nils-herrmann/clean_scirepeval huggingface dataset:
Personality predictors of academic outcomes: big five correlates of GPA and SAT scores.

The authors examined relations between the Big Five personality traits and academic outcomes, specifically SAT scores and grade-point average (GPA). Openness was the strongest predictor of SAT verbal scores, and Conscientiousness was the strongest predictor of both high school and college GPA. These relations replicated across 4 independent samples and across 4 different personality inventories. Further analyses showed that Conscientiousness predicted college GPA, even after controlling for high school GPA and SAT scores, and that the relation between Conscientiousness and college GPA was mediated, both concurrently and longitudinally, by increased academic effort and higher levels of perceived academic ability. The relation between Openness and SAT verbal scores was independent of academic achievement and was mediated, both concurrently and longitudinally, by perceived verbal intelligence. Together, these findings show that personality traits have independent and incremental effects on academic outcomes, even after controlling for traditional predictors of those outcomes.
Biophysical mechanisms complementing "classical" cell biology.

This overview addresses phenomena in cell- and molecular biology which are puzzling by their fast and highly coordinated way of organization. Generally, it appears that informative processes probably involved are more on the biophysical than on the classical biochemical side. The coordination problem is explained within the first part of the review by the topic of endogenous electrical phenomena. These are found e.g. in fast tissue organization and reorganization processes like development, wound healing and regeneration. Here, coupling into classical biochemical signaling and reactions can be shown by modern microscopy, electronics and bioinformatics. Further, one can follow the triggered reactions seamlessly via molecular biology till into genetics. Direct observation of intracellular electric processes is very difficult because of e.g. shielding through the cell membrane and damping by other structures. Therefore, we have to rely on photonic and photon - phonon coupling phenomena like molecular vibrations, which are addressed within the second part. Molecules normally possess different charge moieties and thus small electromagnetic (EMF) patterns arise during molecular vibration. These patterns can now be measured best within the optical part of the spectrum - much less in the lower terahertz till kHz and lower Hz part (third part of this review). Finally, EMFs facilitate quantum informative processes in coherent domains of molecular, charge and electron spin motion. This helps to coordinate such manifold and intertwined processes going on within cells, tissues and organs (part 4). Because the phenomena described in part 3 and 4 of the review still await really hard proofs we need concerted efforts and a combination of biophysics, molecular biology and informatics to unravel the described mysteries in "physics of life".
The economics of crowdfunding platforms

This paper provides a description of the crowdfunding sector, considering investment-based crowdfunding platforms as well as platforms in which funders do not obtain monetary payments. It lays out key features of this quickly developing sector and explores the economic forces at play that can explain the design of these platforms. In particular, it elaborates on cross-group and within-group external e¤ects and asymmetric information on crowdfunding platforms.  Crowdfunding, Platform markets, Network e¤ects, Asymmetric information, P2P lending JEL-Classication: L13, D62, G24 Université catholique de Louvain, CORE and Louvain School of Management, and CESifo yRITM, University of Paris Sud and Digital Society Institute zUniversity of Mannheim, Mannheim Centre for Competition and Innovation (MaCCI), and CERRE.
Occupational Therapy Alone Has Looked Upon Occupation: Future Applications of Occupational Science to Pediatric Occupational Therapy.

Occupational therapy has been an invisible profession, largely because the public has had difficulty grasping the concept of occupation. The emergence of occupational science has the potential of improving this situation. Occupational science is firmly rooted in the founding ideas of occupational therapy. In the future, the nature of human occupation will be illuminated by the development of a basic theory of occupational science. Occupational science, through research and theory development, will guide the practice of occupational therapy. Applications of occupational science to the practice of pediatric occupational therapy are presented. Ultimately, occupational science will prepare pediatric occupational therapists to better meet the needs of parents and their children.
Type-2 fuzzified flappy bird control system

In this study, we will present the novel application of Type-2 (T2) fuzzy control into the popular video game called flappy bird. To the best of our knowledge, our work is the first deployment of the T2 fuzzy control into the computer games research area. We will propose a novel T2 fuzzified flappy bird control system that transforms the obstacle avoidance problem of the game logic into the reference tracking control problem. The presented T2 fuzzy control structure is composed of two important blocks which are the reference generator and Single Input Interval T2 Fuzzy Logic Controller (SIT2-FLC). The reference generator is the mechanism which uses the bird's position and the pipes' positions to generate an appropriate reference signal to be tracked. Thus, a conventional fuzzy feedback control system can be defined. The generated reference signal is tracked via the presented SIT2-FLC that can be easily tuned while also provides a certain degree of robustness to system. We will investigate the performance of the proposed T2 fuzzified flappy bird control system by providing comparative simulation results and also experimental results performed in the game environment. It will be shown that the proposed T2 fuzzified flappy bird control system results with a satisfactory performance both in the framework of fuzzy control and computer games. We believe that this first attempt of the employment of T2-FLCs in games will be an important step for a wider deployment of T2-FLCs in the research area of computer games.
Embedding Multimodal Relational Data for Knowledge Base Completion

Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, images, and numerical values. In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Further, using these learned embedings and different neural decoders, we introduce a novel multimodal imputation model to generate missing multimodal values, like text and images, from information in the knowledge base. We enrich existing relational datasets to create two novel benchmarks that contain additional information such as textual descriptions and images of the original entities. We demonstrate that our models utilize this additional information effectively to provide more accurate link prediction, achieving state-of-the-art results with a considerable gap of 5-7% over existing methods. Further, we evaluate the quality of our generated multimodal values via a user study. We have release the datasets and the opensource implementation of our models at https: //github.com/pouyapez/mkbe.
meta-analysis Vitamin D deficiency and depression in adults : systematic review Material

Depression is associated with significant disability, mortality and healthcare costs. It is the third leading cause of disability in high-income countries, 1 and affects approximately 840 million people worldwide. 2 Although biological, psychological and environmental theories have been advanced, 3 the underlying pathophysiology of depression remains unknown and it is probable that several different mechanisms are involved. Vitamin D is a unique neurosteroid hormone that may have an important role in the development of depression. Receptors for vitamin D are present on neurons and glia in many areas of the brain including the cingulate cortex and hippocampus, which have been implicated in the pathophysiology of depression. 4 Vitamin D is involved in numerous brain processes including neuroimmuno-modulation, regulation of neurotrophic factors, neuroprotection, neuroplasticity and brain development, 5 making it biologically plausible that this vitamin might be associated with depression and that its supplementation might play an important part in the treatment of depression. Over two-thirds of the populations of the USA and Canada have suboptimal levels of vitamin D. 6,7 Some studies have demonstrated a strong relationship between vitamin D and depression, 8,9 whereas others have shown no relationship. 10,11 To date there have been eight narrative reviews on this topic, 12–19 with the majority of reviews reporting that there is insufficient evidence for an association between vitamin D and depression. None of these reviews used a comprehensive search strategy, provided inclusion or exclusion criteria, assessed risk of bias or combined study findings. In addition, several recent studies were not included in these reviews. 9,10,20,21 Therefore, we undertook a systematic review and meta-analysis to investigate whether vitamin D deficiency is associated with depression in adults in case–control and cross-sectional studies; whether vitamin D deficiency increases the risk of developing depression in cohort studies in adults; and whether vitamin D supplementation improves depressive symptoms in adults with depression compared with placebo, or prevents depression compared with placebo, in healthy adults in randomised controlled trials (RCTs). We searched the databases MEDLINE, EMBASE, PsycINFO, CINAHL, AMED and Cochrane CENTRAL (up to 2 February 2011) using separate comprehensive strategies developed in consultation with an experienced research librarian (see online supplement DS1). A separate search of PubMed identified articles published electronically prior to print publication within 6 months of our search and therefore not available through MEDLINE. The clinical trials registries clinicaltrials.gov and Current Controlled Trials (controlled-trials.com) were searched for unpublished data. The reference lists …
An investigation on the design and performance assessment of double-PID and LQR controllers for the inverted pendulum

The widespread application of inverted pendulum principles requires better dynamic performance and steady state performance of the inverted pendulum system. The objective of this paper is to design and investigate the time specification performance of the inverted pendulum controllers. Two control methods are proposed in this paper, an innovative double PID control method and a modern LQR (liner quadratic regulator) control method. Dynamic performance and steady state performance are investigated and compared of the two controllers. This paper proves that the LQR controller can guarantee the inverted pendulum a faster and smoother stabilizing process and with better robustness and less oscillation than the double-PID controller. The novelty of this paper is the design of the two controllers, and the adoption of limits cycles as the performance assessment method for the inverted pendulum, which not only makes the steady state performance assessment available, but also, provides an effective way for the evaluation of any equilibrium control problem with friction involved.
Products Components: Alcohols.

Alcohols (CnHn+2OH) are classified into primary, secondary, and tertiary alcohols, which can be branched or unbranched. They can also feature more than one OH-group (two OH-groups = diol; three OH-groups = triol). Presently, except for ethanol and sugar alcohols, they are mainly produced from fossil-based resources, such as petroleum, gas, and coal. Methanol and ethanol have the highest annual production volume accounting for 53 and 91 million tons/year, respectively. Most alcohols are used as fuels (e.g., ethanol), solvents (e.g., butanol), and chemical intermediates.This chapter gives an overview of recent research on the production of short-chain unbranched alcohols (C1-C5), focusing in particular on propanediols (1,2- and 1,3-propanediol), butanols, and butanediols (1,4- and 2,3-butanediol). It also provides a short summary on biobased higher alcohols (>C5) including branched alcohols.
Texture synthesis through convolutional neural networks and spectrum constraints

This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing methods, the presented method inherits from previous CNN approaches the ability to depict local structures and fine scale details, and at the same time yields coherent large scale structures, even in the case of quasi-periodic images. This is done at no extra computational cost. Synthesis experiments on various images show a clear improvement compared to a recent state-of-the art method relying on CNN constraints only.
Systematic review of “ fi lling ” procedures for lip augmentation regarding types of material , outcomes and complications

The ideal lip augmentation technique provides the longest period of efficacy, lowest complication rate, and best aesthetic results. A myriad of techniques have been described for lip augmentation, but the optimal approach has not yet been established. This systematic review with metaregression will focus on the various filling procedures for lip augmentation (FPLA), with the goal of determining the optimal approach.  A systematic search for all English, French, Spanish, German, Italian, Portuguese and Dutch language studies involving FPLA was performed using these databases: Elsevier Science Direct, PubMed, Highwire Press, Springer Standard Collection, SAGE, DOAJ, Sweetswise, Free E-Journals, Ovid Lippincott Williams & Wilkins, Willey Online Library Journals, and Cochrane Plus. The reference section of every study selected through this database search was subsequently examined to identify additional relevant studies.  The database search yielded 29 studies. Nine more studies were retrieved from the reference sections of these 29 studies. The level of evidence ratings of these 38 studies were as follows: level Ib, four studies; level IIb, four studies; level IIIb, one study; and level IV, 29 studies. Ten studies were prospective.  This systematic review sought to highlight all the quality data currently available regarding FPLA. Because of the considerable diversity of procedures, no definitive comparisons or conclusions were possible. Additional prospective studies and clinical trials are required to more conclusively determine the most appropriate approach for this procedure.
Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation

We propose an online, end-to-end, neural generative conversational model for open-domain dialog. It is trained using a unique combination of offline two-phase supervised learning and online human-inthe-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on a diversity-promoting heuristic for response generation and one-character userfeedback at each step. Experiments show that our model inherently promotes the generation of meaningful, relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.
Privacy-preserving k-means clustering over vertically partitioned data

Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for k-means clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites.
NUMERICAL THERMAL ANALYSIS IN ELECTRON BEAM ADDITIVE MANUFACTURING WITH PREHEATING EFFECTS

In an early study, a thermal model has been developed, using finite element simulations, to study the temperature field and response in the electron beam additive manufacturing (EBAM) process, with an ability to simulate single pass scanning only. In this study, an investigation was focused on the initial thermal conditions, redesigned to analyze a critical substrate thickness, above which the preheating temperature penetration will not be affected. Extended studies are also conducted on more complex process configurations, such as multi-layer raster scanning, which are close to actual operations, for more accurate representations of the transient thermal phenomenon.
A Survey on Spatio-Temporal Data Mining

Data mining is the process of searching valuable information by analyzing large volumes of data through automatic or semiautomatic means to discover meaningful patterns and rules. The field of spatio-temporal data mining is concerned with such analysis in the case of spatial and temporal interdependencies. Many interesting techniques of spatio-temporal data mining are proposed and shown to be useful in many applications. Spatiotemporal data mining brings together techniques from different fields such as machine learning, statistics and databases. Here, we present an overview of spatio-temporal data mining and discuss its various tasks and techniques in detail. We have also listed a few research issues of spatio-temporal data mining.
Two Structures for Compositionally Derived Events

This paper addresses the phenomenon of event composition: t he derivation of a single event description expressed in one clause from two le xical heads which could have been used in the description of independent events, eac h expressed in a distinct clause. In English, this phenomenon is well attested with re spect to sentences whose verb is found in combination with an XP describing a result no t strictly lexically entailed by this verb, as in (1).
Morphological Word-Embeddings

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semisupervised learning, encouraging the vectors to encode a word’s morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study.
Sentence-Level Polarity Detection in a Computer Corpus

The paper presents a preliminary research on possible relations between the syntactic structure and the polarity of a Czech sentence by means of the so-called sentiment analysis of a computer corpus. The main goal of sentiment analysis is the detection of a positive or negative polarity, or neutrality of a sentence (or, more broadly, a text). Most often this process takes place by looking for the polarity items, i.e. words or phrases inherently bearing positive or negative values. These words (phrases) are collected in the subjectivity lexicons and implemented into a computer corpus. However, when using sentences as the basic units to which sentiment analysis is applied, it is always important to look at their semantic and morphological analysis, since polarity items may be influenced by their morphological context. It is expected that some syntactic (and hypersyntactic) relations are useful for the identification of sentence polarity, such as negation, discourse relations or the level of embeddedness of the polarity item in the structure. Thus, we will propose such an analysis for a convenient source of data, the richly annotated Prague Dependency Treebank.
Improving security decisions with polymorphic and audited dialogs

Context-sensitive guidance (CSG) can help users make better security decisions. Applications with CSG ask the user to provide relevant context information. Based on such information, these applications then decide or suggest an appropriate course of action. However, users often deem security dialogs irrelevant to the tasks they are performing and try to evade them. This paper contributes two new techniques for hardening CSG against automatic and false user answers. Polymorphic dialogs continuously change the form of required user inputs and intentionally delay the latter, forcing users to pay attention to security decisions. Audited dialogs thwart false user answers by (1) warning users that their answers will be forwarded to auditors, and (2) allowing auditors to quarantine users who provide unjustified answers. We implemented CSG against email-borne viruses on the Thunderbird email agent. One version, CSG-PD, includes CSG and polymorphic dialogs. Another version, CSG-PAD, includes CSG and both polymorphic and audited dialogs. In user studies, we found that untrained users accept significantly less unjustified risks with CSG-PD than with conventional dialogs. Moreover, they accept significantly less unjustified risks with CSG-PAD than with CSG-PD. CSG-PD and CSG-PAD have insignificant effect on acceptance of justified risks.
GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification

SemEval 2018 Task 7 focuses on relation extraction and classification in scientific literature. In this work, we present our tree-based LSTM network for this shared task. Our approach placed 9th (of 28) for subtask 1.1 (relation classification), and 5th (of 20) for subtask 1.2 (relation classification with noisy entities). We also provide an ablation study of features included as input to the network.
DISCOVER: Keyword Search in Relational Databases

DISCOVER operates on relational databases and facilitates information discovery on them by allowing its user to issue keyword queries without any knowledge of the database schema or of SQL. DISCOVER returns qualified joining networks of tuples, that is, sets of tuples that are associated because they join on their primary and foreign keys and collectively contain all the keywords of the query. DISCOVER proceeds in two steps. First the Candidate Network Generator generates all candidate networks of relations, that is, join expressions that generate the joining networks of tuples. Then the Plan Generator builds plans for the efficient evaluation of the set of candidate networks, exploiting the opportunities to reuse common subexpressions of the candidate networks. We prove that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema. We prove that the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete. We provide a greedy algorithm and we show that it provides near-optimal plan execution time cost. Our experimentation also provides hints on tuning the greedy algorithm.