nils-herrmann/clean_scirepeval huggingface dataset:
Epidemics in Multipartite Networks: Emergent Dynamics

Single virus epidemics over complete networks are widely explored in the literature as the fraction of infected nodes is, under appropriate microscopic modeling of the virus infection, a Markov process. With non-complete networks, this macroscopic variable is no longer Markov. In this paper, we study virus diffusion, in particular, multi-virus epidemics, over non-complete stochastic networks. We focus on multipartite networks. In companying work this http URL, we show that the peer-to-peer local random rules of virus infection lead, in the limit of large multipartite networks, to the emergence of structured dynamics at the macroscale. The exact fluid limit evolution of the fraction of nodes infected by each virus strain across islands obeys a set of nonlinear coupled differential equations, see this http URL In this paper, we develop methods to analyze the qualitative behavior of these limiting dynamics, establishing conditions on the virus micro characteristics and network structure under which a virus persists or a natural selection phenomenon is observed.
Error AMP Chain Graphs

Any regular Gaussian probability distribution that can be represented by an AMP chain graph (CG) can be expressed as a system of linear equations with correlated errors whose structure depends on the CG. However, the CG represents the errors implicitly, as no nodes in the CG correspond to the errors. We propose in this paper to add some deterministic nodes to the CG in order to represent the errors explicitly. We call the result an EAMP CG. We will show that, as desired, every AMP CG is Markov equivalent to its corresponding EAMP CG under marginalization of the error nodes. We will also show that every EAMP CG under marginalization of the error nodes is Markov equivalent to some LWF CG under marginalization of the error nodes, and that the latter is Markov equivalent to some directed and acyclic graph (DAG) under marginalization of the error nodes and conditioning on some selection nodes. This is important because it implies that the independence model represented by an AMP CG can be accounted for by some data generating process that is partially observed and has selection bias. Finally, we will show that EAMP CGs are closed under marginalization. This is a desirable feature because it guarantees parsimonious models under marginalization.
THE EFFICIENCY OF SECOND-ORDER FERMI ACCELERATION BY WEAKLY COMPRESSIBLE MAGNETOHYDRODYNAMIC TURBULENCE

We investigate the effects of pitch-angle scattering on the efficiency of particle heating and acceleration by MHD turbulence using phenomenological estimates and simulations of non-relativistic test particles interacting with strong, subsonic MHD turbulence. We include an imposed pitch-angle scattering rate, which is meant to approximate the effects of high-frequency plasma waves and/or velocity space instabilities. We focus on plasma parameters similar to those found in the near-Earth solar wind, though most of our results are more broadly applicable. An important control parameter is the size of the particle mean free path λmfp relative to the scale of the turbulent fluctuations L. For small scattering rates, particles interact quasi-resonantly with turbulent fluctuations in magnetic field strength. Scattering increases the long-term efficiency of this resonant heating by factors of a few times 10, but the distribution function does not develop a significant non-thermal power-law tail. For higher scattering rates, the interaction between particles and turbulent fluctuations becomes non-resonant, governed by particles heating and cooling adiabatically as they encounter turbulent density fluctuations. Rapid pitch-angle scattering can produce a power-law tail in the proton distribution function, but this requires fine-tuning of parameters. Moreover, in the near-Earth solar wind, a significant power-law tail cannot develop by this mechanism because the particle acceleration timescales are longer than the adiabatic cooling timescale set by the expansion of the solar wind. Our results thus imply that MHD-scale turbulent fluctuations are unlikely to be the origin of the v−5 tail in the proton distribution function observed in the solar wind.
Pion electromagnetic form factor at spacelike momenta.

A novel method is employed to compute the pion electromagnetic form factor, F(π)(Q²), on the entire domain of spacelike momentum transfer using the Dyson-Schwinger equation (DSE) framework in QCD. The DSE architecture unifies this prediction with that of the pion's valence-quark parton distribution amplitude (PDA). Using this PDA, the leading-order, leading-twist perturbative QCD result for Q²F(π)(Q²) underestimates the full computation by just 15% on Q²≳8 GeV², in stark contrast to the result obtained using the asymptotic PDA. The analysis shows that hard contributions to the pion form factor dominate for Q²≳8 GeV², but, even so, the magnitude of Q²F(π)(Q²) reflects the scale of dynamical chiral symmetry breaking, a pivotal emergent phenomenon in the standard model.
On the Hyperbolicity of Large-Scale Networks

Through detailed analysis of scores of publicly available data sets corresponding to a wide range of large-scale networks, from communication and road networks to various forms of social networks, we explore a little-studied geometric characteristic of real-life networks, namely their hyperbolicity. In smooth geometry, hyperbolicity captures the notion of negative curvature; within the more abstract context of metric spaces, it can be generalized as -hyperbolicity or negative curvature in the large. This generalized denition can be applied to graphs, which we explore in this report. We provide strong evidence that large-scale communication and social networks exhibit this fundamental property, and through extensive computations we quantify the degree of hyperbolicity of each network in comparison to its diameter. By contrast, and as evidence of the validity of the methodology, applying the same methods to graphs of road networks shows that they are not hyperbolic, which is as expected. Finally, we present practical computational means for detection of hyperbolicity and show how the test itself may be scaled to much larger graphs than those we examined via renormalization group methodology. Using well-understood mechanisms, we provide evidence through synthetically generated graphs that hyperbolicity is preserved and indeed amplied by renormalization. This allows us to detect hyperbolicity in large networks eciently, through much smaller renormalized versions. These observations indicate that -hyperbolicity is a common feature of large-scale networks, from IP-layer connectivity to citation, collaboration, co-authorship, and friendship graphs. We propose that -hyperbolicity in conjunction with other local characteristics of networks, such as the degree distribution and clustering coecients,
Memory Limited, Streaming PCA

We consider streaming, one-pass principal component analysis (PCA), in the high-dimensional regime, with limited memory. Here, p-dimensional samples are presented sequentially, and the goal is to produce the k-dimensional subspace that best approximates these points. Standard algorithms require O(p2) memory; meanwhile no algorithm can do better than O(kp) memory, since this is what the output itself requires. Memory (or storage) complexity is most meaningful when understood in the context of computational and sample complexity. Sample complexity for high-dimensional PCA is typically studied in the setting of the spiked covariance model, where p-dimensional points are generated from a population covariance equal to the identity (white noise) plus a low-dimensional perturbation (the spike) which is the signal to be recovered. It is now well-understood that the spike can be recovered when the number of samples, n, scales proportionally with the dimension, p. Yet, all algorithms that provably achieve this, have memory complexity O(p2). Meanwhile, algorithms with memory-complexity O(kp) do not have provable bounds on sample complexity comparable to p. We present an algorithm that achieves both: it uses O(kp) memory (meaning storage of any kind) and is able to compute the k-dimensional spike with O(p log p) sample-complexity - the first algorithm of its kind. While our theoretical analysis focuses on the spiked covariance model, our simulations show that our algorithm is successful on much more general models for the data.
Centromere reference models for human chromosomes X and Y satellite arrays.

The human genome sequence remains incomplete, with multimegabase-sized gaps representing the endogenous centromeres and other heterochromatic regions. Available sequence-based studies within these sites in the genome have demonstrated a role in centromere function and chromosome pairing, necessary to ensure proper chromosome segregation during cell division. A common genomic feature of these regions is the enrichment of long arrays of near-identical tandem repeats, known as satellite DNAs, which offer a limited number of variant sites to differentiate individual repeat copies across millions of bases. This substantial sequence homogeneity challenges available assembly strategies and, as a result, centromeric regions are omitted from ongoing genomic studies. To address this problem, we utilize monomer sequence and ordering information obtained from whole-genome shotgun reads to model two haploid human satellite arrays on chromosomes X and Y, resulting in an initial characterization of 3.83 Mb of centromeric DNA within an individual genome. To further expand the utility of each centromeric reference sequence model, we evaluate sites within the arrays for short-read mappability and chromosome specificity. Because satellite DNAs evolve in a concerted manner, we use these centromeric assemblies to assess the extent of sequence variation among 366 individuals from distinct human populations. We thus identify two satellite array variants in both X and Y centromeres, as determined by array length and sequence composition. This study provides an initial sequence characterization of a regional centromere and establishes a foundation to extend genomic characterization to these sites as well as to other repeat-rich regions within complex genomes.
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs (GPGP) consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood. Representations and algorithms from computer graphics are used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on general-purpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and yields accurate, approximately Bayesian inferences about real-world images.
Risk Without Return

Risk-only investment strategies have been growing in popularity as traditional in- vestment strategies have fallen short of return targets over the last decade. However, risk-based investors should be aware of four things. First, theoretical considerations and empirical studies show that apparently dictinct risk-based investment strategies are manifestations of a single effect. Second, turnover and associated transaction costs can be a substantial drag on return. Third, capital diversification benefits may be reduced. Fourth, there is an apparent connection between performance and risk diversification. To analyze risk diversification benefits in a consistent way, we introduce the Risk Diversification Index (RDI) which measures risk concentrations and complements the Herfindahl-Herschman Index (HHI) for capital concentrations.
Free Instrument for Movement Measure

This paper presents the validation of a computational tool that serves to obtain continuous measurements of moving objects. The software uses techniques of computer vision, pattern recognition and optical flow, to enable tracking of objects in videos, generating data trajectory, velocity, acceleration and angular movement. The program was applied to track a ball around a simple pendulum. The methodology used to validate it, taking as a basis to compare the values measured by the program, as well as the theoretical values expected according to the model of a simple pendulum. The experiment is appropriate to the method because it was built within the limits of the linear harmonic oscillator and energy losses due to friction had been minimized, making it the most ideal possible. The results indicate that the tool is sensitive and accurate. Deviations of less than a millimeter to the extent of the trajectory, ensures the applicability of the software on physics, whether in research or in teaching topics.
D-Brane solutions under market panic

The relativistic quantum mechanic approach is used to develop a stock market dynamics. The relativistic is conceptional here as the meaning of big external volatility or volatility shock on a financial market. We used a differential geometry approach with the parallel transport of the prices to obtain a direct shift of the stock price movement. The prices are represented here as electrons with different spin orientation. Up and down orientations of the spin particle are likened here as an increase or a decrease of stock prices. The paralel transport of stock prices is enriched about Riemann curvature which describes some arbitrage opportunities in the market. To solve the stock-price dynamics, we used the Dirac equation for bispinors on the spherical brane-world. We found that when a spherical brane is abbreviated to the disk on the equator, we converge to the ideal behaviour of financial market where Black Scholes as well as semi-classical equations are sufficient. Full spherical brane-world scenarios can descibe a non-equilibrium market behaviour were all arbitrage opportunities as well as transaction costs are take into account.
NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison

Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as - NoSQL, NewSQL and Search-based systems. This paper motivation is to provide - classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.
Features of a 2d gauge theory with vanishing chiral condensate

The Schwinger model with Nf ≥ 2 flavors is a simple example for a fermionic model with zero chiral condensate Σ (in the chiral limit). We consider numerical data for two light flavors, based on simulations with dynamical chiral lattice fermions. We test properties and predictions that were put forward in the recent literature for models with Σ = 0, which include IR conformal theories. In particular, we probe the decorrelation of low lying Dirac eigenvalues, and we discuss the mass anomalous dimension and its IR extrapolation. Here, we encounter subtleties, which may urge caution with analogous efforts in other models, such as multi-flavor QCD.
Algorithms of the LDA model [REPORT]

We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them are variational inference algorithms: Variational Bayesian inference and Online Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC) algorithm -- Collapsed Gibbs sampling. We compare their time complexity and performance. We find that online variational Bayesian inference is the fastest algorithm and still returns reasonably good results.
Revisiting the merit-order effect of renewable energy sources

An on-going debate in the energy economics and power market community has raised the question if energy only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES). The short answer to this is: No, they are not failing. Energy-based power markets are, however, facing several market distortions, namely from the gap between the electricity volume traded at day-ahead markets versus the overall electricity consumption as well as the (wrong) regulatory assumption that variable RES generation, i.e., wind and photovoltaic (PV), truly have zero marginal operation costs.
Physical Determination of the Action

Physical theories ought to be built up from colloquial notions such as 'long bodies', 'energetic sources' etc. in terms of which one can define pre-theoretic ordering relations such as 'longer than', 'more energetic than'. One of the questions addressed in previous work is how to make the transition from these pre-theoretic notions to quantification, such as making the transition from the ordering relation of 'longer than' (if one body covers the other) to the notion of how much longer. In similar way we introduce dynamical notions 'more impulse' (if in a collision one object overruns the other) and 'more energetic' (if the effect of one source exceeds the effect of the other). In a physical model - built by coupling congruent standard actions - those basic pre-theoretic notions become measurable. We uncover the origin of (basic) physical quantities of Energy, Momentum and Inertial Mass. From physical and methodical principles - without mathematical presuppositions - we derive all equations of (classical and relativistic) Dynamics and ultimately the principle of least action.
Dynamical Structure of a Traditional Amazonian Social Network

Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’) drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days) is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.
Complete density perturbations in the Jordan-Fierz-Brans-Dicke theory

In the context of scalar-tensor theories we study the evolution of the density contrast for Jordan-Fierz-Brans-Dicke theories in a Friedmann-Lemaitre-Robertson-Walker universe. Calculations are performed in the Einstein frame with the cosmological background described as Lambda-cold dark matter (Lambda CDM) and supplemented by a Jordan-Fierz-Brans-Dicke field. By using a completely general procedure valid for all scalar-tensor theories, we obtain the exact fourth-order differential equation for the density contrast evolution in modes of arbitrary size. In the case of sub-Hubble modes, the expression reduces to a simpler but still fourth-order equation that is then compared with the standard (quasistatic) approximation. Differences with respect to the evolution as predicted by the standard concordance Lambda CDM model are observed depending on the value of the coupling.
Experimental studies of THGEM in different Ar/CO2 mixtures

In this paper, the performance of a type of domestic THGEM (THick Gaseous Electron Multiplier) working in Ar/CO2 mixtures is reported in detail. This kind of single THGEM can provide a gain range from 100 to 1000, which is very suitable for application in neutron detection. In order to study its basic characteristics as a reference for the development of a THGEM based neutron detector, the counting rate plateau, the energy resolution and the gain of the THGEM have been measured in different Ar/CO2 mixtures with a variety of electrical fields. For the Ar/CO2(90%/10%) gas mixture, a wide counting rate plateau is achieved from 720 V to 770 V, with a plateau slope of 2.4%/100 V, and an excellent energy resolution of about 22% is obtained at the 5.9 keV full energy peak of the Fe-55 X-ray source.