Modeling speech imitation and ecological learning of auditory-motor maps.

Classical models of speech consider an antero-posterior distinction between perceptive and productive functions. However, the selective alteration of neural activity in speech motor centers, via transcranial magnetic stimulation, was shown to affect speech discrimination. On the automatic speech recognition (ASR) side, the recognition systems have classically relied solely on acoustic data, achieving rather good performance in optimal listening conditions. The main limitations of current ASR are mainly evident in the realistic use of such systems. These limitations can be partly reduced by using normalization strategies that minimize inter-speaker variability by either explicitly removing speakers' peculiarities or adapting different speakers to a reference model. In this paper we aim at modeling a motor-based imitation learning mechanism in ASR. We tested the utility of a speaker normalization strategy that uses motor representations of speech and compare it with strategies that ignore the motor domain. Specifically, we first trained a regressor through state-of-the-art machine learning techniques to build an auditory-motor mapping, in a sense mimicking a human learner that tries to reproduce utterances produced by other speakers. This auditory-motor mapping maps the speech acoustics of a speaker into the motor plans of a reference speaker. Since, during recognition, only speech acoustics are available, the mapping is necessary to "recover" motor information. Subsequently, in a phone classification task, we tested the system on either one of the speakers that was used during training or a new one. Results show that in both cases the motor-based speaker normalization strategy slightly but significantly outperforms all other strategies where only acoustics is taken into account

A speaker adaptive DNN training approach for speaker-independent acoustic inversion

We address the speaker-independent acoustic inversion (AI) problem, also referred to as acoustic-to-articulatory mapping. The scarce availability of multi-speaker articulatory data makes it difficult to learn a mapping which generalizes from a limited number of training speakers and reliably reconstructs the articulatory movements of unseen speakers. In this paper, we propose a Multi-task Learning (MTL)-based approach that explicitly separates the modeling of each training speaker AI peculiarities from the modeling of AI characteristics that are shared by all speakers. Our approach stems from the well known Regularized MTL approach and extends it to feed-forward deep neural networks (DNNs). Given multiple training speakers, we learn for each an acoustic-to-articulatory mapping represented by a DNN. Then, through an iterative procedure, we search for a canonical speaker-independent DNN that is "similar" to all speaker-dependent DNNs. The degree of similarity is controlled by a regularization parameter. We report experiments on the University of Wisconsin X-ray Microbeam Database under different training/testing experimental settings. The results obtained indicate that our MTL-trained canonical DNN largely outperforms a standardly trained (i.e., single task learning-based) speaker independent DNN

xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera

We present a new solution to egocentric 3D body pose estimation from monocular images captured from a downward looking fish-eye camera installed on the rim of a head mounted virtual reality device. This unusual viewpoint, just 2 cm away from the user's face, leads to images with unique visual appearance, characterized by severe self-occlusions and strong perspective distortions that result in a drastic difference in resolution between lower and upper body. Our contribution is two-fold. Firstly, we propose a new encoder-decoder architecture with a novel dual branch decoder designed specifically to account for the varying uncertainty in the 2D joint locations. Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches. Our second contribution is a new large-scale photorealistic synthetic dataset - xR-EgoPose - offering 383K frames of high quality renderings ofpeople with a diversity of skin tones, body shapes, clothing, in a variety of backgrounds and lighting conditions, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of the art results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint

Modelling mean radiant temperature in outdoor environments: Contrasting the approaches of different simulation tools

Global warming and increasing urbanization are expected to threaten public health in cities, by increasing the heat stress perceived by the inhabitants. Outdoor thermal comfort conditions are influenced by the material and the geometric features of the surrounding urban fabric at both the urban and building scales. In built environments, performance-aware design choices related to street paving or building façade can enhance outdoor thermal comfort in their surroundings. Reliable estimations of outdoor thermal comfort conditions are required to evaluate and control the micro-bioclimatic influences of different design choices. The mean radiant temperature is the physical variable that has the greatest influence on outdoor thermal comfort conditions during summertime. Since its calculation is complex, the available simulation tools employ different approaches and assumptions to estimate it, and potential users need to be aware of their capabilities and simplifications. This research compares the calculation procedures and assumptions of different performance simulation tools (i.e. ENVI-met, TRNSYS, Ladybug/Honeybee, CitySim, and SOLENE-microclimat) to predict the mean radiant temperature in outdoor spaces, based on the available information in the scientific literature. Their ability to account for different radiative components in both the longwave and shortwave spectra is summarized, and practical information regarding the degree of interoperability with the modelling environments and the level of geometrical detail of the virtual model supported by the tools is provided. This work aims to help potential users in the selection of the most appropriate performance tool, based on the requirement of their projects

Results of low energy background measurements with the Liquid Scintillation Detector (LSD) of the Mont Blanc Laboratory

The 90 tons liquid scintillation detector (LSD) is fully running since October 1984, at a depth of 5,200 hg/sq cm of standard rock underground. The main goal is to search for neutrino bursts from collapsing stars. The experiment is very sensitive to detect low energy particles and has a very good signature to gamma-rays from (n,p) reaction which follows the upsilon e + p yields n + e sup + neutrino capture. The analysis of data is presented and the preliminary results on low energy measurements are discussed

Modulation of aflatoxin B1 cytotoxicity and aflatoxin M1 synthesis by natural antioxidants in a bovine mammary epithelial cell line

Aflatoxin (AF) B1, a widespread food and feed contaminant, is bioactivated by drug metabolizing enzymes (DME) to cytotoxic and carcinogenic metabolites like AFB1-epoxide and AFM1, a dairy milk contaminant. A number of natural antioxidants have been reported to afford a certain degree of protection against AFB1 (cyto)toxicity. As the mammary gland potentially participates in the generation of AFB1 metabolites, we evaluated the role of selected natural antioxidants (i.e. curcumin, quercetin and resveratrol) in the modulation of AFB1 toxicity and metabolism using a bovine mammary epithelial cell line (BME-UV1). Quercetin and, to a lesser extent, resveratrol and curcumin from Curcuma longa (all at 5 \u3bcM) significantly counteracted the AFB1-mediated impairment of cell viability (concentration range: 96\u2013750 nM). Moreover, quercetin was able to significantly reduce the synthesis of AFM1. The quantitative PCR analysis on genes encoding for DME (phase I and II) and antioxidant enzymes showed that AFB1 caused an overall downregulation of the detoxifying systems, and mainly of GSTA1, which mediates the GSH conjugation of the AFB1-epoxide. The negative modulation of GSTA1 was efficiently reversed in the presence of quercetin, which significantly increased GSH levels as well. It is suggested that quercetin exerts its beneficial effects by depressing the bio-transformation of AFB1 and counterbalancing its pro-oxidant effects

{SelfPose}: {3D} Egocentric Pose Estimation from a Headset Mounted Camera

We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches. To tackle the lack of labelled data we also introduced a large photo-realistic synthetic dataset. xR-EgoPose offers high quality renderings of people with diverse skintones, body shapes and clothing, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of theart results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.Comment: 14 pages. arXiv admin note: substantial text overlap with arXiv:1907.1004

The research program of the Liquid Scintillation Detector (LSD) in the Mont Blanc Laboratory

A massive (90 tons) liquid scintillation detector (LSD) has been running since October 1984 in the Mont Blanc Laboratory at a depth of 5,200 hg/sq cm of standard rock. The research program of the experiment covers a variety of topics in particle physics and astrophysics. The performance of the detector, the main fields of research are presented and the preliminary results are discussed