nils-herrmann/clean_scirepeval huggingface dataset:
galpy: A python LIBRARY FOR GALACTIC DYNAMICS

I describe the design, implementation, and usage of galpy, a python package for galactic-dynamics calculations. At its core, galpy consists of a general framework for representing galactic potentials both in python and in C (for accelerated computations); galpy functions, objects, and methods can generally take arbitrary combinations of these as arguments. Numerical orbit integration is supported with a variety of Runge–Kutta-type and symplectic integrators. For planar orbits, integration of the phase-space volume is also possible. galpy supports the calculation of action–angle coordinates and orbital frequencies for a given phase-space point for general spherical potentials, using state-of-the-art numerical approximations for axisymmetric potentials, and making use of a recent general approximation for any static potential. A number of different distribution functions (DFs) are also included in the current release; currently, these consist of two-dimensional axisymmetric and non-axisymmetric disk DFs, a three-dimensional disk DF, and a DF framework for tidal streams. I provide several examples to illustrate the use of the code. I present a simple model for the Milky Way's gravitational potential consistent with the latest observations. I also numerically calculate the Oort functions for different tracer populations of stars and compare them to a new analytical approximation. Additionally, I characterize the response of a kinematically warm disk to an elliptical m = 2 perturbation in detail. Overall, galpy consists of about 54,000 lines, including 23,000 lines of code in the module, 11,000 lines of test code, and about 20,000 lines of documentation. The test suite covers 99.6% of the code. galpy is available at http://github.com/jobovy/galpy with extensive documentation available at http://galpy.readthedocs.org/en/latest.
Natural inflation and quantum gravity.

Cosmic inflation provides an attractive framework for understanding the early Universe and the cosmic microwave background. It can readily involve energies close to the scale at which quantum gravity effects become important. General considerations of black hole quantum mechanics suggest nontrivial constraints on any effective field theory model of inflation that emerges as a low-energy limit of quantum gravity, in particular, the constraint of the weak gravity conjecture. We show that higher-dimensional gauge and gravitational dynamics can elegantly satisfy these constraints and lead to a viable, theoretically controlled and predictive class of natural inflation models.
Monochromatic neutrinos generated by dark matter and the seesaw mechanism

We study a minimal extension of the Standard Model where a scalar field is coupled to the right handed neutrino responsible for the see-saw mechanism for neutrino masses. In the absence of other couplings, below 8 TeV the scalar $A$ has a unique decay mode $A \rightarrow \nu \nu$, $\nu$ being the physical observed light neutrino state. Above 8 (11) TeV, the 3-body (4-body) decay modes dominate. Imposing constraints on neutrino masses $m_\nu$ from atmospheric and solar experiments implies a long lifetime for $A$, much larger than the age of the Universe, making it a natural dark matter candidate. Its lifetime can be as large as $10^{29}$ seconds, and its signature below 8 TeV would be a clear monochromatic neutrino signal, which can be observed by ANTARES or IceCube. Under certain conditions, the scalar $A$ may be viewed as a Goldstone mode of a complex scalar field whose vacuum expectation value generates the Majorana mass for $\nu_R$. In this case, we expect the dark matter scalar to have a mass $\lesssim 10$ GeV.
Hamiltonian truncation study of the $φ^4$ theory in two dimensions

We defend the Fock-space Hamiltonian truncation method, which allows us to calculate numerically the spectrum of strongly coupled quantum field theories, by putting them in a finite volume and imposing a UV cutoff. The accuracy of the method is improved via an analytic renormalization procedure inspired by the usual effective field theory. As an application, we study the two-dimensional.4 theory for a wide range of couplings. The theory exhibits a quantum phase transition between the symmetry-preserving and symmetry-breaking phases. We extract quantitative predictions for the spectrum and the critical coupling and make contact with previous results from the literature. Future directions to further improve the accuracy of the method and enlarge its scope of applications are outlined.
Ultimate target for dark matter searches

The combination of $S$-matrix unitarity and the dynamics of thermal freeze-out for massive relic particles (denoted here simply by WIMPs) implies a lower limit on the density of such particles, that provide a (potentially subdominant) contribution to dark matter. This then translates to lower limits to the signal rates for a variety of techniques for direct and indirect detection of dark matter. For illustration, we focus on models where annihilation is $s$-wave dominated. We derive lower limits to the flux of gamma rays from WIMP annihilation at the Galactic center; direct detection of WIMPs; energetic neutrinos from WIMP annihilation in the Sun; and the effects of WIMPs on the angular power spectrum and frequency spectrum of the cosmic microwave background radiation. The results suggest that a variety of dark-matter-search techniques may provide interesting avenues to seek new physics, even if WIMPs do not constitute all the dark matter. While the limits are quantitatively some distance from the reach of current measurements, they may be interesting for long-range planning exercises.
Quantum deep learning

In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well known classical counterparts.
Multipole moments in scalar-tensor theory of gravity

Stationary, asymptotically flat spacetimes in general relativity can be characterized by their multipole moments. The moments have proved to be very useful tools for extracting information about the spacetime from various observables and, more recently, for establishing universalities in the structure of neutron stars. As a first step toward extending these methods beyond general relativity, we develop the formalism that allows one to define and calculate the multipole moment in scalar-tensor theories of gravity.
Optimizing optical Bragg scattering for single-photon frequency conversion

We develop a systematic theory for optimising single-photon frequency conversion using optical Bragg scattering. The efficiency and phase-matching conditions for the desired Bragg scattering conversion as well as spurious scattering and modulation instability are identified. We find that third-order dispersion can suppress unwanted processes, while dispersion above the fourth order limits the maximum conversion efficiency. We apply the optimisation conditions to frequency conversion in highly nonlinear fiber, silicon nitride waveguides and silicon nanowires. Efficient conversion is confirmed using full numerical simulations. These design rules will assist the development of efficient quantum frequency conversion between multicolour single photon sources for integration in complex quantum networks.
APPROPRIATE CAUSAL MODELS AND THE STABILITY OF CAUSATION

Causal models defined in terms of structural equations have proved to be quite a powerful way of representing knowledge regarding causality. However, a number of authors have given examples that seem to show that the Halpern–Pearl (HP) definition of causality (Halpern & Pearl, 2005) gives intuitively unreasonable answers. Here it is shown that, for each of these examples, we can give two stories consistent with the description in the example, such that intuitions regarding causality are quite different for each story. By adding additional variables, we can disambiguate the stories. Moreover, in the resulting causal models, the HP definition of causality gives the intuitively correct answer. It is also shown that, by adding extra variables, a modification to the original HP definition made to deal with an example of Hopkins & Pearl (2003) may not be necessary. Given how much can be done by adding extra variables, there might be a concern that the notion of causality is somewhat unstable. Can adding extra variables in a “conservative” way (i.e., maintaining all the relations between the variables in the original model) cause the answer to the question “Is X = x a cause of Y = y?” to alternate between “yes” and “no”? It is shown that we can have such alternation infinitely often, but if we take normality into consideration, we cannot. Indeed, under appropriate normality assumptions. Adding an extra variable can change the answer from “yes’ to “no”, but after that, it cannot change back to “yes”.
A note on Hilbert Space Representation of Quantum Mechanics with Minimal Length

We study some fundamental issues related to the Hilbert space representation of quantum mechanics in the presence of a minimal length and maximal momentum. In this framework, the maximally localized states and quasi-position representation introduced by Kempf et al. are reconsidered and modified. We show that all studies in recent years, including [15] and [16] need serious modification in order to be a consistent framework for quantum mechanics in Planck scale.
The Thermodynamics of a Gravitating Vacuum

In the present days of modern cosmology it is assumed that the main ingredient to cosmic energy presently is vacuum energy with an energy density vac that is constant over the cosmic evolution. In this paper here we show, however, that this assumption of constant vacuum energy density is unphysical, since it conflicts with the requirements of cosmic thermodynamics. We start from the total vacuum energy including the negatively valued gravitational binding energy and show that cosmic thermodynamics then requires that the cosmic vacuum energy density can only vary with cosmic scale R = R(t) according to vac R with only two values of being allowed, namely 1 = 2 and 2 = 5=2. We then discuss these two remaining solutions and find, when requiring a universe with a constant total energy, that the only allowed power index is 1 = 2. We discuss the consequences of this scaling of vac and show the results for a cosmic scale evolution of a quasi-empty universe like the one that we are presently faced by.
Predicting percolation thresholds in networks.

We consider different methods, which do not rely on numerical simulations of the percolation process, to approximate percolation thresholds in networks. We perform a systematic analysis on synthetic graphs and a collection of 109 real networks to quantify their effectiveness and reliability as prediction tools. Our study reveals that the inverse of the largest eigenvalue of the nonbacktracking matrix of the graph often provides a tight lower bound for true percolation threshold. However, in more than 40% of the cases, this indicator is less predictive than the naive expectation value based solely on the moments of the degree distribution. We find that the performance of all indicators becomes worse as the value of the true percolation threshold grows. Thus, none of them represents a good proxy for the robustness of extremely fragile networks.
Hybrid recommendation methods in complex networks

We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.
A Topic Modeling Approach to Ranking

We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user behavior in a natural way. We show how the estimation of latent rankings in the new generative model can be formally reduced to the estimation of topics in a statistically equivalent topic modeling problem. We leverage recent advances in the topic modeling literature to develop an algorithm that can learn shared latent rankings with provable consistency as well as sample and computational complexity guarantees. We demonstrate that the new approach is empirically competitive with the current state-of-the-art approaches in predicting preferences on some semi-synthetic and real world datasets.
Compact Part-Based Image Representations

Learning compact, interpretable image representations is a very natural task which has not been solved satisfactorily even for simple classes of binary images. In this paper, we review various ways of composing parts (or experts) for binary data and argue that competitive forms of interaction are best suited to learn low-dimensional representations. We propose a new composition rule which discourages parts from focusing on similar structures and which penalizes opposing votes strongly so that abstaining from voting becomes more attractive. We also introduce a novel sequential initialization procedure based on a process of oversimplification and correction. Experiments show that with our approach very intuitive models can be learned.
In situ fiber‐optical monitoring of cytosolic calcium in tissue explant cultures

We present a fluorescence‐lifetime based method for monitoring cell and tissue activity in situ, during cell culturing and in the presence of a strong autofluorescence background. The miniature fiber‐optic probes are easily incorporated in the tight space of a cell culture chamber or in an endoscope. As a first application we monitored the cytosolic calcium levels in porcine tracheal explant cultures using the Calcium Green‐5N (CG5N) indicator. Despite the simplicity of the optical setup we are able to detect changes of calcium concentration as small as 2.5 nM, with a monitoring time resolution of less than 1 s.
Feature Weight Tuning for Recursive Neural Networks

This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the higher-level representation. The proposed model can be viewed as incorporating a more powerful compositional function for embedding acquisition in recursive neural networks. Experimental results demonstrate the significant improvement over standard neural models.
Certifying and Removing Disparate Impact

What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process. When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses. We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: methods based on Additive Noise Models (ANMs) and Information Geometric Causal Inference (IGCI). We present the benchmark CAUSEEFFECTPAIRS that consists of data for 100 different causee ffect pairs selected from 37 data sets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the method based on Additive Noise Models that has originally been proposed by Hoyer et al. (2009), which obtains an accuracy of 63 ± 10 % and an AUC of 0.74 ± 0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.
Light on curved backgrounds

We consider the motion of light on different spacetime manifolds by calculating the deflection angle, lensing properties and by probing into the possibility of bound states. The metrics in which we examine the light motion include, among other, a general relativistic Dark Matter metric, a dirty Black Hole and a Worm Hole metric, the last two inspired by non-commutative geometry. The lensing in a Holographic Screen metric is discussed in detail. We study also the bending of light around naked singularities like, e.g., the Janis-Newman-Winicour metric and include other cases. A generic property of light behaviour in these exotic metrics is pointed out. For the standard metric like the Schwarzschild and Schwarzschild-de Sitter cases we improve the accuracy of the lensing results for the weak and strong regime.
Belief Revision in the Propositional Closure of a Qualitative Algebra

Belief revision is an operation that aims at modifying old beliefs so that they become consistent with new ones. The issue of belief revision has been studied in various formalisms, in particular, in qualitative algebras (QAs) in which the result is a disjunction of belief bases that is not necessarily representable in a QA. This motivates the study of belief revision in formalisms extending QAs, namely, their propositional closures: in such a closure, the result of belief revision belongs to the formalism. Moreover, this makes it possible to define a contraction operator thanks to the Harper identity. Belief revision in the propositional closure of QAs is studied, an algorithm for a family of revision operators is designed, and an open-source implementation is made freely available on the web.
Local structure entropy of complex networks

Identifying influential nodes in the complex networks is of theoretical and practical significance. There are many methods are proposed to identify the influential nodes in the complex networks. In this paper, a local structure entropy which is based on the degree centrality and the statistical mechanics is proposed to identifying the influential nodes in the complex network. 
In the definition of the local structure entropy, each node has a local network, the local structure entropy of each node is equal to the structure entropy of the local network. The main idea in the local structure entropy is try to use the influence of the local network to replace the node's influence on the whole network. 
The influential nodes which are identified by the local structure entropy are the intermediate nodes in the network. The intermediate nodes which connect those nodes with a big value of degree. 
We use the $Susceptible-Infective$ (SI) model to evaluate the performance of the influential nodes which are identified by the local structure entropy. In the SI model the nodes use as the source of infection. According to the SI model, the bigger the percentage of the infective nodes in the network the important the node to the whole networks. The simulation on four real networks show that the proposed method is efficacious and rationality to identify the influential nodes in the complex networks.
Machine learning for neuroimaging with scikit-learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.