Publications associées à Collision frontale

Measurements of azimuthal anisotropy of nonprompt D0 mesons in PbPb collisions at √sNN=5.02 TeV

Matthias Finger, Konstantin Androsov, Qian Wang, Jan Steggemann, Yiming Li, Anna Mascellani, Varun Sharma, Xin Chen, Rakesh Chawla, Matteo Galli

Measurements of the elliptic (v(2)) and triangular (v(3)) azimuthal anisotropy coefficients are presented for D-0 mesons produced in b hadron decays (nonprompt D-0 mesons) in lead-lead collisions at root s(NN) = 5.02 TeV. The results are compared with prev ...

Elsevier2024

Higher-order moments of the elliptic flow distribution in PbPb collisions at √sNN=5.02 TeV

Matthias Finger, Konstantin Androsov, Qian Wang, Jan Steggemann, Yiming Li, Anna Mascellani, Varun Sharma, Xin Chen, Rakesh Chawla, Matteo Galli

The hydrodynamic flow-like behavior of charged hadrons in high-energy lead-lead collisions is studied through multiparticle correlations. The elliptic anisotropy values based on different orders of multiparticle cumulants, v(2){2k}, are measured up to the ...

Springer Nature2024

Two-particle Bose-Einstein correlations and their Lévy parameters in PbPb collisions at √sNN=5.02 TeV

Matthias Finger, Konstantin Androsov, Qian Wang, Jan Steggemann, Yiming Li, Anna Mascellani, Varun Sharma, Xin Chen, Rakesh Chawla, Matteo Galli

Two -particle Bose-Einstein momentum correlation functions are studied for charged-hadron pairs in lead -lead collisions at a center -of -mass energy per nucleon pair of .'/sNN = 5.02 TeV. The data sample, containing 4.27 x 109 minimum bias events correspo ...

Amer Physical Soc2024

A Gyrokinetic Moment Model of the Plasma Boundary in Fusion Devices

Baptiste Jimmy Frei

The overall performance of fusion devices, such as tokamaks, is strongly correlated to the phenomena that occur in the boundary region, the outer plasma region that faces the wall of the device. The boundary plays a crucial role in regulating the heat and ...

EPFL2023

Implicit Distance Functions: Learning and Applications in Robotics

Mikhail Koptev

In this thesis, we address the complex issue of collision avoidance in the joint space of robots. Avoiding collisions with both the robot's own body parts and obstacles in the environment is a critical constraint in motion planning and is crucial for ensur ...

EPFL2023

Geometric and Learning Methods for Robots to Navigate in Human Crowds with Application to Smart Mobility Devices

David Julian Gonon

The thesis at hand is concerned with robots' navigation in human crowds. Specifically, methods are developed for planning a mobile robot's local motion between pedestrians, and they are evaluated in experiments where a robot interacts with real pedestrians ...

EPFL2023

Near Collision Attack Against Grain V1

Subhadeep Banik, Willi Meier, Daniel Patrick Collins

A near collision attack against the Grain v1 stream cipher was proposed by Zhang et al. in Eurocrypt 18. The attack uses the fact that two internal states of the stream cipher with very low hamming distance between them, produce similar keystream sequences ...

2023

Are socially-aware trajectory prediction models really socially-aware?

Alexandre Massoud Alahi, Seyed Mohsen Moosavi Dezfooli, Saeed Saadatnejad, Mohammadhossein Bahari, Pedram Khorsandi

Our transportation field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigation or pedestrian flow simulations, their adversarial robustne ...

2022

Turbulent transport regimes in the tokamak boundary and operational limits

Paolo Ricci, Maurizio Giacomin

Two-fluid, three-dimensional, flux-driven, global, electromagnetic turbulence simulations carried out by using the GBS (Global Braginskii Solver) code are used to identify the main parameters controlling turbulent transport in the tokamak boundary and to d ...

2022

Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control

Aude Billard, Nadia Barbara Figueroa Fernandez, Mikhail Koptev

In this paper, we present an approach for learning a neural implicit signed distance function expressed in joint space coordinates, that efficiently computes distance-to-collisions for arbitrary robotic manipulator configurations. Computing such distances ...

2022