nils-herrmann/clean_scirepeval huggingface dataset:
Does mediator use contribute to the spacing effect for cued recall? Critical tests of the mediator hypothesis.

When study is spaced across sessions (versus massed within a single session), final performance is greater after spacing. This spacing effect may have multiple causes, and according to the mediator hypothesis, part of the effect can be explained by the use of mediator-based strategies. This hypothesis proposes that when study is spaced across sessions, rather than massed within a session, more mediators will be generated that are longer lasting and hence more mediators will be available to support criterion recall. In two experiments, participants were randomly assigned to study paired associates using either a spaced or massed schedule. They reported strategy use for each item during study trials and during the final test. Consistent with the mediator hypothesis, participants who had spaced (as compared to massed) practice reported using more mediators on the final test. This use of effective mediators also statistically accounted for some - but not all of - the spacing effect on final performance.
The Uncertain Geographic Context Problem in the Analysis of the Relationships between Obesity and the Built Environment in Guangzhou

Traditionally, static units of analysis such as administrative units are used when studying obesity. However, using these fixed contextual units ignores environmental influences experienced by individuals in areas beyond their residential neighborhood and may render the results unreliable. This problem has been articulated as the uncertain geographic context problem (UGCoP). This study investigates the UGCoP through exploring the relationships between the built environment and obesity based on individuals' activity space. First, a survey was conducted to collect individuals' daily activity and weight information in Guangzhou in January 2016. Then, the data were used to calculate and compare the values of several built environment variables based on seven activity space delineations, including home buffers, workplace buffers (WPB), fitness place buffers (FPB), the standard deviational ellipse at two standard deviations (SDE2), the weighted standard deviational ellipse at two standard deviations (WSDE2), the minimum convex polygon (MCP), and road network buffers (RNB). Lastly, we conducted comparative analysis and regression analysis based on different activity space measures. The results indicate that significant differences exist between variables obtained with different activity space delineations. Further, regression analyses show that the activity space delineations used in the analysis have a significant influence on the results concerning the relationships between the built environment and obesity. The study sheds light on the UGCoP in analyzing the relationships between obesity and the built environment.
Routing Protocols for Vehicular Adhoc Networks ( VANETs ) : A Review

Routing in Vehicular Ad hoc Networks is a challenging task due to the unique characteristics of the network such as high mobility of nodes, dynamically changing topology and highly partitioned network. It is a challenge to ensure reliable, continuous and seamless communication in the presence of speeding vehicles. The performance of routing protocols depends on various internal factors such as mobility of nodes and external factors such as road topology and obstacles that block the signal. This demands a highly adaptive approach to deal with the dynamic scenarios by selecting the best routing and forwarding strategies and by using appropriate mobility and propagation models. In this paper we review the existing routing protocols for VANETs and categorise them into a taxonomy based on key attributes such as network architecture, applications supported, routing strategies, forwarding strategies, mobility models and quality of service metrics. Protocols belonging to unicast, multicast, geocast and broadcast categories are discussed. Strengths and weaknesses of various protocols using topology based, position based and cluster based approaches are analysed. Emphasis is given on the adaptive and context-aware routing protocols. Simulation of broadcast and unicast protocols is carried out and the results are presented.
Study of the implementation of STATCOM on DFIG-based wind farm connected to a power system

The integration of Doubly Fed Induction Generator (DFIG) based wind farm into the power grid has become a major concern for power system engineers today. Voltage stability is a key factor to maintain DFIG-based wind farm in service during the grid disturbances. This paper investigates the implementation of STATCOM to overcome the voltage stability issue for DFIG-based wind farm connected to a distribution network. The study includes the implementation of a static synchronous compensator (STATCOM) as a dynamic reactive power compensator at the point of common coupling to maintain stable voltage by protecting DFIG-based wind farm interconnected to a distribution system from going offline during and after the disturbances. The developed system is simulated in MATLAB/Simulink and the results show that the STATCOM improves the transient voltage stability and therefore helps the wind turbine generator system to remain in service during grid faults.
Efficient and Scalable k‑Means on GPUs

k-Means is a versatile clustering algorithm widely used in practice. To cluster large data sets, state-of-the-art implementations use GPUs to shorten the data to knowledge time. These implementations commonly assign points on a GPU and update centroids on a CPU. We identify two main shortcomings of this approach. First, it requires expensive data exchange between processors when switching between the two processing steps point assignment and centroid update. Second, even when processing both steps of k-means on the same processor, points still need to be read two times within an iteration, leading to inefficient use of memory bandwidth. In this paper, we present a novel approach for centroid update that allows us to efficiently process both phases of k-means on GPUs. We fuse point assignment and centroid update to execute one iteration with a single pass over the points. Our evaluation shows that our k-means approach scales to very large data sets. Overall, we achieve up to 20 × higher throughput compared to the state-of-the-art approach.
Prevalence of intimate partner violence: findings from the WHO multi-country study on women's health and domestic violence

Violence against women is a serious human rights abuse and public health issue. Despite growing evidence of the size of the problem, current evidence comes largely from industrialised settings, and methodological differences limit the extent to which comparisons can be made between studies. We aimed to estimate the extent of physical and sexual intimate partner violence against women in 15 sites in ten countries: Bangladesh, Brazil, Ethiopia, Japan, Namibia, Peru, Samoa, Serbia and Montenegro, Thailand, and the United Republic of Tanzania.



Standardised population-based household surveys were done between 2000 and 2003. Women aged 15-49 years were interviewed and those who had ever had a male partner were asked in private about their experiences of physically and sexually violent and emotionally abusive acts.



24,097 women completed interviews, with around 1500 interviews per site. The reported lifetime prevalence of physical or sexual partner violence, or both, varied from 15% to 71%, with two sites having a prevalence of less than 25%, seven between 25% and 50%, and six between 50% and 75%. Between 4% and 54% of respondents reported physical or sexual partner violence, or both, in the past year. Men who were more controlling were more likely to be violent against their partners. In all but one setting women were at far greater risk of physical or sexual violence by a partner than from violence by other people.



The findings confirm that physical and sexual partner violence against women is widespread. The variation in prevalence within and between settings highlights that this violence in not inevitable, and must be addressed.
Contrast Enhancement Using Bi-Histogram Equalization With Brightness Preservation

Contrast enhancement is an important factor in the image preprocessing step. One of the widely accepted contrast enhancement method is the histogram equalization. Although histogram equalization achieves comparatively better performance on almost all types of image, global histogram equalization sometimes produces excessive visual deterioration. A new extension of bi-histogram equalization called Bi-Histogram Equalization with Neighborhood Metric (BHENM). First, large histogram bins that cause washout artifacts are divided into sub-bins using neighborhood metrics, the same intensities of the original image are arranged by neighboring information. Then the histogram of the original image is separated into two sub-histogram based on the mean of the histogram of the original image; the sub-histogram are equalized independently using refined histogram equalization, which produces flatter histogram. BHENM simultaneously preserved the brightness and enhanced the local contrast of the original image. Simulation result shows better brightness preservation.
LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks

Recent advances in machine learning are paving the way for the artificial generation of high quality images and videos. In this paper, we investigate how generating synthetic samples through generative models can lead to information leakage, and, consequently, to privacy breaches affecting individuals’ privacy that contribute their personal or sensitive data to train these models. In order to quantitatively measure privacy leakage, we train a Generative Adversarial Network (GAN), which combines a discriminative model and a generative model, to detect overfitting by relying on the discriminator capacity to learn statistical differences in distributions. We present attacks based on both white-box and black-box access to the target model, and show how to improve it through auxiliary knowledge of samples in the dataset. We test our attacks on several state-of-the-art models such as Deep Convolutional GAN (DCGAN), Boundary Equilibrium GAN (BEGAN), and the combination of DCGAN with a Variational Autoencoder (DCGAN+VAE), using datasets consisting of complex representations of faces (LFW) and objects (CIFAR-10). Our white-box attacks are 100% successful at inferring which samples were used to train the target model, while the best black-box attacks can infer training set membership with over 60% accuracy.
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning

Accelerating convolutional neural networks has recently received ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. To this end, most approaches tend to prune filters in a layerwise fixed manner, which is incapable to dynamically recover the previously removed filter, as well as jointly optimize the pruned network across layers. In this paper, we propose a novel global & dynamic pruning (GDP) scheme to prune redundant filters for CNN acceleration. In particular, GDP first globally prunes the unsalient filters across all layers by proposing a global discriminative function based on prior knowledge of each filter. Second, it dynamically updates the filter saliency all over the pruned sparse network, and then recovers the mistakenly pruned filter, followed by a retraining phase to improve the model accuracy. Specially, we effectively solve the corresponding nonconvex optimization problem of the proposed GDP via stochastic gradient descent with greedy alternative updating. Extensive experiments show that the proposed approach achieves superior performance to accelerate several cutting-edge CNNs on the ILSVRC 2012 benchmark, comparing to the state-of-the-art filter pruning methods.
Active Stiffness Tuning of a Spring-Based Continuum Robot for MRI-Guided Neurosurgery

Deep intracranial tumor removal can be achieved if the neurosurgical robot has sufficient flexibility and stability. Toward achieving this goal, we have developed a spring-based continuum robot, namely a minimally invasive neurosurgical intracranial robot (MINIR-II) with novel tendon routing and tunable stiffness for use in a magnetic resonance imaging (MRI) environment. The robot consists of a pair of springs in parallel, i.e., an inner interconnected spring that promotes flexibility with decoupled segment motion and an outer spring that maintains its smooth curved shape during its interaction with the tissue. We propose a shape memory alloy (SMA) spring backbone that provides local stiffness control and a tendon routing configuration that enables independent segment locking. In this paper, we also present a detailed local stiffness analysis of the SMA backbone and model the relationship between the resistive force at the robot tip and the tension in the tendon. We also demonstrate through experiments, the validity of our local stiffness model of the SMA backbone and the correlation between the tendon tension and the resistive force. We also performed MRI compatibility studies of the three-segment MINIR-II robot by attaching it to a robotic platform that consists of SMA spring actuators with integrated water cooling modules.
Short-term memory to long-term memory transition in a nanoscale memristor.

"Memory" is an essential building block in learning and decision-making in biological systems. Unlike modern semiconductor memory devices, needless to say, human memory is by no means eternal. Yet, forgetfulness is not always a disadvantage since it releases memory storage for more important or more frequently accessed pieces of information and is thought to be necessary for individuals to adapt to new environments. Eventually, only memories that are of significance are transformed from short-term memory into long-term memory through repeated stimulation. In this study, we show experimentally that the retention loss in a nanoscale memristor device bears striking resemblance to memory loss in biological systems. By stimulating the memristor with repeated voltage pulses, we observe an effect analogous to memory transition in biological systems with much improved retention time accompanied by additional structural changes in the memristor. We verify that not only the shape or the total number of stimuli is influential, but also the time interval between stimulation pulses (i.e., the stimulation rate) plays a crucial role in determining the effectiveness of the transition. The memory enhancement and transition of the memristor device was explained from the microscopic picture of impurity redistribution and can be qualitatively described by the same equations governing biological memories.
Epidemiology of brain tumors: the national survey of intracranial neoplasms.

A national survey, based on a probability sample of patients admitted to short-term hospitals in the United States during 1973 to 1974 with a discharge diagnosis of an intracranial neoplasm, was conducted in 157 hospitals. The annual incidence was estimated at 17,000 for primary intracranial neoplasms and 17,400 for secondary intracranial neoplasms--8.2 and 8.3 per 100,000 US population, respectively. Rates of primary intracranial neoplasms increased steadily with advancing age. The age-adjusted rates were higher among men than among women (8.5 versus 7.9 per 100,000). However, although men were more susceptible to gliomas and neuronomas, incidence rates for meningiomas and pituitary adenomas were higher among women.
Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data

Medical image data is naturally distributed among clinical institutions. This partitioning, combined with security and privacy restrictions on medical data, imposes limitations on machine learning algorithms in clinical applications, especially for small and newly established institutions. We present InsuLearn: an intuitive and robust open-source (open-source code available at: https://github.com/ DistributedML/InsuLearn) platform designed to facilitate distributed learning (classification and regression) on medical image data, while preserving data security and privacy. InsuLearn is built on ensemble learning, in which statistical models are developed at each institution independently and combined at secure coordinator nodes. InsuLearn protocols are designed such that the liveness of the system is guaranteed as institutions join and leave the network. Coordination is implemented as a cluster of replicated state machines, making it tolerant to individual node failures. We demonstrate that InsuLearn successfully integrates accurate models for horizontally partitioned data while preserving privacy.
Sexual health in women reporting a history of child sexual abuse.

The present study examined the association between child sexual abuse (CSA) and sexual health outcomes in young adult women. Maladaptive coping strategies and optimism were investigated as possible mediators and moderators of this relationship.



Data regarding sexual abuse, coping, optimism and various sexual health outcomes were collected using self-report and computerized questionnaires with a sample of 889 young adult women from the province of Quebec aged 20-23 years old.



A total of 31% of adult women reported a history of CSA. Women reporting a severe CSA were more likely to report more adverse sexual health outcomes including suffering from sexual problems and engaging in more high-risk sexual behaviors. CSA survivors involving touching only were at greater risk of reporting more negative sexual self-concept such as experiencing negative feelings during sex than were non-abused participants. Results indicated that emotion-oriented coping mediated outcomes related to negative sexual self-concept while optimism mediated outcomes related to both, negative sexual self-concept and high-risk sexual behaviors. No support was found for any of the proposed moderation models.



Survivors of more severe CSA are more likely to engage in high-risk sexual behaviors that are potentially harmful to their health as well as to experience more sexual problems than women without a history of sexual victimization. Personal factors, namely emotion-oriented coping and optimism, mediated some sexual health outcomes in sexually abused women. The results suggest that maladaptive coping strategies and optimism regarding the future may be important targets for interventions optimizing sexual health and sexual well-being in CSA survivors.
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure, and uses them to track the evolving states of participant entities. We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans. The explicit, structured, and evolving knowledge graph representations that our model constructs can be used in downstream question answering tasks to improve machine comprehension of text, as we demonstrate empirically. On two comprehension tasks from the recently proposed PROPARA dataset (Dalvi et al., 2018), our model achieves state-of-the-art results. We further show that our model is competitive on the RECIPES dataset (Kiddon et al., 2015), suggesting it may be generally applicable. We present some evidence that the model’s knowledge graphs help it to impose commonsense constraints on its predictions.
Compositional Neural Semantic Parsing for Compound Virtual

Virtual assistants such as IFTTT and Almond support complex tasks that combine open web APIs for devices and web services. In this work, we explore semantic parsing to understand natural language commands for these tasks and their compositions. We present the ThingTalk dataset, which consists of 22,362 commands, corresponding to 2,681 distinct programs in ThingTalk, a language for compound virtual assistant tasks. To improve compositionality of multiple APIs, we propose SEQ2TT, a Seq2Seq extension using a bottom-up encoding of grammar productions for programs and a maxmargin loss. On the ThingTalk dataset, SEQ2TT obtains 84% accuracy on trained programs and 67% on unseen combinations, an improvement of 12% over a basic sequence-to-sequence model with attention.
Twitter Heron: Stream Processing at Scale

Storm has long served as the main platform for real-time analytics at Twitter. However, as the scale of data being processed in real-time at Twitter has increased, along with an increase in the diversity and the number of use cases, many limitations of Storm have become apparent. We need a system that scales better, has better debug-ability, has better performance, and is easier to manage -- all while working in a shared cluster infrastructure. We considered various alternatives to meet these needs, and in the end concluded that we needed to build a new real-time stream data processing system. This paper presents the design and implementation of this new system, called Heron. Heron is now the de facto stream data processing engine inside Twitter, and in this paper we also share our experiences from running Heron in production. In this paper, we also provide empirical evidence demonstrating the efficiency and scalability of Heron.