nils-herrmann/clean_scirepeval huggingface dataset:
On the geometry of null cones to infinity under curvature flux bounds

The main objective of this paper is to control the geometry of a future outgoing truncated null cone extending smoothly toward infinity in an Einstein-vacuum spacetime. In particular, we wish to do this under minimal regularity assumptions, namely, at the (weighted) L2-curvature level. We show that if the curvature flux and the data on an initial sphere of the cone are sufficiently close to the corresponding values in a standard Minkowski or Schwarzschild null cone, then we can obtain quantitative bounds on the geometry of the entire infinite cone. The same bounds also imply the existence of limits at infinity, along the null cone, of naturally scaled geometric quantities. In Alexakis and Shao (2013 Bounds on the Bondi energy by a flux of curvature ), we will apply these results in order to control various physical quantities—e.g., the Bondi energy and (linear and angular) momenta—associated with such infinite null cones in vacuum spacetimes.
Pricing American options via multi-level approximation methods

In this article we propose a novel approach to reduce the computational complexity of various approximation methods for pricing discrete time American options. Given a sequence of continuation values estimates corresponding to different levels of spatial approximation and time discretization, we propose a multi-level low biased estimate for the price of an American option. It turns out that the resulting complexity gain can be rather high and can even reach the order (\varepsilon^{-1}) with (\varepsilon) denoting the desired precision. The performance of the proposed multilevel algorithm is illustrated by a numerical example of pricing Bermudan max-call options.
Coevolution and correlated multiplexity in multiplex networks

Distinct channels of interaction in a complex networked system define network layers, which coexist and cooperate for the system's function. Towards understanding such multiplex systems, we propose a modeling framework based on coevolution of network layers, with a class of minimalistic growing network models as working examples. We examine how the entangled growth of coevolving layers can shape the network structure and show analytically and numerically that the coevolution can induce strong degree correlations across layers, as well as modulate degree distributions. We further show that such a coevolution-induced correlated multiplexity can alter the system's response to the dynamical process, exemplified by the suppressed susceptibility to a social cascade process.
The a theorem for Gauge-Yukawa theories beyond Banks-Zaks

We investigate the a theorem for nonsupersymmetric gauge-Yukawa theories beyond the leading order in perturbation theory. The exploration is first performed in a model-independent manner and then applied to a specific relevant example. Here, a rich fixed point structure appears including the presence of a merging phenomenon between non-trivial fixed points for which the a theorem has not been tested so far.
Efficient Learning Strategy of Chinese Characters Based on Network Approach

We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.
Power-law of Aggregate-size Spectra in Natural Systems

Patterns of animate and inanimate systems show remarkable similarities in their aggregation. One similarity is the double-Pareto distribution of the aggregate-size of system components. Different models have been developed to predict aggregates of system components. However, not many models have been developed to describe probabilistically the aggregate-size distribution of any system regardless of the intrinsic and extrinsic drivers of the aggregation process. Here we consider natural animate systems, from one of the greatest mammals - the African elephant (\textit{Loxodonta africana}) - to the \textit{Escherichia coli} bacteria, and natural inanimate systems in river basins. Considering aggregates as islands and their perimeter as a curve mirroring the sculpting network of the system, the probability of exceedence of the drainage area, and the Hack's law are shown to be the the Kor\v{c}ak's law and the perimeter-area relationship for river basins. The perimeter-area relationship, and the probability of exceedence of the aggregate-size provide a meaningful estimate of the same fractal dimension. Systems aggregate because of the influence exerted by a physical or processes network within the system domain. The aggregate-size distribution is accurately derived using the null-method of box-counting on the occurrences of system components. The importance of the aggregate-size spectrum relies on its ability to reveal system form, function, and dynamics also as a function of other coupled systems. Variations of the fractal dimension and of the aggregate-size distribution are related to changes of systems that are meaningful to monitor because potentially critical for these systems.
Impact Analysis for Risks in Informatics Systems

In this paper are presented methods of impact analysis on informatics system security accidents, qualitative and quantitative methods, starting with risk and informational system security definitions. It is presented the relationship between the risks of exploiting vulnerabilities of security system, security level of these informatics systems, probability of exploiting the weak points subject to financial losses of a company, respectively impact of a security accident on the company. Herewith are presented some examples concerning losses caused by excesses within informational systems and depicted from the study carried out by CSI.
A new approach for an unitary risk theory

The work deals with the risk assessment theory. An unitary risk algorithm is elaborated. The algorithm is based on parallel curves. The basic curve of risk is a hyperbolic curve, obtained as a multiplication between the probability of occurrence of certain event and its impact. Section 1 contains the problem formulation. Section 2 contains some specific notations and the mathematical background of risk algorithm. A numerical application based on risk algorithm is the content of section 3. Section 4 contains several conclusions.
COHERENCE AND ELICITABILITY

The risk of a financial position is usually summarized by a risk measure. As this risk measure has to be estimated from historical data, it is important to be able to verify and compare competing estimation procedures. In statistical decision theory, risk measures for which such verification and comparison is possible, are called elicitable. It is known that quantile‐based risk measures such as value at risk are elicitable. In this paper, the existing result of the nonelicitability of expected shortfall is extended to all law‐invariant spectral risk measures unless they reduce to minus the expected value. Hence, it is unclear how to perform forecast verification or comparison. However, the class of elicitable law‐invariant coherent risk measures does not reduce to minus the expected value. We show that it consists of certain expectiles.
Using the Big Ideas in Cosmology to Teach College Students

Recent advances in our understanding of the Universe have revolutionized our view of its structure, composition and evolution. However, these new ideas have not necessarily been used to improve the teaching of introductory astronomy students. In this project, we have conducted research into student understanding of cosmological ideas so as to develop effective web-based tools to teach basic concepts important to modern cosmology. The tools are intended for use at the introductory college level. Our research uses several instruments, including open-ended and multiple choice surveys conducted at multiple institutions, as well as interviews and course artifacts at one institution, to ascertain what students know regarding modern cosmological ideas, what common misunderstandings and misconceptions they entertain, and what sorts of materials can most effectively overcome student difficulties in learning this material. These data are being used to create a suite of interactive, web-based tutorials that address the major ideas in cosmology using real data. Having students engage with real data is a powerful means to help students overcome certain misconceptions. Students master the scientific concepts and reasoning processes that lead to our current understanding of the universe through interactive tasks, prediction and reflection, experimentation, and model building.
The orbit of the Chelyabinsk event impactor as reconstructed from amateur and public footage

A ballistic reconstruction of a meteoroid orbit can be made if enough information is available about its trajectory inside the atmosphere. A few methods have been devised in the past and used in several cases to trace back the origin of small impactors. On February 15, 2013, a medium-sized meteoroid hit the atmosphere in the Chelyabinsk region of Russia, causing damage in several large cities. The incident, the largest registered since the Tunguska event, was witnessed by many thousands and recorded by hundreds of amateur and public video recording systems. The amount and quality of the information gathered by those systems is sufficient to attempt a reconstruction of the trajectory of the impactor body in the atmosphere, and from this the orbit of the body with respect to the Sun. Using amateur and public footage taken in four different places close to the event, we have determined precisely the properties of the entrance trajectory and the orbit of the Chelyabinsk event impactor. We found that the object entered the atmosphere at a velocity ranging from 16.0 to 17.4 km/s in a grazing trajectory, almost directly from the east, with an azimuth of velocity vector of 285$^o$, and with an elevation of 15.8$^o$ with respect to the local horizon. The orbit that best fits the observations has, at a 95% confidence level, a semi-major axis a = 1.26$\pm$0.05 AU, eccentricity e = 0.44$\pm$0.03, argument of perihelion $\omega$=95.5$^o\pm2^o$ and longitude of ascending node $\Omega$= 326.5$^o\pm0.3^o$. Using these properties the object can be classified as belonging to the Apollo family of asteroids. The absolute magnitude of the meteoroid was H= 25.8, well below the threshold for its detection and identification as a Potential Hazardous Asteroid (PHA). This result would imply that present efforts intended to detect and characterize PHAs are incomplete.
The Polonnaruwa meteorite: oxygen isotope, crystalline and biological composition

Results of X-Ray Diffraction (XRD) analysis, Triple Oxygen Isotope analysis and Scanning Electron Microscopic (SEM) studies are presented for stone fragments recovered from the North Central Province of Sri Lanka following a witnessed fireball event on 29 December 2012. The existence of numerous nitrogen depleted highly carbonaceous fossilized biological structures fused into the rock matrix is inconsistent with recent terrestrial contamination. Oxygen isotope results compare well with those of CI and CI-like chondrites but are inconsistent with the fulgurite hypothesis.
Advancements in Big Data Processing in the ATLAS and CMS Experiments

The ever-increasing volumes of scientific data present new challenges for distributed computing and Grid technologies. The emerging Big Data revolution drives exploration in scientific fields including nanotechnology, astrophysics, high-energy physics, biology and medicine. New initiatives are transforming data-driven scientific fields enabling massive data analysis in new ways. In petascale data processing scientists deal with datasets, not individual files. As a result, a task (comprised of many jobs) became a unit of petascale data processing on the Grid. Splitting of a large data processing task into jobs enabled fine-granularity checkpointing analogous to the splitting of a large file into smaller TCP/IP packets during data transfers. Transferring large data in small packets achieves reliability through automatic re-sending of the dropped TCP/IP packets. Similarly, transient job failures on the Grid can be recovered by automatic re-tries to achieve reliable six sigma production quality in petascale data processing on the Grid. The computing experience of the ATLAS and CMS experiments provides foundation for reliability engineering scaling up Grid technologies for data processing beyond the petascale.
Finite size induces crossover temperature in growing spin chains.

We introduce a growing one-dimensional quenched spin model that bases on asymmetrical one-side Ising interactions in the presence of external field. Numerical simulations and analytical calculations based on Markov chain theory show that when the external field is smaller than the exchange coupling constant J there is a nonmonotonous dependence of the mean magnetization on the temperature in a finite system. The crossover temperature Tc corresponding to the maximal magnetization decays with system size, approximately as the inverse of the Lambert W function. The observed phenomenon can be understood as an interplay between the thermal fluctuations and the presence of the first cluster determined by initial conditions. The effect exists also when spins are not quenched but fully thermalized after the attachment to the chain. By performing tests on real data we conceive the model is in part suitable for a qualitative description of online emotional discussions arranged in a chronological order, where a spin in every node conveys emotional valence of a subsequent post.
Hierarchy Depth in Directed Networks

In this study, we explore the depth measures for flow hierarchy in directed networks. Two simple measures are defined—rooted depth and relative depth—and their properties are discussed. The method of loop collapse is introduced, allowing investigation of networks containing directed cycles. The behavior of the two depth measures is investigated in Erdös-Rényi random graphs, directed Barabási-Albert networks, and in Gnutella p2p share network. A clear distinction in the behavior between non-hierarchical and hierarchical networks is found, with random graphs featuring unimodal distribution of depths dependent on arc density, while for hierarchical systems the distributions are similar for different network densities. Relative depth shows the same behavior as existing trophic level measure for tree-like networks, but is only statistically correlated for more complex topologies, including acyclic directed graphs.
The Powerful Model Adpredictor for Search Engine Switching Detection Challenge

The purpose of the Switching Detection Challenge in the 2013 WSCD workshop was to predict users' search engine swithcing actions given records about search sessions and logs.Our solution adopted the powerful prediction model Adpredictor and utilized the method of feature engineering. We successfully applied the click through rate (CTR) prediction model Adpredicitor into our solution framework, and then the discovery of effective features and the multiple classification of different switching type make our model outperforms many competitors. We achieved an AUC score of 0.84255 on the private leaderboard and ranked the 5th among all the competitors in the competition.
Scaling behavior of online human activity

The rapid development of the Internet technology enables humans to explore the web and record the traces of online activities. From the analysis of these large-scale data sets (i.e., traces), we can get insights about the dynamic behavior of human activity. In this letter, the scaling behavior and complexity of human activity in the e-commerce, such as music, books, and movies rating, are comprehensively investigated by using the detrended fluctuation analysis technique and the multiscale entropy method. Firstly, the interevent time series of rating behaviors of these three types of media show similar scaling properties with exponents ranging from 0.53 to 0.58, which implies that the collective behaviors of rating media follow a process embodying self-similarity and long-range correlation. Meanwhile, by dividing the users into three groups based on their activities (i.e., rating per unit time), we find that the scaling exponents of the interevent time series in the three groups are different. Hence, these results suggest that a stronger long-range correlations exist in these collective behaviors. Furthermore, their information complexities vary in the three groups. To explain the differences of the collective behaviors restricted to the three groups, we study the dynamic behavior of human activity at the individual level, and find that the dynamic behaviors of a few users have extremely small scaling exponents associated with long-range anticorrelations. By comparing the interevent time distributions of four representative users, we can find that the bimodal distributions may bring forth the extraordinary scaling behaviors. These results of the analysis of the online human activity in the e-commerce may not only provide insight into its dynamic behaviors but may also be applied to acquire potential economic interest.
Complex Support Vector Machines for Regression and Quaternary Classification

The paper presents a new framework for complex support vector regression (SVR) as well as Support Vector Machines (SVM) for quaternary classification. The method exploits the notion of widely linear estimation to model the input-out relation for complex-valued data and considers two cases: 1) the complex data are split into their real and imaginary parts and a typical real kernel is employed to map the complex data to a complexified feature space and 2) a pure complex kernel is used to directly map the data to the induced complex feature space. The recently developed Wirtinger's calculus on complex reproducing kernel Hilbert spaces is employed to compute the Lagrangian and derive the dual optimization problem. As one of our major results, we prove that any complex SVM/SVR task is equivalent with solving two real SVM/SVR tasks exploiting a specific real kernel, which is generated by the chosen complex kernel. In particular, the case of pure complex kernels leads to the generation of new kernels, which have not been considered before. In the classification case, the proposed framework inherently splits the complex space into four parts. This leads naturally to solving the four class-task (quaternary classification), instead of the typical two classes of the real SVM. In turn, this rationale can be used in a multiclass problem as a split-class scenario based on four classes, as opposed to the one-versus-all method; this can lead to significant computational savings. Experiments demonstrate the effectiveness of the proposed framework for regression and classification tasks that involve complex data.
khmer: Working with Big Data in Bioinformatics

The field of bioinformatics seeks to provide tools and analyses that provide understanding of the molecular mechanisms of life on Earth, largely by analyzing and correlating genomic and proteomic information. As increasingly large amounts of genomic information, including both genome sequences and expressed gene sequences, becomes available, more efficient, sensitive, and specific analyses become critical. In DNA sequencing, a chemical and mechanical process essentially “digitizes” the information present in DNA and RNA. These sequences are recorded using an alphabet of one letter per nucleotide. Various analyses are performed on this sequence data to determine how it is structured into larger building blocks and how it relates to other sequence data. This serves as the basis for the study of biological evolution and development, genetics, and, increasingly, medicine. Data on nucleotide chains comes from the sequencing process in strings of letters known as reads. (The use of the term read in the bioinformatics sense is an unfortunate collision with the use of the term in the computer science and software engineering sense. This is especially true as the performance of reading reads can be tuned, as we will discuss. To disambiguate this unfortunate collision we refer to sequences from genomes as genomic reads.) To analyze larger scale structures and processes, multiple genomic reads must be fit together. This fitting is different than a jigsaw puzzle in that the picture is often not known a priori and that the pieces may (and often do) overlap. A further complication is introduced in that not all genomic reads are of perfect fidelity and may contain a variety of errors, such as insertions or deletions of letters or substitutions of the wrong letters for nucleotides. While having redundant reads can help in the assembly or fitting of the puzzle pieces, it is also a hindrance because of this imperfect fidelity in all of the existing sequencing technologies. The appearance of erroneous genomic reads scales with the volume of data and this complicates assembly of the data. As sequencing technology has improved, the volume of sequence data being produced has begun to exceed the capabilities of computer hardware employing
Is Learning to Rank Worth it? A Statistical Analysis of Learning to Rank Methods

The Learning to Rank (L2R) research field has experienced a fast paced growth over the last few years, with a wide variety of benchmark datasets and baselines available for experimentation. We here investigate the main assumption behind this field, which is that, the use of sophisticated L2R algorithms and models, produce significant gains over more traditional and simple information retrieval approaches. Our experimental results surprisingly indicate that many L2R algorithms, when put up against the best individual features of each dataset, may not produce statistically significant differences, even if the absolute gains may seem large. We also find that most of the reported baselines are statistically tied, with no clear winner.
0-th quantization or quantum (information) theory in 42 minutes

We present a concise introduction of basic concepts in quantum information theory and quantum mechanics prepared as an introduction for a general audience. In our approach the rules of quantum mechanics are presented in a simple form of rules describing the method of constructing quantum objects corresponding to classical objects. As a byproduct of the introduced approach, we present some alternative rules and use them to describe basic ingredients of quantum information theory using different types of objects.