nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating Relational Databases by Leveraging Remote Memory and RDMA

Memory is a crucial resource in relational databases (RDBMSs). When there is insufficient memory, RDBMSs are forced to use slower media such as SSDs or HDDs, which can significantly degrade workload performance. Cloud database services are deployed in data centers where network adapters supporting remote direct memory access (RDMA) at low latency and high bandwidth are becoming prevalent. We study the novel problem of how a Symmetric Multi-Processing (SMP) RDBMS, whose memory demands exceed locally-available memory, can leverage available remote memory in the cluster accessed via RDMA to improve query performance. We expose available memory on remote servers using a lightweight file API that allows an SMP RDBMS to leverage the benefits of remote memory with modest changes. We identify and implement several novel scenarios to demonstrate these benefits, and address design challenges that are crucial for efficient implementation. We implemented the scenarios in Microsoft SQL Server engine and present the first end-to-end study to demonstrate benefits of remote memory for a variety of micro-benchmarks and industry-standard benchmarks. Compared to using disks when memory is insufficient, we improve the throughput and latency of queries with short reads and writes by 3X to 10X, while improving the latency of multiple TPC-H and TPC-DS queries by 2X to 100X.
The SP Theory of Intelligence: Benefits and Applications

This article describes existing and expected benefits of the SP theory of intelligence, and some potential applications. The theory aims to simplify and integrate ideas across artificial intelligence, mainstream computing, and human perception and cognition, with information compression as a unifying theme. It combines conceptual simplicity with descriptive and explanatory power across several areas of computing and cognition. In the SP machine—an expression of the SP theory which is currently realized in the form of a computer model—there is potential for an overall simplification of computing systems, including software. The SP theory promises deeper insights and better solutions in several areas of application including, most notably, unsupervised learning, natural language processing, autonomous robots, computer vision, intelligent databases, software engineering, information compression, medical diagnosis and big data. There is also potential in areas such as the semantic web, bioinformatics, structuring of documents, the detection of computer viruses, data fusion, new kinds of computer, and the development of scientific theories. The theory promises seamless integration of structures and functions within and between different areas of application. The potential value, worldwide, of these benefits and applications is at least $190 billion each year. Further development would be facilitated by the creation of a high-parallel, open-source version of the SP machine, available to researchers everywhere.
Health impact assessment of the UK soft drinks industry levy: a comparative risk assessment modelling study

In March, 2016, the UK Government proposed a tiered levy on sugar-sweetened beverages (SSBs; high tax for drinks with >8 g of sugar per 100 mL, moderate tax for 5-8 g, and no tax for <5 g). We estimate the effect of possible industry responses to the levy on obesity, diabetes, and dental caries.



We modelled three possible industry responses: reformulation to reduce sugar concentration, an increase of product price, and a change of the market share of high-sugar, mid-sugar, and low-sugar drinks. For each response, we defined a better-case and worse-case health scenario. We developed a comparative risk assessment model to estimate the UK health impact of each scenario on prevalence of obesity and incidence of dental caries and type 2 diabetes. The model combined data for sales and consumption of SSBs, disease incidence and prevalence, price elasticity estimates, and estimates of the association between SSB consumption and disease outcomes. We drew the disease association parameters from a meta-analysis of experimental studies (SSBs and weight change), a meta-analysis of prospective cohort studies (type 2 diabetes), and a prospective cohort study (dental caries).



The best modelled scenario for health is SSB reformulation, resulting in a reduction of 144 383 (95% uncertainty interval 5102-306 743; 0·9%) of 15 470 813 adults and children with obesity in the UK, 19 094 (6920-32 678; incidence reduction of 31·1 per 100 000 person-years) fewer incident cases of type 2 diabetes per year, and 269 375 (82 211-470 928; incidence reduction of 4·4 per 1000 person-years) fewer decayed, missing, or filled teeth annually. An increase in the price of SSBs in the better-case scenario would result in 81 594 (3588-182 669; 0·5%) fewer adults and children with obesity, 10 861 (3899-18 964; 17·7) fewer incident cases of diabetes per year, and 149 378 (45 231-262 013; 2·4) fewer decayed, missing, or filled teeth annually. Changes to market share to increase the proportion of low-sugar drinks sold in the better-case scenario would result in 91 042 (4289-204 903; 0·6%) fewer adults and children with diabetes, 1528 (4414-21 785; 19·7) fewer incident cases of diabetes per year, and 172 718 (47 919-294 499; 2·8) fewer decayed, missing, or filled teeth annually. The greatest benefit for obesity and oral health would be among individuals aged younger than 18 years, with people aged older than 65 years having the largest absolute decreases in diabetes incidence.



The health impact of the soft drinks levy is dependent on its implementation by industry. Uncertainty exists as to how industry will react and about estimation of health outcomes. Health gains could be maximised by substantial product reformulation, with additional benefits possible if the levy is passed on to purchasers through raising of the price of high-sugar and mid-sugar drinks and activities to increase the market share of low-sugar products.
The influence of perceived value on purchase intention in social commerce context

This study aims to examine the effects of perceived benefits, i.e. utilitarian value, hedonic value and social value, as well as perceived risk, on purchase intention in social commerce context.  To cast light on the factors motivating users’ intention to purchase in the context of social commerce, 277 users of social commerce in China were collected via an online survey.  Results show that satisfaction significantly and positively affects users’ purchase intention in social commerce context. In addition, utilitarian, hedonic and social values have significant and positive impacts on satisfaction and purchase intention; and utilitarian value is found to be the most salient factor influencing purchase intention, while hedonic value has the greatest effect on satisfaction. Moreover, perceived risk significantly and negatively affects satisfaction.  Extant research on social commerce has mainly focused on investigating how the general perceived value affects user behavior, but has less considered different dimensions of perceived value. Also, prior studies have explored the roles of utilitarian and hedonic values on user behavior; however, there is a lack of research on the effect of social value. The current study attempts to fill these research gaps.
The Application of Neural Networks , Image Processing and Cad-based Environments Facilities in Automatic Road Extraction and Vectorization from High Resolution Satellite Images

In this article a new procedure that was designed to extract road centerline from high resolution satellite images, is presented. The results (road Networks) are fully structured in vector formed in Computer Aided Design (CAD) based system that could be used in Geographical Information System (GIS) with minimum edit. The designed procedure is the combination of image processing algorithms and exploiting CAD-based facilities. In the first step, artificial neural networks are used to discriminate between road and non-road pixels. Then road centerlines are extracted using image processing algorithms such as morphological operators, and a road raster map is produced. Some cleaning algorithms were designed to reduce the existing noises and improve the obtained results. Finally, the edited raster map was vectorized using the CAD-based facilities. Obtained results showed that the structured vector based road centerlines are confirming when compared with road network in the reference map.  Corresponding author.
A Cloud Qur'an Application Using Drupal Technology

The paper aims to develop and design a cloud-based Quran portal using Drupal technology and make it available in multiple services. The portal can be hosted on cloud and users around the world can access it using any Internet enabled device. The proposed portal includes different features to become a center of learning resources for various users. The portal is further designed to promote research and development of new tools and applications includes Application Programming Interface (API) and Search API, which exposes the search to public, and make the searching Quran efficient and easy. The cloud application can request various surah or ayah using the API and by passing filter.
Stance Detection for Fake News Identification

The latest election cycle generated sobering examples of the threat that fake news poses to democracy. Primarily disseminated by hyper-partisan media outlets, fake news proved capable of becoming viral sensations that can dominate social media and influence elections. To address this problem, we begin with stance detection, which is a first step towards identifying fake news. The goal of this project is to identify whether given headline-article pairs: (1) agree, (2) disagree, (3) discuss the same topic, or (4) are not related at all, as described in . Our method feeds the headline-article pairs into a bidirectional LSTM which first analyzes the article and then uses the acquired article representation to analyze the headline. On top of the output of the conditioned bidirectional LSTM, we concatenate global statistical features extracted from the headline-article pairs. We report a 9.7% improvement in the Fake News Challenge evaluation metric and a 22.7% improvement in mean F1 compared to the highest scoring baseline. We also present qualitative results that show how our method outperforms state-of-the art algorithms on this challenge.
A survey of the sorghum transcriptome using single-molecule long reads

Alternative splicing and alternative polyadenylation (APA) of pre-mRNAs greatly contribute to transcriptome diversity, coding capacity of a genome and gene regulatory mechanisms in eukaryotes. Second-generation sequencing technologies have been extensively used to analyse transcriptomes. However, a major limitation of short-read data is that it is difficult to accurately predict full-length splice isoforms. Here we sequenced the sorghum transcriptome using Pacific Biosciences single-molecule real-time long-read isoform sequencing and developed a pipeline called TAPIS (Transcriptome Analysis Pipeline for Isoform Sequencing) to identify full-length splice isoforms and APA sites. Our analysis reveals transcriptome-wide full-length isoforms at an unprecedented scale with over 11,000 novel splice isoforms. Additionally, we uncover APA of ∼11,000 expressed genes and more than 2,100 novel genes. These results greatly enhance sorghum gene annotations and aid in studying gene regulation in this important bioenergy crop. The TAPIS pipeline will serve as a useful tool to analyse Iso-Seq data from any organism.
An Augmented Reality Delivery Simulator for Medical Training

This paper presents the extension of a birth simulator for medical training with an augmented reality system. The system presents an add-on of the user interface for our previous work on a mixed reality delivery simulator system . This simulation system comprised direct haptic and auditory feedback, and provided important physiological data including values of blood pressure, heart rates, pain and oxygen supply, necessary for training physicians. Major drawback of the system was the indirect viewing of both the virtual models and the final delivery process. The current paper extends the existing system by bringing in the in-situ visualization. This plays an important role in increasing the efficiency of the training, since the physician now concentrates on the vaginal delivery rather than the remote computer screen. In addition, forceps are modeled and an external optical tracking system is integrated in order to provide visual feedback while training with the simulator for complicated procedures such as forceps delivery.
The relationship between social network usage and some personality traits

The aim of the research, is to examine the relationship between adolescents' five-factor personality features by use of Social Media. As for sample, there are 548 girl and 441 boy students and they are between the ages of 11-18. Adolescents’ data participating in the study, are determined by Big Five Factor personality traits Scale. Prepared data on the use of social media called "Personal Information Form" has been obtained by researcher. In the analysis of data, understanding of social media use times whether it differs according to big five personality traits, According to the social media using time, there was no significant difference between the agreeableness and openness subscales. On the other hand, there is a significant differences between conscientiousness, extraversion and neuroticism. In association with five personality traits of social media purpose, it was found that there are significant differences with different personality traits for each purpose.
Root traits contributing to plant productivity under drought

Geneticists and breeders are positioned to breed plants with root traits that improve productivity under drought. However, a better understanding of root functional traits and how traits are related to whole plant strategies to increase crop productivity under different drought conditions is needed. Root traits associated with maintaining plant productivity under drought include small fine root diameters, long specific root length, and considerable root length density, especially at depths in soil with available water. In environments with late season water deficits, small xylem diameters in targeted seminal roots save soil water deep in the soil profile for use during crop maturation and result in improved yields. Capacity for deep root growth and large xylem diameters in deep roots may also improve root acquisition of water when ample water at depth is available. Xylem pit anatomy that makes xylem less "leaky" and prone to cavitation warrants further exploration holding promise that such traits may improve plant productivity in water-limited environments without negatively impacting yield under adequate water conditions. Rapid resumption of root growth following soil rewetting may improve plant productivity under episodic drought. Genetic control of many of these traits through breeding appears feasible. Several recent reviews have covered methods for screening root traits but an appreciation for the complexity of root systems (e.g., functional differences between fine and coarse roots) needs to be paired with these methods to successfully identify relevant traits for crop improvement. Screening of root traits at early stages in plant development can proxy traits at mature stages but verification is needed on a case by case basis that traits are linked to increased crop productivity under drought. Examples in lesquerella (Physaria) and rice (Oryza) show approaches to phenotyping of root traits and current understanding of root trait genetics for breeding.
Electrophysiological observations on the palmomental reflex in normal neonates

The authors report normative data on the electrical response (EMG response) recorded from the mentoneal muscles by repetitive mechanical stimulation of the thenar eminence surface of the hand in 18 neonates, 1 to 6 days old: the so-called palmomental reflex (PMR) or palm-chin reflex. PMR potentials showed in most cases great variability in amplitude and duration but not in latency, and they were present in all but one of the cases. The PMR was monolateral in one case only. Evoked potentials from the scalp were also recorded after electrical stimulation of the thenar eminence. The afferent branch of the PMR is constituted of impulses originating mainly from median nerve, skin and muscle receptors. Such impulses may reach facial motor nuclei following corticofugal pathways throughout a long-loop circuit.
Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis

An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. Conceptual and emotional mechanisms of languages are considered here along with their functions in the mind and cultural evolution. A fundamental contradiction in human mind is considered: language evolution requires reduced emotionality, but “too low” emotionality makes language “irrelevant to life,” disconnected from sensory-motor experience. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the language instinct, the dual model connecting language and cognition, dynamic logic, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Approximate equations for evolution of human minds and cultures are obtained. Their solutions identify three types of cultures: "conceptual"-pragmatic cultures, in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; “traditional-emotional” cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and “multi-cultural” societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.
Adjustable Real-time Style Transfer

Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn’t have control over the synthesized output. Moreover, the style transfer depends on the hyper-parameters of the model with varying “optimum” for different input images. Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization. In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters. These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image. Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyperparameters. We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.
An Object Co-occurrence Assisted Hierarchical Model for Scene Understanding

Hierarchical methods have been widely explored for object recognition, which is a critical component of scene understanding. However, few existing works are able to model the contextual information (e.g., objects co-occurrence) explicitly within a single coherent framework for scene understanding. Towards this goal, in this paper we propose a novel three-level (superpixel level, object level and scene level) hierarchical model to address the scene categorization problem. Our proposed model is a coherent probabilistic graphical model that captures the object co-occurrence information for scene understanding with a probabilistic chain structure. The efficacy of the proposed model is demonstrated by conducting experiments on the LabelMe dataset.
Husky: Towards a More Efficient and Expressive Distributed Computing Framework

Finding efficient, expressive and yet intuitive programming models for data-parallel computing system is an important and open problem. Systems like Hadoop and Spark have been widely adopted for massive data processing, as coarse-grained primitives like map and reduce are succinct and easy to master. However, sometimes over-simplified API hinders programmers from more fine-grained control and designing more efficient algorithms. Developers may have to resort to sophisticated domain-specific languages (DSLs), or even low-level layers like MPI, but this raises development cost— learning many mutually exclusive systems prolongs the development schedule, and the use of low-level tools may result in bugprone programming. This motivated us to start the Husky open-source project, which is an attempt to strike a better balance between high performance and low development cost. Husky is developed mainly for inmemory large scale data mining, and also serves as a general research platform for designing efficient distributed algorithms. We show that many existing frameworks can be easily implemented and bridged together inside Husky, and Husky is able to achieve similar or even better performance compared with domain-specific systems.
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

We present a method for the sparse greedy approximation of Bayesian Gaussian process regression, featuring a novel heuristic for very fast forward selection. Our method is essentially as fast as an equivalent one which selects the “support” patterns at random, yet it can outperform random selection on hard curve fitting tasks. More importantly, it leads to a sufficiently stable approximation of the log marginal likelihood of the training data, which can be optimised to adjust a large number of hyperparameters automatically. We demonstrate the model selection capabilities of the algorithm in a range of experiments. In line with the development of our method, we present a simple view on sparse approximations for GP models and their underlying assumptions and show relations to other methods.