nils-herrmann/clean_scirepeval huggingface dataset:
Crowdsourcing Dialect Characterization through Twitter

We perform a large-scale analysis of language diatopic variation using geotagged microblogging datasets. By collecting all Twitter messages written in Spanish over more than two years, we build a corpus from which a carefully selected list of concepts allows us to characterize Spanish varieties on a global scale. A cluster analysis proves the existence of well defined macroregions sharing common lexical properties. Remarkably enough, we find that Spanish language is split into two superdialects, namely, an urban speech used across major American and Spanish citites and a diverse form that encompasses rural areas and small towns. The latter can be further clustered into smaller varieties with a stronger regional character.
Mutual Information and Conditional Mean Prediction Error

Mutual information is fundamentally important for measuring statistical dependence between variables and for quantifying information transfer by signaling and communication mechanisms. It can, however, be challenging to evaluate for physical models of such mechanisms and to estimate reliably from data. Furthermore, its relationship to better known statistical procedures is still poorly understood. Here we explore new connections between mutual information and regression-based dependence measures, $\nu^{-1}$, that utilise the determinant of the second-moment matrix of the conditional mean prediction error. We examine convergence properties as $\nu\rightarrow0$ and establish sharp lower bounds on mutual information and capacity of the form $\mathrm{log}(\nu^{-1/2})$. The bounds are tighter than lower bounds based on the Pearson correlation and ones derived using average mean square-error rate distortion arguments. Furthermore, their estimation is feasible using techniques from nonparametric regression. As an illustration we provide bootstrap confidence intervals for the lower bounds which, through use of a composite estimator, substantially improve upon inference about mutual information based on $k$-nearest neighbour estimators alone.
Effective And Simple Vwap Options Pricing Model

Volume weighted average price (VWAP) options are a popular security type in many countries, but despite their popularity very few pricing models have been developed so far for VWAP options. This can be explained by the fact that the VWAP pricing problem is set in an incomplete market since there is no underlying with which to hedge the volume risk, and hence there is no uniquely defined price. Any price, which is obtained will include a market price of volume risk which must be determined from the corresponding volume statistics. Our analysis strongly supports the hypothesis that the empirical volume statistics of ASX equities can be described reasonably well by fitted gamma distributions. Based on this observation we suggest a simple gamma process-based model that allows for the exact analytic pricing of VWAP options in a rather straightforward way.
The SAMI Galaxy Survey: Instrument specification and target selection

The SAMI Galaxy Survey will observe 3400 galaxies with the Sydney-AAO Multi- object Integral-field spectrograph (SAMI) on the Anglo-Australian Telescope (AAT) in a 3-year survey which began in 2013. We present the throughput of the SAMI system, the science basis and specifications for the target selection, the survey observation plan and the combined properties of the selected galaxies. The survey includes four volume-limited galaxy samples based on cuts in a proxy for stellar mass, along with low-stellar-mass dwarf galaxies all selected from the Galaxy And Mass Assembly (GAMA) survey. The GAMA regions were selected because of the vast array of ancillary data available, including ultraviolet through to radio bands. These fields are on the celestial equator at 9, 12, and 14.5 hours, and cover a total of 144 square degrees (in GAMA-I). Higher density environments are also included with the addition of eight clusters. The clusters have spectroscopy from 2dFGRS and SDSS and photometry in regions covered by the Sloan Digital Sky Survey (SDSS) and/or VLT Survey Telescope/ATLAS. The aim is to cover a broad range in stellar mass and environment, and therefore the primary survey targets cover redshifts 0.004 < z < 0.095, magnitudes rpet < 19.4, stellar masses 107– 1012M⊙, and environments from isolated field galaxies through groups to clusters of _ 1015M⊙.
Understanding Random Forests: From Theory to Practice

Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. Yet, caution should avoid using machine learning as a black-box tool, but rather consider it as a methodology, with a rational thought process that is entirely dependent on the problem under study. In particular, the use of algorithms should ideally require a reasonable understanding of their mechanisms, properties and limitations, in order to better apprehend and interpret their results. 
Accordingly, the goal of this thesis is to provide an in-depth analysis of random forests, consistently calling into question each and every part of the algorithm, in order to shed new light on its learning capabilities, inner workings and interpretability. The first part of this work studies the induction of decision trees and the construction of ensembles of randomized trees, motivating their design and purpose whenever possible. Our contributions follow with an original complexity analysis of random forests, showing their good computational performance and scalability, along with an in-depth discussion of their implementation details, as contributed within Scikit-Learn. 
In the second part of this work, we analyse and discuss the interpretability of random forests in the eyes of variable importance measures. The core of our contributions rests in the theoretical characterization of the Mean Decrease of Impurity variable importance measure, from which we prove and derive some of its properties in the case of multiway totally randomized trees and in asymptotic conditions. In consequence of this work, our analysis demonstrates that variable importances [...].
Entropic One-Class Classifiers

The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed nontarget, and therefore, they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation-based approach; we embed the input data into the dissimilarity space (DS) by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented by weighted Euclidean graphs, which we use to determine the entropy of the data distribution in the DS and at the same time to derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking data sets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique.
Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data

Algorithms that discover variables which are causally related to a target may inform the design of experiments. With observational gene expression data, many methods discover causal variables by measuring each variable's degree of statistical dependence with the target using dependence measures (DMs). However, other methods measure each variable's ability to explain the statistical dependence between the target and the remaining variables in the data using conditional dependence measures (CDMs), since this strategy is guaranteed to find the target's direct causes, direct effects, and direct causes of the direct effects in the infinite sample limit. In this paper, we design a new algorithm in order to systematically compare the relative abilities of DMs and CDMs in discovering causal variables from gene expression data. 
 The proposed algorithm using a CDM is sample efficient, since it consistently outperforms other state-of-the-art local causal discovery algorithms when samples sizes are small. However, the proposed algorithm using a CDM outperforms the proposed algorithm using a DM only when sample sizes are above several hundred. These results suggest that accurate causal discovery from gene expression data using current CDM-based algorithms requires datasets with at least several hundred samples. 
Availability: The proposed algorithm is freely available at this https URL
Quantum-confined single photon emission at room temperature from SiC tetrapods.

Controlled engineering of isolated solid state quantum systems is one of the most prominent goals in modern nanotechnology. In this letter we demonstrate a previously unknown quantum system namely silicon carbide tetrapods. The tetrapods have a cubic polytype core (3C) and hexagonal polytype legs (4H)--a geometry that creates spontaneous polarization within a single tetrapod. Modeling of the tetrapod structures predicts that a bound exciton should exist at the 3C-4H interface. The simulations are confirmed by the observation of fully polarized and narrowband single photon emission from the tetrapods at room temperature. The single photon emission provides important insights into understanding the quantum confinement effects in non-spherical nanostructures. Our results pave the way to a new class of crystal phase nanomaterials that exhibit single photon emission at room temperature and therefore are suitable for sensing, quantum information and nanophotonics.
Estimating the accuracies of multiple classifiers without labeled data

In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data. This scenario raises the following questions: Without any labeled data and without any a-priori knowledge about the reliability of these different classifiers, is it possible to consistently and computationally efficiently estimate their accuracies? Furthermore, also in a completely unsupervised manner, can one construct a more accurate unsupervised ensemble classifier? In this paper, focusing on the binary case, we present simple, computationally efficient algorithms to solve these questions. Furthermore, under standard classifier independence assumptions, we prove our methods are consistent and study their asymptotic error. Our approach is spectral, based on the fact that the off-diagonal entries of the classifiers' covariance matrix and 3-d tensor are rank-one. We illustrate the competitive performance of our algorithms via extensive experiments on both artificial and real datasets.
Proceedings 3rd Workshop on GRAPH Inspection and Traversal Engineering, GRAPHITE 2014, Grenoble, France, 5th April 2014

These are the proceedings of the Third Workshop on GRAPH Inspection and Traversal Engineering (GRAPHITE 2014), which took place on April 5, 2014 in Grenoble, France, as a satellite event of the 17th European Joint Conferences on Theory and Practice of Software (ETAPS 2014). The aim of GRAPHITE is to foster the convergence on research interests from several communities dealing with graph analysis in all its forms in computer science, with a particular attention to software development and analysis. Graphs are used to represent data and processes in many application areas, and they are subjected to various computational algorithms in order to analyze them. Just restricting the attention to the analysis of software, graph analysis algorithms are used, for instance, to verify properties using model checking techniques that explore the system's state space graph or static analysis techniques based on control flow graphs. Further application domains include games, planning, and network analysis. Very often, graph problems and their algorithmic solutions have common characteristics, independent of their application domain. The goal of this event is to gather scientists from different communities, who do research on graph analysis algorithms, such that awareness of each others' work is increased.
NMF with Sparse Regularizations in Transformed Domains

Nonnegative blind source separation, which is also referred to as nonnegative matrix factorization (NMF), is a very active field in domains as different as astrophysics, audio processing, and biomedical signal processing. In this context, the efficient retrieval of the sources requires the use of signal priors such as sparsity. Although NMF has been well studied with sparse constraints in the direct domain, only very few algorithms can encompass nonnegativity together with sparsity in a transformed domain since simultaneously dealing with two priors in two different domains is challenging. In this paper, we show how a sparse NMF algorithm called nonnegative generalized morphological component analysis (nGMCA) can be extended to impose nonnegativity in the direct domain along with sparsity in a transformed domain, with both analysis and synthesis formulations. To the best of our knowledge, this work presents the first comparison of analysis and synthesis priors---as well as their reweighted versions---in t...
Statistical Properties of Pairwise Distances between Leaves on a Random Yule Tree

A Yule tree is the result of a branching process with constant birth and death rates. Such a process serves as an instructive null model of many empirical systems, for instance, the evolution of species leading to a phylogenetic tree. However, often in phylogeny the only available information is the pairwise distances between a small fraction of extant species representing the leaves of the tree. In this article we study statistical properties of the pairwise distances in a Yule tree. Using a method based on a recursion, we derive an exact, analytic and compact formula for the expected number of pairs separated by a certain time distance. This number turns out to follow a increasing exponential function. This property of a Yule tree can serve as a simple test for empirical data to be well described by a Yule process. We further use this recursive method to calculate the expected number of the n-most closely related pairs of leaves and the number of cherries separated by a certain time distance. To make our results more useful for realistic scenarios, we explicitly take into account that the leaves of a tree may be incompletely sampled and derive a criterion for poorly sampled phylogenies. We show that our result can account for empirical data, using two families of birds species.
Dust Formation in the Presence of Photons I: Evaporation Rates for Small Dust Grains

The temperature of newly forming dust is controlled by the radiation field. As dust forms around stars, stellar transients, quasars or supernovae, the grains must grow through a regime where they are stochastically heated by individual photons. Since evaporation rates increase exponentially with temperature while cooling times decrease only as a power law, the evaporation rates for these small grains are dominated by the temperature spikes. We calculate effective evaporation temperatures for a broad range of input spectra that are encapsulated in a series of simple interpolation formulae for both graphitic and silicate grains. These can be easily used to first determine if dust formation is possible and then to estimate the radius or time at which it commences for a broad range of radiation environments. With these additional physical effects, very small grains may form earlier than in standard models of AGB winds. Even for very high mass loss rates, the hottest stars that can form dust are G and F stars particularly in the case of silicate dusts. For hotter stars, the higher fluxes of ultraviolet photons prevent dust formation. Thus, episodic dust formation by OH/IR stars and LBVs is primarily driven by fluctuations in their apparent temperatures rather than changes in luminosity or mass loss rates.
How chameleons core dwarfs with cusps

The presence of a scalar field that couples nonminimally and universally to matter can enhance gravitational forces on cosmological scales while restoring general relativity in the Solar neighborhood. In the intermediate regime, kinematically inferred masses experience an additional radial dependence with respect to the underlying distribution of matter, which is caused by the increment of gravitational forces with increasing distance from the Milky Way center. The same effect can influence the internal kinematics of subhalos and cause cuspy matter distributions to appear core-like. Specializing to the chameleon model as a worked example, we demonstrate this effect by tracing the scalar field from the outskirts of the Milky Way halo to its interior, simultaneously fitting observed velocity dispersions of chemo-dynamically discriminated red giant populations in the Fornax and Sculptor dwarf spheroidals. Whereas in standard gravity these observations suggest that the matter distribution of the dwarfs is cored, we find that in the presence of a chameleon field the assumption of a cuspy Navarro-Frenk-White profile becomes perfectly compatible with the data. Importantly, chameleon models also predict the existence of slopes between two stellar subcomponents that in Newtonian gravity would be interpreted as a depletion of matter in the dwarf center. Hence, an observation of such an apparently pathological scenario may serve as a smoking gun for the presence of a chameleon field or a similar modification of gravity, independent of baryonic feedback effects. In general, measuring the dynamic mass profiles of the Milky Way dwarfs provides stronger constraints than those inferred from the screening scale of the Solar System since these are located at greater distances from the halo center.
Exploiting Locality in Quantum Computation for Quantum Chemistry.

Accurate prediction of chemical and material properties from first-principles quantum chemistry is a challenging task on traditional computers. Recent developments in quantum computation offer a route toward highly accurate solutions with polynomial cost; however, this solution still carries a large overhead. In this Perspective, we aim to bring together known results about the locality of physical interactions from quantum chemistry with ideas from quantum computation. We show that the utilization of spatial locality combined with the Bravyi-Kitaev transformation offers an improvement in the scaling of known quantum algorithms for quantum chemistry and provides numerical examples to help illustrate this point. We combine these developments to improve the outlook for the future of quantum chemistry on quantum computers.
Electronic Structure of Liquid Water and a Platinum Surface

Many-body perturbation theory within the G$_0$W$_0$ approximation is used to determine molecular orbital level alignment at a liquid water/Pt(111) interface generated through $ab~ initio$ molecular dynamics. Molecular orbital energy levels are shown to depend both on the position of H$_2$O molecules within the liquid relative to the surface and the details of their local bonding environment. Standard density functional theory calculations disagree qualitatively with level alignment predicted by many-body perturbation theory.
Social conformity despite individual preferences for distinctiveness

We demonstrate that individual behaviours directed at the attainment of distinctiveness can in fact produce complete social conformity. We thus offer an unexpected generative mechanism for this central social phenomenon. Specifically, we establish that agents who have fixed needs to be distinct and adapt their positions to achieve distinctiveness goals, can nevertheless self-organize to a limiting state of absolute conformity. This seemingly paradoxical result is deduced formally from a small number of natural assumptions and is then explored at length computationally. Interesting departures from this conformity equilibrium are also possible, including divergence in positions. The effect of extremist minorities on these dynamics is discussed. A simple extension is then introduced, which allows the model to generate and maintain social diversity, including multimodal distinctiveness distributions. The paper contributes formal definitions, analytical deductions and counterintuitive findings to the literature on individual distinctiveness and social conformity.
Tunable terahertz coherent perfect absorption in a monolayer graphene.

Coherent perfect absorber (CPA) was proposed as the time-reversed counterpart to laser: a resonator containing lossy medium instead of gain medium can absorb the coherent optical fields completely. Here, we exploit a monolayer graphene to realize the CPA in a nonresonant manner. It is found that quasi-CPA point exists in the terahertz regime for suspending monolayer graphene, and the CPA can be implemented with the assistance of proper phase modulation among two incident beams at the quasi-CPA frequencies. The graphene-based CPA is found of broadband angular selectivity: CPA point splits into two frequency bands for the orthogonal s and p polarizations at oblique incidence, and the two bands cover a wide frequency range starting from zero frequency. Furthermore, the coherent absorption can be tuned substantially by varying the gate-controlled Fermi energy. The findings of CPA with nonresonant graphene sheet can be generalized for potential applications in terahertz/infrared detections and signal processing with two-dimensional optoelectronic materials.
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning

We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious hand-tuning, brute search or lengthy sampling runs. Against this background, Bayesian optimisation is finding increasing use in automating parameter tuning, making ML algorithms accessible even to non-experts. However, the state of the art in Bayesian optimisation is incapable of scaling to the large number of evaluations of algorithm performance required to fit realistic models to complex, big data. We here describe a stochastic, sparse, Bayesian optimisation strategy to solve this problem, using many thousands of noisy evaluations of algorithm performance on subsets of data in order to effectively train algorithms for big data. We provide a comprehensive benchmarking of possible sparsification strategies for Bayesian optimisation, concluding that a Nystrom approximation offers the best scaling and performance for real tasks. Our proposed algorithm demonstrates substantial improvement over the state of the art in tuning the parameters of a Gaussian Process time series prediction task on real, big data.