Adaptation (computer science)

About: Adaptation (computer science) is a research topic. Over the lifetime, 19936 publications have been published within this topic receiving 326624 citations.

Guidelines for the process of cross-cultural adaptation of self-report measures.

Abstract: With the increase in the number of multinational and multicultural research projects, the need to adapt health status measures for use in other than the source language has also grown rapidly. 1,4,27 Most questionnaires were developed in English-speaking countries, 11 but even within these countries, researchers must consider immigrant populations in studies of health, especially when their exclusion could lead to a systematic bias in studies of health care utilization or quality of life. 9,11 The cross-cultural adaptation of a health status selfadministered questionnaire for use in a new country, culture, and/or language necessitates use of a unique method, to reach equivalence between the original source and target versions of the questionnaire. It is now recognized that if measures are to be used across cultures, the items must not only be translated well linguistically, but also must be adapted culturally to maintain the content validity of the instrument at a conceptual level across different cultures. 6,11‐13,15,24 Attention to this level of detail allows increased confidence that the impact of a disease or its treatment is described in a similar manner in multinational trials or outcome evaluations. The term “cross-cultural adaptation” is used to encompass a process that looks at both language (translation) and cultural adaptation issues in the process of preparing a questionnaire for use in another setting. Cross-cultural adaptations should be considered for several different scenarios. In some cases, this is more obvious than in others. Guillemin et al 11 suggest five different examples of when attention should be paid to this adaptation by comparing the target (where it is going to be used) and source (where it was developed) language and culture. The first scenario is that it is to be used in the same language and culture in which it was developed. No adaptation is necessary. The last scenario is the opposite extreme, the application of a questionnaire in a different culture, language and country—moving the Short Form 36-item questionnaire from the United States (source) to Japan (target) 7 which would necessitate translation and cultural adaptation. The other scenarios are summarized in Table 1 and reflect situations when some translation and/or adaptation is needed. The guidelines described in this document are based on a review of cross-cultural adaptation in the medical, sociological, and psychological literature. This review led to the description of a thorough adaptation process designed to maximize the attainment of semantic, idiomatic, experiential, and conceptual equivalence between the source and target questionnaires. 13 . Further experience in cross-cultural adaptation of generic and diseasespecific instruments and alternative strategies driven by different research groups 18 have led to some refinements

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Abstract: Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models have shown tremendous progress towards adapting to new environments by focusing either on discovering domain invariant representations or by mapping between unpaired image domains. While feature space methods are difficult to interpret and sometimes fail to capture pixel-level and low-level domain shifts, image space methods sometimes fail to incorporate high level semantic knowledge relevant for the end task. We propose a model which adapts between domains using both generative image space alignment and latent representation space alignment. Our approach, Cycle-Consistent Adversarial Domain Adaptation (CyCADA), guides transfer between domains according to a specific discriminatively trained task and avoids divergence by enforcing consistency of the relevant semantics before and after adaptation. We evaluate our method on a variety of visual recognition and prediction settings, including digit classification and semantic segmentation of road scenes, advancing state-of-the-art performance for unsupervised adaptation from synthetic to real world driving domains.

Adaptive Algorithms and Stochastic Approximations

Abstract: Adaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications.