Miracle-dz/UniFAR-FTUs huggingface dataset:
Type of Primary Nb5Si3 and Precipitation of Nbss in αNb5Si3 in a Nb-8.3Ti-21.1Si-5.4Mo-4W-0.7Hf (at.%) Near Eutectic Nb-Silicide-Based Alloy

The Nb-silicide-based alloy of near eutectic composition (at.%) Nb-21.1Si-8.3Ti-5.4Mo-4W-0.7Hf (alloy CM1) was studied in the cast and heat-treated (1500 °C/100 h) conditions. The alloy was produced in the form of buttons and bars using three different methods, namely arc-melting, arc-melting and suction casting, and optical floating zone (OFZ) melting. In the former two cases the alloy solidified in water-cooled copper crucibles. Buttons and suction-cast bars of different size, respectively of 10 g and 600 g weight and 6 mm and 8 mm diameter, were produced. The OFZ bars were grown at three different growth rates of 12, 60 and 150 mm/h. It was confirmed that the type of Nb5Si3 formed in the cast microstructures depended on the solidification conditions. The primary phase in the alloy CM1 was the βNb5Si3. The transformation of βNb5Si3 to αNb5Si3 had occurred in the as cast large size button and the OFZ bars grown at the three different growth rates, and after the heat treatment of the small size button and the suction-cast bars of the alloy. This transformation was accompanied by subgrain formation in Nb5Si3 and the precipitation of Nbss in the large size as cast button and only by the precipitation of Nbss in the cast OFZ bars. Subgrains and precipitation of Nbss in αNb5Si3 was observed in the small size button and suction-cast bars after the heat treatment. Subgrains formed in αNb5Si3 after the heat treatment of the OFZ bars. The partitioning of solutes and in particular of Mo and Ti was key to this phase transformation. Subgrain formation was not necessary for precipitation of Nbss in αNb5Si3, but the partitioning of solutes was essential for this precipitation.
Mapping the Cortical Network Arising From Up-Regulated Amygdaloidal Activation Using  $\lambda$ -Louvain Algorithm

The amygdala plays an important role in emotion processing. Several studies have proved that its activation can be regulated by real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback training. However, although studies have found brain regions that are functionally closely connected to the amygdala in the cortex, it is not clear whether these brain regions and the amygdala are structurally closely connected, and if they show the same training effect as the amygdala in the process of emotional regulation. In this paper, we instructed subjects to up-regulate the activation of the left amygdala (LA) through rtfMRI-based neurofeedback training. In order to fuse multimodal imaging data, we introduced a network analysis method called the $\lambda $ -Louvain clustering algorithm. This method was used to integrate multimodal data from the training experiment and construct an LA-cortical network. Correlation analysis and main-effect analysis were conducted to determine the signal covariance associated with the activation of the target area; ultimately, we identified the left temporal pole superior as the amygdaloidal-cortical network region. As a deep nucleus in the brain, the treatment and stimulation of the amygdala remains challenging. Our results provide new insights for the regulation of activation in a deep nucleus using more neurofeedback techniques.
Towards Wearable A-Mode Ultrasound Sensing for Real-Time Finger Motion Recognition

It is evident that surface electromyography (sEMG) based human–machine interfaces (HMI) have inherent difficulty in predicting dexterous musculoskeletal movements such as finger motions. This paper is an attempt to investigate a plausible alternative to sEMG, ultrasound-driven HMI, for dexterous motion recognition due to its characteristic of detecting morphological changes of deep muscles and tendons. A multi-channel A-mode ultrasound lightweight device is adopted to evaluate the performance of finger motion recognition; an experiment is designed for both widely acceptable offline and online algorithms with eight able-bodied subjects employed. The experiment result presents that the offline recognition accuracy is up to 98.83% ± 0.79%. The real-time motion completion rate is 95.4% ± 8.7% and online motion selection time is 0.243 ± 0.127 s. The outcomes confirm the feasibility of A-mode ultrasound based wearable HMI and its prosperous applications in prosthetic devices, virtual reality, and remote manipulation.
Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.
A case study of strengths-based coaching of mental toughness in cricket

The purpose of this case study was to explore the effectiveness of an individualized, strengths-based coaching approach with an elite cricketer, when compared to two control participants. The coaching consisted of four meetings that occurred over a one month period at the start of the cricket season. Multisource ratings (i.e., self, coaches, teammates) of mental toughness were obtained at baseline, intervention, and postseason. Visual analysis of data revealed general improvements in raw scores of multisource ratings of mental toughness for the experimental participant, with relatively stable levels of mental toughness indicated for the two control participants. Reliable changes in attentional control and self-belief facets were evidenced according to our statistical criterion. This case study has provided initial evidence for the effectiveness of an individualized, strengths-based coaching approach to develop facets of mental toughness, and supports the need for further evaluative research on positive psychology interventions in sport contexts.
Task-Dependent and Query-Dependent Subspace Learning for Cross-Modal Retrieval

Most existing cross-modal retrieval approaches learn the same couple of projection matrices for different sub-retrieval tasks (such as, image retrieves text and text retrieves image) and various queries. They ignore the important fact that, different sub-retrieval tasks and queries have unique characteristics themselves in real practice. To tackle the problem, we propose a task-dependent and query-dependent subspace learning approach for cross-modal retrieval. Specifically, we first develop a unified cross-modal learning framework, where task-specific and category-specific subspaces can be learned simultaneously via an efficient iterative optimization. Based on this step, a task-category-projection mapping table is built. Subsequently, an efficient linear classifier is trained to learn a semantic mapping function between multimedia documents and their potential categories. In the online retrieval stage, the task-dependent and query-dependent matching subspace is adaptively identified by considering the specific sub-retrieval task type, the potential semantic category of the query, and the task-category-projection mapping table. Experimental results demonstrate the superior performance of the proposed approach compared with several state-of-the-art techniques.
Investigating Deep Neural Forests for Facial Expression Recognition

Facial Expression Recognition (FER) usually involves learning intermediate representations from high-dimensional, potentially noisy images, that can be adapted to unknown face morphologies. Moreover, it needs to fulfil the real-time constraint to be useful, e.g. for consumer robotics or healthcare systems. To tackle this issue, Random Forests (RFs) are convenient predictors, as the hard decisions at each node allow non-linear subdivisions of the space with a very fast evaluation runtime. However RFs are non-differenciable predictors, making it impossible to back-propagate the error down to upstream feature extraction layers (e.g. CNN). In this paper, we investigate the adaptation of deep Neural Forests (NFs) to FER. The latter allows to combine the best of the two worlds, \textit{i.e.} a differentiable classification model that can be trained with backpropagation and stochastic gradient descent but has the runtime of a RF. We show that NFs provides competitive results for FER as compared to state-of-the-art approaches when trained upon various combinations of geometric/CNN-based features.
Hashimoto's Thyroiditis in Children and Adolescents

Hashimoto’s thyroiditis (HT) is an autoimmune disease with genetic background. It is also named as autoimmune thyroiditis or chronic lymphocytic tiroiditis. Hashimoto's thyroiditis is the most common cause of thyroid diseases in children and adolescents and it is also the most common cause of acquired hypothyroidism with or without goiter. Hashimoto’s thyroiditis was first described in 1912 by Hakura Hashimoto in a series of patients with diffusely enlarged, firm thyroid glands with distinct pathologic features, classified as chronic lymphocytic thyroiditis (1). The characteristic histologic features include diffuse lymphocytic infiltration, atrophic follicles, well-developed germinal centers, and fibrosis. Hashimoto's thyroiditis is the most important cause of hypothyroidism in children and adolescents. In an American population with age between 11 and 18 years, five new cases were detected out of 1,000 adolescents screened every year. It is more common among girls, varying from 4:1 to 8:1 depending on the geographical region. Although the disease can be seen before three years of age, it is usually seen after six years of age and its peak ages are 10 and 11 years (2). The prevelance of Hashimoto's thyroiditis between 6-18 years old is 3% in Japan. Thirty40% of the cases have familial history of thyroid disease. It occurs far more often in women than in men (between 10:1 and 20:1), and is most prevalent between 45 and 65 years old. Autoimmune thyroid disease (AITD) has two clinical forms: a goitrous form more common in young age groups, in whom goiter may be the only clinical expression (3), often referred to as classical Hashimoto's disease, and an atrophic one often called atrophic thyroiditis (4). Both are characterized by circulating thyroid autoantibodies and varying degrees of thyroid dysfunction, differing only by the presence or absence of goiter. The prognosis is not known very well, and studies reporting about long-term outcome of the disease are scarce (3,5). Thyroid function tests show variations at the time of diagnosis; mostly euthyroid or hypothyroid and rarely hyperthyroid. Hypothyroidism is thought to be a permanent sequelae of HT. Patients with overt hypothyroidism may have been recommended lifelong levothyroxine (LT4) therapy but it should be checked after puberty if LT4 therapy is still necessary or not.
Overcoming Barriers to Experience Benefits: A Qualitative Analysis of Electronic Health Records and Health Information Exchange Implementation in Local Health Departments

Introduction: Electronic Health Records (EHRs) and Health Information Exchanges (HIEs) are changing surveillance and analytic operations within local health departments (LHDs) across the United States. The objective of this study was to analyze the status, benefits, barriers, and ways of overcoming challenges in the implementation of EHRs and HIEs in LHDs. Methods: This study employed a mixed methods approach, first using the 2013 National Profile of LHDs survey to ascertain the status of EHR and HIE implementation across the US, as well as to aid in selection of respondents for the second, interview-based part of project. Next, forty-nine key-informant interviews of local health department staff were conducted. Data were coded thematically and independently by two researchers. Coding was compared and re-coded using the consensus definitions. Results: Twenty-three percent of LHDs nationwide are using EHRs and 14 percent are using HIEs. The most frequently mentioned benefits for implementation were identified as care coordination, retrieval or managing information, and the ability to track outcomes of care. A few mentioned barriers included financial resources, resistance to change, and IT related issues during implementation. Discussion: Despite financial, technical capacity, and operational constraints, leaders interviewed as part of this project were optimistic about the future of EHRs in local health departments. Recent policy changes and accreditation have implications of improving processes to affect populations served. Conclusions: Overcoming the challenges in implementing EHRs can result in increased efficiencies in surveillance and higher quality patient care and tracking. However, significant opportunity cost does exist.
Finding Convincing Arguments Using Scalable Bayesian Preference Learning

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.
Epidemiology of Clinically Significant Tinnitus: A 10-Year Trend From Nationwide Health Claims Data in South Korea.

OBJECTIVES Prevalence of tinnitus has been reported to vary according to the target population and definition of tinnitus. To improve the understanding of tinnitus, authors used the nation-wide health claim data to study the tinnitus in the entire population of South Korea. STUDY DESIGN Retrospective big data review. SETTING Analysis of big data from the National Health Information Database. PATIENTS Patients diagnosed with tinnitus according to International Classification of Diseases code 10th edition (ICD-10) and requested to receive National Health Insurance claim at least once from January 2006 to December 2015. INTERVENTION None. MAIN OUTCOME MEASURE Epidemiologic data, association of tinnitus with the otologic and systemic diseases. RESULTS The number of patients who received medical care because of tinnitus was 1.44% (0.78 million per 51 million) in 2015. There was a higher prevalence of tinnitus in women, and the overall prevalence increased with increasing age of patients and peaked at patients in their 70s (4.43%). The prevalence of tinnitus among patients aged 10 to 30 years, showed a tendency to increase during study period. In the regional analysis, highest prevalence was observed in Gwangju (2.02%). In comparison with the control group, the patients with tinnitus showed a higher frequency in otologic and systemic disease. Especially, noise induced hearing loss (adjusted odds ratio [AOR] = 82.1, 95% confidence interval [95% CI] = 74.8-90.2) and sudden sensorineueal hearing loss (AOR = 49.7, 95% CI = 48.4-51.0) showed high frequency in tinnitus patient group. CONCLUSIONS The prevalence and incidence of tinnitus in this study for entire nation were lower than previously reported studies. These results have limitation because the study only covered patient using the medical service for tinnitus and missed tinnitus sufferers not seeking medical service. However, this study is meaningful in that it was targeting entire nation, reflected the characteristics of clinically significant tinnitus patient enough to visit medical service.
Influence of Modes of Climate Variability on Global Temperature Extremes

Abstract The influence of large-scale modes of climate variability on worldwide summer and winter temperature extremes has been analyzed, namely, that of the El Nino–Southern Oscillation, the North Atlantic Oscillation, and Pacific interdecadal climate variability. Monthly indexes for temperature extremes from worldwide land areas are used describe moderate extremes, such as the number of exceedences of the 90th and 10th climatological percentiles, and more extreme events such as the annual, most extreme temperature. This study examines which extremes show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show that temperature extremes are substantially affected by large-scale circulation patterns, and they show distinct regional patterns of response to modes of climate variability. The effects of the El Nino–Southern Oscillation are seen throughout the world but most clearly around the Pacific Rim and throughout all of North America. Lik.
Comparing the Latent Structure of the Children’s Category Test-Level 1 among Young Children and Older Adults: A Preliminary Study

The aim of this paper was the comparison of the higher order nonverbal abilities, between young children and older adults. In specific, 42 kindergarten and 56 elementary school students (age range: 5 - 8 years), as well as 118 new-old adults and 27 old-old adults (age range: 61 - 88 years), were examined in the Children’s Category Test-Level 1 (CCT-1). Findings from the Confirmatory Factor Analyses that were applied to data, possibly reflect the delay of development of general abstraction and working memory in the group of kindergarten students and the decline of them in the group of old-old adults.
Life history and reproductive ecology of White ’ s skink

The life history and reproductive ecology of White’s skink, Egernia whitii, was examined in a population in the Australian Capital Territory using both field and genetic studies. Colour pattern polymorphism was evident within the population, with both patterned and plain-back morphs present. Lizards typically took 3 years to reach sexual maturity, with the size at maturity being ~75 mm snout–vent length (SVL) in both sexes. There was an even overall adult sex ratio, although a slight female-bias was evident in plain-back individuals. Sexual dimorphism was evident, with males having longer and wider heads, and females having larger body size. Females generally bred annually, with mating occurring in September–October and parturition in late January–February, although the litter was produced over several days (2–10 days, mean 4 days). Litter size ranged from one to four (mean of 2.5). There was a significant relationship between maternal SVL and both litter size and relative clutch mass, but these trends were not consistent between colour morphs. An inverse relationship between litter size and offspring size (SVL and mass) was found. Comparison of the results with previous investigations of E. whitii indicates substantial geographic variation in life-history traits that is presumably associated with latitudinal variation in climatic conditions. Life history and reproductive ecology of White’s skink, Egernia whitii www.publish. csiro.au/journals/ajz CSIRO PUBLISHING
Sex and age differences in Mycobacterium tuberculosis infection in Brazil

Abstract Globally, the prevalence of tuberculosis (TB) disease is higher in males. This study examined the effect of sex and age on Mycobacterium tuberculosis (Mtb) infection. Demographic and exposure data were collected on household contacts of sputum smear-positive pulmonary TB patients in Brazil. Contacts with tuberculin skin test induration ⩾10 mm at baseline or 12 weeks were considered Mtb infected. The study enrolled 917 household contacts from 160 households; 508 (55.4%) were female, median age was 21.0 years (range 0.30–87.0) and 609 (66.4%) had Mtb infection. The proportion infected increased with age from 63.3% in girls <5 years to 75.4% in women ⩾40 years and from 44.9% in boys <5 years to 73.6% in men ⩾40 years. Multivariable modelling showed the odds of infection increased between age 5 and 14 years among female contacts (OR 1.5 per 5-year age increase; 95% CI 1.1–2.2; P = 0.02) and between ages 0–4 and 15–39 years among male contacts (OR 2.7, 95% CI 0.83–8.9 and 1.1, 95% CI 0.99–1.3 per 5-year age increase; P = 0.10, 0.07, respectively). The study suggests that the age at which Mtb infection increases most is different in females compared with males. Studies are needed to explore whether these findings are due to differences in host susceptibility, exposure outside the household or other factors.
Electrical Power Conditioning System for Thermoelectric Waste Heat Recovery in Commercial Vehicles

A considerable part of the fuel energy in vehicles never reaches the wheels and entirely converts to waste heat. In a heavy duty vehicle (HDV), the heat power that escapes from the exhaust system may reach 170 kW. The waste heat can be converted into useful electrical power using a thermoelectric generator (TEG). During the last decades, many studies on the electrical power conditioning system of TEGs have been conducted. However, there is a lack of studies evaluating the electrical instrumentation, the impact of the converter efficiency, and the TEG arrangement on a real large-scale TEG on-board a drivable vehicle. In this paper, the most important parameters for designing electrical power conditioning systems for two TEGs, developed for a real-scale HDV as well as experimental results demonstrating the recovered electrical power, are presented. Eight synchronous interleaved step-down converters with 98% efficiency with a perturb and observe maximum power point tracker was developed and tested for this purpose. The power conditioning system was communicating with the on-board computers through the controller area network and reported the status of the TEGs and the recovered electrical power. The maximum recovered electrical power from the TEGs reached 1 kW which was transmitted to the electrical system of the vehicle, relieving the internal combustion engine.