EgoBlur: Responsible Innovation in Aria

Project Aria pushes the frontiers of Egocentric AI with large-scale real-world data collection using purposely designed glasses with privacy first approach. To protect the privacy of bystanders being recorded by the glasses, our research protocols are designed to ensure recorded video is processed by an AI anonymization model that removes bystander faces and vehicle license plates. Detected face and license plate regions are processed with a Gaussian blur such that these personal identification information (PII) regions are obscured. This process helps to ensure that anonymized versions of the video is retained for research purposes. In Project Aria, we have developed a state-of-the-art anonymization system EgoBlur. In this paper, we present extensive analysis of EgoBlur on challenging datasets comparing its performance with other state-of-the-art systems from industry and academia including extensive Responsible AI analysis on recently released Casual Conversations V2 dataset

Direct search for multi-nucleon clustering in nuclear interactions —Evidence at ultra-relativistic energy

We present an exhaustive analysis on the direct search of multi-nucleon clusters in 28Si-AgBr, 32S-AgBr and 16O-AgBr interactions at 14.5 AGeV, 200 AGeV and 60 AGeV, respectively. A comparison with Monte Carlo simulation reveals that clusters of three and four particles are present in the multi-nucleon production at 200 AGeV

EEG Spectral Correlates of Rapid and Deep Slow Breathing States and classification using ML

One interpretation of breathing exercise is to enforce mind-body harmony, when someone feels well and healthy, different organs of our body function harmoniously. One dysfunctional organ may disturb the resonating mechanism across multiple organs. There are different breathing techniques, and recent scientific evidence encourages understanding the neural correlates of breathing. This research investigates breathing exercises at two paces: Rapid and Deep Slow using neural signals. We collect Electroencephalography (EEG) recordings of 14 participants performing breathing tasks. EEG signals are primarily decomposed in frequency bands that designate different cognitive functions. We extract six primary frequency bands, including delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), high beta (21-30 Hz), and gamma (30-40 Hz). Two different techniques are utilized to report the findings encompassing power spectral analysis and employing machine learning classifiers to discriminate features among different stages of inhalation and exhalation with the significance of different frequencies bands. Lowered beta power in Slow Deep breathing is observed compared to Rapid Breathing, which may suggest increased relaxation, calmness, and anxiety reduction. Differences between the two conditions observed in the frontoparietal cortex may be attributed to differences in voluntary control between the two tasks. We observed classification accuracy of 72 % using low beta between Rapid and Deep Slow breathing using Decision Tree. Several interesting findings are observed in different scalp regions suggesting future direction for further investigation. This study contributes to the understanding of neural signatures for different breathing practices. The implication of this research in health care is to design personalized therapies and to design better breathing mobile applications for daily use.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Design Aesthetic

Metastatic gastric adenocarcinoma to the cutaneous neck and chest wall

This case describes an atypical cutaneous presentation of metastatic gastric carcinoma in a patient initially presenting with dysphagia and a sclerotic red plaque overlying the anterior neck and chest. Skin biopsy revealed metastatic adenocarcinoma from the upper gastrointestinal tract. Esophagogastroduodenoscopy revealed stage IV metastatic gastric adenocarcinoma. Treatment with chemotherapy was initiated

A critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations

The continuing Machine Learning (ML) revolution indubitably has had a significant positive impact on the analysis of downlinked satellite data. Other aspects of the Earth Observation industry, despite being less susceptible to widespread application of Machine Learning, are also following this trend. These applications, actual use cases, possible prospects and difficulties, as well as anticipated research gaps, are the focus of this review of Machine Learning applied to Earth Observation Operations. A wide range of topics are covered, including mission planning, fault diagnosis, fault prognosis and fault repair, optimization of telecommunications, enhanced GNC, on-board image processing, and the use of Machine Learning models on platforms with constrained compute and power capabilities, as well as recommendations in the respective areas of research. The review tackles all on-board and off-board applications of machine learning to Earth Observation with one notable exception: it omits all post-processing of payload data on the ground, a topic that has been studied extensively by past authors. In addition, this review article discusses the standardization of Machine Learning (i.e., Guidelines and Roadmaps), as well as the challenges and recommendations in Earth Observation operations for the purpose of building better space missions

Molecular Pathogenesis of Wilson Disease Among Indians: A Perspective on Mutation Spectrum in ATP7B gene, Prevalent Defects, Clinical Heterogeneity and Implication Towards Diagnosis

Aims We aim to identify the molecular defects in the ATP7B, the causal gene for Wilson disease (WD), in eastern Indian patients and attempt to assess the overall mutation spectrum in India for detection of mutant allele for diagnostic purposes. Methods Patients from 109 unrelated families and their first-degree relatives comprising 400 individuals were enrolled in this study as part of an ongoing project. Genomic DNA was prepared from the peripheral blood of Indian WD patients. PCR was done to amplify the exons and flanking regions of the WD gene followed by sequencing, to identify the nucleotide variants. Results In addition to previous reports, we recently identified eight mutations including three novel (c.3412 + 1G > A, c.1771 G > A, c.3091 A > G) variants, and identified patients with variable phenotype despite similar mutation background suggesting potential role of modifier locus. Conclusions So far we have identified 17 mutations in eastern India including five common mutations that account for 44% of patients. Comparative study on WD mutations between different regions of India suggests high genetic heterogeneity and the absence of a single or a limited number of common founder mutations. Genotype–phenotype correlation revealed that no particular phenotype could be assigned to a particular mutation and even same set of mutations in different patients showed different phenotypes