nils-herrmann/clean_scirepeval huggingface dataset:
Community extraction for social networks

Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields and has diverse applications. Most community detection methods focus on partitioning the entire network into communities, with the expectation of many ties within communities and few ties between. However, many networks contain nodes that do not fit in with any of the communities, and forcing every node into a community can distort results. Here we propose a new framework that extracts one community at a time, allowing for arbitrary structure in the remainder of the network, which can include weakly connected nodes. The main idea is that the strength of a community should depend on ties between its members and ties to the outside world, but not on ties between nonmembers. The proposed extraction criterion has a natural probabilistic interpretation in a wide class of models and performs well on simulated and real networks. For the case of the block model, we establish asymptotic consistency of estimated node labels and propose a hypothesis test for determining the number of communities.
Robust maximization of asymptotic growth

This paper addresses the question of how to invest in a robust growth-optimal way in a market where the instantaneous expected return of the underlying process is unknown. The optimal investment strategy is identified using a generalized version of the principal eigenfunction for an elliptic second-order differential operator which depends on the covariance structure of the underlying process used for investing. The robust growth-optimal strategy can also be seen as a limit, as the terminal date goes to infinity, of optimal arbitrages in the terminology of Fernholz and Karatzas.
Covariance, correlation matrix, and the multiscale community structure of networks.

Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.
The mid-term periodicities in sunspot areas

The sunspot area fluctuations for the northern and the southern hemispheres of the Sun over the epoch of 12 cycles (12–23) are investigated. Because of the asymmetry of their probability distributions, the positive and the negative fluctuations are considered separately. The auto-correlation analysis of them shows three quasi-periodicities at 10, 17 and 23 solar rotations. The wavelets gives the 10-rotation quasi-periodicity. For the original and the negative fluctuations the correlation coefficient between the wavelet and the auto-correlation results is about 0.9 for 90% of the auto-correlation peaks. For the positive fluctuations it is also 0.9 for 70% of the peaks. For 90% of cycles in both hemispheres the auto-correlation analysis of negative fluctuations shows that two longer periods can be represented as the multiple of the shortest period. For positive fluctuations such dependences are found for more than 50% of cases.
Random Numbers in Scientific Computing: An Introduction

Random numbers play a crucial role in science and industry. Many numerical methods require the use of random numbers, in particular the Monte Carlo method. Therefore it is of paramount importance to have efficient random number generators. The differences, advantages and disadvantages of true and pseudo random number generators are discussed with an emphasis on the intrinsic details of modern and fast pseudo random number generators. Furthermore, standard tests to verify the quality of the random numbers produced by a given generator are outlined. Finally, standard scientific libraries with built-in generators are presented, as well as different approaches to generate nonuniform random numbers. Potential problems that one might encounter when using large parallel machines are discussed.
Characterizing the Community Structure of Complex Networks

Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks.  We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as “fingerprints” of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category.  Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks' evolution.
A database approach to information retrieval: The remarkable relationship between language models and region models

In this report, we unify two quite distinct approaches to information retrieval: region models and language models. Region models were developed for structured document retrieval. They provide a well-defined behaviour as well as a simple query language that allows application developers to rapidly develop applications. Language models are particularly useful to reason about the ranking of search results, and for developing new ranking approaches. The unified model allows application developers to define complex language modeling approaches as logical queries on a textual database. We show a remarkable one-to-one relationship between region queries and the language models they represent for a wide variety of applications: simple ad-hoc search, cross-language retrieval, video retrieval, and web search.
DNA translocation through graphene nanopores.

Nanopores--nanosized holes that can transport ions and molecules--are very promising devices for genomic screening, in particular DNA sequencing. Solid-state nanopores currently suffer from the drawback, however, that the channel constituting the pore is long, approximately 100 times the distance between two bases in a DNA molecule (0.5 nm for single-stranded DNA). This paper provides proof of concept that it is possible to realize and use ultrathin nanopores fabricated in graphene monolayers for single-molecule DNA translocation. The pores are obtained by placing a graphene flake over a microsize hole in a silicon nitride membrane and drilling a nanosize hole in the graphene using an electron beam. As individual DNA molecules translocate through the pore, characteristic temporary conductance changes are observed in the ionic current through the nanopore, setting the stage for future single-molecule genomic screening devices.
Hirsch index as a network centrality measure

We study the h Hirsch index as a local node centrality measure for complex networks in general. The h index is compared with the Degree centrality (a local measure), the Betweenness and Eigenvector centralities (two non-local measures) in the case of a biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II) as test environments. In both networks, the Hirsch index has poor correlation with Betweenness centrality but correlates well with Eigenvector centrality, specially for the more important nodes that are relevant for ranking purposes, say in Search Machine Optimization. In the thesaurus network, the h index seems even to outperform the Eigenvector centrality measure as evaluated by simple linguistic criteria.
Predicting Influential Users in Online Social Networks

Who are the influential people in an online social network? The answer to this question depends not only on the structure of the network, but also on details of the dynamic processes occurring on it. We classify these processes as conservative and non-conservative. A random walk on a network is an example of a conservative dynamic process, while information spread is non-conservative. The influence models used to rank network nodes can be similarly classified, depending on the dynamic process they implicitly emulate. We claim that in order to correctly rank network nodes, the influence model has to match the details of the dynamic process. We study a real-world network on the social news aggregator Digg, which allows users to post and vote for news stories. We empirically define influence as the number of in-network votes a user's post generates. This influence measure, and the resulting ranking, arises entirely from the dynamics of voting on Digg, which represents non-conservative information flow. 
We then compare predictions of different influence models with this empirical estimate of influence. The results show that non-conservative models are better able to predict influential users on Digg. We find that normalized alpha-centrality metric turns out to be one of the best predictors of influence. We also present a simple algorithm for computing this metric and the associated mathematical formulation and analytical proofs.
Towards a better definition of the kilogram

It is widely accepted that improvement of the current International System of Units (SI) is necessary, and that central to this problem is redefinition of the kilogram. This paper compares the relative advantages of two main proposals for a modern scientific definition of the kilogram: an ‘electronic kilogram’ based on a fixed value of Planck's constant, and an ‘atomic kilogram’ based on a fixed value for Avogadro's number. A concrete and straightforward atomic definition of the kilogram is proposed. This definition is argued to be more experimentally neutral than the electronic kilogram, more realizable by school and university laboratories than the electronic kilogram, and more readily comprehensible than the electronic kilogram.
Simulating the Spread of Pandemics with Different Origins Considering International Traffic

Pandemics have the potential to cause immense disruption and damage to communities and societies. In this paper, we propose a hybrid model to determine how the pandemic spread through the world. The model combines the SEIR-based model for local areas and the network model for global connection between countries. We simulate the potential pandemic with different origins and find how the difference of the origin of a pandemic influences the impact in the world. We investigate the travelers network which is derived from real data, and simulate 65 countries, and see how the pandemic spread through the world from different 14 countries as origins of pandemic. We compare the difference in terms of the impact in countries and the impact in the world. As a result, the impact in the world increases when pandemic originates from the United States, India, and China.
Simulating the spread of influenza pandemic of 2009 considering international traffic

Pandemics have the potential to cause immense disruption and damage to communities and societies. In this paper, we model the influenza pandemic of 2009. We propose a hybrid model to determine how the pandemic spreads through the world. The model considers both SEIR-based model for local areas and the network model for global connection between countries referring to data on international travelers. Our interest is to reproduce the situation using the data of the early stage of the pandemic and to predict the future transition by extending the simulation cycle. Our simulation result predicts the second peak of the pandemic in the real world by considering the factors of tendency of seasonal influenza and people’s reaction against the infection. Without considering these factors, the simulation does not predict the second peak of the pandemic in the real world. We conclude that the seasonal tendency and people’s reaction are important factors for the spreading of the pandemic.
Influence of the Cold War Upon Influenza Pandemic of 1957-1958

Influenza Pandemic of 1957-1958, also called Asian Flu Pandemic, was one of the most widespread pandemics in history. In this paper, we model the pandemic, considering the effect of the Cold War. There were some restrictions between Western and Eastern nations due to the Cold War during the pandemic. We expect that such restrictions influenced the spread of the pandemic. We propose a hybrid model to determine how the pandemic spread through the world. The model combines the SEIR-based model for local areas and the network model for global connection between countries. First, we reproduce the situation in 19 countries. Then, we run another experiment to find the influence of the war in the spread of the pandemic, simulation considering international relationships in different years. The simulation results show that the impact of the pandemic in each country was much influenced by international relationships. This study indicates that if there was less effect of the Cold War, Western nations would have larger number of death cases, Eastern nations would have smaller number of death cases, and the world impact would be increased somewhat.
Scaling and Multiscaling in Financial Series: A Simple Model

We propose a simple stochastic volatility model which is analytically tractable, very easy to simulate, and which captures some relevant stylized facts of financial assets, including scaling properties. In particular, the model displays a crossover in the log-return distribution from power-law tails (small time) to a Gaussian behavior (large time), slow decay in the volatility autocorrelation, and multiscaling of moments. Despite its few parameters, the model is able to fit several key features of the time series of financial indexes, such as the Dow Jones Industrial Average, with remarkable accuracy.
Inferring Networks of Diffusion and Influence

Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions (who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks.
 We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.
A New Interpretation of Charge Based on Spinning Sphere Model of Electron

LEP experiments indicate that the charge of the electron is distributed over a small radius $\sim10^{-20} $m. By incorporating this information in spinning sphere model of electron we arrive at a new interpretation of charge as the Schwinger corrected or electromagnetic or magnetic self-energy mass. The charge-energy equivalence is also arrived at in this paper. Further it is predicted that particles with zero mass will not contain any charge.
Centrality metric for dynamic networks

Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static networks. Most networks, on the other hand, are dynamic in nature, evolving over time through the addition or deletion of nodes and edges. A popular approach to analyzing such networks represents them by a static network that aggregates all edges observed over some time period. This approach, however, under or overestimates centrality of some nodes. We address this problem by introducing a novel centrality metric for dynamic network analysis. This metric exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. We demonstrate on an example network that the proposed metric leads to a very different ranking than analysis of an equivalent static network. We use dynamic centrality to study a dynamic citations network and contrast results to those reached by static network analysis.
Emergence of universal scaling in financial markets from mean-field dynamics.

Collective phenomena with universal properties have been observed in many complex systems with a large number of components. Here we present a microscopic model of the emergence of scaling behavior in such systems, where the interaction dynamics between individual components is mediated by a global variable making the mean-field description exact. Using the example of financial markets, we show that asset price can be such a global variable with the critical role of coordinating the actions of agents who are otherwise independent. The resulting model accurately reproduces empirical properties such as the universal scaling of the price fluctuation and volume distributions, long-range correlations in volatility, and multiscaling.
Reducing the Leak Rate from a Damaged Oil Well by Filling It with Dense Streamlined Objects

The enormous pressure lifting the column of oil in a leaking oil well can thwart efforts to seal the top of the well and prevent oil from rising. When the oil cannot be stopped completely, we propose to slow its flow by filling the well with a porous medium. That medium consists of countless small, dense, streamlined objects that are dropped into the well and descend through the rising oil at terminal velocity. The resulting heap of objects couples to the oil via viscous and drag forces, dissipating the oil's energy and upward momentum and significantly reducing its rate of flow.
Predicting Failures in Power Grids: The Case of Static Overloads

Here we develop an approach to predict power grid weak points, and specifically to efficiently identify the most probable failure modes in static load distribution for a given power network. This approach is applied to two examples: Guam's power system and also the IEEE RTS-96 system, both modeled within the static dc power flow model. Our algorithm is a power network adaption of the worst configuration heuristics, originally developed to study low probability events in physics and failures in error-correction. One finding is that, if the normal operational mode of the grid is sufficiently healthy, the failure modes, also called instantons, are sufficiently sparse, i.e., the failures are caused by load fluctuations at only a few buses. The technique is useful for discovering weak links which are saturated at the instantons. It can also identify generators working at the capacity and generators under capacity, thus providing predictive capability for improving the reliability of any power network.
Multiscale Modeling of Granular Flows with Application to Crowd Dynamics

In this paper a new multiscale modeling technique is proposed. It relies on a recently introduced measure-theoretic approach, which allows one to manage the microscopic and the macroscopic scale under a unique framework. In the resulting coupled model the two scales coexist and share information. This way it is possible to perform numerical simulations in which the trajectories and the density of the particles affect each other. Crowd dynamics is the motivating application throughout the paper.
PDF modeling of turbulent flows on unstructured grids

In probability density function (PDF) methods of turbulent flows, the joint PDF of several flow variables is computed by numerically integrating a system of stochastic differential equations for Lagrangian particles. A mathematically exact treatment of advection, viscous effects and arbitrarily complex chemical reactions is possible; these processes are treated without closure assumptions. A set of algorithms is proposed to provide an efficient solution of the PDF transport equation modeling the joint PDF of turbulent velocity, frequency and concentration of a passive scalar in geometrically complex configurations. An unstructured Eulerian grid is employed to extract Eulerian statistics, to solve for quantities represented at fixed locations of the domain and to track particles. All three aspects regarding the grid make use of the finite element method. Compared to hybrid methods, the current methodology is stand-alone, therefore it is consistent both numerically and at the level of turbulence closure without the use of consistency conditions. Several newly developed algorithms are described that facilitate the numerical solution in complex flow geometries, including a stabilized mean-pressure projection scheme, the estimation of conditional and unconditional Eulerian statistics and their derivatives from stochastic particle fields, particle tracking through unstructured grids, an efficient particle redistribution procedure and techniques related to efficient random number generation. The solver has been parallelized and optimized for shared memory and multi-core architectures using the OpenMP standard. Relevant aspects of performance and parallelism on cache-based shared memory machines are discussed and presented in detail. The methodology shows great promise in the simulation of high-Reynolds-number incompressible inert or reactive turbulent flows in realistic configurations.