Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Control Monte Carlo Tree Search Informed by Multiple Experts

Efficient algorithms for 3D character control in continuous control setting remain an open problem in spite of the remarkable recent advances in the field. We present a sampling-based model-predictive controller that comes in the form of a Monte Carlo tree search (MCTS). The tree search utilizes information from multiple sources including two machine learning models. This allows rapid development of complex skills such as 3D humanoid locomotion with less than a million simulation steps, in less than a minute of computing on a modest personal computer. We demonstrate locomotion of 3D characters with varying topologies under disturbances such as heavy projectile hits and abruptly changing target direction. In this paper we also present a new way to combine information from the various sources such that minimal amount of information is lost. We furthermore extend the neural network, involved in the algorithm, to represent stochastic policies. Our approach yields a robust control algorithm that is easy to use. While learning, the algorithm runs in near real-time, and after learning the sampling budget can be reduced for real-time operation.
ECG-Based Concentration Recognition With Multi-Task Regression

Objective: Recognition of human activities and mental states using wearable sensors and smartphones has attracted considerable attention recently. In particular, prediction of the stress level of a subject using an electrocardiogram sensor has been studied extensively. In this paper, we attempt to predict the degree of concentration by using heart-rate features. However, due to strong diversity in individuals and high sampling costs, building an accurate prediction model is still highly challenging. Method: To overcome these difficulties, we propose to use a multitask learning (MTL) technique for effectively sharing information among similar individuals. Result: Through experiments with 18 healthy subjects performing daily office works, such as writing reports, we demonstrate that the proposed method significantly improves the accuracy of concentration prediction in small sample situations. Conclusion: The performance of the MTL method is shown to be stable across different subjects, which is an important advantage over conventional models. Significance: This improvement has significant impact in real-world concentration recognition because the data collection burden of each user can be drastically mitigated.
Gender violence : knowledge and professional conduct of the family health strategy

Objective: To evaluate the knowledge and behaviors of health professionals of units of the Strategies Family opposite gender violence. Methods: This descriptive study with 53 seven of units of the family health strategy professionals from March to July 2015. Data were collected through a self-administered instrument and analyzed in Excel 2007. Results: It was observed that the knowledge of professionals about definitions, epidemiology and management of violence ranged from reasonable to good, despite knowing little about the prevalence of violence during pregnancy. Regarding the conduct was evident difficulty in questioning women about violence and its notification. Professionals with shorter assistance and who received training were more assertive results regarding conduct. Conclusions: It is suggested that educational actions in service be carried out in order to provide subsidies for the professionals’ action against cases of gender violence.
Microwave Breast Imaging: Clinical Advances and Remaining Challenges

Objective: Microwave breast imaging has seen significant academic and commercial development in recent years, with four new operational microwave imaging systems used with patients since 2016. In this paper, a comprehensive review of these recent clinical advances is presented, comparing patient populations and study outcomes. For the first time, the designs of operational microwave imaging systems are compared in detail. Methods: First, the current understanding of dielectric properties of human breast tissues is reviewed, considering evidence from operational microwave imaging systems and from dielectric properties measurement studies. Second, design features of operational microwave imaging systems are discussed in terms of advantages and disadvantages during clinical operation. Results: Reported results from patient imaging trials are compared, contrasting the principal results from each trial. Additionally, clinical experience from each trial is highlighted, identifying desirable system design features for clinical use. Conclusions: Increasingly, evidence from patient imaging studies indicate that a contrast in dielectric properties between healthy and cancerous breast tissues exists. However, despite the significant and encouraging results from patient trials, variation still exists in the microwave imaging system design. Significance: This study seeks to define the current state of the art in microwave breast imaging, and identify suitable design characteristics for ease of clinical use.
Tracking Control of a Class of Cyber-Physical Systems via a FlexRay Communication Network

Due to properties of flexibility, adaptiveness, error tolerance, and time-determinism performance, the FlexRay communication protocol has been widely used to investigate robot systems and new generation of automobiles. In this paper, with the FlexRay communication protocol, the tracking problem of a class of cyber-physical systems are investigated by developing a general hybrid model, in which an emulation controller is utilized. Based on the proposed hybrid model, some sufficient conditions are established to guarantee the convergence of tracking errors. Then, the maximum allowable transmission interval (MATI) of the static/dynamic segment is obtained with a more general formula than the ones in some the previous works. The obtained MATI over the FlexRay communication network can be adjusted via the appropriate length of the static/dynamic segment, which reflects the flexibility of FlexRay. Finally, the results are verified by considering the tracking problem of a single-link robot arm system as well as the stabilization of a batch reactor system.
Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning

Retweet prediction is a challenging problem in social media sites (SMS). In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees. Unlike previous studies, we learn user preference ranking model from their past retweeted image tweets in SMS. We first propose heterogeneous image retweet modeling network (IRM) that exploits users' past retweeted image tweets with associated contexts, their following relations in SMS and preference of their followees. We then develop a novel attentional multi-faceted ranking network learning framework with multi-modal neural networks for the proposed heterogenous IRM network to learn the joint image tweet representations and user preference representations for prediction task. The extensive experiments on a large-scale dataset from Twitter site shows that our method achieves better performance than other state-of-the-art solutions to the problem.
A Photoplethysmographic Signal Isolated From an Additive Motion Artifact by Frequency Translation

Acquiring a precise percentage of oxygen saturation (SpO2) from a finger-probe pulse oximeter is dependent on both artifact-free red and infrared photoplethysmoghaphic (PPG) signals. Nonetheless, in real-life situations, these PPG signals are corrupted by a motion artifact (MA) signal that is generated from either finger or hand movement. To resolve this MA interference, the cause of the adulteration of PPG signals by the MA signal is examined. The MA signal is found to behave like an additive noise. Additionally, the frequency responses of the MA and PPG signals show that these signals are in the same frequency band. Hence, instead of direct current, a sinusoidal wave alternating current is proposed to drive an LED source in order to shift the PPG frequency band away from the MA frequency band. Experimentally, a commercial finger-probe pulse oximeter is employed. To determine the performance of the presented scheme, the resulting PPG signals are compared with those from employing the old-fashioned LED-driving method. In addition, the accuracy is verified by computing the SpO2 value. The results reveal that the proposed approach successfully retains the fundamental morphologies of the PPG structures when motion occurs. Moreover, the calculated SpO2 values from the proposed technique provide an average error of approximately 1.4%, whereas the conventional method yields a mean error approximately 4.2%.
Physical accessibility in primary healthcare: a step towards the embracement.

OBJECTIVE To assess the physical accessibility from the front desk of primary healthcare units. METHOD Descriptive and quantitative research to map the accessibility of the physical space in 157 health units, between August 2014 and May 2015, in the region of Baturité, Ceará, Brazil. The data were collected using a checklist instrument type, and absolute and relative frequencies, binomial and verisimilitude tests for statistical analysis, with statistical significance of p <0.05 were used. RESULTS Of the analyzed items, stairs (24.8%), ramps (47.1%) and floors (75.8%) were inaccessible in most health units. Comparing urban and rural areas, circulation area (0.7x, p=0.293), counter (0.4x, p=0.010), seat (0.7x, p=0.758) and drinking fountain (0.7x, p=0.736) were more inaccessible in the urban area. CONCLUSION The access of persons with physical disabilities to primary care should be seen as a priority. There are physical, architectural and furniture barriers that compromise the full embracement of the user.
Distribution of norovirus and sapovirus genotypes with emergence of NoV GII.P16/GII.2 recombinant strains in Chiang Mai, Thailand

Norovirus (NoV) and sapovirus (SaV) are recognized as the causative agents of acute gastroenteritis, and NoV is one of the leading pathogens reported worldwide. This study reports on the distribution of NoV and SaV genotypes in children hospitalized with acute gastroenteritis in Chiang Mai, Thailand, from January 2015 to February 2017. From a total of 843 stool samples, 170 (20.2%) and 16 (1.9%) were identified as having NoV and SaV infections, respectively. Two samples (0.2%) were positive for both NoV and SaV. Of these, NoV GII.4 (57.2%) was the dominant genotype, followed by GII.2, GII.3, GII.17, GII.6, GII.7, GII.13, GII.14, GII.15, GII.21, GI.6, and GI.5. Among the NoV GII.4 variants, Sydney 2012 was the dominant variant during the period 2015‐2016, while the other variants detected in this study were Asia 2003 and New Orleans 2009. Interestingly, an increase of NoV GII.2 was observed in 2016 and 2017. Characterization of partial RNA‐dependent RNA polymerase and VP1 nucleotide sequences of GII.2 strains revealed that more than half of the GII.2 strains circulating in 2016 and 2017 were recombinant strains of GII.P16/GII.2. For SaV, the majority of strains belonged to GI.1 (55.6%) and GI.2 (33.3%), while GII.5 accounted for 11.1%. In conclusion, this study demonstrates the diversity of NoV and SaV, and the emergence of NoV GII.P16/GII.2 recombinant strains in 2016 and 2017 in Chiang Mai, Thailand.
Cyclin D1 overexpression induces global transcriptional downregulation in lymphoid neoplasms

Cyclin D1 is an oncogene frequently overexpressed in human cancers that has a dual function as cell cycle and transcriptional regulator, although the latter is widely unexplored. Here, we investigated the transcriptional role of cyclin D1 in lymphoid tumor cells with cyclin D1 oncogenic overexpression. Cyclin D1 showed widespread binding to the promoters of most actively transcribed genes, and the promoter occupancy positively correlated with the transcriptional output of targeted genes. Despite this association, the overexpression of cyclin D1 in lymphoid cells led to a global transcriptional downmodulation that was proportional to cyclin D1 levels. This cyclin D1–dependent global transcriptional downregulation was associated with a reduced nascent transcription and an accumulation of promoter-proximal paused RNA polymerase II (Pol II) that colocalized with cyclin D1. Concordantly, cyclin D1 overexpression promoted an increase in the Poll II pausing index. This transcriptional impairment seems to be mediated by the interaction of cyclin D1 with the transcription machinery. In addition, cyclin D1 overexpression sensitized cells to transcription inhibitors, revealing a synthetic lethality interaction that was also observed in primary mantle cell lymphoma cases. This finding of global transcriptional dysregulation expands the known functions of oncogenic cyclin D1 and suggests the therapeutic potential of targeting the transcriptional machinery in cyclin D1–overexpressing tumors.
A Mixed-Effects Model for Detecting Disrupted Connectivities in Heterogeneous Data

The human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease, and autism. functional magnetic resonance imaging has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose a special type of mixed-effects model together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities for developing a neural network in whole brain studies. Results are illustrated with a large data set known as autism brain imaging data exchange which includes 361 subjects from eight medical centers.
Safety evaluation of connected vehicle applications using micro-simulation

Connected vehicles are on the cutting edge of automotive technology with applications expected to improve mobility and safety. Several studies have evaluated the mobility benefits of connected vehicle technology but there is little research on its impact on safety. The first objective of this study is to investigate the ability to evaluate the safety of a connected vehicle applications using surrogate safety measures through a combination of the micro-simulation model VISSIM and the Surrogate Safety Assessment Model (SSAM). Two connected vehicle applications are reviewed, considering two types of connected vehicle communications, specifically Vehicle-to-Vehicle and Vehicle-toInfrastructure. The two applications are a cumulative travel time (CTT) intersection control algorithm connected vehicle environment, and a cooperative adaptive cruise control (CACC) application, facilitating vehicle platooning on a freeway. The CACC study investigates the improvement to the freeway segment through a simulated incident. The CTT study investigates the impacts of calibrating the micro-simulation model using real-world vehicle trajectory and conflict data. The CTT algorithm is applied to a signalized intersection and evaluated under three calibration scenarios: uncalibrated, first step calibrated for desired speed and vehicle arrival types, and second step calibrated for conflicts observed in the field. In both studies, a comparison of safety based on the number of conflicts at different time-to-collision thresholds is provided for the varying scenarios. Results show that the combination of VISSIM and SSAM provide an appropriate tool to use in the evaluation of changes in the level of safety of connected vehicle applications, specifically the CACC application and the CTT intersection control application. Calibration of the
3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks

In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., missing/rotten teeth, feature-less regions, crowding teeth, extra medical attachments, etc.). Furthermore, labeling of individual tooth is hardly enabled in traditional tooth segmentation methods. To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep Neural Networks, namely NNs. The segmentation task is achieved by labeling each mesh face. We extract a set of geometry features as face feature representations. In the training step, the network is fed with those features, and produces a probability vector, of which each element indicates the probability a face belonging to the corresponding model part. To this end, we extensively experiment with various network structures, and eventually arrive at a 2-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labeling and the other for inter-teeth labeling. Further, we propose a novel boundary-aware tooth simplification method to significantly improve efficiency in the stage of feature extraction. After CNNs prediction, we do graph-based label optimization and further refine the boundary with an improved version of fuzzy clustering. The accuracy of our mesh labeling method exceeds that of the state-of-art geometry-based methods, reaching 99.06 percent measured by area which is directly applicable in orthodontic CAD systems. It is also robust to any possible foreign matters on model surface, e.g., air bubbles, dental accessories, and many more.
Use of Lipid‐Lowering Drugs and the Risk of Cataract: A Population‐Based Nested Case‐Control Study

Eye lens membrane cells require high cholesterol concentrations that might be counteracted by lipid‐lowering drugs. Using a nationwide database, we conducted a nested case‐control study to evaluate the risk of cataract development associated with the use of lipid‐lowering drugs. Patients aged 45 years and over with first cataract surgery in 2014 (cases) and up to four controls matched on age, gender, diabetes, hypothyroidism, glucocorticoid use, cardiovascular risk, and area of residence were included in the study. Among the 2,811 cases and 11,106 matched controls included, analyses showed a significantly increased risk of cataract surgery for a cumulative exposure to fibrates exceeding 5 years (adjusted odds ratio (aOR) 1.58; 95% confidence interval (CI): 1.17–2.15), unlike cumulative exposure to statins, whatever the dose or duration of treatment (aORs from 1.00–1.08, none being significant). This study highlighted an increased risk of cataract surgery with prolonged use of fibrates but not of statins.
DRINet for Medical Image Segmentation

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid on brain CT images, multi-organ segmentation on abdominal CT images, and multi-class brain tumor segmentation on MR images.
Real-Time Visualization and Analysis of Clinicians’ Performance During Palpation in Physical Examinations

Objective: Motivated by the fact that palpation skills are challenging to learn and teach, particularly during Digital Rectal Examinations (DRE), and the lack of understanding of what constitutes adequate performance, we present a visualization and analysis system that uses small position and pressure sensors located on the examining finger, allowing the quantitative analysis of duration, steps, and pressure applied. Methods: The system is first described, followed by an experimental study of 20 experts from four clinical specialties performing ten DREs each on a benchtop model using the proposed system. Analysis of the constitutive steps was conducted to improve understanding of the examination. A Markov model representing executed tasks and analysis of pressure applied is also introduced. Results: The proposed system successfully allowed the visualization and analysis during the experimental study. General practitioners and nurses were found to execute more tasks compared to urologists and colorectal surgeons. Urologists executed the least number of tasks and were the most consistent group compared to others. Conclusion: The ability to “see through” allowed us to better characterize the performance of experts when conducting a DRE on a benchtop model, comparing the performance of relevant specialties, and studying executed tasks and the pressure applied. The Markov model presented summarizes task execution of experts and could be used to compare the performance of novices against that of experts. Significance: This approach allows for the analysis of performance based on continuous sensor data recording that can be easily extended to real subjects and other types of physical examinations.
C++ Compile Time Polymorphism for Ray Tracing

Reducing the amount of conditional branching instructions in innermost loops is crucial for high performance code on contemporary hardware architectures. In the context of ray tracing algorithms, typical examples for branching in inner loops are the decisions what type of primitive a ray should be tested against for intersection, or which BRDF implementation should be evaluated at a point of intersection. Runtime polymorphism, which is often used in those cases, can lead to highly expressive but poorly performing code. Optimization strategies often involve reduced feature sets (e.g. by simply supporting only a single geometric primitive type), or an upstream sorting step followed by multiple ray tracing kernel executions, which effectively places the branching instruction outside the inner loop. In this paper we propose C++ compile time polymorphism as an alternative optimization strategy that does on its own not reduce branching, but that can be used to write highly expressive code without sacriﬁcing optimization potential such as early binding or inlining of tiny functions. We present an implementation with modern C++ that we integrate into a ray tracing template library. We evaluate our approach on CPU and GPU architectures.