Vertex Displacements for Acausal Particles: Testing the Lee-Wick Standard Model at the LHC

We propose to search for wrong displaced vertices, where decay products of the secondary vertex move towards the primary vertex instead of away from it, as a signature for microscopic violation of causality. We analyze in detail the leptonic sector of the recently proposed Lee-Wick Standard Model, which provides a well motivated framework to study acausal effects. We find that, assuming Minimal Flavor Violation, the Lee-Wick partners of the electron, {\tilde l}^e and \tilde e, can produce measurable wrong vertices at the LHC, the most promising channel being q \bar{q} --> \bar{\tilde l}^e {\tilde l}^e --> e^+ e^- jjjj. A Monte-Carlo simulation using MadGraph/MadEvent suggests that for M_l < 450 GeV the measurement of these acausal vertex displacements should be accessible in the LHC era.Comment: 29 pages, 7 figures, minor changes, published versio

Social relation recognition in egocentric photostreams

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental’s social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.Peer ReviewedPostprint (author's final draft

Social Relation Recognition in Egocentric Photostreams

This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera 2fpm, by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.Comment: Accepted at ICIP 201

Collisional and molecular spectroscopy in an ultracold Bose-Bose mixture

The route toward a Bose-Einstein condensate of dipolar molecules requires the ability to efficiently associate dimers of different chemical species and transfer them to the stable rovibrational ground state. Here, we report on recent spectroscopic measurements of two weakly bound molecular levels and newly observed narrow d-wave Feshbach resonances. The data are used to improve the collisional model for the Bose-Bose mixture 41K87Rb, among the most promising candidates to create a molecular dipolar BEC.Comment: 13 pages, 3 figure