Spectrum and genus of commuting graphs of some classes of finite rings

We consider commuting graphs of some classes of finite rings and compute their spectrum and genus. We show that the commuting graph of a finite CC-ring is integral. We also characterize some finite rings whose commuting graphs are planar

Common neighborhood spectrum of commuting graphs of finite groups

The commuting graph of a finite non-abelian group G with center Z(G), denoted by Гc(G), is a simple undirected graph whose vertex set is G\ Z(G), and two distinct vertices x and y are adjacent if and only if xy = yx. In this paper, we compute the common neighborhood spectrum of commuting graphs of several classes of finite non-abelian groups and conclude that these graphs are CN-integral

Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs

As more deep learning algorithms enter safety-critical application domains, the importance of analyzing their resilience against hardware faults cannot be overstated. Most existing works focus on bit-flips in memory, fewer focus on compute errors, and almost none study the effect of hardware faults on adversarially trained convolutional neural networks (CNNs). In this work, we show that adversarially trained CNNs are more susceptible to failure due to hardware errors when compared to vanilla-trained models. We identify large differences in the quantization scaling factors of the CNNs which are resilient to hardware faults and those which are not. As adversarially trained CNNs learn robustness against input attack perturbations, their internal weight and activation distributions open a backdoor for injecting large magnitude hardware faults. We propose a simple weight decay remedy for adversarially trained models to maintain adversarial robustness and hardware resilience in the same CNN. We improve the fault resilience of an adversarially trained ResNet56 by 25% for large-scale bit-flip benchmarks on activation data while gaining slightly improved accuracy and adversarial robustness

Influence of Socio-Economic Status on Psychopathology in Ecuadorian Children

The socioeconomic status (SES) of parents has been reported to have a crucial impact on emotional competence in childhood. However, studies have largely been carried out in developed countries and in children in a specific age range, and it is not clear whether the effect of the SES of parents varies by age. The objective of this study was to investigate the psychopathological profile (including externalizing and internalizing problems) of children aged 7, 9, and 11 years old with low SES in a developing country (Ecuador). The study included 274 children (139 boys and 135 girls), who were divided between medium-SES (n = 133) and low-SES (n = 141) groups. Data were gathered on socioeconomic and anthropometric variables of the children, and the parents completed the Child Behavior Check-List (CBCL). In comparison to the medium-SES group, children in the low-SES group obtained higher scores for internalizing and externalizing symptoms and for total problems, and they obtained lower scores for social competence skills. The housing risk index and school competence were the two main predictors of internalizing and externalizing problems in this population.This study was supported by the Spanish Agency for International Development Cooperation (AECID) [A3/042954/11] (PI: FC-Q) and Conselleria d'Educació, Investigació, Cultura I Esport de la Generalitat Valenciana (R+D+i projects developed by emerging research groups) [GV/2017/166] (PI: MF-A)

Differences in Neuropsychological Performance between Refugee and Non-Refugee Children in Palestine

Neuropsychological studies on refugee children are scarce, but there are even less in the case of Palestinian children. This work aims to study the neuropsychological performance of Palestinian refugee children in Palestine compared to other Palestinian children living outside refugee camps. A comprehensive Neuropsychological battery was administrated to 584 Palestinian school children (464 refugees and 120 non-refugees) aged 6, 7, and 8 years old. Results showed that non-refugee children outperformed refugee children in sustained attention, verbal comprehension, verbal memory, and visual memory. This study is the first to have performed a comprehensive neuropsychological assessment, based on a standardized and validated battery with the Palestinian refugee children. It supports professionals in their evaluation of neurodevelopment and neuropsychological alterations in refugee and non-refugee children in Palestine.Center for Development Cooperation Initiatives (Centro de Iniciativas de Cooperación al Desarrollo—CICODE), Granada University, Spain (Reference No. C14P11_9359

HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology

Convolutional neural networks (CNNs) have produced unprecedented accuracy for many computer vision problems in the recent past. In power and compute-constrained embedded platforms, deploying modern CNNs can present many challenges. Most CNN architectures do not run in real-time due to the high number of computational operations involved during the inference phase. This emphasizes the role of CNN optimization techniques in early design space exploration. To estimate their efficacy in satisfying the target constraints, existing techniques are either hardware (HW) agnostic, pseudo-HW-aware by considering parameter and operation counts, or HW-aware through inflexible hardware-in-the-loop (HIL) setups. In this work, we introduce HW-Flow, a framework for optimizing and exploring CNN models based on three levels of hardware abstraction: Coarse, Mid and Fine. Through these levels, CNN design and optimization can be iteratively refined towards efficient execution on the target hardware platform. We present HW-Flow in the context of CNN pruning by augmenting a reinforcement learning agent with key metrics to understand the influence of its pruning actions on the inference hardware. With 2× reduction in energy and latency, we prune ResNet56, ResNet50, and DeepLabv3 with minimal accuracy degradation on the CIFAR-10, ImageNet, and CityScapes datasets, respectively