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1. Is working memory domain-general or domain-specific?

   https://doi.org/10.1016/j.tics.2024.06.006  

   Nozari, Nazbanou; Martin, Randi C (July 2024, Trends in Cognitive Sciences)

   Given the fundamental role of working memory (WM) in all domains of cognition, a central question has been whether WM is domain-general. However, the term ‘domain-general’ has been used in different, and sometimes misleading, ways. By reviewing recent evidence and biologically plausible models of WM, we show that the level of domain-generality varies substantially between three facets of WM: in terms of computations, WM is largely domain-general. In terms of neural correlates, it contains both domain-general and domain-specific elements. Finally, in terms of application, it is mostly domain-specific. This variance encourages a shift of focus towards uncovering domain-general computational principles and away from domain-general approaches to the analysis of individual differences and WM training, favoring newer perspectives, such as training-as-skill-learning. 

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2. Local Domain Adaptation for Cross-Domain Activity Recognition

   https://doi.org/10.1109/THMS.2020.3039196  

   Zhao, Jiachen; Deng, Fang; He, Haibo; Chen, Jie (February 2021, IEEE Transactions on Human-Machine Systems)

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3. Open-Set Graph Domain Adaptation via Separate Domain Alignment

   https://doi.org/10.1609/aaai.v38i8.28765  

   Wang, Yu; Zhu, Ronghang; Ji, Pengsheng; Li, Sheng (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   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 novel and challenging problem for graphs, i.e., open-set domain adaptive node classification, and propose a new approach to solve it. Specifically, we develop an algorithm for efficient knowledge transfer from a labeled source graph to an unlabeled target graph under a separate domain alignment (SDA) strategy, in order to learn discriminative feature representations for the target graph. Our goal is to not only correctly classify target nodes into the known classes, but also classify unseen types of nodes into an unknown class. Experimental results on real-world datasets show that our method outperforms existing methods on graph domain adaptation. 

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4. Domain Concretization From Examples: Addressing Missing Domain Knowledge Via Robust Planning

   https://doi.org/10.1109/LRA.2021.3137549  

   Sharma, Akshay; Medikeri, Piyush Rajesh; Zhang, Yu (April 2022, IEEE Robotics and Automation Letters)
5. Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language

   https://doi.org/10.18653/v1/2023.acl-long.668  

   Farzana, Shahla; Parde, Natalie (January 2023, Association for Computational Linguistics)

   Health-related speech datasets are often small and varied in focus. This makes it difficult to leverage them to effectively support healthcare goals. Robust transfer of linguistic features across different datasets orbiting the same goal carries potential to address this concern. To test this hypothesis, we experiment with domain adaptation (DA) techniques on heterogeneous spoken language data to evaluate generalizability across diverse datasets for a common task: dementia detection. We find that adapted models exhibit better performance across conversational and task-oriented datasets. The feature-augmented DA method achieves a 22% increase in accuracy adapting from a conversational to task-specific dataset compared to a jointly trained baseline. This suggests promising capacity of these techniques to allow for productive use of disparate data for a complex spoken language healthcare task. 

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