MOOC
Path Integral Methods in Atomistic Modelling
Related publications (182)
Flipped classroom in Linear Algebra
Simone Deparis, Roland John Tormey, Cécile Hardebolle
As Saitta et al. report in [1], the flipped classroom model builds on several well-known teaching approaches. In particular, using in-class time for more active learning activities as been constantly shown by research to improve learning [2]. In this prese ...
2019Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
Negar Kiyavash, Saber Salehkaleybar, Kun Zhang
A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equi ...
AAAI Press2019i-PI 2.0: A universal force engine for advanced molecular simulations
Michele Ceriotti, David Mark Wilkins, Bingqing Cheng, Venkat Kapil, Riccardo Petraglia, Gareth Aneurin Tribello, Mariana Rossi Carvalho, Benjamin Aaron Helfrecht, Robert Horst Meissner, Alice Cuzzocrea, Sébastien Pierre Bienvenue, Przemyslaw Juda, Wei Fang
Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explici ...
ELSEVIER SCIENCE BV2019Beyond R&D: the role of embodied technological change in affecting employment
In this work, we test the employment impact of distinct types of innovative investments using a representative sample of Spanish manufacturing firms over the period 2002-2013. Our GMM-SYS estimates generate various results, which are partially in contrast ...
SPRINGER2019Topology classification with deep learning to improve real-time event selection at the LHC
We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real-time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class ...
2019Random sampling of bandlimited signals on graphs
Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay
We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...
Elsevier2018Mirrored Langevin Dynamics
Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018Mirrored Langevin Dynamics
Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...
2018Analysis of the sensitivity of the hydromorphological index of diversity, HMID
Anton Schleiss, Walter Gostner, Mário Jorge Rodrigues Pereira Da Franca, Severin Stähly, Christopher Robinson
Hydromorphological conditions are a key factor for the habitat diversity in riverine ecosystems. The Hydromorphological Index of Diversity (HMID) is a tool to quantify the habitat diversity in a river reach based on local flow depths and flow velocities. T ...
2018A Novel Method for Sampling Bandlimited Graph Signals
Pascal Frossard, Pinar Akyazi, Dion Eustathios Olivier Tzamarias
In this paper we propose a novel vertex based sampling method for k-bandlimited signals lying on arbitrary graphs, that has a reasonable computational complexity and results in low reconstruction error. Our goal is to find the smallest set of vertices that ...
IEEE COMPUTER SOC2018