Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Modeling text-based time-series to make prediction about a future event or outcome is an important task with a wide range of applications. The standard approach is to train and test the model using the same input window, but this approach neglects the data collected in longer input windows between the prediction time and the final outcome, which are often available during training. In this study, we propose to treat this neglected text as privileged information available during training to enhance early prediction modeling through knowledge distillation, presented as Learning using Privileged tIme-sEries Text (LuPIET). We evaluate the method on clinical and social media text, with four clinical prediction tasks based on clinical notes and two mental health prediction tasks based on social media posts. Our results show LuPIET is effective in enhancing text-based early predictions, though one may need to consider choosing the appropriate text representation and windows for privileged text to achieve optimal performance. Compared to two other methods using transfer learning and mixed training, LuPIET offers more stable improvements over the baseline, standard training. As far as we are concerned, this is the first study to examine learning using privileged information for time-series in the NLP context.

We commonly use agreement measures to assess the utility of judgements made by human annotators in Natural Language Processing (NLP) tasks. While inter-annotator agreement is frequently used as an indication of label reliability by measuring consistency between annotators, we argue for the additional use of intra-annotator agreement to measure label stability over time. However, in a systematic review, we find that the latter is rarely reported in this field. Calculating these measures can act as important quality control and provide insights into why annotators disagree. We propose exploratory annotation experiments to investigate the relationships between these measures and perceptions of subjectivity and ambiguity in text items.

Process analytic approaches play a critical role in supporting the practice of business process management and continuous process improvement by leveraging process-related data to identify performance bottlenecks, extracting insights about reducing costs and optimizing the utilization of available resources. Process analytic techniques often have to contend with real-world settings where available logs are noisy or incomplete. In this paper we present an approach that permits process analytics techniques to deliver value in the face of noisy/incomplete event logs. Our approach leverages knowledge graphs to mitigate the effects of noise in event logs while supporting process analysts in understanding variability associated with event logs. Our approach is verified and validated on a sepsis event-log taken from a standard repository.

Based on a report by the International Diabetes Federation, in 2021, 537 million adults globally were suffering from diabetes causing 6.7 million deaths [3]. Furthermore, the number of diabetics is projected to reach 643 million by 2030 and 783 million by 2045 [3]. Diabetes in an individual prevails due to a dynamic interaction between different risk factors such as sleep duration, alcohol consumption, dyslipidemia, physical inactivity, serum uric acid, obesity, hypertension, cardiovascular disease, family history of diabetes, ethnicity, depression, age, and gender [4]. If not treated at an early stage, diabetes can lead to severe complications [5]. The use of machine learning has thus gained wide attention for the prediction of diabetes based on risk factors data [6-13] in context of smart healthcare [14,15].

Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.

However, the first machine learning libraries as we know them today, which provide tools and frameworks for implementing and training machine learning models, did not appear until the 1980s and 1990s. One of the earliest machine learning libraries was the Statlib library, which was developed at Carnegie Mellon University in the 1980s. This library provided tools for statistical analysis and machine learning, including support for decision trees and neural networks. Other early machine learning libraries include the Weka library, developed at the University of Waikato in New Zealand in the 1990s, and the LIBSVM library developed at the National Taiwan University in the late 1990s. These libraries provided tools for a variety of machine learning tasks, including classification, regression, and clustering.

Melbourne – Visually impaired student Adam Whitehead has long relied on a computer and assistive technology to help him read course materials and take exams at the University of Melbourne in Australia. He has watched with concern as universities in Australia and beyond move to crack down on ChatGPT -- a free program that generates original text about virtually any subject in response to a prompt -- over fears of cheating. As the chatbot stirs debate over the use of technology and artificial intelligence (AI) in education, disabled students and educators have said the benefits should not be overlooked in a rush to regulate. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

I'm a full stack developer of most things computer sciency and academic with a true passion for teaching. I've been teaching others about games development, programming, computer graphics, animation and web design for over 25 years in universities in Australia and Europe at the full professor level. I've also consulted for Unity, SAE, the Australian Institute of Entertainment and Wikitude. My best selling textbooks including Holistic Game Development with Unity are used in over 100 institutions world-wide. My graduates work at companies like Apple, Ubisoft, LinkedIn and Deloitte Digital.

Writing with next-phrase suggestions powered by large language models is becoming more pervasive by the day. However, research to understand writers' interaction and decision-making processes while engaging with such systems is still emerging. We conducted a qualitative study to shed light on writers' cognitive processes while writing with next-phrase suggestion systems. To do so, we recruited 14 amateur writers to write two reviews each, one without suggestions and one with suggestions. Additionally, we also positively and negatively biased the suggestion system to get a diverse range of instances where writers' opinions and the bias in the language model align or misalign to varying degrees. We found that writers interact with next-phrase suggestions in various complex ways: Writers abstracted and extracted multiple parts of the suggestions and incorporated them within their writing, even when they disagreed with the suggestion as a whole; along with evaluating the suggestions on various criteria. The suggestion system also had various effects on the writing process, such as altering the writer's usual writing plans, leading to higher levels of distraction etc. Based on our qualitative analysis using the cognitive process model of writing by Hayes as a lens, we propose a theoretical model of 'writer-suggestion interaction' for writing with GPT-2 (and causal language models in general) for a movie review writing task, followed by directions for future research and design.