Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference

Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8×8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy)

Ultra-compact binary neural networks for human activity recognition on RISC-V processors

Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and Random Forests (RFs), whereas deep learning is less common due to its high computational complexity. In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set. BNNs yield very small memory footprints and low inference complexity, thanks to the replacement of arithmetic operations with bit-wise ones. However, existing BNN implementations on general purpose processors impose constraints tailored to complex computer vision tasks, which result in over-parametrized models for simpler problems like HAR. Therefore, we also introduce a new BNN inference library, which targets ultra-compact models explicitly. With experiments on a single-core RISC-V processor, we show that BNNs trained on two HAR datasets obtain higher classification accuracy compared to a state-of-the-art baseline based on RFs. Furthermore, our BNN reaches the same accuracy of a RF with either less memory (up to 91%) or more energy-efficiency (up to 70%), depending on the complexity of the features extracted by the RF

A new Manifestation of Atomic Parity Violation in Cesium: a Chiral Optical Gain induced by linearly polarized 6S-7S Excitation

We have detected, by using stimulated emission, an Atomic Parity Violation (APV) in the form of a chiral optical gain of a cesium vapor on the 7S - 6P$_{3/2}$ transition,consecutive to linearly polarized 6S-7S excitation. We demonstrate the validity of this detection method of APV, by presenting a 9% accurate measurement of expected sign and magnitude. We underline several advantages of this entirely new approach in which the cylindrical symmetry of the set-up can be fully exploited. Future measurements at the percent level will provide an important cross-check of an existing more precise result obtained by a different method.Comment: 4 pages, 2 figure

Asynchronous Testing of Synchronous Components in GALS Systems

International audienceGALS (Globally Asynchronous Locally Synchronous) systems, such as the Internet of Things or autonomous cars, integrate reactive synchronous components that interact asynchronously. The complexity induced by combining synchronous and asynchronous aspects makes GALS systems difficult to develop and debug. Ensuring their functional correctness and reliability requires rigorous design methodologies, based on formal methods and assisted by validation tools. In this paper we propose a testing methodology for GALS systems integrating: (1) synchronous and asynchronous concurrent models; (2) functional unit testing and behavioral conformance testing; and (3) various formal methods and their tool equipments. We leverage the conformance test generation for asynchronous systems to automatically derive realistic scenarios (input constraints and oracle), which are necessary ingredients for the unit testing of individual synchronous components, and are difficult and error-prone to design manually. We illustrate our approach on a simple, but relevant example inspired by autonomous cars

Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on clinical, anatomical, and procedural features to predict all-cause mortality following contemporary bifurcation PCI. Multiple ML models to predict all-cause mortality were tested on a cohort of 2393 patients (training, n = 1795; internal validation, n = 598) undergoing bifurcation PCI with contemporary stents from the real-world RAIN registry. Twenty-five commonly available patient-/lesion-related features were selected to train ML models. The best model was validated in an external cohort of 1701 patients undergoing bifurcation PCI from the DUTCH PEERS and BIO-RESORT trial cohorts. At ROC curves, the AUC for the prediction of 2-year mortality was 0.79 (0.74–0.83) in the overall population, 0.74 (0.62–0.85) at internal validation and 0.71 (0.62–0.79) at external validation. Performance at risk ranking analysis, k-center cross-validation, and continual learning confirmed the generalizability of the models, also available as an online interface. The RAIN-ML prediction model represents the first tool combining clinical, anatomical, and procedural features to predict all-cause mortality among patients undergoing contemporary bifurcation PCI with reliable performance

Enantiopure 24-armed dendritic polyoxometalates: Synthesis and evaluation as recoverable catalysts for asymmetric sulfide oxidation

Enantiopure 24-armed dendritic polyoxometalate (DENDRI-POM) hybrids were prepared by ionic coupling of enantiopure 8-armed n-propyl dendritic ammoniums with a catalytically active peroxophosphotungstate trianion {PO4[WO(O-2)(2)](4)}(3). The catalytic properties of these DENDRI-POM hybrids were evaluated in the oxidation of thioanisole as a model reaction and compared to those of the 12-armed n-propyl analogous previously reported in our group. Up to 10% enantiomeric excess (ee) was obtained, indicating a negative effect on the reaction rate and the enantioselectivity, whereas the selectivity to the chiral sulfoxide versus sulfone was improved. This study aids understanding of how the structure and size of the dendritic wedge around the POM can influence its catalytic properties, especially regarding enantioselectivity. Four catalytic cycles were performed without any obvious change in the activity, selectivity and enantioselectivity. (C) 2016 Elsevier B.V. All rights reserved

Inheritance of resistance of wheat to eyespot at the adult stage

International audienc