nils-herrmann/clean_scirepeval huggingface dataset:
Dilepton Production in Heavy-Ion Collisions

The properties of electromagnetic radiation from hot fireballs as created in ultra-relativistic heavy-ion collisions are reviewed. We first outline how the medium effects in the electromagnetic spectral function, which governs thermal production rates, relate to the (partial) restoration of chiral symmetry. In particular, we show how chiral and QCD sum rules, together with constraints from lattice QCD, can render these relations quantitative. Turning to dilepton data, we elaborate on updates in the space-time evolution and quark-gluon plasma emission rates from lattice-QCD calculations. With a now available excitation function in dilepton spectra from the RHIC beam-energy scan connecting down to SPS energies, we argue that a consistent interpretation of dilepton data emerges. Combining well-constrained space-time evolutions with state-of-the-art emission rates identifies most of the radiation to emanate from around the pseudo-critical temperature, and thus confirms resonance melting as the prevalent mechanism in this regime, compatible with chiral restoration. Recent measurements of a relatively soft slope and large elliptic flow in direct-photon spectra at RHIC and LHC lend further support to this picture.
Thermal Hawking Broadening and Statistical Entropy of Black Hole Wave Packet

34G~ A (1) constitutes a triple point in the phase of physical theories, touching gravity, even beyond general relativity, quantum mechanics, and statistical mechanics. However, despite its central role in physics, and several illuminating derivations , its statistical mechanical roots have not been fully revealed. There are only a few exceptions  where one can actually account, at some limit, for the number of micro states. As far as the prototype Schwarzschild black hole is concerned, we still do not know where the micro states are hiding and even what we are about to enumerate. A black hole is classically characterized by its event horizon, but once ~ is switched on, even an innocent looking question like ’where is this horizon located’ still lacks a meaningful answer in the quantum or even semi classical sense. In this paper, we examine the possibility that the Schwarzschild black hole states are apparently hidden simply because they degenerate into one single general relativistic state at the ~ → 0 limit. We carry out our analysis within the framework of the mini super spacetime (a variant of the mini superspace [4] limit of Wheeler-DeWitt formalism) without relying on string theory and/or loop quantum gravity. We do it by invoking the most classical, that is of minimal uncertainty, Hartle-Hawking wave packet solution. The variance of the emerging statistical mass distribution is then interpreted as thermal Hawking broadening, with the corresponding semi-classical picture resembling a Gaussian horizon profile with Compton width (horizon fluctuations were considered in [5, 6]). The quantum mechanically exposed mass spectrum is Hawking temperature dependent. The analytical calculation of the associated statistical entropy then calls for the Fowler prescription [7] which has been designated to deal with temperature dependent energy levels. Our starting point is the static spherically symmetric line element
Large scale cosmic perturbation from evaporation of primordial black holes

We present a novel mechanism to generate the cosmic perturbation from evaporation of primordial black holes. A mass of a field is fluctuated if it is given by a vacuum expectation value of a light scalar field because of the quantum fluctuation during inflation. The fluctuated mass causes variations of the evaporation time of the primordial black holes. Therefore provided the primordial black holes dominate the Universe when they evaporate, primordial cosmic perturbations are generated. We find that the amplitude of the large scale curvature perturbation generated in this scenario can be consistent with the observed value. Interestingly, our mechanism works even if all fields that are responsible for inflation and the generation of the cosmic perturbation are decoupled from the visible sector except for the gravitational interaction. An implication to the running spectral index is also discussed.
The Stark problem in the Weierstrassian formalism

We present a new general, complete closed-form solution of the Stark problem in terms of Weierstrass elliptic and related functions. With respect to previous treatments of the problem, our analysis is exact and valid for all values of the external force field, and it is expressed via unique formul\ae{} valid for all initial conditions and parameters of the system. The simple form of the solution allows us to perform a thorough investigation of the properties of the dynamical system, including the identification of quasi-periodic and periodic orbits, the formulation of a simple analytical criterion to determine the boundness of the trajectory, and the characterisation of the equilibrium points.
Traffic data reconstruction based on Markov random field modeling

We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from various sensors. Our approach is based on Markov random field modeling of road traffic. The reconstruction is achieved by using a mean-field method and a machine learning method. We numerically verify the performance of our method using realistic simulated traffic data for the real road network of Sendai, Japan.
A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection

As the education fees are becoming more expensive, more students apply for scholarships. Consequently, hundreds and even thousands of applications need to be handled by the sponsor. To solve the problems, some alternatives based on several attributes (criteria) need to be selected. In order to make a decision on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS and Weighted Product (WP) methods is applied to select the candidates for academic and non-academic scholarships at Universitas Islam Negeri Sunan Kalijaga. Data used were a crisp and fuzzy data . The results show that TOPSIS and  Weighted Product FMADM methods can be used to select the most suitable candidates to receive the scholarships since the preference values applied in this method can show applicants with the highest eligibility.
The LSND and MiniBooNE Oscillation Searches at High Δm2

This article reviews the experimental design, analysis, and results of the LSND and MiniBooNE experiments. Their primary goal was to effect sensitive searches for neutrino oscillations in the mass region with Δm2≈1 eV2. The two experiments are complementary, so comparing their results can yield additional information with respect to models with sterile neutrinos. Both experiments obtained evidence for oscillations, and MiniBooNE also observed a νμ→νe excess. We also consider the results within the global context of sterile neutrino oscillation models. The final data sets require a more extended model than the simple single–sterile neutrino model imagined at the time that LSND drew to a close and MiniBooNE began. There are apparent incompatibilities between data sets in models with two sterile neutrinos that may be explained with variations within the systematic error. Overall, models with two (or three) sterile neutrinos seem to succeed in fitting the global data, and they make interesting predictions for...
Real-time bidding for online advertising: measurement and analysis

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.
A note on Keen's model: The limits of Schumpeter's "Creative Destruction"

This paper presents a general solution for a recent model by Keen for endogenous money creation. The solution provides an analytic framework that explains all significant dynamical features of Keen's model and their parametric dependence, including an exact result for both the period and subsidence rate of the Great Moderation. It emerges that Keen's model has just two possible long term solutions: stable growth or terminal collapse. While collapse can come about immediately from economies that are nonviable by virtue of unsuitable parameters or initial conditions, in general the collapse is preceded by an interval of exponential growth. In first approximation, the duration of that exponential growth is half a period of a sinusoidal oscillation. The period is determined by reciprocal of the imaginary part of one root of a certain quintic polynomial. The real part of the same root determines the rate of growth of the economy. The coefficients of that polynomial depend in a complicated way upon the numerous parameters in the problem and so, therefore, the pattern of roots. For a favorable choice of parameters, the salient root is purely real. This is the circumstance that admits the second possible long term solution, that of indefinite stable growth, i.e. an infinite period.
Altmetrics: New Indicators for Scientific Communication in Web 2.0

In this paper we review the socalled altmetrics or alternative metrics. This concept raises from the development of new indicators based on Web 2.0, for the evaluation of the research and academic activity. The basic assumption is that variables such as mentions in blogs, number of twits or of researchers bookmarking a research paper for instance, may be legitimate indicators for measuring the use and impact of scientific publications. In this sense, these indicators are currently the focus of the bibliometric community and are being discussed and debated. We describe the main platforms and indicators and we analyze as a sample the Spanish research output in Communication Studies. Comparing traditional indicators such as citations with these new indicators. The results show that the most cited papers are also the ones with a highest impact according to the altmetrics. We conclude pointing out the main shortcomings these metrics present and the role they may play when measuring the research impact through 2.0 platforms.
A RADIO-SELECTED BLACK HOLE X-RAY BINARY CANDIDATE IN THE MILKY WAY GLOBULAR CLUSTER M62

We report the discovery of a candidate stellar-mass black hole in the Milky Way globular cluster M62. We detected the black hole candidate, which we call M62-VLA1, in the core of the cluster using deep radio continuum imaging from the Karl G. Jansky Very Large Array. M62-VLA1 is a faint source with a flux density of 18.7 ± 1.9 μJy at 6.2 GHz and a flat radio spectrum (α = −0.24 ± 0.42, for Sν = να). M62 is the second Milky Way cluster with a candidate stellar-mass black hole; unlike the two candidate black holes previously found in the cluster M22, M62-VLA1 is associated with a Chandra X-ray source, supporting its identification as a black hole X-ray binary. Measurements of its radio and X-ray luminosity, while not simultaneous, place M62-VLA1 squarely on the well-established radio–X-ray correlation for stellar-mass black holes. In archival Hubble Space Telescope imaging, M62-VLA1 is coincident with a star near the lower red giant branch. This possible optical counterpart shows a blue excess, Hα emission, and optical variability. The radio, X-ray, and optical properties of M62-VLA1 are very similar to those for V404 Cyg, one of the best-studied quiescent stellar-mass black holes. We cannot yet rule out alternative scenarios for the radio source, such as a flaring neutron star or background galaxy; future observations are necessary to determine whether M62-VLA1 is indeed an accreting stellar-mass black hole.
THE MASS DISTRIBUTION OF SUBGIANT PLANET HOSTS

High mass stars are hostile to Doppler measurements due to rotation and activity on the main-sequence, so RV searches for planets around massive stars have relied on evolved stars. A large number of planets have been found around evolved stars with M > 1.5 M☉. To test the robustness of mass determinations, Lloyd compared mass distributions of planet hosting subgiants with distributions from integrating isochrones and concluded that it is unlikely the subgiant planet hosts are this massive, but rather that the mass inferences are systematically in error. The conclusions of Lloyd have been called in to question by Johnson et al., who show TRILEGAL-based mass distributions that disagree with the mass distributions in Lloyd, which they attribute to Malmquist bias. Johnson et al. argue that the very small spectroscopic observational uncertainties favor high masses, and there are a large number of high mass sub giants in RV surveys. However, in this Letter, it is shown that Malmquist bias does not impact the mass distributions, but the mass distribution is sensitive to Galaxy model. The relationship needed to reconcile the subgiant planet host masses with any model of the Galactic stellar population is implausible, and the conclusion of Lloyd that spectroscopic mass determinations of subgiants are likely to have been overestimated is robust.
Effective action and phase transitions of scalar field on the fuzzy sphere

Scalar field theory on the fuzzy two-sphere, represented as a hermitian matrix model that includes kinetic, mass and quartic interaction terms, is studied. The effective action in the symmetric large-N regime is analyzed using a self-consistent bootstrap method which fixes its form up to sixth order in the eigenvalues and gives a closed expression to all orders in the quadratic invariant of the matrix, valid close to semicircular distributions. Using this action the eigenvalue distribution is calculated for the interacting theory in the appropriate scaling limit and the phase transition from the disordered to the symmetric ordered phase is identified, including nonperturbative effects.
Measurements of collective machine intelligence

Independent from the still ongoing research in measuring individual intelligence, we anticipate and provide a framework for measuring collective intelligence. Collective intelligence refers to the idea that several individuals can collaborate in order to achieve high levels of intelligence. We present thus some ideas on how the intelligence of a group can be measured and simulate such tests. We will however focus here on groups of artificial intelligence agents (i.e., machines). We will explore how a group of agents is able to choose the appropriate problem and to specialize for a variety of tasks. This is a feature which is an important contributor to the increase of intelligence in a group (apart from the addition of more agents and the improvement due to common decision making). Our results reveal some interesting results about how (collective) intelligence can be modeled, about how collective intelligence tests can be designed and about the underlying dynamics of collective intelligence. As it will be useful for our simulations, we provide also some improvements of the threshold allocation model originally used in the area of swarm intelligence but further generalized here.
Electron localization in disordered graphene: multifractal properties of the wavefunctions

An analysis of the electron localization properties in doped graphene is performed by doing a numerical multifractal analysis. By obtaining the singularity spectrum of a tight-binding model, it is found that the electron wave functions present a multifractal behavior. Such multifractality is preserved even for second neighbor interaction, which needs to be taken into account if a comparison is desired with experimental results. States close to the Dirac point have a wider multifractal character than those far from this point as the impurity concentration is increased. The analysis of the results allows to conclude that in the split-band limit, where impurities act as vacancies, the system can be well described by a chiral orthogonal symmetry class, with a singularity spectrum transition approaching freezing as disorder increases. This also suggests that in doped graphene, localization is in contrast with the conventional picture of Anderson localization in two dimensions, showing also that the common belief of the absence of quantum percolation in two dimensional systems needs to be revised.
Towards a better insight of RDF triples Ontology-guided Storage system abilities

The vision of the Semantic Web is becoming a reality with billions of RDF triples being distributed over multiple queryable end-points (e.g. Linked Data). Although there has been a body of work on RDF triples persistent storage, it seems that, considering reasoning dependent queries, the problem of providing an efficient, in terms of performance, scalability and data redundancy, partitioning of the data is still open. In regards to recent data partitioning studies, it seems reasonable to think that data partitioning should be guided considering several directions (e.g. ontology, data, application queries). This paper proposes several contributions: describe an overview of what a road map for data partitioning for RDF data efficient and persistent storage should contain, present some preliminary results and analysis on the particular case of ontology-guided (property hierarchy) partitioning and finally introduce a set of semantic query rewriting rules to support querying RDF data needing OWL inferences
Estimation of solar zenith passage on the Earth's surface by a simple harmonic motion

The kinematics of the Solar zenith passage on the Earth was shown as a simple harmonic motion. This analytical model used the uniform circular motion to describe the Earth's rotation relative to its axis and relative to the Sun, also this model considered the sunlight beams parallel and constant the obliquity of the ecliptic. We made a contrast between some data taken from of the software Stellarium 0.12.1 and the model, we obtained an uncertainty $\Delta t=1d$; this result allow to project the model as an astronomical context conducive to develop the kinematics of simple harmonic motion.
The Meaning of Probability of Default for Asset-backed Loans

The authors examine the concept of probability of default for asset-backed loans. In contrast to unsecured loans it is shown that probability of default can be defined as either a measure of the likelihood of the borrower failing to make required payments, or as the likelihood of an insufficiency of collateral value on foreclosure. Assuming expected loss is identical under either definition, this implies a corresponding pair of definitions for loss given default. Industry treatment of probability of default for asset-backed loans appears to inconsistently blend the two types of definition. The authors develop a mathematical treatment of asset-backed loans which consistently applies each type of definition in a framework to produce the same expected loss and allows translation between the two frameworks.
Arabizi Detection and Conversion to Arabic

Arabizi is Arabic text that is written using Latin characters. Arabizi is used to present both Modern Standard Arabic (MSA) or Arabic dialects. It is commonly used in informal settings such as social networking sites and is often with mixed with English. In this paper we address the problems of: identifying Arabizi in text and converting it to Arabic characters. We used word and sequence-level features to identify Arabizi that is mixed with English. We achieved an identification accuracy of 98.5%. As for conversion, we used transliteration mining with language modeling to generate equivalent Arabic text. We achieved 88.7% conversion accuracy, with roughly a third of errors being spelling and morphological variants of the forms in ground truth.
Simultaneous Discrimination Prevention and Privacy Protection in Data Publishing and Mining

Data mining is an increasingly important technology for extracting useful knowledge hidden in large collections of data. There are, however, negative social perceptions about data mining, among which potential privacy violation and potential discrimination. Automated data collection and data mining techniques such as classification have paved the way to making automated decisions, like loan granting/denial, insurance premium computation. If the training datasets are biased in what regards discriminatory attributes like gender, race, religion, discriminatory decisions may ensue. In the first part of this thesis, we tackle discrimination prevention in data mining and propose new techniques applicable for direct or indirect discrimination prevention individually or both at the same time. We discuss how to clean training datasets and outsourced datasets in such a way that direct and/or indirect discriminatory decision rules are converted to legitimate (non-discriminatory) classification rules. In the second part of this thesis, we argue that privacy and discrimination risks should be tackled together. We explore the relationship between privacy preserving data mining and discrimination prevention in data mining to design holistic approaches capable of addressing both threats simultaneously during the knowledge discovery process. As part of this effort, we have investigated for the first time the problem of discrimination and privacy aware frequent pattern discovery, i.e. the sanitization of the collection of patterns mined from a transaction database in such a way that neither privacy-violating nor discriminatory inferences can be inferred on the released patterns. Moreover, we investigate the problem of discrimination and privacy aware data publishing, i.e. transforming the data, instead of patterns, in order to simultaneously fulfill privacy preservation and discrimination prevention.