Relevant bibliographies by topics / Target domain

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Target domain'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Target domain"

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Cao, Meng, and Songcan Chen. "Mixup-Induced Domain Extrapolation for Domain Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11168–76. http://dx.doi.org/10.1609/aaai.v38i10.28994.

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Domain generalization aims to learn a well-performed classifier on multiple source domains for unseen target domains under domain shift. Domain-invariant representation (DIR) is an intuitive approach and has been of great concern. In practice, since the targets are variant and agnostic, only a few sources are not sufficient to reflect the entire domain population, leading to biased DIR. Derived from PAC-Bayes framework, we provide a novel generalization bound involving the number of domains sampled from the environment (N) and the radius of the Wasserstein ball centred on the target (r), which
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Lu, Yuwu, Xue Hu, Waikeung Wong, and Haoyu Huang. "Collaborative Semantic Consistency Alignment for Blended-Target Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19170–79. https://doi.org/10.1609/aaai.v39i18.34110.

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Blended-target domain adaptation (BTDA) leverages learned source knowledge to adapt the model to a blended-target domain that is composed of multiple unlabeled sub-target domains with distinct statistical characteristics. The existing BTDA methods usually overlook semantic correlation information across multiple domains and domain shifts among sub-target domains, resulting in suboptimal adaptation performance. To fully harness semantic knowledge and alleviate domain shifts in hybrid data distribution, we propose a collaborative semantic consistency alignment (CSCA) method for BTDA. Specificall
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Xu, Pengcheng, Boyu Wang, and Charles Ling. "Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3036–44. http://dx.doi.org/10.1609/aaai.v37i3.25407.

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Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes. However, we observe that the cluster assumption in BTDA does not comprehensively hold. The hybrid categorical feat
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Wang, Yu, Ronghang Zhu, Pengsheng Ji, and Sheng Li. "Open-Set Graph Domain Adaptation via Separate Domain Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9142–50. http://dx.doi.org/10.1609/aaai.v38i8.28765.

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Domain adaptation has become an attractive learning paradigm, as it can leverage source domains with rich labels to deal with classification tasks in an unlabeled target domain. A few recent studies develop domain adaptation approaches for graph-structured data. In the case of node classification task, current domain adaptation methods only focus on the closed-set setting, where source and target domains share the same label space. A more practical assumption is that the target domain may contain new classes that are not included in the source domain. Therefore, in this paper, we introduce a n
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Liu, Ming, Dong Ren, Hang Sun, and Simon X. Yang. "Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation." Remote Sensing 14, no. 19 (2022): 4915. http://dx.doi.org/10.3390/rs14194915.

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Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings. Large-scale remote sensing images often have multiple target domains in practical applications, and the simple extension of single-target UDA models to multiple target domains is unstable and costly. Multi-target unsupervised domain adaptation (MTUDA) is a more practical scenario that has great potential for solving the problem of crossing multiple domains in remote sensing images. However, existing MTUDA mod
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Lu, Yuwu, Haoyu Huang, Waikeung Wong, and Xue Hu. "Invertible Projection and Conditional Alignment for Multi-Source Blended-Target Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19180–88. https://doi.org/10.1609/aaai.v39i18.34111.

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Multi-source domain adaptation (MSDA), which utilizes multiple source domains to align the distribution of a single target domain, is a popular and challenging setting in domain adaptation (DA). However, existing MSDA approaches are difficult to obtain sufficient target domain knowledge, which serve as the transfer object. Furthermore, the target distributions are confused in the real world, i.e., the model cannot obtain the domain labels of target domains. To tackle these problems, we consider a more realistic DA setting Multi-Source Blended-Target Domain Adaptation (MBDA) and propose an Inve
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Ye, Fei, and Mingjie Zhang. "Structures and target recognition modes of PDZ domains: recurring themes and emerging pictures." Biochemical Journal 455, no. 1 (2013): 1–14. http://dx.doi.org/10.1042/bj20130783.

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PDZ domains are highly abundant protein–protein interaction modules and are often found in multidomain scaffold proteins. PDZ-domain-containing scaffold proteins regulate multiple biological processes, including trafficking and clustering receptors and ion channels at defined membrane regions, organizing and targeting signalling complexes at specific cellular compartments, interfacing cytoskeletal structures with membranes, and maintaining various cellular structures. PDZ domains, each with ~90-amino-acid residues folding into a highly similar structure, are best known to bind to short C-termi
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Choi, Jongwon, Youngjoon Choi, Jihoon Kim, et al. "Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10655–62. http://dx.doi.org/10.1609/aaai.v34i07.6692.

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We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately t
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Xu, Yifan, Kekai Sheng, Weiming Dong, Baoyuan Wu, Changsheng Xu, and Bao-Gang Hu. "Towards Corruption-Agnostic Robust Domain Adaptation." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–16. http://dx.doi.org/10.1145/3501800.

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Great progress has been achieved in domain adaptation in decades. Existing works are always based on an ideal assumption that testing target domains are independent and identically distributed with training target domains. However, due to unpredictable corruptions (e.g., noise and blur) in real data, such as web images and real-world object detection, domain adaptation methods are increasingly required to be corruption robust on target domains. We investigate a new task, corruption-agnostic robust domain adaptation (CRDA), to be accurate on original data and robust against unavailable-for-trai
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Wei, Yikang, and Yahong Han. "Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15805–13. http://dx.doi.org/10.1609/aaai.v38i14.29510.

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Federated Domain Generalization aims to learn a domain-invariant model from multiple decentralized source domains for deployment on unseen target domain. Due to privacy concerns, the data from different source domains are kept isolated, which poses challenges in bridging the domain gap. To address this issue, we propose a Multi-source Collaborative Gradient Discrepancy Minimization (MCGDM) method for federated domain generalization. Specifically, we propose intra-domain gradient matching between the original images and augmented images to avoid overfitting the domain-specific information withi
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Dissertations / Theses on the topic "Target domain"

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Gustafsson, Fredrik, and Erik Linder-Norén. "Automotive 3D Object Detection Without Target Domain Annotations." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148585.

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In this thesis we study a perception problem in the context of autonomous driving. Specifically, we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. We also study the application of Generative Adversarial Networks in domain adaptation techniques, aiming to improve the 3D object detection model's ability to transfer between different domains. The state-of-the-art Frustum-PointNet architecture for LiDAR-based 3D object detection was implemented and found to closely match
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Collins, K. M. "Target recognition by multi-domain RNA-binding proteins." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1460867/.

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Multi-functional RNA binding proteins regulate and coordinate the many steps of RNA metabolism. Accurate functioning of these processes is vital in cells and misregulation has been linked to many human diseases. RNA binding proteins contain multiple RNA binding domains. The ability to perform multiple functions depends on the recognition of a diverse range of targets and domains are used combinatorially to achieve this. In this thesis I ask how the sequence specificity of low affinity RNA-binding domains and the interplay between said domains plays a role in RNA target selectivity. Within this
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Davis, Alicia Morgan. "CHARACTERIZATION OF INFLUENZA NUCLEOPROTEIN BODY DOMAIN AS ANTIVIRAL TARGET." CSUSB ScholarWorks, 2016. https://scholarworks.lib.csusb.edu/etd/364.

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Influenza is a segmented negative strand RNA virus. Each RNA segment is encapsulated by viral nucleoprotein (NP) and bound by the viral RNA dependent RNA polymerase (RdRP) to form viral ribonucleoproteins (vRNPs) responsible for RNA synthesis. NP is a structural component of the vRNP but also interacts with both viral and host factors to regulate viral RNA expression. NP is conserved among influenza A isolates, making NP interactions compelling antiviral targets. Here I characterize mutations within 5 amino acids of NP that comprise an accessible region of the NP body domain, as determined by
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Atkins, Jane. "Biochemical characterisation of the catalytic domain of neuropathy target esterase." Thesis, University of Leicester, 2000. http://hdl.handle.net/2381/29643.

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Neuropathy target esterase (NTE) is an integral membrane protein found predominantly in neurones. Covalent modification of NTE's active site serine, by certain organophosphates (OPs), results in a neurodegenerative syndrome. NTE's physiological substrate is unknown and its catalytic activity does not seem to be vital in the adult animal. The enzyme domain of human NTE (residues 727-1216), called NEST, was expressed in E. coli and reacted with a carboxyl ester substrate and OP inhibitors the same as native NTE. During purification catalytic activity was lost, but was restored by reconstitution
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Bishoff, Josef P. "Target detection using oblique hyperspectral imagery : a domain trade study /." Online version of thesis, 2008. http://hdl.handle.net/1850/7834.

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Kucheruk, Liliya. "Modern English Legal Terminology : linguistic and cognitive aspects." Thesis, Bordeaux 3, 2013. http://www.theses.fr/2013BOR30016/document.

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La présente étude intitulée «Terminologie juridique moderne de la langue anglaise: aspects linguistiques et cognitifs » aborde le langage juridique contemporain dans le cadre de la linguistique cognitive. Les objectifs de l'étude sont d'étudier les particularités de la terminologie juridique et de proposer des principes de systématisation, en se référant à la théorie cognitive de la métaphore. Il s’agit principalement : 1) de déterminer les concepts de base utilisés métaphoriquement dans la langue juridique ; 2) d'établir les correspondances principales entre domaines et les corrélations entre
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Niemiec, Moritz Sebastian. "Human copper ion transfer : from metal chaperone to target transporter domain." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-100511.

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Many processes in living systems occur through transient interactions among proteins. Those interactions are often weak and are driven by small changes in free energy. Due to the short-living nature of these interactions, our knowledge about driving forces, dynamics and structures of these types of protein-protein heterocomplexes are though limited. This is especially important for cellular copper (Cu) trafficking: Copper ions are essential for all eukaryotes and most bacteria. As a cofactor in many enzymes, copper is especially vital in respiration or detoxification. Since the same features t
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Schaus, Brian M. "Improving maritime domain awareness using neural networks for target of interest classification." Thesis, Monterey, California: Naval Postgraduate School, 2015. http://hdl.handle.net/10945/45252.

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Approved for public release; distribution is unlimited<br>Techniques for classifying maritime domain targets-of-interest within images are explored in this thesis. Geometric and photometric features within each image are extracted from processed images and are used to train a neural network. The trained neural network is tested with features of a known object. In the binary classification case, the neural network is used to determine whether a ship is present or not present in the image. In the multi-class and multi-level classification cases, the neural network is used to determine if the obj
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Ferrari, Giovanna Maria. "The interaction of the α2 chimaerin SH2 domain with target proteins." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325678.

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Chen, Yen-Lun. "Margin and Domain Classifications for Target Detection over Huge Population of Outliers." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1269539889.

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Books on the topic "Target domain"

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Dewey, John K. Electrostatic target detection: A preliminary investigation. Naval Postgraduate School, 1994.

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Lee, Christina. The SH3 domain of the yeast protein Fus1 binds and unusual target sequence. National Library of Canada, 2002.

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Behrle, Charles D. Computer simulation studies of multiple broadband target localization via frequency domain beamforming for planar arrays. Naval Postgraduate School, 1988.

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Berube, Christina Louise. Characterization of PIDD, a death domain-containing p53 target gene. 2006.

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Bohon, Cara. Research Domain Criteria. Edited by W. Stewart Agras and Athena Robinson. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190620998.013.2.

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A primary goal of the research domain criteria (RDoC) project from the National Institute of Mental Health in the United States is to better characterize and understand the pathology and etiology of mental illness by examining constructs with biological underpinnings and their effects on psychiatric symptoms. This endeavor shows promise in helping to better conceptualize dysfunction in the field of eating disorders, where there appears to be great heterogeneity within diagnostic groups. Treatments designed for a particular diagnosis may result in improved remission rates if they instead target
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Anagnostopoulou, Elena. Voice, manners, and results in adjectival passives. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198767886.003.0005.

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The chapter argues that there are two functional heads in the VP domain: a little v head introducing an event and Voice introducing the external argument. Evidence is drawn from adjectival passives, which split into several types that can be described in terms of this architecture. The chapter explores the interaction between Voice, v, and manner vs. result interpretations of verbal meaning in resultant state vs. target state adjectival passives. First, a summary is given of the main arguments for postulating a v head and a Voice head in adjectival passives. The chapter then focuses on the abs
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Goldman, Alvin I. Theory of Mind. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0017.

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The article provides an overview of ‘Theory of Mind’ (ToM) research, guided by two classifications. The first covers four competing approaches to mentalizing such as the theory-theory, modularity theory, rationality theory, and simulation theory. The second classification is the first-person/third-person contrast. Jerry Fodor claimed that commonsense psychology is so good at helping predict behavior that it is practically invisible. It works well because the intentional states it posits genuinely exist and possess the properties generally associated with them. The modularity model has two prin
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Embedded Mechatronics System Design for Uncertain Environments: Linux®-Based, Rasbpian®, ARDUINO® and MATLAB® XPC Target Approaches. Institution of Engineering & Technology, 2018.

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Skopeteas, Stavros. Information Structure in Modern Greek. Edited by Caroline Féry and Shinichiro Ishihara. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199642670.013.15.

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This chapter deals with the prosodic and syntactic reflexes of information structure in Modern Greek. The relevant properties of this language are: (a) the word order is sensitive to information structure, such that topics and foci target positions in the left periphery and background information is right dislocated; (b) the intonational nucleus depends on the focus domain and is realized through pitch accents; and (b) definite complements must be doubled through co-referent clitic pronouns if they are not accented, which depends on information structure. This chapter introduces these phenomen
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Anthony Basham, David. Paul, the Temple, and Building a Metaphor. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9780567718341.

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David Anthony Basham argues that Paul and the Corinthians share a “system of associated commonplaces” about the Jerusalem temple. Basham proposes that when Paul applies temple language to the Corinthians by calling themnaos theou(“God’s temple”), he sparks a creative process of interaction between the temple and the Corinthian assembly—a process of selecting, emphasizing, and organizing information from the source domain (temple) to see the target domain (the Corinthians) in a new light. Basham suggests that, in understanding Paul’s fraught relationship with certain institutions of Second Temp
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Book chapters on the topic "Target domain"

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Chen, Si-Wei. "Polarimetric Rotation Domain Target Detection." In Imaging Radar Polarimetric Rotation Domain Interpretation. CRC Press, 2024. http://dx.doi.org/10.1201/9781003461296-5.

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Ranger, Graham. "Anyway: Configuration by Target Domain." In Discourse Markers. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70905-5_3.

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Dalgaard, Johnny. "ISPS Code Implementation: Overkill and Off-Target." In Sustainability in the Maritime Domain. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69325-1_7.

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Saha, Sudipan, Shan Zhao, Nasrullah Sheikh, and Xiao Xiang Zhu. "Reiterative Domain Aware Multi-target Adaptation." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16788-1_5.

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Zuo, Hua, Guangquan Zhang, and Jie Lu. "Fuzzy Domain Adaptation Using Unlabeled Target Data." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_22.

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Lv, Fengmao, Hao Chen, Jinzhao Wu, Linfeng Zhong, Xiaoyu Li, and Guowu Yang. "Improving Target Discriminability for Unsupervised Domain Adaptation." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04221-9_26.

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Pavan, Matheus Camasmie, and Ivandré Paraboni. "Cross-target Stance Classification as Domain Adaptation." In Advances in Computational Intelligence. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19493-1_2.

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Carrazco, Julio Ivan Davila, Suvarna Kishorkumar Kadam, Pietro Morerio, Alessio Del Bue, and Vittorio Murino. "Target-Driven One-Shot Unsupervised Domain Adaptation." In Image Analysis and Processing – ICIAP 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43148-7_8.

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Bobek, Szymon, Sławomir Nowaczyk, Sepideh Pashami, Zahra Taghiyarrenani, and Grzegorz J. Nalepa. "Towards Explainable Deep Domain Adaptation." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50396-2_6.

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AbstractIn many practical applications data used for training a machine learning model and the deployment data does not always preserve the same distribution. Transfer learning and, in particular, domain adaptation allows to overcome this issue, by adapting the source model to a new target data distribution and therefore generalizing the knowledge from source to target domain. In this work, we present a method that makes the adaptation process more transparent by providing two complementary explanation mechanisms. The first mechanism explains how the source and target distributions are aligned
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Bracamonte, Javier, Michael Ansorge, Fausto Pellandini, and Pierre-André Farine. "Efficient Compressed Domain Target Image Search and Retrieval." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526346_19.

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Conference papers on the topic "Target domain"

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Lu, Yuwu, and Yihan Yang. "Context-Guided Active Domain Adaptation for Blended Target Domain." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890496.

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Kelkar, Varun, H. S. Melihcan Erol, Muhammad Aneeq Uz Zaman, Omer Tanovic, and Ravi Kiran Raman. "Regularized Domain Adaptation for Estimation Tasks in Partially Observed Target Domains." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889315.

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Weeks, Deborah, and Samuel Rivera. "Domain adaptation by topology regularization." In Automatic Target Recognition XXXI, edited by Timothy L. Overman, Riad I. Hammoud, and Abhijit Mahalanobis. SPIE, 2021. http://dx.doi.org/10.1117/12.2585705.

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Jang, Hyunsung, Minseok Lee, Jaeyeob Kim, Nam Koo Ha, and Kwang Hoon Sohn. "Unsupervised domain adaptation for multispectral object detection." In Automatic Target Recognition XXXIII, edited by Timothy L. Overman, Riad I. Hammoud, and Abhijit Mahalanobis. SPIE, 2023. http://dx.doi.org/10.1117/12.2664074.

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Zhang, Xiaohong, Haofeng Zhang, Jianfeng Lu, Ling Shao, and Jingyu Yang. "Target-targeted Domain Adaptation for Unsupervised Semantic Segmentation." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9560785.

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Liang, Jian, Dapeng Hu, and Jiashi Feng. "Domain Adaptation with Auxiliary Target Domain-Oriented Classifier." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01636.

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Tang, Jianheng, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, and Zhiting Hu. "Target-Guided Open-Domain Conversation." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1565.

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Havlicek, Joseph P., Chuong T. Nguyen, and Mark Yeary. "Modulation domain infrared target models." In Defense and Security Symposium, edited by Wendell R. Watkins and Dieter Clement. SPIE, 2006. http://dx.doi.org/10.1117/12.666340.

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Yao, Chun-Han, Boqing Gong, Hang Qi, Yin Cui, Yukun Zhu, and Ming-Hsuan Yang. "Federated Multi-Target Domain Adaptation." In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2022. http://dx.doi.org/10.1109/wacv51458.2022.00115.

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Sun, Zhishu, Zhifeng Shen, Luojun Lin, et al. "Dynamic Domain Generalization." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/187.

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Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this problem, we develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt to the data from different domains. Specifically, we leverage a meta-adjuster to twist
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Reports on the topic "Target domain"

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Hammerman, Peter. Characterization of the Discoidin Domain Receptor 2 Kinase as a Novel Therapeutic Target for Squamous Cell Lung Cancer. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada573105.

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Fluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7697123.bard.

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Innate immune responses in animals and plants involve receptors that recognize microbe-associated molecules. In plants, one set of this defense system is characterized by large families of TIR–nucleotide binding site–leucine-rich repeat (TIR-NBS-LRR) resistance genes. The direct interaction between plant proteins harboring the TIR domain with proteins that transmit and facilitate a signaling pathway has yet to be shown. The Arabidopsis genome encodes TIR-domain containing genes that lack NBS and LRR whose functions are unknown. Here we investigated the functional role of such protein, TLW1 (TI
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Rosen, Jeffrey A., Kesha Hudson, Susan Rotermund, Cheryl Roberts, and Anna-Lisa Mackey. Social Emotional Learning in Middle School: Developing Evidence-Based Programs. RTI Press, 2022. http://dx.doi.org/10.3768/rtipress.2022.op.0075.2207.

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This article focuses attention on a critical need for more evidence-based social emotional learning (SEL) programs for middle school students. First, we explore the definition of SEL, pointing out how it has evolved as our world has changed. We review key SEL domains and skills and describe universal school-based SEL programming as one approach to fostering students’ SEL competencies. We highlight the ongoing need for evidence-based middle school SEL programs by demonstrating how few programs meet the rigorous What Works Clearinghouse (WWC), Collaborative for Academic, Social, and Emotional Le
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Fromm, Hillel, and Joe Poovaiah. Calcium- and Calmodulin-Mediated Regulation of Plant Responses to Stress. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568096.bard.

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We have taken a molecular approach to clone cellular targets of calcium/calmodulin (Ca2+/CaM). A 35S-labeled recombinant CaM was used as a probe to screen various cDNA expression libraries. One of the isolated clones from petunia codes for the enzyme glutamate decarboxylase (GAD) which catalyzes the conversion of glutamate to g-aminobutyric acid (GABA). The activity of plant GAD has been shown to be dramatically enhanced in response to cold and heat shock, anoxia, drought, mechanical manipulations and by exogenous application of the stress phytohormone ABA in wheat roots. We have purified the
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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide si
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are ob
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Lin, Xiao, and Irdanto Saputra Lase. Unsettled Issues Regarding Electric Vehicle Battery Recycling. SAE International, 2024. https://doi.org/10.4271/epr2024030.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In today's world, the electric vehicle (EV) industry is experiencing a remarkable boom with increasing global demand. With it, comes the surging and unprecedented need for EV batteries. Recycling these batteries has become of crucial importance, as it not only plays a vital role in ensuring the security of the battery supply chain but also serves as a key measure for reducing greenhouse gas emissions. However, there are still several issues that remain unresolved in this domain.&lt;/div&gt;&lt;div class="htmlview paragra
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Khan, Samir. Towards MRO 4.0: Challenges for Digitalization and Mapping Emerging Technologies. SAE International, 2023. http://dx.doi.org/10.4271/epr2023007.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;With technological breakthroughs in electric land vehicles revolutionizing their respective industry, maintenance, repair, and overhaul (MRO) facilities in aviation are also adopting digital technologies in their practices. But despite this drive towards digitalization, the industry is still dominated by manual labor and subjective assessments. Today, several technologies, processes, and practices are being championed to resolve some of these outstanding challenges. Considering this, it is important to present current pe
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Raju, Nivedita. Parameters to Assess Escalation Risks in Space. Stockholm International Peace Research Institute, 2024. https://doi.org/10.55163/edtc6801.

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Space-enabled services are critical for various civilian and military purposes. Current military uses of space—for example, in the Russia-Ukraine War—indicate several avenues for unpredictability and ambiguity, which can increase potential for escalation, both in space and on earth. Yet, there is no common understanding of escalation risks in the international community. Four parameters to assess escalation risks in the space domain are identified: the target, the capability used, the effect and the consequences. These parameters can help establish a standardized approach to assess whether an
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Gafni, Yedidya, Moshe Lapidot, and Vitaly Citovsky. Dual role of the TYLCV protein V2 in suppressing the host plant defense. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7597935.bard.

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TYLCV-Is is a major tomato pathogen, causing extensive crop losses in Israel and the U.S. We have identified a TYLCV-Is protein, V2, which acts as a suppressor of RNA silencing. Intriguingly, the counter-defense function of V2 may not be limited to silencing suppression. Our recent data suggest that V2 interacts with the tomato CYP1 protease. CYP1 belongs to the family of papain-like cysteine proteases which participate in programmed cell death (PCD) involved in plant defense against pathogens. Based on these data we proposed a model for dual action of V2 in suppressing the host antiviral defe
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