nils-herrmann/clean_scirepeval huggingface dataset:
LOSS's first supernova? New limits on the ‘impostor’ SN 1997bs

We present new, late-time Hubble Space Telescope and Spitzer Space Telescope observations of the archetypal supernova (SN) impostor SN 1997bs. We show that SN 1997bs remains much fainter than its progenitor, posing a challenge for the canonical picture of late-time obscuration by dust forming in a shell ejected during the transient. The possibility that the star survived cloaked behind a dusty, steady wind is also disfavored. The simplest explanation is that SN 1997bs was a subluminous Type IIn SN, although it is currently impossible to rule out the possibility that the star survived either behind an obscuring dusty shell $\gtrsim 1 M_{\odot}$ or with a significantly decreased intrinsic luminosity. Continued monitoring of the system is the most promising means of ultimately resolving the question.
Characterizing Transiting Planet Atmospheres through 2025

The discovery of planets around other stars is revolutionizing our notions of planet formation and is poised to do the same for planetary climate. Studying transiting planets is complementary to eventual studies of directly imaged planets: (1) we can readily measure the mass and radius of transiting planets, linking atmospheric properties to bulk composition and formation, (2) many transiting planets are strongly irradiated and exhibit novel atmospheric physics, and (3) the most common temperate terrestrial planets orbit close to red dwarf stars and are difficult to image directly. We have only been able to comprehensively characterize the atmospheres of a handful of transiting planets, because most orbit faint stars. The Transiting Exoplanet Survey Satellite (TESS) will discover transiting planets orbiting the brightest stars, enabling, in principle, an atmospheric survey of 102–103 bright hot Jupiters and warm sub-Neptunes. Uniform observations of such a statistically significant sample would provide leverage to understand—and learn from—the diversity of short-period planets, and would identify the minority of truly special planets worthy of more intensive follow-up. We argue that the best way to maximize the scientific returns of TESS is to adopt a triage approach. A space mission consisting of a ∼1 m telescope with an optical–NIR spectrograph could measure molecular absorption for nonterrestrial planets discovered by TESS, as well as eclipses and phase variations for the hottest jovians. Such a mission could observe up to 103 transits per year, thus enabling it to survey a large fraction of the bright (J < 11) hot-Jupiters and warm sub-Neptunes TESS is expected to find. The James Webb Space Telescope (JWST) could be used to perform detailed atmospheric characterization of the most interesting transiting targets (transit, eclipse, and—when possible—phase-resolved spectroscopy). TESS is also expected to discover a few temperate terrestrial planets transiting nearby M-Dwarfs. Characterizing these worlds will be time-intensive: JWST will need months to provide tantalizing constraints on the presence of an atmosphere, planetary rotational state, clouds, and greenhouse gases. Future flagship missions should be designed to provide better constraints on the habitability of M-Dwarf temperate terrestrial planets.
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries

The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces. These and many other applications can be supported by Predictive Analytic Queries (PAQs). A major obstacle to supporting PAQs is the challenging and expensive process of identifying and training an appropriate predictive model. Recent efforts aiming to automate this process have focused on single node implementations and have assumed that model training itself is a black box, thus limiting the effectiveness of such approaches on large-scale problems. In this work, we build upon these recent efforts and propose an integrated PAQ planning architecture that combines advanced model search techniques, bandit resource allocation via runtime algorithm introspection, and physical optimization via batching. The result is TuPAQ, a component of the MLbase system, which solves the PAQ planning problem with comparable quality to exhaustive strategies but an order of magnitude more efficiently than the standard baseline approach, and can scale to models trained on terabytes of data across hundreds of machines.
Deep learning of fMRI big data: a novel approach to subject-transfer decoding

As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individuals has limited the scope of many studies to the development of individual-specific decoders. In this study, we used deep neural network (DNN), a nonlinear hierarchical model, to construct a subject-transfer decoder. Our decoder is the first successful DNN-based subject-transfer decoder. When applied to a large-scale functional magnetic resonance imaging (fMRI) database, our DNN-based decoder achieved higher decoding accuracy than other baseline methods, including support vector machine (SVM). In order to analyze the knowledge acquired by this decoder, we applied principal sensitivity analysis (PSA) to the decoder and visualized the discriminative features that are common to all subjects in the dataset. Our PSA successfully visualized the subject-independent features contributing to the subject-transferability of the trained decoder.
Sparse Dueling Bandits

The dueling bandit problem is a variation of the classical multi-armed bandit in which the allowable actions are noisy comparisons between pairs of arms. This paper focuses on a new approach for finding the "best" arm according to the Borda criterion using noisy comparisons. We prove that in the absence of structural assumptions, the sample complexity of this problem is proportional to the sum of the inverse squared gaps between the Borda scores of each suboptimal arm and the best arm. We explore this dependence further and consider structural constraints on the pairwise comparison matrix (a particular form of sparsity natural to this problem) that can significantly reduce the sample complexity. This motivates a new algorithm called Successive Elimination with Comparison Sparsity (SECS) that exploits sparsity to find the Borda winner using fewer samples than standard algorithms. We also evaluate the new algorithm experimentally with synthetic and real data. The results show that the sparsity model and the new algorithm can provide significant improvements over standard approaches.
Classroom reconstruction of the Schwarzschild metric

A promising way to introduce general relativity (GR) in the classroom is to study the physical implications of certain given metrics, such as the Schwarzschild one. This involves lower mathematical expenditure than an approach focusing on differential geometry in its full glory and permits to emphasize physical aspects before attacking the field equations. Even so, in terms of motivation, lacking justification of the metric employed may pose an obstacle. The paper discusses how to establish the weak-field limit of the Schwarzschild metric with a minimum of relatively simple physical assumptions, avoiding the field equations but admitting the determination of a single parameter from experiment. An attractive experimental candidate is the measurement of the perihelion precession of Mercury, because the result was already known before the completion of GR. It is shown how to determine the temporal and radial coefficients of the Schwarzschild metric to sufficiently high accuracy to obtain quantitative predictions for all the remaining classical tests of GR.
High Dimensional Low Rank Plus Sparse Matrix Decomposition

This paper is concerned with the problem of low-rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems with complexity that scales with the dimension of the data, which limits their scalability. Furthermore, existing randomized approaches mostly rely on uniform random sampling, which is quite inefficient for many real world data matrices that exhibit additional structures (e.g., clustering). In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed. The decomposition is carried out by using a small data sketch formed from sampled columns/rows. Even when the data are sampled uniformly at random, it is shown that the sufficient number of sampled columns/rows is roughly <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(r \mu)$</tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> is the coherency parameter and <inline-formula> <tex-math notation="LaTeX">$r$</tex-math></inline-formula> is the rank of the low-rank component. In addition, adaptive sampling algorithms are proposed to address the problem of columns/rows sampling from structured data. We provide an analysis of the proposed method with adaptive sampling and show that adaptive sampling makes the required number of sampled columns/rows invariant to the distribution of the data. The proposed approach is amenable to online implementation and an online scheme is proposed.
Injury risk prediction for traffic accidents in Porto Alegre/RS, Brazil

This study describes the experimental application of Machine Learning techniques to build prediction models that can assess the injury risk associated with traffic accidents. This work uses an freely available data set of traffic accident records that took place in the city of Porto Alegre/RS (Brazil) during the year of 2013. This study also provides an analysis of the most important attributes of a traffic accident that could produce an outcome of injury to the people involved in the accident.
Optimal Derivative Liquidation Timing Under Path-Dependent Risk Penalties

This paper studies the risk-adjusted optimal timing to liquidate an option at the prevailing market price. In addition to maximizing the expected discounted return from option sale, we incorporate a path-dependent risk penalty based on shortfall or quadratic variation of the option price up to the liquidation time. We establish the conditions under which it is optimal to immediately liquidate or hold the option position through expiration. Furthermore, we study the variational inequality associated with the optimal stopping problem, and prove the existence and uniqueness of a strong solution. A series of analytical and numerical results are provided to illustrate the non-trivial optimal liquidation strategies under geometric Brownian motion (GBM) and exponential Ornstein-Uhlenbeck models. We examine the combined effects of price dynamics and risk penalty on the sell and delay regions for various options. In addition, we obtain an explicit closed-form solution for the liquidation of a stock with quadratic penalty under the GBM model.
Scanning probe microscopy and spectroscopy of graphene on metals

Graphene, a two‐dimensional (2D) material with unique electronic properties, appears to be an ideal object for the application of surface‐science methods. Among them, a family of scanning probe microscopy methods (STM, AFM, KPFM) and the corresponding spectroscopy add‐ons provide information about the structure and electronic properties of graphene on the local scale (from μ m to atoms). This review focuses on the recent applications of these microscopic/spectroscopic methods for the investigation of graphene on metals (interfaces, intercalation‐like systems, graphene nanoribbons, and quantum dots, etc.). It is shown that very important information about interaction strength at the graphene/metal interfaces as well as about modification of the electronic spectrum of graphene at the Fermi level can be obtained on the local scale. The combination of these results with those obtained by other methods and comparison with recent theoretical data demonstrate the power of this approach for the investigation of the graphene‐based systems.
Surface plasmons for doped graphene

Within the Dirac model for the electronic excitations of graphene, we calculate the full polarization tensor with finite mass and chemical potential. It has, besides the (00)-component, a second form factor, which must be accounted for. We obtain explicit formulas for both form factors and for the reflection coefficients. Using these, we discuss the regions in the momentum-frequency plane where plasmons may exist and give numeric solutions for the plasmon dispersion relations. It turns out that plasmons exist for both, TE and TM polarizations over the whole range of the ratio of mass to chemical potential, except for zero chemical potential, where only a TE plasmon exists.
Reply to 'Inuence of cosmic ray variability on the monsoon rainfall and temperature': a false-positive in the eld of solar|terrestrial research

AbstractA litany of research has been published claiming strong solar inuences onthe Earth’s weather and climate. Much of this work includes documentederrors and false-positives, yet is still frequently used to substantiate argu-ments of global warming denial. This manuscript reports on a recent studyby Badruddin & Aslam (2014), hereafter BA14, which claimed a highly sig-ni cant (p= 1:4 10 5 ) relationship between extremes in the intensity of theIndian monsoon and the cosmic ray ux. They further speculated that the re-lationship they observed may apply across the entire tropical and sub-tropicalbelt, and be of global importance. However, their statistical analysis|andconsequently their conclusions|were wrong. Speci cally, their error resultedfrom an assumption that their data’s underlying distribution was Gaussian.But, as demonstrated in this work, their data closely follow an ergodic chaoticdistribution biased towards extreme values. From a probability density func-tion, calculated using a Monte Carlo sampling approach, I estimate the truesigni cance of the BA14 samples to be p= 0:91.
Unsupervised Incremental Online Learning and Prediction of Musical Audio Signals

Guided by the idea that musical human-computer interaction may become more effective, intuitive, and creative when basing its computer part on cognitively more plausible learning principles, we employ unsupervised incremental online learning (i.e. clustering) to build a system that predicts the next event in a musical sequence, given as audio input. The flow of the system is as follows: 1) segmentation by onset detection, 2) timbre representation of each segment by Mel frequency cepstrum coefficients, 3) discretization by incremental clustering, yielding a tree of different sound classes (e.g. timbre categories/instruments) that can grow or shrink on the fly driven by the instantaneous sound events, resulting in a discrete symbol sequence, 4) extraction of statistical regularities of the symbol sequence, using hierarchical N-grams and the newly introduced conceptual Boltzmann machine that adapt to the dynamically changing clustering tree in 3) , and 5) prediction of the next sound event in the sequence, given the last $n$ previous events. The system's robustness is assessed with respect to complexity and noisiness of the signal. Clustering in isolation yields an adjusted Rand index (ARI) of 82.7%/85.7% for data sets of singing voice and drums. Onset detection jointly with clustering achieve an ARI of 81.3%/76.3% and the prediction of the entire system yields an ARI of 27.2%/39.2%.
Joint analysis of BICEP2/keck array and Planck Data.

We report the results of a joint analysis of data from BICEP2/Keck Array and Planck. BICEP2 and Keck Array have observed the same approximately 400  deg^{2} patch of sky centered on RA 0 h, Dec. -57.5°. The combined maps reach a depth of 57 nK deg in Stokes Q and U in a band centered at 150 GHz. Planck has observed the full sky in polarization at seven frequencies from 30 to 353 GHz, but much less deeply in any given region (1.2  μK deg in Q and U at 143 GHz). We detect 150×353 cross-correlation in B modes at high significance. We fit the single- and cross-frequency power spectra at frequencies ≥150  GHz to a lensed-ΛCDM model that includes dust and a possible contribution from inflationary gravitational waves (as parametrized by the tensor-to-scalar ratio r), using a prior on the frequency spectral behavior of polarized dust emission from previous Planck analysis of other regions of the sky. We find strong evidence for dust and no statistically significant evidence for tensor modes. We probe various model variations and extensions, including adding a synchrotron component in combination with lower frequency data, and find that these make little difference to the r constraint. Finally, we present an alternative analysis which is similar to a map-based cleaning of the dust contribution, and show that this gives similar constraints. The final result is expressed as a likelihood curve for r, and yields an upper limit r_{0.05}<0.12 at 95% confidence. Marginalizing over dust and r, lensing B modes are detected at 7.0σ significance.
ESCAPE, ACCRETION, OR STAR FORMATION? THE COMPETING DEPLETERS OF GAS IN THE QUASAR MARKARIAN 231

We report on high resolution CO(1–0), CS(2–1), and 3 mm continuum Combined Array for Research in Millimeter Astronomy (CARMA) observations of the molecular outflow host and nearest quasar Markarian 231. We use the CS(2–1) measurements to derive a dense gas mass within Mrk 231 of 1.8 ± 0.3 × 10 10 M ⊙ ?> , consistent with previous measurements. The CS(2–1) data also seem to indicate that the molecular disk of Mrk 231 is forming stars at about normal efficiency. The high resolution CARMA observations were able to resolve the CO(1–0) outflow into two distinct lobes, allowing for a size estimate to be made and further constraining the molecular outflow dynamical time, further constraining the molecular gas escape rate. We find that 15% of the molecular gas within the Mrk 231 outflow actually exceeds the escape velocity in the central kiloparsec. Assuming that molecular gas is not constantly being accelerated, we find the depletion timescale of molecular gas in Mrk 231 to be 49 Myr, rather than 32 Myr, more consistent with the poststarburst stellar population observed in the system.
Collisional behaviors of astrophysical collisionless plasmas

In collisional fluids, a number of key processes rely on the frequency of binary collisions. Collisions seem necessary to generate a shock wave when two fluids collide fast enough, to fulfill the Rankine–Hugoniot (RH) relations, to establish an equation of state or a Maxwellian distribution. Yet, these seemingly collisional features are routinely either observed or assumed, in relation with collisionless astrophysical plasmas. This article will review our current answers to the following questions: How do colliding collisionless plasmas end-up generating a shock as if they were fluids? To which extent are the RH relations fulfilled in this case? Do collisionless shocks propagate like fluid ones? Can we use an equation of state to describe collisionless plasmas, like MHD codes for astrophysics do? Why are Maxwellian distributions ubiquitous in particle-in-cell simulations of collisionless shocks? Time and length scales defining the border between the collisional and the collisionless behavior will be given when relevant. In general, when the time and length scales involved in the collisionless processes responsible for the fluid-like behavior may be neglected, the system may be treated like a fluid.
Classical inflationary and ekpyrotic universes in the no-boundary wavefunction

This paper investigates the manner in which classical universes are obtained in the no-boundary quantum state. In this context, universes can be characterized as classical [in a Wentzel-Kramers-Brillouin (WKB) sense] when the wavefunction is highly oscillatory, i.e. when the ratio of the change in the amplitude of the wavefunction becomes small compared to the change in the phase. In the presence of a positive or negative exponential potential, the WKB condition is satisfied in proportion to a factor e −ðϵ−3ÞN=ðϵ−1Þ , where ϵ is the (constant) slow-roll/fast-roll parameter and N designates the number of e-folds. Thus classicality is reached exponentially fast in N, but only when ϵ 3 (ekpyrosis). Furthermore, when the potential switches off and the ekpyrotic phase goes over into a phase of kinetic domination, the level of classicality obtained up to that point is preserved. Similar results are obtained in a cyclic potential, where a dark energy plateau is added. Finally, for a potential of the form −ϕ n (with n ¼ 4;6;8), where the classical solution becomes increasingly kinetic dominated, there is an initial burst of classicalization which then quickly levels off. These results demonstrate that inflation and ekpyrosis, which are the only dynamical mechanisms known for smoothing the universe, share the perhaps even more fundamental property of rendering space and time classical in the first place.
An Equilibrium Model for Spot and Forward Prices of Commodities

We consider a market model that consists of financial investors and producers of a commodity. Producers optionally store some production for future sale and go short on forward contracts to hedge the uncertainty of the future commodity price. Financial investors take positions in these contracts to diversify their portfolios. The spot and forward equilibrium commodity prices are endogenously derived as the outcome of the interaction between producers and investors. Assuming that both are utility maximizers, we first prove the existence of an equilibrium in an abstract setting. Then, in a framework where the consumers' demand and the exogenously priced financial market are correlated, we provide semi-explicit expressions for the equilibrium prices and analyze their dependence on the model parameters. The model can explain why increased investors' participation in forward commodity markets and higher correlation between the commodity and the stock market could result in higher spot prices and lower forward premia.
Quasi-centralized limit order books

A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in which financial institutions can only access the trading opportunities offered by counterparties with whom they possess sufficient bilateral credit. In this paper, we perform an empirical analysis of a recent, high-quality data set from a large electronic trading platform that utilizes QCLOBs to facilitate trade. We argue that the quote-relative framework often used to study other LOBs is not a sensible reference frame for QCLOBs, so we instead introduce an alternative, trade-relative framework, which we use to study the statistical properties of order flow and LOB state in our data. We also uncover an empirical universality: although the distributions that describe order flow and LOB state vary considerably across days, a simple, linear rescaling causes them to collapse onto a single curve. Motivated by this finding, we propose a semi-parametric model of order flow and LOB state for a single trading day. Our model provides similar performance to that of parametric curve-fitting techniques but is simpler to compute and faster to implement.