nils-herrmann/clean_scirepeval huggingface dataset:
Will This Paper Increase Your h-index?: Scientific Impact Prediction

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."
Optical outbursts of the cataclysmic variable 1RXS J140429.5+172352

We present the nine-year light curve of the cataclysmic variable 1RXS J140429.5+172352 from 2005 April to 2014 July. We identified four dwarf nova-like outbursts, which typically lasted 3 to 5 days, with an amplitude of 3.1 to 5.3 magnitudes. Time resolved photometry during two outbursts revealed small hump-like structures with peak-to-peak amplitude up to 0.5 mag. They occurred on timescales of 20 min to an hour, but did not exhibit a stable period. We suggest that the system might be an Intermediate Polar.
The velocity distribution in the solar neighbourhood from the LAMOST pilot survey

We use about 15,000 F/G nearby dwarf stars selected from the LAMOST pilot survey to map the U-V velocity distribution in the solar neighbourhood. An extreme deconvolution algorithm is applied to reconstruct an empirical multi-Gaussian model. In addition to the well known substructures, e.g., Sirius, Coma Berenices, Hyades-Pleiades over-densities, several new substructures are unveiled. A ripple-like structure from (U, V) = (-120, -5) to (103, -32)km/s is clearly seen in the U-V distribution. This structure seems associated with resonance induced by the Galactic bar, since it is extended in U while having a small dispersion in V at the same time. A ridge structure between (U, V) = (-60, 40) and (-15, 15) km/s is also found. Although similar substructures have been seen in the Hipparcos data, their origin is still unclear. Another compact over-density is seen at (U, V) = (-102, -24). With this large data sample, we find that the substructure located at V~70 km/s and the Arcturus group are essentially parallel in V, which may indicate that they originate from an unrelaxed disk component perturbed by the rotating bar.
Ray Chaos in a Photonic Crystal -- Supplementary Materials

These supplementary materials detail some calculations and some experimental results related to the propagation of light in the photonic billiard. We first justify why we focus only on the rays that are transmitted through the cylinders and justify the geometrical optics approximation. Then we explain the dynamical properties of ray propagation, demonstrate that asymptotically the Lyapunov exponent grows as λ ∼ ln t⋆ where t⋆ = T /R is the photonic crystal period T divided by the cylinder radius R. Finally we close these supplementary materials by presenting some experimental results demonstrating the exponential sensitivity to the initial conditions.
TiNy Titans: The Role of Dwarf-Dwarf Interactions in the Evolution of Low Mass Galaxies

We introduce TiNy Titans (TNT), the first systematic study of star formation and the subsequent processing of the interstellar medium in interacting dwarf galaxies. Here we present the first results from a multiwavelength observational program based on a sample of 104 dwarf galaxy pairs selected from a range of environments within the spectroscopic portion of the Sloan Digital Sky Survey and caught in various stages of interaction. The TNT dwarf pairs span mass ratios of M∗,1/M∗,2 < 10, projected separations < 50 kpc, and pair member masses of 7 < log(M∗/M⊙) < 9.7. The dwarf-dwarf merger sequence, as defined by TNT at z=0, demonstrates conclusively and for the first time that the star formation enhancement observed for massive galaxy pairs also extends to the dwarf mass range. Star formation is enhanced in paired dwarfs in otherwise isolated environments by factor of 2.3 (± 0.7) at pair separations < 50 kpc relative to unpaired analogs. The enhancement decreases with increasing pair separation and extends out to pair separations as large as 100 kpc. Starbursts, defined by Hα EQW > 100Å, occur in 20% of the TNT dwarf pairs, regardless of environment, compared to only 6-8% of the matched unpaired dwarfs. Starbursts can be triggered throughout the merger (i.e. out to large pair separations) and not just approaching coalescence. Despite their enhanced star formation and triggered starbursts, most TNT dwarf pairs have similar gas fractions relative to unpaired dwarfs of the same stellar mass. Thus, there may be significant reservoirs of diffuse, nonstarforming neutral gas surrounding the dwarf pairs or the gas consumption timescales may be long in the starburst phase. The only TNT dwarf pairs with low gas fractions (fgas < 0.4) and the only dwarfs, either paired or unpaired, with Hα EQW < 2Å are found near massive galaxy hosts. We conclude that dwarf-dwarf interactions are significant drivers of galaxy evolution at the low mass end, but ultimately environment is responsible for the quenching of star formation. This preliminary study is a precursor to an ongoing high resolution H i and optical imaging program to constrain the spatial distribution of star formation and gas throughout the course of the dwarf-dwarf merger sequence.
Neutrosophic information in the framework of multi-valued representation

The paper presents some steps for multi-valued representation of neutrosophic information. These steps are provided in the framework of multi- valued logics using the following logical value: true, false, neutral, unknown and saturated. Also, this approach provides some calculus formulae for the fol- lowing neutrosophic features: truth, falsity, neutrality, ignorance, under- definedness, over-definedness, saturation and entropy. In addition, it was de- fined net truth, definedness and neutrosophic score.
Learning with Pseudo-Ensembles

We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g., dropout  in a deep neural network trains a pseudo-ensemble of child subnetworks generated by randomly masking nodes in the parent network. We examine the relationship of pseudo-ensembles, which involve perturbation in model-space, to standard ensemble methods and existing notions of robustness, which focus on perturbation in observation-space. We present a novel regularizer based on making the behavior of a pseudo-ensemble robust with respect to the noise process generating it. In the fully-supervised setting, our regularizer matches the performance of dropout. But, unlike dropout, our regularizer naturally extends to the semi-supervised setting, where it produces state-of-the-art results. We provide a case study in which we transform the Recursive Neural Tensor Network of [19] into a pseudo-ensemble, which significantly improves its performance on a real-world sentiment analysis benchmark.
Constraining nucleon strangeness

Determining the nonperturbative $s\bar{s}$ content of the nucleon has attracted considerable interest and been the subject of numerous experimental searches. These measurements used a variety of reactions and place important limits on the vector form factors observed in parity-violating (PV) elastic scattering and the parton distributions determined by deep inelastic scattering (DIS). In spite of this progress, attempts to relate information obtained from elastic and DIS experiments have been sparse. To ameliorate this situation, we develop an interpolating model using light-front wave functions capable of computing both DIS and elastic observables. This framework is used to show that existing knowledge of DIS places significant restrictions on our wave functions. The result is that the predicted effects of nucleon strangeness on elastic observables are much smaller than those tolerated by direct fits to PV elastic scattering data alone. Using our model, we find $-0.024 \le \mu_s \le 0.035$, and $-0.137 \le \rho^D_s \le 0.081$ for the strange contributions to the nucleon magnetic moment and charge radius. The model we develop also independently predicts the nucleon's strange spin content $\Delta s$ and scalar density $\langle N| \bar{s}s | N \rangle$, and for these we find agreement with previous determinations.
Time evolution of predictability of epidemics on networks

Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information-i.e., knowing the state of each individual with respect to the disease-the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.
Learn Physics by Programming in Haskell

We describe a method for deepening a student's understanding of basic physics by asking the student to express physical ideas in a functional programming language. The method is implemented in a second-year course in computational physics at Lebanon Valley College. We argue that the structure of Newtonian mechanics is clarified by its expression in a language (Haskell) that supports higher-order functions, types, and type classes. In electromagnetic theory, the type signatures of functions that calculate electric and magnetic fields clearly express the functional dependency on the charge and current distributions that produce the fields. Many of the ideas in basic physics are well-captured by a type or a function.
Structure of Cholesterol in Lipid Rafts.

Rafts, or functional domains, are transient nano-or mesoscopic structures in the plasma membrane and are thought to be essential for many cellular processes such as signal transduction, adhesion, trafficking, and lipid or protein sorting. Observations of these membrane heterogeneities have proven challenging, as they are thought to be both small and short lived. With a combination of coarse-grained molecular dynamics simulations and neutron diffraction using deuterium labeled cholesterol molecules, we observe raftlike structures and determine the ordering of the cholesterol molecules in binary cholesterol-containing lipid membranes. From coarse-grained computer simulations, heterogenous membranes structures were observed and characterized as small, ordered domains. Neutron diffraction was used to study the lateral structure of the cholesterol molecules. We find pairs of strongly bound cholesterol molecules in the liquid-disordered phase, in accordance with the umbrella model. Bragg peaks corresponding to ordering of the cholesterol molecules in the raftlike structures were observed and indexed by two different structures: a monoclinic structure of ordered cholesterol pairs of alternating direction in equilibrium with cholesterol plaques, i.e., triclinic cholesterol bilayers.
Overlapping Communities in Social Networks

Complex networks can be typically broken down into groups or modules. Discovering this "community structure" is an important step in studying the large-scale structure of networks. Many algorithms have been proposed for community detection and benchmarks have been created to evaluate their performance. Typically algorithms for community detection either partition the graph (non-overlapping communities) or find node covers (overlapping communities). 
In this paper, we propose a particularly simple semi-supervised learning algorithm for finding out communities. In essence, given the community information of a small number of "seed nodes", the method uses random walks from the seed nodes to uncover the community information of the whole network. The algorithm runs in time $O(k \cdot m \cdot \log n)$, where $m$ is the number of edges; $n$ the number of links; and $k$ the number of communities in the network. In sparse networks with $m = O(n)$ and a constant number of communities, this running time is almost linear in the size of the network. Another important feature of our algorithm is that it can be used for either non-overlapping or overlapping communities. 
We test our algorithm using the LFR benchmark created by Lancichinetti, Fortunato, and Radicchi specifically for the purpose of evaluating such algorithms. Our algorithm can compete with the best of algorithms for both non-overlapping and overlapping communities as found in the comprehensive study of Lancichinetti and Fortunato.
Estimation of large covariance and precision matrices from temporally dependent observations

We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that several commonly used methods for independent observations can be applied to the temporally dependent data. In particular, the rates of convergence are obtained for the generalized thresholding estimation of covariance and correlation matrices, and for the constrained $\ell_1$ minimization and the $\ell_1$ penalized likelihood estimation of precision matrix. Properties of sparsistency and sign-consistency are also established. A gap-block cross-validation method is proposed for the tuning parameter selection, which performs well in simulations. As a motivating example, we study the brain functional connectivity using resting-state fMRI time series data with long-range temporal dependence.
Convenient Liquidity Measure for Financial Markets

A liquidity measure based on consideration and price range is proposed. Initially defined for daily data, Liquidity Index (LIX) can also be estimated via intraday data by using a time scaling mechanism. The link between LIX and the liquidity measure based on weighted average bid-ask spread is established. Using this liquidity measure, an elementary liquidity algebra is possible: from the estimation of the execution cost, the liquidity of a basket of instruments is obtained. A formula for the liquidity of an ETF, from the liquidity of its constituencies and the liquidity of ETF shares, is derived.
Random Forests Can Hash

Hash codes are a very efficient data representation needed to be able to cope with the ever growing amounts of data. We introduce a random forest semantic hashing scheme with information-theoretic code aggregation, showing for the first time how random forest, a technique that together with deep learning have shown spectacular results in classification, can also be extended to large-scale retrieval. Traditional random forest fails to enforce the consistency of hashes generated from each tree for the same class data, i.e., to preserve the underlying similarity, and it also lacks a principled way for code aggregation across trees. We start with a simple hashing scheme, where independently trained random trees in a forest are acting as hashing functions. We the propose a subspace model as the splitting function, and show that it enforces the hash consistency in a tree for data from the same class. We also introduce an information-theoretic approach for aggregating codes of individual trees into a single hash code, producing a near-optimal unique hash for each class. Experiments on large-scale public datasets are presented, showing that the proposed approach significantly outperforms state-of-the-art hashing methods for retrieval tasks.
Testing and confidence intervals for high dimensional proportional hazards models

The paper considers the problem of hypothesis testing and confidence intervals in high dimensional proportional hazards models. Motivated by a geometric projection principle, we propose a unified likelihood ratio inferential framework, including score, Wald and partial likelihood ratio statistics for hypothesis testing. Without assuming model selection consistency, we derive the asymptotic distributions of these test statistics, establish their semiparametric optimality and conduct power analysis under Pitman alternatives. We also develop new procedures to construct pointwise confidence intervals for the baseline hazard function and conditional hazard function. Simulation studies show that all tests proposed perform well in controlling type I errors. Moreover, the partial likelihood ratio test is empirically more powerful than the other tests. The methods proposed are illustrated by an example of a gene expression data set.
Linear waves on constant radius limits of cosmological black hole spacetimes

In this paper we consider the Klein-Gordon equation on spherically symmetric background spacetimes with a constant area radius. The spacetimes under consideration are Nariai and Pleba\'nski-Hacyan, and can be considered constant radius limits of Reissner-Nordstr\"om-de Sitter spacetimes. We prove boundedness in the case of a non-negative Klein Gordon mass and decay unless the mass is zero. In the latter case we prove decay of solutions that are supported on all harmonic modes with angular momentum l greater or equal to 1. We show that the l=0 modes of solutions to the massless Klein-Gordon equation do not decay. They are subject to conservation laws along degenerate Killing horizons. We apply the estimates in Nariai to give decay of solutions to the massive Klein-Gordon equation on an n-dimensional de Sitter background, using only the vector field method and with no restrictions on the positive Klein-Gordon mass.
Testing MCMC code

Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms. Specifically, we advocate writing code in a modular way, where conditional probability calculations are kept separate from the logic of the sampler. We discuss strategies for both unit testing and integration testing. As a running example, we show how a Python implementation of Gibbs sampling for a mixture of Gaussians model can be tested.
The Supervised Hierarchical Dirichlet Process

We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method on two real-world classification problems and two real-world regression problems. Bayesian nonparametric regression models based on the Dirichlet process, such as the Dirichlet process-generalised linear models (DP-GLM) have previously been explored; these models allow flexibility in modelling nonlinear relationships. However, until now, hierarchical Dirichlet process (HDP) mixtures have not seen significant use in supervised problems with grouped data since a straightforward application of the HDP on the grouped data results in learnt clusters that are not predictive of the responses. The sHDP solves this problem by allowing for clusters to be learnt jointly from the group structure and from the label assigned to each group.
Learning unbiased features

A key element in transfer learning is representation learning; if representations can be developed that expose the relevant factors underlying the data, then new tasks and domains can be learned readily based on mappings of these salient factors. We propose that an important aim for these representations are to be unbiased. Different forms of representation learning can be derived from alternative definitions of unwanted bias, e.g., bias to particular tasks, domains, or irrelevant underlying data dimensions. One very useful approach to estimating the amount of bias in a representation comes from maximum mean discrepancy (MMD) , a measure of distance between probability distributions. We are not the first to suggest that MMD can be a useful criterion in developing representations that apply across multiple domains or tasks . However, in this paper we describe a number of novel applications of this criterion that we have devised, all based on the idea of developing unbiased representations. These formulations include: a standard domain adaptation framework; a method of learning invariant representations; an approach based on noise-insensitive autoencoders; and a novel form of generative model.
Dynamic motifs of strategies in prisoner’s dilemma games

We investigate the win-lose relations between strategies of iterated prisoner’s dilemma games by using a directed network concept to display the replicator dynamics results. In the giant strongly-connected component of the win/lose network, we find win-lose circulations similar to rock-paper-scissors and analyze the fixed point and its stability. Applying the network motif concept, we introduce dynamic motifs, which describe the population dynamics relations among the three strategies. Through exact enumeration, we find 22 dynamic motifs and display their phase portraits. Visualization using directed networks and motif analysis is a useful method to make complex dynamic behavior simple in order to understand it more intuitively. Dynamic motifs can be building blocks for dynamic behavior among strategies when they are applied to other types of games.
Two-photon total annihilation of molecular positronium

The rate for complete two-photon annihilation of molecular positronium Ps2 is reported. This decay channel involves a four-body collision among the fermions forming Ps2, and two photons of 1.022 MeV, each, as the final state. The quantum electrodynamics result for the rate of this process is found to be . This decay channel completes the most comprehensive decay chart for Ps2 up to date.