nils-herrmann/clean_scirepeval huggingface dataset:
Data Smashing

Investigation of the underlying physics or biology from empirical data requires a quantifiable notion of similarity when do two observed data sets indicate nearly identical generating processes, and when they do not. The discriminating characteristics to look for in data is often determined by heuristics designed by experts, , distinct shapes of “folded” lightcurves may be used as “features” to classify variable stars, while determination of pathological brain states might require a Fourier analysis of brainwave activity. Finding good features is non-trivial. Here, we propose a universal solution to this problem: we delineate a principle for quantifying similarity between sources of arbitrary data streams, without a priori knowledge, features or training. We uncover an algebraic structure on a space of symbolic models for quantized data, and show that such stochastic generators may be added and uniqely inverted; and that a model and its inverse always sum to the generator of flat white noise. Therefore, every data stream has an anti-stream: data generated by the inverse model. Similarity between two streams, then, is the degree to which one, when summed to the other’s anti-stream, mutually annihilates all statistical structure to noise. We call this data smashing. We present diverse applications, including disambiguation of brainwaves pertaining to epileptic seizures, detection of anomalous cardiac rhythms, and classification of astronomical objects from raw photometry. In our examples, the data smashing principle, without access to any domain knowledge, meets or exceeds the performance of specialized algorithms tuned by domain experts.
Quantum limit on time measurement in a gravitational field

Good clocks are of importance both to fundamental physics and for applications in astronomy, metrology and global positioning systems. In a recent technological breakthrough, researchers at NIST have been able to achieve a stability of one part in 1018 using an ytterbium clock. This naturally raises the question of whether there are fundamental limits to time keeping. In this article we point out that gravity and quantum mechanics set a fundamental limit on the fractional frequency uncertainty of clocks. This limit comes from a combination of the uncertainty relation, the gravitational redshift and the relativistic time dilation effect. For example, a single ion aluminium clock in a terrestrial gravitational field cannot achieve a fractional frequency uncertainty better than one part in 1022. This fundamental limit explores the interaction between gravity and quantum mechanics on a laboratory scale.
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes

An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in an array of areas. Here we propose a generative probabilistic framework using a reinforced Poisson process to explicitly model the process through which individual items gain their popularity. This model distinguishes itself from existing models via its capability of modeling the arrival process of popularity and its remarkable power at predicting the popularity of individual items. It possesses the flexibility of applying Bayesian treatment to further improve the predictive power using a conjugate prior. Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods.
Finite-size scaling at quantum transitions

We develop the finite-size scaling (FSS) theory at quantum transitions, considering generic boundary conditions, such as open and periodic boundary conditions, and also the corrections to the leading FSS behaviors. Using renormalization-group (RG) theory, we generalize Wegner's scaling Ansatz to the quantum case, classifying the different sources of scaling corrections. We identify nonanalytic corrections due to irrelevant (bulk and boundary) RG perturbations and analytic contributions due to regular backgrounds and analytic expansions of the nonlinear scaling fields. 
To check the general predictions, we consider the quantum XY chain in a transverse field. For this model exact or numerically accurate results can be obtained by exploiting its fermionic quadratic representation. We study the FSS of several observables, such as the free energy, the energy differences between low-energy levels, correlation functions of the order parameter, etc., confirming the general predictions in all cases. Moreover, we consider bipartite entanglement entropies, which are characterized by the presence of additional scaling corrections, as predicted by conformal field theory.
A stochastic model for Case-Based Reasoning

Case-Bsed Reasoning (CBR) is a recent theory for problem-solving and learning in computers and people.Broadly construed it is the process of solving new problems based on the solution of similar past problems. In the present paper we introduce an absorbing Markov chain on the main steps of the CBR process.In this way we succeed in obtaining the probabilities for the above process to be in a certain step at a certain phase of the solution of the corresponding problem, and a measure for the efficiency of a CBR system. Examples are given to illustrate our results.
Concave penalized estimation of sparse Gaussian Bayesian networks

We develop a penalized likelihood estimation framework to estimate the structure of Gaussian Bayesian networks from observational data. In contrast to recent methods which accelerate the learning problem by restricting the search space, our main contribution is a fast algorithm for score-based structure learning which does not restrict the search space in any way and works on high-dimensional datasets with thousands of variables. Our use of concave regularization, as opposed to the more popular $\ell_0$ (e.g. BIC) penalty, is new. Moreover, we provide theoretical guarantees which generalize existing asymptotic results when the underlying distribution is Gaussian. Most notably, our framework does not require the existence of a so-called faithful DAG representation, and as a result the theory must handle the inherent nonidentifiability of the estimation problem in a novel way. Finally, as a matter of independent interest, we provide a comprehensive comparison of our approach to several standard structure learning methods using open-source packages developed for the R language. Based on these experiments, we show that our algorithm is significantly faster than other competing methods while obtaining higher sensitivity with comparable false discovery rates for high-dimensional data. In particular, the total runtime for our method to generate a solution path of 20 estimates for DAGs with 8000 nodes is around one hour.
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing

For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of features greatly exceeds the number of training examples, but where only a few features suffice for accurate classification. We show that sum-product GAMP can be used to (approximately) minimize the classification error rate and max-sum GAMP can be used to minimize a wide variety of regularized loss functions. Furthermore, we describe an expectation-maximization (EM)-based scheme to learn the associated model parameters online, as an alternative to cross-validation, and we show that GAMP's state-evolution framework can be used to accurately predict the misclassification rate. Finally, we present a detailed numerical study to confirm the accuracy, speed, and flexibility afforded by our GAMP-based approaches to binary linear classification and feature selection.
On the gauged Kähler isometry in minimal supergravity models of inflation

In this paper we address the question how to discriminate whether the gauged isometry group 𝒢Σ of the Kähler manifold Σ that produces a D‐type inflaton potential in a Minimal Supergravity Model is elliptic, hyperbolic or parabolic. We show that the classification of isometries of symmetric cosets can be extended to non symmetric Σ.s if these manifolds satisfy additional mathematical restrictions. The classification criteria established in the mathematical literature are coherent with simple criteria formulated in terms of the asymptotic behavior of the Kähler potential 𝒦(C) = 2 J(C) where the real scalar field C encodes the inflaton field. As a by product of our analysis we show that phenomenologically admissible potentials for the description of inflation and in particular α‐attractors are mostly obtained from the gauging of a parabolic isometry, this being, in particular the case of the Starobinsky model. Yet at least one exception exists of an elliptic α‐attractor, so that neither type of isometry can be a priori excluded. The requirement of regularity of the manifold Σ poses instead strong constraints on the α‐attractors and reduces their space considerably. Curiously there is a unique integrable α‐attractor corresponding to a particular value of this parameter.
Lens optics and the continuity problems of the ABCD matrix

Paraxial lens optics is discussed to study the continuity properties of the beam transfer matrix. The two-by-two matrix for the one-lens camera-like system can be converted to an equi-diagonal form by a scale transformation, leaving the off-diagonal elements invariant. It is shown that the matrix remains continuous during the focusing process, but this transition is not analytic. However, its first derivative is still continuous, which leads to the concept of ‘tangential continuity’. It is then shown that this tangential continuity is applicable to matrices pertinent to periodic optical systems, where the equi-diagonalization is achieved by a similarity transformation using rotations. It is also noted that both the scale transformations and the rotations can be unified within the framework of Hermitian transformations.
Hunting quasidegenerate Higgsinos

We present a new strategy to uncover light, quasidegenerate Higgsinos, a likely ingredient in a natural supersymmetric model. Our strategy focuses on Higgsinos with interstate splittings of $O(5\ensuremath{-}50)\text{ }\text{ }\mathrm{GeV}$ that are produced in association with a hard, initial-state jet and decay via off-shell gauge bosons to two or more leptons and missing energy, $pp\ensuremath{\rightarrow}j+{\overline{)E}}_{T}+{2}^{+}\ensuremath{\ell}$. The additional jet is used for triggering, allowing us to significantly loosen the lepton requirements and gain sensitivity to small inter-Higgsino splittings. Focusing on the two-lepton signal, we find the seemingly large backgrounds from diboson plus jet, $\overline{t}t$ and $Z/{\ensuremath{\gamma}}^{*}+j$ can be reduced with careful cuts, and that fake backgrounds appear minor. For Higgsino masses ${m}_{\ensuremath{\chi}}$ just above the current LEP II bound ($\ensuremath{\mu}\ensuremath{\simeq}110\text{ }\text{ }\mathrm{GeV}$) we find the significance can be as high as $3\ensuremath{\sigma}$ at the LHC using the existing $20\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of 8 TeV data. Extrapolating to LHC at 14 TeV with $100\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of data, and as one example ${M}_{1}={M}_{2}=500\text{ }\text{ }\mathrm{GeV}$, we find $5\ensuremath{\sigma}$ evidence for ${m}_{\ensuremath{\chi}}\ensuremath{\lesssim}140\text{ }\text{ }\mathrm{GeV}$ and $2\ensuremath{\sigma}$ evidence for ${m}_{\ensuremath{\chi}}\ensuremath{\lesssim}200\text{ }\text{ }\mathrm{GeV}$. We also present a reinterpretation of ATLAS/CMS monojet bounds in terms of degenerate Higgsino ($\ensuremath{\delta}{m}_{\ensuremath{\chi}}\ensuremath{\ll}5\text{ }\text{ }\mathrm{GeV}$) plus jet production. We find the current monojet bounds on ${m}_{\ensuremath{\chi}}$ are no better than the chargino bounds from LEP II.
Structural Patterns in Complex Systems Using Multidendrograms

Complex systems are usually represented as an intricate set of relations between their components forming a complex graph or network. The understanding of their functioning and emergent properties are strongly related to their structural properties. The finding of structural patterns is of utmost importance to reduce the problem of understanding the structure-function relationships. Here we propose the analysis of similarity measures between nodes using hierarchical clustering methods. The discrete nature of the networks usually leads to a small set of different similarity values, making standard hierarchical clustering algorithms ambiguous. We propose the use of "multidendrograms", an algorithm that computes agglomerative hierarchical clusterings implementing a variable-group technique that solves the non-uniqueness problem found in the standard pair-group algorithm. This problem arises when there are more than two clusters separated by the same maximum similarity (or minimum distance) during the agglomerative process. Forcing binary trees in this case means breaking ties in some way, thus giving rise to different output clusterings depending on the criterion used. Multidendrograms solves this problem grouping more than two clusters at the same time when ties occur.
Optimal network modularity for information diffusion.

We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.
Self-consistent description of single-particle levels of magic nuclei

Single-particle levels of seven magic nuclei are calculated within the Energy Density Functional (EDF) method by Fayans et al. Three versions of the EDF are used, the initial Fayans functional DF3 and its two variations, DF3-a and DF3-b, with different values of spin-orbit parameters. Comparison is made with predictions of the Skyrme-Hartree-Fock method with the HFB-17 functional. For the DF3-a functional, phonon coupling (PC) corrections to single-particle energies are found self-consistently with an approximate account for the tadpole diagram. Account for the PC corrections improves agreement with the data for heavy nuclei, e.g. for 208 Pb. On the other hand, for lighter nuclei, e.g. 40,48 Ca, PC corrections make the agreement a little worse. As estimations show, the main reason is that the approximation we use for the tadpole term is less accurate for the light nuclei.
Exocomets in the circumstellar gas disk of HD 172555

The source HD 172555 is a young A7V star surrounded by a debris disk with a gaseous component. Here, we present the detection of variable absorption features detected simultaneously in the Ca II K and H doublet lines (at λ3933 A and λ3968 A). We identified the presence of these absorption signatures at four different epochs in the 129 HARPS high-resolution spectra gathered between 2004 and 2011. These transient absorption features are most likely due to falling evaporating bodies (FEBs, or exocomets) that produce absorbing gas observed transiting in front of the central star. We also detect a stable Ca II absorption component at the star’s radial velocity. With no corresponding detection in the Na I line, the resulting very low upper limit for the Na I/Ca II ratio suggests that this absorption is due to circumstellar gas.
Accelerating ABC methods using Gaussian processes

Approximate Bayesian computation (ABC) methods are used to approximate posterior distributions using simulation rather than likelihood calculations. We introduce Gaussian process (GP) accelerated ABC, which we show can significantly reduce the number of simulations required. As computational resource is usually the main determinant of accuracy in ABC, GP-accelerated methods can thus enable more accurate inference in some models. GP models of the unknown log-likelihood function are used to exploit continuity and smoothness, reducing the required computation. We use a sequence of models that increase in accuracy, using intermediate models to rule out regions of the parameter space as implausible. The methods will not be suitable for all problems, but when they can be used, can result in significant computational savings. For the Ricker model, we are able to achieve accurate approximations to the posterior distribution using a factor of 100 fewer simulator evaluations than comparable Monte Carlo approaches, and for a population genetics model we are able to approximate the exact posterior for the first time.
THE ATMOSPHERES OF EARTHLIKE PLANETS AFTER GIANT IMPACT EVENTS

It is now understood that the accretion of terrestrial planets naturally involves giant collisions, the moon-forming impact being a well-known example. In the aftermath of such collisions, the surface of the surviving planet is very hot and potentially detectable. Here we explore the atmospheric chemistry, photochemistry, and spectral signatures of post-giant-impact terrestrial planets enveloped by thick atmospheres consisting predominantly of CO2 and H2O. The atmospheric chemistry and structure are computed self-consistently for atmospheres in equilibrium with hot surfaces with composition reflecting either the bulk silicate Earth (which includes the crust, mantle, atmosphere, and oceans) or Earth's continental crust. We account for all major molecular and atomic opacity sources including collision-induced absorption. We find that these atmospheres are dominated by H2O and CO2, while the formation of CH4 and NH3 is quenched because of short dynamical timescales. Other important constituents are HF, HCl, NaCl, and SO2. These are apparent in the emerging spectra and can be indicative that an impact has occurred. The use of comprehensive opacities results in spectra that are a factor of two lower brightness temperature in the spectral windows than predicted by previous models. The estimated luminosities show that the hottest post-giant-impact planets will be detectable with near-infrared coronagraphs on the planned 30 m class telescopes. The 1–4 μm will be most favorable for such detections, offering bright features and better contrast between the planet and a potential debris disk. We derive cooling timescales on the order of 105–6 yr on the basis of the modeled effective temperatures. This leads to the possibility of discovering tens of such planets in future surveys.
Self-force via Green functions and worldline integration

A compact object moving in curved spacetime interacts with its own gravitational field. This leads to 
both dissipative and conservative corrections to the motion, which can be interpreted as a self-force acting 
on the object. The original formalism describing this self-force relied heavily on the Green function 
of the linear differential operator that governs gravitational perturbations. However, because the global 
calculation of Green functions in nontrivial black-hole spacetimes has been an open problem until recently, 
alternative methods were established to calculate self-force effects using sophisticated regularization 
techniques that avoid the computation of the global Green function.We present a method for calculating the 
self-force that employs the global Green function and is therefore closely modeled after the original self-force 
expressions. Our quantitative method involves two stages: (i) numerical approximation of the retarded 
Green function in the background spacetime; (ii) evaluation of convolution integrals along the worldline 
of the object. This novel approach can be used along arbitrary worldlines, including those currently 
inaccessible to more established computational techniques. Furthermore, it yields geometrical insight into 
the contributions to self-interaction from curved geometry (backscattering) and trapping of null geodesics. 
We demonstrate the method on the motion of a scalar charge in Schwarzschild spacetime. This toy model 
retains the physical history dependence of the self-force but avoids gauge issues and allows us to focus on 
basic principles. We compute the self-field and self-force for many worldlines including accelerated 
circular orbits, eccentric orbits at the separatrix, and radial infall. This method, closely modeled after the 
original formalism, provides a promising complementary approach to the self-force problem.
A conjectured class of scale-invariant distances on inner product spaces

Let $V$ be an inner product space, and $x, y \in V$; the conjecture is made that, for any $p \in [1, \infty]$, the function $d_p(x, y):=\|x-y\|/(\|x\|^p+ \|y\|^p)^{1/p}$ is a distance on $V$.
An Explicit Conjectured Determinant Evaluation Whose Proof Would Make Me Happy (and the OEIS richer)

I conjecture a certain explicit determinant evaluation, whose proof would imply the solution of certain enumeration problem that I have been working on, and that I find interesting. I am pledging \$500 to the OEIS Foundation (in honor of the prover!) for a proof, and \$50 (in honor of the disprover)
Proposta di nuovi strumenti per comprendere come funziona la cognizione (Novel tools to understand how cognition works)

I think that the main reason why we do not understand the general principles of how knowledge works (and probably also the reason why we have not yet designed and built efficient machines capable of artificial intelligence), is not the excessive complexity of cognitive phenomena, but the lack of the conceptual and methodological tools to properly address the problem. It is like trying to build up Physics without the concept of number, or to understand the origin of species without including the mechanism of natural selection. In this paper I propose some new conceptual and methodological tools, which seem to offer a real opportunity to understand the logic of cognitive processes. I propose a new method to properly treat the concepts of structure and schema, and to perform on them operations of structural analysis. These operations allow to move straightforwardly from concrete to more abstract representations. With these tools I will suggest a definition for the concept of rule, of regularity and of emergent phenomena. From the analysis of some important aspects of the rules, I suggest to distinguish them in operational and associative rules. I propose that associative rules assume a dominant role in cognition. I also propose a definition for the concept of problem. At the end I will briefly illustrate a possible general model for cognitive systems.
THE SPIN OF THE BLACK HOLE GS 1124−683: OBSERVATION OF A RETROGRADE ACCRETION DISK?

We re-examine archival Ginga data for the black hole binary system GS 1124−683, obtained when the system was undergoing its 1991 outburst. Our analysis estimates the dimensionless spin parameter a* = cJ/GM2 by fitting the X-ray continuum spectra obtained while the system was in the “thermal dominant” state. For likely values of mass and distance, we find the spin to be (90% confidence), implying that the disk is retrograde (i.e., rotating antiparallel to the spin axis of the black hole). We note that this measurement would be better constrained if the distance to the binary and the mass of the black hole were more accurately determined. This result is unaffected by the model used to fit the hard component of the spectrum. In order to be able to recover a prograde spin, the mass of the black hole would need to be at least 15.25 M☉, or the distance would need to be less than 4.5 kpc, both of which disagree with previous determinations of the black hole mass and distance. If we allow fcol to be free, we obtain no useful spin constraint. We discuss our results in the context of recent spin measurements and implications for jet production.