nils-herrmann/clean_scirepeval huggingface dataset:
High Dimensional Forecasting via Interpretable Vector Autoregression

Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional approaches address overparameterization by selecting a low lag order, based on the assumption of short range dependence, assuming that a universal lag order applies to all components. Such an approach constrains the relationship between the components and impedes forecast performance. The lasso-based approaches work much better in high-dimensional situations but do not incorporate the notion of lag order selection. 
We propose a new class of hierarchical lag structures (HLag) that embed the notion of lag selection into a convex regularizer. The key modeling tool is a group lasso with nested groups which guarantees that the sparsity pattern of lag coefficients honors the VAR's ordered structure. The HLag framework offers three structures, which allow for varying levels of flexibility. A simulation study demonstrates improved performance in forecasting and lag order selection over previous approaches, and a macroeconomic application further highlights forecasting improvements as well as HLag's convenient, interpretable output.
The distribution of “time of flight” in three dimensional stationary chaotic advection

The distributions of “time of flight” (time spent by a single fluid particle between two crossings of the Poincare section) are investigated for five different three dimensional stationary chaotic mixers. Above all, we study the large tails of those distributions and show that mainly two types of behaviors are encountered. In the case of slipping walls, as expected, we obtain an exponential decay, which, however, does not scale with the Lyapunov exponent. Using a simple model, we suggest that this decay is related to the negative eigenvalues of the fixed points of the flow. When no-slip walls are considered, as predicted by the model, the behavior is radically different, with a very large tail following a power law with an exponent close to −3.
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews

Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discriminate only between positive and negative sentiment, turning the task into a standard binary classification problem. We compare several ma- chine learning approaches to this problem, and combine them to achieve the best possible results. We show how to use for this task the standard generative lan- guage models, which are slightly complementary to the state of the art techniques. We achieve strong results on a well-known dataset of IMDB movie reviews. Our results are easily reproducible, as we publish also the code needed to repeat the experiments. This should simplify further advance of the state of the art, as other researchers can combine their techniques with ours with little effort.
Comprehensive Time-Series Regression Models Using Gretl — U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013

Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalman filter, transfer-function and intervention models, unit root tests, cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M, Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly time series of GDP and Government Consumption Expenditures & Gross Investment (GCEGI) from 1980 to 2013. The article is organized as: (I) Definition; (II) Regression Models; (III) Discussion. Additionally, I discovered a unique interaction between GDP and GCEGI in both the short-run and the long-run and provided policy makers with some suggestions. For example in the short run, GDP responded positively and very significantly (0.00248) to GCEGI, while GCEGI reacted positively but not too significantly (0.08051) to GDP. In the long run, current GDP responded negatively and permanently (0.09229) to a shock in past GCEGI, while current GCEGI reacted negatively yet temporarily (0.29821) to a shock in past GDP. Therefore, policy makers should not adjust current GCEGI based merely on the condition of current and past GDP. Although increasing GCEGI does help GDP in the short-term, significantly abrupt increase in GCEGI might not be good to the long-term health of GDP. Instead, a balanced, sustainable, and economically viable solution is recommended, so that the short-term benefits to the current economy from increasing GCEGI often largely secured by the long-term loan outweigh or at least equal to the negative effect to the future economy from the long-term debt incurred by the loan. Finally, I found that non-normally distributed volatility models generally perform better than normally distributed ones. More specifically, TARCH-GED performs the best in the group of non-normally distributed, while GARCH-M does the best in the group of normally distributed.
Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses

Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees), they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014) presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistically consistent under the multi-species coalescent model. New data used in this study are available at DOI: http://dx.doi.org/10.6084/m9.figshare.1411146, and the software is available at https://github.com/smirarab/binning.
A perceptual hash function to store and retrieve large scale DNA sequences

This paper proposes a novel approach for storing and retrieving massive DNA sequences.. The method is based on a perceptual hash function, commonly used to determine the similarity between digital images, that we adapted for DNA sequences. Perceptual hash function presented here is based on a Discrete Cosine Transform Sign Only (DCT-SO). Each nucleotide is encoded as a fixed gray level intensity pixel and the hash is calculated from its significant frequency characteristics. This results to a drastic data reduction between the sequence and the perceptual hash. Unlike cryptographic hash functions, perceptual hashes are not affected by "avalanche effect" and thus can be compared. The similarity distance between two hashes is estimated with the Hamming Distance, which is used to retrieve DNA sequences. Experiments that we conducted show that our approach is relevant for storing massive DNA sequences, and retrieving them.
Our supermassive black hole rivaled the Sun in the ancient X-ray sky

Sagittarius A* (SgrA*) lying in the Galactic Centre 8 kpc from Earth, hosts the closest supermassive black hole known to us. It is now inactive, but there is evidence indicating that about six million years ago it underwent a powerful outburst where the luminosity could have approached the Eddington limit. Motivated by the fact that in extragalaxies the supermassive black holes with similar masses and near-Eddington luminosities are usually strong X-ray emitters, we calculate here the X-ray luminosity of SgrA*. For that, we assume that the outburst was due to accretion of gas or the tidal disruption of a star. We show that these cases could precipitate on Earth a hard X-ray (i.e. hν>2 keV) flux comparable to that from the current quiescent sun. The flux in harder energy band 20 keV<hν<100 keV, however, surpasses that from an X-class solar flare, and the irradiation timescale is also much longer, ranging from weeks to 105 years depending on the outburst scenario. In the solar system gas giants will suffer the biggest impact in their atmospheres. Lower-mass planets such as Earth receive a level of radiation that might have played a role in the evolution of their primitive atmospheres, so that a detailed study of the consequences deserves further investigation. Planetary systems closer to SgrA* receive higher irradiance levels, making them more likely uninhabitable.
Floquet systems coupled to particle reservoirs

Open quantum systems, when driven by a periodic field, can relax to effective statistical ensembles that resemble their equilibrium counterparts. We consider a class of problems in which a periodically- driven quantum system is allowed to exchange both energy and particles with a thermal reservoir. We demonstrate that, even for noninteracting systems, effective equilibration to the grand canonical ensemble requires both fine tuning the system-bath coupling and selecting a sufficiently simple driving protocol. We study a tractable subclass of these problems in which the long-time steady state of the system can be determined analytically, and demonstrate that the system effectively thermalizes with fine tuning, but does not thermalize for general values of the system-bath couplings. When the driven system does not thermalize, it supports a tunable persistent current in the steady state without external bias. We compute this current analytically for two examples of interest: 1) a driven double quantum dot, where the current is interpreted as a DC electrical current, and 2) driven Dirac fermions in graphene, where it is interpreted as a valley current.
Effective field theory for spacetime symmetry breaking

We discuss the effective field theory for spacetime symmetry breaking from the local symmetry point of view. By gauging spacetime symmetries, the identification of Nambu-Goldstone (NG) fields and the construction of the effective action are performed based on the breaking pattern of diffeomorphism, local Lorentz, and (an)isotropic Weyl symmetries as well as the internal symmetries including possible central extensions in nonrelativistic systems. Such a local picture distinguishes, e.g., whether the symmetry breaking condensations have spins and provides a correct identification of the physical NG fields, while the standard coset construction based on global symmetry breaking does not. We illustrate that the local picture becomes important in particular when we take into account massive modes associated with symmetry breaking, whose masses are not necessarily high. We also revisit the coset construction for spacetime symmetry breaking. Based on the relation between the Maurer-Cartan one form and connections for spacetime symmetries, we classify the physical meanings of the inverse Higgs constraints by the coordinate dimension of broken symmetries. Inverse Higgs constraints for spacetime symmetries with a higher dimension remove the redundant NG fields, whereas those for dimensionless symmetries can be further classified by the local symmetry breaking pattern.
Entanglement entropy of dispersive media from thermodynamic entropy in one higher dimension.

A dispersive medium becomes entangled with zero-point fluctuations in the vacuum. We consider an arbitrary array of material bodies weakly interacting with a quantum field and compute the quantum mutual information between them. It is shown that the mutual information in D dimensions can be mapped to classical thermodynamic entropy in D+1 dimensions. As a specific example, we compute the mutual information both analytically and numerically for a range of separation distances between two bodies in D=2 dimensions and find a logarithmic correction to the area law at short separations. A key advantage of our method is that it allows the strong subadditivity property to be easily verified.
Support recovery without incoherence: A case for nonconvex regularization

We demonstrate that the primal-dual witness proof method may be used to establish variable selection consistency and $\ell_\infty$-bounds for sparse regression problems, even when the loss function and/or regularizer are nonconvex. Using this method, we derive two theorems concerning support recovery and $\ell_\infty$-guarantees for the regression estimator in a general setting. Our results provide rigorous theoretical justification for the use of nonconvex regularization: For certain nonconvex regularizers with vanishing derivative away from the origin, support recovery consistency may be guaranteed without requiring the typical incoherence conditions present in $\ell_1$-based methods. We then derive several corollaries that illustrate the wide applicability of our method to analyzing composite objective functions involving losses such as least squares, nonconvex modified least squares for errors-in variables linear regression, the negative log likelihood for generalized linear models, and the graphical Lasso. We conclude with empirical studies to corroborate our theoretical predictions.
Stabilizing Value Iteration with and without Approximation Errors

Adaptive optimal control using value iteration (VI) initiated from a stabilizing policy is theoretically analyzed in various aspects including the continuity of the result, the stability of the system operated using any single/constant resulting control policy, the stability of the system operated using the evolving/time-varying control policy, the convergence of the algorithm, and the optimality of the limit function. Afterwards, the effect of presence of approximation errors in the involved function approximation processes is incorporated and another set of results for boundedness of the approximate VI as well as stability of the system operated under the results for both cases of applying a single policy or an evolving policy are derived. A feature of the presented results is providing estimations of the region of attraction so that if the initial condition is within the region, the whole trajectory will remain inside it and hence, the function approximation results will be reliable.
Optimal Triggering of Networked Control Systems

This paper is focused on bandwidth allocation in nonlinear networked control systems. The objective is optimal triggering/scheduling for transmitting sensor measurements to the controller through a communication network. An algorithm based on approximate dynamic programming is developed for problems with fixed final times and then the result is extended to problems with infinite horizon. Zero-order-hold (ZOH), generalized ZOH, and networks with packet dropouts are the investigated cases. Problems with unknown models are also addressed and a model-free scheme is established for learning the (approximate) optimal solution. The convergence, optimality, and stability of the results are analyzed and, afterwards, some numerical analyses are presented for demonstrating the potentials of the algorithms in practice.
Cosmological Dynamical Systems

Preface xviiIntroduction 11 A bird’s eye view on cosmology and cosmological problems 131.1 Cosmological models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.1.1 Homogeneity and isotropy: the Roberson-Walker metric . . . . . 161.1.2 Dynamics: the Friedmann equations .  Flat universes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201.1.4 Including curvature . . . . . . . . . . . . . . . . . . . . . . . . . 211.1.5 Geometry, destiny and dark energy . . . . . . . . . . . . . . . . . 221.1.6 Scalar ﬁelds and Dark Energy . . . . . . . . . . . . . . . . . . . 232 Qualitative theory of dynamical systems 452.1  Qualitative theory of dynamical systems . . . . . . . . . . . . . . . . . . 482.2.1 Deﬁnitions and basic results .  Desirable stability properties of nonlinear vector ﬁ elds . . . . . . 512.2.3  Linear stability theorem .  Flow for autonomous vector ﬁelds . . . . . . . . . . . . . . . . . 5 72.2.5 Invariant sets .  Stable, Unstable and Center subspaces of singularpoints of linear autonomous vector ﬁelds . . . . . . . . 602.2.5.2 Stable, Unstable and Center manifolds of singularpoints of nonlinear autonomous vector ﬁelds . . . . . . 642.2.5.3 Center Manifold Theory . . . . . . . . . . . . . . . . . 662.2.5.4 Normal Forms . . . . . . . . . . . . . . . . . . . . . . 682.2.5.5 Asymptotic behavior .  Procedure for analyzing cosmological dynamical systems . . . . . . . . . 76i
Localized Guided-Mode and Cavity-Mode Double Resonance in Photonic Crystal Nanocavities

We investigate the use of guided modes bound to defects in photonic crystals for achieving double resonances. Photoluminescence enhancement by more than three orders of magnitude has been observed when the excitation and emission wavelengths are simultaneously in resonance with the localized guided mode and cavity mode, respectively. We find that the localized guided modes are relatively insensitive to the size of the defect for one of the polarizations, allowing for flexible control over the wavelength combinations. This double resonance technique is expected to enable enhancement of photoluminescence and nonlinear wavelength conversion efficiencies in a wide variety of systems.
La teoria de supercuerdas

We discuss superstring theory based on the reader's intuition and her college knowledge of general physics and calculus. Nowadays, superstring theory is considered the best candidate to explain physical phenomena that involve all of the known fundamental interactions: the gravitational, electromagnetic, weak and strong forces. The main purpose of this work is to show and explain some of the concepts and details about superstrings, such as supersymmetry, compactification, dualities and branes.
Thermodynamics of heavy-light hadrons

Ratios of cumulants of conserved net charge fluctuations are sensitive to the degrees of freedom that are carriers of the corresponding quantum numbers in different phases of strong interaction matter. We calculate second and fourth order cumulants of net charm and strange fluctuations and their correlations with other conserved charges such as net baryon number and electric charge. Simulation are performed on $N_\tau$=6 and 8 lattices using the Highly Improved Staggered Quark (HISQ) action with a light to strange quark mass ratio of 1/20 and having charm quarks treated in the quenched approximation. Analysing appropriate ratios of these cumulants we observe that both open strange and charm hadrons start to get dissociated in the chiral crossover region. We provide indirect evidence for the existence of additional, experimentally yet unobserved open charm and strange hadrons from QCD thermodynamics. This is done by comparing lattice QCD results to Hadron Resonance Gas (HRG) model calculations performed with a hadron spectrum as listed in the Particle Data Tables as well as with a spectrum predicted in the relativistic quark model and observed in lattice QCD calculations. We also discuss the influence of these experimentally yet unobserved states on the determination of freeze-out temperature and chemical potentials from heavy ion collision experiments. We found that including these additional states in the HRG model leads to a systematic 5-8 MeV decrease in the freeze-out temperature of strange hadrons.
Stochastic Descent Analysis of Representation Learning Algorithms

Although stochastic approximation learning methods have been widely used in the machine learning literature for over 50 years, formal theoretical analyses of specific machine learning algorithms are less common because stochastic approximation theorems typically possess assumptions which are difficult to communicate and verify. This paper presents a new stochastic approximation theorem for state-dependent noise with easily verifiable assumptions applicable to the analysis and design of important deep learning algorithms including: adaptive learning, contrastive divergence learning, stochastic descent expectation maximization, and active learning.
Systematic approximation for QCD at non-zero density

We use the heavy dense formulation of QCD (HD-QCD) as the basis for an analytic expansion as systematic approximation to QCD at non-zero density, keeping the full Yang-Mills action. We analyse the structure of the baryonic density and other quantities and present data from the complex Langevin equation (CLE) and reweighting (RW) calculations for 2 flavours of Wilson fermions.
Tensor product methods and entanglement optimization for ab initio quantum chemistry

The treatment of high-dimensional problems such as the Schrodinger equation can be approached by concepts of tensor product approximation. We present general techniques that can be used for the treatment of high-dimensional optimization tasks and time-dependent equations, and connect them to concepts already used in many-body quantum physics. Based on achievements from the past decade, entanglement-based methods—developed from different perspectives for different purposes in distinct communities already matured to provide a variety of tools—can be combined to attack highly challenging problems in quantum chemistry. The aim of the present paper is to give a pedagogical introduction to the theoretical background of this novel field and demonstrate the underlying benefits through numerical applications on a text book example. Among the various optimization tasks, we will discuss only those which are connected to a controlled manipulation of the entanglement which is in fact the key ingredient of the methods considered in the paper. The selected topics will be covered according to a series of lectures given on the topic “New wavefunction methods and entanglement optimizations in quantum chemistry” at the Workshop on Theoretical Chemistry, February 18–21, 2014, Mariapfarr, Austria.