nils-herrmann/clean_scirepeval huggingface dataset:
Student behavioural engagement in self paced online learning

It remains a challenge in online settings to engage students as independent learners without teacher presence. This has led to increasing attention investigating the factors influencing student engagement in this context. As part of a PhD study, this paper investigates students' behavioural engagement with online learning modules without teacher supervision or peer support. The study examines three key constructs of behavioural engagement: student engagement with the task, effort level the student applies to taskcompletion and finally, following instructions. First, the findings suggest that student engagement was high in 'video' and 'feedback' sections as compared to 'simulation' activities. Second, students invested high effort in task completion when the learning modules were delivered with instructional guidance. Finally, non visual learners exhibit more difficulty following instructions in unsupported online settings. The results of this study will contribute to the burgeoning research field promoting the development of online modules that encourage participation of diverse learners.
Perceived Leadership Practices in Year One Students Enrolled in Professional Entry Level Doctor of Physical Therapy Programs.
Venue: Journal of allied health

Leadership has been identified as an important attribute for health care professionals. Assessing leadership practices can help identify areas of strength or needed improvement for the development of leaders.  To describe the perceived leadership practices of year 1 Doctor of Physical Therapy (DPT) students, to determine if the participants' demographic variables were related to their perceived leadership practices, and to explore differences between participants and the general population.  The Leadership Practices Inventory (LPI Self) developed by Kouzes and Posner was used to assess leadership practices of year 1 DPT students at six Midwestern universities.  The leadership practices of 192 DPT students in highest to lowest order were Enable, Encourage, Model, Challenge, and Inspire. Age was found to be significantly correlated with the Challenge leadership practice. The order and means of some leadership practices of the DPT students were different than the general population.  This study provides a baseline description of how year 1 DPT students perceived their leadership behaviors and how the LPI Self can be used to facilitate the development of leadership skills.
Survey on Curricular Content for Doctor of Physical Therapy Guidelines for Women's Health Content in Professional Physical Therapist Education: 2014 Update

To conduct a survey using evidence from community stakeholders in order to update the Doctor of Physical Therapy Guidelines for Women's Health Content in Professional Physical Therapist Education (Guideline) Survey research report. The original content Guideline was published in 2005 with expectations that it would be updated every 5 years. A task force appointed in 2011 undertook this work. A survey was developed and fielded electronically to all 202 accredited academic programs in December 2011. A modified academic survey was fielded to the clinical community. The clinician survey was sent electronically to 1951 members of the Section on Women's Health in April 2012. Analysis of results and percent agreement were used to define elements included in the updated Guideline. There was a high agreement on content to be taught in Doctor of Physical Therapy entry level curricula. Response rate was 102 (51% for the academic survey and 340 (17% for the clinician survey. Areas with equivocal input were evaluated by the task force. Key stakeholder input attained via survey data assisted the task force in its efforts to produce the Doctor of Physical Therapy Guidelines for Women's Health Content in Professional Physical Therapist Education: 2014 Update.
Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding
Venue: 2016 IEEE 16th International Conference on Data Mining (ICDM)

Evaluating the clinical similarities between pairwisepatients is a fundamental problem in healthcare informatics. Aproper patient similarity measure enables various downstreamapplications, such as cohort study and treatment comparative effectiveness research. One major carrier for conductingpatient similarity research is the Electronic Health Records(EHRs) which are usually heterogeneous, longitudinal, andsparse. Though existing studies on learning patient similarityfrom EHRs have shown being useful in solving real clinicalproblems, their applicability is limited due to the lack of medicalinterpretations. Moreover, most previous methods assume avector based representation for patients, which typically requiresaggregation of medical events over a certain time period. As aconsequence, the temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based ontemporal matching of longitudinal patient EHRs. Two efficientmethods are presented, unsupervised and supervised, both ofwhich preserve the temporal properties in EHRs. The supervisedscheme takes a convolutional neural network architecture, andlearns an optimal representation of patient clinical recordswith medical concept embedding. The empirical results on real world clinical data demonstrate substantial improvement overthe baselines.
Autonomous Collision Avoidance for a Teleoperated UAV Based on a Super Ellipsoidal Potential Function

This thesis presents the design of a super ellipsoidal potential function (SEPF) that can be used, in a static and dynamic environment, for autonomous collision avoidance of an unmanned aerial vehicle (UAV) in a 3 dimensional space. In the design of the SEPF, we have the full control over the shape and size of the potential function. In our proposed approach, a teleoperated UAV can not only autonomously avoid collision with surrounding objects but also track the operator's control input as closely as possible. As a result, an operator can always be in control of the UAV for his/her high level guidance and navigation task without worrying too much about the UAV collision avoidance while it is being teleoperated. The effectiveness of the proposed approach is demonstrated through a human in the loop simulation using virtual robot experimentation platform (v rep) and Matlab programs and experimentation using a physical quadrotor UAV in a laboratory environment.
Online Feature Selection Based on Fuzzy Clustering and Its Applications
Venue: IEEE Transactions on Fuzzy Systems

Fuzzy c means (FCM) clustering has been successfully applied in various pattern recognition areas. While FCM is gaining attention, an important issue arising from these studies is the need to determine which attributes of the data should be used. Answering this question is difficult, because there is no labeled training data available in clustering to guide the search. We present a feature selection for FCM. The advantage of our method is that it is intuitively appealing, avoiding combinatorial searches, and allowing us to prune the feature set. Our method is also adaptable and can change through complex scenes in an online environment. We do not have to wait until all data have been generated before learning begins. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback Leibler divergence information. Numerical experiments are presented to demonstrate the robustness and accuracy of our method.
Scalable and Accurate Online Feature Selection for Big Data
Venue: ACM Trans. Knowl. Discov. Data

Feature selection is important in many big data applications. Two critical challenges closely associate with big data. First, in many big data applications, the dimensionality is extremely high, in millions, and keeps growing. Second, big data applications call for highly scalable feature selection algorithms in an online manner such that each feature can be processed in a sequential scan. We present SAOLA, a Scalable and Accurate OnLine Approach for feature selection in this paper. With a theoretical analysis on bounds of the pairwise correlations between features, SAOLA employs novel pairwise comparison techniques and maintains a parsimonious model over time in an online manner. Furthermore, to deal with upcoming features that arrive by groups, we extend the SAOLA algorithm, and then propose a new group SAOLA algorithm for online group feature selection. The group SAOLA algorithm can online maintain a set of feature groups that is sparse at the levels of both groups and individual features simultaneously. An empirical study using a series of benchmark real datasets shows that our two algorithms, SAOLA and group SAOLA, are scalable on datasets of extremely high dimensionality and have superior performance over the state of the art feature selection methods.
A Scalable and Accurate Online Feature Selection for Big Data

Feature selection is important in many big data applications. Two critical challenges closely associate with big data. Firstly, in many big data applications, the dimensionality is extremely high, in millions, and keeps growing. Secondly, big data applications call for highly scalable feature selection algorithms in an online manner such that each feature can be processed in a sequential scan. We present SAOLA, a Scalable and Accurate OnLine Approach for feature selection in this paper. With a theoretical analysis on bounds of the pairwise correlations between features, SAOLA employs novel pairwise comparison techniques and maintain a parsimonious model over time in an online manner. Furthermore, to deal with upcoming features that arrive by groups, we extend the SAOLA algorithm, and then propose a new group SAOLA algorithm for online group feature selection. The group SAOLA algorithm can online maintain a set of feature groups that is sparse at the levels of both groups and individual features simultaneously. An empirical study using a series of benchmark real data sets shows that our two algorithms, SAOLA and group SAOLA, are scalable on data sets of extremely high dimensionality, and have superior performance over the state of the art feature selection methods.
Folic acid conjugated chitosan derivative for tumor targeted magnetic resonance imaging

Contrast agents with high efficiency and safety are excellent candidates as magnetic resonance imaging probes. In this study, a nanoparticle contrast agent based on chitosan was synthesized. The backbone was chitosan, and the side chains were low molecular oligoethylenimine, which possessed multiple primary amines to increase the grafting number of Gd DTPA per glucose ring. This macromolecular chitosan derivative covalently conjugated with folic acid offers a high loading density of Gd DTPA with targeting capacity. The r1 value was found to be 25.9 mM s, which was 5.4 times higher than Magnevist. The higher r1 value was mainly due to the increased rotational correlation time (tR) after conjugation of DTPA Gd onto chitosan chain and the increased loading density of Gd permolecule. No obvious cytotoxicitywas observed fromWSTassay,which illustrated that the chitosan derivative exhibited excellent biocompatibility. During in vivo animal studies, the probe provided better signal intensity enhancement in the tumor after 5 h post injection. This study demonstrated that the developed oligoethylenimine grafted chitosan route was effective to increase the loading density of Gd and then significantly enhanced the relaxivity of the macromolecular contrast probes. Such an oligoethylenimine grafted chitosan derivative has great potential for developing sensitive and biocompatible MRI probe with targeting capability.
On a Gradient Based Algorithm for Sparse Signal Reconstruction in the Signal/Measurements Domain

Sparse signals can be recovered from a reduced set of samples by using compressive sensing algorithms. In common compressive sensing methods the signal is recovered in the sparsity domain. A method for the reconstruction of sparse signals which reconstructs the missing/unavailable samples/measurements is recently proposed. This method can be efficiently used in signal processing applications where a complete set of signal samples exists. The missing samples are considered as the minimization variables, while the available samples are fixed. Reconstruction of the unavailable signal samples/measurements is preformed using a gradient based algorithm in the time domain, with an adaptive step. Performance of this algorithm with respect to the step size and convergence are analyzed and a criterion for the step size adaptation is proposed in this paper. The step adaptation is based on the gradient direction angles. Illustrative examples and statistical study are presented. Computational efficiency of this algorithm is compared with other two commonly used gradient algorithms that reconstruct signal in the sparsity domain. Uniqueness of the recovered signal is checked using a recently introduced theorem. The algorithm application to the reconstruction of highly corrupted images is presented as well.
Overall optimization of Rankine cycle system for waste heat recovery of heavy duty vehicle diesel engines considering the cooling power consumption

The Rankine cycle system for waste heat recovery of heavy duty vehicle diesel engines has been regarded as a promising technique to reduce fuel consumption. Its heat dissipation in the condensation process, however, should be taken away in time, which is an energy consuming process. A fan assisted auxiliary water cooling system is employed in this paper. Results at 1300 r/min and 50% load indicate that the cooling pump and cooling fan together consume 7.66% of the recovered power. What's worse for the heavy load, cooling accessories may deplete of all the recovered power of the Rankine cycle system. Afterwards, effects of the condensing pressure and water feeding temperature are investigated, based on which a cooling power consumption model is established. Finally, an overall efficiency optimization is conducted to balance the electric power generation and cooling power consumption, taking condensing pressure, pressure ratio and exhaust bypass valve as major variables. The research suggests that the priority is to increase condensing pressure and open exhaust bypass valve appropriately at high speed and heavy load to reduce the cooling power consumption, while at low speed and light load, a lower condensing pressure is favored and the exhaust bypass valve should be closed making the waste heat recovered as much as possible. Within the sub critical region, a larger pressure ratio yields higher overall efficiency improvement at medium low speed and load. But the effects taper off at high speed and heavy load. For a given vehicular heavy duty diesel engine, the overall efficiency can be improved by 3.37% at 1300 r/min and 25% load using a Rankine cycle system to recover exhaust energy. The improvement becomes smaller as engine speed and load become higher.
Building ensemble representations: How the shape of preceding distractor distributions affects visual search
Venue: Cognition

Perception allows us to extract information about regularities in the environment. Observers can quickly determine summary statistics of a group of objects and detect outliers. The existing body of research has, however, not revealed how such ensemble representations develop over time. Moreover, the correspondence between the physical distribution of features in the external world and their potential internal representation as a probability density function (PDF) by the visual system is still unknown. Here, for the first time we demonstrate that such internal PDFs are built during visual search and show how they can be assessed with repetition and role reversal effects. Using singleton search for an oddly oriented target line among differently oriented distractors (a priming of pop out paradigm) we test how different properties of previously observed distractor distributions (mean, variability, and shape) influence search times. Our results indicate that observers learn properties of distractor distributions over and above mean and variance; in fact, response times also depend on the shape of the preceding distractor distribution. Response times decrease as a function of target distance from the mean of preceding Gaussian distractor distributions, and the decrease is steeper when preceding distributions have small standard deviations. When preceding distributions are uniform, however, this decrease in response times can be described by a two piece function corresponding to the uniform distribution PDF. Moreover, following skewed distributions response times function is skewed in accordance with the skew in distributions. Indeed, internal PDFs seem to be specifically tuned to the observed feature distribution.
On the inequivalence of the CH and CHSH inequalities due to finite statistics

Different variants of a Bell inequality, such as CHSH and CH, are known to be equivalent when evaluated on nonsignaling outcome probability distributions. However, in experimental setups, the outcome probability distributions are estimated using a finite number of samples. Therefore the nonsignaling conditions are only approximately satisfied and the robustness of the violation depends on the chosen inequality variant. We explain that phenomenon using the decomposition of the space of outcome probability distributions under the action of the symmetry group of the scenario, and propose a method to optimize the statistical robustness of a Bell inequality. In the process, we describe the finite group composed of relabeling of parties, measurement settings and outcomes, and identify correspondences between the irreducible representations of this group and properties of outcome probability distributions such as normalization, signaling or having uniform marginals.
Immediate effects of hip mobilization with movement in patients with hip osteoarthritis: A randomised controlled trial.
Venue: Manual therapy

Mobilization with movement (MWM) has been shown to reduce pain, increase range of motion (ROM) and physical function in a range of different musculoskeletal disorders. Despite this evidence, there is a lack of studies evaluating the effects of MWM for hip osteoarthritis (OA) OBJECTIVES To determine the immediate effects of MWM on pain, ROM and functional performance in patients with hip OA.  Randomized controlled trial with immediate follow up.  Forty consenting patients (mean age 78 6 years; 54% female) satisfied the eligibility criteria. All participants completed the study. Two forms of MWM techniques (n 20) or a simulated MWM (sham) (n 20) were applied. PRIMARY OUTCOMES pain recorded by numerical rating scale (NRS) SECONDARY OUTCOMES hip flexion and internal rotation ROM, and physical performance (timed up and go, sit to stand, and 40 m self placed walk test) were assessed before and after the intervention.  For the MWM group, pain decreased by 2 points on the NRS, hip flexion increased by 12.2deg, internal rotation by 4.4deg, and functional tests were also improved with clinically relevant effects following the MWM. There were no significant changes in the sham group for any outcome variable.  Pain, hip flexion ROM and physical performance immediately improved after the application of MWM in elderly patients suffering hip OA. The observed immediate changes were of clinical relevance. Future studies are required to determine the long term effects of this intervention.
La fiabilite organisationnelle au prisme des activites interstitielles

La plupart des etudes empiriques menees sur la fiabilite organisationnelle se sont centrees sur les activites les plus visibles et les plus directement liees a cette derniere appontage sur les porte avions, conduite et maintenance des centrales nucleaires, gestion de services d'urgence et de blocs operatoires. Dans cet article, nous entendons au contraire orienter notre analyse vers les activites interstitielles qui, tout en etant en contact direct avec des activites nobles restent a leur frontiere sans y etre pleinement integrees. Une etude de terrain menee sur la manutention lourde dans une industrie du secteur nucleaire permet d'analyser en profondeur ce type d'activite. Nous nous appuierons sur les theories pragmatistes de l'activite et sur les approches communicationnelles de l'organisation pour proposer une definition originale des activites interstitielles et identifier quatre de leurs caracteristiques observables. Nous analyserons egalement les effets de mise en vigilance et de decloisonnement de l'organisation que les activites interstitielles produisent, renforcant ainsi la fiabilite de l'organisation dans son ensemble. Enfin, nous discuterons de la contribution du concept d'activite interstitielle aux recherches sur les organisations distribuees, dans lesquelles l'articulation aux frontieres de celles ci est pensee au travers des dispositifs de gestion, des objets et des individus.
Spectrum and energy efficient survivable routing algorithm in elastic optical network

Multiplexing (WDM) optical networks, which allocate spectrum resource in a fixed grid, may lead to inefficient resource utilization, low flexibility and high energy consumption. Therefore, by introducing the Orthogonal Frequency Division Multiplexing (OFDM) technology to the spectrum elastic optical networks could utilize spectrum resources efficiently. This paper established a power consumption model in spectrum elastic optical networks. Both dedicated and shared path protection were studied with static traffic demand under 6 different modulation modes. Considering the energy consumption of the network, a Routing and Modulation Level Spectrum Allocation (RMLSA) algorithm is proposed. In this algorithm, power saving can be achieved by dynamically adapting the number of bandwidth variable transponders according to the chosen modulation level. Furthermore the paper proposed Spectrum Efficient RMLSA (SERMLSA) to make a trade off between the energy consumption and the fragmentation. Based on the SERMLSA algorithm, we also proposed Allowing n fragments spectrum efficient RMLSA (AnSERMLSA) algorithm which allowed the fragments in certain limitation to save more energy. Simulation results showed that, in terms of energy saving and spectrum utilization, the proposed SERMLSA and AnSERMLSA heuristic algorithms reflected good performances in energy saving and defragmentation.
Routing, code, modulation level and spectrum assignment (RCMLSA) algorithm for elastic optical networks
Venue: 2016 18th International Conference on Transparent Optical Networks (ICTON)

To date, most resource allocation proposals in elastic optical networks have focused either on routing and spectrum assignment (RSA, assuming fixed coding scheme and modulation format) or routing, modulation level and spectrum assignment (RMLSA, assuming a fixed coding scheme) Recent research has studied the impact of adaptive assignment of the code rate, without adapting the modulation format (RCSA algorithm) In this paper, we report for the first time the impact that adapting both the code rate as well as the modulation format has on the blocking performance. To do so, we present a new RCMLSA algorithm and evaluate it in terms of blocking by means of simulation. We compared the performance of our RCMLSA algorithm to that of RMLSA and RCSA algorithms. Results obtained for the NSFNet topology show that our RCMLSA algorithm performs better than the RCSA and RMLSA proposals, with a decrease in blocking of more than one order of magnitude (for traffic loads lower than 0.7) with respect to the RCSA.