Publications associées à Loi Gamma

Dynamics of cavitation bubbles near granular boundaries

Mohamed Farhat, Davide Bernardo Preso, Armand Baptiste Sieber

We study the dynamics of a cavitation bubble near beds of sand of different grain sizes. We use high-speed imaging to observe the motion of the bubble and the sand for different values of the stand-off parameter gamma (dimensionless bubble-boundary distanc ...

CAMBRIDGE UNIV PRESS2022

Dalton: Learned Partitioning for Distributed Data Streams

Anastasia Ailamaki, Eleni Zapridou, Ioannis Mytilinis

To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...

2022

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Sabine Süsstrunk, Majed El Helou, Kaan Okumus

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

2022

A Revisitation of Low-Rate Bounds on the Reliability Function of Discrete Memoryless Channels for List Decoding

Marco Bondaschi

We revise the proof of low-rate upper bounds on the reliability function of discrete memoryless channels for ordinary and list-decoding schemes, in particular Berlekamp and Blinovsky's zero-rate bound, as well as Blahut's bound for low rates. The available ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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 ...

ELSEVIER2022

Capturing trade-offs between daily scheduling choices

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...

ELSEVIER SCI LTD2022

Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France

Camille Roland Marie Minaudo

Elevated nitrate from human activity causes ecosystem and economic harm globally. The factors that control the spatiotemporal dynamics of riverine nitrate concentration remain difficult to describe and predict. We analyzed nitrate concentration from 4450 s ...

SPRINGER2022

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Florent Gérard Krzakala, Alia Abbara

There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we g ...

2022

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Loi Gamma

Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Dynamics of cavitation bubbles near granular boundaries

Dalton: Learned Partitioning for Distributed Data Streams

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

A Revisitation of Low-Rate Bounds on the Reliability Function of Discrete Memoryless Channels for List Decoding

Feature distribution learning by passive exposure

Capturing trade-offs between daily scheduling choices

Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Graph Chatbot

Chattez avec Graph Search

Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Dalton: Learned Partitioning for Distributed Data Streams

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Feature distribution learning by passive exposure

Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France

Capturing trade-offs between daily scheduling choices

Dynamics of cavitation bubbles near granular boundaries

A Revisitation of Low-Rate Bounds on the Reliability Function of Discrete Memoryless Channels for List Decoding

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)