nils-herrmann/clean_scirepeval huggingface dataset:
Splendeurs et mis\`eres de theory of laser beam propagation in nonlinear media

It is demonstrated that current theoretical models utilize equations for description of laser beam propagation in nonlinear media that were deduced under the assumption of homogeneity of dielectric constant of the media and for the case of planar wave front. Here, we deduce complete wave propagation equation that includes inhomogeneity of the dielectric constant and present this propagation equation in compact vector form. Although similar equations are known in the narrow fields, such as, radio wave propagation in ionosphere and electro-magnetic and acoustic wave propagation in stratified media, we develop here a novel approach of using such equations in modeling of laser beam propagation in nonlinear media. The inadequacy of the assumptions under which the propagation equations are derived in the current model is demonstrated. Also, mathematical derivation is presented that describes foundation and validates our previously reported method for modeling of laser beam propagation in nonlinear media by blending solution of linear Helmholtz equation with a correction term representing nonlinear field perturbation expressed in terms of paraxial ray-optics (eikonal) equation. Unlike all previous theoretical approaches developed during past five decades, our approach satisfies correspondence principle since in the limit of zero-length wavelength it reduces from physical to geometrical optics.
Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace

Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problemis exacerbated when we rely unrealistically on a singletraining data source, which is often insufficient to coverthe intrinsically complex face variations. This paperproposes a principled multi-task learning approachbased on Discriminative Gaussian Process Latent VariableModel (DGPLVM), named GaussianFace, for faceverification. In contrast to relying unrealistically on asingle training data source, our model exploits additional data from multiple source-domains to improve the generalization performance of face verification inan unknown target-domain. Importantly, our model can adapt automatically to complex data distributions, and therefore can well capture complex face variations inherent in multiple sources. To enhance discriminative power, we introduced a more efficient equivalent form of Kernel Fisher Discriminant Analysis to DGPLVM.To speed up the process of inference and prediction, we exploited the low rank approximation method. Extensive experiments demonstrated the effectiveness of the proposed model in learning from diverse data sources and generalizing to unseen domains. Specifically, the accuracy of our algorithm achieved an impressive accuracyrate of 98.52% on the well-known and challenging Labeled Faces in the Wild (LFW) benchmark. For the first time, the human-level performance in face verification (97.53%) on LFW is surpassed.
Optimizing the CVaR via Sampling

Conditional Value at Risk (CVaR) is a prominent risk measure that is being used extensively in various domains. We develop a new formula for the gradient of the CVaR in the form of a conditional expectation. Based on this formula, we propose a novel sampling-based estimator for the gradient of the CVaR, in the spirit of the likelihood-ratio method. We analyze the bias of the estimator, and prove the convergence of a corresponding stochastic gradient descent algorithm to a local CVaR optimum. Our method allows to consider CVaR optimization in new domains. As an example, we consider a reinforcement learning application, and learn a risk-sensitive controller for the game of Tetris.
Precision measurement of the neutron twist-3 matrix element d(2)(n): probing color forces.

Double-spin asymmetries and absolute cross sections were measured at large Bjorken x  (0.25≤x≤0.90), in both the deep-inelastic and resonance regions, by scattering longitudinally polarized electrons at beam energies of 4.7 and 5.9 GeV from a transversely and longitudinally polarized (3)He target. In this dedicated experiment, the spin structure function g(2)((3)He) was determined with precision at large x, and the neutron twist-3 matrix element d(2)(n) was measured at ⟨Q(2)⟩ of 3.21 and 4.32  GeV(2)/c(2), with an absolute precision of about 10(-5). Our results are found to be in agreement with lattice QCD calculations and resolve the disagreement found with previous data at ⟨Q(2)⟩=5  GeV(2)/c(2). Combining d(2)(n) and a newly extracted twist-4 matrix element f(2)(n), the average neutron color electric and magnetic forces were extracted and found to be of opposite sign and about 30  MeV/fm in magnitude.
Option Pricing Accuracy for Estimated Heston Models

We consider assets for which price $X_t$ and squared volatility $Y_t$ are jointly driven by Heston joint stochastic differential equations (SDEs). When the parameters of these SDEs are estimated from $N$ sub-sampled data $(X_{nT}, Y_{nT})$, estimation errors do impact the classical option pricing PDEs. We estimate these option pricing errors by combining numerical evaluation of estimation errors for Heston SDEs parameters with the computation of option price partial derivatives with respect to these SDEs parameters. This is achieved by solving six parabolic PDEs with adequate boundary conditions. To implement this approach, we also develop an estimator $\hat \lambda$ for the market price of volatility risk, and we study the sensitivity of option pricing to estimation errors affecting $\hat \lambda$. We illustrate this approach by fitting Heston SDEs to 252 daily joint observations of the S\&P 500 index and of its approximate volatility VIX, and by numerical applications to European options written on the S\&P 500 index.
Global energy conservation in nonlinear spherical characteristic evolutions

Associated to the unique 4-parametric subgroup of translations, normal to the Bondi-Metzner-Sachs group, there exists a generator of the temporal translation asymptotic symmetry. Such a descriptor of the motion along the conformal orbit near null infinity is propagated to finite regions. This allows us to observe the global energy conservation even in extreme situations near the critical behavior of the massless scalar field collapse in spherical symmetry.
Lp Regularized Portfolio Optimization

Investors who optimize their portfolios under any of the coherent risk measures are naturally led to regularized portfolio optimization when they take into account the impact their trades make on the market. We show here that the impact function determines which regularizer is used. We also show that any regularizer based on the norm $L_p$ with $p>1$ makes the sensitivity of coherent risk measures to estimation error disappear, while regularizers with $p
A relational approach to the Mach-Einstein question

Mach's principle is incompatible with general relativity (GR), it has not condensed into an established theory and suffers from inconsistencies. Yet, the problem is that Mach's principle is a consequence of Berkeley's notions, which are as good as irrefutable for their ontological nature. Moreover, the observed coincidence of the "preferred inertial frame" and the frame attached to the "fixed stars" is essentially Machian, while this coincidence is anomalous to both GR and Newtonian physics. Another issue is that GR needs dark energy to explain the accelerating expansion of the universe, while acceleration of receding masses is inherent to the Machian principle. So GR and Mach's principle question each other, while neither one can be falsified easily. This suggest that both are valid in their particular domain. A relational theory may reconcile the two, since it can cover both.
Variational study of polarons in Bose-Einstein condensates

We use a class of variational wave functions to study the properties of an impurity in a Bose-Einstein condensate, i.e. the "Bose polaron". The impurity interacts with the condensate through a contact interaction, which can be tuned by a Feshbach resonance. We find a stable attractive polaron branch that evolves continuously across the resonance to a tight-binding diatomic molecule deep in the positive scattering length side. A repulsive polaron branch with finite lifetime is also observed and it becomes unstable as the interaction strength increases. The effective mass of the attractive polaron also changes smoothly across the resonance connecting the two well-understood limits deep on both sides.
A phenomenological cost model for high energy particle accelerators

Accelerator-based facilities have enabled forefront research in high-energy physics for more than half a century. The accelerator technology of colliders has progressed immensely, while beam energy, luminosity, facility size, and cost have grown by several orders of magnitude. The method of colliding beams has not fully exhausted its potential but has slowed down considerably in its progress. In this paper we derive a simple scaling model for the cost of large accelerators and colliding beam facilities based on costs of 17 big facilities which have been either built or carefully estimated. Although this approach cannot replace an actual cost estimate based on an engineering design, this parameterization is to indicate a somewhat realistic cost range for consideration of what future frontier accelerator facilities might be fiscally realizable.
Gravity resonance spectroscopy constrains dark energy and dark matter scenarios.

We report on precision resonance spectroscopy measurements of quantum states of ultracold neutrons confined above the surface of a horizontal mirror by the gravity potential of Earth. Resonant transitions between several of the lowest quantum states are observed for the first time. These measurements demonstrate that Newton's inverse square law of gravity is understood at micron distances on an energy scale of 10-14  eV. At this level of precision, we are able to provide constraints on any possible gravitylike interaction. In particular, a dark energy chameleon field is excluded for values of the coupling constant β>5.8×108 at 95% confidence level (C.L.), and an attractive (repulsive) dark matter axionlike spin-mass coupling is excluded for the coupling strength gsgp>3.7×10-16 (5.3×10-16) at a Yukawa length of λ=20  μm (95% C.L.).
Invisibility System Using Image Processing and Optical Camouflage Technology

Invisible persons are seen in fiction stories only, but in the real world it is proved that invisibility is possible. This paper describes the creation of invisibility with the help of technologies like Optical camouflage; Image based rendering and Retro- reflective projection. The object that needs to be made transparent or invisible is painted or covered with retro reflective material. Then a projector projects the background image on it making the masking object virtually transparent. Capturing the background image requires a video camera, which sits behind the person wearing the cloak. The video from the camera must be in a digital format so it can be sent to a computer for image processing using image based rendering technical. There are some useful applications for this simple but astonishing technology.
Origin of $1/f$ noise transition in hydration dynamics on a lipid membrane surface

Water molecules on lipid membrane surfaces are known to contribute to membrane stability by connecting lipid molecules and acting as a water bridge. Although the number of water molecules near the membrane fluctuates dynamically, the hydration dynamics has been veiled. Here we investigate residence statistics of water molecules on the surface of a lipid membrane using all-atom molecular dynamics simulations. We show that hydration dynamics on the lipid membrane exhibit $1/f^\beta$ noise with two different power-law exponents, $\beta_l 1$. By constructing a dichotomous process for the hydration dynamics, we find that the process can be regarded as a non-Markov renewal process. The result implies that the origin of the $1/f$ noise transition in hydration dynamics on the membrane surface is a combination of a power-law distribution with cutoff of interoccurrence times of switching events and a long-term correlation between the interoccurrence times.
Speeding Up MCMC by Efficient Data Subsampling

We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where the likelihood function for n observations is estimated from a random subset of m observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to sample from a perturbed posterior, for which we derive the asymptotic error with respect to n and m, respectively. We propose a practical estimator of the error and show that the error is negligible even for a very small m in our applications. We demonstrate that subsampling MCMC is substantially more efficient than standard MCMC in terms of sampling efficiency for a given computational budget, and that it outperforms other subsampling methods for MCMC proposed in the literature.
Nonsingular bouncing cosmologies in light of BICEP2

We confront various nonsingular bouncing cosmologies with the recently released BICEP2 data and investigate the observational constraints on their parameter space. In particular, within the context of the effective field approach, we analyze the constraints on the matter bounce curvaton scenario with a light scalar field, and the new matter bounce cosmology model in which the universe successively experiences a period of matter contraction and an ekpyrotic phase. Additionally, we consider three nonsingular bouncing cosmologies obtained in the framework of modified gravity theories, namely the Hořava-Lifshitz bounce model, the f(T) bounce model, and loop quantum cosmology.
STARS GET DIZZY AFTER LUNCH

Exoplanet searches have discovered a large number of “hot Jupiters”—high-mass planets orbiting very close to their parent stars in nearly circular orbits. A number of these planets are sufficiently massive and close-in to be significantly affected by tidal dissipation in the parent star, to a degree parameterized by the tidal quality factor Q*. This process speeds up their star's rotation rate while reducing the planet's semimajor axis. In this paper, we investigate the tidal destruction of hot Jupiters. Because the orbital angular momenta of these planets are a significant fraction of their star's rotational angular momenta, they spin up their stars significantly while spiraling to their deaths. Using the Monte Carlo simulation, we predict that for Q* = 106, 3.9 × 10−6 of stars with the Kepler Target Catalog's mass distribution should have a rotation period shorter than 1/3 day (8 hr) due to accreting a planet. Exoplanet surveys such as SuperWASP, HATnet, HATsouth, and KELT have already produced light curves of millions of stars. These two facts suggest that it may be possible to search for tidally destroyed planets by looking for stars with extremely short rotational periods, then looking for remnant planet cores around those candidates, anomalies in the metal distribution, or other signatures of the recent accretion of the planet.
FORMATION, TIDAL EVOLUTION, AND HABITABILITY OF THE KEPLER-186 SYSTEM

The Kepler-186 system consists of five planets orbiting an early M dwarf. The planets have physical radii of 1.0–1.50 R⊕ and orbital periods of 4–130 days. The 1.1 R⊕ Kepler-186f with a period of 130 days is of particular interest. Its insolation of roughly 0.32 S⊕ places it within the surface liquid water habitable zone (HZ). We present a multifaceted study of the Kepler-186 system, using two sets of parameters which are consistent with the data and also self-consistent. First, we show that the distribution of planet masses can be roughly reproduced if the planets were accreted from a high surface density disk presumably sculpted by an earlier phase of migration. However, our simulations predict the existence of one to two undetected planets between planets e and f. Next, we present a dynamical analysis of the system including the effect of tides. The timescale for tidal evolution is short enough that the four inner planets must have small obliquities and near-synchronous rotation rates. The tidal evolution of Kepler-186f is slow enough that its current spin state depends on a combination of its initial spin state, its dissipation rate, and the stellar age. Finally, we study the habitability of Kepler-186f with a one-dimensional climate model. The planet's surface temperature can be raised above 273 K with 0.5–5 bars of CO2, depending on the amount of N2 present. Kepler-186f represents a case study of an Earth-sized planet in the cooler regions of the HZ of a cool star.
On the absence of black hole event horizons: a test of De Sitter Yang-Mills Theory

De Sitter Quantum Gravity is a Yang-Mills theory based on the de Sitter or SO(4,1) group and a promising candidate for a quantum theory of gravity. In this paper, an exact, static, spherically symmetric solution of the classical equations is derived. I show that when the Schwarzchild radius to distance ratio is at post-Newtonian order the theory agrees with general relativity for all parameters but that, once the ratio becomes closer to unity, they differ. At the Schwarzchild radius from a black hole singularity, general relativity predicts an event horizon, which has become a controversial topic in quantum gravity because of information preservation issues. In the De Sitter theory I show, however, that time-like escape paths exist for any mass black hole until the singularity itself is reached. Since an event horizon has never been directly observed and there is currently no observation on which the two theories disagree, this provides a powerful test of the De Sitter theory.
Creating one-dimensional nanoscale periodic ripples in a continuous mosaic graphene monolayer.

In previous studies, it has proved difficult to realize periodic graphene ripples with wavelengths of a few nanometers. Here we show that one-dimensional (1D) periodic graphene ripples with wavelengths from 2 nm to tens of nanometers can be implemented in the intrinsic areas of a continuous mosaic (locally N-doped) graphene monolayer by simultaneously using both the thermal strain engineering and the anisotropic surface stress of the Cu substrate. Our result indicates that the constraint imposed at the boundaries between the intrinsic and the N-doped regions play a vital role in creating these 1D ripples. We also demonstrate that the observed rippling modes are beyond the descriptions of continuum mechanics due to the decoupling of graphene's bending and tensional deformations. Scanning tunneling spectroscopy measurements indicate that the nanorippling generates a periodic electronic superlattice and opens a zero-energy gap of about 130 meV in graphene. This result may pave a facile way for tailoring the structures and electronic properties of graphene.
Robustness of H0 determination at intermediate redshifts

The most recent Hubble constant (H0) estimates from local methods (z << 1), H0 = 73.8±2.4 km s 1 Mpc 1 , and the one from high redshits (Planck Collaboration 2013), H0 = 67.3 ± 1.2 km s 1 Mpc 1 , are discrepant at 2.4σ confidence level. Within this context, Lima & Cunha (LC) (ApJL 781, 38, 2014) derived a new determination of H0 using four cosmic probes at intermediate redshifts (0.1 < z < 1.8) based on the
Shear viscosity of hot nuclear matter by the mean free path method

The shear viscosity of hot nuclear matter is investigated by using the mean free path method within the framework of the isospin-dependent quantum molecular dynamics model. Finite size nuclear sources at different density and temperature are initialized based on the Fermi-Dirac distribution. The results show that shear viscosity to entropy density ratio decreases with the increase of temperature and tends toward a constant value for rho similar to rho(0), which is consistent with the previous studies on nuclear matter formed during heavy-ion collisions. At rho similar to 1/2 rho(0), a minimum of eta/s is seen at around T = 10 MeV and a maximum of the multiplicity of intermediate mass fragment (M-IMF) is also observed at the same temperature which is an indication of the liquid-gas phase transition.
The role of centrality for the identification of influential spreaders in complex networks

The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumorlike dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreaders might not necessarily be so important when it comes to analyzing social contagion or rumor propagation. Additionally, it has been shown that different metrics (degree, coreness, etc.) might identify different influential nodes even for the same dynamical processes with diverse degrees of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease and rumor spreading capabilities of the nodes in different synthetic and real-world (both spatial and nonspatial) networks. We also propose a generalization of the random walk accessibility as a new centrality measure and derive analytical expressions for the latter measure for simple network configurations. Our results show that for nonspatial networks, the k-core and degree centralities are the most correlated to epidemic spreading, whereas the average neighborhood degree, the closeness centrality, and accessibility are the most related to rumor dynamics. On the contrary, for spatial networks, the accessibility measure outperforms the rest of the centrality metrics in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of spatial networks.