nils-herrmann/clean_scirepeval huggingface dataset:
The influence of the distance from the contact point to the crest of bone on the presence of the interproximal dental papilla.

Loss of the interproximal dental papilla may cause functional and, especially in the maxillary anterior region, phonetic and severe esthetic problems. The purpose of this study was to investigate whether the distance from the contact point to the bone crest on standardized periapical radiographs of the maxillary anterior teeth could be correlated with the presence of the interproximal papilla in Taiwanese patients.



In total, 200 interproximal sites of maxillary anterior teeth in 45 randomly selected patients were examined. Selected subjects were adult Taiwanese with fully erupted permanent dentition. The presence of the interproximal papilla was determined visually. If there was no visible space apical to the contact area, the papilla was recorded as being present. The distance from the contact point to the crest of bone was measured on standardized periapical radiographs using a paralleling technique with a RinnXCP holder.



Data revealed that when the distance from the contact point to the bone crest on standardized periapical radiographs was 5 mm or less, the papillae were almost 100% present. When the distance was 6 mm, 51% of the papillae were present, and when the distance was 7 mm or greater, only 23% of the papillae were present.



The distance from the contact point to the bone crest on standardized periapical radiographs of the maxillary anterior teeth is highly associated with the presence or absence of the interproximal papilla in Taiwanese patients, and is a useful guide for clinical evaluation.
Litz: Elastic Framework for High-Performance Distributed Machine Learning

Machine Learning (ML) is an increasingly popular application in the cloud and data-center, inspiring new algorithmic and systems techniques that leverage unique properties of ML applications to improve their distributed performance by orders of magnitude. However, applications built using these techniques tend to be static, unable to elastically adapt to the changing resource availability that is characteristic of multi-tenant environments. Existing distributed frameworks are either inelastic, or offer programming models which are incompatible with the techniques employed by high-performance ML applications. Motivated by these trends, we present Litz, an elastic framework supporting distributed ML applications. We categorize the wide variety of techniques employed by these applications into three general themes — stateful workers, model scheduling, and relaxed consistency — which are collectively supported by Litz’s programming model. Our implementation of Litz’s execution system transparently enables elasticity and low-overhead execution. We implement several popular ML applications using Litz, and show that they can scale in and out quickly to adapt to changing resource availability, as well as how a scheduler can leverage elasticity for faster job completion and more efficient resource allocation. Lastly, we show that Litz enables elasticity without compromising performance, achieving competitive performance with state-of-the-art non-elastic ML frameworks.
Effect of pitch type, pitch count, and pitching mechanics on risk of elbow and shoulder pain in youth baseball pitchers.

Joint pain is thought to be an early sign of injury to a pitcher.



To evaluate the association between pitch counts, pitch types, and pitching mechanics and shoulder and elbow pain in young pitchers.



Prospective cohort study.



Four hundred and seventy-six young (ages 9 to 14 years) baseball pitchers were followed for one season. Data were collected from pre- and postseason questionnaires, injury and performance interviews after each game, pitch count logs, and video analysis of pitching mechanics. Generalized estimating equations and logistic regression analysis were used.



Half of the subjects experienced elbow or shoulder pain during the season. The curveball was associated with a 52% increased risk of shoulder pain and the slider was associated with an 86% increased risk of elbow pain. There was a significant association between the number of pitches thrown in a game and during the season and the rate of elbow pain and shoulder pain.



Pitchers in this age group should be cautioned about throwing breaking pitches (curveballs and sliders) because of the increased risk of elbow and shoulder pain. Limitations on pitches thrown in a game and in a season can also reduce the risk of pain. Further evaluation of pain and pitching mechanics is necessary.
A compact thermal model to predict the junction temperature of high power light emitting diode package

In this study, the thermal resistance of a typical LED package was analyzed and a compact thermal model (CTM) was obtained by considering the configuration of each layer of LED package. The building of the CTM was presented in detail. Experiments and finite element simulations were conducted to validate the CTM. The error of the thermal resistance obtained by the CTM and simulations were less than 2% when being referred to the experiments. The comparison results demonstrate that the present CTM can be used to obtain the thermal resistance of the package and to predict the junction temperature.
Clinical approach to rapidly progressive renal failure.

Rapidly progressive renal failure (RPRF) is an initial clinical diagnosis in patients who present with progressive renal impairment of short duration. The underlying etiology may be a primary renal disease or a systemic disorder. Important differential diagnoses include vasculitis (systemic or renal-limited), systemic lupus erythematosus, multiple myeloma, thrombotic microangiopathy and acute interstitial nephritis. Good history taking, clinical examination and relevant investigations including serology and ultimately kidney biopsy are helpful in clinching the diagnosis. Early definitive diagnosis of RPRF is essential to reverse the otherwise relentless progression to end-stage kidney disease.
On the trajectory tracking control of industrial SCARA robot manipulators

In this paper, we discuss, from an experimental point of view, the use of different control strategies for the trajectory tracking control of an industrial selective compliance assembly robot arm robot, which is one of the most employed manipulators in industrial environments, especially for assembly tasks. Specifically, we consider decentralized controllers such as proportional–integral–derivative-based and sliding-mode ones and model-based controllers such as the classical computed-torque one and a neural-network-based controller. A simple procedure for the estimation of the dynamic model of the manipulator is given. Experimental results provide a detailed framework about the cost/benefit ratio regarding the use of the different controllers, showing that the performance obtained with decentralized controllers may suffice in a large number of industrial applications, but in order to achieve low tracking errors also for high-speed trajectories, it might be convenient to adopt a neural-network-based control scheme, whose implementation is not particularly demanding.
Finger pinch force estimation through muscle activations using a surface EMG sleeve on the forearm

For prosthetic hand manipulation, the surface Electromyography(sEMG) has been widely applied. Researchers usually focus on the recognition of hand grasps or gestures, but ignore the hand force, which is equally important for robotic hand control. Therefore, this paper concentrates on the methods of finger forces estimation based on multichannel sEMG signal. A custom-made sEMG sleeve system omitting the stage of muscle positioning is utilised to capture the sEMG signal on the forearm. A mathematic model for muscle activation extraction is established to describe the relationship between finger pinch forces and sEMG signal, where the genetic algorithm is employed to optimise the coefficients. The results of experiments in this paper shows three main contributions: 1) There is a systematical relationship between muscle activations and the pinch finger forces. 2) To estimate the finger force, muscle precise positioning for electrodes placement is not inevitable. 3) In a multi-channel EMG system, selecting specific combinations of several channels can improve the estimation accuracy for specific gestures.
Linearization Techniques for Mixers

In the modern high-performance transceivers, mixers (both upand down-converters) are required to have large dynamic range in order to meet the system specifications. The lower end of the dynamic range is indicated by the noise floor which tells how small a signal may be processed while the high end is determined by the non-linearity which causes distortion, compression and saturation of the signal and thus limits the maximum signal amplitude input to the mixer for the undistorted output. Compared to noise, the linearity requirement is much higher in mixer design because it is generally the limiting factor to the transceiver’s linearity. Therefore, this paper will emphasize on the linearization techniques for analog multipliers and mixers, which have been a very active research area since 1960s.
Evaluating a model for cost-effective data quality management in a real-world CRM setting

a r t i c l e i n f o Keywords: Data quality Utility Cost–benefit analysis Data warehouse CRM Managing data resources at high quality is usually viewed as axiomatic. However, we suggest that, since the process of improving data quality should attempt to maximize economic benefits as well, high data quality is not necessarily economically-optimal. We demonstrate this argument by evaluating a microeconomic model that links the handling of data quality defects, such as outdated data and missing values, to economic outcomes: utility, cost, and net-benefit. The evaluation is set in the context of Customer Relationship Management (CRM) and uses large samples from a real-world data resource used for managing alumni relations. Within this context, our evaluation shows that all model parameters can be measured, and that all model-related assumptions are, largely, well supported. The evaluation confirms the assumption that the optimal quality level, in terms of maximizing net-benefits, is not necessarily the highest possible. Further, the evaluation process contributes some important insights for revising current data acquisition and maintenance policies. Maintaining data resources at a high quality level is a critical task in managing organizational information systems (IS). Data quality (DQ) significantly affects IS adoption and the success of data utilization [10,26]. Data quality management (DQM) has been examined from a variety of technical, functional, and organizational perspectives [22]. Achieving high quality is the primary objective of DQM efforts, and much research in DQM focuses on methodologies, tools and techniques for improving quality. Recent studies (e.g., [14,19]) have suggested that high DQ, although having clear merits, should not necessarily be the only objective to consider when assessing DQM alternatives, particularly in an IS that manages large datasets. As shown in these studies, maximizing economic benefits, based on the value gained from improving quality, and the costs involved in improving quality, may conflict with the target of achieving a high data quality level. Such findings inspire the need to link DQM decisions to economic outcomes and tradeoffs, with the goal of identifying more cost-effective DQM solutions. The quality of organizational data is rarely perfect as data, when captured and stored, may suffer from such defects as inaccuracies and missing values [22]. Its quality may further deteriorate as the real-world items that the data describes may change over time (e.g., a customer changing address, profession, and/or marital status). A plethora of studies have underscored the negative effect of low …
Effects of hippotherapy on postural stability, in persons with multiple sclerosis: a pilot study.

The purpose of this pilot study was to examine the effectiveness of hippotherapy as an intervention for the treatment of postural instability in individuals with multiple sclerosis (MS).



A sample of convenience of 15 individuals with MS (24-72 years) were recruited from support groups and assessed for balance deficits.



This study was a nonequivalent pretest-posttest comparison group design. Nine individuals (4 males, 5 females) received weekly hippotherapy intervention for 14 weeks. The other 6 individuals (2 males, 4 females) served as a comparison group. All participants were assessed with the Berg Balance Scale (BBS) and Tinetti Performance Oriented Mobility Assessment (POMA) at 0, 7, and 14 weeks.



The group receiving hippotherapy showed statistically significant improvement from pretest (0 week) to posttest (14 week) on the BBS (mean increase 9.15 points (x (2) = 8.82, p = 0.012)) and POMA scores (mean increase 5.13 (x (2) = 10.38, p = 0.006)). The comparison group had no significant changes on the BBS (mean increase 0.73 (x (2) = 0.40, p = 0.819)) or POMA (mean decrease 0.13 (x (2) = 1.41, p = 0.494)). A statistically significant difference was also found between the groups' final BBS scores (treatment group median = 55.0, comparison group median 41.0), U = 7, r = -0.49.



Hippotherapy shows promise for the treatment of balance disorders in persons with MS. Further research is needed to refine protocols and selection criteria.
Effects of phenytoin sodium on dura mater healing in a rat model of CSF leakage.

Cerebro-spinal fluid (CSF) leakage caused by defects on the dura mater after trauma or some neurosurgical interventions is an important issue. In this study, we investigated the effects of local and systemic use of phenytoin sodium on dural healing.



Thirty-six male Wistar rats were divided into control, local phenytoin and systemic phenytoin groups with 12 rats in each. For each group, a dura defect was created at thoracic segment. Subjects were sacrificed at following 1st and 6th weeks and damaged segments were isolated. The results were compared histopathologically by Hematoxylin-Eosin and Masson-Trichrome staining. Criteria for the rate of collagen, neovascularization, and granulation formation were assessed semi quantitatively according to the histological assessment scale modified by Ozisik et al.



Better healing was achieved in the systemic and local phenytoin groups than in the control group. The level of healing was significantly higher in the systemic group in both early and late periods than in other groups (p < 0.01). The level of healing in the late-local group was also statistically significantly higher than that in the control group.



We observed that both systemic and local uses of phenytoin sodium (especially systemic) have positive effects on dura healing.
Robust throughput and routing for mobile ad hoc wireless networks

Flows transported across mobile ad hoc wireless networks suffer from route breakups caused by nodal mobility. In a network that aims to support critical interactive real-time data transactions, to provide for the uninterrupted execution of a transaction, or for the rapid transport of a high value file, it is essential to identify robust routes across which such transactions are transported. Noting that route failures can induce long re-routing delays that may be highly interruptive for many applications and message/stream transactions, it is beneficial to configure the routing scheme to send a flow across a route whose lifetime is longer, with sufficiently high probability, than the estimated duration of the activity that it is selected to carry. We evaluate the ability of a mobile ad hoc wireless network to distribute flows across robust routes by introducing the robust throughput measure as a performance metric. The utility gained by the delivery of flow messages is based on the level of interruption experienced by the underlying transaction. As a special case, for certain applications only transactions that are completed without being prematurely interrupted may convey data to their intended users that is of acceptable utility. We describe the mathematical calculation of a network’s robust throughput measure, as well as its robust throughput capacity. We introduce the robust flow admission and routing algorithm (RFAR) to provide for the timely and robust transport of flow transactions across mobile ad hoc wireless net-
Quantum Computing in Big Data Analytics: A Survey

Big Data is a term which denotes data that is beyond storage capacity and processing capabilities of classical computer and getting some insight from large amount of data is a very big challenge at hand. Quantum Computing comes to rescue by offering a lot of promises in information processing systems, particularly in Big Data Analytics. In this paper, we have reviewed the available literature on Big Data Analytics using Quantum Computing for Machine Learning and its current state of the art. We categorized the Quantum Machine learning in different subfields depending upon the logic of their learning followed by a review in each technique. Quantum Walks used to construct Quantum Artificial Neural Networks, which exponentially speed-up the quantum machine learning algorithm is discussed. Quantum Supervised and Unsupervised machine learning and its benefits are compared with that of Classical counterpart. The limitations of some of the existing Machine learning techniques and tools are enunciated, and the significance of Quantum computing in Big Data Analytics is incorporated. Being in its infancy as a totally new field, Quantum computing comes up with a lot of open challenges as well. The challenges, promises, future directions and techniques of the Quantum Computing in Machine Learning are also highlighted.
Risk Prediction on Electronic Health Records with Prior Medical Knowledge

Predicting the risk of potential diseases from Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Compared with traditional machine learning models, deep learning based approaches achieve superior performance on risk prediction task. However, none of existing work explicitly takes prior medical knowledge (such as the relationships between diseases and corresponding risk factors) into account. In medical domain, knowledge is usually represented by discrete and arbitrary rules. Thus, how to integrate such medical rules into existing risk prediction models to improve the performance is a challenge. To tackle this challenge, we propose a novel and general framework called PRIME for risk prediction task, which can successfully incorporate discrete prior medical knowledge into all of the state-of-the-art predictive models using posterior regularization technique. Different from traditional posterior regularization, we do not need to manually set a bound for each piece of prior medical knowledge when modeling desired distribution of the target disease on patients. Moreover, the proposed PRIME can automatically learn the importance of different prior knowledge with a log-linear model.Experimental results on three real medical datasets demonstrate the effectiveness of the proposed framework for the task of risk prediction
Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devices

Brownian Dynamics (BD), also known as Langevin Dynamics, and Dissipative Particle Dynamics (DPD) are implicit solvent methods commonly used in models of soft matter and biomolecular systems. The interaction of the numerous solvent particles with larger particles is coarse-grained as a Langevin thermostat is applied to individual particles or to particle pairs. The Langevin thermostat requires a pseudo-random number generator (PRNG) to generate the stochastic force applied to each particle or pair of neighboring particles during each time step in the integration of Newton’s equations of motion. In a SingleInstruction-Multiple-Thread (SIMT) GPU parallel computing environment, small batches of random numbers must be generated over thousands of threads and millions of kernel calls. In this communication we introduce a one-PRNG-per-kernel-call-per-thread scheme, in which a micro-stream of pseudorandom numbers is generated in each thread and kernel call. These high quality, statistically robust micro-streams require no global memory for state storage, are more computationally efficient than other PRNG schemes in memorybound kernels, and uniquely enable the DPD simulation method without requiring communication between threads.
RNSnet: In-Memory Neural Network Acceleration Using Residue Number System

We live in a world where technological advances are continually creating more data than what we can deal with. Machine learning algorithms, in particular Deep Neural Networks (DNNs), are essential to process such large data. Computation of DNNs requires loading the trained network on the processing element and storing the result in memory. Therefore, running these applications need a high memory bandwidth. Traditional cores are memory limited in terms of the memory bandwidth. Hence, running DNNs on traditional cores results in high energy consumption and slows down processing speed due to a large amount of data movement between memory and processing units. Several prior works tried to address data movement issue by enabling Processing In-Memory (PIM)using crossbar analog multiplication. However, these designs suffer from the large overhead of data conversion between analog and digital domains. In this work, we propose RNSnet, which uses Residue Number System (RNS)to execute neural network completely in the digital domain in memory. RNSnet simplifies the fundamental neural network operations and maps them to in-memory addition and data access. We test the efficiency of the proposed design on several popular neural network applications. Our experimental result shows that RNSnet consumes 145.5x less energy and obtains 35.4x speedup as compared to NVIDIA GPU GTX 1080. In addition, our results show that RNSnet can achieve 8.5 x higher energy-delay product as compared to the state-of-the-art neural network accelerators.
CAD-Based Photogrammetry for Reverse Engineering of Industrial Installations

When designing an industrial installation, construction engineers often make use of a library of standardized CAD components. For instance, in the case of a servicing plant, such a library contains descriptions of simple components such as straight pipes, elbows, and T-junctions. A new installation is constructed by selecting and connecting the appropriate components from the library. This article demonstrates that one can use the same approach for reverse engineering by photogrammetry. In our technique, the operator interprets images and selects the appropriate CAD component from a library. By aligning the edges of the component’s wire frame to the visible edges in the images, we implicitly determine the position, orientation, and shape of the real component. For a fast object reconstruction the alignment process has been split in two parts. Initially, the operator approximately aligns a component to the images. In a second step a fitting algorithm is invoked for an automatic and precise alignment. Further improvement in the efficiency of the reconstruction is obtained by imposing geometric constraints on the CAD components of adjacent object