Ocular biomechanics modelling for visual fatigue assessment in virtual environments

The study objectively quantifies visual fatigue caused by immersion in virtual reality. Visual fatigue assessment is done through ocular biomechanics modelling and eye tracking to analyse eye movement and muscle forces into a visual fatigue index

A review on ocular biomechanic models for assessing visual fatigue in virtual reality

With the wide spread of affordable virtual reality headsets, virtual environments are rapidly changing the way humans interact with reality. Understanding the effects of virtual environments on the mental and cognitive state is essential. In addition, defining methods for measuring and assessing visual fatigue in virtual environments is still needed. While eye movements are tightly coupled to the mental state, analysis of eye movement can add insights for safer virtual environments. Biomechanical analysis has been used extensively in the analysis of human movement. Simulation of different scenarios such as injuries and surgeries provided insights and solutions to problems that were otherwise impossible. This includes understanding the effects of changing insertion points of muscle on range of motion or how muscle activation can affect the motion produced. Extending the use of biomechanical simulation analysis into eye movement can be used to deepen our understanding of how virtual environments affect our visual and mental capabilities. This paper presents a thorough review on ocular biomechanics and ocular models in literature. We start with a brief introduction on the anatomy of the eye and eye kinematics. In addition, properties of the extraocular muscles (EOM) are described and the difference between EOMs and skeletal muscle is highlighted. The challenges facing biomechanical simulation and analysis of eye movement are presented along with the role of ocular models in assessing visual fatigue. Furthermore, the compatibility of available biomechanical tools to analyze ocular movements is discussed

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe