nils-herrmann/clean_scirepeval huggingface dataset:
THE EINSTEIN@HOME SEARCH FOR RADIO PULSARS AND PSR J2007+2722 DISCOVERY

Einstein@Home aggregates the computer power of hundreds of thousands of volunteers from 193 countries, to search for new neutron stars using data from electromagnetic and gravitational-wave detectors. This paper presents a detailed description of the search for new radio pulsars using Pulsar ALFA survey data from the Arecibo Observatory. The enormous computing power allows this search to cover a new region of parameter space; it can detect pulsars in binary systems with orbital periods as short as 11 minutes. We also describe the first Einstein@Home discovery, the 40.8 Hz isolated pulsar PSR J2007+2722, and provide a full timing model. PSR J2007+2722's pulse profile is remarkably wide with emission over almost the entire spin period. This neutron star is most likely a disrupted recycled pulsar, about as old as its characteristic spin-down age of 404 Myr. However, there is a small chance that it was born recently, with a low magnetic field. If so, upper limits on the X-ray flux suggest but cannot prove that PSR J2007+2722 is at least ∼100 kyr old. In the future, we expect that the massive computing power provided by volunteers should enable many additional radio pulsar discoveries.
Time Scales in Probabilistic Models of Wireless Sensor Networks

We consider a stochastic model of clock synchronization in a wireless network consisting of N sensors interacting with one dedicated accurate time server. For large N we find an estimate of the final time sychronization error for global and relative synchronization. Main results concern a behavior of the network on different time scales $t=t_N \to \infty$, $N \to \infty$. We discuss existence of phase transitions and find exact time scales on which an effective clock synchronization of the system takes place.
The Mesh of Civilizations and International Email Flows

In The Clash of Civilizations, Samuel Huntington argued that the primary axis of global conflict was no longer ideological or economic but cultural and religious, and that this division would characterize the “battle lines of the future.” In contrast to the "top down" approach in previous research focused on the relations among nation states, we focused on the flows of interpersonal communication as a bottom-up view of international alignments. To that end, we mapped the locations of the world’s countries in global email networks to see if we could detect cultural fault lines. Using IPgeolocation on a worldwide anonymized dataset obtained from a large Internet company, we constructed a global email network. In computing email flows we employ a novel rescaling procedure to account for differences due to uneven adoption of a particular Internet service across the world. Our analysis shows that email flows are consistent with Huntington’s thesis. In addition to location in Huntington’s “civilizations,” our results also attest to the importance of both cultural and economic factors in the patterning of inter-country communication ties.
A Method for Comparing Hedge Funds

The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system to identify behavioral similarities among time-series representing monthly returns of 11,312 hedge funds operated during approximately one decade (2000 - 2010). The presented approach of cross-category and cross-location classification assists the investor to identify alternative investments.
Multidimensional Social Network in the Social Recommender System

All online sharing systems gather data that reflects users' collective behavior and their shared activities. This data can be used to extract different kinds of relationships which can be grouped into layers and which are basic components of the multidimensional social network (MSN) proposed in the paper. The layers are created on the basis of two types of relations between humans, i.e., direct and object-based ones which, respectively, correspond to either social or semantic links between individuals. For better understanding of the complexity of the social network structure, layers and their profiles were identified and studied on two, spanned in time, snapshots of the `Flickr' population. Additionally, for each layer, a separate strength measure was proposed. The experiments on the `Flickr' photo sharing system revealed that the relationships between users result either from semantic links between objects they operate on or from social connections of these users. Moreover, the density of the social network increases in time. The second part of this paper is devoted to building a social recommender system that supports the creation of new relations between users in a multimedia sharing system. Its main goal is to generate personalized suggestions that are continuously adapted to users' needs depending on the personal weights assigned to each layer in the MSN. The conducted experiments confirmed the usefulness of the proposed model.
The Semantic Web takes Wing: Programming Ontologies with Tawny-OWL

The Tawny-OWL library provides a fully-programmatic environment for ontology building; it enables the use of a rich set of tools for ontology development, by recasting development as a form of programming. It is built in Clojure - a modern Lisp dialect, and is backed by the OWL API. Used simply, it has a similar syntax to OWL Manchester syntax, but it provides arbitrary extensibility and abstraction. It builds on existing facilities for Clojure, which provides a rich and modern programming tool chain, for versioning, distributed development, build, testing and continuous integration. In this paper, we describe the library, this environment and the its potential implications for the ontology development process.
One-Class Support Measure Machines for Group Anomaly Detection

We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points. The OCSMMs generalize well-known one-class support vector machines (OCSVMs) to a space of probability measures. By formulating the problem as quantile estimation on distributions, we can establish an interesting connection to the OCSVMs and variable kernel density estimators (VKDEs) over the input space on which the distributions are defined, bridging the gap between large-margin methods and kernel density estimators. In particular, we show that various types of VKDEs can be considered as solutions to a class of regularization problems studied in this paper. Experiments on Sloan Digital Sky Survey dataset and High Energy Particle Physics dataset demonstrate the benefits of the proposed framework in real-world applications.
Structure of 8 B from elastic and inelastic 7 Be+ p scattering

Detailed experimental knowledge of the level structure of light weakly bound nuclei is necessary to guide the development of new theoretical approaches that combine nuclear structure with reaction dynamics.  The resonant structure of 8 B is studied in this work.  Excitation functions for elastic and inelastic 7 Be+p scattering were measured using a 7 Be rare isotope beam. Excitation energies ranging between 1.6 and 3.4 MeV were investigated. An R-matrix analysis of the excitation functions was performed.  New low-lying resonances at 1.9, 2.5, and 3.3 MeV in 8 B are reported with spin-parity assignment 0 + , 2 + , and 1 + , respectively. Comparison to the Time Dependent Continuum Shell (TDCSM) model and ab initio no-core shell model/resonating-group method (NCSM/RGM) calculations is performed. This work is a more detailed analysis of the data first published as a Rapid Communication.  (2010)]  Identification of the 0 + , 2 + , 1 + states that were predicted by some models at relatively low energy but never observed experimentally is an important step toward understanding the structure of 8 B. Their identification was aided by having both elastic and inelastic scattering data. Direct comparison of the cross sections and phase shifts predicted by the TDCSM and ab initio No Core Shell Model coupled with the resonating group method is of particular interest and provides a good test for these theoretical approaches.
Matrix completion via max-norm constrained optimization

Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting. However, the uniform sampling model is unrealistic for a range of applications and the standard trace-norm relaxation can behave very poorly when the underlying sampling scheme is non-uniform. 
In this paper we propose and analyze a max-norm constrained empirical risk minimization method for noisy matrix completion under a general sampling model. The optimal rate of convergence is established under the Frobenius norm loss in the context of approximately low-rank matrix reconstruction. It is shown that the max-norm constrained method is minimax rate-optimal and yields a unified and robust approximate recovery guarantee, with respect to the sampling distributions. The computational effectiveness of this method is also discussed, based on first-order algorithms for solving convex optimizations involving max-norm regularization.
The Velocity of Censorship: High-Fidelity Detection of Microblog Post Deletions

Weibo and other popular Chinese microblogging sites are well known for exercising internal censorship, to comply with Chinese government requirements. This research seeks to quantify the mechanisms of this censorship: how fast and how comprehensively posts are deleted. Our analysis considered 2.38 million posts gathered over roughly two months in 2012, with our attention focused on repeatedly visiting "sensitive" users. This gives us a view of censorship events within minutes of their occurrence, albeit at a cost of our data no longer representing a random sample of the generalWeibo population. We also have a larger 470 million post sampling from Weibo's public timeline, taken over a longer time period, that is more representative of a random sample. 
 
We found that deletions happen most heavily in the first hour after a post has been submitted. Focusing on original posts, not reposts/retweets, we observed that nearly 30% of the total deletion events occur within 5- 30 minutes. Nearly 90% of the deletions happen within the first 24 hours. Leveraging our data, we also considered a variety of hypotheses about the mechanisms used by Weibo for censorship, such as the extent to which Weibo's censors use retrospective keyword-based censorship, and how repost/retweet popularity interacts with censorship. We also used natural language processing techniques to analyze which topics were more likely to be censored.
Personalized news recommendation with context trees

The proliferation of online news creates a need for filtering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with similar content, and frequently we have insufficient information to profile the reader. In this paper, we introduce a class of news recommendation systems based on context trees. They can provide high-quality news recommendations to anonymous visitors based on present browsing behaviour. Using an unbiased testing methodology, we show that they make accurate and novel recommendations, and that they are sufficiently flexible for the challenges of news recommendation.
A scaling law beyond Zipf's law and its relation to Heaps' law

The dependence on text length of the statistical properties of word occurrences has long been considered a severe limitation on the usefulness of quantitative linguistics. We propose a simple scaling form for the distribution of absolute word frequencies that brings to light the robustness of this distribution as text grows. In this way, the shape of the distribution is always the same, and it is only a scale parameter that increases (linearly) with text length. By analyzing very long novels we show that this behavior holds both for raw, unlemmatized texts and for lemmatized texts. In the latter case, the distribution of frequencies is well approximated by a double power law, maintaining the Zipf's exponent value γ ≃ 2 for large frequencies but yielding a smaller exponent in the low-frequency regime. The growth of the distribution with text length allows us to estimate the size of the vocabulary at each step and to propose a generic alternative to Heaps' law, which turns out to be intimately connected to the distribution of frequencies, thanks to its scaling behavior.
New constraint on the existence of the μ+ → e+ γ decay.

The analysis of a combined data set, totaling 3.6 × 10(14) stopped muons on target, in the search for the lepton flavor violating decay μ(+) → e(+)γ is presented. The data collected by the MEG experiment at the Paul Scherrer Institut show no excess of events compared to background expectations and yield a new upper limit on the branching ratio of this decay of 5.7 × 10(-13) (90% confidence level). This represents a four times more stringent limit than the previous world best limit set by MEG.
Bibliometrics for Internet Media: Applying the h-Index to YouTube

The h‐index can be a useful metric for evaluating a person's output of Internet media. Here I advocate and demonstrate adaption of the h‐index and the g‐index to the top video content creators on YouTube. The h‐index for Internet video media is based on videos and their view counts. The h‐index is defined as the number of videos with ≥h × 105 views. The g‐index is defined as the number of videos with ≥g × 105 views on average. When compared with a video creator's total view count, the h‐index and g‐index better capture both productivity and impact in a single metric.
Effect of the interconnected network structure on the epidemic threshold.

Most real-world networks are not isolated. In order to function fully, they are interconnected with other networks, and this interconnection influences their dynamic processes. For example, when the spread of a disease involves two species, the dynamics of the spread within each species (the contact network) differs from that of the spread between the two species (the interconnected network). We model two generic interconnected networks using two adjacency matrices, A and B, in which A is a 2N×2N matrix that depicts the connectivity within each of two networks of size N, and B a 2N×2N matrix that depicts the interconnections between the two. Using an N-intertwined mean-field approximation, we determine that a critical susceptible-infected-susceptible (SIS) epidemic threshold in two interconnected networks is 1/λ(1)(A+αB), where the infection rate is β within each of the two individual networks and αβ in the interconnected links between the two networks and λ(1)(A+αB) is the largest eigenvalue of the matrix A+αB. In order to determine how the epidemic threshold is dependent upon the structure of interconnected networks, we analytically derive λ(1)(A+αB) using a perturbation approximation for small and large α, the lower and upper bound for any α as a function of the adjacency matrix of the two individual networks, and the interconnections between the two and their largest eigenvalues and eigenvectors. We verify these approximation and boundary values for λ(1)(A+αB) using numerical simulations, and determine how component network features affect λ(1)(A+αB). We note that, given two isolated networks G(1) and G(2) with principal eigenvectors x and y, respectively, λ(1)(A+αB) tends to be higher when nodes i and j with a higher eigenvector component product x(i)y(j) are interconnected. This finding suggests essential insights into ways of designing interconnected networks to be robust against epidemics.
The Most Precise Extra-Galactic Black-Hole Mass Measurement

I use archival data to measure the mass of the central black hole in NGC 4526, M = (4.70 +- 0.14) X 10^8 Msun. This 3% error bar is the most precise for an extra-galactic black hole and is close to the precision obtained for Sgr A* in the Milky Way. The factor 7 improvement over the previous measurement is entirely due to correction of a mathematical error, an error that I suggest may be common among astronomers.
Twistor theory of higher dimensional black holes: I. Theory

The correspondence between stationary, axisymmetric, asymptotically flat spacetimes and bundles over a reduced twistor space has been established in four dimensions. The main impediment for an application of this correspondence to examples in higher dimensions has been the lack of a higher dimensional equivalent of the Ernst potential. This paper will propose such a generalized Ernst potential, point out where the rod structure of the spacetime can be found in the twistor picture and thereby provide a procedure for generating solutions to the Einstein equations in higher dimensions from the rod structure and other asymptotic data. An important result for the study of five-dimensional examples will be the theorem which relates the patching matrices on the outer semi-infinite rods.
LT^2C^2: A language of thought with Turing-computable Kolmogorov complexity

In this paper, we present a theoretical effort to connect the theory of program size to psychology by implementing a concrete language of thought with Turing-computable Kolmogorov complexity (LT^2C^2) satisfying the following requirements: 1) to be simple enough so that the complexity of any given finite binary sequence can be computed, 2) to be based on tangible operations of human reasoning (printing, repeating,...), 3) to be sufficiently powerful to generate all possible sequences but not too powerful as to identify regularities which would be invisible to humans. We first formalize LT^2C^2, giving its syntax and semantics and defining an adequate notion of program size. Our setting leads to a Kolmogorov complexity function relative to LT^2C^2 which is computable in polynomial time, and it also induces a prediction algorithm in the spirit of Solomonoff's inductive inference theory. We then prove the efficacy of this language by investigating regularities in strings produced by participants attempting to generate random strings. Participants had a profound understanding of randomness and hence avoided typical misconceptions such as exaggerating the number of alternations. We reasoned that remaining regularities would express the algorithmic nature of human thoughts, revealed in the form of specific patterns. Kolmogorov complexity relative to LT^2C^2 passed three expected tests examined here: 1) human sequences were less complex than control PRNG sequences, 2) human sequences were not stationary, showing decreasing values of complexity resulting from fatigue, 3) each individual showed traces of algorithmic stability since fitting of partial sequences was more effective to predict subsequent sequences than average fits. This work extends on previous efforts to combine notions of Kolmogorov complexity theory and algorithmic information theory to psychology, by explicitly ...
From Many, One: Genetic Control of Prolificacy during Maize Domestication

A reduction in number and an increase in size of inflorescences is a common aspect of plant domestication. When maize was domesticated from teosinte, the number and arrangement of ears changed dramatically. Teosinte has long lateral branches that bear multiple small ears at their nodes and tassels at their tips. Maize has much shorter lateral branches that are tipped by a single large ear with no additional ears at the branch nodes. To investigate the genetic basis of this difference in prolificacy (the number of ears on a plant), we performed a genome-wide QTL scan. A large effect QTL for prolificacy (prol1.1) was detected on the short arm of chromosome 1 in a location that has previously been shown to influence multiple domestication traits. We fine-mapped prol1.1 to a 2.7 kb “causative region” upstream of the grassy tillers1 (gt1) gene, which encodes a homeodomain leucine zipper transcription factor. Tissue in situ hybridizations reveal that the maize allele of prol1.1 is associated with up-regulation of gt1 expression in the nodal plexus. Given that maize does not initiate secondary ear buds, the expression of gt1 in the nodal plexus in maize may suppress their initiation. Population genetic analyses indicate positive selection on the maize allele of prol1.1, causing a partial sweep that fixed the maize allele throughout most of domesticated maize. This work shows how a subtle cis-regulatory change in tissue specific gene expression altered plant architecture in a way that improved the harvestability of maize.
Neutrinoless double beta decay and lepton number violation at the LHC

This work was supported by EU Network Grant No. UNILHC PITN-GA-2009-237920 and by the Spanish MICINN Grants No. FPA2011-22975 and No. , by the Generalitat Valenciana (Prometeo/2009/091), by Fondecyt Grants No. 11121557 and No. 1100582, and CONICYT Projects No. 791100017 and No. CONICYT/DFG-648. H. P. was supported by DGF Grant No. PA 803/6-1.
An Equivalence between the Lasso and Support Vector Machines

We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are equivalent, in the following sense. Given any instance of an $\ell_2$-loss soft-margin (or hard-margin) SVM, we construct a Lasso instance having the same optimal solutions, and vice versa. 
As a consequence, many existing optimization algorithms for both SVMs and Lasso can also be applied to the respective other problem instances. Also, the equivalence allows for many known theoretical insights for SVM and Lasso to be translated between the two settings. One such implication gives a simple kernelized version of the Lasso, analogous to the kernels used in the SVM setting. Another consequence is that the sparsity of a Lasso solution is equal to the number of support vectors for the corresponding SVM instance, and that one can use screening rules to prune the set of support vectors. Furthermore, we can relate sublinear time algorithms for the two problems, and give a new such algorithm variant for the Lasso. We also study the regularization paths for both methods.
High sensitivity optomechanical reference accelerometer over 10 kHz

We present an optically detected mechanical accelerometer that achieves a sensitivity of 100 ngn/Hz over a bandwidth of 10 kHz and is traceable. We have incorporated a Fabry-Perot fiber-optic micro-cavity that is currently capable of measuring the test-mass displacement with sensitivities of 200 am/Hz, and whose length determination enables traceability to the International System of Units. The compact size and high mQ-product achieved combined with the high sensitivity and simplicity of the implemented optical detection scheme highlight our device and this category of accelerometers, outlining a path for high sensitivity reference acceleration measurements and observations in seismology and gravimetry.
Investment and Consumption with Regime-Switching Discount Rates

This paper considers the problem of consumption and investment in a financial market within a continuous time stochastic economy. The investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switch according to a finite state Markov chain. The change in the discount rate leads to time inconsistencies of the investor's decisions. The randomness in our model is driven by a Brownian motion and a Markov chain. Following Ekeland and Pirvu we introduce and characterize the subgame perfect strategies. Numerical experiments show the effect of time preference on subgame perfect strategies and the pre-commitment strategies.