Neural Simplex Architecture

We present the Neural Simplex Architecture (NSA), a new approach to runtime assurance that provides safety guarantees for neural controllers (obtained e.g. using reinforcement learning) of autonomous and other complex systems without unduly sacrificing performance. NSA is inspired by the Simplex control architecture of Sha et al., but with some significant differences. In the traditional approach, the advanced controller (AC) is treated as a black box; when the decision module switches control to the baseline controller (BC), the BC remains in control forever. There is relatively little work on switching control back to the AC, and there are no techniques for correcting the AC's behavior after it generates a potentially unsafe control input that causes a failover to the BC. Our NSA addresses both of these limitations. NSA not only provides safety assurances in the presence of a possibly unsafe neural controller, but can also improve the safety of such a controller in an online setting via retraining, without overly degrading its performance. To demonstrate NSA's benefits, we have conducted several significant case studies in the continuous control domain. These include a target-seeking ground rover navigating an obstacle field, and a neural controller for an artificial pancreas system.Comment: 12th NASA Formal Methods Symposium (NFM 2020

Coupled element and structural level optimisation framework for cold-formed steel frames

Optimisation of cold-formed steel (CFS) structures can be challenging due to the complex behaviour of thin-walled CFS sections affected by different buckling modes. In this paper, a coupled framework is presented for element and structural level optimisation of CFS portal frames, under serviceability limit state (SLS) and ultimate limit state (ULS) conditions, using Genetic Algorithm. First, CFS lipped-channel beam sections are optimised with respect to their flexural capacity determined in accordance with the effective width method specified in Eurocode 3 (EC3). The relative dimensions of the cross-section are considered as the main design variables, while the EC3 plate dimensions and slenderness limits and a number of manufacturing and end-use constraints are taken into account in the optimisation process. The results show that the optimum CFS sections exhibit significantly higher (up to 84%) ultimate capacity compared to the standard lipped channel sections with the same plate width and thickness. The structural level optimisation is then carried out to obtain the optimal design solution for a long-span CFS portal frame with knee braces under SLS and ULS conditions. Compared to conventional optimisation using standard cross-sections, it is shown that the proposed coupled framework leads to more cost-effective solutions (up to 20% less structural material) by using the more efficient CFS cross-sectional shapes optimised for generic applications. The results also indicate that optimising the frame geometry and knee brace configuration can noticeably improve the structural performance and reduce the required structural weight, especially when both ULS and SLS conditions are considered

Design and optimization of cold-formed steel sections in bolted moment connections considering bimoment

The load transfer mechanism in cold-formed steel (CFS) bolted moment connections is mainly through the bolt group in the web of beam elements, which may lead to relatively large bimoment and warping deformations. While the bimoment effects can be considered in the Direct Strength Method (DSM), ignoring the fact that the bolt-group length in the conventional design process can lead to nonconservative solutions. This paper presents an alternative analytical design approach using Eurocode 3 (EC3) effective width method to determine the ultimate flexural strength of CFS bolted moment connections by considering bimoment effects. The results compare very well with previously published experimental test data as well as detailed finite-element models developed in this study. It is shown that a short bolt-group length may lead to up to 25% reduction in the flexural strength of the CFS bolted connections. However, a longer bolt-group length generally results in a moment capacity almost equal to the flexural strength of the CFS channel section. Shape optimization is then conducted using a genetic algorithm (GA) to improve the flexural capacity of the connections by taking into account the bimoment effects. The main design variables are considered to be the relative CFS beam cross-sectional dimensions, while the plate slenderness and dimension limits suggested by EC3 as well as a number of manufacturing and practical end-use constraints are incorporated as design constraints. It is found that, compared with standard cross-sectional dimensions, the optimized sections can improve the flexural strength by as much as 36% for a bolt-group length equal to the depth of beam element

An Examination of Chimpanzee Use in Human Cancer Research

Advocates of chimpanzee research claim the genetic similarity of humans and chimpanzees make them an indispensable research tool to combat human diseases. Given that cancer is a leading cause of human death worldwide, one might expect that if chimpanzees were needed for, or were productive in, cancer research, then they would have been widely used. This comprehensive literature analysis reveals that chimpanzees have scarcely been used in any form of cancer research, and that chimpanzee tumours are extremely rare and biologically different from human cancers. Often, chimpanzee citations described peripheral use of chimpanzee cells and genetic material in predominantly human genomic studies. Papers describing potential new cancer therapies noted significant concerns regarding the chimpanzee model. Other studies described interventions that have not been pursued clinically. Finally, available evidence indicates that chimpanzees are not essential in the development of therapeutic monoclonal antibodies. It would therefore be unscientific to claim that chimpanzees are vital to cancer research. On the contrary, it is reasonable to conclude that cancer research would not suffer, if the use of chimpanzees for this purpose were prohibited in the US. Genetic differences between humans and chimpanzees, make them an unsuitable model for cancer, as well as other human diseases

A unified convergence analysis for shuffling-type gradient methods

In this paper, we propose a unified convergence analysis for a class of generic shuffling-type gradient methods for solving finite-sum optimization problems. Our analysis works with any sampling without replacement strategy and covers many known variants such as randomized reshuffling, deterministic or randomized single permutation, and cyclic and incremental gradient schemes. We focus on two different settings: strongly convex and nonconvex problems, but also discuss the non-strongly convex case. Our main contribution consists of new non-asymptotic and asymptotic convergence rates for a wide class of shuffling-type gradient methods in both nonconvex and convex settings. We also study uniformly randomized shuffling variants with different learning rates and model assumptions. While our rate in the nonconvex case is new and significantly improved over existing works under standard assumptions, the rate on the strongly convex one matches the existing best-known rates prior to this paper up to a constant factor without imposing a bounded gradient condition. Finally, we empirically illustrate our theoretical results via two numerical examples: nonconvex logistic regression and neural network training examples. As byproducts, our results suggest some appropriate choices for diminishing learning rates in certain shuffling variants

Faktor Penguat Pada Peningkatan Kinerja Karyawan PT. Gading Murni Surabaya

The purpose of this study is to determine which factors as an amplifier to improve the performance of employees of PT. Gading Murni Surabaya, between the leadership style and compensation received by employees. This study uses a survey approach by collecting data using a questionnaire of 50 respondents then analyzed using quantitative methods. The regession equation is Y = 8.009 + 0,223 X1 + 0,240 X2. The results of this study concluded that the leadership style and compensation variables had a corrected item total correlation value exceeding r table = 0.284 and the reliability test of the leadership style variable, and the alpha cronbach's compensation results exceeded 0.060, which means that the variable was valid and reliable. Leadership and compensation styles also simultaneously have a significant effect on employee performance. And the independent variable that has the largest beta coefficient is the compensation variable (X2) with a beta coefficient of 0.240.   Keywords : Leadership style, Compesation and Employee Performance

Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease.

Alzheimer's Disease (AD) is the leading cause of dementia. It results in cortical thickness changes and is associated with a decline in cognition and behaviour. Such decline affects multiple important day-to-day functions, including memory, language, orientation, judgment and problem-solving. Recent research has made important progress in identifying brain regions associated with single outcomes, such as individual AD status and general cognitive decline. The complex projection from multiple brain areas to multiple AD outcomes, however, remains poorly understood. This makes the assessment and especially the prediction of multiple AD outcomes - each of which may unveil an integral yet different aspect of the disease - challenging, particularly when some are not strongly correlated. Here, uniting residual learning, partial least squares (PLS), and predictive modelling, we develop an explainable, generalisable, and reproducible method called the Residual Partial Least Squares Learning (the re-PLS Learning) to (1) chart the pathways between large-scale multivariate brain cortical thickness data (inputs) and multivariate disease and behaviour data (outcomes); (2) simultaneously predict multiple, non-pairwise-correlated outcomes; (3) control for confounding variables (e.g., age and gender) affecting both inputs and outcomes and the pathways in-between; (4) perform longitudinal AD disease status classification and disease severity prediction. We evaluate the performance of the proposed method against a variety of alternatives on data from AD patients, subjects with mild cognitive impairment (MCI), and cognitively normal individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results unveil pockets of brain areas in the temporal, frontal, sensorimotor, and cingulate areas whose cortical thickness may be respectively associated with declines in different cognitive and behavioural subdomains in AD. Finally, we characterise re-PLS' geometric interpretation and mathematical support for delivering meaningful neurobiological insights and provide an open software package (re-PLS) available at https://github.com/thanhvd18/rePLS

Finite-Sum Smooth Optimization with SARAH

The total complexity (measured as the total number of gradient computations) of a stochastic first-order optimization algorithm that finds a first-order stationary point of a finite-sum smooth nonconvex objective function F(w)=1n∑ni=1fi(w) has been proven to be at least Ω(n−−√/ϵ) for n≤O(ϵ−2) where ϵ denotes the attained accuracy E[∥∇F(w~)∥2]≤ϵ for the outputted approximation w~ (Fang et al., 2018). In this paper, we provide a convergence analysis for a slightly modified version of the SARAH algorithm (Nguyen et al., 2017a;b) and achieve total complexity that matches the lower-bound worst case complexity in (Fang et al., 2018) up to a constant factor when n≤O(ϵ−2) for nonconvex problems. For convex optimization, we propose SARAH++ with sublinear convergence for general convex and linear convergence for strongly convex problems; and we provide a practical version for which numerical experiments on various datasets show an improved performance