Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Capitalize Effect of Sensor Position for Training Type Recognition in a Body Weight Training Support System

A body weight training (BWT) means the training which utilizes the self-weight instead of the weight machine. The feedback of form and proper training menu recommendation is important for maximizing the effect of BWT. The objective of this study is to realize a novel support system which allows beginners to perform effective BWT alone, under wearable computing environment. To make an effective feedback, it is necessary to recognize BWT type with high accuracy. However, since the accuracy is greatly affected by the position of wearable sensors, we need to know the sensor position which achieves the high accuracy in recognizing the BWT type. We investigated 10 types BWT recognition accuracy for each sensor position. We found that waist is the best position when only 1 sensor is used. When 2 sensors are used, we found that the best combination is of waist and wrist. We conducted an evaluation experiment to show the effectiveness of sensor position. As a result of leave-one-person-out cross-validation from 13 subjects to confirm validity, we calculated the F-measure of 93.5% when sensors are placed on both wrist and waist.
Automatic Human Sleep Stage Scoring Using Deep Neural Networks

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep.
Haematological profile of chronic kidney disease in a mixed-ancestry South African population: a cross-sectional study

Objectives The objectives were to characterise the haematological profile of screen-detected chronic kidney disease (CKD) participants and to correlate the complete blood count measures with the commonly advocated kidney function estimators. Methods The current cross-sectional study used data, collected between February 2015 and November 2016, of 1564 adults of mixed-ancestry, who participated in the Cape Town Vascular and Metabolic Health study. Kidney function was estimated using the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations. CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, and anaemia as haemoglobin level <13.5 g/dL (men) and <12 g/dL (women). Results Based on the MDRD and CKD-EPI equations, the crude prevalence of CKD was 6% and 3%. Irrespective of the equation used, median red blood cell (RBC) indices were consistently lower in those with CKD compared with those without CKD (all p<0.0001). Despite not showing any significant difference in total white blood cell (WBC) count between the two groups, the number of lymphocytes were lower (p=0.0001 and p<0.0001 for MDRD and CKD-EPI, respectively) and neutrophil count (both p<0.0297) and the ratio of lymphocytes to neutrophil (both p<0.0001) higher in the CKD group compared with those without CKD; with the remaining WBC indices similar in the two groups. The platelet count was similar in both groups. Of the screen-detected CKD participants, 45.5% (MDRD) and 57.8% (CKD-EPI) were anaemic, with the prevalence increasing with increasing severity of CKD, from 37.2% (stage 3) to 82.4% (stages 4–5). Furthermore, CKD-EPI-estimated kidney function, but not MDRD, was positively associated with RBC indices. Conclusion Though it remains unclear whether common kidney function estimators provide accurate estimates of CKD in Africans, the correlation of their estimates with deteriorating RBC profile, suggests that advocated estimators, to some extent approximate kidney function in African populations.
Association of interleukin-17a rs2275913 gene polymorphism and asthma risk: a meta-analysis

Introduction Interleukin-17A (IL-17A), a pro-inflammatory cytokine, plays an important role in the pathogenesis of asthma. A number of studies have investigated the relationship between IL-17A rs2275913 polymorphism and risk of asthma. However, the results obtained are inconclusive. The aim of this meta-analysis is to clarify the relationship between IL-17A rs2275913 polymorphism and asthma risk. Material and methods Searches were conducted in PubMed, Web of Science, Elsevier, Google Scholar, Wanfang and Chinese National Knowledge Infrastructure (CNKI) databases, and data were extracted from eligible studies by two independent reviewers. The pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated. Publication bias, heterogeneity and sensitivity analysis were also assessed. Results Ten studies with a total of 5016 subjects were included. Overall, the results indicated a significant association between the IL-17A rs2275913 polymorphism and the risk of asthma (G vs. A: OR = 0.866, 95% CI: 0.789–0.951, p = 0.003; GG+GA vs. AA: OR = 0.752, 95% CI: 0.633–0.895, p = 0.001). In subgroup analysis by age and ethnicity, the G allele of rs2275913 in IL-17A was significantly associated with a reduced risk of asthma in children and Asians. Conclusions The results of this meta-analysis indicate that the G allele of rs2275913 in IL-17A is a protective factor for the development of asthma.
Controllable Top-down Feature Transformer

We study the intrinsic transformation of feature maps across convolutional network layers with explicit top-down control. To this end, we develop top-down feature transformer (TFT), under controllable parameters, that are able to account for the hidden layer transformation while maintaining the overall consistency across layers. The learned generators capture the underlying feature transformation processes that are independent of particular training images. Our proposed TFT framework brings insights to and helps the understanding of, an important problem of studying the CNN internal feature representation and transformation under the top-down processes. In the case of spatial transformations, we demonstrate the significant advantage of TFT over existing data-driven approaches in building data-independent transformations. We also show that it can be adopted in other applications such as data augmentation and image style transfer.
LBSLab: A User Data Collection System in Mobile Environments

Thanks to the emergence of mobile computing technologies, location-based services (LBS) have been widely used. Massive data of LBS user activities would be useful for studying human mobility and urban computing. In this paper, we design and implement LBSLab, a system which facilitates large-scale data collection of mobile user activities, and provides data visualization in an informative way. LBSLab interacts with users via a mini-program built in the WeChat app, assembling several representative location-related functions such as conducting check-ins. A mobile user can join a data collection experiment by simply scanning a QR code. LBSLab can serve as an efficient system to study the user behavior of LBS-related applications.
The roots of civic cooperative behavior : the roles of trust, shared identity and diversity

results were most pronounced in non-Secondo Females, followed Secondo Secondo Females and least non-secondo Males. Furthermore, the results show that civic nationalism increases national identification as does. The study has shown that social trusts, as well as national identification, are associated with higher levels of civic cooperation. the concerns about possible adverse effects of ethnic diversity on mutual trust, national identity, and civic cooperation cannot entirely Therefore, is recommended to target demographic trust, and an inclusive civic form of to enhance civic cooperation. differences observed in this Then the further are
Developing Notations for Data Infrastructuring in Participatory Design

Data has become a core asset for organizations. However, the data infrastructures, which makes data accessible often require IT expertise, which consequently places high demands on organiza-tions' computational knowledge and skills. This paper reports on preliminary results of an action research intervention that questions how data can become an accessible enabler for service innovation in small and medium-sized organiza-tions (SMEs). On this basis, I highlight three in-frastructuring challenges for data infrastructures that are relevant from a Participatory Design (PD) perspective. The paper concludes by suggesting future work and advice sought.
Unifying Isolated and Overlapping Audio Event Detection with Multi-label Multi-task Convolutional Recurrent Neural Networks

We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent neural network architectures; convolutional layers learn effective features over which higher recurrent layers perform sequential modelling. Furthermore, the output layer is designed to handle arbitrary degrees of event overlap. At each time step in the recurrent output sequence, an output triple is dedicated to each event category of interest to jointly model event occurrence and temporal boundaries. That is, the network jointly determines whether an event of this category occurs, and when it occurs, by estimating onset and offset positions at each recurrent time step. We then introduce three sequential losses for network training: multi-label classification loss, distance estimation loss, and confidence loss. We demonstrate good generalization on two datasets: ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for overlapping audio event detection.
A Meta-Analysis Including Pre-selected Sequence Variants Associated With Seven Traits in Three French Dairy Cattle Populations

A within-breed genome-wide association study (GWAS) is useful when identifying the QTL that segregates in a breed. However, an across-breed meta-analysis can be used to increase the power of identification and precise localization of QTL that segregate in multiple breeds. Precise localization will allow including QTL information from other breeds in genomic prediction due to the persistence of the linkage phase between the causal variant and the marker. This study aimed to identify and confirm QTL detected in within-breed GWAS through a meta-analysis in three French dairy cattle breeds. A set of sequence variants selected based on their functional annotations were imputed into 50 k genotypes for 46,732 Holstein, 20,096 Montbeliarde, and 11,944 Normande cows to identify QTL for milk production, the success rate at insemination of cows (fertility) and stature. We conducted within-breed GWAS followed by across-breed meta-analysis using a weighted Z-scores model on the GWAS summary data (i.e., P-values, effect direction, and sample size). After Bonferroni correction, the GWAS result identified 21,956 significantly associated SNP (PFWER < 0.05), while meta-analysis result identified 9,604 significant SNP (PFWER < 0.05) associated with the phenotypes. The meta-analysis identified 36 QTL for milk yield, 48 QTL for fat yield and percentage, 29 QTL for protein yield and percentage, 13 QTL for fertility, and 16 QTL for stature. Some of these QTL were not significant in the within-breed GWAS. Some previously identified causal variants were confirmed, e.g., BTA14:1802265 (fat percentage, P = 1.5 × 10−760; protein percentage, P = 7.61 × 10−348) both mapping the DGAT1-K232A mutation and BTA14:25006125 (P = 8.58 × 10−140) mapping PLAG1 gene was confirmed for stature in Montbeliarde. New QTL lead SNP shared between breeds included the intronic variant rs109205829 (NFIB gene), and the intergenic variant rs41592357 (1.38 Mb upstream of the CNTN6 gene and 0.65 Mb downstream of the CNTN4 gene). Rs110425867 (ZFAT gene) was the top variant associated with fertility, and new QTL lead SNP included rs109483390 (0.1 Mb upstream of the TNFAIP3 gene and 0.07 Mb downstream of PERP gene), and rs42412333 (0.45 Mb downstream of the RPL10L gene). An across-breed meta-analysis had greater power to detect QTL as opposed to a within breed GWAS. The QTL detected here can be incorporated in routine genomic predictions.
Extent of Resection and Residual Tumor Thresholds for Postoperative Total Seizure Freedom in Epileptic Adult Patients Harboring a Supratentorial Diffuse Low-Grade Glioma.

BACKGROUND Epileptic seizures impair quality of life in diffuse low-grade glioma (DLGG) patients. Tumor resection significantly impacts postoperative seizure control, but the precise extent of resection (EOR) required for optimal seizure control is not clear yet. OBJECTIVE To identify the EOR and residual tumor volume that correlated to postoperative seizure control, defined as a total seizure freedom (Class 1A in reference to Engel classification system) with and without antiepileptic drugs in patients undergoing surgical resection of supratentorial DLGG. METHODS A retrospective review was conducted of all patients who underwent first-line surgical resection of supratentorial DLGG who presented with preoperative seizures without adjuvant oncological treatment. EOR and residual tumor volume were quantified from pre- and post-operative magnetic resonance imagings. Receiver operating characteristic curves were plotted to determine the EOR and residual tumor volume that corresponded to optimal postoperative seizure control. RESULTS Of the 346 included patients, 65.5% had controlled seizures postoperatively, with higher age at resection (adjusted OR per unit, 1.03 [95% confidence interval:1.01-1.06], P = .043) and higher percentage of resection (adjusted OR per unit, 1.02 [95% confidence interval:1.00-1.03], P < .001) found as independent predictors of postoperative seizure control. Optimal EOR was ≥91% and optimal residual tumor volume was ≤19 cc to improve postoperative seizure control. CONCLUSION Postoperative seizure control is more likely when EOR is ≥91% and/or when residual tumor volume is ≤19 cc in supratentorial DLGG gliomas who present with seizures. Resected peritumoral cortex should, however, be taken into account in future studies.
Monitoring Daily Activities of Multiple Sclerosis Patients with Connected Health Devices

We report results from a pilot study that focuses mainly on understanding the everyday life quality of patients suffering from multiple sclerosis through the lens of connected Nokia Health devices. Our dataset comprises of 198 individuals (184 females and 14 males) and the study lasted over six months. By analyzing carefully crafted user-studies and correlating with personal sensor data collected with Nokia devices, we found that the level of fatigue is one of the main sources of discomfort across the majority of the patients. We further perform an exploratory analysis, which provides an early indication that by actively monitoring and perturbing users' daily activity levels, such as increasing daily step-counts, sleep duration and decreasing body weight, patients can potentially reduce their daily fatigue level.
SPECTRE: Seedless Network Alignment via Spectral Centralities

Network alignment consists of finding a structure-preserving correspondence between the nodes of two correlated, but not necessarily identical, networks. This problem finds applications in a wide variety of fields, from the alignment of proteins in computational biology, to the de-anonymization of social networks, as well as recognition tasks in computer vision. In this work we introduce SPECTRE, a scalable algorithm that uses spectral centrality measures and percolation techniques. Unlike most network alignment algorithms, SPECTRE requires no seeds (i.e., pairs of nodes identified beforehand), which in many cases are expensive, or impossible, to obtain. Instead, SPECTRE generates an initial noisy seed set via spectral centrality measures which is then used to robustly grow a network alignment via bootstrap percolation techniques. We show that, while this seed set may contain a majority of incorrect pairs, SPECTRE is still able to obtain a high-quality alignment. Through extensive numerical simulations, we show that SPECTRE allows for fast run times and high accuracy on large synthetic and real-world networks, even those which do not exhibit a high correlation.
Interest Detection While Reading Newspaper Articles by Utilizing a Physiological Sensing Wristband

In this paper, we present how physiological measures including heart rate (HR), electrodermal activity (EDA) and blood volume pulse (BVP) can be retrieved from a wristband device like an E4 wristband and further used to detect the interest of a user during a reading task. From the data of 13 university students on 18 newspaper articles, we have classified their interest level into four classes with an accuracy of 50%, and 68% with binary classification (interesting or boring). This research can be incorporated in the real-time prediction of a user's interest while reading, for the betterment of future designs of human-document interaction.
Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intelligence applications, but their very large and deep models impose high computational requirements during training. Multi-GPU parallelization is a popular option to accelerate demanding computations in DNN training, but most state-of-the-art multi-GPU deep learning frameworks not only require users to have an in-depth understanding of the implementation of the frameworks themselves, but also apply parallelization in a straight-forward way without optimizing GPU utilization. In this work, we propose a workload-aware auto-parallelization framework (WAP) for DNN training, where the work is automatically distributed to multiple GPUs based on the workload characteristics. We evaluate WAP using TensorFlow with popular DNN benchmarks (AlexNet and VGG-16), and show competitive training throughput compared with the state-of-the-art frameworks, and also demonstrate that WAP automatically optimizes GPU assignment based on the workload’s compute requirements, thereby improving energy efficiency.
Advantages of the New Tao Mask for Bag Mask Ventilation: a randomized crossover trial

Abstract Background and Aims: Manual bag mask ventilation is a life saving skill. An investigation was made to compare two different facemasks used in bag mask ventilation, the standard and the novel Tao face mask, and evaluate the ability of novices to achieve adequate tidal volume. Methods: The study design was a crossover trial, which randomized forty medical students with no previous airway experience to learn bag mask ventilation with the standard mask and the Tao face mask. Primary outcome measures were mean and median tidal volume per mask, and secondary measures were hand area, age, gender, and order of mask usage. Results: Medical students who used the Tao mask first achieved significantly more tidal volume than those who used the standard mask first (p = 0.002). However, when comparing face masks that were used second, the tidal volume did not differ significantly between the two masks (p = 1.000). Greater tidal volume was achieved on the second attempt relative to the first attempt with each mask. There was significantly more tidal volume achieved with greater hand size with the standard mask, whether it was used first or second (p < 0.001 and p = 0.012 respectively). Greater hand size was associated with greater tidal volume in the Tao mask also, but only when used first (p < 0.001). When first attempting bag mask ventilation, inexperienced students achieved greater tidal volume with the Tao Mask. The results also suggest that hand size matters less when using the Tao Mask. Conclusion: When first attempting bag mask ventilation inexperienced students achieved greater tidal volume with the Tao Mask. The results also suggest that hand size matters less when using the Tao mask.
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.
Biased landscapes for random constraint satisfaction problems

The typical complexity of constraint satisfaction problems (CSPs) can be investigated by means of random ensembles of instances. The latter exhibit many threshold phenomena besides their satisfiability phase transition, in particular a clustering or dynamic phase transition (related to the tree reconstruction problem) at which their typical solutions shatter into disconnected components. In this paper we study the evolution of this phenomenon under a bias that breaks the uniformity among solutions of one CSP instance, concentrating on the bicoloring of k-uniform random hypergraphs. We show that for small k the clustering transition can be delayed in this way to higher density of constraints, and that this strategy has a positive impact on the performances of simulated annealing algorithms. We characterize the modest gain that can be expected in the large k limit from the simple implementation of the biasing idea studied here. This paper contains also a contribution of a more methodological nature, made of a review and extension of the methods to determine numerically the discontinuous dynamic transition threshold.