nils-herrmann/clean_scirepeval huggingface dataset:
Scheduling Multithreaded Computations by Work Stealing

This paper studies the problem of efficiently schedulling fully strict (i.e., well-structured) multithreaded computations on parallel computers. A popular and practical method of scheduling this kind of dynamic MIMD-style computation is “work stealing,” in which processors needing work steal computational threads from other processors. In this paper, we give the first provably good work-stealing scheduler for multithreaded computations with dependencies.
Specifically, our analysis shows that the expected time to execute a fully strict computation on <italic>P</italic> processors using our work-stealing scheduler is <italic>T</italic><subscrpt>1</subscrpt>/<italic>P</italic> + <italic>O</italic>(<italic>T</italic><subscrpt><inline-equation> <f> ∞ </f></inline-equation></subscrpt>, where  <italic>T</italic><subscrpt>1</subscrpt> is the minimum serial execution time of the multithreaded computation and (<italic>T</italic><subscrpt><inline-equation> <f> ∞</f></inline-equation></subscrpt> is the minimum execution time with an infinite number of processors. Moreover, the space required by the execution is at most <italic>S</italic><subscrpt>1</subscrpt><italic>P</italic>, where  <italic>S</italic><subscrpt>1</subscrpt> is the minimum serial space requirement. We also show that the expected total communication of the algorithm is at most <italic>O</italic>(<italic>PT</italic><subscrpt><inline-equation> <f> ∞</f> </inline-equation></subscrpt>( 1 +  <italic>n<subscrpt>d</subscrpt></italic>)<italic>S</italic><subscrpt>max</subscrpt>), where <italic>S</italic><subscrpt>max</subscrpt> is the size of the largest activation record of any thread and  <italic>n<subscrpt>d</subscrpt></italic> is the maximum number of times that any thread synchronizes with its parent. This communication bound justifies the folk wisdom that work-stealing schedulers are more communication efficient than their work-sharing counterparts. All three of these bounds are existentially optimal to within a constant factor.
Comparing support vector machines with Gaussian kernels to radial basis function classifiers

The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined byk-means clustering, and the weights are computed using error backpropagation. We consider three machines, namely, a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system. The SV approach is thus not only theoretically wellfounded but also superior in a practical application.
Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge

Modern microprocessors are evolving into system-on-a-chip designs with high integration levels, catering to ever-shrinking form factors. Portability without compromising performance is a driving market need. An architectural approach that's adaptive to and cognizant of workload behavior and platform physical constraints is indispensable to meeting these performance and efficiency goals. This article describes power-management innovations introduced on Intel's Sandy Bridge microprocessor.
Understanding neurocognitive developmental disorders can improve education for all.

Specific learning disabilities (SLDs) are estimated to affect up to 10% of the population, and they co-occur far more often than would be expected, given their prevalences. We need to understand the complex etiology of SLDs and their co-occurrences in order to underpin the training of teachers, school psychologists, and clinicians, so that they can reliably recognize SLDs and optimize the learning contexts for individual learners.
Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.

While several resources are available to help researchers determine the minimum sample size needed to achieve target power for a wide variety of hypothesis tests, such resources are generally not available for determining the sample size when accurate parameter estimation is of interest. Sample size tables and procedures used to determine sample size for hypothesis tests should not be used for estimation because providing evidence that a parameter is not equal to some specific value is a fundamentally different task than accurately estimating the parameter. In particular, the necessary sample size required for hypothesis testing declines as the difference between the parameter and the specified value increases, but this difference does not have the same relationship to the sample size needed for accurate estimation. As interest in reporting estimates of effect sizes increases, sample size guidelines are needed for accurate estimation of these parameters. The present article focuses on the squared multiple correlation coefficient and presents regression equations that permit determination of sample size for estimating this parameter for up to 20 predictor variables. A comparison of the sample sizes reported here with those needed to test the hypothesis of no relationship between the predictor and criterion variables demonstrates the need for researchers to consider the purpose of their research and what is to be reported when determining the sample size for the study.
Crowdsourcing Inference-Rule Evaluation

The importance of inference rules to semantic applications has long been recognized and extensive work has been carried out to automatically acquire inference-rule resources. However, evaluating such resources has turned out to be a non-trivial task, slowing progress in the field. In this paper, we suggest a framework for evaluating inference-rule resources. Our framework simplifies a previously proposed “instance-based evaluation” method that involved substantial annotator training, making it suitable for crowdsourcing. We show that our method produces a large amount of annotations with high inter-annotator agreement for a low cost at a short period of time, without requiring training expert annotators.
Improving late life depression and cognitive control through the use of therapeutic video game technology: A proof-of-concept randomized trial.

Existing treatments for depression are known to have only modest effects, are insufficiently targeted, and are inconsistently utilized, particularly in older adults. Indeed, older adults with impaired cognitive control networks tend to demonstrate poor response to a majority of existing depression interventions. Cognitive control interventions delivered using entertainment software have the potential to not only target the underlying cerebral dysfunction associated with depression, but to do so in a manner that is engaging and engenders adherence to treatment protocol.



In this proof-of-concept trial (Clinicaltrials.gov ), individuals with late life depression (LLD) (22; 60+ years old) were randomized to either problem solving therapy (PST, n = 10) or a neurobiologically inspired digital platform designed to enhance cognitive control faculties (Project: EVO™, n = 12). Given the overlapping functional neuroanatomy of mood disturbances and executive dysfunction, we explored the impact of an intervention targeting cognitive control abilities, functional disability, and mood in older adults suffering from LLD, and how those outcomes compare to a therapeutic gold standard.



EVO participants demonstrated similar improvements in mood and self-reported function after 4 weeks of treatment to PST participants. The EVO participants also showed generalization to untrained measures of working memory and attention, as well as negativity bias, a finding not evident in the PST condition. Individuals assigned to EVO demonstrated 100% adherence.



This study provides preliminary findings that this therapeutic video game targeting cognitive control deficits may be an efficacious LLD intervention. Future research is needed to confirm these findings.
TrialChain: A Blockchain-Based Platform to Validate Data Integrity in Large, Biomedical Research Studies

The governance of data used for biomedical research and clinical trials is an important requirement for generating accurate results. To improve the visibility of data quality and analysis, we developed TrialChain, a blockchain-based platform that can be used to validate data integrity from large, biomedical research studies. We implemented a private blockchain using the MultiChain platform and integrated it with a data science platform deployed within a large research center. An administrative web application was built with Python to manage the platform, which was built with a microservice architecture using Docker. The TrialChain platform was integrated during data acquisition into our existing data science platform. Using NiFi, data were hashed and logged within the local blockchain infrastructure. To provide public validation, the local blockchain state was periodically synchronized to the public Ethereum network. The use of a combined private/public blockchain platform allows for both public validation of results while maintaining additional security and lower cost for blockchain transactions. Original data and modifications due to downstream analysis can be logged within TrialChain and data assets or results can be rapidly validated when needed using API calls to the platform. The TrialChain platform provides a data governance solution to audit the acquisition and analysis of biomedical research data. The platform provides cryptographic assurance of data authenticity and can also be used to document data analysis.
On the Functions Realized by Stochastic Computing Circuits

Stochastic computing (SC) employs conventional logic circuits to implement analog-style arithmetic functions acting on digital bit-streams. It exploits the advantages of analog computation -powerful basic operations, high operating speed, and error tolerance- in important applications such as sensory image processing and neuromorphic systems. At the same time, SC exhibits the analog drawbacks of low precision and complex underlying behavior. Although studied since the 1960s, many of SC"s fundamental properties are not well known or well understood. This paper presents, in a uniform manner and notation, what is known about the relations between the logical and stochastic behavior of stochastic circuits. It also considers how correlation among input bit-streams and the presence of memory elements influences stochastic behavior. Some related research challenges posed by SC are also discussed.
An Introduction to Random Indexing

Word space models enjoy considerable attention in current research on semantic indexing. Most notably, Latent Semantic Analysis/Indexing (LSA/LSI; Deerwester et al., 1990, Landauer & Dumais, 1997) has become a household name in information access research, and deservedly so; LSA has proven its mettle in numerous applications, and has more or less spawned an entire research field since its introduction around 1990. Today, there is a rich flora of word space models available, and there are numerous publications that report exceptional results in many different applications, including information retrieval (Dumais et al., 1988), word sense disambiguation (Schütze, 1993), various semantic knowledge tests (Lund et al., 1995, Karlgren & Sahlgren, 2001), and text categorization (Sahlgren & Karlgren, 2004). This paper introduces the Random Indexing word space approach, which presents an efficient, scalable and incremental alternative to standard word space methods. The paper is organized as follows: in the next section, we review the basic word space methodology. We then look at some of the problems that are inherent in the basic methodology, and also review some of the solutions that have been proposed in the literature. In the final section, we introduce the Random Indexing word space approach, and briefly review some of the experimental results that have been achieved with Random Indexing.
Evidence for van der Waals adhesion in gecko setae.

Geckos have evolved one of the most versatile and effective adhesives known. The mechanism of dry adhesion in the millions of setae on the toes of geckos has been the focus of scientific study for over a century. We provide the first direct experimental evidence for dry adhesion of gecko setae by van der Waals forces, and reject the use of mechanisms relying on high surface polarity, including capillary adhesion. The toes of live Tokay geckos were highly hydrophobic, and adhered equally well to strongly hydrophobic and strongly hydrophilic, polarizable surfaces. Adhesion of a single isolated gecko seta was equally effective on the hydrophobic and hydrophilic surfaces of a microelectro-mechanical systems force sensor. A van der Waals mechanism implies that the remarkable adhesive properties of gecko setae are merely a result of the size and shape of the tips, and are not strongly affected by surface chemistry. Theory predicts greater adhesive forces simply from subdividing setae to increase surface density, and suggests a possible design principle underlying the repeated, convergent evolution of dry adhesive microstructures in gecko, anoles, skinks, and insects. Estimates using a standard adhesion model and our measured forces come remarkably close to predicting the tip size of Tokay gecko seta. We verified the dependence on size and not surface type by using physical models of setal tips nanofabricated from two different materials. Both artificial setal tips stuck as predicted and provide a path to manufacturing the first dry, adhesive microstructures.
Uneasy lies the head that wears the crown: the link between guilt proneness and leadership.

We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.
Optimal discriminant plane for a small number of samples and design method of classifier on the plane

In a previous work (Zi-Quan Hong and Jing-Yu Yang, Minimum distance classifier on the optimal discriminant plane), we suggested and derived a method for constructing classifier on the optimal discriminant plane using minimum distance criteria. In this paper, the problem of solving optimal discriminant plane for a small number of samples is discussed, which is based on the above paper by the same authors. In the case of a small number of samples, generalized eigenequation AX = ,WX established for a large number of samples usually has no solution because within-class scatter matrix is singular. To obtain the solution of the generalized eigenequation, a new method is suggested, in which the Singular Value Perturbation is added to the within-class scatter matrix such that the matrix becomes a nonsingular matrix. Therefore, the generalized eigenequation can be solved using existing algorithms. We proved that the generalized eigenequations on the optimal discriminant plane are stable in respect of eigenvalues and the generalized eigenvectors are indeed the optimal discriminant directions, if the perturbation is subject to some certain conditions. The experimental results have shown that our method works well and the minimum distance classifier on the optimal discriminant plane can be constructed with high performance even in the case of a small number of samples. Discriminant plane Classifier design Dimension compression Singular value decomposition Discriminant vectors Eigenvalue problem Matrix perturbation Mapping
Open circuit fault detection in PWM voltage source inverter for PMSM drive system

In this paper, a simple and low-cost open-circuit fault detection and identification method for a pulse-width modulated (PWM) voltage-source inverter (VSI) employing a permanent magnet synchronous motor is proposed. An open-circuit fault of a power switch in the PWM VSI changes the corresponding terminal voltage and introduces the voltage distortions to each phase voltage. The proposed open-circuit fault diagnosis method employs the model reference adaptive system techniques and requires no additional sensors or electrical devices to detect the fault condition and identify the faulty switch. The proposed method has the features of fast diagnosis time, simple structure, and being easily inserted to the existing control algorithms as a subroutine without major modifications. The simulations and experiments are carried out and the results show the effectiveness of the proposed method.
RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials

To develop and evaluate RobotReviewer, a machine learning (ML) system that automatically assesses bias in clinical trials. From a (PDF-formatted) trial report, the system should determine risks of bias for the domains defined by the Cochrane Risk of Bias (RoB) tool, and extract supporting text for these judgments.



We algorithmically annotated 12,808 trial PDFs using data from the Cochrane Database of Systematic Reviews (CDSR). Trials were labeled as being at low or high/unclear risk of bias for each domain, and sentences were labeled as being informative or not. This dataset was used to train a multi-task ML model. We estimated the accuracy of ML judgments versus humans by comparing trials with two or more independent RoB assessments in the CDSR. Twenty blinded experienced reviewers rated the relevance of supporting text, comparing ML output with equivalent (human-extracted) text from the CDSR.



By retrieving the top 3 candidate sentences per document (top3 recall), the best ML text was rated more relevant than text from the CDSR, but not significantly (60.4% ML text rated 'highly relevant' v 56.5% of text from reviews; difference +3.9%, [-3.2% to +10.9%]). Model RoB judgments were less accurate than those from published reviews, though the difference was <10% (overall accuracy 71.0% with ML v 78.3% with CDSR).



Risk of bias assessment may be automated with reasonable accuracy. Automatically identified text supporting bias assessment is of equal quality to the manually identified text in the CDSR. This technology could substantially reduce reviewer workload and expedite evidence syntheses.
Fast (Spherical) Light Field Rendering with Per-Pixel Depth

Image-based rendering techniques are a powerful alternative to traditional polygon-based computer graphics. This paper presents a novel light field rendering technique which performs per-pixel depth correction of rays for high-quality reconstruction. Our technique stores combined RGB and depth values in a parabolic 2D texture for every light field sample acquired at discrete positions on a uniformly sampled sphere. Image synthesis is implemented on the GPU as a fragment program which extracts the correct image information from adjacent cameras for each fragment by applying a per-pixel depth correction of rays. We show that the presented image-based rendering technique provides a significant improvement compared to previous approaches. We explain two different rendering implementations which make use of a uniformly sampled parameterisation to minimize disparity problems and ensure full six degrees of freedom for virtual view synthesis. While one rendering algorithm implements a run-time efficient iterative refinement approach for rendering light fields with per pixel depth correction the other approach employs a raycaster for per pixel depth correction and provides superior rendering quality at moderate frame rates. GPU based per-fragment depth correction of rays, used in both implementations, reduces ghosting artefacts to a non noticeable amount and provides a rendering technique that performs without exhaustive pre-processing for 3D object reconstruction or real-time ray-object intersection calculations at rendering time.
Generative adversarial networks and adversarial methods in biomedical image analysis

Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. We provide an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.
Theory and Application of Magnetic Flux Leakage Pipeline Detection

Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.
Active learning: theory and applications to automatic speech recognition

We are interested in the problem of adaptive learning in the context of automatic speech recognition (ASR). In this paper, we propose an active learning algorithm for ASR. Automatic speech recognition systems are trained using human supervision to provide transcriptions of speech utterances. The goal of Active Learning is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. In this paper we describe how to estimate the confidence score for each utterance through an on-line algorithm using the lattice output of a speech recognizer. The utterance scores are filtered through the informativeness function and an optimal subset of training samples is selected. The active learning algorithm has been applied to both batch and on-line learning scheme and we have experimented with different selective sampling algorithms. Our experiments show that by using active learning the amount of labeled data needed for a given word accuracy can be reduced by more than 60% with respect to random sampling.