nils-herrmann/clean_scirepeval huggingface dataset:
A Stochastic Model of Order Book Dynamics using Bouncing Geometric Brownian Motions

We consider a limit order book, where buyers and sellers register to trade a security at specific prices. The largest price buyers on the book are willing to offer is called the market bid price, and the smallest price sellers on the book are willing to accept is called the market ask price. Market ask price is always greater than market bid price, and these prices move upwards and downwards due to new arrivals, market trades, and cancellations. We model these two price processes as "bouncing geometric Brownian motions (GBMs)", which are defined as exponentials of two mutually reflected Brownian motions. We then modify these bouncing GBMs to construct a discrete time stochastic process of trading times and trading prices, which is parameterized by a positive parameter $\delta$. Under this model, it is shown that the inter-trading times are inverse Gaussian distributed, and the logarithmic returns between consecutive trading times follow a normal inverse Gaussian distribution. Our main results show that the logarithmic trading price process is a renewal reward process, and under a suitable scaling, this process converges to a standard Brownian motion as $\delta\to 0$. We also prove that the modified ask and bid processes approach the original bouncing GBMs as $\delta\to0$. Finally, we derive a simple and effective prediction formula for trading prices, and illustrate the effectiveness of the prediction formula with an example using real stock price data.
Word Embedding Based Correlation Model for Question/Answer Matching

The large scale of Q&A archives accumulated in community based question answering (CQA) servivces are important information and knowledge resource on the web. Question and answer matching task has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, a Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding. Given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs, while it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising potential.
Scenario Generation for Single-Period Portfolio Selection Problems with Tail Risk Measures: Coping with High Dimensions and Integer Variables

In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problematic: because the value of a tail risk measure only depends on a small subset of the support of the distribution of asset returns, traditional scenario based methods, which spread scenarios evenly across the whole support of the distribution, yield very unstable solutions unless we use a very large number of scenarios. The proposed approach works by prioritizing the construction of scenarios in the areas of a probability distribution which correspond to the tail losses of feasible portfolios. The proposed approach can be applied to difficult instances of the portfolio selection problem characterized by high-dimensions, non-elliptical distributions of asset returns, and the presence of integer variables. It is also observed that the methodology works better as the feasible set of portfolios becomes more constrained. Based on this fact, a heuristic algorithm based on the sample average approximation method is proposed. This algorithm works by adding artificial constraints to the problem which are gradually tightened, allowing one to telescope onto high quality solutions.
Estimating the Degree Centrality Ranking of a Node

Complex networks have gained more attention from the last few years. The size of real-world complex networks, such as online social networks, WWW network, collaboration networks, is increasing exponentially with time. It is not feasible to collect the complete data and store and process it. In the present work, we propose a method to estimate the degree centrality rank of a node without having the complete structure of the graph. The proposed algorithm uses the degree of a node and power-law exponent of the degree distribution to calculate the ranking. Simulation results on the Barabasi-Albert networks show that the average error in the estimated ranking is approximately $5\%$ of the total number of nodes.
Variational Gaussian Process

Representations offered by deep generative models are fundamentally tied to their inference method from data. Variational inference methods require a rich family of approximating distributions. We construct the variational Gaussian process (VGP), a Bayesian nonparametric model which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity. We prove a universal approximation theorem for the VGP, demonstrating its representative power for learning any model. For inference we present a variational objective inspired by autoencoders and perform black box inference over a wide class of models. The VGP achieves new state-of-the-art results for unsupervised learning, inferring models such as the deep latent Gaussian model and the recently proposed DRAW.
Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments

Real-world robotics problems often occur in domains that differ significantly from the robot's prior training environment. For many robotic control tasks, real world experience is expensive to obtain, but data is easy to collect in either an instrumented environment or in simulation. We propose a novel domain adaptation approach for robot perception that adapts visual representations learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a target real-world domain, without requiring expensive manual data annotation of real world data before policy search. Supervised domain adaptation methods minimize cross-domain differences using pairs of aligned images that contain the same object or scene in both the source and target domains, thus learning a domain-invariant representation. However, they require manual alignment of such image pairs. Fully unsupervised adaptation methods rely on minimizing the discrepancy between the feature distributions across domains. We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains. Focusing on adapting from simulation to real world data using a PR2 robot, we evaluate our approach on a manipulation task and show that by using weakly paired images, our method compensates for domain shift more effectively than previous techniques, enabling better robot performance in the real world.
Bootstrapping Ternary Relation Extractors

Binary relation extraction methods have been widely studied in recent years. However, few methods have been developed for higher n-ary relation extraction. One limiting factor is the effort required to generate training data. For binary relations, one only has to provide a few dozen pairs of entities per relation, as training data. For ternary relations (n=3), each training instance is a triplet of entities, placing a greater cognitive load on people. For example, many people know that Google acquired Youtube but not the dollar amount or the date of the acquisition and many people know that Hillary Clinton is married to Bill Clinton by not the location or date of their wedding. This makes higher n-nary training data generation a time consuming exercise in searching the Web. We present a resource for training ternary relation extractors. This was generated using a minimally supervised yet effective approach. We present statistics on the size and the quality of the dataset.
How the medium shapes the message: Printing and the rise of the arts and sciences

Communication technologies, from printing to social media, affect our historical records by changing the way ideas are spread and recorded. Yet, finding statistical evidence of this fact has been challenging. Here we combine a common causal inference technique (instrumental variable estimation) with a dataset on nearly forty thousand biographies from Wikipedia (Pantheon 2.0), to study the effect of the introduction of printing in European cities on Wikipedia’s digital biographical records. By using a city’s distance to Mainz as an instrument for the adoption of the movable type press, we show that European cities that adopted printing earlier were more likely to become the birthplace of a famous scientist or artist during the years following the invention of printing. We bring these findings to recent communication technologies by showing that the number of radios and televisions in a country correlates with the number of globally famous performing artists and sports players born in that country, even after controlling for GDP, population, and including country and year fixed effects. These findings support the hypothesis that the introduction of communication technologies can bias historical records in the direction of the content that is best suited for each technology.
Mutual Information and Diverse Decoding Improve Neural Machine Translation

Sequence-to-sequence neural translation models learn semantic and syntactic relations between sentence pairs by optimizing the likelihood of the target given the source, i.e., $p(y|x)$, an objective that ignores other potentially useful sources of information. We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets. We implement the model with a simple re-ranking method, and also introduce a decoding algorithm that increases diversity in the N-best list produced by the first pass. Applied to the WMT German/English and French/English tasks, the proposed models offers a consistent performance boost on both standard LSTM and attention-based neural MT architectures.
Unified Growth Theory Contradicted by the Economic Growth in Latin America

Historical economic growth in Latin America is analysed using the data of Maddison. Unified Growth Theory is found to be contradicted by these data in the same way as it is contradicted by the economic growth in Africa, Asia, former USSR, Western Europe, Eastern Europe and by the world economic growth. Paradoxically, Unified Growth Theory is repeatedly and consistently contradicted by the same data, which were used, but never properly analysed, during the formulation of this theory. Unified Growth Theory does not explain the mechanism of the economic growth because it explains features contradicted by data. This theory is based fundamentally on the unfortunate lack of understanding of the properties of hyperbolic distribution and on the unscientific analysis of data. There was no transition from stagnation to growth at the end of the alleged Malthusian regime because the economic growth was hyperbolic. There was no escape from Malthusian trap because there was no trap. There was no takeoff. On the contrary, at the time of the alleged takeoff economic growth started to be diverted to a slower trajectory. Unified Growth Theory is dissociated from the reality. This theory needs to be revised or replaced. In its present form, it is a collection of irrelevant stories based on impressions and on the unscientific use of data.
Computationally Feasible Near-Optimal Subset Selection for Linear Regression under Measurement Constraints

Computationally feasible and statistically near-optimal subset selection strategies are derived to select a small portion of design (data) points in a linear regression model $y=X\beta+\varepsilon$ to reduce measurement cost and data efficiency. We consider two subset selection algorithms for estimating model coefficients $\beta$: the first algorithm is a random subsampling based method that achieves optimal statistical performance with a small $(1+\epsilon)$ relative factor under the with replacement model, and an $O(\log k)$ multiplicative factor under the without replacement model, with $k$ denoting the measurement budget. The second algorithm is fully deterministic and achieves $(1+\epsilon)$ relative approximation under the without replacement model, at the cost of slightly worse dependency of $k$ on the number of variables (data dimension) in the linear regression model. Finally, we show how our method could be extended to the corresponding prediction problem and also remark on interpretable sampling (selection) of data points under random design frameworks.
Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people’s argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.
Unified Growth Theory Contradicted by the Absence of Takeoffs in the Gross Domestic Product

Data describing historical economic growth are analysed. They demonstrate convincingly that the takeoffs from stagnation to growth, claimed in the Unified Growth Theory, never happened. This theory is again contradicted by data, which were used, but never properly analysed, during its formulation. The absence of the claimed takeoffs demonstrates also that the postulate of the differential takeoffs is contradicted by data.
Bayesian phylogenetic estimation of fossil ages

Recent advances have allowed for both morphological fossil evidence and molecular sequences to be integrated into a single combined inference of divergence dates under the rule of Bayesian probability. In particular, the fossilized birth–death tree prior and the Lewis-Mk model of discrete morphological evolution allow for the estimation of both divergence times and phylogenetic relationships between fossil and extant taxa. We exploit this statistical framework to investigate the internal consistency of these models by producing phylogenetic estimates of the age of each fossil in turn, within two rich and well-characterized datasets of fossil and extant species (penguins and canids). We find that the estimation accuracy of fossil ages is generally high with credible intervals seldom excluding the true age and median relative error in the two datasets of 5.7% and 13.2%, respectively. The median relative standard error (RSD) was 9.2% and 7.2%, respectively, suggesting good precision, although with some outliers. In fact, in the two datasets we analyse, the phylogenetic estimate of fossil age is on average less than 2 Myr from the mid-point age of the geological strata from which it was excavated. The high level of internal consistency found in our analyses suggests that the Bayesian statistical model employed is an adequate fit for both the geological and morphological data, and provides evidence from real data that the framework used can accurately model the evolution of discrete morphological traits coded from fossil and extant taxa. We anticipate that this approach will have diverse applications beyond divergence time dating, including dating fossils that are temporally unconstrained, testing of the ‘morphological clock', and for uncovering potential model misspecification and/or data errors when controversial phylogenetic hypotheses are obtained based on combined divergence dating analyses. This article is part of the themed issue ‘Dating species divergences using rocks and clocks’.
Collapsing shells, critical phenomena and black hole formation

We study the gravitational collapse of two thin shells of matter, in asymptotically flat spacetime or constrained to move within a spherical box. We show that this simple two-body system has surprisingly rich dynamics, which includes prompt collapse to a black hole, perpetually oscillating solutions or black hole formation at arbitrarily large times. Collapse is induced by shell crossing and the black hole mass depends sensitively on the number of shell crossings. At certain critical points, the black hole mass exhibits critical behavior, determined by the change in parity (even or odd) of the number of crossings, with or without mass-gap during the transition. Some of the features we observe are reminiscent of confined scalars undergoing ``turbulent'' dynamics.
What is the entropy in entropic gravity

We investigate theories in which gravity arises as a consequence of entropy. We distinguish between two approaches to this idea: holographic gravity, in which Einstein’s equation arises from keeping entropy stationary in equilibrium under variations of the geometry and quantum state of a small region, and thermodynamic gravity, in which Einstein’s equation emerges as a local equation of state from constraints on the area of a dynamical light sheet in a fixed spacetime background. Examining holographic gravity, we argue that its underlying assumptions can be justified in part using recent results on the form of the modular energy in quantum field theory. For thermodynamic gravity, on the other hand, we find that it is difficult to formulate a self-consistent definition of the entropy, which represents an obstacle for this approach. This investigation points the way forward in understanding the connections between gravity and entanglement.
The Equation of State Approach to Cosmological Perturbations in f(R) Gravity

The discovery of apparent cosmological acceleration has spawned a huge number of dark energy and modified gravity theories. The f(R) models of gravity are obtained when one replaces the Ricci scalar in the Einstein-Hilbert action by an arbitrary function of the Ricci scalar. In this work we provide expressions for the equations of state (EoS) for perturbations which completely characterize the linearized perturbations in f(R) gravity, including the scalar, vector, and tensor modes. The EoS formalism is a powerful and elegant parametrization where the modification to General Relativity are treated as a dark-energy-fluid. The perturbed dark-energy-fluid variables such as the anisotropic stress or the entropy perturbation are explicitly given in terms of parameters of the model of interest.
Sufficiency on the Stock Market

It is well-known that there are a number of relations between theoretical finance theory and information theory.Some of these relations are exact and some are approximate. In thi s paper we will explore some of these relations and determine under which conditions the relations are exact. It turns out that portfolio theory always leads to Bregman divergences. The Bregman divergence is only proportional to information divergence in situations that are essentially equal to the t ype of gambling studied by Kelly. This can be related an abstract sufficiency condition.
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.
K2-30 b and K2-34 b: Two inflated hot Jupiters around solar-type stars

We report the discovery of the two hot Jupiters K2-30 b and K2-34 b. The two planets were detected during campaigns 4 and 5 of the extension of the Kepler mission, K2; they transit their main-sequence stars with periods of ~4.099 and ~2.996 days. Subsequent ground-based radial velocity follow-up with SOPHIE, HARPS-N, and CAFE established the planetary nature of the transiting objects. We analyzed the transit signal, radial velocity, and spectral energy distributions of the two systems to characterize their properties. Both planets (K2-30 b and K2-34 b) are bloated hot Jupiters (1.20 RJup and 1.22 RJup) around relatively bright (V = 13.5 and V = 11.5) slow rotating main-sequence (G8 and F9) stars. Thus, these systems are good candidates for detecting the Rossiter-MacLaughlin effect in order to measure their obliquity and for atmospheric studies.
Probing neutron-proton dynamics by pions

In order to investigate the nuclear symmetry energy at high density, we study the pion production in central collisions of neutron-rich nuclei ${}^{132}\mathrm{Sn}+{}^{124}\mathrm{Sn}$ at 300 MeV/nucleon using a new approach by combining the antisymmetrized molecular dynamics (AMD) and a hadronic cascade model (JAM). The dynamics of neutrons and protons is solved by AMD, and then pions and $\Delta$ resonances in the reaction process are handled by JAM. We see the mechanism how the $\Delta$ resonance and pions are produced reflecting the dynamics of neutrons and protons. We also investigate the impacts of cluster correlations as well as of the high-density symmetry energy on the nucleon dynamics and consequently on the pion ratio. We find that the $\Delta^-/\Delta^{++}$ production ratio agrees very well with the neutron-proton squared ratio $(N/Z)^2$ in the high-density and high-momentum region. We show quantitatively that $\Delta$ production ratio, and therefore $(N/Z)^2$, are directly reflected in the $\pi^-/\pi^+$ ratio, with modification in the final stage of the reaction.