nils-herrmann/clean_scirepeval huggingface dataset:
Quasi-steady linked vortices with chaotic streamlines

This paper describes the motion and the flow geometry of two or more linked ring vortices in an otherwise quiescent, ideal fluid. The vortices are thin tubes of near-circular shape which lie on the surface of an immaterial torus of small aspect ratio. Since the vortices are assumed to be identical and evenly distributed on any meridional section of the torus, the flow evolution depends only on the number of vortices ($n$) and the torus aspect ratio ( / {r}_{0} $, where ${r}_{0} $ is the centreline radius and ${r}_{1} $ is the cross-section radius). Numerical simulations based on the Biot–Savart law showed that a small number of vortices ($n= 2, 3$) coiled on a thin torus (${r}_{1} / {r}_{0} \leq 0. 16$) progressed along and rotated around the symmetry axis of the torus in an almost uniform manner while each vortex approximately preserved its shape. In the comoving frame the velocity field always possesses two stagnation points. The transverse intersection, along $2n$ streamlines, of the stream tube emanating from the front stagnation point and the stream tube ending at the rear stagnation point creates a three-dimensional chaotic tangle. It was found that the volume of the chaotic region increases with increasing torus aspect ratio and decreasing number of vortices.
Virus and Warning Spread in Dynamical Networks

Recent work on information survival in sensor and human P2P networks try to study the datum preservation or the virus spreading in a network under the dynamical system approach. Some interesting solutions propose to use non-linear dynamical systems and fixed point stability theorems, providing closed form formulas that depend on the largest eigenvalue of the dynamic system matrix. Given that in the Web there can be messages from one place to another, and that these messages can be, with some probability, new unclassified virus warning messages as well as worms or other kind of viruses, the sites can be infected very fast. The question to answer is how and when a network infection can become global and how it can be controlled or at least how to stabilize the spreading in such a way that it becomes confined below a fixed portion of the network. In this paper, we try to be a step ahead in this direction and apply classic results of the dynamical systems theory to model the behavior of a network where warning messages and viruses spread.
DPIV Measurements of Olympic Skeleton Athletes

The Olympic sport of skeleton involves an athlete riding a small sled face first down a bobsled track at speeds up to 130 km/hr. In these races, the difference between gold and missing the medal stand altogether can be hundredths of a second per run. As such, reducing aerodynamic drag through proper body positioning is of first order importance. To better study the flow behavior and to improve the performance of the athletes, we constructed a mock section of a bobsled track which was positioned at the exit of an open loop wind tunnel. DPIV measurements were made along with video recordings of body position to aid the athletes in determining their optimal aerodynamic body position. In the fluid dynamics video shown, the athlete slowly raised his head while DPIV measurements were made behind the helmet in the separated flow region.
The Emerging Web of Social Machines

We define a notion of social machine and envisage an algebra that can describe networks of such. To start with, social machines are defined as tuples of input, output, processes, constraints, state, requests and responses, apart from defining the machines themselves, we define their connectors and conditionals that can be used to describe the interactions between any number of machines in a multitude of ways, as a means to represent real machines interacting in the real web, such as Twitter, Twitter running on top of Amazon AWS, mashups built using Twitter and, obviously, other social machines. This work is not a theoretical paper as yet, but, in more than one sense, we think we have found a way to describe web based information systems and are starting to work on what could be a practical way of dealing with the complexity of this emerging web of social machines that is all around us.
Using explosive percolation in analysis of real-world networks.

We apply a variant of the explosive percolation procedure to large real-world networks and show with finite-size scaling that the university class, ordinary or explosive, of the resulting percolation transition depends on the structural properties of the network, as well as the number of unoccupied links considered for comparison in our procedure. We observe that in our social networks, the percolation clusters close to the critical point are related to the community structure. This relationship is further highlighted by applying the procedure to model networks with predefined communities.
The Tibetan Singing Bowl

We present the results of an experimental investigation of the acoustics and uid dynamics of Tibetan singing bowls. Their acoustic behavior is rationalized in terms of the related dynamics of standing bells and wine glasses. Striking or rubbing a uid-lled bowl excites wall vibrations, and concomitant waves at the uid surface. Acoustic excitation of the bowl’s natural vibrational modes allows for a controlled study in which the evolution of the surface waves with increasing forcing amplitude is detailed. Particular attention is given to rationalizing the observed criteria for the onset of edge-induced Faraday waves and droplet generation via surface fracture. Our study indicates that drops may be levitated on the uid surface, induced to bounce on or skip across the vibrating uid surface.
Experimental Investigation of Shock Wave Surfing

Shock wave surfing is investigated experimentally in GALCIT's Mach 4.0 Ludwieg Tube. Shock wave surfing occurs when a secondary free-body follows the bow shock formed by a primary free-body; an example of shock wave surfing occurs during meteorite breakup. The free-bodies in the current investigation are nylon spheres. During each run in the Ludwieg tube a high speed camera is used to capture a series of schlieren images; edge tracking software is used to measure the position of each sphere. Velocity and acceleration are had from processing the position data. The radius ratio and initial orientation of the two spheres are varied in the test matrix. The variation of sphere radius ratio and initial angle between the centers of gravity are shown to have a significant effect on the dynamics of the system. 
The air flow in each fluid dynamics video is from left to right. The Mach number is 4.0 with total pressure in each run of roughly 200kPa. Each run begins with the spheres tethered and no flow. Flow arrives and the tethers are quickly broken. The spheres are then free-bodies with the dominant force being the pressure distribution around the surface of the sphere, which, in the case of the secondary sphere, is strongly manipulated by the shock wave interaction. Gravity is not a considerable body force because of the short test times in these experiments. The secondary spheres are visibly seen to be affected by the bow shock wave of the primary bodies in the form of quite beautiful trajectories. Computational images were obtained from a simulation using the AMROC code. A fully three-dimensional simulation of two free-flying bodies was performed, assuming inviscid flow, whereby the trajectory of each sphere was calculated according to the instantaneous aerodynamic forces experienced.
Lens Inquiry: An Astronomy Lab for Non-science Majors at Hartnell Community College

We describe a three hour inquiry activity involving converging lenses and telescopes as part of a semester-long astronomy lab course for non-science majors at Hartnell Community College in Salinas, CA. Students were shown several short demonstrations and given the chance to experiment with the materials, after which there was a class discussion about the phenomena they observed. Students worked in groups of 2-4 to design their own experiments to address a particular question of interest to them and then presented their findings to the class. An instructor-led presentation highlighted the students' discoveries and the lab's content goals, followed by a short worksheet-based activity that guided them in applying their new knowledge to build a simple telescope using two converging lenses. The activity was successful in emphasizing communication skills and giving students opportunities to engage in the process of science in different ways. One of the biggest challenges in designing this activity was covering all of the content given the short amount of time available. Future implementations may have more success by splitting the lab into two sessions, one focusing on converging lenses and the other focusing on telescopes.
Wet-dog shake

The drying of wet fur is a critical to mammalian heat regulation. In this fluid dynamics video, we show a sequence of films demonstrating how hirsute animals to rapidly oscillate their bodies to shed water droplets, nature's analogy to the spin cycle of a washing machine. High-speed videography and fur-particle tracking is employed to determine the angular position of the animal's shoulder skin as a function of time. X-ray cinematography is used to track the motion of the skeleton. We determine conditions for drop ejection by considering the balance of surface tension and centripetal forces on drops adhering to the animal. Particular attention is paid to rationalizing the relationship between animal size and oscillation frequency required to self-dry.
Fifteen Years of Econophysics Research

Econophysics is a new research field, which makes an attempt to bring economics in the fold of natural sciences or specifically attempts for a "physics of economics". The term Econophysics was formally born in Kolkata in 1995. The entry on Econophysics in The New Palgrave Dictionary of Economics, 2nd Ed., Vol 2, Macmillan, NY (2008), pp 729-732, begins with "... the term 'econophysics' was neologized in 1995 at the second Statphys- Kolkata conference in Kolkata (formerly Calcutta), India ...". The Econophysics research therefore formally completes fifteen years of research by the end of this year! The importance and proliferation of the interdisciplinary research of Econophysics is highlighted in the special issue of Science & Culture, which presents a collection of twenty nine papers (giving country wise perspectives, reviews of the recent developments and original research communications), written by more than forty renowned experts in physics, mathematics or economics, from all over the world. We present here the list of contents and the editorial. The manuscript files are available at http://fiquant.mas.ecp.fr/chakraboa for preview. This special issue will be published online at http://www.scienceandculture-isna.org/journal.htm, at the end of October 2010.
Tracing scientific influence

Scientometrics is the field of quantitative studies of scholarly activity. It has been used for systematic studies of the fundamentals of scholarly practice as well as for evaluation purposes. Although advocated from the very beginning the use of scientometrics as an additional method for science history is still under explored. In this paper we show how a scientometric analysis can be used to shed light on the reception history of certain outstanding scholars. As a case, we look into citation patterns of a specific paper by the American sociologist Robert K. Merton.
Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes—flexibility and selection—must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
Dark matter at the end of the Galaxy

Dark matter density profiles based upon {Lambda}CDM cosmology motivate an ansatz velocity distribution function with fewer high-velocity particles than the Maxwell-Boltzmann distribution or proposed variants. The high-velocity tail of the distribution is determined by the outer slope of the dark matter halo--the large radius behavior of the Galactic dark matter density. N-body simulations of Galactic halos reproduce the high-velocity behavior of this ansatz. Predictions for direct detection rates are dramatically affected for models where the threshold scattering velocity is within 30% of the escape velocity.
Cross-Community Dynamics in Science: How Information Retrieval Affects Semantic Web and Vice Versa

Community effects on the behaviour of individuals, the community itself and other communities can be observed in a wide range of applications. This is true in scientific research, where communities of researchers have increasingly to justify their impact and progress to funding agencies. While previous work has tried to explain and analyse such phenomena, there is still a great potential for increasing the quality and accuracy of this analysis, especially in the context of cross-community effects. In this work, we propose a general framework consisting of several different techniques to analyse and explain such dynamics. The proposed methodology works with arbitrary community algorithms and incorporates meta-data to improve the overall quality and expressiveness of the analysis. We suggest and discuss several approaches to understand, interpret and explain particular phenomena, which themselves are identified in an automated manner. We illustrate the benefits and strengths of our approach by exposing highly interesting in-depth details of cross-community effects between two closely related and well established areas of scientific research. We finally conclude and highlight the important open issues on the way towards understanding, defining and eventually predicting typical life-cycles and classes of communities in the context of cross-community effects.
Critical effect of dependency groups on the function of networks

Current network models assume one type of links to define the relations between the network entities. However, many real networks can only be correctly described using two different types of relations. Connectivity links that enable the nodes to function cooperatively as a network and dependency links that bind the failure of one network element to the failure of other network elements. Here we present an analytical framework for studying the robustness of networks that include both connectivity and dependency links. We show that a synergy exists between the failure of connectivity and dependency links that leads to an iterative process of cascading failures that has a devastating effect on the network stability. We present exact analytical results for the dramatic change in the network behavior when introducing dependency links. For a high density of dependency links, the network disintegrates in a form of a first-order phase transition, whereas for a low density of dependency links, the network disintegrates in a second-order transition. Moreover, opposed to networks containing only connectivity links where a broader degree distribution results in a more robust network, when both types of links are present a broad degree distribution leads to higher vulnerability.
An Aboriginal Australian record of the Great Eruption of Eta Carinae

We present evidence that the Boorong Aboriginal people of northwestern Victoria observed the Great Eruption of Eta ({\eta}) Carinae in the nineteenth century and incorporated the event into their oral traditions. We identify this star, as well as others not specifically identified by name, using descriptive material presented in the 1858 paper by William Edward Stanbridge in conjunction with early southern star catalogues. This identification of a transient astronomical event supports the assertion that Aboriginal oral traditions are dynamic and evolving, and not static. This is the only definitive indigenous record of {\eta} Carinae's outburst identified in the literature to date.
Hot Conformal Gauge Theories

We compute the nonzero temperature free energy up to the order g 6 ln(1/g) in the coupling constant for vectorlike SU(N) gauge theories featuring matter transforming according to different representations of the underlying gauge group. The number of matter fields, i.e. flavors, is arranged in such a way that the theory develops a perturbative stable infrared fixed point at zero temperature. Because of large distance conformality we trade the coupling constant with its fixed point value and define a reduced free energy which depends only on the number of flavors, colors, and matter representation. We show that the reduced free energy changes sign, at the second, fifth, and sixth order in the coupling, when decreasing the number of flavors from the upper end of the conformal window. If the change in sign is interpreted as a signal of an instability of the system then we infer a critical number of flavors. Surprisingly this number, if computed to the order g 2 , agrees with previous predictions for the lower boundary of the conformal window for nonsupersymmetric gauge theories. The higher order results tend to predict a higher number of critical flavors. These are universal properties, i.e. they are independent of the specific matter representation.
Correlated couplings and robustness of coupled networks

Most real-world complex systems can be modelled by coupled networks with multiple layers. How and to what extent the pattern of couplings between network layers may influence the interlaced structure and function of coupled networks are not clearly understood. Here we study the impact of correlated inter-layer couplings on the network robustness of coupled networks using percolation concept. We found that the positive correlated inter-layer coupling enhaces network robustness in the sense that it lowers the percolation threshold of the interlaced network than the negative correlated coupling case. At the same time, however, positive inter-layer correlation leads to smaller giant component size in the well-connected region, suggesting potential disadvantage for network connectivity, as demonstrated also with some real-world coupled network structures.
Estimating network parameters for selecting community detection algorithms

This paper considers the problem of algorithm selection for community detection. The aim of community detection is to identify sets of nodes in a network which are more interconnected relative to their connectivity to the rest of the network. A large number of algorithms have been developed to tackle this problem, but as with any machine learning task there is no “one-size-fits-all” and each algorithm excels in a specific part of the problem space. This paper examines the performance of algorithms developed for weighted networks against those using unweighted networks for different parts of the problem space (parameterised by the intra/inter community links). It is then demonstrated how the choice of algorithm (weighted/unweighted) can be made based only on the observed network.
On the Convexity of Latent Social Network Inference

In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data. We present a maximum likelihood approach based on convex programming with a l1-like penalty term that encourages sparsity. Experiments on real and synthetic data reveal that our method near-perfectly recovers the underlying network structure as well as the parameters of the contagion propagation model. Moreover, our approach scales well as it can infer optimal networks of thousands of nodes in a matter of minutes.
Percolation in self-similar networks

We provide a simple proof that graphs in a general class of self-similar networks have zero percolation threshold. The considered self-similar networks include random scale-free graphs with given expected node degrees and zero clustering, scale-free graphs with finite clustering and metric structure, growing scale-free networks, and many real networks. The proof and the derivation of the giant component size do not require the assumption that networks are treelike. Our results rely only on the observation that self-similar networks possess a hierarchy of nested subgraphs whose average degree grows with their depth in the hierarchy. We conjecture that this property is pivotal for percolation in networks.
Robustness of a Network of Networks

Network research has been focused on studying the properties of a single isolated network, which rarely exists. We develop a general analytical framework for studying percolation of n interdependent networks. We illustrate our analytical solutions for three examples: (i) For any tree of n fully dependent Erdős-Rényi (ER) networks, each of average degree k, we find that the giant component is P∞ =p[1-exp(-kP∞)](n) where 1-p is the initial fraction of removed nodes. This general result coincides for n = 1 with the known second-order phase transition for a single network. For any n>1 cascading failures occur and the percolation becomes an abrupt first-order transition. (ii) For a starlike network of n partially interdependent ER networks, P∞ depends also on the topology-in contrast to case (i). (iii) For a looplike network formed by n partially dependent ER networks, P∞ is independent of n.
Random Graph Generator for Bipartite Networks Modeling

The purpose of this article is to introduce a new iterative algorithm with properties resembling real life bipartite graphs. The algorithm enables us to generate wide range of random bigraphs, which features are determined by a set of parameters.We adapt the advances of last decade in unipartite complex networks modeling to the bigraph setting. This data structure can be observed in several situations. However, only a few datasets are freely available to test the algorithms (e.g. community detection, influential nodes identification, information retrieval) which operate on such data. Therefore, artificial datasets are needed to enhance development and testing of the algorithms. We are particularly interested in applying the generator to the analysis of recommender systems. Therefore, we focus on two characteristics that, besides simple statistics, are in our opinion responsible for the performance of neighborhood based collaborative filtering algorithms. The features are node degree distribution and local clustering coeficient.