nils-herrmann/clean_scirepeval huggingface dataset:
Accreting Black Holes

I outline the theory of accretion onto black holes, and its application to observed phenomena such as X-ray binaries, active galactic nuclei, tidal disruption events, and gamma-ray bursts. The dynamics as well as radiative signatures of black hole accretion depend on interactions between the relatively simple black-hole spacetime and complex radiation, plasma and magnetohydrodynamical processes in the surrounding gas. I will show how transient accretion processes could provide clues to these interactions. Larger global magnetohydrodynamic simulations as well as simulations incorporating plasma microphysics and full radiation hydrodynamics will be needed to unravel some of the current mysteries of black hole accretion.
THE POWER SPECTRUM OF THE MILKY WAY: VELOCITY FLUCTUATIONS IN THE GALACTIC DISK

We investigate the kinematics of stars in the mid-plane of the Milky Way (MW) on scales between 25 pc and 10 kpc with data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), the Radial Velocity Experiment (RAVE), and the Geneva-Copenhagen survey (GCS). Using red-clump (RC) stars in APOGEE, we determine the large-scale line-of-sight velocity field out to 5 kpc from the Sun in (0.75 kpc)2 bins. The solar motion V☉ − c with respect to the circular velocity Vc is the largest contribution to the power on large scales after subtracting an axisymmetric rotation field; we determine the solar motion by minimizing the large-scale power to be V☉ − c = 24 ± 1 (ran.) ± 2 (syst. [Vc]) ± 5 (syst.[large-scale]) km s−1, where the systematic uncertainty is due to (1) a conservative 20 km s−1 uncertainty in Vc and (2) the estimated power on unobserved larger scales. Combining the APOGEE peculiar-velocity field with RC stars in RAVE out to 2 kpc from the Sun and with local GCS stars, we determine the power spectrum of residual velocity fluctuations in the MW's disk on scales between 0.2 kpc−1 ⩽ k ⩽ 40 kpc−1. Most of the power is contained in a broad peak between 0.2 kpc−1 < k < 0.9 kpc−1. We investigate the expected power spectrum for various non-axisymmetric perturbations and demonstrate that the central bar with commonly used parameters but of relatively high mass can explain the bulk of velocity fluctuations in the plane of the Galactic disk near the Sun. Streaming motions ≈10 km s−1 on ≳ 3 kpc scales in the MW are in good agreement with observations of external galaxies and directly explain why local determinations of the solar motion are inconsistent with global measurements.
AN ACCURATE NEW METHOD OF CALCULATING ABSOLUTE MAGNITUDES AND K-CORRECTIONS APPLIED TO THE SLOAN FILTER SET

We describe an accurate new method for determining absolute magnitudes, and hence also K-corrections, that is simpler than most previous methods, being based on a quadratic function of just one suitably chosen observed color. The method relies on the extensive and accurate new set of 129 empirical galaxy template spectral energy distributions from Brown et al. A key advantage of our method is that we can reliably estimate random errors in computed absolute magnitudes due to galaxy diversity, photometric error and redshift error. We derive K-corrections for the five Sloan Digital Sky Survey filters and provide parameter tables for use by the astronomical community. Using the New York Value-Added Galaxy Catalog, we compare our K-corrections with those from kcorrect. Our K-corrections produce absolute magnitudes that are generally in good agreement with kcorrect. Absolute griz magnitudes differ by less than 0.02 mag and those in the u band by ∼0.04 mag. The evolution of rest-frame colors as a function of redshift is better behaved using our method, with relatively few galaxies being assigned anomalously red colors and a tight red sequence being observed across the whole 0.0 < z < 0.5 redshift range.
Real-space calculation of the conductivity tensor for disordered topological matter.

We describe an efficient numerical approach to calculate the longitudinal and transverse Kubo conductivities of large systems using Bastin's formulation. We expand the Green's functions in terms of Chebyshev polynomials and compute the conductivity tensor for any temperature and chemical potential in a single step. To illustrate the power and generality of the approach, we calculate the conductivity tensor for the quantum Hall effect in disordered graphene and analyze the effect of the disorder in a Chern insulator in Haldane's model on a honeycomb lattice.
ZOMBIE VORTEX INSTABILITY. I. A PURELY HYDRODYNAMIC INSTABILITY TO RESURRECT THE DEAD ZONES OF PROTOPLANETARY DISKS

There is considerable interest in hydrodynamic instabilities in dead zones of protoplanetary disks as a mechanism for driving angular momentum transport and as a source of particle-trapping vortices to mix chondrules and incubate planetesimal formation. We present simulations with a pseudo-spectral anelastic code and with the compressible code Athena, showing that stably stratified flows in a shearing, rotating box are violently unstable and produce space-filling, sustained turbulence dominated by large vortices with Rossby numbers of order ∼0.2–0.3. This Zombie Vortex Instability (ZVI) is observed in both codes and is triggered by Kolmogorov turbulence with Mach numbers less than ∼0.01. It is a common view that if a given constant density flow is stable, then stable vertical stratification should make the flow even more stable. Yet, we show that sufficient vertical stratification can be unstable to ZVI. ZVI is robust and requires no special tuning of boundary conditions, or initial radial entropy or vortensity gradients (though we have studied ZVI only in the limit of infinite cooling time). The resolution of this paradox is that stable stratification allows for a new avenue to instability: baroclinic critical layers. ZVI has not been seen in previous studies of flows in rotating, shearing boxes because those calculations frequently lacked vertical density stratification and/or sufficient numerical resolution. Although we do not expect appreciable angular momentum transport from ZVI in the small domains in this study, we hypothesize that ZVI in larger domains with compressible equations may lead to angular transport via spiral density waves.
Pricing and Hedging Long-Term Options

In this article, we investigate the behavior of long-term options. In many cases, option prices follow an exponential decay (or growth) rate for further maturity dates. We determine under what conditions option prices are characterized by this property. To see this, we use the martingale extraction method through which a pricing operator is transformed into a semigroup operator, which is easier to address. We also explore notions of hedging long-term options. Hedging is an attempt to reduce market risks, and we investigate the price sensitivities (Greeks) with respect to such risks, which are typically repre- sented by variations in the underlying process of an option. We combine the Malliavin calculus with the martingale extraction method to analyze Greeks. We see that the ratios between Greeks and the option price are expressed in a simple form in the long term.
Rayleigh-Bénard instability in graphene

Motivated by the observation that electrons in graphene, in the hydrodynamic regime of transport, can be treated as a two-dimensional ultrarelativistic gas with very low shear viscosity, we examine the existence of the Rayleigh-Benard instability in a massless electron-hole plasma. First, we perform a linear stability analysis, derive the leading contributions to the relativistic Rayleigh number, and calculate the critical value above which the instability develops. By replacing typical values for graphene, such as thermal conductivity, shear viscosity, temperature, and sample sizes, we find that the instability might be experimentally observed in the near future. Additionally, we have performed simulations for vanishing reduced chemical potential and compare the measured critical Rayleigh number with the theoretical prediction, finding good agreement.
Quantum Riemannian geometry of phase space and nonassociativity

Noncommutative or ‘quantum’ differential geometry has emerged in recent years as a process for quantizing not only a classical space into a noncommutative algebra (as familiar in quantum mechanics) but also differential forms, bundles and Riemannian structures at this level. The data for the algebra quantisation is a classical Poisson bracket while the data for quantum differential forms is a Poisson-compatible connection. We give an introduction to our recent result whereby further classical data such as classical bundles, metrics etc. all become quantised in a canonical ‘functorial’ way at least to 1st order in deformation theory. The theory imposes compatibility conditions between the classical Riemannian and Poisson structures as well as new physics such as typical nonassociativity of the differential structure at 2nd order. We develop in detail the case of ℂℙn where the commutation relations have the canonical form [wi, w̄j] = iλδij similar to the proposal of Penrose for quantum twistor space. Our work provides a canonical but ultimately nonassociative differential calculus on this algebra and quantises the metric and Levi-Civita connection at lowest order in λ.
Interferometric tests of Planckian quantum geometry models

The effect of Planck scale quantum geometrical effects on measurements with interferometers is estimated with standard physics, and with a variety of proposed extensions. It is shown that effects are negligible in standard field theory with canonically quantized gravity. Statistical noise levels are estimated in a variety of proposals for nonstandard metric fluctuations, and these alternatives are constrained using upper bounds on stochastic metric fluctuations from LIGO. Idealized models of several interferometer system architectures are used to predict signal noise spectra in a quantum geometry that cannot be described by a fluctuating metric, in which position noise arises from holographic bounds on directional information. Predictions in this case are shown to be close to current and projected experimental bounds.
A Generalized Linear Transport Model for Spatially Correlated Stochastic Media

We formulate a new model for transport in stochastic media with long-range spatial correlations where exponential attenuation (controlling the propagation part of the transport) becomes power law. Direct transmission over optical distance τ(s), for fixed physical distance s, thus becomes (1 + τ(s)/a)− a, with standard exponential decay recovered when a → ∞. Atmospheric turbulence phenomenology for fluctuating optical properties rationalizes this switch. Foundational equations for this generalized transport model are stated in integral form for d = 1, 2, 3 spatial dimensions. A deterministic numerical solution is developed in d = 1 using Markov Chain formalism, verified with Monte Carlo, and used to investigate internal radiation fields. Standard two-stream theory, where diffusion is exact, is recovered when a = ∞. Differential diffusion equations are not presently known when a < ∞, nor is the integro-differential form of the generalized transport equation. Monte Carlo simulations are performed in d = 2, as a model for transport on random surfaces, to explore scaling behavior of transmittance T when transport optical thickness τt ≫ 1. Random walk theory correctly predicts T∝τ− min {1, a/2}t in the absence of absorption. Finally, single scattering theory in d = 3 highlights the model’s violation of angular reciprocity when a < ∞, a desirable property at least in atmospheric applications. This violation is traced back to a key trait of generalized transport theory, namely, that we must distinguish more carefully between two kinds of propagation: one that ends in a virtual or actual detection and the other in a transition from one position to another in the medium.
Efficient price dynamics in a limit order market: an utility indifference approach

We construct an utility-based dynamic asset pricing model for a limit order market. The price is nonlinear in volume and subject to market impact. We solve an optimal hedging problem under the market impact and derive the dynamics of the efficient price, that is, the asset price when a representative liquidity demander follows an optimal strategy. We show that a Pareto efficient allocation is achieved under a completeness condi- tion. We give an explicit representation of the efficient price for several examples. In particular, we observe that the volatility of the asset depends on the convexity of an initial endowment. Further, we observe that an asset price crash is invoked by an endowment shock. We establish a dynamic programming principle under an incomplete framework.
Two new approaches to compressed sensing exhibiting both robust sparse recovery and the grouping effect

In this paper we introduce a new optimization formulation for sparse regression and compressed sensing, called CLOT (Combined L-One and Two), wherein the regularizer is a convex combination of the ℓ1- and ℓ2-norms. This formulation differs from the Elastic Net (EN) formulation, in which the regularizer is a convex combination of the ℓ1- and ℓ2-norm squared. This seemingly simple modification has fairly significant consequences. In particular, it is shown in this paper that the EN formulation does not achieve robust recovery of sparse vectors in the context of compressed sensing, whereas the new CLOT formulation does so. Also, like EN but unlike LASSO, the CLOT formulation achieves the grouping effect, wherein coefficients of highly correlated columns of the measurement (or design) matrix are assigned roughly comparable values. It is noteworthy that LASSO does not have the grouping effect and EN (as shown here) does not achieve robust sparse recovery. Therefore the CLOT formulation combines the best features of both LASSO (robust sparse recovery) and EN (grouping effect). The CLOT formulation is a special case of another one called SGL (Sparse Group LASSO) which was introduced into the literature previously, but without any analysis of either the grouping effect or robust sparse recovery. It is shown here that SGL achieves robust sparse recovery, and also achieves a version of the grouping effect in that coefficients of highly correlated columns of the measurement (or design) matrix are assigned roughly comparable values, if the columns belong to the same group.
Do Artificial Reinforcement-Learning Agents Matter Morally?

Artificial reinforcement learning (RL) is a widely used technique in artificial intelligence that provides a general method for training agents to perform a wide variety of behaviours. RL as used in computer science has striking parallels to reward and punishment learning in animal and human brains. I argue that present-day artificial RL agents have a very small but nonzero degree of ethical importance. This is particularly plausible for views according to which sentience comes in degrees based on the abilities and complexities of minds, but even binary views on consciousness should assign nonzero probability to RL programs having morally relevant experiences. While RL programs are not a top ethical priority today, they may become more significant in the coming decades as RL is increasingly applied to industry, robotics, video games, and other areas. I encourage scientists, philosophers, and citizens to begin a conversation about our ethical duties to reduce the harm that we inflict on powerless, voiceless RL agents.
Bootstrap-Based Regularization for Low-Rank Matrix Estimation

We develop a flexible framework for low-rank matrix estimation that allows us to transform noise models into regularization schemes via a simple bootstrap algorithm. Effectively, our procedure seeks an autoencoding basis for the observed matrix that is stable with respect to the specified noise model; we call the resulting procedure a stable autoencoder. In the simplest case, with an isotropic noise model, our method is equivalent to a classical singular value shrinkage estimator. For non-isotropic noise models, e.g., Poisson noise, the method does not reduce to singular value shrinkage, and instead yields new estimators that perform well in experiments. Moreover, by iterating our stable autoencoding scheme, we can automatically generate low-rank estimates without specifying the target rank as a tuning parameter.
On the energy partition in oscillations and waves

A class of generally nonlinear dynamical systems is considered, for which the Lagrangian is represented as a sum of homogeneous functions of the displacements and their derivatives. It is shown that an energy partition as a single relation follows directly from the Euler–Lagrange equation in its general form. The partition is defined solely by the homogeneity orders. If the potential energy is represented by a single homogeneous function, as well as the kinetic energy, the partition between these energies is defined uniquely. For a steady-state solitary wave, where the potential energy consists of two functions of different orders, the Derrick–Pohozaev identity is involved as an additional relation to obtain the partition. Finite discrete systems, finite continuous bodies, homogeneous and periodic-structure waveguides are considered. The general results are illustrated by examples of various types of oscillations and waves: linear and nonlinear, homogeneous and forced, steady-state and transient, periodic and non-periodic, parametric and resonant, regular and solitary.
Towards Learning Object Affordance Priors from Technical Texts

Everyday activities performed by artificial assistants can potentially be executed naively and dangerously given their lack of common sense knowledge. This paper presents conceptual work towards obtaining prior knowledge on the usual modality (passive or active) of any given entity, and their affordance estimates, by extracting high-confidence ability modality semantic relations (X can Y relationship) from non-figurative texts, by analyzing co-occurrence of grammatical instances of subjects and verbs, and verbs and objects. The discussion includes an outline of the concept, potential and limitations, and possible feature and learning framework adoption.
A construction of the Schr\"odinger Functional for M\"obius Domain Wall Fermions

We construct the Schr\"odinger Functional (SF) setup for the M\"obius domain wall fermions (MDWF). The method is an extension of the method proposed by Takeda for the standard domain wall fermion. In order to fulfill the requirement that the lattice Dirac operator with the SF boundary obeys the L\"uscher's universality argument: the lattice chiral fermion with the SF boundary condition breaks the chiral symmetry at the temporal boundary, we impose the parity symmetry with respect to the fifth-direction on the MDWF operator. This additional symmetry restricts the choice of the parameter of the MDWF so that the optimal parameter from the Zolotarev optimal approximation cannot be applied. We introduce a modified parameter set having the fifth-dimensional parity symmetry. We investigate the MDWF with the SF boundary by observing eigenvalues of the Hermitian operator and the Ginsparg-Wilson relation violation at the tree-level. We compare the computational cost with that of the standard DWF with the SF scheme.
On Estimating $L_2^2$ Divergence

We give a comprehensive theoretical characterization of a nonparametric estimator for the $L_2^2$ divergence between two continuous distributions. We first bound the rate of convergence of our estimator, showing that it is $\sqrt{n}$-consistent provided the densities are sufficiently smooth. In this smooth regime, we then show that our estimator is asymptotically normal, construct asymptotic confidence intervals, and establish a Berry-Ess\'{e}en style inequality characterizing the rate of convergence to normality. We also show that this estimator is minimax optimal.
Standard model predictions and new physics sensitivity in B → D D decays

An extensive model-independent analysis of $B\ensuremath{\rightarrow}DD$ decays is carried out employing $SU(3)$ flavor symmetry, including symmetry-breaking corrections. Several theoretically clean observables are identified which allow for testing the standard model. These include the known time-dependent $CP$ asymmetries, the penguin pollution of which can be controlled in this framework, but notably also quasi-isospin relations which are experimentally well accessible and unaffected by symmetry-breaking corrections. Theoretical assumptions can be kept to a minimum and controlled by additional sum rules. Available data are used in global fits to predict the branching ratio for the ${B}^{0}\ensuremath{\rightarrow}{D}_{s}^{+}{D}_{s}^{\ensuremath{-}}$ decay as well as several $CP$ asymmetries which have not been measured so far, and future prospects are analyzed.
Training for Fast Sequential Prediction Using Dynamic Feature Selection

We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the features into a sequence of templates which are ordered such that high confidence can often be reached using only a small fraction of all features. Parameter estimation is arranged to maximize accuracy and early confidence in this sequence. We present experiments in left-to-right part-of-speech tagging on WSJ, demonstrating that we can preserve accuracy above 97% with over a five-fold reduction in run-time.
Directional antineutrino detection.

We propose the first event-by-event directional antineutrino detector using inverse beta decay (IBD) interactions on hydrogen, with potential applications including monitoring for nuclear nonproliferation, spatially mapping geoneutrinos, characterizing the diffuse supernova neutrino background and searching for new physics in the neutrino sector. The detector consists of adjacent and separated target and capture scintillator planes. IBD events take place in the target layers, which are thin enough to allow the neutrons to escape without scattering elastically. The neutrons are detected in the thicker boron-loaded capture layers. The location of the IBD event and the momentum of the positron are determined by tracking the positron's trajectory through the detector. Our design is a straightforward modification of existing antineutrino detectors; a prototype could be built with existing technology.
Team size matters: Collaboration and scientific impact since 1900

This article provides the first historical analysis of the relationship between collaboration and scientific impact using three indicators of collaboration (number of authors, number of addresses, and number of countries) derived from articles published between 1900 and 2011. The results demonstrate that an increase in the number of authors leads to an increase in impact, from the beginning of the last century onward, and that this is not due simply to self‐citations. A similar trend is also observed for the number of addresses and number of countries represented in the byline of an article. However, the constant inflation of collaboration since 1900 has resulted in diminishing citation returns: Larger and more diverse (in terms of institutional and country affiliation) teams are necessary to realize higher impact. The article concludes with a discussion of the potential causes of the impact gain in citations of collaborative papers.