nils-herrmann/clean_scirepeval huggingface dataset:
On a dynamic adaptation of The Distribution Builder approach to investment decisions

Sharpe et al. proposed the idea of having an expected utility maximizer choose a probability distribution for future wealth as an input to her investment problem instead of a utility function. They developed a computer program, called The Distribution Builder, as one way to elicit such a distribution. In a single-period model, they then showed how this desired distribution for terminal wealth can be used to infer the investor's risk preferences. We adapt their idea, namely that a risk-averse investor can choose a desired distribution for future wealth as an alternative input attribute for investment decisions, to continuous time. In a variety of scenarios, we show how the investor's desired distribution combines with her initial wealth and market-related input to determine the feasibility of her distribution, her implied risk preferences, and her optimal policies throughout her investment horizon. We then provide several examples.
Coupling between time series: A network view

Recently, the visibility graph has been introduced as a novel method for analyzing time series, which maps a time series to a complex network. In this paper we introduce a new algorithm of visibility, “cross-visibility”, which reveals the conjugation of two coupled time series. The correspondence between the two time series is mapped to a network, “the cross-visibility graph”, to demonstrate the correlation between them. We have applied the algorithm to several correlated and uncorrelated time series, generated by the linear stationary ARFIMA process, in order to better understand the results of the cross-visibility of empirical series. The comparison between the degree distribution of coupled and uncoupled (shuffled) series' networks demonstrates the emergence of super nodes (extremely high-degree nodes) in the uncoupled ones. Furthermore, we have applied the algorithm to real-world data from the financial trades of two companies and oil, and observed significant small-scale coupling in their dynamics.
Hawkes model for price and trades high-frequency dynamics

We introduce a multivariate Hawkes process that accounts for the dynamics of market prices through the impact of market order arrivals at microstructural level. Our model is a point process mainly characterized by four kernels associated with, respectively, the trade arrival self-excitation, the price changes mean reversion, the impact of trade arrivals on price variations and the feedback of price changes on trading activity. It allows one to account for both stylized facts of market price microstructure (including random time arrival of price moves, discrete price grid, high-frequency mean reversion, correlation functions behaviour at various time scales) and the stylized facts of market impact (mainly the concave-square-root-like/relaxation characteristic shape of the market impact of a meta-order). Moreover, it allows one to estimate the entire market impact profile from anonymous market data. We show that these kernels can be empirically estimated from the empirical conditional mean intensities. We provide numerical examples, application to real data and comparisons to former approaches.
Finding the True Frequent Itemsets

Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $\theta$ of a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the understanding of the underlying process that generated it. Specifically, in many applications $\mathcal{D}$ is a collection of samples obtained from an unknown probability distribution $\pi$ on transactions, and by extracting the FIs in $\mathcal{D}$ one attempts to infer itemsets that are frequently (i.e., with probability at least $\theta$) generated by $\pi$, which we call the True Frequent Itemsets (TFIs). Due to the inherently stochastic nature of the generative process, the set of FIs is only a rough approximation of the set of TFIs, as it often contains a huge number of \emph{false positives}, i.e., spurious itemsets that are not among the TFIs. In this work we design and analyze an algorithm to identify a threshold $\hat{\theta}$ such that the collection of itemsets with frequency at least $\hat{\theta}$ in $\mathcal{D}$ contains only TFIs with probability at least $1-\delta$, for some user-specified $\delta$. Our method uses results from statistical learning theory involving the (empirical) VC-dimension of the problem at hand. This allows us to identify almost all the TFIs without including any false positive. We also experimentally compare our method with the direct mining of $\mathcal{D}$ at frequency $\theta$ and with techniques based on widely-used standard bounds (i.e., the Chernoff bounds) of the binomial distribution, and show that our algorithm outperforms these methods and achieves even better results than what is guaranteed by the theoretical analysis.
Bayes and Frequentism: a particle physicist’s perspective

In almost every scientific field, an experiment involves collecting data and then analysing it. The analysis stage will often consist in trying to extract some physical parameter and estimating its uncertainty; this is known as Parameter Determination. An example would be the determination of the mass of the top quark, from data collected from high energy proton–proton collisions. A different aim is to choose between two possible hypotheses. For example, are data on the recession speed s of distant galaxies proportional to their distance d, or do they fit better to a model where the expansion of the Universe is accelerating? There are two fundamental approaches to such statistical analyses – Bayesian and Frequentist. This article discusses the way they differ in their approach to probability, and then goes on to consider how this affects the way they deal with Parameter Determination and Hypothesis Testing. The examples are taken from everyday life and from Particle Physics.
The closest black holes

Starting from the assumption that there is a large population (> 10^8) of isolated, stellar-mass black holes (IBH) distributed throughout our galaxy, we consider the detectable signatures of accretion from the interstellar medium (ISM) that may be associated with such a population. We simulate the nearby (radius 250 pc) part of this population, corresponding to the closest ~35 000 black holes, using current best estimates of the mass distribution of stellar mass black holes combined with two models for the velocity distribution of stellar-mass IBH which bracket likely possibilities. We distribute this population of objects appropriately within the different phases of the ISM and calculate the Bondi-Hoyle accretion rate, modified by a further dimensionless efficiency parameter \lambda. Assuming a simple prescription for radiatively inefficient accretion at low Eddington ratios, we calculate the X-ray luminosity of these objects, and similarly estimate the radio luminosity from relations found empirically for black holes accreting at low rates. The latter assumption depends crucially on whether or not the IBH accrete from the ISM in a manner which is axisymmetric enough to produce jets. Comparing the predicted X-ray fluxes with limits from hard X-ray surveys, we conclude that either the Bondi-Hoyle efficiency parameter \lambda, is rather small (< 0.01), the velocities of the IBH are rather high, or some combination of both. The predicted radio flux densities correspond to a population of objects which, while below current survey limits, should be detectable with the Square Kilometre Array (SKA). Converting the simulated space velocities into proper motions, we further demonstrate that such IBH could be identified as faint high proper motion radio sources in SKA surveys.
The Foster-Hart Measure of Riskiness for General Gambles

Foster and Hart proposed an operational measure of riskiness for discrete random variables. We show that their defining equation has no solution for many common continuous distributions. We show how to extend consistently the definition of riskiness to continuous random variables. For many continuous random variables, the risk measure is equal to the worst-case risk measure, i.e. the maximal possible loss incurred by that gamble. We also extend the Foster-Hart risk measure to dynamic environments for general distributions and probability spaces, and we show that the extended measure avoids bankruptcy in infinitely repeated gambles.
MULTIVARIATE RISK MEASURES: A CONSTRUCTIVE APPROACH BASED ON SELECTIONS

Since risky positions in multivariate portfolios can be offset by various choices of capital requirements that depend on the exchange rules and related transaction costs, it is natural to assume that the risk measures of random vectors are set‐valued. Furthermore, it is reasonable to include the exchange rules in the argument of the risk measure and so consider risk measures of set‐valued portfolios. This situation includes the classical Kabanov's transaction costs model, where the set‐valued portfolio is given by the sum of a random vector and an exchange cone, but also a number of further cases of additional liquidity constraints. We suggest a definition of the risk measure based on calling a set‐valued portfolio acceptable if it possesses a selection with all individually acceptable marginals. The obtained selection risk measure is coherent (or convex), law invariant, and has values being upper convex closed sets. We describe the dual representation of the selection risk measure and suggest efficient ways of approximating it from below and from above. In the case of Kabanov's exchange cone model, it is shown how the selection risk measure relates to the set‐valued risk measures considered by Kulikov (2008), Hamel and Heyde (2010, ), and Hamel, Heyde, and Rudloff (2013,
A Comparative Analysis of Transcription Factor Expression during Metazoan Embryonic Development

During embryonic development, a complex organism is formed from a single starting cell. These processes of growth and differentiation are driven by large transcriptional changes, which are following the expression and activity of transcription factors (TFs). This study sought to compare TF expression during embryonic development in a diverse group of metazoan animals: representatives of vertebrates (Danio rerio, Xenopus tropicalis), a chordate (Ciona intestinalis) and invertebrate phyla such as insects (Drosophila melanogaster, Anopheles gambiae) and nematodes (Caenorhabditis elegans) were sampled, The different species showed overall very similar TF expression patterns, with TF expression increasing during the initial stages of development. C2H2 zinc finger TFs were over-represented and Homeobox TFs were under-represented in the early stages in all species. We further clustered TFs for each species based on their quantitative temporal expression profiles. This showed very similar TF expression trends in development in vertebrate and insect species. However, analysis of the expression of orthologous pairs between more closely related species showed that expression of most individual TFs is not conserved, following the general model of duplication and diversification. The degree of similarity between TF expression between Xenopus tropicalis and Danio rerio followed the hourglass model, with the greatest similarity occuring during the early tailbud stage in Xenopus tropicalis and the late segmentation stage in Danio rerio. However, for Drosophila melanogaster and Anopheles gambiae there were two periods of high TF transcriptome similarity, one during the Arthropod phylotypic stage at 8–10 hours into Drosophila development and the other later at 16–18 hours into Drosophila development.
Google Matrix Analysis of DNA Sequences

For DNA sequences of various species we construct the Google matrix of Markov transitions between nearby words composed of several letters. The statistical distribution of matrix elements of this matrix is shown to be described by a power law with the exponent being close to those of outgoing links in such scale-free networks as the World Wide Web (WWW). At the same time the sum of ingoing matrix elements is characterized by the exponent being significantly larger than those typical for WWW networks. This results in a slow algebraic decay of the PageRank probability determined by the distribution of ingoing elements. The spectrum of is characterized by a large gap leading to a rapid relaxation process on the DNA sequence networks. We introduce the PageRank proximity correlator between different species which determines their statistical similarity from the view point of Markov chains. The properties of other eigenstates of the Google matrix are also discussed. Our results establish scale-free features of DNA sequence networks showing their similarities and distinctions with the WWW and linguistic networks.
Spacetime noncommutative effect on black hole as particle accelerators

We study the spacetime noncommutative effect on black hole as particle accelerators and, find that the particles falling from infinity with zero velocity cannot collide with unbound energy, either near the horizon or on the prograde ISCO when the noncommutative Kerr black hole is exactly extremal. Our results also show that the bigger of the spinning black hole's mass is the higher of center of mass energy that the particles obtain. For small and medium noncommutative Schwarzschild black hole, the collision energy depends on the black hole's mass.
A Genome-Wide Survey of Genetic Variation in Gorillas Using Reduced Representation Sequencing

All non-human great apes are endangered in the wild, and it is therefore important to gain an understanding of their demography and genetic diversity. Whole genome assembly projects have provided an invaluable foundation for understanding genetics in all four genera, but to date genetic studies of multiple individuals within great ape species have largely been confined to mitochondrial DNA and a small number of other loci. Here, we present a genome-wide survey of genetic variation in gorillas using a reduced representation sequencing approach, focusing on the two lowland subspecies. We identify 3,006,670 polymorphic sites in 14 individuals: 12 western lowland gorillas (Gorilla gorilla gorilla) and 2 eastern lowland gorillas (Gorilla beringei graueri). We find that the two species are genetically distinct, based on levels of heterozygosity and patterns of allele sharing. Focusing on the western lowland population, we observe evidence for population substructure, and a deficit of rare genetic variants suggesting a recent episode of population contraction. In western lowland gorillas, there is an elevation of variation towards telomeres and centromeres on the chromosomal scale. On a finer scale, we find substantial variation in genetic diversity, including a marked reduction close to the major histocompatibility locus, perhaps indicative of recent strong selection there. These findings suggest that despite their maintaining an overall level of genetic diversity equal to or greater than that of humans, population decline, perhaps associated with disease, has been a significant factor in recent and long-term pressures on wild gorilla populations.
Biases in the Experimental Annotations of Protein Function and Their Effect on Our Understanding of Protein Function Space

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the “few articles - many proteins” phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.
Strange quark content of the nucleon and dark matter searches

The strange quark scalar content plays an important role in both the description of nucleon structure and in the determination of dark matter direct detection cross sections. As a measure of the strange-quark contribution to the nucleon mass, the strange-quark sigma term (\sigma_s) provides important insight into the nature of mass generation in QCD. The phenomenological determination of \sigma_s exhibits a wide range of variation, with values suggesting that the strange quark contributes anywhere between 0 and more than 30% of the nucleon mass. In the context of dark matter searches, coupled with relatively large Higgs coupling to strangeness, this variation dominates the uncertainty in predicted cross sections for a large class of dark matter models. Here we report on the recent results in lattice QCD, which are now giving a far more precise determination of \sigma_s than can be inferred from phenomenology. As a consequence, the lattice determinations of \sigma_s can now dramatically reduce the uncertainty in dark matter cross sections associated with the hadronic matrix elements.
Nuclear ρ meson transparency in a relativistic Glauber model

The recent Jefferson Laboratory data for the nuclear transparency in rho(0) electroproduction have the potential to settle the scale for the onset of color transparency (CT) in vector meson production. 
 To compare the data to calculations in a relativistic and quantum-mechanical Glauber model and to investigate whether they are in accordance with results including color transparency given that the computation of rho-nucleus attenuations is subject to some uncertainties. 
 We compute the nuclear transparencies in a multiple-scattering Glauber model and account for effects stemming from color transparency, from rho-meson decay, and from short-range correlations (SRC) in the final-state interactions (FSI). 
 The robustness of the model is tested by comparing the mass dependence and the hard-scale dependence of the A(e, e'p) nuclear transparencies with the data. The hard-scale dependence of the (e, e', rho(0)) nuclear transparencies for C-12 and Fe-56 are only moderately affected by SRC and by rho(0) decay. 
 The RMSGA calculations confirm the onset of CT at four-momentum transfers of a few (GeV/c)(2) in. meson electroproduction data. A more precise determination of the scale for the onset of CT is hampered by the lack of precise input in the FSI and rho-meson decay calculations.
History of solar wind and space plasma physics revisited

A paper published by Scottish geophysicist J.A. Broun in 1858 contained several pioneering and remarkable ideas in solar-terrestrial physics. He could anticipate more or less correctly the nature and origin of solar wind, solar magnetic fields, sunspot activity and geomagnetic storms in the middle of the 19th century. Broun applied the experimental results of the behavior of ionized gases in discharge tubes for the first time to Space Physics which may be considered as the beginning of the astrophysical plasma physics. In this context he attempted to explain the plasma interactions of solar wind with the comet tails and earth's magnetosphere. Most of the postulates or hypotheses put forward by Broun in 1858 and later in 1874 was rediscovered during the 20th century, after the advent of Space age.
Kinetic theory and hydrodynamics of cosmic strings

We develop further a kinetic theory of strings and derive a transport equation for a network of cosmic strings with Nambu-Goto evolution, interactions and background gravitational effects taken into account. We prove an H-theorem and obtain necessary and sufficient conditions for a thermodynamic equilibrium. At the lowest order the equilibrium is estimated by the von Mises-Fisher distributions parametrized by mean directions and dispersions of the right- and left-moving tangent vectors. Under assumption of a local equilibrium we derive a complete set of hydrodynamic equations that govern the evolution of strings on large scales. We also argue that on small scales the assumption of a local equilibrium would break down, and non-equilibrium steady states, described by the Sinai-Ruelle-Bowen distributions, should be used instead.
A new calibration for the Blue Horizontal Branch

We suggest a simple analytic approximation for magnitudes and hence distances of Blue Horizontal Branch (BHB) stars in sloan colours. Precedent formulations do not offer a simple closed formula, nor do they cover the full dependences, e.g., on metallicity. Furthermore, using BHB star samples from the Sloan Digital Sky Survey, we validate our distance calibration directly on field stars instead of globular clusters and assess the performance of other available distance calibrations. Our method to statistically measure distances is sufficiently accurate to measure the colour and metallicity dependence on our sample and can be applied to other sets of stars or filters.
A Cascading Failure Model by Quantifying Interactions

Cascading failures triggered by trivial initial events are encountered in many complex systems. It is the interaction and coupling between components of the system that causes cascading failures. We propose a simple model to simulate cascading failure by using the matrix that determines how components interact with each other. A careful comparison is made between the original cascades and the simulated cascades by the proposed model. It is seen that the model can capture general features of the original cascades, suggesting that the interaction matrix can well reflect the relationship between components. An index is also defined to identify important links and the distribution follows an obvious power law. By eliminating a small number of most important links the risk of cascading failures can be significantly mitigated, which is dramatically different from getting rid of the same number of links randomly.
Modeling the History of Astronomy: Ptolemy, Copernicus, and Tycho

This paper describes a series of activities in which students investigate and use the Ptolemaic, Copernican, and Tychonic models of planetary motion. The activities guide students through using open source software to discover important observational facts, learn the necessary vocabulary, understand the fundamental properties of different theoretical models, and relate the theoretical models to observational data. After completing these activities students can make observations of a fictitious solar system and use those observations to construct models for that system.