Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Radio Frequency Cooperation at the Wavelength Level Using Wireless Phase Synchronization

Coordinating the operations of separate wireless systems at the wavelength level can lead to significant improvements in wireless capabilities. We address a fundamental challenge in distributed radio frequency system cooperation - inter-node phase alignment - which must be accomplished wirelessly, and is particularly challenging when the nodes are in relative motion. We present a solution to this problem that is based on a novel combined high-accuracy ranging and frequency transfer technique. Using this approach, we present the design of the first fully wireless distributed system operating at the wavelength level. We demonstrate the system in the first open-loop coherent distributed beamforming experiment. Internode range estimation to support phase alignment was performed using a two-tone stepped frequency waveform with a single pulse, while a two-tone waveform was used for frequency synchronization, where the oscillator of a secondary node was disciplined to the primary node. In this concept, secondary nodes are equipped with an adjunct self-mixing circuit that is able to extract the reference frequency from the captured synchronization waveform. The approach was implemented on a two-node dynamic system using Ettus X310 software-defined radios, with coherent beamforming at 1.5 GHz. We demonstrate distributed beamforming with greater than 90% of the maximum possible coherent gain throughout the displacement of the secondary node over one full cycle of the beamforming frequency.
Educational Case: Hematologic and Immunologic Response to Allergic Rhinitis With Other Causes of Leukocytosis

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub. com/doi/10.1177/2374289517715040.1
Robot Design With Neural Networks, MILP Solvers and Active Learning

Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem. This paper presents CNMA, a new method of solving this problem that is conservative in the number of potentially expensive blackbox function evaluations; allows specifying complex, even recursive constraints directly rather than as hard-to-design penalty or barrier functions; and is resilient to the non-termination of function evaluations. CNMA leverages the ability of neural networks to approximate any continuous function, their transformation into equivalent mixed integer linear programs (MILPs) and their optimization subject to constraints with industrial strength MILP solvers. A new learning-from-failure step guides the learning to be relevant to solving the constrained optimization problem. Thus, the amount of learning is orders of magnitude smaller than that needed to learn functions over their entire domains. CNMA is illustrated with the design of several robotic systems: wave-energy propelled boat, lunar lander, hexapod, cartpole, acrobot and parallel parking. These range from 6 real-valued dimensions to 36. We show that CNMA surpasses the Nelder-Mead, Gaussian and Random Search optimization methods against the metric of number of function evaluations.
Text Classification of Manifestos and COVID-19 Press Briefings using BERT and Convolutional Neural Networks.

We build a sentence-level political discourse classifier using existing human expert annotated corpora of political manifestos from the Manifestos Project (Volkens et al., 2020a) and applying them to a corpus ofCOVID-19Press Briefings (Chatsiou, 2020). We use manually annotated political manifestos as training data to train a local topic ConvolutionalNeural Network (CNN) classifier; then apply it to the COVID-19PressBriefings Corpus to automatically classify sentences in the test corpus.We report on a series of experiments with CNN trained on top of pre-trained embeddings for sentence-level classification tasks. We show thatCNN combined with transformers like BERT outperforms CNN combined with other embeddings (Word2Vec, Glove, ELMo) and that it is possible to use a pre-trained classifier to conduct automatic classification on different political texts without additional training.
The Parent’s Chronotype and Child’s Sleeping Quality in Association with Relationship Satisfaction

The prospective Ulm-SPATZ study was investigated to assess the role of child sleeping quality between 4 to 6 years of age in affecting a partner’s sleeping and relationship satisfaction within a couple. The study was conducted using a triadic approach in which the child was included in the Actor-Partner-Interdependence Model (APIM). Sleeping quality of the child was determined by using the German version of the children’s sleep habits questionnaire, sleeping features of the parents were assessed by using the Munich chronotype questionnaire, and the partner relationship assessment was performed by employing the German version of the parenting stress index questionnaire. In 211 German triads, we observed that sleeping characteristics and partner relationship scores at different child ages are consistent for both men and women. Higher and statistically significant sleep duration, time spent in bed, the midpoint of sleep, time getting out of bed, and sleep onset in women compared to men during the working days were observed. The APIM analyses showed a significant direct effect of child sleep quality on the partner relationship satisfaction. In women, a mediated effect of child sleep quality acted through sleep duration and time spent in bed on the partner relationship satisfaction score during both free and working days. In men, low child sleep quality was found to be associated with increased sleep onset during both free and working days. Child sleep quality influences relationship satisfaction mostly in mothers, likely because of their higher involvement in childcare during working days. Distress in the couple could be counteracted by a major involvement of the fathers in child management.
Glycated albumin versus HbA1c as indicators of glycemic control in type I diabetic children with iron deficiency anemia

Abstract. We evaluated the clinical usefulness of glycated albumin (GA) and glycated hemoglobin (HbA1c) as indicators of glycemic control in type I diabetic (T1DM) children with and without iron deficiency anemia (IDA). Our prospective cross-sectional study was conducted on 147 T1DM children who were classified into Group I (with IDA) and Group II (without anemia). The participants were classified as controlled and uncontrolled based on mean blood glucose (MBG) in the past 30 days. The 5–12-yr-olds with MBG above 200 and 12–15-yr-olds with levels above 180 md/dl were considered uncontrolled. HbA1c increased significantly in the participants with IDA compared to those without anemia (p < 0.01). HbA1c in those with IDA showed insignificant difference between the controlled and uncontrolled (p = 0.5), while GA was significantly higher in the uncontrolled than the controlled (p = 0.3). Receiver operating characteristic (ROC) curve analysis showed that GA had 87.2% sensitivity and 75.8% specificity at a cut-off point of 16.9%. HbA1c at a cut-off point of 7.09% showed 80% sensitivity and 57.6% specificity. For prediction of uncontrolled diabetes in children with IDA, we concluded that HbA1c increases significantly in diabetic children with IDA. GA may be a useful alternative biomarker for evaluating the glycemic control in such children.
Emotion Regulation in eSports Gaming

Today eSports gaming is enjoying growing popularity in the world and much attention from various research areas, including CSCW. eSports gaming is a highly competitive environment commonly associated with negative emotions such as anxiety and stress. However, little attention has been paid to emotion regulation in eSports gaming. In this study, we empirically investigated how players experience emotion and regulate emotions in League of Legends, one of the largest eSports games today. We identify four emotive factors, as well as emotion regulation strategies that players deploy to manage the emotions of their selves, teammates, and opponents. We further report on how they use emotion regulation in emotional self-care and emotional leadership. Building upon this set of findings, we discuss how the competitive eSports gaming context conditions emotion regulation in League of Legends, foreground emotion regulation expertise in competitive gaming, and derive implications for designing emotion regulation technologies.
Injuries among children and adolescents in a rapidly growing urban African metropolis: a cross-sectional survey of 1,968 households in Dar es Salaam, Tanzania

Objectives To assess the patterns and incidence of child and adolescent injury and explore associations with household deprivation and child characteristics in a low-income urban setting. Study Design Cross-sectional household survey in Dar es Salaam, Tanzania. Methods Data collection took place during July 2009. Injuries requiring medical attention were recorded with a one month period of recall. A total of 1,968 households representing 3,927 children and adolescents were visited by health workers. Gender-, age-, and type-specific injury incidence was compiled. Odds ratios were calculated to measure associations with child injury, perceived deprivation, household characteristics and child characteristics. Results One household in five reported injuries. The estimated incidence was 3.2 per 10,000 child-years. The most common identifiable injuries were falls (41%), cuts (22%) and burns (16%). Male and younger children aged 1–4 years were at higher risk (respectively OR = 1.36; p = 0.004; OR = 1.47; p ≤ 0.001). Conclusions In Dar e Salaam injuries are common. Future investigations should take into account both subjective and objective measurements of relative household deprivation and a clear criteria for the assessment of injury severity in community-based survey contexts.
Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare

Background: Atrial fibrillation (AF) in the elderly population is projected to increase over the next several decades. Catheter ablation shows promise as a treatment option and is becoming increasingly available. We examined 90-day hospital readmission for AF patients undergoing catheter ablation and utilized machine learning methods to explore the risk factors associated with these readmission trends. Methods: Data from the 2013 Nationwide Readmissions Database on AF cases were used to predict 90-day readmissions for AF with catheter ablation. Multiple machine learning methods such as k-Nearest Neighbors, Decision Tree, and Support Vector Machine were employed to determine variable importance and build risk prediction models. Accuracy, precision, sensitivity, specificity, and area under the curve were compared for each model. Results: The 90-day hospital readmission rate was 17.6%; the average age of the patients was 64.9 years; 62.9% of patients were male. Important variables in predicting 90-day hospital readmissions in patients with AF undergoing catheter ablation included the age of the patient, number of diagnoses on the patient’s record, and the total number of discharges from a hospital. The k-Nearest Neighbor had the best performance with a prediction accuracy of 85%. This was closely followed by Decision Tree, but Support Vector Machine was less ideal. Conclusions: Machine learning methods can produce accurate models in predicting hospital readmissions for patients with AF. The likelihood of readmission to the hospital increases as the patient age, total number of hospital discharges, and total number of patient diagnoses increase. Findings from this study can inform quality improvement in healthcare and in achieving patient-centered care.
On the Misinformation Beat

Journalists are increasingly investigating and reporting on problematic online content such as misinformation, disinformation, and conspiracy theories, leading to the creation of a new misinformation beat. The process of collecting, analyzing, and reporting on this kind of data is complex and nuanced. It is especially challenging as online actors attempt to undermine their work. Through in-depth interviews with twelve journalists, we explore how they investigate and report on online misinformation and disinformation. Our findings reveal some of the unique challenges of reporting on this beat, as well as the ways in which reporters overcome those challenges. We highlight and discuss how journalistic values could be better embedded into the design of tools to support their work, the power dynamics between social media companies and journalists, and the promise of collaborations as a way to support and educate journalists on this beat. This work provides contextual knowledge to researchers looking to better support investigative journalists - on the misinformation beat and beyond - as their work becomes more entangled in sociotechnical systems.
Human-AI Collaboration in a Cooperative Game Setting

Human-AI interaction is pervasive across many areas of our day to day lives. In this paper, we investigate human-AI collaboration in the context of a collaborative AI-driven word association game with partially observable information. In our experiments, we test various dimensions of subjective social perceptions (rapport, intelligence, creativity and likeability) of participants towards their partners when participants believe they are playing with an AI or with a human. We also test subjective social perceptions of participants towards their partners when participants are presented with a variety of confidence levels. We ran a large scale study on Mechanical Turk (n=164) of this collaborative game. Our results show that when participants believe their partners were human, they found their partners to be more likeable, intelligent, creative and having more rapport and use more positive words to describe their partner's attributes than when they believed they were interacting with an AI partner. We also found no differences in game outcome including win rate and turns to completion. Drawing on both quantitative and qualitative findings, we discuss AI agent transparency, include design implications for tools incorporating or supporting human-AI collaboration, and lay out directions for future research. Our findings lead to implications for other forms of human-AI interaction and communication.
5-Ions: An Automatic Method for Predicting Entity Performance

Textual documents on the Web (e.g., news, comments) may impact public opinion towards certain entities (e.g., currencies, people, institutions, products). Several indicators can be extracted from these documents, correlated to real measures of entity performance (e.g., currency quotation, company stock valuation) and used to explain or estimate real performance of some entities. This paper proposes an automatic method, named 5-Ions, which employes state-of-the-art off-the-shelf natural language processing tools to find relevant entity mentions along with their associated sentiment in text, and use them to calculate consolidated performance indicators for entities that are related in a semantic network (e.g. entities related to a particular currency). The results of the experiments with text documents about the Brazilian economy show that these consolidated metrics are better correlated with the real quotation of the Brazilian currency and allow better predictions of currency fluctuations than measures calculated for single entities.
Research and Application of Progressive Loading Technology for Web Publishing of Complex Mechanical Product

In order to solve the problem of long model loading time in the process of web publishing for complex mechanical product, a progressive loading technology for 3D model web publishing is proposed. Firstly, the different application modes of 3D model are distinguished; then, the classified mesh data of 3D model is constructed according to the application mode. Finally, the publishing of 3D model is realized on the web side. The verification in the developed prototype software shows that the web publishing of the 3D model is progressively loaded.
Effects of Low-Fat, Mediterranean, or Low-Carbohydrate Weight Loss Diets on Serum Urate and Cardiometabolic Risk Factors: A Secondary Analysis of the Dietary Intervention Randomized Controlled Trial (DIRECT)

OBJECTIVE Weight loss diets may reduce serum urate (SU) by lowering insulin resistance while providing cardiometabolic benefits, something urate-lowering drugs have not shown in trials. We aimed to examine the effects of weight loss diets on SU and cardiometabolic risk factors. RESEARCH DESIGN AND METHODS This secondary study of the Dietary Intervention Randomized Controlled Trial (DIRECT) used stored samples from 235 participants with moderate obesity randomly assigned to low-fat, restricted-calorie (n = 85); Mediterranean, restricted-calorie (n = 76); or low-carbohydrate, non–restricted-calorie (n = 74) diets. We examined SU changes at 6 and 24 months overall and among those with hyperuricemia (SU ≥416 μmol/L), a relevant subgroup at risk for gout. RESULTS Among all participants, average SU decreases were 48 μmol/L at 6 months and 18 μmol/L at 24 months, with no differences between diets (P > 0.05). Body weight, HDL cholesterol (HDL-C), total cholesterol:HDL-C ratio, triglycerides, and insulin concentrations also improved in all three groups (P < 0.05 at 6 months). Adjusting for covariates, changes in weight and fasting plasma insulin concentrations remained associated with SU changes (P < 0.05). SU reductions among those with hyperuricemia were 113, 119, and 143 μmol/L at 6 months for low-fat, Mediterranean, and low-carbohydrate diets (all P for within-group comparison < 0.001; P > 0.05 for between-group comparisons) and 65, 77, and 83 μmol/L, respectively, at 24 months (all P for within-group comparison < 0.01; P > 0.05 for between-group comparisons). CONCLUSIONS Nonpurine-focused weight loss diets may simultaneously improve SU and cardiovascular risk factors likely mediated by reducing adiposity and insulin resistance. These dietary options could provide personalized pathways to suit patient comorbidity and preferences for adherence.
Classification of submandibular salivary stones based on ultrastructural studies

Introduction: Sialolithiasis remains a clinical problem with unclear etiopathogenesis, lack of prevention methods, and only surgical treatment. Materials and Methods: An ultrastructure examination of submandibular sialoliths obtained from patients with chronic sialolithiasis was conducted using a scanning electron microscope and X-ray photoelectron spectroscopy. Results: Based on the results, we divided sialoliths into three types: calcified (CAL), organic/lipid (LIP), and mixed (MIX). The core structure of the CAL and MIX is very similar. The core of the LIP has a prevalence of organic components. The intermediate layers' structure of the CAL is different from LIP and MIX. In LIP and MIX, the organic component begins to increase in intermediate layers rapidly. The structure of the superficial layers for all types of sialoliths is similar. Conclusions: We introduced a new classification of the submandibular salivary gland stones. Based on the results, it can be said that sialoliths type CAL and LIP have their separate path of origin and development, while MIX is formed as CAL stone, and the further pathway of their growth passes as LIP stones. Organic components were much more than inorganic in all layers of salivary gland stones, which highly prevents their dissolution in the patient's salivary gland duct.
Replicator Evolution of Welfare Stigma: Welfare Fraud vs. Incomplete Take-Up

There are two important problems in welfare benefit programs: the prevalence of welfare fraud, in which ineligible people receive welfare benefits, and incomplete take-up, whereby eligible poor people are reluctant to claim welfare benefits. This study investigates both of these opposing phenomena using simple replicator models of statistical discrimination and the tax-payer resentment view welfare stigma suggested by Besley and Coate (1992). We find multiple stable equilibria in the long run, one of which entails low welfare fraud and 100% incomplete take-up and the other of which entails high welfare fraud and complete take-up in either model, and, moreover, that an interior stationary equilibrium that allows for the coexistence of welfare fraud and incomplete take-up is unstable in the model of statistical discrimination view welfare stigma, but it is stable in the model of the tax-payer resentment view welfare stigma. This difference arises from the different nature of stigma cost functions in these two models.
The aerobic mitochondrial ATP synthesis from a comprehensive point of view

Most of the ATP to satisfy the energetic demands of the cell is produced by the F1Fo-ATP synthase (ATP synthase) which can also function outside the mitochondria. Active oxidative phosphorylation (OxPhos) was shown to operate in the photoreceptor outer segment, myelin sheath, exosomes, microvesicles, cell plasma membranes and platelets. The mitochondria would possess the exclusive ability to assemble the OxPhos molecular machinery so to share it with the endoplasmic reticulum (ER) and eventually export the ability to aerobically synthesize ATP in true extra-mitochondrial districts. The ER lipid rafts expressing OxPhos components is indicative of the close contact of the two organelles, bearing different evolutionary origins, to maximize the OxPhos efficiency, exiting in molecular transfer from the mitochondria to the ER. This implies that its malfunctioning could trigger a generalized oxidative stress. This is consistent with the most recent interpretations of the evolutionary symbiotic process whose necessary prerequisite appears to be the presence of the internal membrane system inside the eukaryote precursor, of probable archaeal origin allowing the engulfing of the α-proteobacterial precursor of mitochondria. The process of OxPhos in myelin is here studied in depth. A model is provided contemplating the biface arrangement of the nanomotor ATP synthase in the myelin sheath.
Medicinal mushrooms: Valuable biological resources of high exploitation potential

Abstract Higher Basidiomycetes and Ascomycetes mushrooms possess various immunological and anticancer properties. They also offer important health benefits and exhibit a broad spectrum of pharmacological activities including antibacterial, antifungal, antiviral, cytotoxic, immunomodulating, anti-inflammatory, antioxidative, antiallergic, antidepressive, antihyperlipidemic, antidiabetic, digestive, hepatoprotective, neuroprotective, nephroprotective, osteoprotective, and hypotensive activities. This minireview summarizes the perspectives, recent advances, and major challenges of medicinal mushrooms with reference to their nutraceutical properties and dietary value, the production of mushroom biomass on various substrates, and the purification, characterization, and pharmaceutical effects of biologically active compounds from medicinal mushrooms.