Towards spike-based machine intelligence with neuromorphic computing.

Guided by brain-like 'spiking' computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence-promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm-hardware codesign.

From evolutionary computation to the evolution of things.

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

Prevalence of Computer Vision Syndrome and Associated Factors among Instructors in Ethiopian Universities: A Web-Based Cross-Sectional Study.

BACKGROUND: In this globalized and high-tech era, the computer has become an integral part of daily life. A constant use of computer for 3 hours and more per day can cause computer vision syndrome (CVS), which is one of the leading occupational hazards of the 21st century. The visual difficulties are the most common health problems associated with excessive computer use. Therefore, this study aimed to assess the prevalence and associated factors of CVS among instructors working in Ethiopian universities. METHODS: A web-based cross-sectional study was conducted among 422 university instructors in Ethiopia from February 02 to March 24, 2021. A structured and self-administered questionnaire prepared by Google Forms was shared among instructors through their e-mail addresses, Facebook, and Telegram accounts. Data cleanup and cross-checking were done before analysis using SPSS version 23. A multivariable logistic regression was applied to identify factors associated with CVS using p value <0.05 and 95% confidence interval. RESULTS: Of the total 416 participants, about 293 (70.4%) were reported to have CVS (95% CI: 65.9-74.5%), of which 54.6% were aged 24-33 years. Blurred vision, pain in and around the eye, and eye redness were the main symptoms reported. Working in third-established universities (AOR = 8.44, 95% CI: 5.47-21.45), being female (AOR = 2.69, 95% CI: 1.28-5.64), being 44 years old and above (AOR = 2.73, 95% CI: 1.31-5.70), frequently working on the computer (AOR = 5.51, 95% CI: 2.05-14.81), and sitting in bent back position (AOR = 8.10, 95% CI: 2.42-23.45) were the factors associated with computer vision syndrome. CONCLUSIONS: In this study, nearly seven-tenths of instructors in Ethiopian universities reported having symptoms of computer vision syndrome. Working in third-generation universities, being female, age, frequently working on the computer, and sitting in bent back position were statistically significant predictors in computer vision syndrome. Therefore, optimizing exposure time, addressing ergonomic hazards associated with computer usage through on-the-job and off-the-job training, and making the safety guidelines accessible for all university instructors would be critical to address the problem.

CAT: computer aided triage improving upon the Bayes risk through ε-refusal triage rules.

BACKGROUND: Manual extraction of information from electronic pathology (epath) reports to populate the Surveillance, Epidemiology, and End Result (SEER) database is labor intensive. Systematizing the data extraction automatically using machine-learning (ML) and natural language processing (NLP) is desirable to reduce the human labor required to populate the SEER database and to improve the timeliness of the data. This enables scaling up registry efficiency and collection of new data elements. To ensure the integrity, quality, and continuity of the SEER data, the misclassification error of ML and NPL algorithms needs to be negligible. Current algorithms fail to achieve the precision of human experts who can bring additional information in their assessments. Differences in registry format and the desire to develop a common information extraction platform further complicate the ML/NLP tasks. The purpose of our study is to develop triage rules to partially automate registry workflow to improve the precision of the auto-extracted information. RESULTS: This paper presents a mathematical framework to improve the precision of a classifier beyond that of the Bayes classifier by selectively classifying item that are most likely to be correct. This results in a triage rule that only classifies a subset of the item. We characterize the optimal triage rule and demonstrate its usefulness in the problem of classifying cancer site from electronic pathology reports to achieve a desired precision. CONCLUSIONS: From the mathematical formalism, we propose a heuristic estimate for triage rule based on post-processing the soft-max output from standard machine learning algorithms. We show, in test cases, that the triage rule significantly improve the classification accuracy.

Pain, pain intensity and pain disability in high school students are differently associated with physical activity, screening hours and sleep.

BACKGROUND: Studies exploring the association between physical activity, screen time and sleep and pain usually focus on a limited number of painful body sites. Nevertheless, pain at different body sites is likely to be of different nature. Therefore, this study aims to explore and compare the association between time spent in self-reported physical activity, in screen based activities and sleeping and i) pain presence in the last 7-days for 9 different body sites; ii) pain intensity at 9 different body sites and iii) global disability. METHODS: Nine hundred sixty nine students completed a questionnaire on pain, time spent in moderate and vigorous physical activity, screen based time watching TV/DVD, playing, using mobile phones and computers and sleeping hours. Univariate and multivariate associations between pain presence, pain intensity and disability and physical activity, screen based time and sleeping hours were investigated. RESULTS: Pain presence: sleeping remained in the multivariable model for the neck, mid back, wrists, knees and ankles/feet (OR 1.17 to 2.11); moderate physical activity remained in the multivariate model for the neck, shoulders, wrists, hips and ankles/feet (OR 1.06 to 1.08); vigorous physical activity remained in the multivariate model for mid back, knees and ankles/feet (OR 1.05 to 1.09) and screen time remained in the multivariate model for the low back (OR = 2.34. Pain intensity: screen time and moderate physical activity remained in the multivariable model for pain intensity at the neck, mid back, low back, shoulder, knees and ankles/feet (Rp2 0.02 to 0.04) and at the wrists (Rp2 = 0.04), respectively. Disability showed no association with sleeping, screen time or physical activity. CONCLUSIONS: This study suggests both similarities and differences in the patterns of association between time spent in physical activity, sleeping and in screen based activities and pain presence at 8 different body sites. In addition, they also suggest that the factors associated with the presence of pain, pain intensity and pain associated disability are different.

[Education and Training in Public Health - Results of the Working Group 9 of the Forum Future Public Health, Berlin 2016]. / Lehre, Fort- und Weiterbildung in Public Health.

The need for a qualified public health workforce can only be met by appropriate provision of a wide spectrum of basic, advanced and continuing education and training programs on public health that meet international standards. At the same time, efforts must be made to offer young academics attractive career opportunities. Training in public health competences must also be provided for allied professionals in health care and for professions with influence on the determinants of health such as urban planning or agricultural science. This report from a working group meeting at the 'Public Health Zukunftsforum 2016' in Berlin presents ideas for the further development of training in public health in Germany.

Teledildonics and New Ways of "Being in Touch": A Phenomenological Analysis of the Use of Haptic Devices for Intimate Relations.

The aim of this paper is to analyse teledildonics from a phenomenological perspective in order to show the possible effects they will have on ourselves and on our society. The new way of using digital technologies is to merge digital activities with our everyday praxes, and there are already devices which enable subjects to be digitally connected in every moment of their lives. Even the most intimate ones are becoming mediated by devices such as teledildonics which digitally provide a tactual stimulation allowing users to have sexual intercourse through them. The efforts made in order to provide such an intertwinement of our everyday lives and digital technologies are evident, but the effects produced by them are not clear at all. This paper will analyse these technologies from a phenomenological perspective in order to understand their effects on the constitution of the subjects and on our society at the intimate level.

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.

BACKGROUND: What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. OBJECTIVE: The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. METHODS: We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. RESULTS: We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. CONCLUSIONS: We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.