Relevant bibliographies by topics / Feature Adaptation

Academic literature on the topic 'Feature Adaptation'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Feature Adaptation"

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O'Brien, Michael J., and Thomas D. Holland. "The Role of Adaptation in Archaeological Explanation." American Antiquity 57, no. 1 (January 1992): 36–59. http://dx.doi.org/10.2307/2694834.

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Adaptation, a venerable icon in archaeology, often is afforded the vacuous role of being an ex-post-facto argument used to "explain" the appearance and persistence of traits among prehistoric groups—a position that has seriously impeded development of a selectionist perspective in archaeology. Biological and philosophical definitions of adaptation—and by extension, definitions of adaptedness—vary considerably, but all are far removed from those usually employed in archaeology. The prevailing view in biology is that adaptations are features that were shaped by natural selection and that increase the adaptedness of an organism. Thus adaptations are separated from other features that may contribute to adaptedness but are products of other evolutionary processes. Analysis of adaptation comprises two stages: showing that a feature was under selection and how the feature functioned relative to the potential adaptedness of its bearers. The archaeological record contains a wealth of information pertinent to examining the adaptedness of prehistoric groups, but attempts to use it will prove successful only if a clear understanding exists of what adaptation is and is not.
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Cui, Pengcheng, Yongqian Zheng, Peimin Xu, Bin Li, Mingsheng Ma, and Guiyu Zhou. "The comparison of adjoint-based grid adaptation and feature-based grid adaptation method." Journal of Physics: Conference Series 2280, no. 1 (June 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2280/1/012003.

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Abstract Grid adaption is a popular method to enhance the resolution of flow field and the precision of numerical simulation, which automatically optimizes the grid distribution instead of manual complicated work. There exist usually two grid adaptation methods, the feature based grid adaption and adjoint based grid adaption, the former focuses on shocks, vortexes and other features of flow field, and the latter focuses on lift, drag and other aerodynamic characteristics. The comparison of adjoint based grid adaption and feature based grid adaption method is investigated in this paper. Numerical simulations show that both feature adaption and adjoint adaption could improve the resolution of flow field and the precision of numerical simulation such as lift and drag. As for the flow features, the feature adaptation could capture the obvious shock waves and vortexes in the flow field, the adjoint adaptation, however, only captures the flow features that are contributory to the accuracy of aerodynamic characteristics. As for the aerodynamic characteristics, some shock waves and vortexes have little influences to the forces, so the feature adaptation is not efficient as adjoint adaptation, which could greatly improve the accuracy of aerodynamic characteristics.
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Qin, Ning, and Xueqiang Liu. "Flow feature aligned grid adaptation." International Journal for Numerical Methods in Engineering 67, no. 6 (2006): 787–814. http://dx.doi.org/10.1002/nme.1648.

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Chen, Cheng, Qi Dou, Hao Chen, Jing Qin, and Pheng-Ann Heng. "Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 865–72. http://dx.doi.org/10.1609/aaai.v33i01.3301865.

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This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of crossmodality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.
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Li, Shuang, Chi Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, and Jian Tang. "Domain Conditioned Adaptation Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11386–93. http://dx.doi.org/10.1609/aaai.v34i07.6801.

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Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.
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Chen, Qingchao, and Yang Liu. "Structure-Aware Feature Fusion for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10567–74. http://dx.doi.org/10.1609/aaai.v34i07.6629.

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Unsupervised domain Adaptation (UDA) aims to learn and transfer generalized features from a labelled source domain to a target domain without any annotations. Existing methods only aligning high-level representation but without exploiting the complex multi-class structure and local spatial structure. This is problematic as 1) the model is prone to negative transfer when the features from different classes are misaligned; 2) missing the local spatial structure poses a major obstacle in performing the fine-grained feature alignment. In this paper, we integrate the valuable information conveyed in classifier prediction and local feature maps into global feature representation and then perform a single mini-max game to make it domain invariant. In this way, the domain-invariant feature not only describes the holistic representation of the original image but also preserves mode-structure and fine-grained spatial structural information. The feature integration is achieved by estimating and maximizing the mutual information (MI) among the global feature, local feature and classifier prediction simultaneously. As the MI is hard to measure directly in high-dimension spaces, we adopt a new objective function that implicitly maximizes the MI via an effective sampling strategy and a discriminator design. Our STructure-Aware Feature Fusion (STAFF) network achieves the state-of-the-art performances in various UDA datasets.
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Sun, Feng, Hanrui Wu, Zhihang Luo, Wenwen Gu, Yuguang Yan, and Qing Du. "Informative Feature Selection for Domain Adaptation." IEEE Access 7 (2019): 142551–63. http://dx.doi.org/10.1109/access.2019.2944226.

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Li, Jingyao, Zhanshan Li, and Shuai Lü. "Feature concatenation for adversarial domain adaptation." Expert Systems with Applications 169 (May 2021): 114490. http://dx.doi.org/10.1016/j.eswa.2020.114490.

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Wen, Jun, Risheng Liu, Nenggan Zheng, Qian Zheng, Zhefeng Gong, and Junsong Yuan. "Exploiting Local Feature Patterns for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5401–8. http://dx.doi.org/10.1609/aaai.v33i01.33015401.

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Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation methods focus on holistic feature alignment by matching source and target holistic feature distributions, without considering local features and their multi-mode statistics. We show that the learned local feature patterns are more generic and transferable and a further local feature distribution matching enables fine-grained feature alignment. In this paper, we present a method for learning domain-invariant local feature patterns and jointly aligning holistic and local feature statistics. Comparisons to the state-of-the-art unsupervised domain adaptation methods on two popular benchmark datasets demonstrate the superiority of our approach and its effectiveness on alleviating negative transfer.
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Stephens, John, and Sung-Ae Lee. "Transcultural Adaptation of Feature Films: South Korea’s My Sassy Girl and its Remakes." Adaptation 11, no. 1 (February 9, 2018): 75–95. http://dx.doi.org/10.1093/adaptation/apy001.

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Dissertations / Theses on the topic "Feature Adaptation"

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Harris, Matthew. "Flow feature aligned mesh generation and adaptation." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/4192/.

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Methods which allow for construction of flow feature aligned meshes in two- and three-dimensions have been developed in this thesis to investigate their potential for improvements in the numerical solution relative to globally refining the mesh. Of particular interest in the work is the generation of high-quality quadrilateral and hexahedral elements aligned with the dominant flow features. The two-dimensional techniques are applied on unstructured quad-dominant meshes, whilst the three-dimensional problems involve embedding high-quality hex-dominant mesh blocks into a hybrid volume mesh to improve their ability to capture anisotropic flow features such as shock waves, trailing shear layers/wakes and wing tip vortices. A method involving the medial axis has been studied to provide a geometric representation of two-dimensional flow features to allow feature-aligned meshes to be generated. Due to the flexibility of the approach, a range of complex features can be represented as simple geometric entities. These curves are embedded into the domain as virtual geometries to force alignment of unstructured quad-dominant surface mesh elements. The mesh locally mimics the attributes of a structured grid and provides high quality numerical solutions due to the alignment of the cell interfaces with the flow features. To improve the capability of hybrid meshes to resolve anisotropic flow physics, a method involving the extrusion of quad-dominant surface meshes has been developed. Surface meshes are extruded in the direction of extracted flow features, yielding feature-aligned semi-structured hex-dominant mesh blocks which can be embedded into the hybrid volume mesh. The presence of feature-aligned hexahedra has been shown to greatly enhance the resolution of anisotropic flow features compared with both isotropic and anisotropic tetrahedral elements, due to a significant reduction in numerical diffusion. Furthermore, improvements in the numerical solution have been also been obtained in a more efficient manner than isotropically refining the hybrid mesh. The results indicate that the type, orientation and size of the elements are significant contributing factors in the resolution of the dominant flow features.
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Stoltzfus, Daniel Paul. "Predictions on markedness and feature resilience in loanword adaptation." Doctoral thesis, Université Laval, 2014. http://hdl.handle.net/20.500.11794/25567.

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Normalement, un emprunt est adapté afin que ses éléments étrangers s’intègrent au système phonologique de la langue emprunteuse. Certains auteurs (cf. Miao 2005; Steriade 2001b, 2009) ont soutenu que, lors de l’adaptation d’une consonne, les traits de manière d’articulation sont plus résistants au changement que les traits laryngaux (ex. : le voisement) ou que ceux de place. Mes résultats montrent cependant que les traits de manière (ex. : [±continu]) sont impliqués dans les adaptations consonantiques aussi fréquemment que les autres traits (ex. [±voisé] et [±antérieur]). Par exemple, le /Z/ français est illicite à l’initiale en anglais. Les options d’adaptation incluent /Z/ → [z] (changement de place), /Z/ → [S] (changement de voisement) et /Z/ → [dZ] (changement de manière). Contrairement aux prédictions des auteurs précités, l’adaptation primaire en anglais est /Z/ → [dZ], avec changement de manière (ex. français [Zelatin] gélatine → anglais [dZElœtIn]). Plutôt qu’une résistance des traits de manière, les adaptations étudiées dans ma thèse font ressortir une nette tendance à la simplification. Mon hypothèse est que les langues adaptent les consonnes étrangères en en éliminant les complexités. Donc un changement impliquant l’élimination plutôt que l’insertion d’un trait marqué sera préféré. Ma thèse innove aussi en montrant qu’une consonne est le plus souvent importée lorsque sa stratégie d’adaptation primaire implique l’insertion d’un trait marqué. Les taux d’importation sont systématiquement élevés pour les consonnes dont l’adaptation impliquerait l’insertion d’un tel trait (ici [+continu] ou [+voisé]). Par exemple, /dZ/ en anglais, lorsque adapté, devient /Z/ en français après l’insertion de [+continu]; cependant, l’importation de /dZ/ est de loin préférée à son adaptation (89%). En comparaison, /dZ/ est rarement importé (10%) en germano-pennsylvanien (GP) parce que l’adaptation de /dZ/ à [tS] (élision du trait marqué [+voisé]) est disponible, contrairement au cas du français. Cependant, le /t/ anglais à l’initiale, lui, est majoritairement importé (74%) en GP parce que son adaptation en /d/ impliquerait l’insertion du trait marqué [+voisé]. Ma thèse permet non seulement de mieux cerner la direction des adaptations, mais repère aussi ce qui favorise fortement les importations sur la base d’une notion déjà établie en phonologie : la marque.
A loanword is normally adapted to fit its foreign elements to the phonological system of the borrowing language (L1). Recently, some authors (e.g. Miao 2005; Steriade 2001b, 2009) have proposed that during the adaptation process of a second language (L2) consonant, manner features are more resistant to change than are non-manner features. A careful study of my data indicate that manner features (e.g. [±continuant]) are as likely to be involved in the adaptation process as are non-manner [±voice] and [±anterior]. For example, French /Z/ is usually not tolerated word-initially in English. Adaptation options include /Z/ → [z] (change of place), /Z/ → [S] (change of voicing) and /Z/ → [dZ] (change of manner). The primary adaptation in English is /Z/ → [dZ] (e.g. French [Zelatin] gélatine → English [dZElœtIn]) where manner is in fact the less resistant. Instead, during loanword adaptation there is a clear tendency towards unmarkedness. My hypothesis is that languages overwhelmingly adapt with the goal of eliminating the complexities of the L2; a change that involves deletion instead of insertion of a marked feature is preferred. Furthermore, my thesis shows for the first time that a consonant is statistically most likely to be imported if its preferred adaptation strategy involves insertion of a marked feature (e.g. [+continuant] or [+voice]). For example, the adaptation of English /dZ/ is /Z/ in French after insertion of marked [+continuant], but /dZ/ is overwhelmingly imported (89%), instead of adapted in French. I argue that this is to avoid the insertion of marked [+continuant]. This contrasts with Pennsylvania German (PG) where English /dZ/ is rarely imported (10%). This is because unlike in French, there is an option to adapt /dZ/ to /tS/ (deletion of marked [+voice]) in PG. However, English word-initial /t/ is heavily imported (74%), not adapted, in PG because adaptation to /d/ involves insertion of marked [+voice]. Not only does my thesis better determine the direction of adaptations but it also establishes the circumstances where L2 consonants are most likely to be imported instead of being adapted, on the basis of a well-known notion in phonology: markedness.
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Lu, Jianhua. "Missing feature decoding and model adaptation for noisy speech recognition." Thesis, Queen's University Belfast, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527938.

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Zennaro, Fabio. "Feature distribution learning for covariate shift adaptation using sparse filtering." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/feature-distribution-learning-for-covariate-shift-adaptation-using-sparse-filtering(67989db2-b8a0-4fac-8832-f611e9236ed5).html.

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This thesis studies a family of unsupervised learning algorithms called feature distribution learning and their extension to perform covariate shift adaptation. Unsupervised learning is one of the most active areas of research in machine learning, and a central challenge in this field is to develop simple and robust algorithms able to work in real-world scenarios. A traditional assumption of machine learning is the independence and identical distribution of data. Unfortunately, in realistic conditions this assumption is often unmet and the performances of traditional algorithms may be severely compromised. Covariate shift adaptation has then developed as a lively sub-field concerned with designing algorithms that can account for covariate shift, that is for a difference in the distribution of training and test samples. The first part of this dissertation focuses on the study of a family of unsupervised learning algorithms that has been recently proposed and has shown promise: feature distribution learning; in particular, sparse filtering, the most representative feature distribution learning algorithm, has commanded interest because of its simplicity and state-of-the-art performance. Despite its success and its frequent adoption, sparse filtering lacks any strong theoretical justification. This research questions how feature distribution learning can be rigorously formalized and how the dynamics of sparse filtering can be explained. These questions are answered by first putting forward a new definition of feature distribution learning based on concepts from information theory and optimization theory; relying on this, a theoretical analysis of sparse filtering is carried out, which is validated on both synthetic and real-world data sets. In the second part, the use of feature distribution learning algorithms to perform covariate shift adaptation is considered. Indeed, because of their definition and apparent insensitivity to the problem of modelling data distributions, feature distribution learning algorithms seems particularly fit to deal with covariate shift. This research questions whether and how feature distribution learning may be fruitfully employed to perform covariate shift adaptation. After making explicit the conditions of success for performing covariate shift adaptation, a theoretical analysis of sparse filtering and another novel algorithm, periodic sparse filtering, is carried out; this allows for the determination of the specific conditions under which these algorithms successfully work. Finally, a comparison of these sparse filtering-based algorithms against other traditional algorithms aimed at covariate shift adaptation is offered, showing that the novel algorithm is able to achieve competitive performance. In conclusion, this thesis provides a new rigorous framework to analyse and design feature distribution learning algorithms; it sheds light on the hidden assumptions behind sparse filtering, offering a clear understanding of its conditions of success; it uncovers the potential and the limitations of sparse filtering-based algorithm in performing covariate shift adaptation. These results are relevant both for researchers interested in furthering the understanding of unsupervised learning algorithms and for practitioners interested in deploying feature distribution learning in an informed way.
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Collet, Philippe. "Taming Complexity of Large Software Systems: Contracting, Self-Adaptation and Feature Modeling." Habilitation à diriger des recherches, Université de Nice Sophia-Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00657444.

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Nos travaux s'inscrivent dans le domaine du génie logiciel pour les systèmes informatiques à large échelle. Notre objectif est de fournir des techniques et des outils pour aider les architectes logiciels à maîtriser la complexité toujours grandissante de ces systèmes. Principalement fondées sur des approches par ingénierie des modèles, nos contributions s'organisent autour de trois axes. Le premier axe concerne le développement de systèmes à la fois fiables et flexibles, et ce à base de composants hiérarchiques équipés de capacités de reconfiguration dynamique. Par l'utilisation de nouvelles formes de contrats logiciels, les systèmes et frameworks que nous proposons prennent en compte differents formalismes de spécification et maintiennent les contrats à jour pendant l'exécution. Une seconde partie de nos travaux s'intéresse à fournir des capacités auto-adaptatives à ces systèmes contractuels, à travers des mécanismes de négociation de contrats et des sous-systèmes de monitoring eux-mêmes auto-adaptatifs. Un troisième axe concerne les lignes de produits logiciels dans lesquelles les features models sont largement utilisés pour modéliser la variabilité. Nos contributions consistent en un ensemble d'opérateurs de composition bien définis et implémentés efficacement pour les feature models, ainsi qu'un langage dédié permettant leur gestion à large échelle.
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Yang, Baoyao. "Distribution alignment for unsupervised domain adaptation: cross-domain feature learning and synthesis." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/556.

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In recent years, many machine learning algorithms have been developed and widely applied in various applications. However, most of them have considered the data distributions of the training and test datasets to be similar. This thesis concerns on the decrease of generalization ability in a test dataset when the data distribution is different from that of the training dataset. As labels may be unavailable in the test dataset in practical applications, we follow the effective approach of unsupervised domain adaptation and propose distribution alignment methods to improve the generalization ability of models learned from the training dataset in the test dataset. To solve the problem of joint distribution alignment without target labels, we propose a new criterion of domain-shared group sparsity that is an equivalent condition for equal conditional distribution. A domain-shared group-sparse dictionary learning model is built with the proposed criterion, and a cross-domain label propagation method is developed to learn a target-domain classifier using the domain-shared group-sparse representations and the target-specific information from the target data. Experimental results show that the proposed method achieves good performance on cross-domain face and object recognition. Moreover, most distribution alignment methods have not considered the difference in distribution structures, which results in insufficient alignment across domains. Therefore, a novel graph alignment method is proposed, which aligns both data representations and distribution structural information across the source and target domains. An adversarial network is developed for graph alignment by mapping both source and target data to a feature space where the data are distributed with unified structure criteria. Promising results have been obtained in the experiments on cross-dataset digit and object recognition. Problem of dataset bias also exists in human pose estimation across datasets with different image qualities. Thus, this thesis proposes to synthesize target body parts for cross-domain distribution alignment, to address the problem of cross-quality pose estimation. A translative dictionary is learned to associate the source and target domains, and a cross-quality adaptation model is developed to refine the source pose estimator using the synthesized target body parts. We perform cross-quality experiments on three datasets with different image quality using two state-of-the-art pose estimators, and compare the proposed method with five unsupervised domain adaptation methods. Our experimental results show that the proposed method outperforms not only the source pose estimators, but also other unsupervised domain adaptation methods.
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Lock, G. "'Petticoat Sailor' to 'She Crossing' : adaptation in process : a writer's reflection on adapting a feature length screenplay into a novel." Thesis, Nottingham Trent University, 2015. http://irep.ntu.ac.uk/id/eprint/27947/.

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This PhD thesis consists of a novel entitled 'She Crossing', and a Commentary. The Commentary reflects on my practice of constructing the novel by adapting it from my previously existing screenplay, 'Petticoat Sailor'. They both derive from 'The Seafaring Maiden', a Nova Scotian newspaper article dating from 1957. All of these narratives are concerned with a nineteenth-century woman who had to captain a commercial sailing ship across the Atlantic. My screenplay, 'Petticoat Sailor', is set entirely in the nineteenth century. Both the newspaper article and my novel frame the protagonist’s voyage as a twentieth-century reminiscence. My novel also introduces a fictional subplot, which was not present in the screenplay. This subplot derives from my research into accounts of cross-dressed women who went to sea when only men were legally employed as sailors. The direction of my adaptation from a screenplay into a novel is unusual. Most adaptations move from novel to script and this is reflected in the secondary literature about adaptation. Novels resulting from adapting scripts have attracted little academic analysis as artefacts, and even less theorization of their creative processes. There is also an absence of sustained reflection by other writers who have turned their screenplays into novels. Aiming to increase understanding of the novelizing process, my thesis addresses these absences. The Commentary discusses the differences and similarities in writing screenplay and writing prose fiction by reflecting on my processes in writing this novel. I particularly explore the effects of contingency in adjusting theoretical principles during the creative process of novelization. I also examine to what extent the ways in which I write are themselves adapted from my own experience of directing and acting for stage and film.
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Kleynhans, Neil Taylor. "Automatic speech recognition for resource-scarce environments / N.T. Kleynhans." Thesis, North-West University, 2013. http://hdl.handle.net/10394/9668.

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Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. In this thesis we present research into developing techniques and tools to (1) harvest audio data, (2) rapidly adapt ASR systems and (3) select “useful” training samples in order to assist with resource-scarce ASR system development. We demonstrate an automatic audio harvesting approach which efficiently creates a speech recognition corpus by harvesting an easily available audio resource. We show that by starting with bootstrapped acoustic models, trained with language data obtain from a dialect, and then running through a few iterations of an alignment-filter-retrain phase it is possible to create an accurate speech recognition corpus. As a demonstration we create a South African English speech recognition corpus by using our approach and harvesting an internet website which provides audio and approximate transcriptions. The acoustic models developed from harvested data are evaluated on independent corpora and show that the proposed harvesting approach provides a robust means to create ASR resources. As there are many acoustic model adaptation techniques which can be implemented by an ASR system developer it becomes a costly endeavour to select the best adaptation technique. We investigate the dependence of the adaptation data amount and various adaptation techniques by systematically varying the adaptation data amount and comparing the performance of various adaptation techniques. We establish a guideline which can be used by an ASR developer to chose the best adaptation technique given a size constraint on the adaptation data, for the scenario where adaptation between narrow- and wide-band corpora must be performed. In addition, we investigate the effectiveness of a novel channel normalisation technique and compare the performance with standard normalisation and adaptation techniques. Lastly, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions.
Thesis (PhD (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.
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Cardace, Adriano. "Learning Features Across Tasks and Domains." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20050/.

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The absence of in-domain labeled data hinders the applicability of powerful deep neural networks. Unsupervised Domain Adaptation (UDA) methods have emerged to exploit such models even when labeled data is not available in the target domain. All these techniques aim to reduce the distribution shift problem that afflicts these models when trained on one dataset and tested in a different one. However, most of the works, do not consider relationships among tasks to further boost performances. In this thesis, we study a recent method called AT/DT (Across Tasks Domain Transfer), that seeks to apply Domain Adaptation together with Task Adaptation, leveraging on the correlation of two popular Vision tasks such as Semantic Segmentation and Monocular Depth Estimation. Inspired by the Domain Adaptation literature, we propose many extensions to the original work and show how these enhance the framework performances. Our contributions are applied at different levels: we first study how different architectures affect the transferability of features across tasks. We further improve performances by deploying Adversarial training. Finally, we explore the possibility of replacing Depth Estimation with popular Self-supervised tasks, demonstrating that two tasks must be semantically connected to be able to transfer features among them.
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Svensson, Ylva. "Embedded in a context : the adaptation of immigrant youth." Doctoral thesis, Örebro universitet, Institutionen för juridik, psykologi och socialt arbete, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-24172.

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With rising levels of immigration comes a need to know what fosters positive adaptation for the youth growing up in a new culture of settlement.The issue is increasingly studied; however, little of the research conducted has combined a developmental with a contextual approach. The aim of this dissertation was to explore the adaptation of immigrant youth on the basis of developmental theories and models which put emphasis on setting or contextual conditions. This entailed viewing immigrant youths as developing organisms that actively interact with their environments. Further, immigrant youths were seen as embedded in multiple settings, at different levels and with different contextual features. Two of the overall research questions addressed how contextual features of the settings in which the youth are embedded were related to adaptation. Results from all three studies combined to show that the contextual feature of a setting is not of prime or sole importance for the adaptation of immigrant youth, and that the contextual feature of SES diversity is of greater importance than theethnic compositions of settings. The next two overall research questions addressed how the linkage between settings was related to adaptation. The results indicated that adaptation is not always setting specific and that what is happening in one setting can be related to adaptation in anothersetting. Further, it was found that the cultural distance between settings is related to adaption, but that contextual factors affect this relationship. Overall, the results of the dissertation suggests that the adaptation of immigrant youth is a complex matter that is explained better by interaction and indirect effects than by main and direct effects. This highlights the importance of taking all settings in which the immigrant youths are embedded into account and to account for how the settings interact to understand the factors that foster and hinder positive adaptation of immigrant youth.

The article "Homophily in friendship networks of immigrant and nonimmigrantyouth: Does context matter?" in the list of studies is published electronically as "Peer selection and influence of delinquent behavior of immigrant and nonimmigrant youths: does context matter?"

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Books on the topic "Feature Adaptation"

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Loiselle, André. Stage-bound: Feature film adaptations of Canadian and Québécois drama. Montreal, QC: McGill-Queen's University Press, 2004.

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Stage-bound: Feature film adaptations of Canadian and Québécois drama. Montreal: McGill-Queen's University Press, 2003.

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Valut︠s︡kiĭ, Vladimir, and Leonid Kvinikhidze. Mėri Poppins, do svidanii︠a︡. Moskva: "Krupnyĭ Plan", 2011.

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Paradzhanov, Sergeĭ. The legend of Suram fortress: Ashik Kerib. New York: Kino Video, 2001.

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Kundera, Milan. Žert: The joke. Chicago: Facets Video, 2001.

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Stoppard, Tom, and Rainer Werner Fassbinder. Despair. United States]: Olive Films, 2011.

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Nicholson, Jack. The shining. Burbank, CA: Warner Home Video, 1999.

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The hawk's gray feather: A book of the Keltiad. New York: ROC, 1990.

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Wajda, Andrzej. Pan Tadeusz. New York, NY: Filmart, 2000.

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Danelii︠a︡, Georgiĭ Nikolaevich, and Viktorii︠a︡ Tokareva. Sovsem propashchiĭ. Moskva: Krupnyĭ plan, 2004.

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Book chapters on the topic "Feature Adaptation"

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Monaghan, Whitney, and Stuart Richards. "Adapting Queer Shorts to Feature Films: Does Size Really Matter?" In Queer/Adaptation, 137–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05306-2_9.

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Bi, Ying, Bing Xue, and Mengjie Zhang. "Random Forest-Assisted GP for Feature Learning." In Adaptation, Learning, and Optimization, 207–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65927-1_9.

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Bi, Ying, Bing Xue, and Mengjie Zhang. "GP with Image-Related Operators for Feature Learning." In Adaptation, Learning, and Optimization, 145–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65927-1_7.

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Bi, Ying, Bing Xue, and Mengjie Zhang. "GP for Simultaneous Feature Learning and Ensemble Learning." In Adaptation, Learning, and Optimization, 179–205. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65927-1_8.

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Mori, Marco, and Anthony Cleve. "Feature-Based Adaptation of Database Schemas." In Model-Based Methodologies for Pervasive and Embedded Software, 85–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38209-3_6.

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Jing, Zhang, and Ren Yonggong. "Robust Multi-feature Extreme Learning Machine." In Proceedings in Adaptation, Learning and Optimization, 150–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01520-6_13.

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Cui, Fengli, Yinghao Chen, Yuntao Du, Yikang Cao, and Chongjun Wang. "Joint Feature and Labeling Function Adaptation for Unsupervised Domain Adaptation." In Advances in Knowledge Discovery and Data Mining, 432–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05933-9_34.

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Bhatnagar, Shalabh, Vivek S. Borkar, and L. A. Prashanth. "Adaptive Feature Pursuit: Online Adaptation of Features in Reinforcement Learning." In Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, 517–34. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118453988.ch23.

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Yan, Weiqing, Shuigen Wang, Guanghui Yue, Jindong Xu, Xiangrong Tong, and Laihua Wang. "Feature Points Selection for Rectangle Panorama Stitching." In Proceedings in Adaptation, Learning and Optimization, 118–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_13.

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Yue, Caitong, Jing Liang, Boyang Qu, Zhuopei Lu, Baolei Li, and Yuhong Han. "Sparse Representation Feature for Facial Expression Recognition." In Proceedings in Adaptation, Learning and Optimization, 12–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01520-6_2.

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Conference papers on the topic "Feature Adaptation"

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Pukall, Mario, Norbert Siegmund, and Walter Cazzola. "Feature-oriented runtime adaptation." In the 2009 ESEC/FSE workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1596495.1596506.

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Uribe, Diego, and Enrique Cuan. "Feature Distributions in Domain Adaptation." In 2012 IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA). IEEE, 2012. http://dx.doi.org/10.1109/cerma.2012.33.

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Amja, Anne Marie, Abdel Obaid, Hafedh Mili, and Zahi Jarir. "Feature-Based Adaptation and Its Implementation." In 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC). IEEE, 2016. http://dx.doi.org/10.1109/cic.2016.050.

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Barddal, Jean Paul, Heitor Murilo Gomes, and Fabricio Enembreck. "A Survey on Feature Drift Adaptation." In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2015. http://dx.doi.org/10.1109/ictai.2015.150.

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Zhou, Wubai, Tao Li, Larisa Shwartz, and Genady Ya Grabarnik. "Recommending ticket resolution using feature adaptation." In 2015 11th International Conference on Network and Service Management (CNSM). IEEE, 2015. http://dx.doi.org/10.1109/cnsm.2015.7367333.

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Chi Zhang, Hao Zhang, and Lynne E. Parker. "Feature Space Decomposition for effective robot adaptation." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7353410.

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Volpi, Riccardo, Pietro Morerio, Silvio Savarese, and Vittorio Murino. "Adversarial Feature Augmentation for Unsupervised Domain Adaptation." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00576.

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Chen, Chaoqi, Weiping Xie, Wenbing Huang, Yu Rong, Xinghao Ding, Yue Huang, Tingyang Xu, and Junzhou Huang. "Progressive Feature Alignment for Unsupervised Domain Adaptation." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00072.

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Chuang-Hua Chueh and Jen-Tzung Chien. "Reliable feature selection for language model adaptation." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518803.

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Visweswariah, Karthik, and Peder Olsen. "Feature adaptation using projection of Gaussian posteriors." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-163.

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Reports on the topic "Feature Adaptation"

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Morse, P. D., R. J. H. Parker, W. E. Sladen, S L Smith, and H. B. O'Neill. Remote permafrost terrain mapping, Grays Bay-Yellowknife corridor, Northwest Territories and Nunavut. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330206.

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The new Grays Bay - Yellowknife corridor will provide transportation, energy, and telecommunications to northcentral Canada, and connect existing highway infrastructure to a deep-sea port on the Arctic Ocean. This infrastructure will improve access for exploration and development, and reduce operating costs in this mineral-rich region of the Northwest Territories and Nunavut, one of the biggest impediments to development in this region. Relatively little information on permafrost and ground ice conditions is available for most of the corridor. To fill this information gap, the Geological Survey of Canada's GEM-GeoNorth program is compiling permafrost and surficial geology information, mapping periglacial terrain features, and modelling ground ice conditions along the corridor. This poster presents the periglacial feature mapping component. Periglacial features are landforms associated with cold environments and typically contain permafrost. Permafrost, and its related ground ice, affect terrain sensitivity to climate change and surface disturbance. With development of this periglacial terrain and accelerated climate warming in the north, communities, planners, and regulators require this information to make informed decisions on how and where to build and manage environmentally sustainable and climate change-resilient infrastructure, and determine best adaptation strategies.
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Kelly, Luke. Lessons on Disaster Resilience Pogramming in Pakistan. Institute of Development Studies, February 2021. http://dx.doi.org/10.19088/k4d.2021.057.

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This rapid literature review finds that lessons drawn from disaster resilience programmes in Pakistan are focused on the best ways to co-ordinate between different resilience work in different sectors. This can be difficult because of the number of NGOs with different sectoral expertise, short time frames for intervention, and the differing capacities of levels of Pakistani government. More generally, although the benefits of linking disaster risk reduction (DRR) to climate change adaptation (CCA) and development work are advocated in many policies, the fact that they are often undertaken by different actors limits synergies. The report also finds that most DRR work is focused on reducing risk from hazards rather than social vulnerabilities. Pakistan is vulnerable to climate change and a range of natural disasters. Following the earthquake in 2005 and floods in 2010, the Pakistani government and international donors have sought to increase the country's resilience to natural hazards. This literature review focuses on disaster risk reduction (DRR) efforts, as these constitute a significant portion of the resilience work in Pakistan. It first defines resilience, DRR and the related concept of CCA, as these all aim to improve resilience. It then surveys lessons learned in implementing resilience and DRR programmes in Pakistan. It focuses on lessons relating to sectoral focus, target populations, as well as strategic framework and operating models. It is mainly based on evaluations written by NGOs, UN bodies and international financial institutions. NGO reports are typically focused on specific programmes, although some point to broader features of the landscape for resilience programming in Pakistan. Academic papers charting the trends and issues in resilience programming have also been consulted.
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Iatsyshyn, Anna V., Valeriia O. Kovach, Yevhen O. Romanenko, Iryna I. Deinega, Andrii V. Iatsyshyn, Oleksandr O. Popov, Yulii G. Kutsan, Volodymyr O. Artemchuk, Oleksandr Yu Burov, and Svitlana H. Lytvynova. Application of augmented reality technologies for preparation of specialists of new technological era. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3749.

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Augmented reality is one of the most modern information visualization technologies. Number of scientific studies on different aspects of augmented reality technology development and application is analyzed in the research. Practical examples of augmented reality technologies for various industries are described. Very often augmented reality technologies are used for: social interaction (communication, entertainment and games); education; tourism; areas of purchase/sale and presentation. There are various scientific and mass events in Ukraine, as well as specialized training to promote augmented reality technologies. There are following results of the research: main benefits that educational institutions would receive from introduction of augmented reality technology are highlighted; it is determined that application of augmented reality technologies in education would contribute to these technologies development and therefore need increase for specialists in the augmented reality; growth of students' professional level due to application of augmented reality technologies is proved; adaptation features of augmented reality technologies in learning disciplines for students of different educational institutions are outlined; it is advisable to apply integrated approach in the process of preparing future professionals of new technological era; application of augmented reality technologies increases motivation to learn, increases level of information assimilation due to the variety and interactivity of its visual representation. Main difficulties of application of augmented reality technologies are financial, professional and methodical. Following factors are necessary for introduction of augmented reality technologies: state support for such projects and state procurement for development of augmented reality technologies; conduction of scientific research and experimental confirmation of effectiveness and pedagogical expediency of augmented reality technologies application for training of specialists of different specialties; systematic conduction of number of national and international events on dissemination and application of augmented reality technology. It is confirmed that application of augmented reality technologies is appropriate for training of future specialists of new technological era.
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Introduction Success of Less Common Species from the Genus Berberis L. Ukrainian Journal of Ecology, 2019. http://dx.doi.org/10.31812/123456789/3641.

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The study dealt with the patterns of adaptation of the genus Berberis L. low-abundant alien plants to the climatic conditions of the steppe zone of Ukraine. The success of the introduction, which was conducted on the territory of the Botanical Garden of Oles Honchar Dnipro National University (city of Dnipro) near 60 years ago, was evaluated by a set of indicators. According to the study of phenological rhythms, introduced plants can be characterized as plants with a relatively short growing season, a relatively long shoot growth, a short flowering period and a long maturing period. This indicates the lability of the phenological rhythms of the introduced plants, which respond to changes in environmental conditions in terms of the beginning and end of the phenological phases. The aquatic regime of introduced plants reflects the level of their adaptation to the environment due to the ability of plant organism to withstand water consumption. The study of water exchange features of Berberis L. plants revealed a moderate decrease in the intensity of transpiration and a moderate water deficit in the plant leaves, which indicates the adaptation of the species to the difficult climatic conditions of the steppe zone. The response of introduced plants to the level of moisture in the steppe region confirmed the universal mechanism of plant adaptation to arid conditions. Comparative analysis of the reproductive ability of introduced plants of the genus Berberis L. in the conditions of the Botanical Garden revealed a significant variability of quantitative indicators. The weight of 100 fruits ranged from 12.5 g (B. vulgaris) to 16.3 g (B. declinata). Seed length varied from 4.5 mm (B. canadensis) to 5.2 mm (B. declinata, B. amurensis), seed width varied from 1.8 mm (B. amurensis, B. vulgaris) to 2.1 mm (B. koreana). The highest mass of 1000 seeds was observed in the species B. koreana. To compare sample averages, the criterion for the significant difference of Tukey's group averages was used. The investigated plants of the genus Berberis L. can be recommended for the introduction into large-scale and individual gardening in order to expand the range of fruit products and obtain biologically valuable raw materials for the industrial production of functional products.
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