2,778 research outputs found
Editorial: Explainable artificial intelligence models and methods in finance and healthcare
No full textThis article is a foreword to a special issue on "Explainable artificial intelligence models and methods in finance and healthcare" and introduces the main articles of the collection. The core topic of this special issue is explainability and trusting algorithmic output
In Honour of Brian MacWhinney: A Personal Account
No full textWhile this volume and the writings have made it amply clear what significant contributions Professor Brian MacWhinney has made to the field at large, in this afterword, we begin with a senior member of our author team (Ping Li, PL) followed by a mid-career member (Helen Zhao, HZ) and an early career member (Zhe Gao, ZG), to provide our personal accounts of Brian not only as a leading scholar but also as a role model who touches and changes people’s lives
Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms
No full textLangrock R, Swihart BJ, Caffo BS, Punjabi NM, Crainiceanu CM. Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine. 2013;32(19):3342-3356
‘Through the unknown, remembered gate’: the Brian Nettleton lecture – Outdoors Victoria conference, 2022
No full textThis paper is an adapted version of the Brian Nettleton Lecture given at the Outdoors Victoria Conference, 2022. It explores how the last two decades have seen an ever-accelerating Digital Revolution which has impacted on almost every aspect of human experience to the point that it is now omnipresent. Life is now mediated through the screen. As a result, children and young people have become hyper-vigilant, overly anxious, experience a sense of climate trauma, and have decreasing access to, and time spent in, the outdoors. In addition, children have just experienced two years of isolation as a result of the Covid-19 pandemic and evidence suggests that they are already experiencing significant mental health issues as a result. This paper considers the implications of this for Outdoors Victoria and Outdoor Education. © The Author(s) 2023
Japan’s New Basic Energy Plan
No full textAuthor accepted manuscript version of an article published in:
John S. Duffield and Brian Woodall, “Japan’s New Basic Energy Plan,” Energy Policy 39, no. 6 (June 2011): 3741-49. https://doi.org/10.1016/j.enpol.2011.04.00
FACE TOUCH DETECTION TO REDUCE DISEASE TRANSMISSION
No full textIn the last several years, multiple research teams have investigated the face-touch detection problem for the purpose of developing technology that can detect and subsequently prevent face-touching. IMU-based datasets have been cultivated under controlled settings of trial participants performing different face-touches. This thesis seeks to use one well-outlined dataset in particular, to build a “pre-face touch detection model” and a “completed face-touch detection model”. These two Human Activity Recognition (HAR) models are designed to be situated in a free-living study of participants “in the wild”. The pre-face touch detection model is the focus of this thesis, as we primarily seek to build a face-touch prevention tool that potentially can reduce disease transmission for its user. The need for researchers to collect free-living face-touch trials such that a pre-face touch detection model can be built that is generalizable to users “in the wild” is what motivated the development of the completed face-touch detection model. The main contribution of this thesis to the sub-problem of face-touch detection within the HAR space is our investigation of utilizing Discrete Wavelet Transform (DWT) on IMU sensor signals in order to extract localized time-frequency features relevant to the tasks of pre-face touch detection and completed face-touch detection. Additionally, we identify constraints on Precision and Specificity metrics in order to minimize the invasiveness of the pre-face touch detection model, and we maximize Recall score with these constraints considered
FACE TOUCH DETECTION TO REDUCE DISEASE TRANSMISSION
No full textIn the last several years, multiple research teams have investigated the face-touch detection problem for the purpose of developing technology that can detect and subsequently prevent face-touching. IMU-based datasets have been cultivated under controlled settings of trial participants performing different face-touches. This thesis seeks to use one well-outlined dataset in particular, to build a “pre-face touch detection model” and a “completed face-touch detection model”. These two Human Activity Recognition (HAR) models are designed to be situated in a free-living study of participants “in the wild”. The pre-face touch detection model is the focus of this thesis, as we primarily seek to build a face-touch prevention tool that potentially can reduce disease transmission for its user. The need for researchers to collect free-living face-touch trials such that a pre-face touch detection model can be built that is generalizable to users “in the wild” is what motivated the development of the completed face-touch detection model. The main contribution of this thesis to the sub-problem of face-touch detection within the HAR space is our investigation of utilizing Discrete Wavelet Transform (DWT) on IMU sensor signals in order to extract localized time-frequency features relevant to the tasks of pre-face touch detection and completed face-touch detection. Additionally, we identify constraints on Precision and Specificity metrics in order to minimize the invasiveness of the pre-face touch detection model, and we maximize Recall score with these constraints considered
The Ethnogenesis of the Crimean Tatars. An Historical Reinterpretation. (Brian Glyn Williams)
No full texthis article is the translation of Brian Glyn Williams`s work “The Ethnogenesis of the Crimean Tatars. An Historical Reinterpretation”. Brian Williams is the professor of history of Islam on the Chair of Massachusetts University in Dartmouth, the USA. The source is: Journal of the Royal Asiatic Society. Third Series, Vol. 11, № 3, (Nov., 2001), pp. 329–348. The article considers the issue of the Crimean Tatar`s ethnogenesis, their communal identity, social-economic conditions of the activities. On the basis of the studied material, the author comes to conclusion of necessity of Crimean Tatar`s History Reinterpretation
Autoencoder-based deep learning methods for single cell and spatial transcriptomics
No full textThe advent of single-cell and spatial transcriptomics has provided unprecedented resolution into cellular heterogeneity but introduced major computational challenges for data integration and interpretation. In this thesis, we develop and explore deep learning-based methods to address these challenges through the lens of autoencoder architectures.
In Chapter 1, we benchmark Variational Autoencoders (VAEs) against traditional linear decomposition methods, demonstrating that VAEs more effectively capture nonlinear biological variation in single-cell RNA-seq datasets. However, we also highlight inherent limitations in interpretability of VAE latent spaces and show through simulation studies that interpretability cannot be guaranteed without additional model constraints.
Building on these findings, Chapter 2 addresses the critical problem of cross-sample integration in spatial transcriptomics. Using the human dorsolateral prefrontal cortex dataset, we evaluate the performance of adversarial domain adaptation methods and show that standard domain classifier approaches may falter when scaling to multiple samples, motivating the need for more stable integration frameworks.
Finally, in Chapter 3, we introduce a novel autoencoder-based model that explicitly disentangles latent representations into common and dataset-specific components. We propose using the Sliced Wasserstein Distance as a stable and interpretable alternative to adversarial losses for multi-sample integration. Proof-of-concept experiments on synthetic datasets show that our approach successfully recovers and disentangles shared and residual signals, laying the groundwork for future applications to real-world biological data.
Overall, this thesis advances the application of deep learning for high-dimensional biomedical data analysis by proposing interpretable, stable, and scalable representation learning frameworks
Turning the R Console Into an Interactive Learning Environment with swirl
No full textInteractive platforms for learning computer programming have exploded in recent years. Sadly, the R programming language is overlooked by most of these tools. swirl is an open source software package for the R programming language that allows users to learn R and statistics interactively, right in the R console. swirl supports multiple questions types, but emphasis is on having the user interact directly with the R prompt, while receiving helpful and immediate feedback. We (swirl’s creators) have pioneered early content creation, but swirl is constructed in such a way that anyone can create his or her own content and share it freely with others. While swirl has been downloaded over 70,000 times in its first nine months, development of the software and instructional content is ongoing. We hope that the R and statistics communities will continue to rally around the platform and truly embrace it as their own
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