nils-herrmann/clean_scirepeval huggingface dataset:
A quantitative stock prediction system based on financial news

We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText). The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText’s predictions against existing quantitative funds and human stock pricing experts. We approach this line of research using textual representation and statistical machine learning methods on financial news articles partitioned by similar industry and sector groupings. Through our research, we discovered that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score of 0.1954, predicted Directional Accuracy of 71.18% and a Simulated Trading return of 8.50% (compared to 5.62% for the S&P 500 index). In direct comparisons to existing market experts and quantitative mutual funds, our system’s trading return of 8.50% outperformed well-known trading experts. Our system also performed well against the top 10 quantitative mutual funds of 2005, where our system would have placed fifth. When comparing AZFinText against only those quantitative funds that monitor the same securities, AZFinText had a 2% higher return than the best performing quant fund.
TweetLDA: supervised topic classification and link prediction in Twitter

L-LDA is a new supervised topic model for assigning "topics" to a collection of documents (e.g., Twitter profiles). User studies have shown that L-LDA effectively performs a variety of tasks in Twitter that include not only assigning topics to profiles, but also re-ranking feeds, and suggesting new users to follow. Building upon these promising qualitative results, we here run an extensive quantitative evaluation of L-LDA. We test the extent to which, compared to the competitive baseline of Support Vector Machines (SVM), L-LDA is effective at two tasks: 1) assigning the correct topics to profiles; and 2) measuring the similarity of a profile pair. We find that L-LDA generally performs as well as SVM, and it clearly outperforms SVM when training data is limited, making it an ideal classification technique for infrequent topics and for (short) profiles of moderately active users. We have also built a web application that uses L-LDA to classify any given profile and graphically map predominant topics in specific geographic regions.
Recognizing entailments in legal texts using sentence encoding-based and decomposable attention models

This paper presents an end-to-end question answering system for legal texts. This system includes two main phases. In the first phase, our system will retrieve articles from Japanese Civil Code that are relevant with the given question using the cosine distance after the given question and articles are converted into vectors using TF-IDF weighting scheme. Then, a ranking model can be applied to re-rank these retrieved articles using a learning to rank algorithm and annotated corpus. In the second phase, we adapted two deep learning models, which has been proposed for the Natural language inference task, to check the entailment relationship between a question and its related articles including a sentence encoding-based model and a decomposable attention model. Experimental results show that our approaches can be a promising approach for information extraction/entailment in legal texts.
A Survey on Collaborative Deep Learning and Privacy-Preserving

In the era of big data, the amount of data that individuals and enterprises hold is increasing, and the efficiency and effectiveness of data analysis are increasingly demanding. Collaborative deep learning, as a machine learning framework that can share users' data and improve learning efficiency, has drawn more and more attention and started to be applied in practical problems. In collaborative deep learning, data sharing and interaction among multi users may lead data leakage especially when data are very sensitive to the user. Therefore, how to protect the data privacy when processing collaborative deep learning becomes an important problem. In this paper, we review the current state of art researches in this field and summarize the application of privacy-preserving technologies in two phases of collaborative deep learning. Finally we discuss the future direction and trend on this problem.
A Predictive Approach Using Deep Feature Learning for Electronic Medical Records: A Comparative Study

Massive amount of electronic medical records accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to extract knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. In this paper, we propose a new predictive approach based on feature representation using deep feature learning and word embedding techniques. Our method uses different deep architectures for feature representation in higher-level abstraction to obtain effective and more robust features from EMRs, and then build prediction models on the top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled one is scarce. We investigate the performance of representation learning through a supervised approach. First, we apply our method on a small dataset related to a specific precision medicine problem, which focuses on prediction of left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk in a vulnerable demographic subgroup (African-Americans). Then we use two large datasets from eICU collaborative research database to predict the length of stay in Cardiac-ICU and Neuro-ICU based on high dimensional features. Finally we provide a comparative study and show that our predictive approach leads to better results in comparison with others.
Identifying the Academic Rising Stars

Predicting the fast-rising young researchers (Academic Rising Stars) in the future provides useful guidance to the research community, e.g., offering competitive candidates to university for young faculty hiring as they are expected to have success academic careers. In this work, given a set of young researchers who have published the first first-author paper recently, we solve the problem of how to effectively predict the top k% researchers who achieve the highest citation increment in ∆t years. We explore a series of factors that can drive an author to be fast-rising and design a novel impact increment ranking learning (IIRL) algorithm that leverages those factors to predict the academic rising stars. Experimental results on the large ArnetMiner dataset with over 1.7 million authors demonstrate the effectiveness of IIRL. Specifically, it outperforms all given benchmark methods, with over 8% average improvement. Further analysis demonstrates that the prediction models for different research topics follow the similar pattern. We also find that temporal features are the best indicators for rising stars prediction, while venue features are less relevant.
Computational mass spectrometry for small molecules

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.
Compact Quad-Band Polarization-Insensitive Ultrathin Metamaterial Absorber With Wide Angle Stability

In this letter, a compact ultrathin quad-band polarization-insensitive metamaterial absorber with a wide angle of absorption is proposed. The unit cell of the proposed structure comprises conductive cross dipoles loaded with split-ring resonators. The proposed absorber exhibits simulated peak absorption of <inline-formula> <tex-math notation="LaTeX">$\text{96.15}\% $</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX"> $\text{99.17}\% $</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{99.75}\%,$</tex-math> </inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{98.75}\% $</tex-math></inline-formula> at 3.68, 8.58, 10.17, and 14.93 GHz, respectively. The proposed multiband absorber is ultrathin and compact in configuration with a thickness of <inline-formula><tex-math notation="LaTeX">$\text{0.0122}\,\lambda $</tex-math> </inline-formula> and a unit cell size of 0.122 <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math> </inline-formula> (corresponding to the lowest frequency). Moreover, by understanding the interaction of the unit cell with incident electromagnetic radiation, a conceptual equivalent circuit model is developed, which is used to understand the influence of coupling on the quad band of absorption. The simulated response of the proposed design demonstrates that it has quad-band polarization-insensitive absorption characteristics. In addition, the proposed absorber shows high absorption for an oblique incidence angle up to <inline-formula><tex-math notation="LaTeX">$6{0^ \circ }$</tex-math></inline-formula> for both transverse-electric and transverse-magnetic polarizations.
RNN Approaches to Text Normalization: A Challenge

This paper presents a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. We present a data set of general text where the normalizations were generated using an existing text normalization component of a text-to-speech system. This data set will be released open-source in the near future. We also present our own experiments with this data set with a variety of different RNN architectures. While some of the architectures do in fact produce very good results when measured in terms of overall accuracy, the errors that are produced are problematic, since they would convey completely the wrong message if such a system were deployed in a speech application. On the other hand, we show that a simple FST-based filter can mitigate those errors, and achieve a level of accuracy not achievable by the RNN alone. Though our conclusions are largely negative on this point, we are actually not arguing that the text normalization problem is intractable using an pure RNN approach, merely that it is not going to be something that can be solved merely by having huge amounts of annotated text data and feeding that to a general RNN model. Andwhenwe open-source our data, we will be providing a novel data set for sequenceto-sequence modeling in the hopes that the the community can find better solutions.
IMPACTS OF JOB PERFORMANCE LEVEL ON NURSES IN PUBLIC SECTOR HOSPITALS

Job performance refers to how effective employees a r in accomplishing their tasks and responsibilitie s related to direct patient care. Improving the perfo rmance of employees has been a topic of great inter est to practitioners as well as researchers. The aim of th e s udy is to analysis the impacts of job performan ce level on nurses’ performance working in public hospitals. In order to achieve the study objective, a survey conducted. Questionnaires distributed to the public sector hospital’s manager in Saudi Arabia. The fin dings of the study turn out to be true; the study will co ntribute to both theory and practice. Through the p r sent study, the researcher expects the findings to shed light on the research conducted regression to analy sis the impacts of job performance level on nurses’ in publ ic sector hospitals in Saudi Arabia.
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization

Recently, there has been a lot of activity in learning distributed representations of words in vector spaces. Although there are models capable of learning high-quality distributed representations of words, how to generate vector representations of the same quality for phrases or documents still remains a challenge. In this paper, we propose to model each document as a multivariate Gaussian distribution based on the distributed representations of its words. We then measure the similarity between two documents based on the similarity of their distributions. Experiments on eight standard text categorization datasets demonstrate the effectiveness of the proposed approach in comparison with stateof-the-art methods.
Ontology-Based Interface Specifications for a NLP Pipeline Architecture

The high level of heterogeneity between linguistic annotations usually complic ates the interoperability of processing modules within an NLP pipeline. In this paper, a framework for the interoperation of NLP co mp nents, based on a data-driven architecture, is presented. Here, ontologies of linguistic annotation are employed to provide a conceptu al basis for the tag-set neutral processing of linguistic annotations. The framework proposed here is based on a set of struc tured OWL ontologies: a reference ontology, a set of annotation models which formalize different annotation schemes, and a declarativ e linking between these, specified separately. This modular architecture is particularly scalable and flexible as it allows for the integration of different reference ontologies of linguistic annotations in order to overcome the absence of a consensus for an ontology of ling uistic terminology. Our proposal originates from three lines of research from different fields: research on annotation type systems in UIMA; the ontological architecture OLiA, originally developed for sustainable documentation and annotation-independent corpus browsin g, and the ontologies of the OntoTag model, targeted towards the processing of linguistic annotations in Semantic Web applications. We describ how UIMA annotations can be backed up by ontological specifications of annotation schemes as in the OLiA model, and how these ar e linked to the OntoTag ontologies, which allow for further ontological processing.
Conceptual issues concerning mediation , interventions and composition

Concepts concerning mediation in the causal inference literature are reviewed. Notions of direct and indirect effects from a counterfactual approach to mediation are compared with those arising from the standard regression approach to mediation of Baron and Kenny (1986), commonly utilized in the social science literature. It is shown that concepts of direct and indirect effect from causal inference generalize those described by Baron and Kenny and that under appropriate identification assumptions these more general direct and indirect effects from causal inference can be estimated using regression even when there are interactions between the primary exposure of interest and the mediator. A number of conceptual issues are discussed concerning the interpretation of identification conditions for mediation, the notion of counterfactuals based on hypothetical interventions and the so called consistency and composition assumptions.
The history of twins, as a criterion of the relative powers of nature and nurture (1,2).

In the article ?The History of Twins, As a Criterion of the Relative Powers of Nature and Nurture,? Francis Galton  describes his study of twins. Published in 1875 in Fraser?s Magazine in London, England, the article lays out Galton?s use of twins to examine and distinguish between the characteristics people have at birth and the characteristics they receive from the circumstances of life and experience. Galton calls those factors nature and nurture. Based on his study, Galton concluded that nature has a larger effect than nurture on development. By studying twins, Galton introduced a way to examine the effects of nature and nurture in people who were born with similar traits, which allowed him to focus on the effects of experience on a person?s development.
Bootstrapping Dialog Systems with Word Embeddings

One of the main tasks of a dialog system is to assign intents to user utterances, which is a form of text classification. Since intent labels are application-specific, bootstrapping a new dialog system requires collecting and annotating in-domain data. To minimize the need for a long and expensive data collection process, we explore ways to improve the performance of dialog systems with very small amounts of training data. In recent years, word embeddings have been shown to provide valuable features for many different language tasks. We investigate the use of word embeddings in a text classification task with little training data. We find that count and vector features complement each other and their combination yields better results than either type of feature alone. We propose a simple alternative, vector extrema, to replace the usual averaging of a sentence’s vectors. We show how taking vector extrema is well suited for text classification and compare it against standard vector baselines in three different applications.
Genotypes and Haplotypes of the Estrogen Receptor α Gene (ESR1) Are Associated With Female-to-Male Gender Dysphoria.

Gender dysphoria, a marked incongruence between one's experienced gender and biological sex, is commonly believed to arise from discrepant cerebral and genital sexual differentiation. With the discovery that estrogen receptor β is associated with female-to-male (FtM) but not with male-to-female (MtF) gender dysphoria, and given estrogen receptor α involvement in central nervous system masculinization, it was hypothesized that estrogen receptor α, encoded by the ESR1 gene, also might be implicated.



To investigate whether ESR1 polymorphisms (TA)n-rs3138774, PvuII-rs2234693, and XbaI-rs9340799 and their haplotypes are associated with gender dysphoria in adults.



Molecular analysis was performed in peripheral blood samples from 183 FtM subjects, 184 MtF subjects, and 394 sex- and ethnically-matched controls.



Genotype and haplotype analyses of the (TA)n-rs3138774, PvuII-rs2234693, and XbaI-rs9340799 polymorphisms.



Allele and genotype frequencies for the polymorphism XbaI were statistically significant only in FtM vs control XX subjects (P = .021 and P = .020). In XX individuals, the A/G genotype was associated with a low risk of gender dysphoria (odds ratio [OR] = 0.34; 95% CI = 0.16-0.74; P = .011); in XY individuals, the A/A genotype implied a low risk of gender dysphoria (OR = 0.39; 95% CI = 0.17-0.89; P = .008). Binary logistic regression showed partial effects for all three polymorphisms in FtM but not in MtF subjects. The three polymorphisms were in linkage disequilibrium: a small number of TA repeats was linked to the presence of PvuII and XbaI restriction sites (haplotype S-T-A), and a large number of TA repeats was linked to the absence of these restriction sites (haplotype L-C-G). In XX individuals, the presence of haplotype L-C-G carried a low risk of gender dysphoria (OR = 0.66; 95% CI = 0.44-0.99; P = .046), whereas the presence of haplotype L-C-A carried a high susceptibility to gender dysphoria (OR = 3.96; 95% CI = 1.04-15.02; P = .044). Global haplotype was associated with FtM gender dysphoria (P = .017) but not with MtF gender dysphoria.



XbaI-rs9340799 is involved in FtM gender dysphoria in adults. Our findings suggest different genetic programs for gender dysphoria in men and women. Cortés-Cortés J, Fernández R, Teijeiro N, et al. Genotypes and Haplotypes of the Estrogen Receptor α Gene (ESR1) Are Associated With Female-to-Male Gender Dysphoria.  2017;14:464-472.
Virtual Reality : Opportunities and Challenges

Virtual Reality systems have drawn much attention by researchers and companies in the last few years. Virtual Reality is a term that applies to computer-simulated environments that can simulate physical presence in places in the real world, as well as in imaginary worlds. Interactivity and its captivating power, contribute to the feeling of being the part of the action on the virtual safe environment, without any real danger. So, Virtual Reality has been a promising technology applicable in various domains of application such as training simulators, medical and health care, education, scientific visualization, and entertainment industry. Virtual reality can lead to state of the art technologies like Second Life, too. Like many advantageous technologies, beside opportunities of Virtual Reality and Second Life, inevitable challenges appear, too. This paper is a technical brief on Virtual Reality technology and its opportunities and challenges in different areas.
Deep Belief Networks with Feature Selection for Sentiment Classification

Due to the complexity of human languages, most of sentiment classification algorithms are suffered from a huge-scale dimension of vocabularies which are mostly noisy and redundant. Deep Belief Networks (DBN) tackle this problem by learning useful information in input corpus with their several hidden layers. Unfortunately, DBN is a time-consuming and computationally expensive process for large-scale applications. In this paper, a semi-supervised learning algorithm, called Deep Belief Networks with Feature Selection (DBNFS) is developed. Using our chi-squared based feature selection, the complexity of the vocabulary input is decreased since some irrelevant features are filtered which makes the learning phase of DBN more efficient. The experimental results of our proposed DBNFS shows that the proposed DBNFS can achieve higher classification accuracy and can speed up training time compared with others well-known semi-supervised learning algorithms.
Corpus-Oriented Grammar Development for Acquiring a Head-Driven Phrase Structure Grammar from the Penn Treebank

This paper describes a method of semi-automatically acquiring an English HPSG grammar from the Penn Treebank. First, heuristic rules are employed to annotate the treebank with partially-specified derivation trees. Lexical entries are automatically extracted from the annotated corpus by inversely applying schemata to partially-specified derivation trees.
School finance reform, the distribution of school spending, and the distribution of student test scores

This paper studies the effect of school finance reforms on the distribution of school spending across richer and poorer districts, and the consequences of spending equalization for the relative test performance of students from different family backgrounds. We find that states where the school finance system was declared unconstitutional in the 1980s increased the relative funding of low-income districts. Increases in the amount of state aid available to poorer districts led to increases in the spending of these districts, narrowing the spending gap between richer and poorer districts. Using micro samples of SAT scores from this same period, we then test whether changes in spending inequality affect the gap in achievement between different family background groups. We find evidence that equalization of spending leads to a narrowing of test score outcomes across family background groups.