nils-herrmann/clean_scirepeval huggingface dataset:
VARIABILITY-INDUCED MOTION IN KEPLER DATA

Variability-induced motion (VIM) is an observable effect in simultaneous astrometric and photometric measurements caused by brightness variation in one of the components of a double source or blended image, which manifests itself as a strongly correlated shift of the optical photocenter. We have processed the entire collection of Kepler long-cadence light curve data, looking for correlated signals in astrometry and photometry on the time basis of a quarter-year. Limiting the VIM correlation coefficient to 0.3, VIM events are detected for 129,525 Kepler stars in at least one quarter. Of 7305 Kepler objects of interest, 4440 are detected as VIM at least once. Known variable stars and resolved double stars have elevated rates of VIM detection. Confident VIM occurrences are found for stars with suggested superflare events, indicating possible signal contamination. We present a complete catalog of all quarterly VIM detections. This catalog should be checked for such astrophysically significant events as transits of exoplanets, new eclipsing stars, and superflares of solar-type stars.
CEMP stars: possible hosts to carbon planets in the early universe

We explore the possibility of planet formation in the carbon-rich protoplanetary disks of carbon-enhanced metal-poor (CEMP) stars, possible relics of the early Universe. The chemically anomalous abundance patterns ([C/Fe] $\geq$ 0.7) in this subset of low-mass stars suggest pollution by primordial core-collapsing supernovae (SNe) ejecta that are particularly rich in carbon dust grains. By comparing the dust-settling timescale in the protoplanetary disks of CEMP stars to the expected disk lifetime (assuming dissipation via photoevaporation), we determine the maximum distance $r_{max}$ from the host CEMP star at which carbon-rich planetesimal formation is possible, as a function of the host star's [C/H] abundance. We then use our linear relation between $r_{max}$ and [C/H], along with the theoretical mass-radius relation derived for a solid, pure carbon planet, to characterize potential planetary transits across host CEMP stars. Given that the related transits are detectable with current and upcoming space-based transit surveys, we suggest initiating an observational program to search for carbon planets around CEMP stars in hopes of shedding light on the question of how early planetary systems may have formed after the Big Bang.
Electron-phonon interactions from first principles

This article reviews the theory of electron-phonon interactions in solids from the point of view of ab-initio calculations. While the electron-phonon interaction has been studied for almost a century, predictive non-empirical calculations have become feasible only during the past two decades. Today it is possible to calculate from first principles many materials properties related to the electron-phonon interaction, including the critical temperature of conventional superconductors, the carrier mobility in semiconductors, the temperature dependence of optical spectra in direct and indirect-gap semiconductors, the relaxation rates of photoexcited carriers, the electron mass renormalization in angle-resolved photoelectron spectra, and the non-adiabatic corrections to phonon dispersion relations. Here we review the theoretical and computational framework underlying modern electron-phonon calculations from first principles, as well as landmark investigations of the electron-phonon interaction in real materials. In the first part of the article we summarize the elementary theory of electron-phonon interactions and their calculations based on density-functional theory. In the second part we discuss a general field-theoretic formulation of the electron-phonon problem, and establish the connection with practical first-principles calculations. In the third part we review a number of recent investigations of electron-phonon interactions in the areas of vibrational spectroscopy, photoelectron spectroscopy, optical spectroscopy, transport, and superconductivity.
Effective quantum gravity observables and locally covariant QFT

Perturbative algebraic quantum field theory (pAQFT) is a mathematically rigorous framework that allows to construct models of quantum field theories on a general class of Lorentzian manifolds. Recently this idea has been applied also to perturbative quantum gravity, treated as an effective theory. The difficulty was to find the right notion of observables that would in an appropriate sense be diffeomorphism invariant. In this article I will outline a general framework that allows to quantize theories with local symmetries (this includes infinitesimal diffeomorphism transformations) with the use of the BV (Batalin-Vilkovisky) formalism. This approach has been successfully applied to effective quantum gravity in a recent paper by R. Brunetti, K. Fredenhagen and myself. In the same paper we also proved perturbative background independence of the quantized theory, which is going to be discussed in the present work as well.
Cyclical Properties of Supply-Side and Demand-Side Shocks in Oil-Based Commodity Markets

Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as demand-side benchmarks, we document new stylized facts about cyclical properties of the transmission mechanism generated by volatility shocks with heterogeneous frequency responses. Our first key finding is that shocks to volatility with response shorter than one week are increasingly important to the transmission mechanism over the studied period. Second, demand-side shocks to volatility are becoming increasingly important in creating short-run connectedness. Third, the supply-side shocks to volatility resonating in both the long run and short run are important sources of connectedness.
Recurrent Neural Network Encoder with Attention for Community Question Answering

We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechanism that encourages reasoning over entire sequences. To deal with practical issues such as data sparsity and imbalanced labels, we apply various techniques such as transfer learning and multitask learning. Our experiments on the SemEval-2016 cQA task show 10% improvement on a MAP score compared to an information retrieval-based approach, and achieve comparable performance to a strong handcrafted feature-based method.
Cosolver2B: An efficient local search heuristic for the Travelling Thief Problem

Real-world problems are very difficult to optimize. However, many researchers have been solving benchmark problems that have been extensively investigated for the last decades even if they have very few direct applications. The Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to provide a more realistic model. TTP targets particularly routing problem under packing/loading constraints which can be found in supply chain management and transportation. In this paper, TTP is presented and formulated mathematically. A combined local search algorithm is proposed and compared with Random Local Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite promising since new better solutions were found.
On Random Convex Analysis

Recently, based on the idea of randomizing space theory, random convex analysis has been being developed in order to deal with the corresponding problems in random environments such as analysis of conditional convex risk measures and the related variational problems and optimization problems. Random convex analysis is convex analysis over random locally convex modules. Since random locally convex modules have the more complicated topological and algebraic structures than ordinary locally convex spaces, establishing random convex analysis will encounter harder mathematical challenges than classical convex analysis so that there are still a lot of fundamentally important unsolved problems in random convex analysis. This paper is devoted to solving some important theoretic problems. First, we establish the inferior limit behavior of a proper lower semicontinuous $L^0$--convex function on a random locally convex module endowed with the locally $L^0$--convex topology, which makes perfect the Fenchel--Moreau duality theorem for such functions. Then, we investigate the relations among continuity, locally $L^0$--Lipschitzian continuity and almost surely sequent continuity of a proper $L^0$--convex function. And then, we establish the elegant relationships among subdifferentiability, G\^ateaux--differentiability and Fr\'ech\'et--differentiability for a proper $L^0$--convex function defined on random normed modules. At last, based on the Ekeland's variational principle for a proper lower semicontinuous $\bar{L}^0$--valued function, we show that $\varepsilon$--subdifferentials can be approximated by subdifferentials. We would like to emphasize that the success of this paper lies in simultaneously considering the $(\varepsilon, \lambda)$--topology and the locally $L^0$--convex topology for a random locally convex module.
Static scalar field solutions in symmetric gravity

We study an extension of general relativity with a second metric and an exchange symmetry between the two metrics. Such an extension might help to address some of the outstanding problems with general relativity, for example the smallness of the cosmological constant. We here derive a family of exact solutions for this theory. In this two-parameter family of solutions the gravitational field is sourced by a time-independent massless scalar field. We find that the only limit in which the scalar field entirely vanishes is flat space. The regular Schwarzschild-solution is left with a scalar field hidden in the second metric’s sector.
Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors

This study addresses the issue of predicting the glaucomatous visual field loss from patient disease datasets. Our goal is to accurately predict the progress of the disease in individual patients. As very few measurements are available for each patient, it is difficult to produce good predictors for individuals. A recently proposed clustering-based method enhances the power of prediction using patient data with similar spatiotemporal patterns. Each patient is categorized into a cluster of patients, and a predictive model is constructed using all of the data in the class. Predictions are highly dependent on the quality of clustering, but it is difficult to identify the best clustering method. Thus, we propose a method for aggregating cluster-based predictors to obtain better prediction accuracy than from a single cluster-based prediction. Further, the method shows very high performances by hierarchically aggregating experts generated from several cluster-based methods. We use real datasets to demonstrate that our method performs significantly better than conventional clustering-based and patient-wise regression methods, because the hierarchical aggregating strategy has a mechanism whereby good predictors in a small community can thrive.
The Anatomy of a Search and Mining System for Digital Archives

Samtla (Search And Mining Tools with Linguistic Analysis) is a digital humanities system designed in collaboration with historians and linguists to assist them with their research work in quantifying the content of any textual corpora through approximate phrase search and document comparison. The retrieval engine uses a character-based n-gram language model rather than the conventional word-based one so as to achieve great flexibility in language agnostic query processing. 
The index is implemented as a space-optimised character-based suffix tree with an accompanying database of document content and metadata. A number of text mining tools are integrated into the system to allow researchers to discover textual patterns, perform comparative analysis, and find out what is currently popular in the research community. 
Herein we describe the system architecture, user interface, models and algorithms, and data storage of the Samtla system. We also present several case studies of its usage in practice together with an evaluation of the systems' ranking performance through crowdsourcing.
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings

We apply distributed language embedding methods from Natural Language Processing to assign a vector to each database entity associated token (for example, a token may be a word occurring in a table row, or the name of a column). These vectors, of typical dimension 200, capture the meaning of tokens based on the contexts in which the tokens appear together. To form vectors, we apply a learning method to a token sequence derived from the database. We describe various techniques for extracting token sequences from a database. The techniques differ in complexity, in the token sequences they output and in the database information used (e.g., foreign keys). The vectors can be used to algebraically quantify semantic relationships between the tokens such as similarities and analogies. Vectors enable a dual view of the data: relational and (meaningful rather than purely syntactical) text. We introduce and explore a new class of queries called cognitive intelligence (CI) queries that extract information from the database based, in part, on the relationships encoded by vectors. We have implemented a prototype system on top of Spark to exhibit the power of CI queries. Here, CI queries are realized via SQL UDFs. This power goes far beyond text extensions to relational systems due to the information encoded in vectors. We also consider various extensions to the basic scheme, including using a collection of views derived from the database to focus on a domain of interest, utilizing vectors and/or text from external sources, maintaining vectors as the database evolves and exploring a database without utilizing its schema. For the latter, we consider minimal extensions to SQL to vastly improve query expressiveness.
A Tutorial on Deep Neural Networks for Intelligent Systems

Developing Intelligent Systems involves artificial intelligence approaches including artificial neural networks. Here, we present a tutorial of Deep Neural Networks (DNNs), and some insights about the origin of the term "deep"; references to deep learning are also given. Restricted Boltzmann Machines, which are the core of DNNs, are discussed in detail. An example of a simple two-layer network, performing unsupervised learning for unlabeled data, is shown. Deep Belief Networks (DBNs), which are used to build networks with more than two layers, are also described. Moreover, examples for supervised learning with DNNs performing simple prediction and classification tasks, are presented and explained. This tutorial includes two intelligent pattern recognition applications: hand- written digits (benchmark known as MNIST) and speech recognition.
Neural Summarization by Extracting Sentences and Words

Traditional approaches to extractive summarization rely heavily on humanengineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor. This architecture allows us to develop different classes of summarization models which can extract sentences or words. We train our models on large scale corpora containing hundreds of thousands of document-summary pairs 1 . Experimental results on two summarization datasets demonstrate that our models obtain results comparable to the state of the art without any access to linguistic annotation.
Evaluating semantic models with word-sentence relatedness

Semantic textual similarity (STS) systems are designed to encode and evaluate the semantic similarity between words, phrases, sentences, and documents. One method for assessing the quality or authenticity of semantic information encoded in these systems is by comparison with human judgments. A data set for evaluating semantic models was developed consisting of 775 English word-sentence pairs, each annotated for semantic relatedness by human raters engaged in a Maximum Difference Scaling (MDS) task, as well as a faster alternative task. As a sample application of this relatedness data, behavior-based relatedness was compared to the relatedness computed via four off-the-shelf STS models: n-gram, Latent Semantic Analysis (LSA), Word2Vec, and UMBC Ebiquity. Some STS models captured much of the variance in the human judgments collected, but they were not sensitive to the implicatures and entailments that were processed and considered by the participants. All text stimuli and judgment data have been made freely available.
A guide to convolution arithmetic for deep learning

We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers, as well as the relationship between convolutional and transposed convolutional layers. Relationships are derived for various cases, and are illustrated in order to make them intuitive.
Debugging Machine Learning Tasks

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl's theory of causation, and uses Pearl's PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis

Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015). While this one posterior sample (OPS) approach elegantly provides privacy "for free," it is data inefficient in the sense of asymptotic relative efficiency (ARE). We show that a simple alternative based on the Laplace mechanism, the workhorse of differential privacy, is as asymptotically efficient as non-private posterior inference, under general assumptions. This technique also has practical advantages including efficient use of the privacy budget for MCMC. We demonstrate the practicality of our approach on a time-series analysis of sensitive military records from the Afghanistan and Iraq wars disclosed by the Wikileaks organization.
CONDITOR1: Topic Maps and DITA labelling tool for textual documents with historical information

Conditor is a software tool which works with textual documents containing historical information. The purpose of this work two-fold: firstly to show the validity of the developed engine to correctly identify and label the entities of the universe of discourse with a labelled-combined XTM-DITA model. Secondly to explain the improvements achieved in the information retrieval process thanks to the use of a object-oriented database (JPOX) as well as its integration into the Lucene-type database search process to not only accomplish more accurate searches, but to also help the future development of a recommender system [21]. We finish with a brief demo in a 3D-graph of the results of the aforementioned search.
Acceleration of Universe by Nonlinear Electromagnetic Fields

A new model of nonlinear electromagnetic fields possessing a dimensional parameter $\beta$ is proposed. Electromagnetic fields are considered as the source of the gravitation field and accelerated expansion of the universe is driven by nonlinear electromagnetic fields. We consider the magnetic universe and the stochastic magnetic field is a source of the universe acceleration. After the universe inflation and the accelerated expansion the universe decelerates. We show the causality of the model and a classical stability at the deceleration phase. The spectral index, the tensor-to-scalar ratio, and the running of the spectral index were estimated that approximately fulfil the PLANK, WMAP, and BICEP2 data.
POST-MAXIMUM NEAR-INFRARED SPECTRA OF SN 2014J: A SEARCH FOR INTERACTION SIGNATURES

We present near-infrared (NIR) spectroscopic and photometric observations of the nearby Type Ia SN 2014J. The 17 NIR spectra span epochs from +15.3 to +92.5 days after B-band maximum light, while the JHK s photometry include epochs from −10 to +71 days. These data are used to constrain the progenitor system of SN 2014J utilizing the Paβ line, following recent suggestions that this phase period and the NIR in particular are excellent for constraining the amount of swept-up hydrogen-rich material associated with a non-degenerate companion star. We find no evidence for Paβ emission lines in our post-maximum spectra, with a rough hydrogen mass limit of ≲ 0.1 M⊙, which is consistent with previous limits in SN 2014J from late-time optical spectra of the Hα line. Nonetheless, the growing data set of high-quality NIR spectra holds the promise of very useful hydrogen constraints.
Physical properties of local star-forming analogues to z ∼ 5 Lyman-break galaxies

Intense, compact, star-forming galaxies are rare in the local Universe but ubiquitous at high redshift. We interpret the 0.1-22 μm spectral energy distributions (SED) of a sample of 180 galaxies at 0.05 < z < 0.25 selected for extremely high surface densities of inferred star formation in the ultraviolet. By comparison with well-established stellar population synthesis models we find that our sample comprises young (∼ 60 - 400 Myrs), moderate mass (∼6 × 109 M⊙) star-forming galaxies with little dust extinction (mean stellar continuum extinction Econt(B − V) ∼ 0.1) and find star formation rates of a few tens of Solar masses per year. We use our inferred masses to determine a mean specific star formation rate for this sample of ∼10−9 yr−1, and compare this to the specific star formation rates in distant Lyman break galaxies (LBGs), and in other low redshift populations. We conclude that our sample's characteristics overlap significantly with those of the z ∼ 5 LBG population, making ours the first local analogue population well tuned to match those high redshift galaxies. We consider implications for the origin and evolution of early galaxies.