nils-herrmann/clean_scirepeval huggingface dataset:
Reducing population salt intake worldwide: from evidence to implementation.

Raised blood pressure is a major cause of cardiovascular disease, responsible for 62% of stroke and 49% of coronary heart disease. There is overwhelming evidence that dietary salt is the major cause of raised blood pressure and that a reduction in salt intake lowers blood pressure, thereby, reducing blood pressure-related diseases. Several lines of evidence including ecological, population, and prospective cohort studies, as well as outcome trials, demonstrate that a reduction in salt intake is related to a lower risk of cardiovascular disease. Increasing evidence also suggests that a high salt intake may directly increase the risk of stroke, left ventricular hypertrophy, and renal disease; is associated with obesity through soft drink consumption; is related to renal stones and osteoporosis; is linked to the severity of asthma; and is probably a major cause of stomach cancer. In most developed countries, a reduction in salt intake can be achieved by a gradual and sustained reduction in the amount of salt added to foods by the food industry. In other countries where most of the salt consumed comes from salt added during cooking or from sauces, a public health campaign is needed to encourage consumers to use less salt. Several countries have already reduced salt intake. The challenge now is to spread this out to all other countries. A modest reduction in population salt intake worldwide will result in a major improvement in public health.
A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions

A photovoltaic (PV) generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement, and environmental friendliness. For large PGS, the probability for partially shaded condition (PSC) to occur is also high. Under PSC, the P-V curve of PGS exhibits multiple peaks, which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods. In this paper, a particle swarm optimization (PSO)-based MPPT algorithm for PGS operating under PSC is proposed. The standard version of PSO is modified to meet the practical consideration of PGS operating under PSC. The problem formulation, design procedure, and parameter setting method which takes the hardware limitation into account are described and explained in detail. The proposed method boasts the advantages such as easy to implement, system-independent, and high tracking efficiency. To validate the correctness of the proposed method, simulation, and experimental results of a 500-W PGS will also be provided to demonstrate the effectiveness of the proposed technique.
Modeling bike availability in a bike-sharing system using machine learning

This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.
Performance Sensitivity of Space Sharing Processor Scheduling in Distributed-Memory Multicomputers

Processor scheduling in distributed-memory systems has received considerable attention in recent years. Several commercial distributed-memory systems use spacesharing processor scheduling. In space-sharing, the set of processors in a system is partitioned and each partition is assigned for the exclusive use of a job. Space-sharing policies can be divided into fixed, static, or dynamic categories. For distributed-memory systems, dynamic policies incur high overhead. Thus, static policies are considered as these policies provide a better performance than the fixed policies. Several static policies have been proposed in the literature. In a previously proposed adaptive static policy, the partition size is a function of the number of queued jobs. This policy, however, tends to underutilize the system resources. To improve the performance of this policy, we propose a new policy in which the partition size is a function of the total number of jobs in the system, as opposed to only the queued jobs. The results presented here demonstrate that the new policy performs substantially better than the original policy for the various workload and system parameters. Another major contribution is the evaluation of the performance sensitivity to job structure, variances in inter-arrival times and job service times, and network topology.
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks

We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a specific short video interval. It exploits both temporal-and spatial-attention mechanisms to selectively focus on visual elements during generation. The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator. We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel. The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively.
Angular Brushing of Extended Parallel Coordinates

In this paper we present angularbrushingfor parallel coordinates (PC) as a new approach to high-light rational dataproperties, i.e., features which depend on two data dimensions (instead of one). We also demonstrate smoothbrushing of PC as an intuitive tool to specify non-binary degreeof-interest functions (then used for F+C visualization). We also shortly describe our implementation as well as its application to the visualization of CFD data.
The development and malleability of executive control abilities

Executive control (EC) generally refers to the regulation of mental activity. It plays a crucial role in complex cognition, and EC skills predict high-level abilities including language processing, memory, and problem solving, as well as practically relevant outcomes such as scholastic achievement. EC develops relatively late in ontogeny, and many sub-groups of developmental populations demonstrate an exaggeratedly poor ability to control cognition even alongside the normal protracted growth of EC skills. Given the value of EC to human performance, researchers have sought means to improve it through targeted training; indeed, accumulating evidence suggests that regulatory processes are malleable through experience and practice. Nonetheless, there is a need to understand both whether specific populations might particularly benefit from training, and what cortical mechanisms engage during performance of the tasks used in the training protocols. This contribution has two parts: in Part I, we review EC development and intervention work in select populations. Although promising, the mixed results in this early field make it difficult to draw strong conclusions. To guide future studies, in Part II, we discuss training studies that have included a neuroimaging component - a relatively new enterprise that also has not yet yielded a consistent pattern of results post-training, preventing broad conclusions. We therefore suggest that recent developments in neuroimaging (e.g., multivariate and connectivity approaches) may be useful to advance our understanding of the neural mechanisms underlying the malleability of EC and brain plasticity. In conjunction with behavioral data, these methods may further inform our understanding of the brain-behavior relationship and the extent to which EC is dynamic and malleable, guiding the development of future, targeted interventions to promote executive functioning in both healthy and atypical populations.
Global optimization of Lipschitz functions

The goal of the paper is to design sequential strategies which lead to efficient optimization of an unknown function under the only assumption that it has a finite Lipschitz constant. We first identify sufficient conditions for the consistency of generic sequential algorithms and formulate the expected minimax rate for their performance. We introduce and analyze a first algorithm called LIPO which assumes the Lipschitz constant to be known. Consistency, minimax rates for LIPO are proved, as well as fast rates under an additional Hölder like condition. An adaptive version of LIPO is also introduced for the more realistic setup where the Lipschitz constant is unknown and has to be estimated along with the optimization. Similar theoretical guarantees are shown to hold for the adaptive algorithm and a numerical assessment is provided at the end of the paper to illustrate the potential of this strategy with respect to state-of-the-art methods over typical benchmark problems for global optimization.
Deep Image Segmentation by Quality Inference

Traditionally, convolutional neural networks are trained for semantic segmentation by having an image given as input and the segmented mask as output. In this work, we propose a neural network trained by being given an image and mask pair, with the output being the quality of that pairing. The segmentation is then created afterwards through backpropagation on the mask. This allows enriching training with semi-supervised synthetic variations on the ground-truth. The proposed iterative segmentation technique allows improving an existing segmentation or creating one from scratch. We compare the performance of the proposed methodology with state-of-the-art deep architectures for image segmentation and achieve competitive results, being able to improve their segmentations.
Platform Competition in Two-Sided Markets : The Case of Payment Networks

In this article, we construct a model to study competing payment networks, where networks offer differentiated products in terms of benefits to consumers and merchants. We study market equilibria for a variety of market structures: duopolistic competition and cartel, symmetric and asymmetric networks, and alternative assumptions about multihoming and consumer preferences. We find that competition unambiguously increases consumer and merchant welfare. We extend this analysis to competition among payment networks providing different payment instruments and find similar results.
Context-Dependent Knowledge Graph Embedding

We consider the problem of embedding knowledge graphs (KGs) into continuous vector spaces. Existing methods can only deal with explicit relationships within each triple, i.e., local connectivity patterns, but cannot handle implicit relationships across different triples, i.e., contextual connectivity patterns. This paper proposes context-dependent KG embedding, a twostage scheme that takes into account both types of connectivity patterns and obtains more accurate embeddings. We evaluate our approach on the tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods.
Mapping the world's photos

We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.
Fitting conic sections to "very scattered" data: An iterative refinement of the bookstein algorithm

An examination of the geometric interpretation of the error-of-fit measure of the Bookstein algorithm for fitting conic sections shows why it may not be entirely satisfactory when the data are “very scattered” in the sense that the data points are distributed rather widely about an underlying smooth curve. A simple iterative refinement of the Bookstein algorithm, similar in spirit to iterative weighted least-squares methods in regression analysis, results in a fitted conic section that approximates the conic that would minimize the sum of squared orthogonal distances of data points from the fitted conic. The usefulness and limitations of the refined algorithm are demonstrated on two different types of “very scattered” data.
Defining Media Enjoyment as the Satisfaction of Intrinsic Needs

This article presents a model of enjoyment rooted in self-determination theory (Deci & Ryan, 1985) that includes the satisfaction of three needs related to psychological wellbeing: autonomy, competence, and relatedness. In an experiment designed to validate this conceptualization of enjoyment, we manipulate video game characteristics related to the satisfaction of these needs and examine their relative effects on enjoyment. The validated model explains 51% of the variance in enjoyment, even without including needs usually studied in relation to enjoyment such as pleasure seeking. Results indicate the utility of defining enjoyment as need satisfaction. These results are discussed in terms of a broader conceptualization of enjoyment represented as the satisfaction of a comprehensive set of functional needs.
Learning spatiotemporal graphs of human activities

Complex human activities occurring in videos can be defined in terms of temporal configurations of primitive actions. Prior work typically hand-picks the primitives, their total number, and temporal relations (e.g., allow only followed-by), and then only estimates their relative significance for activity recognition. We advance prior work by learning what activity parts and their spatiotemporal relations should be captured to represent the activity, and how relevant they are for enabling efficient inference in realistic videos. We represent videos by spatiotemporal graphs, where nodes correspond to multiscale video segments, and edges capture their hierarchical, temporal, and spatial relationships. Access to video segments is provided by our new, multiscale segmenter. Given a set of training spatiotemporal graphs, we learn their archetype graph, and pdf's associated with model nodes and edges. The model adaptively learns from data relevant video segments and their relations, addressing the “what” and “how.” Inference and learning are formulated within the same framework - that of a robust, least-squares optimization - which is invariant to arbitrary permutations of nodes in spatiotemporal graphs. The model is used for parsing new videos in terms of detecting and localizing relevant activity parts. We out-perform the state of the art on benchmark Olympic and UT human-interaction datasets, under a favorable complexity-vs.-accuracy trade-off.
Comparison of Pooling Methods for Handwritten Digit Recognition Problem

Convolutional Neural Networks (CNNs) are used as a current approach to the recognition of handwritten digits for the design of pattern recognition systems. The fact that Convolutional neural networks have a multilayered structure and a large number of items in each layer increases the level of complexity. Increasing the level of complexity makes extremely difficult to discover the optimum configuration for these networks. Modelling the layers of convolutional neural networks independently may be an effective solution to overcome this difficulty and successfully classify images. In this paper, we have investigated the design of the pool layer, which is one of the three basic layers of convoluted neural networks, with optimum techniques. For this purpose primarily a basic convolutional network structure is formed. Then effect of different pooling methods on the classification performance of these networks was investigated using the MNIST dataset. In this study, in addition to the average and maximum pooling methods commonly used in the literature, Mixed pooling, Stochastic pooling, random pooling, Gaussian pooling, median pooling, minimum pooling methods are also used. Experimental results show that the Mixed pooling method has highest accuracy and minimum pooling method has the lowest accuracy .
Skin Cancer Detection and Tracking Using Data Synthesis and Deep Learning

Dermatology is a medical field which stands to be heavily augmented by the use of artificial intelligence techniques. Diseases are visually screened for, and many disease diagnoses are performed strictly with an in-clinic visual examination. Discerning between skin lesions is difficult the difference between skin cancer (melanoma, carcinoma) and benign lesions (nevi, seborrheic keratosis) is minute . With 5.4 million cases of skin cancer diagnosed each year in the United States alone, the need for quick and effective clinical screenings is rising [1, 2]. Patients with skin cancer tend to be afflicted with many moles, and so one of the challenges in skin cancer screenings is identifying them amongst a myriad of benign lesions. Another key element of these diagnoses is based on inspecting temporal changes in lesions a fast changing lesion is more likely to be malignant. As such, patients and providers need tools to support this at scale.
Microscopic and Macroscopic Spatio-Temporal Topic Models for Check-in Data

Twitter, together with other online social networks, such as Facebook, and Gowalla have begun to collect hundreds of millions of check-ins. Check-in data captures the spatial and temporal information of user movements and interests. To model and analyze the spatio-temporal aspect of check-in data and discover temporal topics and regions, we first propose a spatio-temporal topic model, i.e., Upstream Spatio-Temporal Topic Model (USTTM). USTTM can discover temporal topics and regions, i.e., a user’s choice of region and topic is affected by time in this model. We use continuous time to model check-in data, rather than discretized time, avoiding the loss of information through discretization. In addition, USTTM captures the property that user’s interests and activity space will change over time, and users have different region and topic distributions at different times in USTTM. However, both USTTM and other related models capture “microscopic patterns” within a single city, where users share POIs, and cannot discover “macroscopic” patterns in a global area, where users check-in to different POIs. Therefore, we also propose a macroscopic spatio-temporal topic model, MSTTM, employing words of tweets that are shared between cities to learn the topics of user interests. We perform an experimental evaluation on Twitter and Gowalla data sets from New York City and on a Twitter US data set. In our qualitative analysis, we perform experiments with USTTM to discover temporal topics, e.g., how topic “tourist destinations” changes over time, and to demonstrate that MSTTM indeed discovers macroscopic, generic topics. In our quantitative analysis, we evaluate the effectiveness of USTTM in terms of perplexity, accuracy of POI recommendation, and accuracy of user and time prediction. Our results show that the proposed USTTM achieves better performance than the state-of-the-art models, confirming that it is more natural to model time as an upstream variable affecting the other variables. Finally, the performance of the macroscopic model MSTTM is evaluated on a Twitter US dataset, demonstrating a substantial improvement of POI recommendation accuracy compared to the microscopic models.
Getting Formal with Dopamine and Reward

Recent neurophysiological studies reveal that neurons in certain brain structures carry specific signals about past and future rewards. Dopamine neurons display a short-latency, phasic reward signal indicating the difference between actual and predicted rewards. The signal is useful for enhancing neuronal processing and learning behavioral reactions. It is distinctly different from dopamine's tonic enabling of numerous behavioral processes. Neurons in the striatum, frontal cortex, and amygdala also process reward information but provide more differentiated information for identifying and anticipating rewards and organizing goal-directed behavior. The different reward signals have complementary functions, and the optimal use of rewards in voluntary behavior would benefit from interactions between the signals. Addictive psychostimulant drugs may exert their action by amplifying the dopamine reward signal.
Current Sharing Control Strategy for IGBTs Connected in Parallel

This work focuses on current sharing between punch-through insulated gate bipolar transistors (IGBTs) connected in parallel and evaluates the mechanisms that allow overall current balancing. Two different control strategies are presented. These strategies are based on the modification of transistor gate-emitter control voltage VGE by using an active gate driver circuit. The first strategy relies on the calculation of the average value of the current flowing through all parallel-connected IGBTs. The second strategy is proposed by the authors on the basis of a current cross reference control scheme. Finally, the simulation and experimental results of the application of the two current sharing control algorithms are presented.
Information systems interoperability: What lies beneath?

Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various semantic conflicts is proposed. Our proposed framework provides a unified view of the underlying representational and reasoning formalism for the semantic mediation process. This framework is then used as a basis for automating the detection and resolution of semantic conflicts among heterogeneous information sources. We define several types of semantic mediators to achieve semantic interoperability. A domain-independent ontology is used to capture various semantic conflicts. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous databases. A usable prototype is implemented as a proof-of-concept for this work. Finally, the usefulness of our approach is evaluated using three cases in different application domains. Various heterogeneous datasets are used during the evaluation phase. The results of the evaluation suggest that correct identification and construction of both schema and ontology-schema mapping knowledge play very important roles in achieving interoperability at both the data and schema levels.