nils-herrmann/clean_scirepeval huggingface dataset:
A Multibody Factorization Method for Independently Moving Objects

The structure-from-motion problem has been extensively studied in the field of computer vision. Yet, the bulk of the existing work assumes that the scene contains only a single moving object. The more realistic case where an unknown number of objects move in the scene has received little attention, especially for its theoretical treatment. In this paper we present a new method for separating and recovering the motion and shape of multiple independently moving objects in a sequence of images. The method does not require prior knowledge of the number of objects, nor is dependent on any grouping of features into an object at the image level. For this purpose, we introduce a mathematical construct of object shapes, called the shape interaction matrix, which is invariant to both the object motions and the selection of coordinate systems. This invariant structure is computable solely from the observed trajectories of image features without grouping them into individual objects. Once the matrix is computed, it allows for segmenting features into objects by the process of transforming it into a canonical form, as well as recovering the shape and motion of each object. The theory works under a broad set of projection models (scaled orthography, paraperspective and affine) but they must be linear, so it excludes projective “cameras”.
A least-squares approach to anomaly detection in static and sequential data

We describe a probabilistic, nonparametric method for anomaly detection, based on a squared-loss objective function which has a simple analytical solution. The method emerges from extending recent work in nonparametric leastsquares classification to include a “none-of-the-above” class which models anomalies in terms of non-anamalous training data. The method shares the flexibility of other kernel-based anomaly detection methods, yet is typically much faster to train and test. It can also be used to distinguish between multiple inlier classes and anomalies. The probabilistic nature of the output makes it straightforward to apply even when test data has structural dependencies; we show how a hidden Markov model framework can be incorporated in order to identify anomalous subsequences in a test sequence. Empirical results on datasets from several domains show the method to have comparable discriminative performance to popular alternatives, but with a clear speed advantage.
Sport motivation scale-6 (SMS-6): A revised six-factor sport motivation scale

The sport motivation scale (SMS; [Pelletier, L. G., Fortier, M. S., Vallerand, R. J., Tuson, K. M., Brière, N. M., & Blais, M. R. (1995). Toward a new measure of intrinsic motivation, extrinsic motivation, and amotivation in sports: The sport motivation scale (SMS). Journal of Sport and Exercise Psychology, 17, 35–53]) was developed to measure an athlete’s motivation toward sport participation. However, the SMS does not measure the most autonomous form of extrinsic motivation, integrated regulation, which is inconsistent with self-determination theory (SDT; [Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour.  Plenum Press]) upon which the instrument is based. Moreover, several studies (e.g., [Martens, M. P., & Webber, S. N. (2002). Psychometric properties of the sport motivation scale: An evaluation with college varsity athletes from the U.S. Journal of Sport and Exercise Psychology, 24, 254–270]) have questioned the factorial validity of the SMS. Hence, the purpose of this study was to develop a revised version of the SMS, including integrated regulation.  In Stage 1, the factorial validity of the SMS was examined using confirmatory factor analysis (CFA) on data collected from 614 Australians (elite athletes and university students). In Stage 2, the scale was revised by including integrated regulation items and replacing problematic items through an iterative process using CFA for data collected from 557 Australian university students. Concurrent validity of the revised scale was examined by evaluating correlations with the dispositional flow scale-2 (DFS-2) [Jackson, ARTICLE IN PRESS www.elsevier.com/locate/psychsport 1469-0292/$ see front matter r 2007  Corresponding author.  E-mail addresses: cmallett@hms.uq.edu.au (C. Mallett), masato@hms.uq.edu.au (M. Kawabata), newc@psy.uq.edu.au (P. Newcombe), andres.otero@gmail.com (A. Otero-Forero). Author's personal copy S. A., & Eklund, R. C. (2004). The flow scales manual. Morgantown, WV: Fitness Information Technology].  The revision led to development of a six-factor 24-item scale (SMS-6) that indicated a more parsimonious and improved fitting model consistent with SDT. Correlations between the SMS-6 and DFS-2 factors support the concurrent validity of the revised scale.  From statistical and theoretical viewpoints, the revised SMS-6 was preferable to the original SMS, except for the discriminant validity issue of identification regulation. Further examination of the instrument is necessary by cross-validating the findings from this study. r 2007
Mirror therapy enhances lower-extremity motor recovery and motor functioning after stroke: a randomized controlled trial.

To evaluate the effects of mirror therapy, using motor imagery training, on lower-extremity motor recovery and motor functioning of patients with subacute stroke.



Randomized, controlled, assessor-blinded, 4-week trial, with follow-up at 6 months.



Rehabilitation education and research hospital.



A total of 40 inpatients with stroke (mean age, 63.5 y), all within 12 months poststroke and without volitional ankle dorsiflexion.



Thirty minutes per day of the mirror therapy program, consisting of nonparetic ankle dorsiflexion movements or sham therapy, in addition to a conventional stroke rehabilitation program, 5 days a week, 2 to 5 hours a day, for 4 weeks.



The Brunnstrom stages of motor recovery, spasticity assessed by the Modified Ashworth Scale (MAS), walking ability (Functional Ambulation Categories [FAC]), and motor functioning (motor items of the FIM instrument).



The mean change score and 95% confidence interval (CI) of the Brunnstrom stages (mean, 1.7; 95% CI, 1.2-2.1; vs mean, 0.8; 95% CI, 0.5-1.2; P=.002), as well as the FIM motor score (mean, 21.4; 95% CI, 18.2-24.7; vs mean, 12.5; 95% CI, 9.6-14.8; P=.001) showed significantly more improvement at follow-up in the mirror group compared with the control group. Neither MAS (mean, 0.8; 95% CI, 0.4-1.2; vs mean, 0.3; 95% CI, 0.1-0.7; P=.102) nor FAC (mean, 1.7; 95% CI, 1.2-2.1; vs mean, 1.5; 95% CI, 1.1-1.9; P=.610) showed a significant difference between the groups.



Mirror therapy combined with a conventional stroke rehabilitation program enhances lower-extremity motor recovery and motor functioning in subacute stroke patients.
A highly area-efficient controller for capacitive touch screen panel systems

In this paper, a highly area-efficient controller for capacitive touch screen panels (TSPs) is proposed. The proposed controller uses a 10-bit successive approximation register analog-to-digital converter (SAR ADC) with an adder to compensate for the capacitance variation in the TSP and for the offset voltage variation in the charge amplifier of the sensing circuit. By using the proposed compensation method, the area of the controller can be reduced by 90.3% of the area of the conventional controllers. The measurement results showed that the signal-to-noise ratio (SNR) of the controller increases from 12.5 to 21.3 dB after compensation. Also, its spatial jitter decreases from ±1.5 to ±0.46 mm, which is 7% of the sensor pitch of 8 mm.
Rotor Failures in Squirrel Cage Induction Motors

During the past two decades, tremendous improvement has been made in the design and manufacture of stator windings. This has been accomplished primarily through the development of improved insulation materials and treatment processes. As a result, the expected life from a thermal, dielectrical, mechanical, and environmental standpoint has been significantly increased. The rotor design and manufacturing remains basically unchanged. A survey of warranty data and service facilities suggests that rotor failures now account for a larger percentage of the total induction motor failures. The majority of these failures are caused by various stresses which act on the rotor assembly. These various stresses and how they affect the life of the motor and contribute to premature failure are discussed.
SybilInfer: Detecting Sybil Nodes using Social Networks

SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indicating its degree of certainty. We prove through analytical results as well as experiments on simulated and real-world network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully distinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Furthermore, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applicable, as well as providing vastly more accurate results.
Comparisons of waist circumferences measured at 4 sites.

Waist circumference (WC) is now accepted as a practical measure of adipose tissue distribution. Four body sites for WC measurements are commonly used, as follows: immediately below the lowest ribs (WC1), the narrowest waist (WC2), the midpoint between the lowest rib and the iliac crest (WC3), and immediately above the iliac crest (WC4).



We sought to compare the magnitude and reliability of WC measured at these 4 sites in males and females.



WC was measured at each site 1 time in all subjects [49 males and 62 females, aged 7-83 y, with a body mass index (in kg/m(2)) of 9-43] and 3 times in a subgroup (n = 93) by one experienced observer using a heavy-duty inelastic tape. Body fat was measured in a subgroup (n = 74) with the use of dual-energy X-ray absorptiometry.



The mean values of WC were WC2 < WC1 < WC3 < WC4 (P < 0.01) in females and WC2 < WC1, WC3, and WC4 (P < 0.01) in males. For all 4 sites, measurement reproducibility was high, with intraclass correlation (r) values > 0.99. WC values were significantly correlated with fatness; correlations with trunk fat were higher than correlations with total body fat in both sexes.



WC values at the 4 commonly used anatomic sites differ in magnitude depending on sex, are highly reproducible, and are correlated with total body and trunk adiposity in a sex-dependent manner. These observations have implications for the use of WC measurements in clinical practice and patient-oriented research.
A sub-µW, 1.0V CMOS temperature sensor circuit insensitive to device parameters

In this paper, we firstly propose the CMOS temperature sensor circuit operating in the subthreshold region. The circuit has advantages of 1.0 V operation and sub-µW power consumption. Furthermore, it is insensitive to the device parameter of the fabrication process. Next, we design the high sensitivity temperature sensor circuit that can be realized by cascade-connection of the proposed temperature sensor circuits. The proposed circuits were evaluated through HSPICE with a standard 0.35 µm CMOS device parameter. The simulation demonstrate the maximum power consumption is 0.27 µW under the condition that the temperature range is from 0 °C to 100 °C, the sensitivity is 1.01 mV/ °C and the maximum differential error is 0.87 %.
Extensions of black-litterman portfolio optimization model with downside risk measure

This paper considers extensions of the Black-Litterman(BL) portfolio optimization model. Recognizing only the expected returns of the stocks are updated by investor's private information in traditional BL model, we extend such approach by integrating the information of the variance(volatility) of the stock from the correspondent options. By using the inverse optimization technique, it is possible to find the updated estimation of the expected return and covariance matrix by solving one Semidefinite Programming problem(SDP). Furthermore, we combine these improved estimations of the return statistics with the mean-CVaR based portfolio optimization model. Without assuming the prior distribution of the random return of the assets, we use the moment matching technique to generate the discrete scenarios, which is a key step in computing mean-CVaR portfolio. Our real market data based computational example demonstrates the prominent feature of our method.
Novel approach to nonlinear / non-Gaussian Bayesian state estimation

An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.
Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems

One of the main goals of natural language processing (NLP) is synthetic understanding of natural language documents, especially reading comprehension (RC). An obstacle to the further development of RC systems is the absence of a synthetic methodology to analyze their performance. It is difficult to examine the performance of systems based solely on their results for tasks because the process of natural language understanding is complex. In order to tackle this problem, we propose in this paper a methodology inspired by unit testing in software engineering that enables the examination of RC systems from multiple aspects. Our methodology consists of three steps. First, we define a set of prerequisite skills for RC based on existing NLP tasks. We assume that RC capability can be divided into these skills. Second, we manually annotate a dataset for an RC task with information regarding the skills needed to answer each question. Finally, we analyze the performance of RC systems for each skill based on the annotation. The last two steps highlight two aspects: the characteristics of the dataset, and the weaknesses in and differences among RC systems. We tested the effectiveness of our methodology by annotating the Machine Comprehension Test (MCTest) dataset and analyzing four existing systems (including a neural system) on it. The results of the annotations showed that answering questions requires a combination of skills, and clarified the kinds of capabilities that systems need to understand natural language. We conclude that the set of prerequisite skills we define are promising for the decomposition and analysis of RC.
DendroPy: a Python library for phylogenetic computing

DendroPy is a cross-platform library for the Python programming language that provides for object-oriented reading, writing, simulation and manipulation of phylogenetic data, with an emphasis on phylogenetic tree operations. DendroPy uses a splits-hash mapping to perform rapid calculations of tree distances, similarities and shape under various metrics. It contains rich simulation routines to generate trees under a number of different phylogenetic and coalescent models. DendroPy's data simulation and manipulation facilities, in conjunction with its support of a broad range of phylogenetic data formats (NEXUS, Newick, PHYLIP, FASTA, NeXML, etc.), allow it to serve a useful role in various phyloinformatics and phylogeographic pipelines.


AVAILABILITY
The stable release of the library is available for download and automated installation through the Python Package Index site (http://pypi.python.org/pypi/DendroPy), while the active development source code repository is available to the public from GitHub (http://github.com/jeetsukumaran/DendroPy).
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic’s complex morphology.
A multimode-beamforming 77-GHz FMCW radar system

In this work a multimode-beamforming 77-GHz frequency-modulated continuous-wave radar system is presented. Four transceiver chips with integrated inphase/quadrature modulators in the transmit path are used in order to simultaneously realize a short-range frequency-division multiple-access (FDMA) multiple-input multiple-output (MIMO) and a long-range transmit phased-array (PA) radar system with the same antennas. It combines the high angular resolution of FDMA MIMO radars and the high-gain and steerable beam of PA transmit antennas. Several measurements were carried out to show the potential benefits of using this concept for a linear antenna array with four antennas and methods of digital beamforming in the receive path.
CRMP4 and CRMP2 Interact to Coordinate Cytoskeleton Dynamics, Regulating Growth Cone Development and Axon Elongation

Cytoskeleton dynamics are critical phenomena that underpin many fundamental cellular processes. Collapsin response mediator proteins (CRMPs) are highly expressed in the developing nervous system, mediating growth cone guidance, neuronal polarity, and axonal elongation. However, whether and how CRMPs associate with microtubules and actin coordinated cytoskeletal dynamics remain unknown. In this study, we demonstrated that CRMP2 and CRMP4 interacted with tubulin and actin in vitro and colocalized with the cytoskeleton in the transition-zone in developing growth cones. CRMP2 and CRMP4 also interacted with one another coordinately to promote growth cone development and axonal elongation. Genetic silencing of CRMP2 enhanced, whereas overexpression of CRMP2 suppressed, the inhibitory effects of CRMP4 knockdown on axonal development. In addition, knockdown of CRMP2 or overexpression of truncated CRMP2 reversed the promoting effect of CRMP4. With the overexpression of truncated CRMP2 or CRMP4 lacking the cytoskeleton interaction domain, the promoting effect of CRMP was suppressed. These data suggest a model in which CRMP2 and CRMP4 form complexes to bridge microtubules and actin and thus work cooperatively to regulate growth cone development and axonal elongation.
Transcutaneous measurement of glomerular filtration rate using FITC-sinistrin in rats.

Inulin/sinistrin (I/S) clearance is a gold standard for an accurate assessment of glomerular filtration rate (GFR). Here we describe and validate an approach for a transcutaneous determination of GFR by using fluorescein-isothiocyanate-labelled sinistrin (FITC-S) in rats.



Using a small animal imager, fluorescence is measured over the depilated ear of a rat after the injection of FITC-S. The decay curve of fluorescence is used for the calculation of half-life and GFR. The thus obtained transcutaneous data were validated by simultaneously performed enzymatic and fluorometric measurements in plasma of both FITC-S and sinistrin.



The results of enzymatic sinistrin determination versus transcutaneous half-life of FITC-S or plasma fluorescence correlated well with each other (R(2) > 0.90). Furthermore, Bland-Altman analyses proved a good degree of agreement of the three methods used. The measurements performed in healthy animals as well as different models of renal failure demonstrate its appropriateness in a wide range of renal function.



The transcutaneous method described offers a precise assessment of GFR in small animals. As neither blood and/or urine sampling nor time-consuming lab work is required, GFR can be determined immediately after the clearance procedure is finished. This method, therefore, simplifies and fastens GFR determinations in small lab animals compared to conventional bolus clearance techniques based on blood sampling. A low-cost device for the measurement of transcutaneous fluorescence intensity over time is under construction.
DLM: Decentralized Linearized Alternating Direction Method of Multipliers

This paper develops the Decentralized Linearized Alternating Direction Method of Multipliers (DLM) that minimizes a sum of local cost functions in a multiagent network. The algorithm mimics operation of the decentralized alternating direction method of multipliers (DADMM) except that it linearizes the optimization objective at each iteration. This results in iterations that, instead of successive minimizations, implement steps whose cost is akin to the much lower cost of the gradient descent step used in the distributed gradient method (DGM). The algorithm is proven to converge to the optimal solution when the local cost functions have Lipschitz continuous gradients. Its rate of convergence is shown to be linear if the local cost functions are further assumed to be strongly convex. Numerical experiments in least squares and logistic regression problems show that the number of iterations to achieve equivalent optimality gaps are similar for DLM and ADMM and both much smaller than those of DGM. In that sense, DLM combines the rapid convergence of ADMM with the low computational burden of DGM.
Segmentation of edges into lines and arcs

.4 long standing problem in computer vision is the extraction of meaningfulfeatures from images. This paper describes a method of segmenting curves in images into a combination of circular arcs and straight lines. This uses a recursive algorithm that first analyses lists of connected edge points and finds a polygonal description, and then analyses this description fitting arcs to groups of connected lines. The result is a description of image edges consisting of circular arcs and lines. The algorithm uses no thresholding. Instead the best option is chosen at each decision stage.
Metallic Biomaterials: Current Challenges and Opportunities

Metallic biomaterials are engineered systems designed to provide internal support to biological tissues and they are being used largely in joint replacements, dental implants, orthopaedic fixations and stents. Higher biomaterial usage is associated with an increased incidence of implant-related complications due to poor implant integration, inflammation, mechanical instability, necrosis and infections, and associated prolonged patient care, pain and loss of function. In this review, we will briefly explore major representatives of metallic biomaterials along with the key existing and emerging strategies for surface and bulk modification used to improve biointegration, mechanical strength and flexibility of biometals, and discuss their compatibility with the concept of 3D printing.