nils-herrmann/clean_scirepeval huggingface dataset:
Frequentism and Bayesianism: A Python-driven Primer

This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python statistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo.
TWO LOCAL VOLUME DWARF GALAXIES DISCOVERED IN 21 cm EMISSION: PISCES A AND B

We report the discovery of two dwarf galaxies, Pisces A and B, from a blind 21 cm H i search. These were the only two galaxies found via optical imaging and spectroscopy of 22 H i clouds identified in the GALFA-H i survey as dwarf galaxy candidates. They have properties consistent with being in the Local Volume (<10 Mpc), and one has resolved stellar populations such that it may be on the outer edge of the Local Group (∼1 Mpc from M31). While the distance uncertainty makes interpretation ambiguous, these may be among the faintest star-forming galaxies known. Additionally, rough estimates comparing these galaxies to ΛCDM dark matter simulations suggest consistency in number density, implying that the dark matter halos likely to host these galaxies are primarily H i-rich. The galaxies may thus be indicative of a large population of dwarfs at the limit of detectability that are comparable to the faint satellites of the Local Group. Because they are outside the influence of a large dark matter halo to alter their evolution, these galaxies can provide critical anchors to dwarf galaxy formation models.
Runaway Electrification of Friable Self-Replicating Granular Matter

We establish that the nonlinear dynamics of collisions between particles favors the charging of an insulating, friable, self-replicating granular material that undergoes nucleation, growth, and fission processes; we demonstrate with a minimal dynamical model that secondary nucleation produces a positive feedback in an electrification mechanism that leads to runaway charging. We discuss ice as an example of such a self-replicating granular material: We confirm with laboratory experiments in which we grow ice from the vapor phase in situ within an environmental scanning electron microscope that charging causes fast-growing and easily breakable palmlike structures to form, which when broken off may form secondary nuclei. We propose that thunderstorms, both terrestrial and on other planets, and lightning in the solar nebula are instances of such runaway charging arising from this nonlinear dynamics in self-replicating granular matter.
Spiral precipitation patterns in confined chemical gardens

Chemical gardens are plant-like mineral forms existing in nature at various scales. Even though they have been described for more than three centuries thanks to their beauty and analogies with biological forms, their growth mechanisms remain largely not understood. To gain insight into their formation processes, we develop an innovative strategy to study these systems in which chemical gardens grow in a quasi-2D confined geometry upon injecting one reagent solution into the other. The advantage of this procedure over previous works is to allow studying, in controlled and reproducible conditions, the effects of variable concentrations and injection rates on the selected morphology. Chemical gardens are mineral aggregates that grow in three dimensions with plant-like forms and share properties with self-assembled structures like nanoscale tubes, brinicles, or chimneys at hydrothermal vents. The analysis of their shapes remains a challenge, as their growth is influenced by osmosis, buoyancy, and reaction–diffusion processes. Here we show that chemical gardens grown by injection of one reactant into the other in confined conditions feature a wealth of new patterns including spirals, flowers, and filaments. The confinement decreases the influence of buoyancy, reduces the spatial degrees of freedom, and allows analysis of the patterns by tools classically used to analyze 2D patterns. Injection moreover allows the study in controlled conditions of the effects of variable concentrations on the selected morphology. We illustrate these innovative aspects by characterizing quantitatively, with a simple geometrical model, a new class of self-similar logarithmic spirals observed in a large zone of the parameter space.
Towards a theory of societal co-evolution: Individualism versus collectivism

Substantial empirical research has shown that the level of individualism vs. collectivism is one of the most critical and important determinants of societal traits, such as economic growth, economic institutions and health conditions. But the exact nature of this impact has thus far not been well understood in an analytical setting. In this work, we develop one of the first theoretical models that analytically studies the impact of individualism-collectivism on the society. We model the growth of an individual's welfare (wealth, resources and health) as depending not only on himself, but also on the level of collectivism, i.e. the level of dependence on the rest of the individuals in the society, which leads to a co-evolutionary setting. Based on our model, we are able to predict the impact of individualism-collectivism on various societal metrics, such as average welfare, average lifetime, total population, cumulative welfare and average inequality. We analytically show that individualism has a positive impact on average welfare and cumulative welfare, but comes with the drawbacks of lower average life-time, lower total population and higher average inequality.
Large deviations of the realized (co-)volatility vector

Realized statistics based on high frequency returns have become very popular in financial economics. In recent years, different non-parametric estimators of the variation of a log-price process have appeared. These were developed by many authors and were motivated by the existence of complete records of price data. Among them are the realized quadratic (co-)variation which is perhaps the most well known example, providing a consistent estimator of the integrated (co-)volatility when the logarithmic price process is continuous. Limit results such as the weak law of large numbers or the central limit theorem have been proved in different contexts. In this paper, we propose to study the large deviation properties of realized (co-)volatility (i.e., when the number of high frequency observations in a fixed time interval increases to infinity. More specifically, we consider a bivariate model with synchronous observation schemes and correlated Brownian motions of the following form: $dX\_{\ell,t} = \sigma\_{\ell,t}dB\_{\ell,t}+b\_{\ell}(t,\omega)dt$ for $\ell=1,2$, where $X\_{\ell}$ denotes the log-price, we are concerned with the large deviation estimation of the vector $V\_t^n(X)=(Q\_{1,t}^n(X), Q\_{2,t}^n(X), C\_{t}^n(X))$ where $Q\_{\ell,t}^n(X)$ and $C\_{t}^n(X)$ represente the estimator of the quadratic variational processes $Q\_{\ell,t}=\int\_0^t\sigma\_{\ell,s}^2ds$ and the integrated covariance $C\_t=\int\_0^t\sigma\_{1,s}\sigma\_{2,s}\rho\_sds$ respectively, with $\rho\_t=cov(B\_{1,t}, B\_{2,t})$. Our main motivation is to improve upon the existing limit theorems. Our large deviations results can be used to evaluate and approximate tail probabilities of realized (co-)volatility. As an application we provide the large deviation for the standard dependence measures between the two assets returns such as the realized regression coefficients up to time $t$, or the realized correlation. Our study should contribute to the recent trend of research on the (co-)variance estimation problems, which are quite often discussed in high-frequency financial data analysis.
Global surface wave resonances of the earth magnetosphere and their possible manifestation

In this paper global surface wave modes supported by plasma discontinuities at both the magnetopause and the plasmapause are considered. The ionosphere at the ends of the magnetic field lines of the earth outer magnetosphere is considered as reflecting boundaries of the surface waves that propagate along the plasma boundaries. As a result a standing wave structure along the magnetic field fluxes of the outer magnetosphere, i.e. surface wave resonance structure can be formed. Due to quantized wavenumbers along the magnetic field lines, the surface wave resonance possesses quantified frequencies in a following way: f = nfres, where fres is frequency of the corresponding fundamental surface wave resonance and n is an integer. Global Pc5 pulsations have been observed and interpreted mostly as cavity modes of the earth magnetosphere. The global Pc5 pulsations however could alternatively be interpreted as ultra low-frequency surface wave resonances of the earth magnetosphere that do not necessarily involve the cavity mode-field aligned resonance transformation concept.
Existential Rule Languages with Finite Chase: Complexity and Expressiveness

Finite chase, or alternatively chase termination, is an important condition to ensure the decidability of existential rule languages. In the past few years, a number of rule languages with finite chase have been studied. In this work, we propose a novel approach for classifying the rule languages with finite chase. Using this approach, a family of decidable rule languages, which extend the existing languages with the finite chase property, are naturally defined. We then study the complexity of these languages. Although all of them are tractable for data complexity, we show that their combined complexity can be arbitrarily high. Furthermore, we prove that all the rule languages with finite chase that extend the weakly acyclic language are of the same expressiveness as the weakly acyclic one, while rule languages with higher combined complexity are in general more succinct than those with lower combined complexity.
Can we build a conscious machine?

The underlying physiological mechanisms of generating conscious states are still unknown. To make progress on the problem of consciousness, we will need to experimentally design a system that evolves in a similar way our brains do. Recent experimental data show that the multiscale nature of the evolving human brain can be implemented by reprogramming human cells. A hybrid system can be designed to include an evolving brain equipped with digital computers that maintain homeostasis and provide the right amount of nutrients and oxygen for the brain growth. Shaping the structure of the evolving brain will be progressively achieved by controlling spatial organization of various types of cells. Following a specific program, the evolving brain can be trained using substitutional reality to learn and experience live scenes. We already know from neuroelectrodynamics that meaningful information in the brain is electrically (wirelessly) read out and written fast in neurons and synapses at the molecular (protein) level during the generation of action potentials and synaptic activities. Since with training, meaningful information accumulates and is electrically integrated in the brain, one can predict, that this gradual process of training will trigger a tipping point for conscious experience to emerge in the hybrid system.
Quantifying error in estimates of human brain fiber directions using Earth Mover's Distance

Diffusion-weighted MR imaging (DWI) is the only method we currently have to measure connections between different parts of the human brain in vivo. To elucidate the structure of these connections, algorithms for tracking bundles of axonal fibers through the subcortical white matter rely on local estimates of the fiber orientation distribution function (fODF) in different parts of the brain. These functions describe the relative abundance of populations of axonal fibers crossing each other in each location. Multiple models exist for estimating fODFs. The quality of the resulting estimates can be quantified by means of a suitable measure of distance on the space of fODFs. However, there are multiple distance metrics that can be applied for this purpose, including smoothed $L_p$ distances and the Wasserstein metrics. Here, we give four reasons for the use of the Earth Mover's Distance (EMD) equipped with the arc-length, as a distance metric. (continued)
Ontology Module Extraction via Datalog Reasoning

Module extraction — the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S — has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results.
Inflationary dynamics of kinetically-coupled gauge fields

We investigate the inflationary dynamics of two kinetically-coupled massless U(1) gauge fields with time-varying kinetic-term coefficients. Ensuring that the system does not have strongly coupled regimes shrinks the parameter space. Also, we further restrict ourselves to systems that can be quantized using the standard creation, annihilation operator algebra. This second constraint limits us to scenarios where the system can be diagonalized into the sum of two decoupled, massless, vector fields with a varying kinetic-term coefficient. Such a system might be interesting for magnetogenesis because of how the strong coupling problem generalizes. We explore this idea by assuming that one of the gauge fields is the Standard Model U(1) field and that the other dark gauge field has no particles charged under its gauge group. We consider whether it would be possible to transfer a magnetic field from the dark sector, generated perhaps before the coupling was turned on, to the visible sector. We also investigate whether the simple existence of the mixing provides more opportunities to generate magnetic fields. We find that neither possibility works efficiently, consistent with the well-known difficulties in inflationary magnetogenesis.
Interactions along an entanglement cut in 2+1 D Abelian topological phases

A given fractional quantum Hall state may admit multiple, distinct edge phases on its boundary. We explore the implications that multiple edge phases have for the entanglement spectrum and entropy of a given bulk state. We describe the precise manner in which the entanglement spectrum depends upon local (tunneling) interactions along an entanglement cut and throughout the bulk. The sensitivity to local conditions near the entanglement cut appears not only in gross features of the spectrum, but can also manifest itself in an additive, positive constant correction to the topological entanglement entropy, i.e., it increases its magnitude. A natural interpretation for this result is that the tunneling interactions across an entanglement cut can function as a barrier to certain types of quasiparticle transport across the cut, thereby, lowering the total entanglement between the two regions.
Unification of Field Theory and Maximum Entropy Methods for Learning Probability Densities

The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such approaches. Both have origins in statistical physics, but the relationship between them has remained unclear. Here I unify these two methods by showing that every maximum entropy density estimate can be recovered in the infinite smoothness limit of an appropriate Bayesian field theory. I also show that Bayesian field theory estimation can be performed without imposing any boundary conditions on candidate densities, and that the infinite smoothness limit of these theories recovers the most common types of maximum entropy estimates. Bayesian field theory thus provides a natural test of the maximum entropy null hypothesis and, furthermore, returns an alternative (lower entropy) density estimate when the maximum entropy hypothesis is falsified. The computations necessary for this approach can be performed rapidly for one-dimensional data, and software for doing this is provided.
Johnson-Lindenstrauss Compression with Neuroscience-Based Constraints

Johnson-Lindenstrauss (JL) matrices implemented by sparse random synaptic connections are thought to be a prime candidate for how convergent pathways in the brain compress information. However, to date, there is no complete mathematical support for such implementations given the constraints of real neural tissue. The fact that neurons are either excitatory or inhibitory implies that every so implementable JL matrix must be sign-consistent (i.e., all entries in a single column must be either all non-negative or all non-positive), and the fact that any given neuron connects to a relatively small subset of other neurons implies that the JL matrix had better be sparse. 
We construct sparse JL matrices that are sign-consistent, and prove that our construction is essentially optimal. Our work answers a mathematical question that was triggered by earlier work and is necessary to justify the existence of JL compression in the brain, and emphasizes that inhibition is crucial if neurons are to perform efficient, correlation-preserving compression.
Stochastic Block Transition Models for Dynamic Networks

There has been great interest in recent years on statistical models for dynamic networks. In this paper, I propose a stochastic block transition model (SBTM) for dynamic networks that is inspired by the well-known stochastic block model (SBM) for static networks and previous dynamic extensions of the SBM. Unlike most existing dynamic network models, it does not make a hidden Markov assumption on the edge-level dynamics, allowing the presence or absence of edges to directly influence future edge probabilities while retaining the interpretability of the SBM. I derive an approximate inference procedure for the SBTM and demonstrate that it is significantly better at reproducing durations of edges in real social network data.
Nonlocal General Relativity

A brief account of the present status of the recent nonlocal generalization of Einstein's theory of gravitation is presented. The main physical assumptions that underlie this theory are described. We clarify the physical meaning and significance of Weitzenbock's torsion, and emphasize its intimate relationship with the gravitational field, characterized by the Riemannian curvature of spacetime. In this theory, nonlocality can simulate dark matter; in fact, in the Newtonian regime, we recover the phenomenological Tohline-Kuhn approach to modified gravity. To account for the observational data regarding dark matter, nonlocality is associated with a characteristic length scale of order 1 kpc. The confrontation of nonlocal gravity with observation is briefly discussed.
Quantum dynamics of the early universe

Quantum gravity may shed light on the prehistory of the universe. Quantum corrections to gravity affect the dynamics of the expansion of the universe. Their influence is studied on the example of the exactly solvable quantum model. The corrections to the energy density and pressure lead to the emergence of an additional attraction (like dark matter) or repulsion (like dark energy) in the quantum system of the gravitating matter and radiation. The model explains the accelerating expansion (inflation) in the early universe (the domain of comparatively small values of quantum numbers) and a later transition from the decelerating expansion to the accelerating expansion of the universe (the domain of the very large values of quantum numbers) from a single approach. The generation of primordial fluctuations of the energy density at the expense of the change of sign of the quantum correction to the pressure is discussed.
Temporal network sparsity and the slowing down of spreading

nteractions in time-varying complex systems are often very heterogeneous at the topological level (who interacts with whom) and at the temporal level (when interactions occur and how often). While it is known that temporal heterogeneities often have strong effects on dynamical processes, e.g. the burstiness of contact sequences is associated with slower spreading dynamics, the picture is far from complete. In this paper, we show that temporal heterogeneities result in temporal sparsity} at the time scale of average inter-event times, and that temporal sparsity determines the amount of slowdown of Susceptible-Infectious (SI) spreading dynamics on temporal networks. This result is based on the analysis of several empirical temporal network data sets. An approximate solution for a simple network model confirms the association between temporal sparsity and slowdown of SI spreading dynamics. Since deterministic SI spreading always follows the fastest temporal paths, our results generalize -- paths are slower to traverse because of temporal sparsity, and therefore all dynamical processes are slower as well.
Functional metamirrors using bianisotropic elements.

Conventional mirrors obey the simple reflection law that a plane wave is reflected as a plane wave, at the same angle. To engineer spatial distributions of fields reflected from a mirror, one can either shape the reflector or position some phase-correcting elements on top of a mirror surface. Here we show, both theoretically and experimentally, that full-power reflection with general control over the reflected wave phase is possible with a single-layer array of deeply subwavelength inclusions. These proposed artificial surfaces, metamirrors, provide various functions of shaped or nonuniform reflectors without utilizing any mirror. This can be achieved only if the forward and backward scattering of the inclusions in the array can be engineered independently, and we prove that it is possible using electrically and magnetically polarizable inclusions. The proposed subwavelength inclusions possess desired reflecting properties at the operational frequency band, while at other frequencies the array is practically transparent. The metamirror concept leads to a variety of applications over the entire electromagnetic spectrum, such as optically transparent focusing antennas for satellites, multifrequency reflector antennas for radio astronomy, low-profile conformal antennas for telecommunications, and nanoreflectarray antennas for integrated optics.
Lattice QCD Study of $B$-meson Decay Constants from ETMC

We discuss a lattice QCD computation of the $B$-meson decay constants by the ETM collaboration where suitable ratios allow to reach the bottom quark sector by combining simulations around the charm-quark mass with an exactly known static limit. The different steps involved in this ratio method are discussed together with an account of the assessment of various systematic effects. A comparison of results from simulations with two and four flavour dynamical quarks is presented.