University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Context (language use). The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

Search for new physics in the multijet and missing transverse momentum final state in proton-proton collisions at √s=8 Tev

Abstract: A search for new physics is performed in multijet events with large missing transverse momentum produced in proton-proton collisions at sqrt(s)=8 TeV using a data sample corresponding to an integrated luminosity of 19.5 inverse femtobarns collected with the CMS detector at the LHC. The data sample is divided into three jet multiplicity categories (3-5, 6-7, and 8 or more jets), and studied further in bins of two variables: the scalar sum of jet transverse momenta and the missing transverse momentum. The observed numbers of events in various categories are consistent with backgrounds expected from standard model processes. Exclusion limits are presented for several simplified supersymmetric models of squark or gluino pair production.

Compressive Sensing in Electromagnetics - A Review

Abstract: Several problems arising in electromagnetics can be directly formulated or suitably recast for an effective solution within the compressive sensing (CS) framework. This has motivated a great interest in developing and applying CS methodologies to several conventional and innovative electromagnetic scenarios. This work is aimed at presenting, to the best of the authors’ knowledge, a review of the state-of-the-art and most recent advances of CS formulations and related methods as applied to electromagnetics. Toward this end, a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed. Current challenges and trends in the application of CS to the solution of traditional and new electromagnetic problems are also discussed.

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Abstract: There has been a resurgence of interest in multiagent reinforcement learning (MARL), due partly to the recent success of deep neural networks. The simplest form of MARL is independent reinforcement learning (InRL), where each agent treats all of its experience as part of its (non stationary) environment. In this paper, we first observe that policies learned using InRL can overfit to the other agents' policies during training, failing to sufficiently generalize during execution. We introduce a new metric, joint-policy correlation, to quantify this effect. We describe a meta-algorithm for general MARL, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game theoretic analysis to compute meta-strategies for policy selection. The meta-algorithm generalizes previous algorithms such as InRL, iterated best response, double oracle, and fictitious play. Then, we propose a scalable implementation which reduces the memory requirement using decoupled meta-solvers. Finally, we demonstrate the generality of the resulting policies in three partially observable settings: gridworld coordination problems, emergent language games, and poker.