unimodular planar graphs

Laszlo Marton Toth

When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...

WILEY2023

The effects of visual distractors on serial dependence

David Pascucci

Attractive serial dependence occurs when perceptual decisions are attracted toward previous stimuli. This effect is mediated by spatial attention and is most likely to occur when similar stimuli are attended at nearby locations. Attention, however, also in ...

Association for Research in Vision and Ophthalmology (ARVO)2023

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Fabio Nobile, Sundar Subramaniam Ganesh

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method ...

2023

Optimal Matching of Random Parts

Thomas Alois Weber

This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...

2022