Miracle-dz/UniFAR-FTUs huggingface dataset:
Does unmeasured confounding influence associations between the retail food environment and body mass index over time? The Coronary Artery Risk Development in Young Adults (CARDIA) study.

Background Findings in the observational retail food environment and obesity literature are inconsistent, potentially due to a lack of adjustment for residual confounding. Methods Using data from the CARDIA study (n = 12 174 person-observations; 6 examinations; 1985-2011) across four US cities (Birmingham, AL; Chicago, IL; Minneapolis, MN; Oakland, CA), we used instrumental-variables (IV) regression to obtain causal estimates of the longitudinal associations between the percentage of neighbourhood food stores or restaurants (per total food outlets within 1 km network distance of respondent residence) with body mass index (BMI), adjusting for individual-level socio-demographics, health behaviours, city, year, total food outlets and market-level prices. To determine the presence and extent of bias, we compared the magnitude and direction of results with ordinary least squares (OLS) and random effects (RE) regression, which do not control for residual confounding, and with fixed effects (FE) regression, which does not control for time-varying residual confounding. Results Relative to neighbourhood supermarkets (which tend to be larger and have healthier options than grocery stores), a higher percentage of grocery stores [mean = 53.4%; standard deviation (SD) = 31.8%] was positively associated with BMI [β = 0.05; 95% confidence interval (CI) = 0.01, 0.10] using IV regression. However, associations were negligible or null using OLS (β = -0.001; 95% CI = -0.01, 0.01), RE (β = -0.003; 95% CI = -0.01, 0.0001) and FE (β = -0.003; 95% CI = -0.01, 0.0002) regression. Neighbourhood convenience stores and fast-food restaurants were not associated with BMI in any model. Conclusions Longitudinal associations between neighbourhood food outlets and BMI were greater in magnitude using a causal model, suggesting that weak findings in the literature may be due to residual confounding.
Finite Element Analysis of One-dimensional Classical Coupled Thermoelasticity of a Layered Slab

The behavior of thermoelastic waves at the interface of layered media and distributions of these waves through the domain are examined by using direct finite element method to obtain the field variables directly in the space and time domains. The analysis is performed in a onedimensional domain with two different layers to provide a means to follow the behavior of the reflected thermoelastic waves at the interface. The material characteristics of layers are selected in both hypothetical and practical ways. This can assure us that the method is applicable for both theoretical and practical purposes. It found that the distributions of thermoelastic waves in an isotropic slab with one layer are significantly different from those in multilayered slabs. This method may be generalized to simulate the propagation of thermoelastic waves in different multilayer regions and analyze the behavior of the layered composite structures under the mechanical or thermal impact loads.
Was Dinosaurian Physiology Inherited by Birds? Reconciling Slow Growth in Archaeopteryx

Background Archaeopteryx is the oldest and most primitive known bird (Avialae). It is believed that the growth and energetic physiology of basalmost birds such as Archaeopteryx were inherited in their entirety from non-avialan dinosaurs. This hypothesis predicts that the long bones in these birds formed using rapidly growing, well-vascularized woven tissue typical of non-avialan dinosaurs. Methodology/Principal Findings We report that Archaeopteryx long bones are composed of nearly avascular parallel-fibered bone. This is among the slowest growing osseous tissues and is common in ectothermic reptiles. These findings dispute the hypothesis that non-avialan dinosaur growth and physiology were inherited in totality by the first birds. Examining these findings in a phylogenetic context required intensive sampling of outgroup dinosaurs and basalmost birds. Our results demonstrate the presence of a scale-dependent maniraptoran histological continuum that Archaeopteryx and other basalmost birds follow. Growth analysis for Archaeopteryx suggests that these animals showed exponential growth rates like non-avialan dinosaurs, three times slower than living precocial birds, but still within the lowermost range for all endothermic vertebrates. Conclusions/Significance The unexpected histology of Archaeopteryx and other basalmost birds is actually consistent with retention of the phylogenetically earlier paravian dinosaur condition when size is considered. The first birds were simply feathered dinosaurs with respect to growth and energetic physiology. The evolution of the novel pattern in modern forms occurred later in the group's history.
W.O.U.S.: Widgets of Unusual Size

Recent work in tangible interfaces, including widget sets like . NET Gadgeteer and Phidgets, has enabled prototyping of rich physical interaction at a handheld or tabletop scale. But it remains unclear how participants respond to physical widgets at larger scales. What kinds of interaction would larger widgets enable, and what kinds of systems - if any - can or should be built with them? We built unusually-sized widgets, or "mega-widgets" in order to explore this territory. We present the results of two iterations of building mega-widgets and accompanying user studies designed to help understand participants» reactions to mega-widgets and probe possible applications. Responses indicated, among other things, a correlation between widget size and the perceived size or importance of what it might control. Mega-widgets were also perceived as increasing the precision of user input control and providing a fun and playful element. We hope that knowledge gained from this exploratory work can help lay groundwork for further exploration of widgets at larger scales.
Relationship of PTSD With impulsivity Dimensions While Controlling the Effect of Anxiety and Depression in a Sample of Inpatients With Alcohol Use Disorder

ABSTRACT Objective: The aim of the present study was to evaluate the relationship of posttraumatic stress disorder (PTSD) with impulsivity dimensions while controlling the effect of anxiety and depression in a sample of inpatients with alcohol use disorder (AUD). Methods: Participants were 190 male patients admitted to a specialized center for substance use disorders within a six month period. Participants were evaluated with the State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BDI), the Short Form Barratt Impulsiveness Scale (BIS-11-SF), the Traumatic Experiences Checklist, and PTSD Checklist-Civilian version. Results: Age was lower in the group with PTSD (n = 63, 33.2%) than the group without PTSD (n = 127, 66.8%). Duration of education, marital, and employment status did not differ between the groups. STAI, BDI, and BIS-11-SF scores were higher in the group with PTSD. Trait anxiety, depression, and impulsivity predicted high PTSD risk in a logistic regression model. Same variables predicted the severity of PTSD symptoms in a linear regression. Among dimensions of impulsivity attentional component was the only predictor of PTSD symptoms severity, not motor or nonplanning impulsivity. Conclusions: These findings suggest that the PTSD may be related to impulsivity, particularly attentional impulsivity, even after controlling anxiety and depression among inpatients with AUD.
Particle Swarm Optimization based on Vector Gaussian Learning

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm’s optimal location, increases the learning ability of the swarm’s optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.
QUEST: A 7.49TOPS multi-purpose log-quantized DNN inference engine stacked on 96MB 3D SRAM using inductive-coupling technology in 40nm CMOS

A key consideration for deep neural network (DNN) inference accelerators is the need for large and high-bandwidth external memories. Although an architectural concept for stacking a DNN accelerator with DRAMs has been proposed previously, long DRAM latency remains problematic and limits the performance [1]. Recent algorithm-level optimizations, such as network pruning and compression, have shown success in reducing the DNN memory size [2]; however, since networks become irregular and sparse, they induce an additional need for agile random accesses to the memory systems.
Modeling of a Permanent Magnet Synchronous Machine With Internal Magnets Using Magnetic Equivalent Circuits

The design of control strategies for permanent magnet synchronous machines (PSM) is almost exclusively based on classical dq0-models. These models are, however, not able to systematically describe saturation or nonhomogenous air gap geometries typically occurring in PSM. This paper deals with a framework for the mathematical modeling of PSM based on magnetic equivalent circuits. Different to existing works, the model equations are derived by means of graph theory allowing for a systematic choice of a minimal set of state variables of the model and a systematic consideration of the electrical connection of the coils of the motor. The resulting model is calibrated and verified by means of measurement results. Finally, a magnetically linear and a dq0-model are derived and their performance is compared with the nonlinear model and measurement results.
Brain-actuated Control of Wheelchair Using Fuzzy Neural Networks

In this paper, a brain-actuated control of the wheelchair for physically disabled people is presented. The design of the system is focused on receiving, processing and classification of the brain signals and then performing control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Using obtained data including brain signals and control commands the design of classification system based on fuzzy neural networks (FNN) is performed. The structure and learning algorithm of FNN used for brain-actuated control are presented. The training data is used to design the system and then test data is applied to measure the performance of the control under real conditions. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.
Rapid production of pure recombinant actin isoforms in Pichia pastoris

ABSTRACT Actins are major eukaryotic cytoskeletal proteins, and they are involved in many important cell functions, including cell division, cell polarity, wound healing and muscle contraction. Despite obvious drawbacks, muscle actin, which is easily purified, is used extensively for biochemical studies of the non-muscle actin cytoskeleton. Here, we report a rapid and cost-effective method to purify heterologous actins expressed in the yeast Pichia pastoris. Actin is expressed as a fusion with the actin-binding protein thymosin β4 and purified by means of an affinity tag introduced in the fusion. Following cleavage of thymosin β4 and the affinity tag, highly purified functional full-length actin is liberated. We purify actins from Saccharomyces cerevisiae and Schizosaccharomyces pombe, and the β- and γ-isoforms of human actin. We also report a modification of the method that facilitates expression and purification of arginylated actin, a form of actin thought to regulate dendritic actin networks in mammalian cells. The methods we describe can be performed in all laboratories equipped for molecular biology, and should greatly facilitate biochemical and cell biological studies of the actin cytoskeleton. Summary:Here, we describe a method to purify recombinant actin to homogeneity by expression in Pichia pastoris. The purified actin is polymerisation competent and should facilitate biochemical and cell biological studies of the actin cytoskeleton.
Complete mitochondrial genome of the Triplophysa bombifrons and Triplophysa strauchii

Abstract China is very rich in mud fish and some detailed studies were classified and biological studies have been carried out. But many, such as genetics and evolutionary aspects, require further investigation. Here, we sequenced the complete mitogenome sequence of Triplophysa bombifrons and Triplophysa strauchii. They are circular molecule of 16 571 and 16 568 bp in size, containing 13 protein-coding genes, 22 transfer RNAs, 2 ribosomal RNAs and 1 non-coding control region (D-loop).
Network structure and dynamics of the mental workspace

Significance We do not know how the human brain mediates complex and creative behaviors such as artistic, scientific, and mathematical thought. Scholars theorize that these abilities require conscious experience as realized in a widespread neural network, or “mental workspace,” that represents and manipulates images, symbols, and other mental constructs across a variety of domains. Evidence for such a complex, interconnected network has been difficult to produce with current techniques that mainly study brain activity in isolation and are insensitive to distributed informational processes. The present work takes advantage of emerging techniques in network and information analysis to provide empirical support for such a widespread and interconnected information processing network in the brain that supports the manipulation of visual imagery. The conscious manipulation of mental representations is central to many creative and uniquely human abilities. How does the human brain mediate such flexible mental operations? Here, multivariate pattern analysis of functional MRI data reveals a widespread neural network that performs specific mental manipulations on the contents of visual imagery. Evolving patterns of neural activity within this mental workspace track the sequence of informational transformations carried out by these manipulations. The network switches between distinct connectivity profiles as representations are maintained or manipulated.
A maneuverable robotic wheelchair able to move adaptively with a caregiver by considering the situation

Although it is desirable for wheelchair users to move alone by operating wheelchairs themselves, they are often pushed or accompanied by caregivers, especially in elderly care facilities. In designing robotic wheelchairs, therefore, it is important to consider how to reduce the caregiver's load and support their activities. We particularly focus on communication among wheelchair users and caregivers because face-to-face communication is known to be effective in ameliorating elderly mental health issues. Hence, we propose a maneuverable robotic wheelchair that can move autonomously towards a goal while considering the situation of the accompanying caregiver. Our robotic wheelchair starts to move autonomously towards the goal as soon as the caregiver sets the goal position, such as a bathroom, by utilizing its touch panel interface. While moving towards the goal, the robotic wheelchair autonomously controls its speed and direction of movement to facilitate natural communication between the wheelchair user and the caregiver, by observing the situation of the accompanying caregiver.
Failure Detection in Large Scale Systems: a Survey

Failure detection is a basic service for building dependable systems. The large scale distribution of computing systems naturally makes failure detectors much harder to build. Moreover, providing QoS (quality of service) guarantees in this context is a challenging task. The objective of this paper is twofold: (1) proposing a complete set of classification criteria to compare different failure detection mechanisms, and based on these criteria (2) surveying the main failure detection solutions for large scale distributed systems.
Augmented-Reality Survey: from Concept to Application

The recent advances in the field of augmented reality (AR) have shown that the technology is a fundamental part of modern immersive interactive systems for the achievement of user engagement and a dynamic user experience. This survey paper presents the descriptions of a variety of the new AR explorations, and the issues that are relevant to the contemporary development of the fundamental technologies and applications are discussed. Most of the literature regarding the pertinent topics-taxonomy, the core tracking and sensing technologies, the hardware and software platforms, and the domain-specific applications-are then chronologically surveyed, and in varying detail, this is supplemented with the cited papers. This paper portrays the diversity of the research regarding the AR field together with an overview of the benefits and the limitations of the competing and complementary technologies.
SourcererCC: Scaling Code Clone Detection to Big-Code

Despite a decade of active research, there has been a marked lack in clone detection techniques that scale to large repositories for detecting near-miss clones. In this paper, we present a token-based clone detector, SourcererCC, that can detect both exact and near-miss clones from large inter-project repositories using a standard workstation. It exploits an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks: (1) a big benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (25K projects, 250MLOC) using a standard workstation.
Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties

As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism.
Gastrointestinal Stromal Tumor: Genotype Frequency and Prognostic Relevance

Background: Genotyping has an important role in the prognosis and prediction of response to tyrosine kinase inhibitor therapy. KIT exon 11 deletions serve as an adverse prognostic marker. Gastrointestinal stromal tumor (GIST) genotype has been described in developed countries; however, data from India are lacking. The aim of this study was to determine the genotype frequency and its prognostic relevance. Materials and Methods: Eighty consecutive cases of resected GIST were evaluated for histologic and immunohistochemical findings. Mutation analysis for exons 9, 11, 13, and 17 of KIT and 12 and 18 of PDGFRA was carried out by PCR-Sanger sequencing. Genotypes were correlated with risk groups, recurrence, and imatinib therapy. Results: Forty-seven of 80 cases (58.7%) showed mutations, including 30 cases (37.5%) in KIT exon 11, 9 cases (11.2%) in KIT exon 9, and 8 cases (10%) in PDGFRA exon 18. Codon 557-558 deletion was present in 15 cases. D842E was the most common in PDGFRA, with similar histologic features as D842V. KIT exon 11 deletion had higher mitotic rate, larger tumor size, high-risk stratification, and lower recurrence-free survival. Recurrences were seen in 12 (16.4%) patients. Nine patients (75%) with recurrence were on imatinib therapy. Conclusions: GIST genotype frequency is lower in Indians. KIT exon 11 deletion is associated with poor prognosis compared with wild-type and other missense mutations. D842E is a common PDGFRA mutation in Indian patients. Patients with a wild genotype are not suitable candidates for imatinib therapy. Genotyping can serve as an important prognostic marker.
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge.