nils-herrmann/clean_scirepeval huggingface dataset:
Cosmological Ontology and Epistemology

In cosmology, we would like to explain our observations and predict future observations from theories of the entire universe. Such cosmological theories make ontological assumptions of what entities exist and what their properties and relationships are. One must also make epistemological assumptions or metatheories of how one can test cosmological theories. Here I shall propose a Bayesian analysis in which the likelihood of a complete theory is given by the normalized measure it assigns to the observation used to test the theory. In this context, a discussion is given of the trade-off between prior probabilities and likelihoods of the measure problem of cosmology, of the death of Born's rule, of the Boltzmann brain problem, of whether there is a better principle for prior probabilities than mathematical simplicity, and of an Optimal Argument for the Existence of God. Cosmology is the study of the entire universe. Ideally in science, one would like the simplest possible theory from which one could logically deduce a complete description of the universe. Such theories must make implicit assumptions about the ontology of the universe, what are its existing entities and their nature. They may also make implicit assumptions about the ontology of the entire world (all that exists), particularly if other entities beyond the universe have relationships with the universe. For example, I am a Christian who believes that the universe was created by an omnipotent, omniscient, omnibenevolent, personal God who exists outside it but who relates to it as His creation. Therefore, the ontology I assume for the world includes not only our universe but also God and other entities He may have created (such as new heavens and new earth for us after death). However, my assumption that God has created our universe as an entity essentially separate from Himself means that one can also look for a theory of the universe itself, without including its relationship to God, although there may be some aspects of such a theory that can only be explained by the assumption that the universe was created by God. (For example, even though a complete theory of our universe would necessarily imply the facts that there is life and consciousness within it, since such entities do exist within our universe, there may not be a good explanation of these aspects of the theory apart from the concept of creation by a personal God.)
Anisotropy of the extragalactic radio background from dark matter annihilation

Observations of the extragalactic radio background have uncovered a significant isotropic emission across multiple frequencies spanning from 22 MHz to 10 GHz. The intensity of this nonthermal emission component significantly exceeds the expected contribution from known astrophysical sources. Interestingly, models have indicated that the annihilation of dark matter particles may reproduce both the flux and spectrum of the excess. However, the lack of a measurable anisotropy in the residual emission remains challenging for both dark matter and standard astrophysical interpretations of the ARCADE-2 data. We calculate the expected synchrotron anisotropy from dark matter annihilation and show that these models can produce very small anisotropies, though this requires galaxy clusters to have large substructure contributions and strong magnetic fields. We show that this constraint can be significantly relaxed, however, in scenarios where electrons produced via dark matter annihilation can be efficiently reaccelerated by Alfven waves in the intra-Cluster medium. Our analysis indicates that any source capable of explaining the intensity and isotropy of the extragalactic radio excess must have a spatial extension far larger than typical for baryons in galaxies, hinting at a novel physics interpretation.
A Pilot Study on Coupling CT and MRI through Use of Semiconductor Nanoparticles

CT and MRI are the two most widely used imaging modalities in healthcare, each with its own merits and drawbacks. Combining these techniques in one machine could provide unprecedented resolution and sensitivity in a single scan, and serve as an ideal platform to explore physical coupling of x-ray excitation and magnetic resonance. Molecular probes such as functionalized nanophosphors present an opportunity to demonstrate a synergy between these modalities. However, a simultaneous CT-MRI scanner does not exist at this moment. As a pilot study, here we propose a mechanism in which water solutions containing LiGa5O8:Cr3+ nanophosphors can be excited with x-rays to store energy, and these excited particles may subsequently influence the T2 relaxation times of the solutions so that a difference in T2 can be measured by MRI before and after x-ray excitation. The trends seen in our study suggest that a measurable effect may exist from x-ray excitation of the nanophosphors. However, there are several experimental conditions that hinder the clarity of the results to be statistically significant up to a commonly accepted level (p=0.05), including insoluble nanoparticles and inter-scan variability. Nevertheless, the initial results from our experiments seem a consistent and inspiring story that x-rays modify MRI T2 values around nanophosphors. Upon availability of soluble nanophosphors, we will repeat our experiments to confirm these observations.
Cosmological Consequences of a Variable Cosmological Constant Model

We derive a model of dark energy which evolves with time via the scale factor. The equation of state $\omega=(1-2\alpha)/(1+2\alpha)$ is studied as a function of a parameter $\alpha$ introduced in this model. In addition to the recent accelerated expansion, the model predicts another decelerated phase. The age of the universe is found to be almost consistent with observation. In the limiting case, the cosmological constant model, we find that vacuum energy gravitates with a gravitational strength, different than Newton's constant. This enables degravitation of the vacuum energy which in turn produces the tiny observed curvature, rather than a 120 orders of magnitude larger value.
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transform convolution implementations: one based on NVIDIA's cuFFT library, and another based on a Facebook authored FFT implementation, fbfft, that provides significant speedups over cuFFT (over 1.5x) for whole CNNs. Both of these convolution implementations are available in open source, and are faster than NVIDIA's cuDNN implementation for many common convolutional layers (up to 23.5x for some synthetic kernel configurations). We discuss different performance regimes of convolutions, comparing areas where straightforward time domain convolutions outperform Fourier frequency domain convolutions. Details on algorithmic applications of NVIDIA GPU hardware specifics in the implementation of fbfft are also provided.
Superluminal Spot Pair Events in Astronomical Settings: Sweeping Beams

Sweeping beams of light can cast spots moving with superluminal speeds across scattering surfaces. Such faster-than-light speeds are well-known phenomena that do not violate special relativity. It is shown here that under certain circumstances, superluminal spot pair creation and annihilation events can occur that provide unique information to observers. These spot pair events are not particle pair events—they are the sudden creation or annihilation of a pair of relatively illuminated spots on a scattering surface. Real spot pair illumination events occur unambiguously on the scattering surface when spot speeds diverge, while virtual spot pair events are observer dependent and perceived only when real spot radial speeds cross the speed of light. Specifically, a virtual spot pair creation event will be observed when a real spot’s speed toward the observer drops below c, while a virtual spot pair annihilation event will be observed when a real spot’s radial speed away from the observer rises above c. Superluminal spot pair events might be found angularly, photometrically, or polarimetrically, and might carry useful geometry or distance information. Two example scenarios are briefly considered. The first is a beam swept across a scattering spherical object, exemplified by spots of light moving across Earth’s Moon and pulsar companions. The second is a beam swept across a scattering planar wall or linear filament, exemplified by spots of light moving across variable nebulae including Hubble’s Variable Nebula. In local cases where the sweeping beam can be controlled and repeated, a three-dimensional map of a target object can be constructed. Used tomographically, this imaging technique is fundamentally different from lens photography, radar, and conventional lidar.
Differential Privacy and Machine Learning: a Survey and Review

The objective of machine learning is to extract useful information from data, while privacy is preserved by concealing information. Thus it seems hard to reconcile these competing interests. However, they frequently must be balanced when mining sensitive data. For example, medical research represents an important application where it is necessary both to extract useful information and protect patient privacy. One way to resolve the conflict is to extract general characteristics of whole populations without disclosing the private information of individuals. 
In this paper, we consider differential privacy, one of the most popular and powerful definitions of privacy. We explore the interplay between machine learning and differential privacy, namely privacy-preserving machine learning algorithms and learning-based data release mechanisms. We also describe some theoretical results that address what can be learned differentially privately and upper bounds of loss functions for differentially private algorithms. 
Finally, we present some open questions, including how to incorporate public data, how to deal with missing data in private datasets, and whether, as the number of observed samples grows arbitrarily large, differentially private machine learning algorithms can be achieved at no cost to utility as compared to corresponding non-differentially private algorithms.
Seaborg's Plutonium?

Passive x-ray and gamma-ray analysis was performed on UC Berkeley's EH&S Sample S338. The object was found to contain Pu-239 and no other radioactive isotopes. The mass of Pu-239 contained in this object was determined to be 2.0 +- 0.3 micrograms. These observations are consistent with the identification of this object being the 2.77-microgram plutonium oxide sample described by Glenn Seaborg and his collaborators as the first sample of Pu-239 that was large enough to be weighed.
Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations

In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a social collaborative filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilise multiple types of relations in a heterogeneous social network. More importantly, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on a real-world dataset DBLP (a typical heterogeneous information network)demonstrate the effectiveness of our algorithm.
An Effective Semi-supervised Divisive Clustering Algorithm

Nowadays, data are generated massively and rapidly from scientific fields as bioinformatics, neuroscience and astronomy to business and engineering fields. Cluster analysis, as one of the major data analysis tools, is therefore more significant than ever. We propose in this work an effective Semi-supervised Divisive Clustering algorithm (SDC). Data points are first organized by a minimal spanning tree. Next, this tree structure is transitioned to the in-tree structure, and then divided into sub-trees under the supervision of the labeled data, and in the end, all points in the sub-trees are directly associated with specific cluster centers. SDC is fully automatic, non-iterative, involving no free parameter, insensitive to noise, able to detect irregularly shaped cluster structures, applicable to the data sets of high dimensionality and different attributes. The power of SDC is demonstrated on several datasets.
Inference for Sparse Conditional Precision Matrices

Given $n$ i.i.d. observations of a random vector $(X,Z)$, where $X$ is a high-dimensional vector and $Z$ is a low-dimensional index variable, we study the problem of estimating the conditional inverse covariance matrix $\Omega(z) = (E[(X-E[X \mid Z])(X-E[X \mid Z])^T \mid Z=z])^{-1}$ under the assumption that the set of non-zero elements is small and does not depend on the index variable. We develop a novel procedure that combines the ideas of the local constant smoothing and the group Lasso for estimating the conditional inverse covariance matrix. A proximal iterative smoothing algorithm is used to solve the corresponding convex optimization problems. We prove that our procedure recovers the conditional independence assumptions of the distribution $X \mid Z$ with high probability. This result is established by developing a uniform deviation bound for the high-dimensional conditional covariance matrix from its population counterpart, which may be of independent interest. Furthermore, we develop point-wise confidence intervals for individual elements of the conditional inverse covariance matrix. We perform extensive simulation studies, in which we demonstrate that our proposed procedure outperforms sensible competitors. We illustrate our proposal on a S&P 500 stock price data set.
Tail Risk Constraints and Maximum Entropy

Portfolio selection in the financial literature has essentially been analyzed under two central assumptions: full knowledge of the joint probability distribution of the returns of the securities that will comprise the target portfolio; and investors’ preferences are expressed through a utility function. In the real world, operators build portfolios under risk constraints which are expressed both by their clients and regulators and which bear on the maximal loss that may be generated over a given time period at a given confidence level (the so-called Value at Risk of the position). Interestingly, in the finance literature, a serious discussion of how much or little is known from a probabilistic standpoint about the multi-dimensional density of the assets’ returns seems to be of limited relevance. Our approach in contrast is to highlight these issues and then adopt throughout a framework of entropy maximization to represent the real world ignorance of the “true” probability distributions, both univariate and multivariate, of traded securities’ returns. In this setting, we identify the optimal portfolio under a number of downside risk constraints. Two interesting results are exhibited: (i) the left- tail constraints are sufficiently powerful to override all other considerations in the conventional theory; (ii) the “barbell portfolio” (maximal certainty/ low risk in one set of holdings, maximal uncertainty in another), which is quite familiar to traders, naturally emerges in our construction.
Gravitational Anomalies in the Solar System

Mindful of the anomalous perihelion precession of Mercury discovered by Le Verrier in the second half of the nineteenth century and its successful explanation by Einstein with his General Theory of Relativity in the early years of the twentieth century, discrepancies among observed effects in our Solar system and their theoretical predictions on the basis of the currently accepted laws of gravitation applied to known matter-energy distributions have the potential of paving the way for remarkable advances in fundamental physics. This is particularly important now more than ever, given that most of the universe seems to be made of unknown substances dubbed Dark Matter and Dark Energy. Should this not be directly the case, Solar system's anomalies could anyhow lead to advancements in either cumulative science, as shown to us by the discovery of Neptune in the first half of the nineteenth century, and technology itself. Moreover, investigations in one of such directions can serendipitously enrich the other one as well. The current status of some alleged gravitational anomalies in the Solar system is critically reviewed. They are: (a) Possible anomalous advances of planetary perihelia.  The secular decrease of the mass parameter of the Sun. (f) The Flyby Anomaly. (g) The Pioneer Anomaly. (h) The anomalous secular increase of the astronomical unit.
MAGNETIZED JETS DRIVEN BY THE SUN: THE STRUCTURE OF THE HELIOSPHERE REVISITED

The classic accepted view of the heliosphere is a quiescent, comet-like shape aligned in the direction of the Sun’s travel through the interstellar medium (ISM) extending for thousands of astronomical units (AUs). Here, we show, based on magnetohydrodynamic (MHD) simulations, that the tension (hoop) force of the twisted magnetic field of the Sun confines the solar wind plasma beyond the termination shock and drives jets to the north and south very much like astrophysical jets. These jets are deflected into the tail region by the motion of the Sun through the ISM similar to bent galactic jets moving through the intergalactic medium. The interstellar wind blows the two jets into the tail but is not strong enough to force the lobes into a single comet-like tail, as happens to some astrophysical jets. Instead, the interstellar wind flows around the heliosphere and into the equatorial region between the two jets. As in some astrophysical jets that are kink unstable, we show here that the heliospheric jets are turbulent (due to large-scale MHD instabilities and reconnection) and strongly mix the solar wind with the ISM beyond 400 AU. The resulting turbulence has important implications for particle acceleration in the heliosphere. The two-lobe structure is consistent with the energetic neutral atom (ENA) images of the heliotail from IBEX where two lobes are visible in the north and south and the suggestion from the Cassini ENAs that the heliosphere is “tailless.”
Concentration for matrix martingales in continuous time and microscopic activity of social networks

This paper gives new concentration inequalities for the spectral norm of matrix martingales in continuous time. Both cases of purely discountinuous and continuous martingales are considered. The analysis is based on a new supermartingale property of the trace exponential, based on tools from stochastic calculus. Matrix martingales in continuous time are probabilistic objects that naturally appear for statistical learning of time-dependent systems. We focus here on the the microscopic study of (social) networks, based on self-exciting counting processes, such as the Hawkes process, together with a low-rank prior assumption of the self-exciting component. A consequence of these new concentration inequalities is a push forward of the theoretical analysis of such models.
Static- and stationary-complete spacetimes: algebraic and causal structures

This is intended as an analysis of the global properties of static and stationary spacetimes with complete (timelike) Killing field, with particular attention to quotients by group actions. This is presented in terms of algebraic structures which are fairly simple for the static case and more involved for the stationary case; the most important tool, the fundamental cocycle, is a cohomological class for static spacetimes but of somewhat looser structure in the stationary case. In particular: (1) A new measurement, similar to the spacetime interval in Minkowski space, is devised for detecting whether two points are causally related in a stationary spacetime; this proves very useful for analysis. (2) All stationary spacetimes are categorized by how they behave with respect to the fundamental cocycle; this enables a complete characterization of global causality properties. (3) It is shown how these tools determine whether global hyperbolicity of a stationary spacetime is inherited by its quotients. (4) Examples are examined in detail, a large range including both ones of mathematical curiosity and ones of physical interest, such as cosmic strings in flat, accelerated, Schwarzschild, Kerr, and other backgrounds.
Constraints on changes in the proton–electron mass ratio using methanol lines

We report Karl G. Jansky Very Large Array (VLA) absorption spectroscopy in four methanol (CH3OH) lines in the z = 0.885 82 gravitational lens towards PKS1830-211. Three of the four lines have very different sensitivity coefficients K-mu to changes in the proton-electron mass ratio mu; a comparison between the line redshifts thus allows us to test for temporal evolution in mu. We obtain a stringent statistical constraint on changes in mu by comparing the redshifted 12.179 and 60.531 GHz lines, [Delta mu/mu] 4 sigma significance). The sensitivity of the above result, and that of all earlier CH3OH studies, is thus likely to be limited by unknown systematic errors, probably arising due to the frequency-dependent structure of PKS1830-211. A robust result is obtained by combining the three lines at similar frequencies, 48.372, 48.377 and 60.531 GHz, whose line profiles are found to be in good agreement. This yields the 2 sigma constraint [Delta mu/mu] less than or similar to 4 x 10(-7), the most stringent current constraint on changes in mu. We thus find no evidence for changes in the proton-electron mass ratio over a lookback time of approximate to 7.5 Gyr.
Three transiting planet discoveries from the Wide Angle Search for Planets: WASP-85 A b; WASP-116 b, and WASP-149 b

(abridged) We report the discovery of three new transiting planets: WASP-85 A b, WASP-116 b, and WASP-149 b. WASP-85 b orbits its host star every 2.66 days, and has a mass of 1.25 M_Jup and a radius of 1.25 R_Jup. The host star is of G5 spectral type, with magnitude V = 11.2, and lies 141 pc distant. The system has a K-dwarf binary companion, WASP-85 B, at a separation of ~1.5". The close proximity of this companion leads to contamination of our photometry, decreasing the apparent transit depth that we account for during our analysis. Analysis of the Ca II H+K lines shows strong emission that implies that both binary components are strongly active. WASP-116 b is a warm, mildly inflated super-Saturn, with a mass of 0.59 M_Jup and a radius of 1.43 R_Jup. It was discovered orbiting a metal-poor ([Fe/H] = -0.28 dex), cool (T_eff = 5950 K) G0 dwarf every 6.61 days. WASP-149 b is a typical hot Jupiter, orbiting a G6 dwarf with a period of 1.33 days. The planet has a mass and radius of 1.05 M_Jup and 1.29 R_Jup, respectively. The stellar host has an effective temperature of T_eff = 5750 K and has a metallicity of [Fe/H] = 0.16 dex. WASP photometry of the system is contaminated by a nearby star; we therefore corrected the depth of the WASP transits using the measured dilution. WASP-149 lies inside the 'Neptune desert' identified in the planetary mass-period plane by Mazeh, Holczer & Faigler (2016). 
We model the modulation visible in the K2 lightcurve of WASP-85 using a simple three-spot model consisting of two large spots on WASP-85 A, and one large spot on WASP-85 B, finding rotation periods of 13.1+/-0.1 days for WASP-85 A and 7.5+/-0.03 days for WASP-85 B. We estimate stellar inclinations of I_A = 66.8+/-0.7 degrees and I_B = 39.7+/-0.2 degrees, and constrain the obliquity of WASP-85 A b to be psi<27 degrees. We therefore conclude that WASP-85 A b is very likely to be aligned.