nils-herrmann/clean_scirepeval huggingface dataset:
Gossip: Identifying Central Individuals in a Social Network

Can we identify the members of a community who are best- placed to diffuse information simply by asking a random sample of individuals? We show that boundedly-rational individuals can, simply by tracking sources of gossip, identify those who are most central in a network according to "diffusion centrality," which nests other standard centrality measures. Testing this prediction with data from 35 Indian villages, we find that respondents accurately nominate those who are diffusion central (not just those with many friends). Moreover, these nominees are more central in the network than traditional village leaders and geographically central individuals.
A Fast, Minimal Memory, Consistent Hash Algorithm

We present jump consistent hash, a fast, minimal memory, consistent hash algorithm that can be expressed in about 5 lines of code. In comparison to the algorithm of Karger et al., jump consistent hash requires no storage, is faster, and does a better job of evenly dividing the key space among the buckets and of evenly dividing the workload when the number of buckets changes. Its main limitation is that the buckets must be numbered sequentially, which makes it more suitable for data storage applications than for distributed web caching.
HALO MASS DEPENDENCE OF H i AND O vi ABSORPTION: EVIDENCE FOR DIFFERENTIAL KINEMATICS

We studied a sample of 14 galaxies (0.1 < z < 0.7) using HST/WFPC2 imaging and high-resolution HST/COS or HST/STIS quasar spectroscopy of Lyα, Lyβ, and O vi λλ1031, 1037 absorption. The galaxies, having 10.8 ⩽ log (M h/M☉) ⩽ 12.2, lie within D = 300 kpc of quasar sightlines, probing out to D/Rvir = 3. When the full range of M h and D/Rvir of the sample are examined, ∼40% of the H i absorbing clouds can be inferred to be escaping their host halo. The fraction of bound clouds decreases as D/Rvir increases such that the escaping fraction is ∼15% for D/Rvir < 1, ∼45% for 1 ⩽ D/Rvir < 2, and ∼90% for 2 ⩽ D/Rvir < 3. Adopting the median mass log M h/M☉ = 11.5 to divide the sample into “higher” and “lower” mass galaxies, we find a mass dependency for the hot circumgalactic medium kinematics. To our survey limits, O vi absorption is found in only ∼40% of the H i clouds in and around lower mass halos as compared to ∼85% around higher mass halos. For D/Rvir < 1, lower mass halos have an escape fraction of ∼65%, whereas higher mass halos have an escape fraction of ∼5%. For 1 ⩽ D/Rvir < 2, the escape fractions are ∼55% and ∼35% for lower mass and higher mass halos, respectively. For 2 ⩽ D/Rvir < 3, the escape fraction for lower mass halos is ∼90%. We show that it is highly likely that the absorbing clouds reside within 4Rvir of their host galaxies and that the kinematics are dominated by outflows. Our finding of “differential kinematics” is consistent with the scenario of “differential wind recycling” proposed by Oppenheimer et al. We discuss the implications for galaxy evolution, the stellar to halo mass function, and the mass–metallicity relationship of galaxies.
SO(5) symmetry in the quantum Hall effect in graphene

Electrons in graphene have four flavors associated with low-energy spin and valley degrees of freedom. The fractional quantum Hall effect in graphene is dominated by long-range Coulomb interactions which are invariant under rotations in spin-valley space. This SU(4) symmetry is spontaneously broken at most filling factors, and also weakly broken by atomic scale valley-dependent and valley-exchange interactions with coupling constants $g_{z}$ and $g_{\perp}$. In this paper we demonstrate that when $g_{z}=-g_{\perp}$ an exact SO(5) symmetry survives which unifies the N\'eel spin order parameter of the antiferromagnetic state and the $XY$ valley order parameter of the Kekul\'e distortion state into a single five-component order parameter. The proximity of the highly insulating quantum Hall state observed in graphene at $\nu=0$ to an ideal SO(5) symmetric quantum Hall state remains an open experimental question. We illustrate the physics associated with this SO(5) symmetry by studying the multiplet structure and collective dynamics of filling factor $\nu=0$ quantum Hall states based on exact-diagonalization and low-energy effective theory approaches. This allows to illustrate how manifestations of the SO(5) symmetry would survive even when it is weakly broken.
Neutrino trident production: a powerful probe of new physics with neutrino beams.

The production of a μ+ μ- pair from the scattering of a muon neutrino off the Coulomb field of a nucleus, known as neutrino trident production, is a subweak process that has been observed in only a couple of experiments. As such, we show that it constitutes an exquisitely sensitive probe in the search for new neutral currents among leptons, putting the strongest constraints on well-motivated and well-hidden extensions of the standard model gauge group, including the one coupled to the difference of the lepton number between the muon and tau flavor, Lμ-Lτ. The new gauge boson Z', increases the rate of neutrino trident production by inducing additional (μγαμ)(νγ(α)ν) interactions, which interfere constructively with the standard model contribution. Existing experimental results put significant restrictions on the parameter space of any model coupled to muon number Lμ, and disfavor a putative resolution to the muon g-2 discrepancy via the loop of Z' for any mass mZ'≳400  MeV. The reach to the models' parameter space can be widened with future searches of the trident production at high-intensity neutrino facilities such as the LBNE.
Optical transport and manipulation of an ultracold atomic cloud using focus-tunable lenses

We present an optical setup with focus-tunable lenses to dynamically control the waist and focus position of a laser beam, in which we transport a trapped ultracold cloud of 87Rb over a distance of 28 cm ?> . The scheme allows us to shift the focus position at constant waist, providing uniform trapping conditions over the full transport length. The fraction of atoms that are transported over the entire distance comes near to unity, while the heating of the cloud is in the range of a few microkelvin. We characterize the position stability of the focus and show that residual drift rates in focus position can be compensated for by counteracting with the tunable lenses. Beyond being a compact and robust scheme to transport ultracold atoms, the reported control of laser beams makes dynamic tailoring of trapping potentials possible. As an example, we steer the size of the atomic cloud by changing the waist size of the dipole beam.
Inferring the geographic focus of online documents from social media sharing patterns

Determining the geographic focus of digital media is an essential first step for modern geographic information retrieval. However, publicly-visible location annotations are remarkably sparse in online data. In this work, we demonstrate a method which infers the geographic focus of an online document by examining the locations of Twitter users who share links to the document. 
We apply our geotagging technique to multiple datasets built from different content: manually-annotated news articles, GDELT, YouTube, Flickr, Twitter, and Tumblr.
Expert Bayes: Automatically Refining Manually Built Bayesian Networks

Bayesian network structures are usually built using only the data and starting from an empty network or from a naive Bayes structure. Very often, in some domains, like medicine, a prior structure is already known based on expert knowledge. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers, while maintaining most of the interpretability of the original network.
Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.
Probabilistic ODE Solvers with Runge-Kutta Means

Runge-Kutta methods are the classic family of solvers for ordinary differential equations (ODEs), and the basis for the state of the art. Like most numerical methods, they return point estimates. We construct a family of probabilistic numerical methods that instead return a Gauss-Markov process defining a probability distribution over the ODE solution. In contrast to prior work, we construct this family such that posterior means match the outputs of the Runge-Kutta family exactly, thus inheriting their proven good properties. Remaining degrees of freedom not identified by the match to Runge-Kutta are chosen such that the posterior probability measure fits the observed structure of the ODE. Our results shed light on the structure of Runge-Kutta solvers from a new direction, provide a richer, probabilistic output, have low computational cost, and raise new research questions.
Graph Approximation and Clustering on a Budget

We consider the problem of learning from a similarity matrix (such as spectral clustering and lowd imensional embedding), when computing pairwise similarities are costly, and only a limited number of entries can be observed. We provide a theoretical analysis using standard notions of graph approximation, significantly generalizing previous results (which focused on spectral clustering with two clusters). We also propose a new algorithmic approach based on adaptive sampling, which experimentally matches or improves on previous methods, while being considerably more general and computationally cheaper.
Equivalence of Learning Algorithms

The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms. We define two notions of algorithmic equivalence, namely, weak and strong equivalence. These notions are of paramount importance for identifying when learning prop erties from one learning algorithm can be transferred to another. Using regularized kernel machines as a case study, we illustrate the importance of the introduced equivalence concept by analyzing the relation between kernel ridge regression (KRR) and m-power regularized least squares regression (M-RLSR) algorithms.
On the initial state and consistency relations

We study the effect of the initial state on the consistency conditions for adiabatic perturbations. In order to be consistent with the constraints of General Relativity, the initial state must be diffeomorphism invariant. As a result, we show that initial wavefunctional/density matrix has to satisfy a Slavnov-Taylor identity similar to that of the action. We then investigate the precise ways in which modified initial states can lead to violations of the consistency relations. We find two independent sources of violations: i) the state can include initial non-Gaussianities; ii) even if the initial state is Gaussian, such as a Bogoliubov state, the modified 2-point function can modify the q⃗→ 0 analyticity properties of the vertex functional and result in violations of the consistency relations.
Higher dimensional numerical relativity: Code comparison

The nonlinear behavior of higher dimensional black hole spacetimes is of interest in several contexts, ranging from an understanding of cosmic censorship to black hole production in high-energy collisions. However, nonlinear numerical evolutions of higher dimensional black hole spacetimes are tremendously complex, involving different diagnostic tools and “dimensional reduction methods.” In this work we compare two different successful codes to evolve Einstein’s equations in higher dimensions, and show that the results of such different procedures agree to numerical precision, when applied to the collision from rest of two equal-mass black holes. We calculate the total radiated energy to be E_rad/M=(9.0±0.5)×10^(−4) in five dimensions and E_rad/M=(8.1±0.4)×10^(−4) in six dimensions.
The Effect of Social Learning on Individual Learning and Evolution

We consider the effects of social learning on the individual learning and genetic evolution of a colony of artificial agents capable of genetic, individual and social modes of adaptation. We confirm that there is strong selection pressure to acquire traits of individual learning and social learning when these are adaptive traits. We show that selection pressure for learning of either kind can supress selection pressure for reproduction or greater fitness. We show that social learning differs from individual learning in that it can support a second evolutionary system that is decoupled from the biological evolutionary system. This decoupling leads to an emergent interaction where immature agents are more likely to engage in learning activities than mature agents.
The Natural Science Underlying Big History

's many varied complex systems—including galaxies, stars, planets, life, and society—are islands of order within the increasingly disordered Universe. All organized systems are subject to physical, biological, or cultural evolution, which together comprise the grander interdisciplinary subject of cosmic evolution. A wealth of observational data supports the hypothesis that increasingly complex systems evolve unceasingly, uncaringly, and unpredictably from big bang to humankind. These are global history greatly extended, big history with a scientific basis, and natural history broadly portrayed across ∼14 billion years of time. Human beings and our cultural inventions are not special, unique, or apart from Nature; rather, we are an integral part of a universal evolutionary process connecting all such complex systems throughout space and time. Such evolution writ large has significant potential to unify the natural sciences into a holistic understanding of who we are and whence we came. No new science (beyond frontier, nonequilibrium thermodynamics) is needed to describe cosmic evolution's major milestones at a deep and empirical level. Quantitative models and experimental tests imply that a remarkable simplicity underlies the emergence and growth of complexity for a wide spectrum of known and diverse systems. Energy is a principal facilitator of the rising complexity of ordered systems within the expanding Universe; energy flows are as central to life and society as they are to stars and galaxies. In particular, energy rate density—contrasting with information content or entropy production—is an objective metric suitable to gauge relative degrees of complexity among a hierarchy of widely assorted systems observed throughout the material Universe. Operationally, those systems capable of utilizing optimum amounts of energy tend to survive, and those that cannot are nonrandomly eliminated.
Are 140 Characters Enough? A Large-Scale Linkability Study of Tweets

Microblogging is a very popular Internet activity that informs and entertains great multitudes of people world-wide via quickly and scalably disseminated terse messages containing all kinds of newsworthy utterances. Even though microblogging is neither designed nor meant to emphasize privacy, numerous contributors hide behind pseudonyms and compartmentalize their different incarnations via multiple accounts within the same, or across multiple, site(s). Prior work has shown that stylometric analysis is a very powerful tool capable of linking product or service reviews and blogs that are produced by the same author when the number of authors is large. In this paper, we explore linkability of tweets. Our results, based on a very large corpus of tweets, clearly demonstrate that, at least for relatively active tweeters, linkability of tweets by the same author is easily attained even when the number of tweeters is large. We also show that our linkability results hold for a set of actual Twitter users who tweet from multiple accounts. This has some obvious privacy implications, both positive and negative.
Quantum Monte Carlo calculations of light nuclei using chiral potentials.

We present the first Green's function Monte Carlo calculations of light nuclei with nuclear interactions derived from chiral effective field theory up to next-to-next-to-leading order. Up to this order, the interactions can be constructed in a local form and are therefore amenable to quantum Monte Carlo calculations. We demonstrate a systematic improvement with each order for the binding energies of A=3 and A=4 systems. We also carry out the first few-body tests to study perturbative expansions of chiral potentials at different orders, finding that higher-order corrections are more perturbative for softer interactions. Our results confirm the necessity of a three-body force for correct reproduction of experimental binding energies and radii, and pave the way for studying few- and many-nucleon systems using quantum Monte Carlo methods with chiral interactions.
On Optimal Reinsurance Policy with Distortion Risk Measures and Premiums

In this paper, we consider the problem of optimal reinsurance design, when the risk is measured by a distortion risk measure and the premium is given by a distortion risk premium. First, we show how the optimal reinsurance design for the ceding company, the reinsurance company and the social planner can be formulated in the same way. Second, by introducing the “marginal indemnification functions”, we characterize the optimal reinsurance contracts. We show that, for an optimal policy, the associated marginal indemnification function only takes the values zero and one. We will see how the roles of the market preferences and premiums and that of the total risk are separated.
A machine-compiled macroevolutionary history of Phanerozoic life

Many aspects of macroevolutionary theory and our understanding of biotic responses to global environmental change derive from literature-based compilations of palaeontological data. Existing manually assembled databases are, however, incomplete and difficult to assess and enhance. Here, we develop and validate the quality of a machine reading system, PaleoDeepDive, that automatically locates and extracts data from heterogeneous text, tables, and figures in publications. PaleoDeepDive performs comparably to humans in complex data extraction and inference tasks and generates congruent synthetic macroevolutionary results. Unlike traditional databases, PaleoDeepDive produces a probabilistic database that systematically improves as information is added. We also show that the system can readily accommodate sophisticated data types, such as morphological data in biological illustrations and associated textual descriptions. Our machine reading approach to scientific data integration and synthesis brings within reach many questions that are currently underdetermined and does so in ways that may stimulate entirely new modes of inquiry.
Techniques for Learning Binary Stochastic Feedforward Neural Networks

Stochastic binary hidden units in a multi-layer perceptron (MLP) network give at least three potential benefits when compared to deterministic MLP networks. (1) They allow to learn one-to-many type of mappings. (2) They can be used in structured prediction problems, where modeling the internal structure of the output is important. (3) Stochasticity has been shown to be an excellent regularizer, which makes generalization performance potentially better in general. However, training stochastic networks is considerably more difficult. We study training using M samples of hidden activations per input. We show that the case M=1 leads to a fundamentally different behavior where the network tries to avoid stochasticity. We propose two new estimators for the training gradient and propose benchmark tests for comparing training algorithms. Our experiments confirm that training stochastic networks is difficult and show that the proposed two estimators perform favorably among all the five known estimators.
EXOPLANET POPULATION INFERENCE AND THE ABUNDANCE OF EARTH ANALOGS FROM NOISY, INCOMPLETE CATALOGS

No true extrasolar Earth analog is known. Hundreds of planets have been found around Sun-like stars that are either Earth-sized but on shorter periods, or else on year-long orbits but somewhat larger. Under strong assumptions, exoplanet catalogs have been used to make an extrapolated estimate of the rate at which Sun-like stars host Earth analogs. These studies are complicated by the fact that every catalog is censored by non-trivial selection effects and detection efficiencies, and every property (period, radius, etc.) is measured noisily. Here we present a general hierarchical probabilistic framework for making justified inferences about the population of exoplanets, taking into account survey completeness and, for the first time, observational uncertainties. We are able to make fewer assumptions about the distribution than previous studies; we only require that the occurrence rate density be a smooth function of period and radius (employing a Gaussian process). By applying our method to synthetic catalogs, we demonstrate that it produces more accurate estimates of the whole population than standard procedures based on weighting by inverse detection efficiency. We apply the method to an existing catalog of small planet candidates around G dwarf stars. We confirm a previous result that the radius distribution changes slope near Earth's radius. We find that the rate density of Earth analogs is about 0.02 (per star per natural logarithmic bin in period and radius) with large uncertainty. This number is much smaller than previous estimates made with the same data but stronger assumptions.
DETECTING INDUSTRIAL POLLUTION IN THE ATMOSPHERES OF EARTH-LIKE EXOPLANETS

Detecting biosignatures, such as molecular oxygen in combination with a reducing gas, in the atmospheres of transiting exoplanets has been a major focus in the search for alien life. We point out that in addition to these generic indicators, anthropogenic pollution could be used as a novel biosignature for intelligent life. To this end, we identify pollutants in the Earth's atmosphere that have significant absorption features in the spectral range covered by the James Webb Space Telescope. We focus on tetrafluoromethane (CF4) and trichlorofluoromethane (CCl3F), which are the easiest to detect chlorofluorocarbons (CFCs) produced by anthropogenic activity. We estimate that ∼1.2 days (∼1.7 days) of total integration time will be sufficient to detect or constrain the concentration of CCl3F (CF4) to ∼10 times the current terrestrial level.