Continual Learning with Adaptive Weights (CLAW)

Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner. Recently, several frameworks have been developed which enable deep learning to be deployed in this learning scenario. A key modelling decision is to what extent the architecture should be shared across tasks. On the one hand, separately modelling each task avoids catastrophic forgetting but it does not support transfer learning and leads to large models. On the other hand, rigidly specifying a shared component and a task-specific part enables task transfer and limits the model size, but it is vulnerable to catastrophic forgetting and restricts the form of task-transfer that can occur. Ideally, the network should adaptively identify which parts of the network to share in a data driven way. Here we introduce such an approach called Continual Learning with Adaptive Weights (CLAW), which is based on probabilistic modelling and variational inference. Experiments show that CLAW achieves state-of-the-art performance on six benchmarks in terms of overall continual learning performance, as measured by classification accuracy, and in terms of addressing catastrophic forgetting

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Teaching Physical Education through student-centered approaches: A year-long action research study of an early-career teacher

A presente tese teve como objetivo compreender como é que uma professora de Educação Física (EF) em início de carreira desenvolveu a sua intervenção pedagógica durante a implementação de pedagogias centradas no aluno, através de um desenho longitudinal de investigação-ação (IA). Adicionalmente, procurou incluir a voz dos alunos acerca das suas experiências de aprendizagem. Participaram 25 alunos com idades compreendidas entre os 16-17 anos, a frequentar o 12º ano de uma escola secundária em Portugal, e uma professora com dois anos de experiência profissional, que assumiu o duplo papel de professora-investigadora. Foram utilizadas múltiplas metodologias ecléticas de recolha de dados, analisadas através de análise temática. Os resultados mostraram que a aplicação inovadora de um design de IA durante um ano e o recurso a fontes de dados qualitativos proporcionaram uma análise profunda, contextualizada e próxima das transformações do processo de ensino-aprendizagem, de acordo com as vozes dos alunos e as suas necessidades individuais. Tais como: (i) As interpretações concetuais erradas da professora no início do processo, e consequente falta de habilidade para atribuir aos alunos um papel central nas experiências de aprendizagem, foi resolvida através do uso combinado de estratégias de ensino mais explícitas e implícitas; (ii) A dificuldade inicial dos alunos em trabalhar cooperativamente foi resolvida através do Modelo de Aprendizagem Cooperativa, que promoveu a interdependência positiva entre os alunos; (iii) Experiências de EF significativas foram alcançadas através da combinação de pedagogias centradas no aluno de acordo com as suas necessidades de aprendizagem, habilidade da professora e constrangimentos contextuais; e (iv) O uso de uma unidade híbrida onde o ambiente centrado no aluno (Modelo de Educação Desportiva) e a especificidade do conteúdo e da matéria (Modelo de Abordagem Progressiva ao Jogo) são considerados, ajudou a professora a atuar como facilitadora da aprendizagem. PALAVRAS-CHAVE: ABORDAGENS DE ENSINO CENTRADAS NO ALUNO, PROFESSOR NOVATO, EDUCAÇÃO FÍSICA, INVESTIGAÇÃO-AÇÃO, LONGITUDINAL, QUALITATIVOThe aim of the present thesis was to understand how a novice Physical Education (PE) teacher developed her pedagogical intervention during the implementation of student-centered pedagogies, through a long-term action-research (AR) design. Additionally, it aimed to consider students' voices about their student-centered learning experiences. Participate 25 students aged 16-17 years, enrolled in the 12th grade at a high school in Portugal, and a teacher with two years of professional experience, who assumed the dual role of teacher-researcher. Multiple eclectic methodologies were used to collect data, analyzed through thematic analyzis. The findings showed that the innovative application of a year-long insider AR-design and the use of qualitative data sources afforded a deep, contextualized, and close analyzis of the transformations of the teaching-learning process according to students' voices and their individual needs. Such as: (i) Teacher' conceptual misinterpretations at the beginning of the process, and consequently her lack of ability to assing students a central role in their learning experience, was solved through the combined use of more explicit and implicit teaching strategies; (ii) The initial difficulty of students to work cooperatively was solved through the use of Cooperative Leaning model, which promoted students' positive interdependence; (iii) Meaningful PE experiences were achieved through the combination of student-centered pedagogies according to students' learning needs, teacher' abilities and contextual constrains; (iv) The use of a hybrid season where the students-centered environment (Sport Education) and the specificity of the content subject-knowledge (Step-Game Approach for non-invasion games) were considerated, helped the teacher to act as facilitator of learning

Jumping into the Cloud: Privacy, Security and Trust of Cloud-Based Computing Within K-12 American Public Education

The purpose of this study is to gain a deeper understanding of how faculty view Cloud-based computing, how they perceive issues of privacy, security, and trust when using Cloud-based systems in schools, and what differences, if any, exist between their at home use of Cloud-based computer systems and their use of these and similar systems at work. Educators who took part in this study (a) demonstrated a relatively good understanding of the Cloud; (b) perceived the issues of privacy, security, and trust as related to Cloud-based computing as a serious matter, which strongly influenced their acceptance of the Cloud, and to a lesser extent, their use of the Cloud; and (c) had noticeable differences in their perceptions of the Cloud when used for school related tasks, and then, as used for personal, non-work related tasks. The theoretical framework utilized is an adaption of F.D. Davis’s 1989 Technology Acceptance Model, which according to Venkatesh (2000), is the most widely applied model of users\u27 acceptance and usage. Findings from this study inform efforts to improve educators’ understanding of the Cloud as a dynamic technology with constantly evolving trade offs of convenience that are increasingly becoming the enemy of privacy, security, and trust

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation