nils-herrmann/clean_scirepeval huggingface dataset:
Potentially malignant disorders of the oral and oropharyngeal mucosa; terminology, classification and present concepts of management.

In a recently held WHO workshop it has been recommended to abandon the distinction between potentially malignant lesions and potentially malignant conditions and to use the term potentially malignant disorders instead. Of these disorders, leukoplakia and erythroplakia are the most common ones. These diagnoses are still defined by exclusion of other known white or red lesions. In spite of tremendous progress in the field of molecular biology there is yet no single marker that reliably enables to predict malignant transformation in an individual patient. The general advice is to excise or laser any oral of oropharyngeal leukoplakia/erythroplakia, if feasible, irrespective of the presence or absence of dysplasia. Nevertheless, it is actually unknown whether such removal truly prevents the possible development of a squamous cell carcinoma. At present, oral lichen planus seems to be accepted in the literature as being a potentially malignant disorder, although the risk of malignant transformation is lower than in leukoplakia. There are no means to prevent such event. The efficacy of follow-up of oral lichen planus is questionable. Finally, brief attention has been paid to oral submucous fibrosis, actinic cheilitis, some inherited cancer syndromes and immunodeficiency in relation to cancer predisposition.
SonarBeat: Sonar Phase for Breathing Beat Monitoring with Smartphones

Vital sign (e.g., breathing rate) monitoring has become increasingly more important because it can offer useful clues to medical conditions such as sleep disorders or anomalies. There is a compelling need for technologies that enable contact-free, easy deployment, and long-term vital sign monitoring for healthcare. In this paper, we present a SonarBeat system to leverage a phase based active sonar to monitor breathing rates with smartphones. We design and implement the SonarBeat system, with components including signal generation, data extraction, received signal preprocessing, and breathing rate estimation, with Andriod smartphones. Our experimental results validate the superior performance of SonarBeat in different indoor environment settings.
Coordinate-based Texture Inpainting for Pose-Guided Image Generation

We present a new deep learning approach to pose-guided resynthesis of human photographs. At the heart of the new approach is the estimation of the complete body surface texture based on a single photograph. Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body. Rather than working directly with colors of texture elements, the inpainting network estimates an appropriate source location in the input image for each element of the body surface. This correspondence field between the input image and the texture is then further warped into the target image coordinate frame based on the desired pose, effectively establishing the correspondence between the source and the target view even when the pose change is drastic. The final convolutional network then uses the established correspondence and all other available information to synthesize the output image using a fully-convolutional architecture with deformable convolutions. We show stateof-the-art result for pose-guided image synthesis. Additionally, we demonstrate the performance of our system for garment transfer and pose-guided face resynthesis.
Modular array-based GPU computing in a dynamically-typed language

Nowadays, GPU accelerators are widely used in areas with large data-parallel computations such as scientific computations or neural networks. Programmers can either write code in low-level CUDA/OpenCL code or use a GPU extension for a high-level programming language for better productivity. Most extensions focus on statically-typed languages, but many programmers prefer dynamically-typed languages due to their simplicity and flexibility. 
 This paper shows how programmers can write high-level modular code in Ikra, a Ruby extension for array-based GPU computing. Programmers can compose GPU programs of multiple reusable parallel sections, which are subsequently fused into a small number of GPU kernels. We propose a seamless syntax for separating code regions that extensively use dynamic language features from those that are compiled for efficient execution. Moreover, we propose symbolic execution and a program analysis for kernel fusion to achieve performance that is close to hand-written CUDA code.
Taking up the challenge of measuring prolonged pain in (premature) neonates: the COMFORTneo scale seems promising.

Pain assessment is essential to tailor intensive care of neonates. The present focus is on acute procedural pain; assessment of pain of longer duration remains a challenge. We therefore tested a modified version of the COMFORT-behavior scale-named COMFORTneo-for its psychometric qualities in the Neonatal Intensive Care Unit setting.



In a clinical observational study, nurses assessed patients with COMFORTneo and Numeric Rating Scales (NRS) for pain and distress, respectively. Interrater reliability, concurrent validity, and sensitivity to change were calculated as well as sensitivity and specificity for different cut-off scores for subsets of patients.



Interrater reliability was good: median linearly weighted Cohen kappa 0.79. Almost 3600 triple ratings were obtained for 286 neonates. Internal consistency was good (Cronbach alpha 0.84 and 0.88). Concurrent validity was demonstrated by adequate and good correlations, respectively, with NRS-pain and NRS-distress: r=0.52 (95% confidence interval 0.44-0.59) and r=0.70 (95% confidence interval 0.64-0.75). COMFORTneo cut-off scores of 14 or higher (score range is 6 to 30) had good sensitivity and specificity (0.81 and 0.90, respectively) using NRS-pain or NRS-distress scores of 4 or higher as criterion.



The COMFORTneo showed preliminary reliability. No major differences were found in cut-off values for low birth weight, small for gestational age, neurologic impairment risk levels, or sex. Multicenter studies should focus on establishing concurrent validity with other instruments in a patient group with a high probability of ongoing pain.
Smart grid sensor data collection, communication, and networking: a tutorial

The smart grid is an innovative energy network that will improve the conventional electrical grid network to be more reliable, cooperative, responsive, and economical. Within the context of the new capabilities, advanced data sensing, communication, and networking technology will play a significant role in shaping the future of the smart grid. The smart grid will require a flexible and efficient framework to ensure the collection of timely and accurate information from various locations in power grid to provide continuous and reliable operation. This article presents a tutorial on the sensor data collection, communications, and networking issues for the smart grid. First, the applications of data sensing in the smart grid are reviewed. Then, the requirements for data sensing and collection, the corresponding sensors and actuators, and the communication and networking architecture are discussed. The communication technologies and the data communication network architecture and protocols for the smart grid are described. Next, different emerging techniques for data sensing, communications, and sensor data networking are reviewed. The issues related to security of data sensing and communications in the smart grid are then discussed. To this end, the standardization activities and use cases related to data sensing and communications in the smart grid are summarized. Finally, several open issues and challenges are outlined.
Reference range of fetal transverse cerebellar diameter between 18 and 24 weeks of pregnancy in a Brazilian population.

To determine a reference range of fetal transverse cerebellar diameter in Brazilian population. This was a retrospective cross-sectional study with 3772 normal singleton pregnancies between 18 and 24 weeks of pregnancy. The transverse cerebellar diameter was measured on the axial plane of the fetal head at the level of the lateral ventricles, including the thalamus, cavum septum pellucidum, and third ventricle. To assess the correlation between transverse cerebellar diameter and gestational age, polynomial equations were calculated, with adjustments by the determination coefficient (R2). The mean of fetal transverse cerebellar diameter ranged from 18.49 ± 1.24 mm at 18 weeks to 25.86 ± 1.66 mm at 24 weeks of pregnancy. We observed a good correlation between transverse cerebellar diameter and gestational age, which was best represented by a linear equation: transverse cerebellar diameter: -6.21 + 1.307*gestational age (R2 = 0.707). We determined a reference range of fetal transverse cerebellar diameter for the second trimester of pregnancy in Brazilian population.
Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics

In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer size of tfMRI data, its intrinsic complex structure, and lack of ground truth of underlying neural activities, modeling tfMRI data is hard and challenging. Previously proposed data-modeling methods including Independent Component Analysis (ICA) and Sparse Dictionary Learning only provided a weakly established model based on blind source separation under the strong assumption that original fMRI signals could be linearly decomposed into time series components with corresponding spatial maps. Meanwhile, analyzing and learning a large amount of tfMRI data from a variety of subjects has been shown to be very demanding but yet challenging even with technological advances in computational hardware. Given the Convolutional Neural Network (CNN), a robust method for learning high-level abstractions from low-level data such as tfMRI time series, in this work we propose a fast and scalable novel framework for distributed deep Convolutional Autoencoder model. This model aims to both learn the complex hierarchical structure of the tfMRI data and to leverage the processing power of multiple GPUs in a distributed fashion. To implement such a model, we have created an enhanced processing pipeline on the top of Apache Spark and Tensorflow library, leveraging from a very large cluster of GPU machines. Experimental data from applying the model on the Human Connectome Project (HCP) show that the proposed model is efficient and scalable toward tfMRI big data analytics, thus enabling data-driven extraction of hierarchical neuroscientific information from massive fMRI big data in the future.
The Tamiflu fiasco and lessons learnt

Oseltamivir (Tamiflu), a neuraminidase inhibitor, was approved for seasonal flu by US Food and Drug Administration in 1999. A number of randomized controlled trials, systematic reviews, and meta-analysis emphasized a favorable efficacy and safety profile. Majority of them were funded by Roche, which also first marketed and promoted this drug. In 2005 and 2009, the looming fear of pandemic flu led to recommendation by prominent regulatory bodies such as World Health Organization (WHO), Centers for Disease Control and Prevention, European Medicines Agency and others for its use in treatment and prophylaxis of influenza, and it's stockpiling as a measure to tide over the crisis. Serious Adverse Events, especially neuropsychiatric events associated with Tamiflu started getting reported leading to a cascade of questions on clinical utility of this drug. A recent Cochrane review and related articles have questioned the risk-benefit ratio of the drug, besides raising doubts about the regulatory decision of approving it. The recommendations for stockpiling the said drug as given by various international organizations viz WHO have also been put to scrutiny. Although many reviewers have labeled the Tamiflu saga as a "costly mistake," the episode leaves us with some important lessons. This article takes a comprehensive relook on the subject, and we proceed to suggest some ways and means to avoid a similar situation in the future.
Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals

This study mainly integrates the mature Technology-Organization-Environment (TOE) framework and recently developed Human-Organization-Technology (HOT) fit model to identify factors that affect the hospital decision in adopting Hospital Information System (HIS).



Accordingly, a hybrid Multi-Criteria-Decision-Making (MCDM) model is used to address the dependence relationships of factors with the aid of Analytic Network Processes (ANP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) approaches. The initial model of the study is designed by considering four main dimensions with 13 variables as organizational innovation adoption factors with respect to HIS. By using DEMATEL, the interdependencies strength among the dimensions and variables are tested. The ANP method is then adopted in order to determine the relative importance of the adoption factors, and is used to identify how these factors are weighted and prioritized by the public hospital professionals, who are wholly familiar with the HIS and have years of experience in decision making in hospitals' Information System (IS) department.



The results of this study indicate that from the experts' viewpoint "Perceived Technical Competence" is the most important factor in the Human dimension. In the Technology dimension, the experts agree that the "Relative Advantage" is more important in relation to the other factors. In the Organization dimension, "Hospital Size" is considered more important rather than others. And, in the Environment dimension, according to the experts judgment, "Government Policy" is the most important factor. The results of ANP survey from experts also reveal that the experts in the HIS field believed that these factors should not be overlooked by managers of hospitals and the adoption of HIS is more related to more consideration of these factors. In addition, from the results, it is found that the experts are more concerned about Environment and Technology for the adoption HIS.



The findings of this study make a novel contribution in the context of healthcare industry that is to improve the decision process of innovation in adoption stage and to help enhance more the diffusion of IS in the hospital setting, which by doing so, can provide plenty of profits to the patient community and the hospitals.
Vsion: Vehicle Occlusion Handling for Traffic Monitoring

Recently, the pervasiveness of street cameras for security and traffic monitoring opens new challenges to the computer vision technology to provide reliable monitoring schemes. These monitoring schemes require the basic processes of detecting and tracking objects, such as vehicles. However, object detection performance often suffers under occlusion. This work proposes a vehicle occlusion handling improvement of an existing traffic video monitoring system, which was later integrated. Two scenarios were considered in occlusion: indistinct and distinct - wherein the occluded vehicles have similar and dissimilar colors, respectively. K-means clustering using the HSV color space was used for distinct occlusion while sliding window algorithm was used for indistinct occlusion. The proposed method also applies deep convolutional neural networks to further improve vehicle recognition and classification. The CNN model obtained a 97.21% training accuracy and a 98.27% testing accuracy. Moreover, it minimizes the effect of occlusion to vehicle detection and classification. It also identifies common vehicle types (bus, truck, van, sedan, SUV, jeepney, and motorcycle) rather than classifying these as small, medium and large vehicles, which were the previous categories. Despite the implementation and results, it is recommended to improve the occlusion handling issue. The disadvantage of the sliding window algorithm is that it requires a lot of memory and is time-consuming. In case of deploying this research for more substantial purposes and intentions, it is ideal to enhance the CNN model by training it with more varied images of vehicles and to implement the system real-time. The results of this work can serve as a contribution for future works that are significant to traffic monitoring and air quality surveillance.
Bandwidth-adaptive partitioning for distributed execution optimization of mobile applications

Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices, while such applications drain increasingly more battery power of mobile devices. Offloading some parts of the application running on mobile devices onto remote servers/clouds is a promising approach to extend the battery life of mobile devices. However, as data transmission of offloading causes delay and energy costs for mobile devices, it is necessary to carefully design application partitioning/offloading schemes to weigh the benefits against the transmission delay and costs. Due to bandwidth fluctuations in the wireless environment, static partitionings in previous work are unsuitable for mobile platforms with a fixed bandwidth assumption, while dynamic partitionings result in high overhead of continuous partitioning for mobile devices. Therefore, we propose a novel partitioning scheme taking the bandwidth as a variable to improve static partitioning and avoid high costs of dynamic partitioning. Firstly, we construct application Object Relation Graphs (ORGs) by combining static analysis and dynamic profiling to propose partitioning optimization models. Then based on our novel executiontime and energy optimization partitioning models, we propose the Branch-and-Bound based Application Partitioning (BBAP) algorithm and Min-Cut based Greedy Application Partitioning (MCGAP) algorithm. BBAP is suited to finding the optimal partitioning solutions for small applications, while MCGAP is applicable to quickly obtaining suboptimal solutions for large-scale applications. Experimental results demonstrate that both algorithms can adapt to bandwidth fluctuations well, and significantly reduce application execution time and energy consumption by optimally distributing components between mobile devices and servers. & 2013 Elsevier Ltd. All rights reserved.
RFID technology: Beyond cash-based methods in vending machine

This paper characterizes the design, implementation and employment of cashless and secure payment system in vending machine by using radio frequency identification technology, to improve the traditional cash-based payment system that involved lot of problems and risks i.e., hacking, auditing, storing, currency and material of coins and notes. RFID is achieving momentum in a multiple sectors like retail, security, transportation, pharmaceuticals, defense, healthcare etc., and a host of other fields, and now vending machines. Our proposed methodology consists of passive RFID identification cards and reader for consumers, Arduino Mega microcontroller, SPI protocol for RFID and Arduino interfacing, keypad for password protection, liquid crystal display (LCD) for displaying consumer name and current balance, and SMS is sent for notification using GSM module. Spiral coil architecture is supported by DC motors powered by relays in mechanical structure of vending machine. It also describes how such product-oriented RFID card based vending machine can maximum facilitates the system engineering.
A simple and efficient estimator for hyperbolic location

An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed. The approach is noniterative and gives au explicit solution. It is an approximate realization of the maximum-likelihood estimator and is shown to attain the Cramer-Rao lower bound near the small error region. Comparisons of performance with existing techniques of beamformer, sphericat-interpolation, divide and conquer, and iterative Taylor-series methods are made. The proposed technique performs significantly better than sphericalinterpolation, and has a higher noise threshold than divide and conquer before performance breaks away from the Cramer-Rao lower bound. It provides an explicit solution form that is not available in the beamformmg and Taylor-series methods. Computational complexity is comparable to spherical-interpolation but substantially less than the Taylor-series method.
Design of an energy-based controller for a 2D ball segway

A ball segway is a ballbot-type robot that has a car-like structure. It can move with three omnidirectional-wheel mechanisms to drive the ball while maintaining balance. To obtain stable balancing and transferring simultaneously of the 2D ball segway which is an underactuated system, a control law is designed based on energy method. The energy storage function is formulated to prove the passivity property of the system. Simulation results show the effectiveness of our approach.
PAR Sheets , probabilities , and slot machine play : Implications for problem and non-problem gambling

Through the Freedom of Information and Protection of Privacy Act, we obtained design documents, called PAR Sheets, for slot machine games that are in use in Ontario, Canada. From our analysis of these PAR Sheets and observations from playing and watching others play these games, we report on the design of the structural characteristics of Ontario slots and their implications for problem gambling. We discuss characteristics such as speed of play, stop buttons, bonus modes, hand-pays, nudges, near misses, how some wins are in fact losses, and how two identical looking slot machines can have very different payback percentages. We then discuss how these characteristics can lead to multi-level reinforcement schedules (different reinforcement schedules for frequent and infrequent gamblers playing the same game) and how they may provide an illusion of control and contribute in other ways to irrational thinking, all of which are known risk factors for problem gambling.
[Pterigium colli: secondary surgical correction of one severe case].

Congenital web neck is a deformity hardly ever reported in the English literature. It is usually associated to Ulrrich-Turner syndrome. There are several options to correct this deformity, but in severe cases complete correction of the web and the abnormal back hair is not always possible. We present our experience with a secondary case where previous butterfly method was employed, a combined procedure was used achieving a satisfactory result. We considered that this technique is useful and offers an important improvement of the contour.