Rational unified process

Summary

The rational unified process (RUP) is an iterative software development process framework created by the Rational Software Corporation, a division of IBM since 2003. RUP is not a single concrete prescriptive process, but rather an adaptable process framework, intended to be tailored by the development organizations and software project teams that will select the elements of the process that are appropriate for their needs. RUP is a specific implementation of the Unified Process. History Rational Software originally developed the rational unified process as a software process product. The product includes a hyperlinked knowledge-base with sample artifacts and detailed descriptions for many different types of activities. RUP is included in the IBM Rational Method Composer (RMC) product which allows customization of the process. Philippe Kruchten, an experienced Rational technical representative was tasked with heading up the original RUP team. These initial versions combin

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https://en.wikipedia.org/wiki/Rational_unified_process

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TCB-spline-based isogeometric analysis method with high-quality parameterizations

Juan Cao, Xiaodong Wei

Isogeometric analysis (IGA) was introduced to integrate methods for analysis and computer-aided design (CAD) into a unified process. High-quality parameterization of a physical domain plays a crucial role in IGA. However, obtaining high-quality parameterizations for complex geometries retains a challenge. Triangle configuration based bivariate simplex splines (TCB-splines) and their rational extensions provide an attractive alternative to classical nonuniform rational B-splines (NURBS) in the context of IGA, as they not only share many desired properties with NURBS but also can be defined over general polygonal domains. In this work, using TCB-splines in IGA is suggested to overcome the limits posed by the tensor-product structure of NURBS. We present the methodology for IGA to use rational TCB-splines over general polygonal domains. Then a method to generate the parametric domain according to given physical domain boundaries is proposed. This allows us to obtain a high-quality parameterization easily without resorting to the optimization method. Several numerical examples with complex physical domains demonstrate the flexibility of our TCB-spline-based IGA method, the high quality of the parameterization, and the accuracy of the numerical solutions. (C) 2022 Elsevier B.V. All rights reserved.

ELSEVIER SCIENCE SA2022

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The screening of molecular targets benefits greatly from rational design criteria, which can detect the most promising candidates. We demonstrate that π-depleted polyaromatic molecules present superior π-stacking ability. This counterintuitive realization is quantified using a novel computational criterion, LOLIPOP that identifies π-conjugated frameworks presenting the desired electronic features. The utility of the LOLIPOP criterion is demonstrated by identifing chemosensors presenting enhanced π-stacking ability.

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Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, John Stephan

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence of some misbehaving (a.k.a., Byzantine) workers. Although a myriad of techniques addressing the problem have been proposed, the field arguably rests on fragile foundations. These techniques are hard to prove correct and rely on assumptions that are (a) quite unrealistic, i.e., often violated in practice, and (b) heterogeneous, i.e., making it difficult to compare approaches. We present RESAM (RESilient Averaging of Momentums), a unified framework that makes it simple to establish optimal Byzantine resilience, relying only on standard machine learning assumptions. Our framework is mainly composed of two operators: resilient averaging at the server and distributed momentum at the workers. We prove a general theorem stating the convergence of distributed SGD under RESAM. Interestingly, demonstrating and comparing the convergence of many existing techniques become direct corollaries of our theorem, without resorting to stringent assumptions. We also present an empirical evaluation of the practical relevance of RESAM.

PMLR2022