nils-herrmann/clean_scirepeval huggingface dataset:
Failed Agility LP Total Ankle Replacement with Severe >40 Varus DeformityRevision with Prosthesis Conversion and Soft-Tissue Procedures Alone

Achieving frontal plane alignment of the ankle joint during primary and revision total ankle replacement is essential for long-term success. This manuscript presents the revision of a failed AgilityTM LP total ankle replacement system with severe > 40° varus ankle deformity that was corrected with explanation and conversion to an INBONETM II total ankle system coupled with soft-tissue release/reconstruction employing percutaneous Achilles tendon lengthening, posterior tibial tendon intramuscular recession lengthening and modified Evans peroneus longus lateral ankle stabilization. Additionally, prophylactic tarsal tunnel release was performed to reduce potential for postoperative nerve compression. At short-term 2.5-years follow-up the patient had a well-balanced and painless foot/ ankle and return of a functional lower limb.
Is Customer Satisfaction an Indicator of Customer Loyalty ?

Customer satisfaction and loyalty is a well known and established concept in several areas like marketing, consumer research, economic psychology, welfare-economics, and economics. And has long been a topic of high interest in both academia and practice. The aim of the study was to investigate whether customer satisfaction is an indicator of customer loyalty. The findings of the study supported the contention that strong relationship exist between customer satisfaction and loyalty. However, customer satisfaction alone cannot achieve the objective of creating a loyal customer base. Some researchers also argued, that customer satisfaction and loyalty are not directly correlated, particularly in competitive business environments because there is a big difference between satisfaction, which is a passive customer condition, and loyalty, which is an active or proactive relationship with the organization.
A Survey of Content-Aware Video Analysis for Sports

Sports data analysis is becoming increasingly large scale, diversified, and shared, but difficulty persists in rapidly accessing the most crucial information. Previous surveys have focused on the methodologies of sports video analysis from the spatiotemporal viewpoint instead of a content-based viewpoint, and few of these studies have considered semantics. This paper develops a deeper interpretation of content-aware sports video analysis by examining the insight offered by research into the structure of content under different scenarios. On the basis of this insight, we provide an overview of the themes particularly relevant to the research on content-aware systems for broadcast sports. Specifically, we focus on the video content analysis techniques applied in sportscasts over the past decade from the perspectives of fundamentals and general review, a content hierarchical model, and trends and challenges. Content-aware analysis methods are discussed with respect to object-, event-, and context-oriented groups. In each group, the gap between sensation and content excitement must be bridged using proper strategies. In this regard, a content-aware approach is required to determine user demands. Finally, this paper summarizes the future trends and challenges for sports video analysis. We believe that our findings can advance the field of research on content-aware video analysis for broadcast sports.
Compiling a high-level language for GPUs: (via language support for architectures and compilers)

Languages such as OpenCL and CUDA offer a standard interface for general-purpose programming of GPUs. However, with these languages, programmers must explicitly manage numerous low-level details involving communication and synchronization. This burden makes programming GPUs difficult and error-prone, rendering these powerful devices inaccessible to most programmers.
 We desire a higher-level programming model that makes GPUs more accessible while also effectively exploiting their computational power. This paper presents features of Lime, a new Java-compatible language targeting heterogeneous systems, that allow an optimizing compiler to generate high quality GPU code. The key insight is that the language type system enforces isolation and immutability invariants that allow the compiler to optimize for a GPU without heroic compiler analysis.
 Our compiler attains GPU speedups between 75% and 140% of the performance of native OpenCL code.
Higher Order Positive Semidefinite Diffusion Tensor Imaging

Due to the well-known limitations of diffusion tensor imaging, high angular resolution diffusion imaging (HARDI) is used to characterize non-Gaussian diffusion processes. One approach to analyzing HARDI data is to model the apparent diffusion coefficient (ADC) with higher order diffusion tensors. The diffusivity function is positive semidefinite. In the literature, some methods have been proposed to preserve positive semidefiniteness of second order and fourth order diffusion tensors. None of them can work for arbitrarily high order diffusion tensors. In this paper, we propose a comprehensive model to approximate the ADC profile by a positive semidefinite diffusion tensor of either second or higher order. We call this the positive semidefinite diffusion tensor (PSDT) model. PSDT is a convex optimization problem with a convex quadratic objective function constrained by the nonnegativity requirement on the smallest Z-eigenvalue of the diffusivity function. The smallest Z-eigenvalue is a computable measure of the extent of positive definiteness of the diffusivity function. We also propose some other invariants for the ADC profile analysis. Experiment results show that higher order tensors could improve the estimation of anisotropic diffusion and that the PSDT model can depict the characterization of diffusion anisotropy which is consistent with known neuroanatomy.
Designing a virtual assistant for in-car child entertainment

Driving is an attention-demanding task, especially with children in the back seat. While most recommendations prefer to reduce children's screen time in common entertainment systems, e.g. DVD players and tablets, parents often rely on these systems to entertain the children during car trips. These systems often lack key components that are important for modern parents, namely, sociability and educational content. In this contribution we introduce PANDA, a parental affective natural driving assistant. PANDA is a virtual in-car entertainment agent that can migrate around the car to interact with the parent-driver or with children in the back seat. PANDA supports the parent-driver via speech interface, helps to mediate her interaction with children in the back seat, and works to reduce distractions for the driver while also engaging, entertaining and educating children. We present the design of PANDA system and preliminary tests of the prototype system in a car setting.
Prenatal diagnosis and post-mortem examination in a fetus with thrombocytopenia-absent radius (TAR) syndrome due to compound heterozygosity for a 1q21.1 microdeletion and a RBM8A hypomorphic allele: a case report

Thrombocytopenia–absent radius syndrome is a rare autosomal recessive disorder characterized by megakaryocytic thrombocytopenia and longitudinal limb deficiencies mostly affecting the radial ray. Most patients are compound heterozygotes for a 200 kb interstitial microdeletion in 1q21.1 and a hypomorphic allele in RBM8A, mapping in the deleted segment. At the moment, the complete molecular characterization of thrombocytopenia–absent radius syndrome is limited to a handful of patients mostly ascertained in the pediatric age We report on a fetus with bilateral upper limb deficiency found at standard prenatal ultrasound examination. The fetus had bilateral radial agenesis and humeral hypo/aplasia with intact thumbs, micrognathia and urinary anomalies, indicating thrombocytopenia–absent radius syndrome. Molecular studies demonstrated compound heterozygosity for the 1q21.1 microdeletion and the RBM8A rs139428292 variant at the hemizygous state, inherited from the mother and father, respectively The molecular information allowed prenatal diagnosis in the following pregnancy resulting in the birth of a healthy carrier female. A review was carried out with the attempt to the trace the fetal ultrasound presentation of thrombocytopenia–absent radius syndrome and discussing opportunities for second-tier molecular studies within a multidisciplinary setting.
Salient Structure Detection by Context-Guided Visual Search

Visual search is necessary for rapid scene analysis because information processing in the visual system is limited to one or a few regions at one time . To select potential regions or objects of interest rapidly with a task-independent manner, the so-called "visual saliency", is important for reducing the complexity of scenes. From the perspective of engineering, modeling visual saliency usually facilitates subsequent higher visual processing, such as image re-targeting , image compression , object recognition [16], etc. Visual attention model is deeply studied in recent decades. Most of existing models are built on the biologically-inspired architecture based on the famous Feature Integration Theory (FIT) [19, 20]. For instance, Itti et al. proposed a famous saliency model which computes the saliency map with local contrast in multiple feature dimensions, such as color, orientation, etc.  . However, FIT-based methods perhaps risk being immersed in local saliency (e.g., object boundaries), because they employ local contrast of features in limited regions and ignore the global information. Visual attention models usually provide location information of the potential objects, but miss some object-related information (e.g., object surfaces) that is necessary for further object detection and recognition. Distinguished from FIT, Guided Search Theory (GST)   provides a mechanism to search the regions of interest (ROI) or objects with the guidance from scene layout or top-down sources. The recent version of GST claims that the visual system searches objects of interest along two parallel pathways, i.e., the non-selective pathway and the selective pathway . This new visual search strategy allows observers to extract spatial layout (or gist) information rapidly from entire scene via non-selective pathway. Then, this context information of scene acts as top-down modulation to guide the salient object search along the selective pathway. This two-pathway-based search strategy provides a parallel processing of global and local information for rapid visual search. Referring to the GST, we assume that the non-selective pathway provides "where" information and prior of multiple objects for visual search, a counterpart to visual selective saliency, and we use certain simple and fast fixation prediction method to provide an initial estimate of where the objects present. At the same time, the bottom-up visual selective pathway extracts fine image features in multiple cue channels, which could be regarded as a counterpart to the "what" pathway in visual system for object recognition. When these bottom-up features meet "where" information of objects, the visual system …
Churn Prediction in Telecommunication Industry Using Rough Set Approach

The Customer churn is a crucial activity in rapidly growing and mature competitive telecommunication sector and is one of the greatest importance for a project manager. Due to the high cost of acquiring new customers, customer churn prediction has emerged as an indispensable part of telecom sectors’ strategic decision making and planning process. It is important to forecast customer churn behavior in order to retain those customers that will churn or possible may churn. This study is another attempt which makes use of rough set theory, a rule-based decision making technique, to extract rules for churn prediction. Experiments were performed to explore the performance of four different algorithms (Exhaustive, Genetic, Covering, and LEM2). It is observed that rough set classification based on genetic algorithm, rules generation yields most suitable performance out of the four rules generation algorithms. Moreover, by applying the proposed technique on publicly available dataset, the results show that the proposed technique can fully predict all those customers that will churn or possibly may churn and also provides useful information to strategic decision makers as well.
Shear Reinforcements in the Reinforced Concrete Beams

This study focuses on the use of different types of shear reinforcement in the reinforced concrete beams. Four different types of shear reinforcement are investigated; traditional stirrups, welded swimmer bars, bolted swimmer bars, and u-link bolted swimmer bars. Beam shear strength as well as beam deflection are the main two factors considered in this study. Shear failure in reinforced concrete beams is one of the most undesirable modes of failure due to its rapid progression. This sudden type of failure made it necessary to explore more effective ways to design these beams for shear. The reinforced concrete beams show different behavior at the failure stage in shear compare to the bending, which is considered to be unsafe mode of failure. The diagonal cracks that develop due to excess shear forces are considerably wider than the flexural cracks. The cost and safety of shear reinforcement in reinforced concrete beams led to the study of other alternatives. Swimmer bar system is a new type of shear reinforcement. It is a small inclined bars, with its both ends bent horizontally for a short distance and welded or bolted to both top and bottom flexural steel reinforcement. Regardless of the number of swimmer bars used in each inclined plane, the swimmer bars form plane-crack interceptor system instead of bar-crack interceptor system when stirrups are used. Several reinforced concrete beams were carefully prepared and tested in the lab. The results of these tests will be presented and discussed. The deflection of each beam is also measured at incrementally increased applied load.
MENOPOST - Calcium and vitamin D supplementation in postmenopausal osteoporosis treatment: a descriptive cohort study

Adequate vitamin D/calcium supplementation during osteoporosis (OP) treatments seems insufficient. This cohort study within a national claims database evaluated calcium/vitamin D co-prescription in postmenopausal women initiating an OP treatment. A high co-prescription rate was observed with three quarters of women supplemented with calcium and/or vitamin D in agreement with current recommendations. Adequate calcium/vitamin D supplementation should be taken in combination with antiresorptive drugs in OP treatment. Despite guidelines, supplementation appears to be insufficient. The objective of this study was to describe and estimate co-prescription rates of calcium/vitamin D among postmenopausal women initiating an OP treatment. All women over 50 years with a first claim for a bisphosphonates, raloxifene, or strontium prescription filled between May and August 2010 were included in a retrospective cohort study. Data source was the health insurance claims database of the Rhône-Alpes area. Among 4,415 women, 77.0 % had co-prescription of calcium or vitamin D with initial OP treatment, of which 2,150 (49.7 %) had both calcium and vitamin D. The proportion of women with calcium and/or vitamin D (81.7 %) was significantly higher when OP treatment was a bisphosphonate compared to strontium (70.9 %) or raloxifene (67.0 %) (p < 0.05). Among women prescribed both calcium and vitamin D, 7.6 % received a bisphosphonate and vitamin D ± calcium fixed-combination pack. General practitioners prescribed two thirds of initial supplementation treatment (66.9 %). Patients were twice as likely to be prescribed supplementation when the prescriber was a rheumatologist (OR = 2; 95 % CI = 1.57–2.54). Three quarters of women initiating OP treatment were supplemented with calcium and/or vitamin D in agreement with current recommendations. This represents a high co-prescription rate.
Characterization, Stability and Convergence of Hierarchical Clustering Methods

Ulrike von Luxburg  We study hierarchical clustering schemes under an axiomatic view. We show that within this framework, one can prove a theorem analogous to one of J. Kleinberg (Kleinberg, 2002), in which one obtains an existence and uniqueness theorem instead of a non-existence result. We explore further properties of this unique scheme: stability and convergence are established. We represent dendrograms as ultrametric spaces and use tools from metric geometry, namely the Gromov-Hausdor↵ distance, to quantify the degree to which perturbations in the input metric space a↵ect the result of hierarchical methods.
Load balancing in 5G cloud radio access networks supporting IoT communications for smart communities

Internet of Things (IoT) communication is vital for the developing of smart communities. The rapid growth of IoT depends on reliable wireless networks. The evolving 5G cellular system addresses this challenge by adopting cloud computing technology in Radio Access Network (RAN); namely Cloud RAN or CRAN. CRAN enables better scalability, flexibility, and performance that allows 5G to provide connectivity for the vast volume of IoT devices envisioned for smart cities. This work investigates the load balance (LB) problem in CRAN, with the goal of reducing latencies experienced by IoT communications. Eight practical LB algorithms are studied and evaluated in CRAN environment, based on real cellular network traffic characteristics provided by Nokia Research. Experiment results on queue-length analysis show that the simple, light-weight queue-based LB is almost as effectively as the much more complex waiting-time-based LB. We believe that this study is significant in enabling 5G networks for providing IoT communication backbone in the emerging smart communities; it also has wide applications in other distributed systems.
Big data on cloud for government agencies: benefits, challenges, and solutions

Big Data and Cloud computing are the most important technologies that give the opportunity for government agencies to gain a competitive advantage and improve their organizations. On one hand, Big Data implementation requires investing a significant amount of money in hardware, software, and workforce. On the other hand, Cloud Computing offers an unlimited, scalable and on-demand pool of resources which provide the ability to adopt Big Data technology without wasting on the financial resources of the organization and make the implementation of Big Data faster and easier. The aim of this study is to conduct a systematic literature review in order to collect data to identify the benefits and challenges of Big Data on Cloud for government agencies and to make a clear understanding of how combining Big Data and Cloud Computing help to overcome some of these challenges. The last objective of this study is to identify the solutions for related challenges of Big Data. Four research questions were designed to determine the information that is related to the objectives of this study. Data is collected using literature review method and the results are deduced from there.
A review of the evidence supporting the aesthetic orthodontic treatment need indices.

Aesthetic improvement and psychological enhancement have been cited as justifications for orthodontic treatment. This paper reviews the evidence that relates malocclusion to psychological health and quality of life and explores whether this evidence supports the most commonly used aesthetic Orthodontic Treatment Need Indices (OTNI).



The relevant cited material from the MEDLINE, Web of Science, Scopus, Cochrane databases, and scientific textbooks were used. The citation rate was confirmed by using the Google Scholar.



The subjective nature of aesthetic indices and the variable perception of attractiveness between clinicians and patients, and among various cultures or countries are a few limitations of aesthetic OTNI. The available evidence of mainly cross-sectional studies on the link between malocclusion and either psychosocial well-being or quality of life is not conclusive, and sometimes contradictory, to suggest these characteristics are affected by malocclusions. Further, the long-term longitudinal studies did not suggest that people with malocclusion are disadvantaged psychologically, or their quality of life would be worse off, which challenges using aesthetic OTNI to assess the social and psychological implications of malocclusion.



The subjective nature of aesthetic OTNI and the minor contributory role of malocclusion in psychosocial health or quality of life undermine using aesthetic indices to assess the likely social and psychological implications of malocclusion. Consequently, using aesthetic OTNI, as a method to quantify malocclusion remains open to debate. Various soft and hard-tissue analyses are used before formulating a treatment plan (i.e., assessment of sagittal and vertical skeletal relationships). The addition of a shortened version of these analyses to the aesthetic OTNI can be a good substitute for the aesthetic components of OTNI, if an assessment of the aesthetic aspects of malocclusion is intended. This reduces subjectivity and improves the validity of the OTNI that incorporate an aesthetic component.
Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems

Visual analytics (VA) system development started in academic research institutions where novel visualization techniques and open source toolkits were developed. Simultaneously, small software companies, sometimes spin-offs from academic research institutions, built solutions for specific application domains. In recent years we observed the following trend: some small VA companies grew exponentially; at the same time some big software vendors such as IBM and SAP started to acquire successful VA companies and integrated the acquired VA components into their existing frameworks. Generally the application domains of VA systems have broadened substantially. This phenomenon is driven by the generation of more and more data of high volume and complexity, which leads to an increasing demand for VA solutions from many application domains. In this paper we survey a selection of state-of-the-art commercial VA frameworks, complementary to an existing survey on open source VA tools. From the survey results we identify several improvement opportunities as future research directions.
Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition

Classification of electroencephalogram (EEG) signals is an important task in the brain computer interface system. This paper presents two combination strategies of feature extraction on EEG signals. In the first strategy, Autoregressive coefficients and approximate entropy are calculated respectively, and the features are obtained by assembling them. In the second strategy, the EEG signals are first decomposed into sub-bands by wavelet packet decomposition. Wavelet packet coefficients are then sent to the autoregressive model to calculate autoregressive coefficients, which are used as features extracted from the original EEG signals. These features are fed to support vector machine for classifying the EEG signals. The classification accuracy has been used for evaluating the classification performance. Experimental results in five mental tasks show that the combination strategies can effectively improve the classification performance when the order of autoregressive model is greater than 5, and the second strategy is superior to the first one in terms of the classification accuracy.
Design of an array with modified ultra-wideband Quasi-Yagi antennas

An array with modified ultra-wideband (UWB) Quasi-Yagi antennas is designed. The Quasi-Yagi antenna is adopted in this design which has obtain the characteristics of UWB (3.1–4.1GHz) and high-gain (10dB) in practical project effectively, through modifying the radiation part, designing a broadband feeding network, and grouping an array. The final size of this antenna is 218×110mm2, and the thickness is about 1mm.