title: Adaptive Neural Networks for Online Domain Incremental Continual Learning

authors: Gunasekara, Nuwan

subjects: computer science

abstract: Continual Learning (CL) poses a significant challenge to Neural Network (NN)s, where the data distribution changes from one task to another. In Online domain incremental continual learning (OD-ICL), this distribution change happens in the input space without affecting the label distribution. In order to adapt to such changes, the model being trained risks forgetting previously learned knowledge (stability). On the other hand, enforcing that the model preserves past knowledge will cause it to fail to learn new concepts (plasticity). We propose Online Domain Incremental Networks (ODIN), a novel method to alleviate catastrophic forgetting by automatically detecting the end of a task using concept drift detection. As a consequence, ODIN does not require the specification of task ids. ODIN maintains a pool of NNs, each trained on a single task and frozen for further updates. A Task Predictor (TP) is trained to select the most suitable NN from the frozen pool for prediction. We compare ODIN against popular regularization and replay methods. It outperforms regularization methods and achieves comparable predictive performance to replay methods.

text: Adaptive Neural Networks for Online Domain Incremental Continual Learning Continual Learning (CL) poses a significant challenge to Neural Network (NN)s, where the data distribution changes from one task to another. In Online domain incremental continual learning (OD-ICL), this distribution change happens in the input space without affecting the label distribution. In order to adapt to such changes, the model being trained risks forgetting previously learned knowledge (stability). On the other hand, enforcing that the model preserves past knowledge will cause it to fail to learn new concepts (plasticity). We propose Online Domain Incremental Networks (ODIN), a novel method to alleviate catastrophic forgetting by automatically detecting the end of a task using concept drift detection. As a consequence, ODIN does not require the specification of task ids. ODIN maintains a pool of NNs, each trained on a single task and frozen for further updates. A Task Predictor (TP) is trained to select the most suitable NN from the frozen pool for prediction. We compare ODIN against popular regularization and replay methods. It outperforms regularization methods and achieves comparable predictive performance to replay methods.

year: 2022

title: A Q-Slot Monopole for UWB Body-Centric Wireless Communications

authors: Yeboah-Akowuah, Bright

abstract: This paper presents a novel and simple ultrawideband printed rectangular monopole antenna for body-centric wireless communications. The design is based on etching a Q-slot on a rectangular radiator and is optimized to produce the largest bandwidth in free space and close to the human body. We analyze the design of the proposed antenna and assess its performance in terms of bandwidth, gain, efficiency, and radiation patterns. We also characterize the antenna in the time-domain by calculating its fidelity factor. Our results show that the Q-slot antenna maintains its bandwidth when placed in close contact with the human body, or in contact with breast-mimicking tissue phantoms. The very good agreement between the calculated and measured antenna performances in free space and on body suggests that the antenna is immune to variations in the human tissue and is also robust to fabrication tolerances.

text: A Q-Slot Monopole for UWB Body-Centric Wireless Communications This paper presents a novel and simple ultrawideband printed rectangular monopole antenna for body-centric wireless communications. The design is based on etching a Q-slot on a rectangular radiator and is optimized to produce the largest bandwidth in free space and close to the human body. We analyze the design of the proposed antenna and assess its performance in terms of bandwidth, gain, efficiency, and radiation patterns. We also characterize the antenna in the time-domain by calculating its fidelity factor. Our results show that the Q-slot antenna maintains its bandwidth when placed in close contact with the human body, or in contact with breast-mimicking tissue phantoms. The very good agreement between the calculated and measured antenna performances in free space and on body suggests that the antenna is immune to variations in the human tissue and is also robust to fabrication tolerances.

year: 2017

title: Claiming collective space: Kaupapa Maori in psychology

authors: Levy, Michelle Patricia

subjects: Maori

abstract: From its inception, the Psychology Department at the University of Waikato has had a focus on issues of culture, particularly those of relevance for Maori, with these early foundations laid by Emeritus Professor James Ritchie, who in 1965 took up the founding chair of the Department. These foundations have continued to be built on, utilising a variety of different strategies. Drawing on a research base which has emerged primarily from within the Maori and Psychology Research Unit at Waikato University, this paper explores strategies which have served to carve out and claim space for Maori, both within the department, and within the wider discipline of psychology.

text: Claiming collective space: Kaupapa Maori in psychology From its inception, the Psychology Department at the University of Waikato has had a focus on issues of culture, particularly those of relevance for Maori, with these early foundations laid by Emeritus Professor James Ritchie, who in 1965 took up the founding chair of the Department. These foundations have continued to be built on, utilising a variety of different strategies. Drawing on a research base which has emerged primarily from within the Maori and Psychology Research Unit at Waikato University, this paper explores strategies which have served to carve out and claim space for Maori, both within the department, and within the wider discipline of psychology.

year: 2008