Predicting Three Types of Freezing of Gait Events Using Deep Learning Models

Freezing of gait is a Parkinson's Disease symptom that episodically inflicts a patient with the inability to step or turn while walking. While medical experts have discovered various triggers and alleviating actions for freezing of gait, the underlying causes and prediction models are still being explored today. Current freezing of gait prediction models that utilize machine learning achieve high sensitivity and specificity in freezing of gait predictions based on time-series data; however, these models lack specifications on the type of freezing of gait events. We develop various deep learning models using the transformer encoder architecture plus Bidirectional LSTM layers and different feature sets to predict the three different types of freezing of gait events. The best performing model achieves a score of 0.427 on testing data, which would rank top 5 in Kaggle's Freezing of Gait prediction competition, hosted by THE MICHAEL J. FOX FOUNDATION. However, we also recognize overfitting in training data that could be potentially improved through pseudo labelling on additional data and model architecture simplification.Comment: 5 page

Preface: Trends in natural and machine intelligence

AbstractTrends in natural and machine intelligence are increasingly reflecting a convergence in these two well-established fields of study. The Third International Neural Network Society Winter Conference (INNS-WC 2012) was held in Bangkok, Thailand, on October 3-5, 2012. INNS-WC2012, with an aim to bring together scientists, practitioners, and students worldwide, to discuss the past, present, and future challenges and trends in the area of natural and machine intelligence. This event has been a bi-annual conference of the International Neural Network Society (INNS) to provide a forum for international researchers to exchange latest ideas and advances on neural networks and related discipline

Genome-wide DNA-(de)methylation is associated with Noninfectious Bud-failure exhibition in Almond (Prunus dulcis [Mill.] D.A.Webb).

Noninfectious bud-failure (BF) remains a major threat to almond production in California, particularly with the recent rapid expansion of acreage and as more intensive cultural practices and modern cultivars are adopted. BF has been shown to be inherited in both vegetative and sexual progeny, with exhibition related to the age and propagation history of scion clonal sources. These characteristics suggest an epigenetic influence, such as the loss of juvenility mediated by DNA-(de)methylation. Various degrees of BF have been reported among cultivars as well as within sources of clonal propagation of the same cultivar. Genome-wide methylation profiles for different clones within almond genotypes were developed to examine their association with BF levels and association with the chronological time from initial propagation. The degree of BF exhibition was found to be associated with DNA-(de)methylation and clonal age, which suggests that epigenetic changes associated with ageing may be involved in the differential exhibition of BF within and among almond clones. Research is needed to investigate the potential of DNA-(de)methylation status as a predictor for BF as well as for effective strategies to improve clonal selection against age related deterioration. This is the first report of an epigenetic-related disorder threatening a major tree crop

Immersive Technologies in Virtual Companions: A Systematic Literature Review

The emergence of virtual companions is transforming the evolution of intelligent systems that effortlessly cater to the unique requirements of users. These advanced systems not only take into account the user present capabilities, preferences, and needs but also possess the capability to adapt dynamically to changes in the environment, as well as fluctuations in the users emotional state or behavior. A virtual companion is an intelligent software or application that offers support, assistance, and companionship across various aspects of users lives. Various enabling technologies are involved in building virtual companion, among these, Augmented Reality (AR), and Virtual Reality (VR) are emerging as transformative tools. While their potential for use in virtual companions or digital assistants is promising, their applications in these domains remain relatively unexplored. To address this gap, a systematic review was conducted to investigate the applications of VR, AR, and MR immersive technologies in the development of virtual companions. A comprehensive search across PubMed, Scopus, and Google Scholar yielded 28 relevant articles out of a pool of 644. The review revealed that immersive technologies, particularly VR and AR, play a significant role in creating digital assistants, offering a wide range of applications that brings various facilities in the individuals life in areas such as addressing social isolation, enhancing cognitive abilities and dementia care, facilitating education, and more. Additionally, AR and MR hold potential for enhancing Quality of life (QoL) within the context of virtual companion technology. The findings of this review provide a valuable foundation for further research in this evolving field

EHR-Based Care Coordination Performance Measures in Ambulatory Care

Describes electronic health record-based measures for assessing coordination in referrals, including information communicated with referral, communication to patient, and specialist report to primary care physician. Offers preliminary evaluation findings

Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks

The current COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With more and more people getting affected during the second wave, the hospitals were over-burdened, running out of supplies and oxygen. In this scenario, prediction of the number of COVID-19 cases beforehand might have helped in the better utilization of limited resources and supplies. This manuscript deals with the prediction of new COVID-19 cases, new deaths and total active cases for multiple days in advance. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models that are pre-trained on the data from four different countries (United States of America, Brazil, Spain and Bangladesh) and are fine-tuned or retrained on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then give a multiple days ahead predictions using recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the combination of different models. This method with two countries, Spain and Brazil, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.Comment: 8 pages, 7 figure

Meson Decay Constants from Isospin Mass Splittings in the Quark Model

Decay constants of $D$ and $B$ mesons are estimated within the framework of a heavy-quark approach using measured isospin mass splittings in the $D$, $D^*$, and $B$ states to isolate the electromagnetic hyperfine interaction between quarks. The values $f_D = (262 \pm 29)$ MeV and $f_B = (160 \pm 17)$ MeV are obtained. Only experimental errors are given; possible theoretical ambiguities, and suggestions for reducing them, are noted.Comment: 7 pages, LaTeX, EFI-92-3

Design, Qualification and Integration Testing of the High-Temperature Resistance Temperature Device for Stirling Power System

The Advanced Stirling Radioisotope Generator (ASRG), developed from 2006 to 2013 under the joint sponsorship of the United States Department of Energy (DOE) and National Aeronautics and Space Administration (NASA) to provide a high-efficiency power system for future deep space missions, employed Sunpower Incorporated's Advanced Stirling Convertors (ASCs) with operating temperature up to 840 C. High-temperature operation was made possible by advanced heater head materials developed to increase reliability and thermal-to-mechanical conversion efficiency. During a mission, it is desirable to monitor the Stirling hot-end temperature as a measure of convertor health status and assist in making appropriate operating parameter adjustments to maintain the desired hot-end temperature as the radioisotope fuel decays. To facilitate these operations, a Resistance Temperature Device (RTD) that is capable of high-temperature, continuous long-life service was designed, developed and qualified for use in the ASRG. A thermal bridge was also implemented to reduce the RTD temperature exposure while still allowing an accurate projection of the ASC hot-end temperature. NASA integrated two flight-design RTDs on the ASCs and assembled into the high-fidelity Engineering Unit, the ASRG EU2, at Glenn Research Center (GRC) for extended operation and system characterization. This paper presents the design implementation and qualification of the RTD, and its performance characteristics and calibration in the ASRG EU2 testing