nils-herrmann/clean_scirepeval huggingface dataset:
Managing Research Data in Big Science

The project which led to this report was funded by JISC in 2010--2011 as part of its 'Managing Research Data' programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big science data is different: it comes in large volumes, and it is shared and exploited in ways which may differ from other disciplines. This project has explored these differences using as a case-study Gravitational Wave data generated by the LSC, and has produced recommendations intended to be useful variously to JISC, the funding council (STFC) and the LSC community. In Sect. 1 we define what we mean by 'big science', describe the overall data culture there, laying stress on how it necessarily or contingently differs from other disciplines. In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and the cases for open data and for well-preserved data that follow from that. This leads to our recommendations that, in essence, funders should adopt rather light-touch prescriptions regarding data preservation planning: normal data management practice, in the areas under study, corresponds to notably good practice in most other areas, so that the only change we suggest is to make this planning more formal, which makes it more easily auditable, and more amenable to constructive criticism. In Sect. 3 we briefly discuss the LIGO data management plan, and pull together whatever information is available on the estimation of digital preservation costs. The report is informed, throughout, by the OAIS reference model for an open archive.
Global Inflation Dynamics: regularities & forecasts

The analysis of dollar inflation performed by the authors through the approximation of empirical data for 1913-2012 with a power-law function with an accelerating log-periodic oscillation superimposed over it has made it possible to detect a quasi-singularity point around the 17th of December, 2012. It is demonstrated that, if adequate measures are not taken, one may expect a surge of inflation around the end of this year that may also mark the start of stagflation as there are no sufficient grounds to expect the re-start of the dynamic growth of the world economy by that time. On the other hand, as the experience of the 1970s and the 1980s indicates, the stagflation consequences can only be eliminated with great difficulties and at a rather high cost, because the combination of low levels of economic growth and employment with high inflation leads to a sharp decline in consumption, aggravating the economic depression. In order to mitigate the inflationary consequences of the explosive growth of money (and, first of all, US dollar) supply it is necessary to take urgently the world monetary emission under control. This issue should become central at the forthcoming G8 and G20 summits.
DO ARBITRAGE‐FREE PRICES COME FROM UTILITY MAXIMIZATION?

In this paper we ask whether, given a stock market and an illiquid derivative, there exists arbitrage‐free prices at which a utility‐maximizing agent would always want to buy the derivative, irrespectively of his own initial endowment of derivatives and cash. We prove that this is false for any given investor if one considers all initial endowments with finite utility, and that it can instead be true if one restricts to the endowments in the interior. We show, however, how the endowments on the boundary can give rise to very odd phenomena; for example, an investor with such an endowment would choose not to trade in the derivative even at prices arbitrarily close to some arbitrage price.
A new algorithm for extracting a small representative subgraph from a very large graph

Many real-world networks are prohibitively large for data retrieval, storage and analysis of all of its nodes and links. Understanding the structure and dynamics of these networks entails creating a smaller representative sample of the full graph while preserving its relevant topological properties. In this report, we show that graph sampling algorithms currently proposed in the literature are not able to preserve network properties even with sample sizes containing as many as 20% of the nodes from the original graph. We present a new sampling algorithm, called Tiny Sample Extractor, with a new goal of a sample size smaller than 5% of the original graph while preserving two key properties of a network, the degree distribution and its clustering co- efficient. Our approach is based on a new empirical method of estimating measurement biases in crawling algorithms and compensating for them accordingly. We present a detailed comparison of best known graph sampling algorithms, focusing in particular on how the properties of the sample subgraphs converge to those of the original graph as they grow. These results show that our sampling algorithm extracts a smaller subgraph than other algorithms while also achieving a closer convergence to the degree distribution, measured by the degree exponent, of the original graph. The subgraph generated by the Tiny Sample Extractor, however, is not necessarily representative of the full graph with regard to other proper ties such as assortativity. This indicates that the problem of extracting a truly representative small subgraph from a large graph remains unsolved.
Inference of Extreme Synchrony with an Entropy Measure on a Bipartite Network

This article proposes a method to quantify the structure of a bipartite graph using a network entropy per link. Collective behavior of participants' quotes and trades in the foreign exchange market is quantified by applying the proposed method to high frequency data. The network entropy per link corresponded to the macroeconomic situation. A finite mixture of Gumbel distributions is used to fit the empirical distribution for the minimum values of network entropy per link in each week. The mixture of Gumbel distributions with parameter estimates by segmentation procedure was verified by the Kolmogorov–Smirnov test. The finite mixture of Gumbel distributions that extrapolate the empirical probability of extreme events has explanatory power at a statistically significant level. These findings provide insight into how to manage collective behavior in the foreign exchange market.
New physics signal in top physics

Effective Field Theory (EFT) provides a model-independent way to search for new physics if the new degrees of freedom are heavy. The new physics appears then as new interactions between the known particles including modification of the SM vertices. In the Lagrangian, they are written as new operators built from the SM fields and invariant under its symmetries. These operators have dimension higher than four and are suppressed by negative powers of the new physics scale Λ to get the required dimension for the Lagrangian. Only the operators with the lower dimension, i.e. dimension-six, can be kept in good approximation since the new physics scale is well above the energies probed by the experiments. Consequently, EFT is valid only below the scale of the new physics. In this region, the unitary bound is never reached and no form factors are needed unlike for anomalous couplings (see  for a complete discussion of the advantages of EFT compared to anomalous couplings). EFT is also more predictive due to the symmetries. For example, an operator often contains several vertices with different numbers of legs all depending on its coefficient only. The symmetries also make the EFT Lagrangian renormalizable and allow loop computation. Despite that the number of dimension-six operators that can be added to the SM Lagrangian is large, only a few contribute to a particular process and they can usually be distinguished using several observables. This will be illustrated in the following for top pair production. The complete discussion and list of references can be found in ref. . The only references hereafter are new measurements or computations that have been used to update the results from ref. .
Black Hole Formation with an Interacting Vacuum Energy Density

We discuss the gravitational collapse of a spherically symmetric massive core of a star in which the fluid component is interacting with a growing vacuum energy density. The influence of the variable vacuum in the collapsing core is quantified by a phenomenological \beta-parameter as predicted by dimensional arguments and the renormalization group approach. For all reasonable values of this free parameter, we find that the vacuum energy density increases the collapsing time but it cannot prevent the formation of a singular point. However, the nature of the singularity depends on the values of \beta. In the radiation case, a trapped surface is formed for \beta 1/2, a naked singularity is developed. In general, the critical value is \beta=1-2/3(1+\omega), where the \omega-parameter describes the equation of state of the fluid component.
ARCHITECTURE OF PLANETARY SYSTEMS BASED ON KEPLER DATA: NUMBER OF PLANETS AND COPLANARITY

We investigated the underlying architecture of planetary systems by deriving the distribution of planet multiplicity (number of planets) and the distribution of orbital inclinations based on the sample of planet candidates discovered by the Kepler mission. The scope of our study included solar-like stars and planets with orbital periods less than 200 days and with radii between 1.5 and 30 Earth radii, and was based on Kepler planet candidates detected during Quarters 1–6. We created models of planetary systems with different distributions of planet multiplicity and inclinations, simulated observations of these systems by Kepler, and compared the properties of the transits of detectable objects to actual Kepler planet detections. Specifically, we compared with both the Kepler sample's transit numbers and normalized transit duration ratios in order to determine each model's goodness of fit. We did not include any constraints from radial velocity surveys. Based on our best-fit models, 75%–80% of planetary systems have one or two planets with orbital periods less than 200 days. In addition, over 85% of planets have orbital inclinations less than 3° (relative to a common reference plane). This high degree of coplanarity is comparable to that seen in our solar system. These results have implications for planet formation and evolution theories. Low inclinations are consistent with planets forming in a protoplanetary disk, followed by evolution without significant and lasting perturbations from other bodies capable of increasing inclinations.
Quantum Randi Challenge

Observed violations of Bell type inequalities exclude all relativistic micro causal ("local"), counterfactual definite ("real") hidden variable models of nature. This further relativization of our concept of reality triggers a growing pseudoscientific resistance against quantum mechanics (QM). I define Didactic Randi Challenges (DRC) via five characteristics. These are challenges which, according to the laws of nature, are impossible to meet. They effectively refute pseudoscientific claims according to which the challenge could easily be met. DRC work by being known to exist while never having been overcome, despite the large rewards which would follow from meeting the challenge. Pseudoscience exploits well meaning engagement in argument to create the appearance of an expert dispute (sowing doubt). DRC decline to discuss "until the challenge is met", without solidifying the perception of establishment conspiracy. This requires transparency, thus DRC are efficient didactic tools. The Quantum Randi Challenge (QRC) is a DRC designed to reject hidden variable models by simply teaching QM; there is no bet or interaction with challengers. The QRC is a computer game that anybody can modify. The present version includes a simulation of true QM behavior that violates Bell 99% of the time, hidden variables that violate the Bell and CHSH inequality with 50% probability, and ones which violate Bell 85% of the time when missing 13% anti-correlation. The DRC challenge is to modify the hidden variables so that the predicted QM behavior arises, including anti-correlation. If such were possible, the presented programs would make it trivial to meet the challenge. This fact and the whole QRC can be taught to a wide audience via the presented heuristics. Demanding anti-correlation is argued to be superior to employing CHSH.
Towards a Statistical Physics of Human Mobility

In this paper, we extend some ideas of statistical physics to describe the properties of human mobility. From a physical point of view, we consider the statistical empirical laws of private cars mobility, taking advantage of a GPS database which contains a sampling of the individual trajectories of 2% of the whole vehicle population in an Italian region. Our aim is to discover possible ”universal laws” that can be related to the dynamical cognitive features of individuals. Analyzing the empirical trip length distribution we study if the travel time can be used as universal cost function in a mesoscopic model of mobility. We discuss the implications of the elapsed times distribution between successive trips that shows an underlying Benford’s law, and we study the rank distribution of the average visitation frequency to understand how people organize their daily agenda. We also propose simple stochastic models to suggest possible explanations of the empirical observations and we compare our results with analogous results on statistical properties of human mobility presented in the literature.
Ordering dynamics of the multi-state voter model

The voter model is a paradigm of ordering dynamics. At each time step, a random node is selected and copies the state of one of its neighbors. Traditionally, this state has been considered as a binary variable. Here, we address the case in which the number of states is a parameter that can assume any value, from 2 to ∞, in the thermodynamic limit. We derive mean-field analytical expressions for the exit probability, the consensus time, and the number of different states as a function of time for the case of an arbitrary number of states. We finally perform a numerical study of the model in low-dimensional lattices, comparing the case of multiple states with the usual binary voter model. Our work sheds light on the role of the parameter accounting for the number of states.
Dark matter results from 225 live days of XENON100 data.

We report on a search for particle dark matter with the XENON100 experiment, operated at the Laboratori Nazionali del Gran Sasso for 13 months during 2011 and 2012. XENON100 features an ultralow electromagnetic background of (5.3 ± 0.6) × 10(-3) events/(keV(ee) × kg × day) in the energy region of interest. A blind analysis of 224.6 live days × 34 kg exposure has yielded no evidence for dark matter interactions. The two candidate events observed in the predefined nuclear recoil energy range of 6.6-30.5 keV(nr) are consistent with the background expectation of (1.0 ± 0.2) events. A profile likelihood analysis using a 6.6-43.3 keV(nr) energy range sets the most stringent limit on the spin-independent elastic weakly interacting massive particle-nucleon scattering cross section for weakly interacting massive particle masses above 8 GeV/c(2), with a minimum of 2 × 10(-45) cm(2) at 55 GeV/c(2) and 90% confidence level.
Effective retrieval of resources in folksonomies using a new tag similarity measure

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.
Measuring Similarity in Large-scale Folksonomies

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites.  
taxonomies, which overimpose a hierarchical categorisation of content, folksonomies enable end-users to freely create and choose the categories (in this case, tags) that best 
describe some content. However, as tags are informally de- 
ﬁned, continually changing, and ungoverned, social tagging 
has often been criticised for lowering, rather than increasing, the efﬁciency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of 
users and the noise they introduce. To address this issue, a 
variety of approaches have been proposed that recommend 
users what tags to use, both when labelling and when looking for resources. As we illustrate in this paper, real world 
folksonomies are characterized by power law distributions 
of tags, over which commonly used similarity metrics, including the Jaccard coefﬁcient and the cosine similarity, fail 
to compute. We thus propose a novel metric, speciﬁcally 
developed to capture similarity in large-scale folksonomies, 
that is based on a mutual reinforcement principle: that is, 
two tags are deemed similar if they have been associated to 
similar resources, and vice-versa two resources are deemed 
similar if they have been labelled by similar tags. We offer an efﬁcient realisation of this similarity metric, and assess its quality experimentally, by comparing it against cosine similarity, on three large-scale datasets, namely Bibsonomy, MovieLens and CiteULike.
THE CHANDRA HRC VIEW OF THE SUBARCSECOND STRUCTURES IN THE NUCLEAR REGION OF NGC 1068

We have obtained a high spatial resolution X-ray image of the nucleus of NGC 1068 using the High Resolution Camera (HRC-I) on board the Chandra X-ray Observatory, which provides an unprecedented view of the innermost 1 arcsec radius region of this galaxy. The HRC image resolves the narrow-line region into X-ray emission clumps matching bright emission-line clouds in the HST [OIII] λ5007 images and allows comparison with subarcsecond-scale radio jet for the first time. Two distinct X-ray knots are revealed at 1.3–1.4 arcsec northeast and southwest of the nucleus. Based on the combined X-ray, [O iii], and radio continuum morphology, we identify the locations of intense radio jet–cloud interaction. The [O iii] to soft X-ray ratios show that some of these clouds are strongly affected by shock heating, whereas in other locations the jet simply thrusts through with no signs of strong interaction. This is further strengthened by the presence of a kT ∼ 1 keV collisionally ionized component in the ACIS spectrum of a shock-heated cloud HST-G. We estimate that the kinematic luminosity of the jet-driven shocks is 6 × 1038 erg s−1, a negligible fraction (10−4) of the estimated total jet power.
A Dynamical Model for Operational Risk in Banks

Operational risk is the risk relative to monetary losses caused by failures of bank internal processes due to heterogeneous causes. A dynamical model including both spontaneous generation of losses and generation via interactions between different processes is presented; the efforts made by the bank to avoid the occurrence of losses is also taken into account. Under certain hypotheses, the model can be exactly solved and, in principle, the solution can be exploited to estimate most of the model parameters from real data. The forecasting power of the model is also investigated and proved to be surprisingly remarkable.
Shaping communities out of triangles

Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its importance in many fields such as biology, social networks or network traffic analysis. The metrics proposed to shape communities are too lax and do not consider the internal layout of the edges in the community, which lead to undesirable results. We define a new community metric called WCC. The proposed metric meets a minimum set of basic properties that guarantees communities with structure and cohesion. We experimentally show that WCC correctly quantifies the quality of communities and community partitions using real and synthetic datasets, and compare some of the most used community detection algorithms in the state of the art.
Large tick assets: implicit spread and optimal tick size

In this work, we provide a framework linking microstructural properties of an asset to the tick value of the exchange. In particular, we bring to light a quantity, referred to as implicit spread, playing the role of spread for large tick assets, for which the effective spread is almost always equal to one tick. The relevance of this new parameter is shown both empirically and theoretically. This implicit spread allows us to quantify the tick sizes of large tick assets and to define a notion of optimal tick size. Moreover, our results open the possibility of forecasting the behavior of relevant market quantities after a change in the tick value and to give a way to modify it in order to reach an optimal tick size.
Reactor ν̄e disappearance in the Double Chooz experiment

The Double Chooz experiment has observed 8,249 candidate electron antineutrino events in 227.93 live days with 33.71 GW-ton-years (reactor power x detector mass x livetime) exposure using a 10.3 cubic meter fiducial volume detector located at 1050 m from the reactor cores of the Chooz nuclear power plant in France. The expectation in case of theta13 = 0 is 8,937 events. The deficit is interpreted as evidence of electron antineutrino disappearance. From a rate plus spectral shape analysis we find sin^2 2{\theta}13 = 0.109 \pm 0.030(stat) \pm 0.025(syst). The data exclude the no-oscillation hypothesis at 99.9% CL (3.1{\sigma}).
PYNPOINT: An image processing package for finding exoplanets

We present the scientic performance results of PynPoint, our Python-based software package that uses principle component analysis to detect and estimate the ux of exoplanets in two dimensional imaging data. Recent advances in adaptive optics and imaging technology at visible and infrared wavelengths have opened the door to direct detections of planetary companions to nearby stars, but image processing techniques have yet to be optimized. We show that the performance of our approach gives a marked improvement over what is presently possible using existing methods such as LOCI. To test our approach, we use real angular dierential imaging (ADI) data taken with the adaptive optics assisted high resolution near-infrared camera NACO at the VLT. These data were taken during the commissioning of the apodising phase plate (APP) coronagraph. By inserting simulated planets into these data, we test the performance of our method as a function of planet brightness for dierent positions on the image. We nd that in all cases PynPoint has a detection threshold that is superior to that given by our LOCI analysis when assessed in a common statistical framework. We obtain our best improvements for smaller inner working angles (IWA). For an IWA of 0.29 00 we