nils-herrmann/clean_scirepeval huggingface dataset:
VisAn MHD : a toolbox in Matlab for MHD computer model data visualisation and analysis

Among the many challenges facing modern space physics today is the need for a visualisation and analysis package which can examine the results from the diversity of numerical and empirical computer models as well as observational data. Magnetohydrodynamic (MHD) models represent the latest numerical models of the complex Earth’s space environment and have the unique ability to span the enormous distances present in the magnetosphere from several hundred kilometres to several thousand kilometres above the Earth surface. This feature enables scientist to study complex structures of processes where otherwise only point measurements from satellites or ground-based instruments are available. Only by combining these observational data and the MHD simulations it is possible to enlarge the scope of the point-to-point observations and to fill the gaps left by measurements in order to get a full 3-D representation of the processes in our geospace environment. In this paper we introduce the VisAn MHD toolbox for Matlab as a tool for the visualisation and analysis of observational data and MHD simulations. We have created an easy to use tool which is capable of highly sophisticated visualisations and data analysis of the results from a diverse set of MHD models in combination with in situ measurements from satellites and groundbased instruments. The toolbox is being released under an open-source licensing agreement to facilitate and encourage community use and contribution.
Chick Chorioallantoic Membrane (CAM) Assay as an In Vivo Model to Study the Effect of Newly Identified Molecules on Ovarian Cancer Invasion and Metastasis

The majority of ovarian cancer patients present with advanced disease and despite aggressive treatment, prognosis remains poor. Significant improvement in ovarian cancer survival will require the development of more effective molecularly targeted therapeutics. Commonly, mouse models are used for the in vivo assessment of potential new therapeutic targets in ovarian cancer. However, animal models are costly and time consuming. Other models, such as the chick embryo chorioallantoic membrane (CAM) assay, are therefore an attractive alternative. CAM assays have been widely used to study angiogenesis and tumor invasion of colorectal, prostate and brain cancers. However, there have been limited studies that have used CAM assays to assess ovarian cancer invasion and metastasis. We have therefore developed a CAM assay protocol to monitor the metastatic properties of ovarian cancer cells (OVCAR-3, SKOV-3 and OV-90) and to study the effect of potential therapeutic molecules in vivo. The results from the CAM assay are consistent with cancer cell motility and invasion observed in in vitro assays. Our results demonstrate that the CAM assay is a robust and cost effective model to study ovarian cancer cell metastasis. It is therefore a very useful in vivo model for screening of potential novel therapeutics.
Neural network based detection of hard exudates in retinal images

Diabetic retinopathy (DR) is an important cause of visual impairment in developed countries. Automatic recognition of DR lesions in fundus images can contribute to the diagnosis of the disease. The aim of this study is to automatically detect one of these lesions, hard exudates (EXs), in order to help ophthalmologists in the diagnosis and follow-up of the disease. We propose an algorithm which includes a neural network (NN) classifier for this task. Three NN classifiers were investigated: multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM). Our database was composed of 117 images with variable colour, brightness, and quality. 50 of them (from DR patients) were used to train the NN classifiers and 67 (40 from DR patients and 27 from healthy retinas) to test the method. Using a lesion-based criterion, we achieved a mean sensitivity (SE(l)) of 88.14% and a mean positive predictive value (PPV(l)) of 80.72% for MLP. With RBF we obtained SE(l)=88.49% and PPV(l)=77.41%, while we reached SE(l)=87.61% and PPV(l)=83.51% using SVM. With an image-based criterion, a mean sensitivity (SE(i)) of 100%, a mean specificity (SP(i)) of 92.59% and a mean accuracy (AC(i)) of 97.01% were obtained with MLP. Using RBF we achieved SE(i)=100%, SP(i)=81.48% and AC(i)=92.54%. With SVM the image-based results were SE(i)=100%, SP(i)=77.78% and AC(i)=91.04%.
CONSTRAINT PROGRAMMING APPROACH TO THE TAEMS SCHEDULING PROBLEM By

characteristics of method execution that the agent would want to maximize. So a higher quality method is better whereas a lower cost method is usually preferred. If each outcome oi has a quality distribution ((qi,1, pi,1), (qi,2, pi,2), ..., (qi,m, pi,m)),, the probability that it will execute with quality q is computed as [24] , , , ( ) : i i j i j i j P q p p q q     The expected quality E(q) is computed as [24] :
Digital culture clash : “ massive ” education in the E-learning and Digital Cultures MOOC

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The Role of Business Intelligence Tools in Decision Making Process

Business organization sheds the lights on the development in marketing to be able to accompaniment with the last even in marketing and to handle market management. The organizations create their own business decisions and operations through using business intelligence justification. Therefore organization can do that through knowledge, and convey the correct information. As a result, business intelligence becomes the main criterion and the strategic performance in the modern organization to achieve the dominant character. This study will show the impact of using business intelligence strategy on the decision making process by showing a study of the Jordanian customs department.
Codelets: linking interactive documentation and example code in the editor

Programmers frequently use instructive code examples found on the Web to overcome cognitive barriers while programming. These examples couple the concrete functionality of code with rich contextual information about how the code works. However, using these examples necessitates understanding, configuring, and integrating the code, all of which typically take place after the example enters the user's code and has been removed from its original instructive context. In short, a user's interaction with an example continues well after the code is pasted. This paper investigates whether treating examples as "first-class" objects in the code editor - rather than simply as strings of text - will allow programmers to use examples more effectively. We explore this through the creation and evaluation of Codelets. A Codelet is presented inline with the user's code, and consists of a block of example code and an interactive helper widget that assists the user in understanding and integrating the example. The Codelet persists throughout the example's lifecycle, remaining accessible even after configuration and integration is done. A comparative laboratory study with 20 participants found that programmers were able to complete tasks involving examples an average of 43% faster when using Codelets than when using a standard Web browser.
Facts, Events, and Inflection: When Language and Memory Dissociate

We report on two brain-damaged patients who show contrasting patterns of deficits in memory and language functioning. One patient (AW) suffers from a lexical retrieval deficit and failed to produce many irregularly inflected words such as spun, forgotten, andmice, but demonstrated intact production of regularly inflected words such aswalked andrats. She also had preserved declarative memory for facts and events. The other patient (VP) presented with a severe declarative memory deficit but showed no signs of impairment in producing either regular or irregular inflections. These patterns of deficits reveal that the retrieval of irregular inflections proceeds relatively autonomously with respect to declarative memory. We interpret these deficits with reference to three current theories of lexical structure: (a) Pinker's words and rules account, which assumes distinct mechanisms for processing regular and irregular inflections and proposes that lexical and semantic processing are subserved by distinct but interacting cognitive systems; (b) Ullman's declarative/procedural model, which assumes that mechanisms for the retrieval of irregular inflections are part of declarative memory; (c) Joanisse and Seidenberg's connectionist model, in which semantic information is critical for the retrieval of irregular inflections.
ARAS human activity datasets in multiple homes with multiple residents

The real world human activity datasets are of great importance in development of novel machine learning methods for automatic recognition of human activities in smart environments. In this study, we present the details of ARAS (Activity Recognition with Ambient Sensing) human activity recognition datasets that are collected from two real houses with multiple residents during two months. The datasets contain the ground truth labels for 27 different activities. Each house was equipped with 20 binary sensors of different types that communicate wirelessly using the ZigBee protocol. A full month of information which contains the sensor data and the activity labels for both residents was gathered from each house, resulting in a total of two months data. In the paper, particularly, we explain the details of sensor selection, targeted activities, deployment of the sensors and the characteristics of the collected data and provide the results of our preliminary experiments on the datasets.
Incremental graph pattern matching

Graph pattern matching has become a routine process in emerging applications such as social networks. In practice a data graph is typically large, and is frequently updated with small changes. It is often prohibitively expensive to recompute matches from scratch via batch algorithms when the graph is updated. With this comes the need for incremental algorithms that compute changes to the matches in response to updates, to minimize unnecessary recomputation. This paper investigates incremental algorithms for graph pattern matching defined in terms of graph simulation, bounded simulation and subgraph isomorphism. (1) For simulation, we provide incremental algorithms for unit updates and certain graph patterns. These algorithms are optimal: in linear time in the size of the changes in the input and output, which characterizes the cost that is inherent to the problem itself. For general patterns we show that the incremental matching problem is unbounded, i.e., its cost is not determined by the size of the changes alone. (2) For bounded simulation, we show that the problem is unbounded even for unit updates and path patterns. (3) For subgraph isomorphism, we show that the problem is intractable and unbounded for unit updates and path patterns. (4) For multiple updates, we develop an incremental algorithm for each of simulation, bounded simulation and subgraph isomorphism. We experimentally verify that these incremental algorithms significantly outperform their batch counterparts in response to small changes, using real-life data and synthetic data.
Analysis of k-Nearest Neighbor Distances with Application to Entropy Estimation

Estimating entropy and mutual information consistently is important for many machine learning applications. The Kozachenko-Leonenko (KL) estimator ( Kozachenko & Leonenko , 1987) is a widely used nonparametric estimator for the entropy of multivariate continuous random variables, as well as the basis of the mutual information estimator ofKraskov et al.(2004), perhaps the most widely used estimator of mutual information in this setting. Despite the practical importance of these estimators, major theoretical questions regarding their finite-sample behavior remain open. This paper proves finite-sample bounds on the bias and variance of the KL estimator, showing that it achieves the minimax convergence rate for certain classes of smooth functions. In proving these bounds, we analyze finitesample behavior of k-nearest neighbors ( k-NN) distance statistics (on which the KL estimator is based). We derive concentration inequalities for k-NN distances and a general expectation bound for statistics ofk-NN distances, which may be useful for other analyses of k-NN methods.
A 12.5Gb/s SerDes in 65nm CMOS Using a Baud-Rate ADC with Digital Receiver Equalization and Clock Recovery

A DSP-based low-power 12.5Gb/s SerDes using a baud-rate ADC and a digital data-path is developed for backplane data communication. A digital 2-tap FFE and a 5-tap DFE in the RX provide channel compensation. A BER of <10-15 is measured over legacy backplanes with 24dB loss at Nyquist. The power consumption and die area are 330mW and 0.45mm2 per TX/RX pair
Parkinson's Disease Motor Symptoms in Machine Learning: A Review

This paper reviews related work and state-of-the-art publications for recognizing motor symptoms of Parkinson's Disease (PD). It presents research efforts that were undertaken to inform on how well traditional machine learning algorithms can handle this task. In particular, four PD related motor symptoms are highlighted (i.e. tremor, bradykinesia, freezing of gait and dyskinesia) and their details summarized. Thus the primary objective of this research is to provide a literary foundation for development and improvement of algorithms for detecting PD related motor symptoms.
A microprocessor survey course: exploring advanced computer architecture in practice

A seminar that surveys state-of-the-art microprocessors offers an excellent forum for students to see how computer architecture techniques are employed in practice and for them to gain a detailed knowledge of the state of the art in microprocessor design. Princeton and the University of Virginia have developed such a seminar, organized around student presentations and a substantial research project. The course can accommodate a range of students, from advanced undergraduates to senior graduate students. The course can also be easily adapted to a survey of embedded processors. This paper describes the version taught at the University of Virginia and lessons learned from the experience.
Fine tuning CNNS with scarce training data — Adapting imagenet to art epoch classification

Deep Convolutional Neural Networks (CNN) have recently been shown to outperform previous state of the art approaches for image classification. Their success must in parts be attributed to the availability of large labeled training sets such as provided by the ImageNet benchmarking initiative. When training data is scarce, however, CNNs have proven to fail to learn descriptive features. Recent research shows that supervised pre-training on external data followed by domain-specific fine-tuning yields a significant performance boost when external data and target domain show similar visual characteristics. Transfer-learning from a base task to a highly dissimilar target task, however, has not yet been fully investigated. In this paper, we analyze the performance of different feature representations for classification of paintings into art epochs. Specifically, we evaluate the impact of training set sizes on CNNs trained with and without external data and compare the obtained models to linear models based on Improved Fisher Encodings. Our results underline the superior performance of fine-tuned CNNs but likewise propose Fisher Encodings in scenarios were training data is limited.
Child Neglect : Developmental Consequences , Intervention , and Policy Implications

This paper reviews the literature on child neglect with respect to child outcomes, prevention and intervention, and implications for policy. First, the prevalence of the problem is discussed and then potential negative outcomes for neglected children, including behavior problems, low self-esteem, poor school performance, and maladjustment/psychopathology, are discussed. Risk factors and current child neglect interventions are then reviewed. Popular family support programs, such as family preservation, have mixed success rates for preventing child neglect. The successes and shortcomings of other programs are also examined with a focus on implications for future research and policy. Overall, the research supports a multidisciplinary approach to assessment, intervention, and research on child neglect. Furthermore, the need for a combined effort among parents, community members, professionals, and policymakers to increase awareness and prevention endeavors is discussed. Targeted attempts to educate all involved parties should focus on early intervention during specific encounters with atrisk families via medical settings, school settings, and parent education programs.
A Dynamic Graph-Based Scheduling and Interference Coordination Approach in Heterogeneous Cellular Networks

To meet the demand of increasing mobile data traffic and provide better user experience, heterogeneous cellular networks (HCNs) have become a promising solution to improve both the system capacity and coverage. However, due to dense self-deployment of small cells in a limited area, serious interference from nearby base stations may occur, which results in severe performance degradation. To mitigate downlink interference and utilize spectrum resources more efficiently, we present a novel graph-based resource allocation and interference management approach in this paper. First, we divide small cells into cell clusters, considering their neighborhood relationships in the scenario. Then, we develop another graph clustering scheme to group user equipment (UE) in each cell cluster into UE clusters with minimum intracluster interference. Finally, we utilize a proportional fairness scheduling scheme to assign subchannels to each UE cluster and allocate power using water-filling method. To show the efficacy and effectiveness of our proposed approach, we propose a dual-based approach to search for optimal solutions as the baseline for comparisons. Furthermore, we compare the graph-based approach with the state of the art and a distributed approach without interference coordination. The simulation results show that our graph-based approach reaches more than 90% of the optimal performance and achieves a significant improvement in spectral efficiency compared with the state of the art and the distributed approach both under cochannel and orthogonal deployments. Moreover, the proposed graph-based approach has low computation complexity, making it feasible for real-time implementation.
PPG embedded system for blood pressure monitoring

In this work, we have designed and implemented a microcontroller-based embedded system for blood pressure monitoring through a PhotoPlethysmoGraphic (PPG) technique. In our system, it is possible to perform PPG measurements via reflectance mode. Hardware novelty of our system consists in the adoption of Silicon PhotoMultiplier detectors. The signal received from the photodetector is used to calculate the instantaneous heart rate and therefore the heart rate variability. The obtained results show that, by using our system, it is possible to easily extract both the PPG and the breath signal. These signals can be used to monitor the patients during the convalescence both in hospital and at home.
The prediction of professional behaviour.

The purpose of this study was to establish outcome measures for professionalism in medical students and to identify predictors of these outcomes.



Retrospective cohort study.



A US medical school.



All students entering in 1995 and graduating within 5 years.



Outcome measures included review board identification of professionalism problems and clerkship evaluations for items pertaining to professionalism. Pre-clinical predictor variables included material from the admissions application, completion of required course evaluations, students' self-reporting of immunisation compliance, students' performance on standardised patient (SP) exercises, and students' self-assessed performance on SP exercises.



The outcome measures of clerkship professionalism scores were found to be highly reliable (alpha 0.88-0.96). No data from the admissions material was found to be predictive of professional behaviour in the clinical years. Using multivariate regression, failing to complete required course evaluations (B = 0.23) and failing to report immunisation compliance (B = 0.29) were significant predictors of unprofessional behaviour found by the review board in subsequent years. Immunisation non-compliance predicted low overall clerkship professional evaluation scores (B = - 0.34). Student self-assessment accuracy (SP score minus self-assessed score) (B = 0.03) and immunisation non-compliance (B = 0.54) predicted the internal medicine clerkship professionalism score.



This study identifies a set of reliable, context-bound outcome measures in professionalism. Although we searched for predictors of behaviour in the admissions application and other domains commonly felt to be predictive of professionalism, we found significant predictors only in domains where students had had opportunities to demonstrate conscientious behaviour or humility in self-assessment.
The leaves of Stevia rebaudiana (Bertoni), their constituents and the analyses thereof: a review.

The plant Stevia rebaudiana is well-known due to the sweet-tasting ent-kaurene diterpenoid glycosides. Stevioside and rebaudioside A are the most abundant and best analyzed, but more than 30 additional steviol glycosides have been described in the scientific literature to date. Most of them were detected in the last two years. This paper reviews these new compounds and provides an overview about novel trends in their determination, separation, analysis, detection, and quantification. The detection and analysis of further constituents such as nonglycosidic diterpenes, flavonoids, chlorogenic acids, vitamins, nutrients, and miscellaneous minor compounds in the leaves of Stevia rebaudiana are reviewed as well. A critical review of the antioxidant capacity of Stevia leaves and its analysis is also included. These different aspects are discussed in consideration of the scientific literature of the last 10 years.