nils-herrmann/clean_scirepeval huggingface dataset:
One pendulum to run them all

The analytical solution for the three-dimensional linear pendulum in a rotating frame of reference is obtained, including Coriolis and centrifugal accelerations, and expressed in terms of initial conditions. This result offers the possibility of treating Foucault and Bravais pendula as trajectories of the same system of equations, each of them with particular initial conditions. We compare them with the common two-dimensional approximations in textbooks. A previously unnoticed pattern in the three-dimensional Foucault pendulum attractor is presented.
Probing Dark Energy through Scale Dependence

We consider the consequences of having no prior knowledge of the true dark energy model for the interpretation of cosmological observations. The magnitude of redshift-space distortions and weak-lensing shear is determined by the metric on the geodesics of which galaxies and light propagate. We show that, given precise enough observations, we can use these data to completely reconstruct the metric on our past lightcone and therefore to measure the scale- and time-dependence of the anisotropic stress and the evolution of the gravitational potentials in a model-independent manner. Since both dark matter and dark energy affect the visible sector only through the gravitational field they produce, they are inseparable without a model for dark energy: galaxy bias cannot be measured and therefore the distribution of dark matter determined; the peculiar velocity of dark matter can be identified with that of the galaxies only when the equivalence principle holds. Given these limitations, we show how one can nonetheless build tests for classes of dark energy models which depend on making measurements at multiple scales at a particular redshift. They are null tests on the model-independent observables, do not require modeling evolution in time and do not require any parametrization of the free functions of these models, such as the sound speed. We show how one can rule out or constrain the whole class of the most-general scalar-tensor theories even without assuming the quasi-static limit.
Inferring ground truth from multi-annotator ordinal data: a probabilistic approach

A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy ordinal labels obtained from multiple annotators of varying and unknown expertise levels. Annotation models for ordinal data have been proposed mostly as extensions of their binary/categorical counterparts and have received little attention in the crowdsourcing literature. We propose a new model for crowdsourced ordinal data that accounts for instance difficulty as well as annotator expertise, and derive a variational Bayesian inference algorithm for parameter estimation. We analyze the ordinal extensions of several state-of-the-art annotator models for binary/categorical labels and evaluate the performance of all the models on two real world datasets containing ordinal query-URL relevance scores, collected through Amazon's Mechanical Turk. Our results indicate that the proposed model performs better or as well as existing state-of-the-art methods and is more resistant to `spammy' annotators (i.e., annotators who assign labels randomly without actually looking at the instance) than popular baselines such as mean, median, and majority vote which do not account for annotator expertise.
Revealing Social Networks of Spammers Through Spectral Clustering

To date, most studies on spam have focused only on the spamming phase of the spam cycle and have ignored the harvesting phase, which consists of the mass acquisition of email addresses. It has been observed that spammers conceal their identity to a lesser degree in the harvesting phase, so it may be possible to gain new insights into spammers' behavior by studying the behavior of harvesters, which are individuals or bots that collect email addresses. In this paper, we reveal social networks of spammers by identifying communities of harvesters with high behavioral similarity using spectral clustering. The data analyzed was collected through Project Honey Pot, a distributed system for monitoring harvesting and spamming. Our main findings are (1) that most spammers either send only phishing emails or no phishing emails at all, (2) that most communities of spammers also send only phishing emails or no phishing emails at all, and (3) that several groups of spammers within communities exhibit coherent temporal behavior and have similar IP addresses. Our findings reveal some previously unknown behavior of spammers and suggest that there is indeed social structure between spammers to be discovered.
Distilled single-cell genome sequencing and de novo assembly for sparse microbial communities

Identification of every single genome present in a microbial sample is an important and challenging task with crucial applications. It is challenging because there are typically millions of cells in a microbial sample, the vast majority of which elude cultivation. The most accurate method to date is exhaustive single-cell sequencing using multiple displacement amplification, which is simply intractable for a large number of cells. However, there is hope for breaking this barrier, as the number of different cell types with distinct genome sequences is usually much smaller than the number of cells.



Here, we present a novel divide and conquer method to sequence and de novo assemble all distinct genomes present in a microbial sample with a sequencing cost and computational complexity proportional to the number of genome types, rather than the number of cells. The method is implemented in a tool called Squeezambler. We evaluated Squeezambler on simulated data. The proposed divide and conquer method successfully reduces the cost of sequencing in comparison with the naïve exhaustive approach.


AVAILABILITY
Squeezambler and datasets are available at http://compbio.cs.wayne.edu/software/squeezambler/.
Pricing public goods for private sale

We consider the pricing problem faced by a seller who assigns a price to a good that confers its benefits not only to its buyers, but also to other individuals around them. For example, a snow-blower is potentially useful not only to the household that buys it, but also to others on the same street. Given that the seller is constrained to selling such a (locally) public good via individual private sales, how should he set his prices given the distribution of values held by the agents?
 We study this problem as a two-stage game. In the first stage, the seller chooses and announces a price for the product. In the second stage, the agents (each having a private value for the good) decide simultaneously whether or not they will buy the product. In the resulting game, which can exhibit a multiplicity of equilibria, agents must strategize about whether they will themselves purchase the good to receive its benefits.
 In the case of a fully public good (where all agents benefit whenever any agent purchases), we describe a pricing mechanism that is approximately revenue-optimal (up to a constant factor) when values are drawn from a regular distribution. We then study settings in which the good is only "locally" public: agents are arranged in a network and share benefits only with their neighbors. We describe a pricing method that approximately maximizes revenue, in the worst case over equilibria of agent behavior, for any d-regular network. Finally, we show that approximately optimal prices can be found for general networks in the special case that private values are drawn from a uniform distribution. We also discuss some barriers to extending these results to general networks and regular distributions.
Quantile Regression for Large-Scale Applications

Quantile regression is a method to estimate the quantiles of the conditional distribution of a response variable, and as such it permits a much more accurate portrayal of the relationship between the response variable and observed covariates than methods such as Least-squares or Least Absolute Deviations regression. It can be expressed as a linear program, and, with appropriate preprocessing, interior-point methods can be used to find a solution for moderately large problems. Dealing with very large problems, \emph(e.g.), involving data up to and beyond the terabyte regime, remains a challenge. Here, we present a randomized algorithm that runs in nearly linear time in the size of the input and that, with constant probability, computes a $(1+\epsilon)$ approximate solution to an arbitrary quantile regression problem. As a key step, our algorithm computes a low-distortion subspace-preserving embedding with respect to the loss function of quantile regression. Our empirical evaluation illustrates that our algorithm is competitive with the best previous work on small to medium-sized problems, and that in addition it can be implemented in MapReduce-like environments and applied to terabyte-sized problems.
Bubbles are rational

As we show using the notion of equilibrium in the theory of infinite sequential games, bubbles and escalations are rational for economic and environmental agents, who believe in an infinite world. This goes against a vision of a self regulating, wise and pacific economy in equilibrium. In other words, in this context, equilibrium is not a synonymous of stability. We attempt to draw from this statement methodological consequences and a new approach to economics. To the mindware of economic agents (a concept due to cognitive psychology) we propose to add coinduction to properly reason on infinite games. This way we refine the notion of rationality.
Semi Markov Model for Market Microstructure

We introduce a new model for describing the fluctuations of a tick-by-tick single asset price. Our model is based on Markov renewal processes. We consider a point process associated to the timestamps of the price jumps, and marks associated to price increments. By modeling the marks with a suitable Markov chain, we can reproduce the strong mean-reversion of price returns known as microstructure noise. Moreover, by using Markov renewal processes, we can model the presence of spikes in intensity of market activity, i.e. the volatility clustering, and consider dependence between price increments and jump times. We also provide simple parametric and nonparametric statistical procedures for the estimation of our model. We obtain closed-form formula for the mean signature plot, and show the diffusive behavior of our model at large scale limit. We illustrate our results by numerical simulations, and that our model is consistent with empirical data on the Euribor future.
Adaptive reference-free compression of sequence quality scores

Rapid technological progress in DNA sequencing has stimulated interest in compressing the vast datasets that are now routinely produced. Relatively little attention has been paid to compressing the quality scores that are assigned to each sequence, even though these scores may be harder to compress than the sequences themselves. By aggregating a set of reads into a compressed index, we find that the majority of bases can be predicted from the sequence of bases that are adjacent to them and, hence, are likely to be less informative for variant calling or other applications. The quality scores for such bases are aggressively compressed, leaving a relatively small number at full resolution. As our approach relies directly on redundancy present in the reads, it does not need a reference sequence and is, therefore, applicable to data from metagenomics and de novo experiments as well as to re-sequencing data.



We show that a conservative smoothing strategy affecting 75% of the quality scores above Q2 leads to an overall quality score compression of 1 bit per value with a negligible effect on variant calling. A compression of 0.68 bit per quality value is achieved using a more aggressive smoothing strategy, again with a very small effect on variant calling.


AVAILABILITY
Code to construct the BWT and LCP-array on large genomic data sets is part of the BEETL library, available as a github repository at git@github.com:BEETL/BEETL.git.
A Community Based Algorithm for Large Scale Web Service Composition

Web service composition is the process of synthesizing a new composite service using a set of available Web services in order to satisfy a client request that cannot be treated by any available Web services. The Web services space is a dynamic environment characterized by a huge number of elements. Furthermore, many Web services are offering similar functionalities. In this paper we propose a model for Web service composition designed to address the scale effect and the redundancy issue. The Web services space is represented by a two-layered network architecture. A concrete similarity network layer organizes the Web services operations into communities of functionally similar operations. An abstract interaction network layer represents the composition relationships between the sets of communities. Composition synthesis is performed by a two-phased graph search algorithm. First, the interaction network is mined in order to discover abstract solutions to the request goal. Then, the abstract compositions are instantiated with concrete operations selected from the similarity network. This strategy allows an efficient exploration of the Web services space. Furthermore, operations grouped in a community can be easily substituted if necessary during the composition's synthesis's process.
Web Services Dependency Networks Analysis

Along with a continuously growing number of publicly available Web services (WS), we are witnessing a rapid development in semantic-related web technologies, which lead to the apparition of semantically described WS. In this work, we perform a comparative analysis of the syntactic and semantic approaches used to describe WS, from a complex network perspective. First, we extract syntactic and semantic WS dependency networks from a collection of publicly available WS descriptions. Then, we take advantage of tools from the complex network field to analyze them and determine their topological properties. We show WS dependency networks exhibit some of the typical characteristics observed in real-world networks, such as small world and scale free properties, as well as community structure. By comparing syntactic and semantic networks through their topological properties, we show the introduction of semantics in WS description allows modeling more accurately the dependencies between parameters, which in turn could lead to improved composition mining methods.
Tests of Lorentz Invariance Violation with Gamma Rays to probe Quantum Gravity

Special and general relativity extended our understanding of the concepts of space and time,two of the most basic topics of investigation of modern physics. However, quantum theory hasshown that there is more to learn regarding these concepts. The group of Lorentz transformationsis scale invariant. However, considerations of how to combine the concepts of quantum mechanicsand gravity (quantum gravity) indicate that there is a “natural scale” at which the physics ofspace-time predicted by relativity theory breaks down and thus requires modiﬁcation, or a newparadigm (Saslow, 1998). This natural scale is the Planck scale,
Magneto-optical conductivity of Weyl semimetals

Weyl semimetals are a topological phase of matter that have drawn recent interest and have been suggested as a possible phase of the pyrochlore irridates, among other materials. Here, we compute the magneto-optical response of an isolated Weyl point. We find that the conductivity is a series of asymmetric peaks lying on top of a linear background. We find additional features in the conductivity that are explained by the underlying Landau level structure. We show that weak disorder tends to blur out the peaks, but they still show up as measurable oscillations on a linear background.
Densities and energies of nuclei in dilute matter at zero temperature

We explore the properties of medium-mass and heavy nuclear clusters embedded in a gas of nucleons with the help of Skyrme-Hartree-Fock microscopic calculations at zero temperature. Two alternative representations of clusters are introduced, namely coordinate-space and energy-space clusters. We parametrize their density profiles in spherical symmetry in terms of basic properties of the energy density functionals used and propose an analytical, Woods-Saxon density profile whose parameters depend, not only on the composition of the cluster, but also on that of the nucleon gas. We study the clusters’ energies with the help of the local-density approximation, validated through our microscopic results. We find that the volume energies of coordinate-space clusters are determined by the saturation properties of matter, while the surface energies are strongly affected by the presence of the gas. We conclude that both the density profiles and the cluster energies are strongly affected by the gas and discuss implications for the nuclear equation of state and related perspectives. Our study provides a simple but microscopically motivated modeling of the energetics of clusterized matter at subsaturation densities for applications of astrophysical interest.
Insights into the gamma* N -> Delta transition

The \gamma* N -> \Delta(1232) transition is a window on hadron shape deformation, the applicability of perturbative QCD at moderate momentum transfers, and the influence of nonperturbative phenomena on hadronic observables. We explain that the Ash-convention magnetic transition form factor must fall faster than the neutron's magnetic form factor and nonzero values for the associated quadrupole ratios reveal the impact of quark orbital angular momentum within the nucleon and \Delta(1232); and show that these quadrupole ratios do approach their predicted asymptotic limits, albeit slowly.
Fission properties of the BCPM functional

We explore the properties of the Barcelona Catania Paris Madrid (BCPM) energy density functional concerning fission dynamics. Potential energy surfaces as well as collective inertias relevant in the fission process are computed for several nuclei where experimental data exists. Inner and outer barrier heights as well as fission isomer excitation energies are reproduced quite well in all the cases. The spontaneous fission half lives $t_{\textrm{\textrm{SF}}}$ are also computed using the standard semiclassical approach and the results are compared with the experimental data. A reasonable agreement with experiment is found over a range of 27 orders of magnitude but the theoretical predictions suffer from large uncertainties associated to the values of the parameters entering the spontaneous fission half life formula. The impact that increasing the pairing correlations strengths has in the spontaneous fission half lives is analyzed and found to be large in all the nuclei considered. Given the satisfactory description of the trend of fission properties with mass number we explore the fission properties of the even-even uranium isotope chain from $^{226}$U to $^{282}$U. Very large half lives are found when getting close to neutron number N=184.
Predicted rectification and negative differential spin Seebeck effect at magnetic interfaces

We study the nonequilibrium Seebeck spin transport across metal-magnetic insulator interfaces. The conjugate-converted thermal-spin transport is assisted by the exchange interaction at the interface, between conduction electrons in the metal lead and localized spins in the insulating magnet lead. We predict the rectification and negative differential spin Seebeck effect and resolve their microscopic mechanism, as a consequence of the strongly-fluctuated electronic density of states in the metal lead. The rectification of spin Peltier effect is also discussed. The phenomena predicted here are relevant for designing efficient spin/magnon diode and transistor, which could play crucial roles in controlling energy and information in functional devices.
Network-Centric Quantum Communications with Application to Critical Infrastructure Protection

Network-centric quantum communications (NQC) - a new, scalable instantiation of quantum cryptography providing key management with forward security for lightweight encryption, authentication and digital signatures in optical networks - is briefly described. Results from a multi-node experimental test-bed utilizing integrated photonics quantum communications components, known as QKarDs, include: quantum identification; verifiable quantum secret sharing; multi-party authenticated key establishment, including group keying; and single-fiber quantum-secured communications that can be applied as a security retrofit/upgrade to existing optical fiber installations. A demonstration that NQC meets the challenging simultaneous latency and security requirements of electric grid control communications, which cannot be met without compromises using conventional cryptography, is described.
Abundance-weighted phylogenetic diversity measures distinguish microbial community states and are robust to sampling depth

In microbial ecology studies, the most commonly used ways of investigating alpha (within-sample) diversity are either to apply non-phylogenetic measures such as Simpson’s index to Operational Taxonomic Unit (OTU) groupings, or to use classical phylogenetic diversity (PD), which is not abundance-weighted. Although alpha diversity measures that use abundance information in a phylogenetic framework do exist, they are not widely used within the microbial ecology community. The performance of abundance-weighted phylogenetic diversity measures compared to classical discrete measures has not been explored, and the behavior of these measures under rarefaction (sub-sampling) is not yet clear. In this paper we compare the ability of various alpha diversity measures to distinguish between different community states in the human microbiome for three different datasets. We also present and compare a novel one-parameter family of alpha diversity measures, BWPDθ, that interpolates between classical phylogenetic diversity (PD) and an abundance-weighted extension of PD. Additionally, we examine the sensitivity of these phylogenetic diversity measures to sampling, via computational experiments and by deriving a closed form solution for the expectation of phylogenetic quadratic entropy under re-sampling. On the three datasets, a phylogenetic measure always performed best, and two abundance-weighted phylogenetic diversity measures were the only measures ranking in the top four across all datasets. OTU-based measures, on the other hand, are less effective in distinguishing community types. In addition, abundance-weighted phylogenetic diversity measures are less sensitive to differing sampling intensity than their unweighted counterparts. Based on these results we encourage the use of abundance-weighted phylogenetic diversity measures, especially for cases such as microbial ecology where species delimitation is difficult.