nils-herrmann/clean_scirepeval huggingface dataset:
Planck intermediate results - XXX. The angular power spectrum of polarized dust emission at intermediate and high Galactic latitudes

The polarized thermal emission from diffuse Galactic dust is the main foreground present in measurements of the polarization of the cosmic microwave background (CMB) at frequencies above 100 GHz. In this paper we exploit the uniqueness of the Planck HFI polarization data from 100 to 353 GHz to measure the polarized dust angular power spectra C_l^(EE) and C_l^(BB) over the multipole range 40 <l< 600 well away from the Galactic plane. These measurements will bring new insights into interstellar dust physics and allow a precise determination of the level of contamination for CMB polarization experiments. Despite the non-Gaussian and anisotropic nature of Galactic dust, we show that general statistical properties of the emission can be characterized accurately over large fractions of the sky using angular power spectra. The polarization power spectra of the dust are well described by power laws in multipole, C_l ∝ l_α, with exponents α^(EE,BB) = −2.42 ± 0.02. The amplitudes of the polarization power spectra vary with the average brightness in a way similar to the intensity power spectra. The frequency dependence of the dust polarization spectra is consistent with modified blackbody emission with β_d = 1.59 and T_d = 19.6 K down to the lowest Planck HFI frequencies. We find a systematic difference between the amplitudes of the Galactic B- and E-modes, C_l^(BB)/C_l^(EE) = 0.5. We verify that these general properties are preserved towards high Galactic latitudes with low dust column densities. We show that even in the faintest dust-emitting regions there are no “clean” windows in the sky where primordial CMB B-mode polarization measurements could be made without subtraction of foreground emission. Finally, we investigate the level of dust polarization in the specific field recently targeted by the BICEP2 experiment. Extrapolation of the Planck 353 GHz data to 150 GHz gives a dust power
Unitarity bounds on scalar dark matter effective interactions at LHC

We study the compatibility of the unitarity bound and the 8TeV LHC on the effective theory of the scalar dark matter. In several signals of effective interactions, mono-jet with missing energy events are studied. We found that, at least, if the dark matter mass is about 800GeV or heavier, contributions of events violating the unitarity are not negligible. The unitarity conditions in the 14TeV LHC are also calculated.
GRB 080517: a local, low-luminosity gamma-ray burst in a dusty galaxy at z = 0.09

We present an analysis of the photometry and spectroscopy of the host galaxy of Swift-detected GRB 080517. From our optical spectroscopy, we identify a redshift of z = 0.089 ± 0.003, based on strong emission lines, making this a rare example of a very local, low-luminosity, long gamma-ray burst. The galaxy is detected in the radio with a flux density of S4.5 GHz = 0.22 ± 0.04 mJy - one of relatively few known gamma-ray bursts hosts with a securely measured radio flux. Both optical emission lines and a strong detection at 22 μm suggest that the host galaxy is forming stars rapidly, with an inferred star formation rate ∼16 M⊙ yr−1 and a high dust obscuration (E(B − V) > 1, based on sightlines to the nebular emission regions). The presence of a companion galaxy within a projected distance of 25 kpc, and almost identical in redshift, suggests that star formation may have been triggered by galaxy-galaxy interaction. However, fitting of the remarkably flat spectral energy distribution from the ultraviolet through to the infrared suggests that an older, 500 Myr post-starburst stellar population is present along with the ongoing star formation. We conclude that the host galaxy of GRB 080517 is a valuable addition to the still very small sample of well-studied local gamma-ray burst hosts.
Impact of star formation history on the measurement of star formation rates

Measuring star formation across the Universe is key to constrain models of galaxy formation and evolution. Yet, determining the SFR (star formation rate) of galaxies remains a challenge.  In this paper we investigate in isolation the impact of a variable star formation history on the measurement of the SFR.  We combine 23 state-of-the-art hydrodynamical simulations of 1<z<2 galaxies on the main sequence with the cigale spectral energy distribution modelling code. This allows us to generate synthetic spectra every 1 Myr for each simulation, taking the stellar populations and the nebular emission into account. Using these spectra, we estimate the SFR from classical estimators which we compare with the true SFR we know from the simulations.  We find that except for the Lyman continuum, classical SFR estimators calibrated over 100 Myr overestimate the SFR from ~25% in the FUV band to ~65% in the U band. Such biases are due 1) to the contribution of stars living longer than 100 Myr, and 2) to variations of the SFR on timescales longer than a few tens of Myr. Rapid variations of the SFR increase the uncertainty on the determination of the instantaneous SFR but have no long term effect.  The discrepancies between the true and estimated SFR may explain at least part of the tension between the integral of the star formation rate density and the stellar mass density at a given redshift. To reduce possible biases, we suggest to use SFR estimators calibrated over 1 Gyr rather than the usually adopted 100 Myr timescales.
Quasinormal modes of weakly charged Kerr-Newman spacetimes

The resonant mode spectrum of the Kerr-Newman spacetime is presently unknown. These modes, called the quasinormal modes, play a central role in determining the stability of Kerr-Newman black holes and their response to perturbations. We present a new formalism, generalized from time-independent perturbation theory in quantum mechanics, for calculating the quasinormal mode frequencies of weakly charged Kerr-Newman spacetimes of arbitrary spin. Our method makes use of an original technique for applying perturbation theory to zeroth-order solutions that are not square-integrable, and it can be applied to other problems in theoretical physics. The new formalism reveals no unstable modes, which together with previous results in the slow-rotation limit strongly indicates the modal stability of the Kerr-Newman spacetime. Our techniques and results are of interest in the areas of holographic duality, foundational problems in general relativity, and possibly in astrophysical systems.
A proposal of Quantization in flat space-time with a minimal length present

The 4-dimensional space-time is extended to pseudo-complex coordinates. Proposing the standard quantization rules in this extended space, the ones for the 4-dimensional sub-space acquire, as one solution, the commutation relations with non- commuting coordinates. This demonstrates that the algebraic extension keeps the simple structure of Quantum Mechanics, while it also introduces an effective quite involved structure in the 4-dimensional sub-space. The similarities to H. S. Snyder's work, a former proposal to include the effects of a minimal length, are exposed. The first steps to pseudo-complex Quantum Mechanics in 1-dimension are outlined, awaiting still the interpretation of some new emerging structures. As an example, two waves, out of phase by 90 degrees, are added which classically annihilate each other, while in the pseudo-complex description there is a non-zero amplitude.
General Relativity and Gravitation: A Centennial Perspective

Part I. Einstein's Triumph: 1. 100 years of general relativity George F. R. Ellis 2. Was Einstein right? Clifford M. Will 3. Cosmology David Wands, Misao Sasaki, Eiichiro Komatsu, Roy Maartens and Malcolm A. H. MacCallum 4. Relativistic astrophysics Peter Schneider, Ramesh Narayan, Jeffrey E. McClintock, Peter Meszaros and Martin J. Rees Part II. New Window on the Universe: 5. Receiving gravitational waves Beverly K. Berger, Karsten Danzmann, Gabriela Gonzalez, Andrea Lommen, Guido Mueller, Albrecht Rudiger and William Joseph Weber 6. Sources of gravitational waves. Theory and observations Alessandra Buonanno and B. S. Sathyaprakash Part III. Gravity is Geometry, After All: 7. Probing strong field gravity through numerical simulations Frans Pretorius, Matthew W. Choptuik and Luis Lehner 8. The initial value problem of general relativity and its implications Gregory J. Galloway, Pengzi Miao and Richard Schoen 9. Global behavior of solutions to Einstein's equations Stefanos Aretakis, James Isenberg, Vincent Moncrief and Igor Rodnianski Part IV.  Quantum fields in curved space-times Stefan Hollands and Robert M. Wald 11. From general relativity to quantum gravity Abhay Ashtekar, Martin Reuter and Carlo Rovelli 12. Quantum gravity via unification Henriette Elvang and Gary T. Horowitz.
Bayesian Prediction for The Winds of Winter

Predictions are made for the number of chapters told from the point of view of each character in the next two novels in George R. R. Martin's \emph{A Song of Ice and Fire} series by fitting a random effects model to a matrix of point-of-view chapters in the earlier novels using Bayesian methods.  readers who have not read all five existing novels in the series should not read further, as major plot points will be spoiled.}}
Bounds on Portfolio Quality

The signal-noise ratio of a portfolio of p assets, its expected return divided by its risk, is couched as an estimation problem on the sphere. When the portfolio is built using noisy data, the expected value of the signal-noise ratio is bounded from above via a Cramer-Rao bound, for the case of Gaussian returns. The bound holds for `biased' estimators, thus there appears to be no bias-variance tradeoff for the problem of maximizing the signal-noise ratio. An approximate distribution of the signal-noise ratio for the Markowitz portfolio is given, and shown to be fairly accurate via Monte Carlo simulations, for Gaussian returns as well as more exotic returns distributions. These findings imply that if the maximal population signal-noise ratio grows slower than the universe size to the 1/4 power, there may be no diversification benefit, rather expected signal-noise ratio can decrease with additional assets. As a practical matter, this may explain why the Markowitz portfolio is typically applied to small asset universes. Finally, the theorem is expanded to cover more general models of returns and trading schemes, including the conditional expectation case where mean returns are linear in some observable features, subspace constraints (i.e., dimensionality reduction), and hedging constraints.
Distributed Robust Learning

We propose a framework for distributed robust statistical learning on {\em big contaminated data}. The Distributed Robust Learning (DRL) framework can reduce the computational time of traditional robust learning methods by several orders of magnitude. We analyze the robustness property of DRL, showing that DRL not only preserves the robustness of the base robust learning method, but also tolerates contaminations on a constant fraction of results from computing nodes (node failures). More precisely, even in presence of the most adversarial outlier distribution over computing nodes, DRL still achieves a breakdown point of at least $ \lambda^*/2 $, where $ \lambda^* $ is the break down point of corresponding centralized algorithm. This is in stark contrast with naive division-and-averaging implementation, which may reduce the breakdown point by a factor of $ k $ when $ k $ computing nodes are used. We then specialize the DRL framework for two concrete cases: distributed robust principal component analysis and distributed robust regression. We demonstrate the efficiency and the robustness advantages of DRL through comprehensive simulations and predicting image tags on a large-scale image set.
Distributed Clustering and Learning Over Networks

Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications, agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lead to undesired results. In this paper, we propose an adaptive clustering and learning scheme that allows agents to learn which neighbors they should cooperate with and which other neighbors they should ignore. In doing so, the resulting algorithm enables the agents to identify their clusters and to attain improved learning and estimation accuracy over networks. We carry out a detailed mean-square analysis and assess the error probabilities of Types I and II, i.e., false alarm and misdetection, for the clustering mechanism. Among other results, we establish that these probabilities decay exponentially with the step-sizes so that the probability of correct clustering can be made arbitrarily close to one.
Estimating topological properties of weighted networks from limited information

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.
Enabling Incremental Query Re-Optimization

As declarative query processing techniques expand to the Web, data streams, network routers, and cloud platforms, there is an increasing need to re-plan execution in the presence of unanticipated performance changes. New runtime information may affect which query plan we prefer to run. Adaptive techniques require innovation both in terms of the algorithms used to estimate costs, and in terms of the search algorithm that finds the best plan. We investigate how to build a cost-based optimizer that recomputes the optimal plan incrementally given new cost information, much as a stream engine constantly updates its outputs given new data. Our implementation especially shows benefits for stream processing workloads. It lays the foundations upon which a variety of novel adaptive optimization algorithms can be built. We start by leveraging the recently proposed approach of formulating query plan enumeration as a set of recursive datalog queries; we develop a variety of novel optimization approaches to ensure effective pruning in both static and incremental cases. We further show that the lessons learned in the declarative implementation can be equally applied to more traditional optimizer implementations.
Double soft limits of cosmological correlations

Correlation functions of two long-wavelength modes with several short-wavelength modes are shown to be related to lower order correlation functions, using the background wave method, and independently, by exploiting symmetries of the wavefunction of the Universe. These soft identities follow from the non-linear extension of the adiabatic modes of Weinberg, and their generalization by Hinterbichler et al. The extension is shown to be unique. A few checks of the identities are presented.
Semianalytical quantum model for graphene field-effect transistors

We develop a semianalytical model for monolayer graphene field-effect transistors in the ballistic limit. Two types of devices are considered: in the first device, the source and drain regions are doped by charge transfer with Schottky contacts, while, in the second device, the source and drain regions are doped electrostatically by a back gate. The model captures two important effects that influence the operation of both devices: (i) the finite density of states in the source and drain regions, which limits the number of states available for transport and can be responsible for negative output differential resistance effects, and (ii) quantum tunneling across the potential steps at the source-channel and drain-channel interfaces. By comparison with a self-consistent non-equilibrium Green's function solver, we show that our model provides very accurate results for both types of devices, in the bias region of quasi-saturation as well as in that of negative differential resistance.
BOLT : Efficient Betweenness Ordering in almost Linear Time

Centrality measures, erstwhile popular amongst the sociologists and psychologists, have seen wide and increasing applications across several disciplines of late. In conjunction with the big data problems there came the need to analyze big networks and in this connection, centrality measures became of great interest to the community of mathematicians, computer scientists and physicists. While it is an important question to ask how one can rank vertices based on their importance in a network, there hasn't been a commonly accepted definition, mainly due to the subjectivity of the term "importance". Amongst a plethora of application specific definitions available in the literature to rank the vertices, closeness centrality, betweenness centrality and eigenvector centrality (page-rank) have been the most important and widely applied ones. In the current paper, we formulate a method to determine the betweenness ordering of $k$ vertices without exactly computing their betweenness indices - which is a daunting task for networks of large size. The method results very efficient ordering even when runs for linear time in the number of edges. We apply our approach to find the betweenness ordering of $k$ vertices in several synthetic and real world graphs. We compare our method with the available techniques in the literature and show that our method fares several times better than the currently known techniques. We further show that the accuracy of our algorithm gets better with the increase in size and density of the network.
Learning to Match for Multi-criteria Document Relevance

In light of the tremendous amount of data produced by social media, a large body of research have revisited the relevance estimation of the users' generated content. Most of the studies have stressed the multidimensional nature of relevance and proved the effectiveness of combining the different criteria that it embodies. Traditional relevance estimates combination methods are often based on linear combination schemes. However, despite being effective, those aggregation mechanisms are not effective in real-life applications since they heavily rely on the non-realistic independence property of the relevance dimensions. In this paper, we propose to tackle this issue through the design of a novel fuzzy-based document ranking model. We also propose an automated methodology to capture the importance of relevance dimensions, as well as information about their interaction. This model, based on the Choquet Integral, allows to optimize the aggregated documents relevance scores using any target information retrieval relevance metric. Experiments within the TREC Microblog task and a social personalized information retrieval task highlighted that our model significantly outperforms a wide range of state-of-the-art aggregation operators, as well as a representative learning to rank methods.
Tensors, BICEP2 results, prior dependence, and dust

We investigate the prior dependence on the inferred spectrum of primordial tensor perturbations, in light of recent results from BICEP2 and taking into account a possible dust contribution to polarized anisotropies. We highlight an optimized parametrization of the tensor power spectrum, and adoption of a logarithmic prior on its amplitude AT, leading to results that transform more evenly under change of pivot scale. In the absence of foregrounds the tension between the results of BICEP2 and Planck drives the tensor spectral index nT to be blue tilted in a joint analysis, which would be in contradiction to the standard inflation prediction (nT<0). When foregrounds are accounted for, the BICEP2 results no longer require nonstandard inflationary parameter regions. We present limits on primordial AT and nT, adopting foreground scenarios put forward by Mortonson and Seljak and motivated by Planck 353 GHz observations, and assess what dust contribution leaves a detectable cosmological signal. We find that if there is sufficient dust for the signal to be compatible with standard inflation, then the primordial signal is too weak to be robustly detected by BICEP2 if Planck+WMAP upper limits from temperature and E-mode polarization are correct.
The first billion years of a warm dark matter universe

We present results of cosmological N-body hydrodynamic chemistry simulations of primordial structure growth and evolution in a scenario with warm dark matter (WDM) having a mass of 3 keV (thermal relic) and compare with a model consisting of standard cold dark matter (CDM). We focus on the high-redshift universe ($z>6$), where the structure formation process should better reflect the primordial (linear) differences in terms of matter power spectrum. We find that early epochs can be exceptional probes of the dark-matter nature. Non-linear WDM power spectra and mass functions are up to 2 dex lower than in CDM and show spreads of factor of a few persisting in the whole first Gyr. Runaway molecular cooling in WDM haloes results severely inhibited because of the damping of power at large $k$ modes and hence cosmic (population III and II-I) star formation rate (SFR) is usually suppressed with respect to CDM predictions. Luminous objects formed in a WDM background are very rare at $z>10$, due to the sparser and retarded evolution of early WDM mini-haloes during the dark ages and their lack can be fitted with a simple analytical formula depending only on magnitude and redshift. Future high-$z$ observations of faint galaxies have the potential to discriminate between CDM and WDM scenarios by means of cosmic stellar mass density (SMD) and specific SFR, as well. When compared to the effects of alternative cosmologies (e.g. non-Gaussian or dark-energy models) or of high-order corrections at large $z$ (e.g. primordial streaming motions or changes in the pristine IMF) the ones caused by WDM are definitely more dramatic.