Multiple path prediction for traffic scenes using LSTMs and mixture density models

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye vie

Unitarity of the Leptonic Mixing Matrix

We determine the elements of the leptonic mixing matrix, without assuming unitarity, combining data from neutrino oscillation experiments and weak decays. To that end, we first develop a formalism for studying neutrino oscillations in vacuum and matter when the leptonic mixing matrix is not unitary. To be conservative, only three light neutrino species are considered, whose propagation is generically affected by non-unitary effects. Precision improvements within future facilities are discussed as well.Comment: Standard Model radiative corrections to the invisible Z width included. Some numerical results modified at the percent level. Updated with latest bounds on the rare tau decay. Physical conculsions unchange

Impact of the Intensive Program of Emotional Intelligence (IPEI) on middle man-agers’ emotional intelligence

Background: This study aimed to evaluate the effect of the Intensive Program of Emo-tional Intelligence (IPEI; Fernández, 2015) on middle managers’ emotional intelligence, as this variable may have a significant impact on personal satisfaction, task performance, and work environment. Method: The intervention was applied on work team supervisors from a big call center, as it is an overlooked sector in this topic. Two-hundred and eighty-two supervisors from a Spanish multinational Madrid-based company (51.4% males and 48.6% females) participated in this study. Participants were assigned to the experimental group (n = 190) or the control group (n = 92) by availability, according to management decision. All supervisors filled in two questionnaires to evaluate the differ-ent components of intrapersonal emotional intelligence (i.e., attention, clarity, and repair; TMMS-24; Fernández-Berrocal, Extremera, & Ramos, 2004) and cognitive and affec-tive empathy (i.e., perspective taking, emotion understanding, empathic joy, and person-al distress; TECA; López-Pérez, Fernández, & Abad, 2008). Results: The findings showed an increase in the studied variables for the experimental group. Conclusions: The obtained results support middle managers’ training on emotional competences through short, efficient, and economic programs, and it was discussed potential limita-tions and implications of the obtained results

A test generation framework for quiescent real-time systems

We present an extension of Tretmans theory and algorithm for test generation for input-output transition systems to real-time systems. Our treatment is based on an operational interpretation of the notion of quiescence in the context of real-time behaviour. This gives rise to a family of implementation relations parameterized by observation durations for quiescence. We define a nondeterministic (parameterized) test generation algorithm that generates test cases that are sound with respect to the corresponding implementation relation. Also, the test generation is exhaustive in the sense that for each non-conforming implementation a test case can be generated that detects the non-conformance