nils-herrmann/clean_scirepeval huggingface dataset:
Public Spheres in Twitter- and Blogosphere. Evidence from the US

The political requires a forum for its deliberation and Habermas has located it in the public spheres. Originally, mass media's role was one of a facilitator of these debates. However, under the immense pressures of free market competition and mobile audiences, mass media prefers episodic over thematic news. On the opposite end of the spectrum, social media has been heralded as a new forum, a reincarnation of the ailing public spheres to further the deliberation of the political. But do the followers of political parties in social media endorse thematic or episodic content? 
To answer this question, I look at the most recent 3,200 tweets that were broadcast from the Republican and Democratic Twitter accounts. By employing Latent dirichlet allocation, I extract the prevailing topics of these tweets and linked websites. Generalized linear models are used to describe the relationship between episodicity, thematicity and the endorsement counts of the posts analyzed. 
I find that there is a stark contrast between the behavior of Democratic and Republican followers. In general, there seems to be a slight preference for thematic messages. Interestingly, the distance to an election increases the odds of a message to be endorsed.
The Large Observatory for x-ray timing

The Large Observatory For x-ray Timing (LOFT) was studied within ESA M3 Cosmic Vision framework and participated in the final downselection for a launch slot in 2022-2024. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument, LOFT will study the behaviour of matter under extreme conditions, such as the strong gravitational field in the innermost regions of accretion flows close to black holes and neutron stars, and the supranuclear densities in the interior of neutron stars. The science payload is based on a Large Area Detector (LAD, 10 m2 effective area, 2-30 keV, 240 eV spectral resolution, 1° collimated field of view) and a Wide Field Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e.g. GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the status of the mission at the end of its Phase A study.
The fast multipole method and point dipole moment polarizable force fields.

We present an implementation of the fast multipole method for computing Coulombic electrostatic and polarization forces from polarizable force-fields based on induced point dipole moments. We demonstrate the expected O(N) scaling of that approach by performing single energy point calculations on hexamer protein subunits of the mature HIV-1 capsid. We also show the long time energy conservation in molecular dynamics at the nanosecond scale by performing simulations of a protein complex embedded in a coarse-grained solvent using a standard integrator and a multiple time step integrator. Our tests show the applicability of fast multipole method combined with state-of-the-art chemical models in molecular dynamical systems.
Short-range magnetic correlations in the highly correlated electron compound CeCu 4 Ga

We present experimental results for the heavy-electron compound CeCu$_{4}$Ga which show that it possesses short-range magnetic correlations down to a temperature of $T = 0.1$ K. Our neutron scattering data show no evidence of long-range magnetic order occurring despite a peak in the specific heat at $T^{*} =1.2$ K. Rather, magnetic diffuse scattering occurs which corresponds to short-range magnetic correlations occurring across two unit cells. The specific heat remains large as $T\sim0$ K resulting in a Sommerfeld coefficient of $\gamma_{0} = 1.44(2)$ J/mol-K$^{2}$, and, below $T^{*}$, the resistivity follows $T^{2}$ behavior and the ac magnetic susceptibility becomes temperature independent. A magnetic peak centered at an energy transfer of $E_{\rm{c}}=0.24(1)$ meV is seen in inelastic neutron scattering data which shifts to higher energies and broadens under a magnetic field. We discuss the coexistence of large specific heat, magnetic fluctuations, and short-range magnetic correlations at low temperatures and compare our results to those for materials possessing spin-liquid behavior.
Fluctuation theorem for a small engine and magnetization switching by spin torque.

We consider a reversal of the magnetic moment of a nanomagnet by a fluctuating spin torque induced by a nonequilibrium current of electron spins. This is an example of the problem of the escape of a particle from a metastable state subjected to a fluctuating nonconservative force. Spin torque is a nonconservative force, and its fluctuations are beyond the description of the fluctuation-dissipation theorem. We estimate the joint probability distribution of work done by the spin torque and the Joule heat generated by the current, which satisfies the fluctuation theorem for a small engine. We predict a threshold voltage above which the spin-torque shot noise induces probabilistic switching events and below which such events are blocked. We adopt the theory of full-counting statistics under the adiabatic pumping of spin angular momentum. This enables us to account for the backaction effect, which is crucial to maintain consistency with the fluctuation theorem.
Uncertainty Aware Query Execution Time Prediction

Predicting query execution time is a fundamental issue underlying many database management tasks. Existing predictors rely on information such as cardinality estimates and system performance constants that are difficult to know exactly. As a result, accurate prediction still remains elusive for many queries. However, existing predictors provide a single, point estimate of the true execution time, but fail to characterize the uncertainty in the prediction. In this paper, we take a first step towards providing uncertainty information along with query execution time predictions. We use the query optimizer's cost model to represent the query execution time as a function of the selectivities of operators in the query plan as well as the constants that describe the cost of CPU and I/O operations in the system. By treating these quantities as random variables rather than constants, we show that with low overhead we can infer the distribution of likely prediction errors. We further show that the estimated prediction errors by our proposed techniques are strongly correlated with the actual prediction errors.
Emergence of clustering in an acquaintance model without homophily

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.
RNA structure prediction: progress and perspective

Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three dimensional (3D) structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.
$\Lambda\Lambda$ interaction from relativistic heavy-ion collisions

We investigate the two-particle intensity correlation function ofin relativistic heavy-ion colli- sions. We find that the behavior of the �� correlation function at small relative momenta is fairly sensitive to the interaction potential and collective flows. By comparing the results of different source functions and potentials, we explore the effect of intrinsic collective motions on the correla- tion function. We find that the recent STAR data gives a strong constraint on the scattering length and effective range of �� interaction as, 1.8 fm 1 < 1/a0 < 0.8 fm 1 and 3.5 fm < reff < 7 fm, respectively, ifsamples do not include feed-down contribution from long-lived particles. We find that feed-down correction for � 0 decay reduces the sensitivity of the correlation function to the de- tail of the �� interaction. As a result, we obtain a weaker constraint 1/a0 < 0.8 fm 1 . Implication for the signal of existence of H-dibaryon is discussed. Comparison with the scattering parameters obtained from the doublehypernucleus may reveal in-medium effects in the �� interaction.
Sparse Generalized Eigenvalue Problem Via Smooth Optimization

In this paper, we consider an ℓ0-norm penalized formulation of the generalized eigenvalue problem (GEP), aimed at extracting the leading sparse generalized eigenvector of a matrix pair. The formulation involves maximization of a discontinuous nonconcave objective function over a nonconvex constraint set, and is therefore computationally intractable. To tackle the problem, we first approximate the ℓ0-norm by a continuous surrogate function. Then an algorithm is developed via iteratively majorizing the surrogate function by a quadratic separable function, which at each iteration reduces to a regular generalized eigenvalue problem. A preconditioned steepest ascent algorithm for finding the leading generalized eigenvector is provided. A systematic way based on smoothing is proposed to deal with the “singularity issue” that arises when a quadratic function is used to majorize the nondifferentiable surrogate function. For sparse GEPs with special structure, algorithms that admit a closed-form solution at every iteration are derived. Numerical experiments show that the proposed algorithms match or outperform existing algorithms in terms of computational complexity and support recovery.
VoID-graph: Visualize Linked Datasets on the Web

The Linked Open Data (LOD) cloud diagram is a picture that helps us grasp the contents and the links of globally available data sets. Such diagram has been a powerful dissemination method for the Linked Data movement, allowing people to glance at the size and structure of this distributed, interconnected database. However, generating such image for third-party datasets can be a quite complex task as it requires the installation and understanding of a variety of tools which are not easy to setup. In this paper we present VoID-graph (this http URL), a standalone web-tool that, given a VoID description, can visualize a diagram similar to the LOD cloud. It is novel because the diagram is autonomously shaped from VoID descriptions directly within a Web-browser, which doesn't require any server cooperation. This makes it not only easy to use, as no installation or configuration is required, but also makes it more sustainable, as it is built using Open Web standards such as JavaScript and SVG.
What is faster—light or gravity?

General relativity lacks the notion of the speed of gravity. This is inconvenient, and the current paper is aimed at filling this gap. To that end I introduce the concept of the ‘alternative’ and argue that its variation, called the ‘superluminal alternative’, describes exactly what one understands by the ‘superluminal gravitational signal’. Another, closely related, object called the ‘semi-superluminal alternative’ corresponds to the situation in which a massive (and therefore gravitating) body reaches its destination sooner than a photon would if the latter were sent instead of the body. I prove that in general relativity constrained by the condition that only globally hyperbolic spacetimes are allowed, (1) semi-superluminal alternatives are absent, and (2) under some natural conditions and conventions, admissible superluminal alternatives are absent too.
Lyth bound of inflation with a tilt

We provide strong evidence for universality of the inflationary field range: given an accurate measurement of (n(s), r), one can infer Delta phi in a model-independent way in the sub-Planckian regime for a range of universality classes of inflationary models. Both the tensor-to-scalar ratio as well as the spectral tilt are essential for the field range. Given the Planck constraints on ns, the Lyth bound is strengthened by two orders of magnitude: whereas the original bound gives a sub-Planckian field range for r less than or similar to 2 x 10(-3), we find that n(s) = 0.96 brings this down to r less than or similar to 2 x 10(-5).
Effect of small interpulsar distances in stochastic gravitational wave background searches with pulsar timing arrays

One of the primary objectives for pulsar timing arrays (PTAs) is to detect a stochastic background generated by the incoherent superposition of gravitational waves (GWs), in particular from the cosmic population of supermassive black hole binaries. Current stochastic background searches assume that pulsars in a PTA are separated from each other and the Earth by many GW wavelengths. As more millisecond pulsars are discovered and added to PTAs, some may be separated by only a few radiation wavelengths or less, resulting in correlated GW phase changes between close pulsars in the array. Here we investigate how PTA overlap reduction functions (ORFs), up to quadrupole order, are affected by these additional correlated phase changes, and how they are in turn affected by relaxing the assumption that all pulsars are equidistant from the solar system barycenter. We find that in the low-frequency GW background 
limit of f ∼ 10^(−9) Hz, and for pulsars at varying distances from the Earth, these additional correlations only 
affect the ORFs by a few percent for pulsar pairs at large angular separations, as expected. However, when nearby (order 100 pc) pulsars are separated by less than a few degrees, the correlated phase changes can introduce variations of a few tens of percent in the magnitude of the isotropic ORF, and much larger fractional differences in the anisotropic ORFs—up to 188 in the m = 0, l = 2 ORF for equidistant pulsars separated by 3°. In fact, the magnitude of most of the anisotropic ORFs is largest at small, but nonzero, pulsar separations. Finally, we write down a small angle approximation for the correlated phase changes which can easily be implemented in search pipelines, and for completeness, examine the behavior of the ORFs for pulsars which lie at a radiation wavelength from the Earth.
The Illustris simulation: the evolving population of black holes across cosmic time

We study the properties of black holes and their host galaxies across cosmic time in the Illustris simulation. Illustris is a large-scale cosmological hydrodynamical simulation which resolves a (106.5 Mpc)^3 volume with more than 12 billion resolution elements and includes state-of-the-art physical models relevant for galaxy formation. We find that the black hole mass density for redshifts z = 0–5 and the black hole mass function at z = 0 predicted by Illustris are in very good agreement with the most recent observational constraints. We show that the bolometric and hard X-ray luminosity functions of active galactic nuclei (AGN) at z = 0 and 1 reproduce observational data very well over the full dynamic range probed. Unless the bolometric corrections are largely underestimated, this requires radiative efficiencies to be on average low, ϵ_r ≲ 0.1, noting however that in our model radiative efficiencies are degenerate with black hole feedback efficiencies. Cosmic downsizing of the AGN population is in broad agreement with the findings from X-ray surveys, but we predict a larger number density of faint AGN at high redshifts than currently inferred. We also study black hole–host galaxy scaling relations as a function of galaxy morphology, colour and specific star formation rate. We find that black holes and galaxies co-evolve at the massive end, but for low mass, blue and star-forming galaxies there is no tight relation with either their central black hole masses or the nuclear AGN activity.
Software-Defined Cloud Computing: Architectural elements and open challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.
AI Evaluation: past, present and future

Artificial intelligence develops techniques and systems whose performance must be evaluated on a regular basis in order to certify and foster progress in the discipline. We will describe and critically assess the different ways AI systems are evaluated. We first focus on the traditional task-oriented evaluation approach. We see that black-box (behavioural evaluation) is becoming more and more common, as AI systems are becoming more complex and unpredictable. We identify three kinds of evaluation: Human discrimination, problem benchmarks and peer confrontation. We describe the limitations of the many evaluation settings and competitions in these three categories and propose several ideas for a more systematic and robust evaluation. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more general approaches under the perspective of universal psychometrics.
Heterogeneous recovery rates against SIS epidemics in directed networks

The nodes in communication networks are possibly and most likely equipped with different recovery resources, which allow them to recover from a virus with different rates. In this paper, we aim to understand know how to allocate the limited recovery resources to efficiently prevent the spreading of epidemics. We study the susceptible-infected-susceptible (SIS) epidemic model on directed scale-free networks. In the classic SIS model, a susceptible node can be infected by an infected neighbor with the infection rate β and an infected node can be recovered to be susceptible again with the recovery rate δ. In the steady state a fraction y<inf>∞</inf> of nodes are infected, which shows how severely the network is infected. Instead of considering the same recovery rate for all the nodes in the SIS model, we propose to allocate the recovery rate δ<inf>ί</inf> for node i according to its indegree and outdegree-δ<inf>i</inf>∼ k<sup>α in</sup><inf>i, in</inf> k<sup>α out</sup><inf>i, out</inf> given the finite average recovery rate (δ) representing the limited recovery resources over the whole network. We consider directed networks with the same power law indegree and outdegree distribution, but different values of the directionality ξ (the proportion of unidirectional links) and linear correlation coefficients ρ between the indegree and outdegree. We find that, by tuning the two scaling exponents α<inf>in</inf> and α<inf>out</inf>, we can always reduce the infection fraction y<inf>∞</inf> thus reducing the extent of infections, comparing to the homogeneous recovery rates allocation. Moreover, we can find our optimal strategy via the optimal choice of the exponent α<inf>in</inf> and α<inf>out</inf>. Our optimal strategy indicates that when the recovery resources are sufficient, more resources should be allocated to the nodes with a larger indegree or outdegree, but when the recovery resource is very limited, only the nodes with a larger outdegree should be equipped with more resources. We also find that our optimal strategy works better when the recovery resources are sufficient but not yet able to make the epidemic die out, and when the indegree outdegree correlation is small.
How structurally stable are global socioeconomic systems?

The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions.