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Survey on lie group machine learning
Lie group machine learning is recognized as the theoretical basis of brain intelligence, brain learning, higher machine learning, and higher artificial intelligence. Sample sets of Lie group matrices are widely available in practical applications. Lie group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed further. This study aims to provide a comprehensive survey on recent advances in Lie group machine learning. We introduce Lie group machine learning techniques in three major categories: supervised Lie group machine learning, semisupervised Lie group machine learning, and unsupervised Lie group machine learning. In addition, we introduce the special application of Lie group machine learning in image processing. This work covers the following techniques: Lie group machine learning model, Lie group subspace orbit generation learning, symplectic group learning, quantum group learning, Lie group fiber bundle learning, Lie group cover learning, Lie group deep structure learning, Lie group semisupervised learning, Lie group kernel learning, tensor learning, frame bundle connection learning, spectral estimation learning, Finsler geometric learning, homology boundary learning, category representation learning, and neuromorphic synergy learning. Overall, this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine learning. It will enable researchers to comprehensively understand the state of the field, identify the most appropriate tools for particular applications, and identify directions for future research.
Journal Article
Power, Status, and Learning in Organizations
This paper reviews the scholarly literature on the effects of social hierarchy-differences in power and status among organizational actors-on collective learning in organizations and groups. We begin with the observation that theories of organization and group learning have tended to adopt a rational system model, a model that emphasizes goal-directed and cooperative interactions between and among actors who may differ in knowledge and expertise but are undifferentiated with respect to power and status. Our review of the theoretical and empirical literatures on power, status, and learning suggests that social hierarchy can complicate a rational system model of collective learning by disrupting three critical learning-related processes: anchoring on shared goals, risk taking and experimentation, and knowledge sharing. We also find evidence to suggest that the stifling effects of power and status differences on collective learning can be mitigated when advantaged actors are collectively oriented. Indeed, our review suggests that higher-ranking actors who use their power and status in more \"socialized\" ways can play critical roles in stimulating collective learning behavior. We conclude by articulating several promising directions for future research that were suggested by our review.
Journal Article
Blending talents for innovation
Despite the upsurge in cross-border R&D collaboration within multinational corporations (MNCs), firms often fail to realize the full potential of cross-border R&D teams. We examine under what conditions geographic diversity might lead to higher or lower innovation performance by focusing on the moderating roles of team composition. We first demonstrate that the geographic diversity of an MNC’s research team has a curvilinear (inverted U-shaped) relationship with the team’s innovation performance. Building upon group learning theory, we further claim that this non-linear relationship is strengthened by the technical experience heterogeneity of researchers but weakened by repeated collaboration among researchers. Our analyses on the top 25 multinational pharmaceutical companies and their 59,998 patents registered from 1981 to 2012 provide strong support for our hypotheses. When geographic diversity is relatively low, teams with different levels of technical experience and more fresh collaborators improve performance by amplifying the benefits of sourcing diverse knowledge. With high geographic dispersion, on the other hand, minimal experience heterogeneity and more instances of past collaboration lead to better performance by facilitating the integration of diverse knowledge. The results shed light on the importance of technical and social relationships among researchers in sourcing and integrating location-specific knowledge and ultimately enhancing team performance.
Journal Article
Barriers and Facilitators to the Learning and Acquisition of Research Competencies among Nursing Students through Active Methodologies: A Qualitative Study Using Reflective Writing
Background: The development of educational research, critical thinking skills, and evidence-based practice requires proposals for educational innovation. The purpose of this study was to explore the perspectives of undergraduate nursing students on the barriers and facilitators after the implementation of a novel activity within the course of research methodology, composed of three active learning strategies: (a) project-based learning; (b) small-group learning; and (c) self-directed learning. Methods: A qualitative exploratory study using reflective writing was conducted at the Nursing Department of the Red Cross School (Spain). Seventy-four nursing students participated in the study, enrolled in the research methodology course. Purposive sampling was used. Online reflective notes were collected from a script of open-ended questions. An inductive thematic analysis was performed. Results: The new proposals facilitated learning of the subject matter and its contents. They were useful and enabled the students to put the contents into practice. In addition, they improved the students’ organization, planning, and involvement. The barriers identified were a lack of time, ambiguity, inadequate tutoring or novelty of the work, and inequity in the distribution of tasks and workloads. Conclusions: Our findings shed light on the barriers and facilitators identified by nursing students when implementing an educational innovation proposal, using three active learning methodologies as learning tools for the subject of nursing research.
Journal Article
Student Accountability in Team-based Learning Classes
Team-based learning (TBL) is a form of small-group learning that assumes stable teams promote accountability. Teamwork promotes communication among members; application exercises promote active learning. Students must prepare for each class; failure to do so harms their team's performance. Therefore, TBL promotes accountability. As part of the course grade, students assess the performance of their teammates. The evaluation forces students to rank their teammates and to provide rationale for the highest and lowest rankings. These evaluations provide rich data on small-group dynamics. In this paper, we analyze 211 student teammate assessments. We find evidence that teams consistently give the lowest evaluations to their least involved members, suggesting that the team component increases accountability, which can promote learning. From these findings we draw implications about small-group dynamics in general and the pedagogy of TBL in particular.
Journal Article