nils-herrmann/clean_scirepeval huggingface dataset:
Systemic risk through contagion in a core-periphery structured banking network

We contribute to the understanding of how systemic risk arises in a network of credit-interlinked agents. Motivated by empirical studies we formulate a network model which, despite its simplicity, depicts the nature of interbank markets better than a homogeneous model. The components of a vector Ornstein-Uhlenbeck process living on the vertices of the network describe the financial robustnesses of the agents. For this system, we prove a LLN for growing network size leading to a propagation of chaos result. We state properties, which arise from such a structure, and examine the effect of inhomogeneity on several risk management issues and the possibility of contagion.
A Scaled Gradient Projection Method for Bayesian Learning in Dynamical Systems

A crucial task in system identification problems is the selection of the most appropriate model class and is classically addressed resorting to cross-validation or using order selection criteria based on asymptotic arguments. As recently suggested in the literature, this can be addressed in a Bayesian framework, where model complexity is regulated by a few hyperparameters, which can be estimated via marginal likelihood maximization. It is thus of primary importance to design effective optimization methods to solve the corresponding optimization problem. If the unknown impulse response is modeled as a Gaussian process with a suitable kernel, the maximization of the marginal likelihood leads to a challenging nonconvex optimization problem, which requires a stable and effective solution strategy. In this paper we address this problem by means of a scaled gradient projection algorithm, in which the scaling matrix and the steplength parameter play a crucial role to provide a meaningful solution in a computatio...
Initial mass function of intermediate-mass black hole seeds

We study the initial mass function (IMF) and hosting halo properties of intermediate-mass black holes (IMBHs, 104-6 M⊙) formed inside metal-free, UV-illuminated atomic-cooling haloes (virial temperature Tvir ≥ 104 K) either via the direct collapse of the gas or via an intermediate supermassive star (SMS) stage. These IMBHs have been recently advocated as the seeds of the supermassive black holes observed at z ≈ 6. We achieve this goal in three steps: (a) we derive the gas accretion rate for a proto-SMS to undergo General Relativity instability and produce a direct collapse black hole (DCBH) or to enter the zero-age main sequence and later collapse into an IMBH; (b) we use merger-tree simulations to select atomic-cooling haloes in which either a DCBH or SMS can form and grow, accounting for metal enrichment and major mergers that halt the growth of the proto-SMS by gas fragmentation. We derive the properties of the hosting haloes and the mass distribution of black holes at this stage, and dub it the `birth mass function'; (c) we follow the further growth of the DCBH by accreting the leftover gas in the parent halo and compute the final IMBH mass. We consider two extreme cases in which minihaloes (Tvir <104 K) can (fertile) or cannot (sterile) form stars and pollute their gas leading to a different IMBH IMF. In the (fiducial) fertile case, the IMF is bimodal extending over a broad range of masses, M ≈ (0.5-20) × 105 M⊙, and the DCBH accretion phase lasts from 10 to 100 Myr. If minihaloes are sterile, the IMF spans the narrower mass range M ≈ (1-2.8) × 106 M⊙, and the DCBH accretion phase is more extended (70-120 Myr). We conclude that a good seeding prescription is to populate haloes (a) of mass 7.5
Link Analysis for Communities Detection on Facebook

Social networks have become a part in the daily life of millions of users, which offer wide range of interests and practices. The main characteristic of social networks is its ability to gather different individuals around a common point of view or collective beliefs. Among the current social networking sites, Facebook is the most popular, which has the highest number of users. However, in Facebook, the existence of communities (groups)is a critical question; thus, many researchers focus on potential communities by using techniques like data mining and web mining. In this work, we present four approaches based on link analysis techniques to detect prospective groups and their members.
A non-resonant dielectric metamaterial for the enhancement of thin-film solar cells

Recently, we have suggested a dielectric metamaterial composed of an array of submicron dielectric spheres located on top of an amorphous thin-film solar cell. We have theoretically shown that this metamaterial can decrease the reflection and simultaneously suppress the transmission through the photovoltaic layer because it transforms the incident plane wave into a set of focused light beams. This theoretical concept has been strongly developed and experimentally confirmed in the present paper. Here we consider the metamaterial for oblique angle illumination, redesign the solar cell and present a detailed experimental study of the whole structure. In contrast to our previous theoretical study we show that our omnidirectional light-trapping structure may operate better than the optimized flat coating obtained by plasma-enhanced chemical vapor deposition.
Credit Bubbles in Arbitrage Markets: The Geometric Arbitrage Approach to Credit Risk

We apply Geometric Arbitrage Theory (GAT) to obtain results in mathematical finance for credit markets, which do not need stochastic differential geometry in their formulation. The remarkable aspect of the GAT is the gauge symmetry, which can be translated to the financial context, by packaging all of the asset model information into a (stochastic) principal fiber bundle. We obtain closed-form equations involving default intensities and loss-given defaults characterizing the no-free-lunch-with-vanishing-risk condition for government and corporate bond markets while relying on the spread-term structure with default intensity and loss-given default. Moreover, we provide a sufficient condition equivalent to the Novikov condition implying the absence of arbitrage. Furthermore, the generic dynamics for an isolated credit market allowing for arbitrage possibilities (and minimizing the total quantity of potential arbitrage) are derived, and arbitrage credit bubbles for both base credit assets and credit derivatives are explicitly computed. The existence of an approximated risk-neutral measure allowing the definition of fundamental values for the assets is inferred through spectral theory. We show that instantaneous bond returns are serially uncorrelated and centered, that the expected value of credit bubbles remains constant for future times where no coupons are paid, and that the variance of the market portfolio nominals is concurrent with that of the corresponding bond deflators.
Online learning in MDPs with side information

We study online learning of finite Markov decision process (MDP) problems when a side information vector is available. The problem is motivated by applications such as clinical trials, recommendation systems, etc. Such applications have an episodic structure, where each episode corresponds to a patient/customer. Our objective is to compete with the optimal dynamic policy that can take side information into account. 
We propose a computationally efficient algorithm and show that its regret is at most $O(\sqrt{T})$, where $T$ is the number of rounds. To best of our knowledge, this is the first regret bound for this setting.
Overlapping Community Detection Optimization and Nash Equilibrium

Community detection in social networks is the focus of many algorithms. Recent methods aimed at optimizing the so-called modularity function proceed by maximizing relations within communities while minimizing inter-community relations. However, given the NP-completeness of the problem, these algorithms are heuristics that do not guarantee an optimum. In this paper, we introduce a new algorithm along with a function that takes an approximate solution and modifies it in order to reach an optimum. This reassignment function is considered a `potential function' and becomes a necessary condition to asserting that the computed optimum is indeed a Nash Equilibrium. We also use this function to simultaneously show two detection and visualization modes: partitioned and overlaped communities, of great value in revealing interesting features in a social network. Our approach is successfully illustrated through several experiments on either real unipartite, multipartite or directed graphs of medium and large-sized datasets.
Pattern-wave model of brain. Mechanisms of information processing, memory organization

The structure of the axon-dendrite connections of neurons of the brain creates a rich spatial structure in which provided various combinations of signals surrounding neurons. Structure of dendritic trees and shape of dendritic spines allow repeatedly increase combinatorial component through cross synapses influence neighboring neurons. In this paper it is shown that the diffuse spreading of neurotransmitters allows neurons to detect and remember significant set of environmental activity patterns. As a core element fixation described extrasynaptic metabotropic receptive clusters. The described mechanism leads to the appearance of wave processes, based on the propagation of the front-line areas of spontaneous activity. In the proposed model, any compact pattern of neural activity is seen as a source emitting a diverging wave endogenous spikes. It is shown that the spike pattern of the wave front is strictly unique and uniquely defined pattern that started the wave. The propagation of waves with a unique pattern allows anywhere in nature undergoing brain wave patterns there to judge the whole brain processes information. In these assumptions naturally described mechanism of projection information between regions of the cortex. Performed computer simulations show the high effectiveness of such information model.
Some Observable Effects ofModified Gravity inCosmology and Astrophysics

In Chapter 1 I present the current picture of the universe and briefly review the cosmological constant problem and some of the theories proposed to solve it. The following Chapters essentially contain the published papers with some modifications and additions: in Chapter 2 I study the radiation-dominated epoch in $R^2$ gravity, gravitational particle production and its implications for the early and contemporary universe; in Chapter 3 I investigate the formation of curvature singularities in contracting astronomical systems; in Chapter 4, I discuss some mechanisms to prevent such singularities and calculate the related cosmic-ray production; finally, in Chapter 5, I deal with spherically symmetric solutions and explore the possibility of gravitational repulsion.
Fluid breakup during simultaneous two-phase flow through a three-dimensional porous medium

We use confocal microscopy to directly visualize the simultaneous flow of both a wetting and a non-wetting fluid through a model three-dimensional (3D) porous medium. We find that, for small flow rates, both fluids flow through unchanging, distinct, connected 3D pathways; in stark contrast, at sufficiently large flow rates, the non-wetting fluid is broken up into discrete ganglia. By performing experiments over a range of flow rates, using fluids of different viscosities, and with porous media having different geometries, we show that this transition can be characterized by a state diagram that depends on the capillary numbers of both fluids, suggesting that it is controlled by the competition between the viscous forces exerted on the flowing oil and the capillary forces at the pore scale. Our results thus help elucidate the diverse range of behaviors that arise in two-phase flow through a 3D porous medium.
Using multi-categorization semantic analysis and personalization for semantic search

Semantic search technology has received more attention in the last years. Compared with the keyword based search, semantic search is used to excavate the latent semantics information and help users find the information items that they want indeed. In this paper, we present a novel approach for semantic search which combines Multi-Categorization Semantic Analysis with personalization technology. The MCSA approach can classify documents into multiple categories, which is distinct from the existing approaches of classifying documents into a single category. Then, the search history and personal information for users are significantly considered in analysing and matching the original search result by Term Vector DataBase. A series of personalization algorithms are proposed to match personal information and search history. At last, the related experiments are made to validate the effectiveness and efficiency of our method. The experimental results show that our method based on MCSA and personalization outperforms some existing methods with the higher search accuracy and the lower extra time cost.
Evolution of online user behavior during a social upheaval

Social media represent powerful tools of mass communication and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We analyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similarity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this conversation and what roles they played. We study how roles and individual influence evolved during the period of the upheaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly intertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior.
Remnant mass, spin, and recoil from spin aligned black-hole binaries

We perform a set of 36 nonprecessing black-hole binary simulations with spins either aligned or counteraligned with the orbital angular momentum in order to model the final mass, spin, and recoil of the merged black hole as a function of the individual black-hole spin magnitudes and the mass ratio of the progenitors. We find that the maximum recoil for these configurations is ${V}_{\mathrm{max}}=526\ifmmode\pm\else\textpm\fi{}23\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$, which occurs when the progenitor spins are maximal, the mass ratio is ${q}_{\mathrm{max}}={m}_{1}/{m}_{2}=0.623\ifmmode\pm\else\textpm\fi{}0.038$, the smaller black-hole spin is aligned with the orbital angular momentum, and the larger black-hole spin is counteraligned (${\ensuremath{\alpha}}_{1}=\ensuremath{-}{\ensuremath{\alpha}}_{2}=1$). This maximum recoil is about $80\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ larger than previous estimates, but most importantly, because the maximum occurs for smaller mass ratios, the probability for a merging binary to recoil faster than $400\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ can be as large as 17%, while the probability for recoils faster than $250\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ can be as large as 45% when the spins are aligned or counteraligned by accretion. We provide explicit phenomenological formulas for the final mass, spin, and recoil as a function of the individual black-hole spins and the mass difference between the two black holes. Here we include terms up through fourth order in the initial spins and mass difference and find excellent agreement (within a few percent) with independent results available in the literature. The maximum radiated energy is ${E}_{\text{rad}}/m\ensuremath{\approx}11.3%$ and final spin ${\ensuremath{\alpha}}_{\text{rem}}^{\mathrm{max}}\ensuremath{\approx}0.952$ for equal-mass, aligned, maximally spinning binaries.
Entanglement entropy and nonabelian gauge symmetry

Entanglement entropy has proven to be an extremely useful concept in quantum field theory. Gauge theories are of particular interest, but for these systems the entanglement entropy is not clearly defined because the physical Hilbert space does not factor as a tensor product according to regions of space. Here we review a definition of entanglement entropy that applies to abelian and nonabelian lattice gauge theories. This entanglement entropy is obtained by embedding the physical Hilbert space into a product of Hilbert spaces associated to regions with boundary. The latter Hilbert spaces include degrees of freedom on the entangling surface that transform like surface charges under the gauge symmetry. These degrees of freedom are shown to contribute to the entanglement entropy, and the form of this contribution is determined by the gauge symmetry. We test our definition using the example of two-dimensional Yang–Mills theory, and find that it agrees with the thermal entropy in de Sitter space, and with the results of the Euclidean replica trick. We discuss the possible implications of this result for more complicated gauge theories, including quantum gravity.
Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization

We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able to simultaneously leverage the correlations: (a) among stock prices, (b) among news articles, and (c) between stock prices and news articles. Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity. We carry out extensive back testing on trading strategies based on our algorithm. The result shows that our model has substantially better accuracy rate (55.7%) compared to many widely used algorithms. The return (56%) and Sharpe ratio due to a trading strategy based on our model are also much higher than baseline indices.
Similarity between nuclear rainbow and meteorological rainbow - evidence for nuclear ripples -

We present evidence for the nuclear ripples superimposed on the Airy structure of the nuclear rainbow, which is similar to the meteorological rainbow. The mechanism of the nuclear ripples is also similar to that of the meteorological rainbow, which is caused by the interference between the externally reflective waves and refractive waves. The nuclear ripple structure was confirmed by analyzing the elastic angular distribution in $^{16}\mathrm{O}+^{12}\mathrm{C}$ rainbow scattering at ${E}_{L}=115.9$ MeV using the coupled channels method by taking account of coupling to the excited states of $^{12}\mathrm{C}$ and $^{16}\mathrm{O}$ with a double folding model derived from a density-dependent effective nucleon-nucleon force with realistic wave functions for $^{12}\mathrm{C}$ and $^{16}\mathrm{O}$. The coupling to the excited states plays the role of creating the external reflection.
Optical-phonon resonances with saddle-point excitons in twisted-bilayer graphene.

Twisted-bilayer graphene (tBLG) exhibits van Hove singularities in the density of states that can be tuned by changing the twisting angle θ. A θ-defined tBLG has been produced and characterized with optical reflectivity and resonance Raman scattering. The θ-engineered optical response is shown to be consistent with persistent saddle-point excitons. Separate resonances with Stokes and anti-Stokes Raman scattering components can be achieved due to the sharpness of the two-dimensional saddle-point excitons, similar to what has been previously observed for one-dimensional carbon nanotubes. The excitation power dependence for the Stokes and anti-Stokes emissions indicate that the two processes are correlated and that they share the same phonon.
Exponentially Increasing the Capacity-to-Computation Ratio for Conditional Computation in Deep Learning

Many state-of-the-art results obtained with deep networks are achieved with the largest models that could be trained, and if more computation power was available, we might be able to exploit much larger datasets in order to improve generalization ability. Whereas in learning algorithms such as decision trees the ratio of capacity (e.g., the number of parameters) to computation is very favorable (up to exponentially more parameters than computation), the ratio is essentially 1 for deep neural networks. Conditional computation has been proposed as a way to increase the capacity of a deep neural network without increasing the amount of computation required, by activating some parameters and computation "on-demand", on a per-example basis. In this note, we propose a novel parametrization of weight matrices in neural networks which has the potential to increase up to exponentially the ratio of the number of parameters to computation. The proposed approach is based on turning on some parameters (weight matrices) when specific bit patterns of hidden unit activations are obtained. In order to better control for the overfitting that might result, we propose a parametrization that is tree-structured, where each node of the tree corresponds to a prefix of a sequence of sign bits, or gating units, associated with hidden units.
Towards a Pion Generalized Parton Distribution Model from Dyson-Schwinger Equations

We compute the pion quark Generalized Parton Distribution H and quark Double Distributions F and G in a coupled Bethe-Salpeter and Dyson-Schwinger approach in terms of quark flavors or isospin states. We use simple analytic expressions inspired by the numerical resolution of Dyson-Schwinger and Bethe-Salpeter equations. We explicitly check the support and polynomiality properties, and the behavior under charge conjugation or time invariance of our model. We obtain analytic expressions for the pion Double Distributions and Generalized Parton Distribution at vanishing pion momentum transfer at a low scale. Our model compares very well to experimental pion form factor or Parton Distribution Function data. This paper is the first stage of a GPD-modeling program which will be pursued by incorporating more realistic solutions of the Bethe-Salpeter and Dyson-Schwinger equations.
Encouraging Knowledge Sharing Using Web 2.0 Technologies In Higher Education: A Survey

As the technology continuous to advance new technologies have emerged with the capability to revolutionize knowledge sharing practices. Web 2.0 exemplifies such new technologies which provides dynamic way of interactions of people and businesses. In learning environment Web 2.0 technologies support and enhance teaching and learning of students. Therefore the main aim of this study focuses on the determining the ways to encourage knowledge sharing through web 2.0 technologies from students' point of views. A total of 287 students responded to the online questionnaire in International Islamic University Malaysia. Descriptive statistics was used in data analysis. The results show that students used web 2.0 technologies in learning and sharing knowledge among them. In addition the study found eight items on ways to encourage and enhance knowledge sharing among students in the University. These items include Create Awareness Provide facilities Internet Accessibility Ease of use Encourage Teamwork Materials Availability Improved Responses and Motivation.