nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Determination of Shoulder Joint Limits Using Quaternion Field Boundaries

To provide efficient tools for the capture and modeling of acceptable virtual human poses, we propose a method for constraining the underlying joint structures based on real life data. Current tools for delimiting valid postures often employ techniques that do not represent joint limits in an intuitively satisfying manner, and furthermore are seldom directly derived from experimental data. Here, we propose a semi-automatic scheme for determining ball-and-socket joint limits by actual measurement and apply it to modeling the shoulder complex, which—along with the hip complex—can be approximated by a 3 degree-of-freedom ball-and-socket joint. Our first step is to measure the joint motion range using optical motion capture. We next convert the recorded values to joint poses encoded using a coherent quaternion field representation of the joint orientation space. Finally, we obtain a closed, continuous implicit surface approximation for the quaternion orientation-space boundary whose interior represents the complete space of valid orientations, enabling us to project invalid postures to the closest valid ones. The work reported here was supported in part by the Swiss National Science Foundation.
Precision-Recall-Gain Curves: PR Analysis Done Right

Precision-Recall analysis abounds in applications of binary classification where true negatives do not add value and hence should not affect assessment of the classifier’s performance. Perhaps inspired by the many advantages of receiver operating characteristic (ROC) curves and the area under such curves for accuracybased performance assessment, many researchers have taken to report PrecisionRecall (PR) curves and associated areas as performance metric. We demonstrate in this paper that this practice is fraught with difficulties, mainly because of incoherent scale assumptions – e.g., the area under a PR curve takes the arithmetic mean of precision values whereas the Fβ score applies the harmonic mean. We show how to fix this by plotting PR curves in a different coordinate system, and demonstrate that the new Precision-Recall-Gain curves inherit all key advantages of ROC curves. In particular, the area under Precision-Recall-Gain curves conveys an expected F1 score on a harmonic scale, and the convex hull of a PrecisionRecall-Gain curve allows us to calibrate the classifier’s scores so as to determine, for each operating point on the convex hull, the interval of β values for which the point optimises Fβ . We demonstrate experimentally that the area under traditional PR curves can easily favour models with lower expected F1 score than others, and so the use of Precision-Recall-Gain curves will result in better model selection.
Archaea in Natural and Impacted Brazilian Environments

In recent years, archaeal diversity surveys have received increasing attention. Brazil is a country known for its natural diversity and variety of biomes, which makes it an interesting sampling site for such studies. However, archaeal communities in natural and impacted Brazilian environments have only recently been investigated. In this review, based on a search on the PubMed database on the last week of April 2016, we present and discuss the results obtained in the 51 studies retrieved, focusing on archaeal communities in water, sediments, and soils of different Brazilian environments. We concluded that, in spite of its vast territory and biomes, the number of publications focusing on archaeal detection and/or characterization in Brazil is still incipient, indicating that these environments still represent a great potential to be explored.
Sensing Margin Enhancement Techniques for Ultra-Low-Voltage SRAMs Utilizing a Bitline-Boosting Current and Equalized Bitline Leakage

A small bitline sensing margin is one of the most challenging design obstacles for reliable ultra-low-voltage static random access memory (SRAM) implementation. This paper presents design techniques for bitline sensing margin enhancement using decoupled SRAMs. The proposed bitline-boosting current scheme improves the bitline sensing margin at a given bitline configuration. The bitline sensing margin can be further augmented by equalizing bitline leakage. Simulation using a 40-nm CMOS process shows that the proposed techniques achieve larger bitline sensing margin, wider operating temperature and supply range, and a larger number of cells per bitline.
Algebraic Stability of Zigzag Persistence Modules

The stability theorem for persistent homology is a central result in topological data analysis. While the original formulation of the result concerns the persistence barcodes of R-valued functions, the result was later cast in a more general algebraic form, in the language of persistence modules and interleavings. In this paper, we establish an analogue of this algebraic stability theorem for zigzag persistence modules. To do so, we functorially extend each zigzag persistence module to a two-dimensional persistence module, and establish an algebraic stability theorem for these extensions. One part of our argument yields a stability result for free two-dimensional persistence modules. As an application of our main theorem, we strengthen a result of Bauer et al. on the stability of the persistent homology of Reeb graphs. Our main result also yields an alternative proof of the stability theorem for level set persistent homology of Carlsson et al.
The Three Domains of Disgust Scale: factor structure, psychometric properties, and conceptual limitations.

This investigation examined the measurement properties of the Three Domains of Disgust Scale (TDDS). Principal components analysis in Study 1 (n = 206) revealed three factors of Pathogen, Sexual, and Moral Disgust that demonstrated excellent reliability, including test-retest over 12 weeks. Confirmatory factor analyses in Study 2 (n = 406) supported the three factors. Supportive evidence for the validity of the Pathogen and Sexual Disgust subscales was found in Study 1 and Study 2 with strong associations with disgust/contamination and weak associations with negative affect. However, the validity of the Moral Disgust subscale was limited. Study 3 (n = 200) showed that the TDDS subscales differentially related to personality traits. Study 4 (n = 47) provided evidence for the validity of the TDDS subscales in relation to multiple indices of disgust/contamination aversion in a select sample. Study 5 (n = 70) further highlighted limitations of the Moral Disgust subscale given the lack of a theoretically consistent association with moral attitudes. Lastly, Study 6 (n = 178) showed that responses on the Moral Disgust scale were more intense when anger was the response option compared with when disgust was the response option. The implications of these findings for the assessment of disgust are discussed.
Design and implementation of a bi-directional power converter for electric bike with charging feature

This paper presents a bi-directional converter applied in electric bike. The main structure is a cascade buck-boost converter, which transfers the energy stored in battery for driving motor, and can recycle the energy resulted from the back electromotive force (BEMF) to charge battery by changing the operation mode. Moreover, the proposed converter can also serve as a charger by connecting with AC line directly. Besides, the single-chip DSP TMS320F2812 is adopted as a control core to manage the switching behaviors of each mode and to detect the battery capacity. In this paper, the equivalent models of each mode and complete design considerations are all detailed. All the experimental results are used to demonstrate the feasibility.
The relationship between obesity and hypertension: an updated comprehensive overview on vicious twins

Obesity is a growing global health concern, with a rapid increase being observed in morbid obesity. Obesity is associated with an increased cardiovascular risk and earlier onset of cardiovascular morbidity. The growing obesity epidemic is a major source of unsustainable health costs and morbidity and mortality because of hypertension, type 2 diabetes mellitus, dyslipidemia, certain cancers and major cardiovascular diseases. Similar to obesity, hypertension is a key unfavorable health metric that has disastrous health implications: currently, hypertension is the leading contributor to global disease burden, and the direct and indirect costs of treating hypertension are exponentially higher. Poor lifestyle characteristics and health metrics often cluster together to create complex and difficult-to-treat phenotypes: excess body mass is such an example, facilitating a cascade of pathophysiological sequelae that create such as a direct obesity–hypertension link, which consequently increases cardiovascular risk. Although some significant issues regarding assessment/management of obesity remain to be addressed and the underlying mechanisms governing these disparate effects of obesity on cardiovascular disease are complex and not completely understood, a variety of factors could have a critical role. Consequently, a comprehensive and exhaustive investigation of this relationship should analyze the pathogenetic factors and pathophysiological mechanisms linking obesity to hypertension as they provide the basis for a rational therapeutic strategy in the aim to fully describe and understand the obesity–hypertension link and discuss strategies to address the potential negative consequences from the perspective of both primordial prevention and treatment for those already impacted by this condition.
Polycystic ovary syndrome: definition, aetiology, diagnosis and treatment

Polycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders in premenopausal women. Heterogeneous by nature, PCOS is defined by a combination of signs and symptoms of androgen excess and ovarian dysfunction in the absence of other specific diagnoses. The aetiology of this syndrome remains largely unknown, but mounting evidence suggests that PCOS might be a complex multigenic disorder with strong epigenetic and environmental influences, including diet and lifestyle factors. PCOS is frequently associated with abdominal adiposity, insulin resistance, obesity, metabolic disorders and cardiovascular risk factors. The diagnosis and treatment of PCOS are not complicated, requiring only the judicious application of a few well-standardized diagnostic methods and appropriate therapeutic approaches addressing hyperandrogenism, the consequences of ovarian dysfunction and the associated metabolic disorders. This article aims to provide a balanced review of the latest advances and current limitations in our knowledge about PCOS while also providing a few clear and simple principles, based on current evidence-based clinical guidelines, for the proper diagnosis and long-term clinical management of women with PCOS.
Credit risk models : An analysis of default correlation

This paper examines one of the major problems in credit risk models widely used in the financial industry to forecast future defaults and bankruptcies. We find that even after proper calibration, a representative credit risk model can severely underestimate default correlation. We further find that a likely reason for the underestimation of default correlation is the problematic common practice in the financial industry of using observable equity correlation as a proxy for unobservable asset correlation when the model is applied to predict default correlation. However, our results show that this proxy in common practice is not valid.
ARK: Augmented Reality for Kilobots

Working with large swarms of robots has challenges in calibration, sensing, tracking, and control due to the associated scalability and time requirements. Kilobots solve this through their ease of maintenance and programming, and are widely used in several research laboratories worldwide where their low cost enables large-scale swarms studies. However, the small, inexpensive nature of the Kilobots limits their range of capabilities as they are only equipped with a single sensor. In some studies, this limitation can be a source of motivation and inspiration, while in others it is an impediment. As such, we designed, implemented, and tested a novel system to communicate personalized location-and-state-based information to each robot, and receive information on each robots’ state. In this way, the Kilobots can sense additional information from a virtual environment in real time; for example, a value on a gradient, a direction toward a reference point or a pheromone trail. The augmented reality for Kilobots ( ARK) system implements this in flexible base control software which allows users to define varying virtual environments within a single experiment using integrated overhead tracking and control. We showcase the different functionalities of the system through three demos involving hundreds of Kilobots. The ARK  provides Kilobots with additional and unique capabilities through an open-source tool which can be implemented with inexpensive, off-the-shelf hardware.
Polymorphic Malware Detection Using Sequence Classification Methods

Polymorphic malware detection is challenging due to the continual mutations miscreants introduce to successive instances of a particular virus. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we argue that these methods can be usefully applied to malware detection. Unfortunately, gene classification tools are usually optimized for and restricted to an alphabet of four letters (nucleic acids). Consequently, we have selected the Strand gene sequence classifier, which offers a robust classification strategy that can easily accommodate unstructured data with any alphabet including source code or compiled machine code. To demonstrate Stand's suitability for classifying malware, we execute it on approximately 500GB of malware data provided by the Kaggle Microsoft Malware Classification Challenge (BIG 2015) used for predicting 9 classes of polymorphic malware. Experiments show that, with minimal adaptation, the method achieves accuracy levels well above 95% requiring only a fraction of the training times used by the winning team's method.
Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data

Previous work has demonstrated that in the context of Massively Open Online Courses (MOOCs), doing activities is more predictive of learning than reading text or watching videos (Koedinger et al., 2015). This paper breaks down the general behaviors of reading and watching into finer behaviors, and considers how these finer behaviors may provide evidence for active learning as well. By characterizing learner strategies through patterns in their data, we can evaluate which strategies (or measures of them) are predictive of learning outcomes. We investigated strategies such as page re-reading (active reading) and video watching in response to an incorrect attempt (active watching) and found that they add predictive power beyond mere counts of the amount of doing, reading, and watching.
In vitro and in vivo corrosion, mechanical properties and biocompatibility evaluation of MgF2-coated Mg-Zn-Zr alloy as cancellous screws.

Magnesium (Mg) and its alloys as biodegradable materials have received much attention in the orthopedics applications; however, the corrosion behavior of these metals in vivo remains challenging. In this work, a dense and nanoscale magnesium fluoride (MgF2) coating was deposited on the surface of Mg-Zn-Zr (MZZ) alloy cancellous screw. The MZZ cancellous screw with MgF2 coating maintained an integrated shape and high yield tensile stress after 30days immersion in SBF, comparing with the bare screw. Hydrogen releasing rate of the MZZ samples was suppressed at a lower level at the initial stage, which is in favour of the adhesion of the cells. And in vivo experiments indicated that MgF2-coated MZZ screws presented advantages in cytocompatibility, osteoconductivity and osteogenesis of cancellous bone in rabbits. Corrosion rate in vivo perfusion environment increased very slowly with time in long-term study, which was an opposite trend in vitro static immersion test. Moreover, maximum corrosion rate (CRmax), a critical calculation method of corrosion rate was introduced to predict fracture regions of the sample. The MZZ alloy with MgF2 coating possesses a great potential for clinical applications for internal fracture fixation repair.
Survival analysis of composite Dahl restorations provided to manage localised anterior tooth wear (ten year follow-up)

To evaluate ten-year survival and clinical performance of resin-based composite restorations placed at increased vertical dimension as a 'Dahl' type appliance to manage localised anterior tooth wear. A prospective survival analysis of restorations provided at a single centre.Setting UK NHS hospital and postgraduate institute. The clinical performance of 283 composite resin restorations on 26 patients with localised anterior tooth wear was reviewed after a ten year follow-up period. The study used modified United States Public Health Service (USPHS) criteria for assessing the restorations. Survival of the restorations was analysed using Kaplan-Meier survival curves, the log-rank test, and the Cox proportional hazards regression analysis. The results indicated that the median survival time for composite resin restorations was 5.8 years and 4.75 years for replacement restorations when all types of failure were considered. The restorations commonly failed as a result of wear, fracture and marginal discoloration. The factors that significantly influenced the survival of these restorations were the incisal relationship, aetiology, material used, and the nature of opposing dentition. The biological complications associated with this treatment regime were rare. Patient satisfaction remained high despite the long term deterioration of the restorations. With some degree of maintenance, repeated use of composite resin restorations to treat localised anterior tooth wear at an increased occlusal vertical dimension is a viable treatment option over a ten-year period.
3-D radar imaging using extended 2-D range migration technique

A three dimensional (3-D) imaging system is implemented by employing 2-D range migration algorithm (RMA) for frequency modulated continuous wave synthetic aperture radar (FMCW-SAR). The backscattered data of a 1-D synthetic aperture at specific altitudes are coherently integrated to form 2-D images. These 2-D images at different altitudes are stitched vertically to form a 3-D image. Numerical simulation for near-field scenario are also presented to validate the proposed algorithm.
Urban point-of-interest recommendation by mining user check-in behaviors

In recent years, researches on recommendation of urban Points-Of-Interest (POI), such as restaurants, based on social information have attracted a lot of attention. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. It leads to that the recommended POIs list is usually constrained within the users' or friends' living area. Furthermore, since context-aware and environmental information changes quickly, especially in urban areas, how to extract appropriate features from such kind of heterogeneous data to facilitate the recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Urban POI-Mine (UPOI-Mine) that integrates location-based social networks (LBSNs) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space, so as to support the prediction of interestingness of POI related to each user's preference. Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Gowalla, the proposed UPOI-Mine is shown to deliver excellent performance.
An Arabic-Moroccan Darija Code-Switched Corpus

In multilingual communities, speakers often switch between languages or dialects within the same context. This phenomenon is called code-switching. It can be observed, e.g., in the Arab world, where Modern Standard Arabic and Dialectal Arabic coexist. Recently, the computational treatment of code-switching has received attention. Just as other natural language processing tasks, this task requires annotated linguistic resources. In our work, we turn to a particular under-resourced Arabic Dialect, Moroccan Darija. While other dialects such as Egyptian Arabic have received their share of attention, very limited effort has been devoted to the development of basic linguistic resources that would support a computational treatment of Darija. Motivated by these considerations, we describe our effort in the development and annotation of a large scale corpus collected from Moroccan social media sources, namely blogs and internet discussion forums. It has been annotated on token-level by three Darija native speakers. Crowd-sourcing has not been used. The final corpus has a size of 223k tokens. It is, to our knowledge, currently the largest resource of its kind.
An autopilot system based on ROS distributed architecture and deep learning

An autopilot system includes several modules, and the software architecture has a variety of programs. As we all know, it is necessary that there exists one brand with a compatible sensor system till now, owing to complexity and variety of sensors before. In this paper, we apply (Robot Operating System) ROS-based distributed architecture. Deep learning methods also adopted by perception modules. Experimental results demonstrate that the system can reduce the dependence on the hardware effectively, and the sensor involved is convenient to achieve well the expected functionalities. The system adapts well to some specific driving scenes, relatively fixed and simple driving environment, such as the inner factories, bus lines, parks, highways, etc. This paper presents the case study of autopilot system based on ROS and deep learning, especially convolution neural network (CNN), from the perspective of system implementation. And we also introduce the algorithm and realization process including the core module of perception, decision, control and system management emphatically.