Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews

Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. In this work, we first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic . We investigate the automatic detection of the two conditions, and demonstrate a good linear classifier that can be learnt using a down-selected set of features from the different aspects of the interviews and a novel approach of summarising these features. Finally, we find that different sets of features characterise BD and BPD, thus providing insights into the difference between the automatic screening of the two conditions

Modelling paralinguistic properties in conversational speech to detect bipolar disorder and borderline personality disorder

Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms. In this work, we investigate the automatic detection of these two conditions by modelling both verbal and non-verbal cues in a set of interviews. We propose a new approach of modelling short-term features with visibility-signature transform, and compare it with widely used high-level statistical functions. We demonstrate the superior performance of our proposed signature-based model. Furthermore, we show the role of different sets of features in characterising BD and BPD

Terrestrial applications: An intelligent Earth-sensing information system

For Abstract see A82-2214

The French Atlantic littoral and the Massif Armoricain, part 1

The author has identified the following significant results. For interpretation of Isle of Jersey imagery, two types of taxons were defined according to their variability in time. On the whole, taxons with a similar spectral signature were opposed to those with strongly varying spectral signature. The taxon types were low diachronic variations and strong diachronic variation. Imagery interpretation was restricted to the landward part of the Fromentine area, including the sand beaches which were often difficult to spectrally separate from the barren coastal dunes in the southern part of Noirmoutier Island as well as along the Breton marsh. From 1972 to 1976, sandbanks reduced in area. Two high river discharge images showed over a two year period an identical outline for the Bilho bank to seaward, whereas upstream, the bank has receeded in the same time to a line joining Paimboeuf to Montoir. The Brillantes bank has receeded at both ends, partly due to dredging operations in the access channel to Donges harbor

Generative Language Models for Personalized Information Understanding

A major challenge in information understanding stems from the diverse nature of the audience, where individuals possess varying preferences, experiences, educational and cultural backgrounds. Consequently, adopting a one-size-fits-all approach to provide information may prove suboptimal. While prior research has predominantly focused on delivering pre-existing content to users with potential interests, this thesis explores generative language models for personalized information understanding. By harnessing the potential of generative language models, our objective is to generate novel personalize content for individual users. As a result, users from diverse backgrounds can be provided with content that are tailored for their need and better aligns with their interests. The crux of this research hinges on addressing the following two aspects: 1. Personalized Content: How to harness user profiles to create tailored content for individual users; 2. Effective Communication: How to engage with users in order to proficiently convey information. For the first aspect, i.e. personalized content, we explored personalized news headline generation. By analyzing users\u27 reading history, our proposed framework identifies perspectives that users are interested in, which can further guide generating news headlines that are attractive to users. For the second aspect, i.e. effective communication, we developed personalized reading assistive agent, which assist users understand complex information in news article or academic documents through conversations. Compared to reading, obtaining information through conversations is more interactive and requires shorter attention span. We further incorporate the above aspects in personalized information systems in a real-life scenario, i.e. patient education. Specifically, we propose a novel after-visit summaries (AVS) writing assistant. After-visit summaries notes are documents given to patients to help them understand their clinical visits and disease self-management. Our approach not only automatically generates AVS drafts, but also detects potential errors in the generated drafts, allowing physicians to revise and produce AVS notes with higher efficiency and accuracy. Moreover, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients’ discharge instructions and then formulates personalized educational questions for distinctive patients. In addition, PaniniQA is also equipped with answer verification functionality to provide timely feedback to correct patients’ misunderstandings. Overall, we aspire to contribute to the advancement of information dissemination techniques, promoting a more inclusive and effective means of communication in our information-driven world

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Functional design for operational earth resources ground data processing

The author has identified the following significant results. Study emphasis was on developing a unified concept for the required ground system, capable of handling data from all viable acquisition platforms and sensor groupings envisaged as supporting operational earth survey programs. The platforms considered include both manned and unmanned spacecraft in near earth orbit, and continued use of low and high altitude aircraft. The sensor systems include both imaging and nonimaging devices, operated both passively and actively, from the ultraviolet to the microwave regions of the electromagnetic spectrum