nils-herrmann/clean_scirepeval huggingface dataset:
Study of neutron spectra in a water bath from a Pb target irradiated by 250 MeV protons

Spallation neutrons were produced by the irradiation of Pb with 250 MeV protons. The Pb target was surrounded by water which was used to slow down the emitted neutrons. The moderated neutrons in the water bath were measured by using the resonance detectors of Au, Mn and In with a cadmium (Cd) cover. According to the measured activities of the foils, the neutron flux at different resonance energies were deduced and the epithermal neutron spectra were proposed. Corresponding results calculated with the Monte Carlo code MCNPX were compared with the experimental data to check the validity of the code. The comparison showed that the simulation could give a good prediction for the neutron spectra above 50 eV, while the finite thickness of the foils greatly effected the experimental data in low energy. It was also found that the resonance detectors themselves had great impact on the simulated energy spectra.
How memory generates heterogeneous dynamics in temporal networks

Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks, little work has been devoted to the understanding of how such heterogeneities can emerge from microscopic mechanisms at the level of nodes and links. Here we show that long-term memory effects are present in the creation and disappearance of links in empirical networks. We thus consider a simple generative modeling framework for temporal networks able to incorporate these memory mechanisms. This allows us to study separately the role of each of these mechanisms in the emergence of heterogeneous network dynamics. In particular, we show analytically and numerically how heterogeneous distributions of contact durations, of intercontact durations, and of numbers of contacts per link emerge. We also study the individual effect of heterogeneities on dynamical processes, such as the paradigmatic susceptible-infected epidemic spreading model. Our results confirm in particular the crucial role of the distributions of intercontact durations and of the numbers of contacts per link.
Back-reaction of the Hawking radiation flux on a gravitationally collapsing star II: Fireworks instead of firewalls

A star collapsing gravitationally into a black hole emits a flux of radiation, known as Hawking radiation. When the initial state of a quantum field on the background of the star, is placed in the Unruh vacuum in the far past, then in the exterior Hawking radiation corresponds to a flux of positive energy radiation travelling outwards from near the surface to future infinity. Based on pair creation, the evaporation of the collapsing star can be equivalently described by the absorption of an ingoing negative energy flux of radiation travelling towards the center of the star. Here, we are interested in the evolution of the star during its collapse. Thus we include the backreaction of the negative energy Hawking flux in the interior geometry of the collapsing star when writing the full 4-dimensional Einstein and hydrodynamic equations. Hawking radiation emitted before the star passes through its Schwarzschild radius slows down and reverses the collapse of the star. The star evaporates without forming an horizon or a singularity. This study provides a more realistic investigation than the one first presented in , since the backreaction of Hawking radiation flux on the collapsing star is studied in the case when the initial state of the field is in Unruh's vacuum.
Exploring exoplanet populations with NASA’s Kepler Mission

The Kepler Mission is exploring the diversity of planets and planetary systems. Its legacy will be a catalog of discoveries sufficient for computing planet occurrence rates as a function of size, orbital period, star type, and insolation flux. The mission has made significant progress toward achieving that goal. Over 3,500 transiting exoplanets have been identified from the analysis of the first 3 y of data, 100 planets of which are in the habitable zone. The catalog has a high reliability rate (85–90% averaged over the period/radius plane), which is improving as follow-up observations continue. Dynamical (e.g., velocimetry and transit timing) and statistical methods have confirmed and characterized hundreds of planets over a large range of sizes and compositions for both single- and multiple-star systems. Population studies suggest that planets abound in our galaxy and that small planets are particularly frequent. Here, I report on the progress Kepler has made measuring the prevalence of exoplanets orbiting within one astronomical unit of their host stars in support of the National Aeronautics and Space Administration’s long-term goal of finding habitable environments beyond the solar system.
Quantum Nonlinear Optics with Polar J-Aggregates in Microcavities.

We predict that an ensemble of organic dye molecules with permanent electric dipole moments embedded in a microcavity can lead to strong optical nonlinearities at the single-photon level. The strong long-range electrostatic interaction between chromophores due to their permanent dipoles introduces the desired nonlinearity of the light-matter coupling in the microcavity. We develop a semiclassical model to obtain the absorption spectra of a weak probe field under the influence of strong exciton-photon coupling with the cavity field. Using realistic parameters, we demonstrate that a cavity field with an average photon number near unity can significantly modify the absorptive and dispersive response of the medium to a weak probe field at a different frequency. Finally, we show that the system is in the regime of cavity-induced transparency with a broad transparency window for dye dimers. We illustrate our findings using pseudoisocyanine chloride (PIC) J-aggregates in currently available optical microcavities.
A Martingale Approach to Detect Peak of News in Social Network

Nowadays, social medias such as Twitter, Memetracker and Blogs have become powerful tools to propagate information. They facilitate quick dissemination sequence of information such as news article, blog posts, user’s interests and thoughts through large- scale. Providing strong means to analyzing social networks structure and how information diffuse through them is essential. Many recent studies emphasize on modeling information diffusion and their patterns to gain some useful knowledge. In this paper, we propose a statistical approach to online detect peak points of news when spread over social networks, to the best of our knowledge has never investigated before. The proposed model use martingale approach to predict peak points (when news reached the peak of its popularity). Experimental results on real datasets show good performance of our approach to online detect these peak points.
Green Algae as Model Organisms for Biological Fluid Dynamics.

In the past decade the volvocine green algae, spanning from the unicellular Chlamydomonas to multicellular Volvox, have emerged as model organisms for a number of problems in biological fluid dynamics. These include flagellar propulsion, nutrient uptake by swimming organisms, hydrodynamic interactions mediated by walls, collective dynamics and transport within suspensions of microswimmers, the mechanism of phototaxis, and the stochastic dynamics of flagellar synchronization. Green algae are well suited to the study of such problems because of their range of sizes (from 10 μm to several millimetres), their geometric regularity, the ease with which they can be cultured and the availability of many mutants that allow for connections between molecular details and organism-level behavior. This review summarizes these recent developments and highlights promising future directions in the study of biological fluid dynamics, especially in the context of evolutionary biology, that can take advantage of these remarkable organisms.
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.
Quanta of geometry: noncommutative aspects.

In the construction of spectral manifolds in noncommutative geometry, a higher degree Heisenberg commutation relation involving the Dirac operator and the Feynman slash of real scalar fields naturally appears and implies, by equality with the index formula, the quantization of the volume. We first show that this condition implies that the manifold decomposes into disconnected spheres, which will represent quanta of geometry. We then refine the condition by involving the real structure and two types of geometric quanta, and show that connected spin manifolds with large quantized volume are then obtained as solutions. The two algebras M_{2}(H) and M_{4}(C) are obtained, which are the exact constituents of the standard model. Using the two maps from M_{4} to S^{4} the four-manifold is built out of a very large number of the two kinds of spheres of Planckian volume. We give several physical applications of this scheme such as quantization of the cosmological constant, mimetic dark matter, and area quantization of black holes.
Emergent gravity requires kinematic nonlocality.

This Letter refines arguments forbidding nonlinear dynamical gravity from appearing in the low energy effective description of field theories with local kinematics, even for those with instantaneous long-range interactions. Specifically, we note that gravitational theories with universal coupling to energy-an intrinsically nonlinear phenomenon-are characterized by Hamiltonians that are pure boundary terms on shell. In order for this to be the low energy effective description of a field theory with local kinematics, all bulk dynamics must be frozen and, thus, irrelevant to the construction. The result applies to theories defined either on a lattice or in the continuum, and requires neither Lorentz invariance nor translation invariance.
Sparse Additive Model using Symmetric Nonnegative Definite Smoothers

We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers. Unlike the previous sparse backfitting algorithm, our method is essentially a block coordinate descent algorithm that guarantees to converge to the optimal solution. It bridges the gap between the population backfitting algorithm and that of the data version. We also prove variable selection consistency under suitable conditions. Numerical studies on both synthesis and real data are conducted to show that adaptive sparse backfitting algorithm outperforms previous sparse backfitting algorithm in fitting and predicting high dimensional nonparametric models.
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, problem domain knowledge can be built into the constraints of the model, typically at the expense of difficulties during inference. In contrast, deterministic deep neural networks are constructed in such a way that inference is straightforward, but their architectures are generic and it is unclear how to incorporate knowledge. This work aims to obtain the advantages of both approaches. To do so, we start with a model-based approach and an associated inference algorithm, and \emph{unfold} the inference iterations as layers in a deep network. Rather than optimizing the original model, we \emph{untie} the model parameters across layers, in order to create a more powerful network. The resulting architecture can be trained discriminatively to perform accurate inference within a fixed network size. We show how this framework allows us to interpret conventional networks as mean-field inference in Markov random fields, and to obtain new architectures by instead using belief propagation as the inference algorithm. We then show its application to a non-negative matrix factorization model that incorporates the problem-domain knowledge that sound sources are additive. Deep unfolding of this model yields a new kind of non-negative deep neural network, that can be trained using a multiplicative backpropagation-style update algorithm. We present speech enhancement experiments showing that our approach is competitive with conventional neural networks despite using far fewer parameters.
Custom v. Standardized Risk Models

We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: 1) longer horizon risk factors (value, growth, etc.) increase noise trades and trading costs; 2) arbitrary risk factors can neutralize alpha; 3) "standardized" industries are artificial and insufficiently granular; 4) normalization of style risk factors is lost for the trading universe; 5) diversifying risk models lowers P&L correlations, reduces turnover and market impact, and increases capacity. We discuss various aspects of custom risk model building.
Fano resonances in hexagonal zigzag graphene rings under external magnetic flux

We study transport properties of hexagonal zigzag graphene quantum rings connected to semi-infinite nanoribbons. Open two-fold symmetric structures support localized states that can be traced back to those existing in the isolated six-fold symmetric rings. Using a tight-binding Hamiltonian within the Green's function formalism, we show that an external magnetic field promotes these localized states to Fano resonances with robust signatures in transport. Local density of states and current distributions of the resonant states are calculated as a function of the magnetic flux intensity. For structures on corrugated substrates we analyze the effect of strain by including an out-of-plane centro-symmetric deformation in the model. We show that small strains shift the resonance positions without further modifications, while high strains introduce new ones.
Optimal Execution with Dynamic Order Flow Imbalance

We examine optimal execution models that take into account both market microstructure impact and informational costs. Informational footprint is related to order flow and is represented by the trader's influence on the flow imbalance process, while microstructure influence is captured by instantaneous price impact. We propose a continuous-time stochastic control problem that balances between these two costs. Incorporating order flow imbalance leads to the consideration of the current market state and specifically whether one's orders lean with or against the prevailing order flow, key components often ignored by execution models in the literature. In particular, to react to changing order flow, we endogenize the trading horizon $T$. After developing the general indefinite-horizon formulation, we investigate several tractable approximations that sequentially optimize over price impact and over $T$. These approximations, especially a dynamic version based on receding horizon control, are shown to be very ac...
Contagion in an interacting economy

We investigate the credit risk model defined in Hatchett and Kühn (2006  2231) under more general assumptions, in particular using a general degree distribution for sparse graphs. Expanding upon earlier results, we show that the model is exactly solvable in the N → ∞ limit and demonstrate that the exact solution is described by the message-passing approach outlined in Karrer and Newman (2010 ), generalized to include heterogeneous agents and couplings. We provide comparisons with simulations of graph ensembles with power-law degree distributions.
Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge

We introduce a minorization-maximization approach to optimizing common measures of discovery significance in high energy physics. The approach alternates between solving a weighted binary classification problem and updating class weights in a simple, closed-form manner. Moreover, an argument based on convex duality shows that an improvement in weighted classification error on any round yields a commensurate improvement in discovery significance. We complement our derivation with experimental results from the 2014 Higgs boson machine learning challenge.
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate

We describe and analyze a simple algorithm for principal component analysis and singular value decomposition, VR-PCA, which uses computationally cheap stochastic iterations, yet converges exponentially fast to the optimal solution. In contrast, existing algorithms suffer either from slow convergence, or computationally intensive iterations whose runtime scales with the data size. The algorithm builds on a recent variance-reduced stochastic gradient technique, which was previously analyzed for strongly convex optimization, whereas here we apply it to an inherently non-convex problem, using a very different analysis.
Raman spectra and electron-phonon coupling in disordered graphene with gate-tunable doping

We report a Raman spectroscopy study of graphene field-effect transistors with a controlled amount of defects introduced in graphene by exposure to electron-beam irradiation. Raman spectra are taken at T = 8 K over a range of back gate voltages (Vg) for various irradiation dosages (Re). We study effects in the Raman spectra due to Vg-induced doping and artificially created disorder at various Re. With moderate disorder (irradiation), the Raman G peak with respect to the graphene carrier density (nFE) exhibits a minimum in peak frequency and a maximum in peak width near the charge-neutral point (CNP). These trends are similar to those seen in previous works on pristine graphene and have been attributed to a reduction of electron-phonon coupling strength (D) and removal of the Kohn anomaly as the Fermi level moves away from the CNP. We also observe a maximum in I2D/IG and weak maximum in ID/IG near the CNP. All the observed dependences of Raman parameters on nFE weaken at stronger disorder (higher Re), impl...
Scalable Genomics with R and Bioconductor

This paper reviews strategies for solving problems encountered when analyzing large genomic data sets and describes the implementation of those strategies in R by packages from the Bioconductor project. We treat the scalable processing, summarization and visualization of big genomic data. The general ideas are well established and include restrictive queries, compression, iteration and parallel computing. We demonstrate the strategies by applying Bioconductor packages to the detection and analysis of genetic variants from a whole genome sequencing experiment.
The white dwarf's carbon fraction as a secondary parameter of Type Ia supernovae

Binary stellar evolution calculations predict that Chandrasekhar-mass carbon/oxygen white dwarfs (WDs) show a radially varying profile for the composition with a carbon depleted core. Many recent multi-dimensional simulations of Type Ia supernovae (SNe Ia), however, assume the progenitor WD has a homogeneous chemical composition.  In this work, we explore the impact of di erent initial carbon profiles of the progenitor WD on the explosion phase and on synthetic observables in the Chandrasekhar-mass delayed detonation model. Spectra and light curves are compared to observations to judge the validity of the model.  The explosion phase is simulated using the finite volume supernova code Leafs, which is extended to treat di erent compositions of the progenitor WD. The synthetic observables are computed with the Monte Carlo radiative transfer code Artis.  Di erences in binding energies of carbon and oxygen lead to a lower nuclear energy release for carbon depleted material; thus, the burning fronts that develop are weaker and the total nuclear energy release is smaller. For otherwise identical conditions, carbon depleted models produce less 56 Ni. Comparing di erent models with similar 56 Ni yields shows lower kinetic energies in the ejecta for carbon depleted models, but only small di erences in velocity distributions and line velocities in spectra. The light curve width‐luminosity relation (WLR) obtained for models with di ering carbon depletion is roughly perpendicular to the observed WLR, hence the carbon mass fraction is probably only a secondary parameter in the family of SNe Ia.
Self-scattering for Dark Matter with an excited state

Self-interacting dark matter scenarios have recently attracted much attention, as a possible means to alleviate the tension between N-body simulations and observations of the dark matter distribution on galactic and sub-galactic scales. The presence of internal structure for the dark matter—for example, a nearly-degenerate state in the spectrum that could decay, or be collisionally excited or de-excited—has also been proposed as a possible means to address these discrepancies. Such internal structure can be a source of interesting signatures in direct and indirect dark matter searches, for example providing a novel explanation for the 3.5 keV line recently observed in galaxies and galaxy clusters. We analyze a simple model of dark matter self-scattering including a nearly-degenerate excited state, and develop an accurate analytic approximation for the elastic and inelastic s-wave cross sections, which is valid outside the perturbative regime provided the particle velocity is sufficiently low (this condition is also required for the s-wave to dominate over higher partial waves). We anticipate our results will be useful in incorporating inelastic self-scattering into N-body simulations, in order to study the quantitative impact of nearly-degenerate states in the dark matter spectrum on galactic structure and dynamics, and in computing the indirect signatures of multi-state dark matter.
Entropy mode loops and cosmological correlations during perturbative reheating

Recently, it has been shown that during preheating the entropy modes circulating in the loops, which correspond to the inflaton decay products, meaningfully modify the cosmological correlation functions at superhorizon scales. In this paper, we determine the significance of the same effect when reheating occurs in the perturbative regime. In a typical two scalar field model, the magnitude of the loop corrections are shown to depend on several parameters like the background inflaton amplitude in the beginning of reheating, the inflaton decay rate and the inflaton mass. Although the loop contributions turn out to be small as compared to the preheating case, they still come out larger than the loop effects during inflation.