nils-herrmann/clean_scirepeval huggingface dataset:
Image Reconstruction with Analytical Point Spread Functions

The image degradation produced by atmospheric turbulence and optical aberrations is usually alleviated using post-facto image reconstruction techniques, even when observing with adaptive optics systems. These techniques rely on the development of the wavefront using Zernike functions and the non-linear optimization of a certain metric. The resulting optimization procedure is computationally heavy. Our aim is to alleviate this computationally burden. To this aim, we generalize the recently developed extended Zernike-Nijboer theory to carry out the analytical integration of the Fresnel integral and present a natural basis set for the development of the point spread function in case the wavefront is described using Zernike functions. We present a linear expansion of the point spread function in terms of analytic functions which, additionally, takes defocusing into account in a natural way. This expansion is used to develop a very fast phase-diversity reconstruction technique which is demonstrated through some applications. This suggest that the linear expansion of the point spread function can be applied to accelerate other reconstruction techniques in use presently and based on blind deconvolution.
Optimality in Social Choice

Marengo and Settepanella (2010) have developed a geometric model of social choice when it takes place among bundles of interdependent elements, showing that by bundling and unbundling the same set of constituent elements an authority has the power of determining the social outcome. In this article, we will tie the model above to tournament theory, solving some of the mathematical problems arising in their work and opening new questions which are interesting from both a mathematical and social choice point of view. In particular, we will introduce the notion of u-local optima and study it from both a theoretical and a numerically probabilistic point of view; we will also describe an algorithm that computes the universal basin of attraction of a social outcome in O(M 3log M) time (where M is the number of social outcomes).
Dynamic Principles of Center of Mass in Human Walking

We present results of an analytic and numerical calculation that studies the relationship between the time of initial foot contact and the ground reaction force of human gait and explores the dynamic principle of center of mass. Assuming the ground reaction force of both feet to be the same in the same phase of a stride cycle, we establish the relationships between the time of initial foot contact and the ground reaction force, acceleration, velocity, displacement and average kinetic energy of center of mass. We employ the dispersion to analyze the effect of the time of the initial foot contact that imposes upon these physical quantities. Our study reveals that when the time of one foot's initial contact falls right in the middle of the other foot's stride cycle, these physical quantities reach extrema. An action function has been identified as the dispersion of the physical quantities and optimized analysis used to prove the least-action principle in gait. In addition to being very significant to the research domains such as clinical diagnosis, biped robot's gait control, the exploration of this principle can simplify our understanding of the basic properties of gait.
Governance in Social Media: A Case Study of the Wikipedia Promotion Process

Social media sites are often guided by a core group of committed users engaged in various forms of governance. A crucial aspect of this type of governance is deliberation, in which such a group reaches decisions on issues of importance to the site. Despite its crucial — though subtle — role in how a number of prominent social media sites function, there has been relatively little investigation of the deliberative aspects of social media governance. Here we explore this issue, investigating a particular deliberative process that is extensive, public, and recorded: the promotion of Wikipedia admins, which is determined by elections that engage committed members of the Wikipedia community. We find that the group decision-making at the heart of this process exhibits several fundamental forms of relative assessment. First we observe that the chance that a voter will support a candidate is strongly dependent on the relationship between characteristics of the voter and the candidate. Second we investigate how both individual voter decisions and overall election outcomes can be based on models that take into account the sequential, public nature of the voting.
SCALING OF INTERNODE DISTANCES IN WEIGHTED COMPLEX NETWORKS

We extend the previously observed scaling equation connecting the internode distances and nodes' degrees onto the case of weighted networks. We show that the scaling takes a similar form in the empirical data obtained from networks characterized by different relations between node's strength and its degree. In the case of explicit equation for s (k) (e.g. linear or scale-free), the new coefficients of scaling equation can be easily obtained. We support our analysis with numerical simulations for Erdos–Renyi random graphs with different weight distributions.
Flow of emotional messages in artificial social networks

Models of message flows in an artificial group of users communicating via the Internet are introduced and investigated using numerical simulations. We assumed that messages possess an emotional character with a positive valence and that the willingness to send the next affective message to a given person increases with the number of messages received from this person. As a result, the weights of links between group members evolve over time. Memory effects are introduced, taking into account that the preferential selection of message receivers depends on the communication intensity during the recent period only. We also model the phenomenon of secondary social sharing when the reception of an emotional e-mail triggers the distribution of several emotional e-mails to other people.
Schrodinger's Cat is not Alone

We introduce the `Complete Wave Function' and deduce that all living beings, not just Schroedinger's cat, are actually described by a superposition of `alive' and `dead' quantum states; otherwise they would never die. Therefore this proposal provides a quantum mechanical explanation to the world-wide observation that we all pass away. Next we consider the Measurement problem in the framework of M-theory. For this purpose, together with Schroedinger's cat we also place inside the box Rasputin's cat, which is unaffected by poison. We analyse the system identifying its excitations (catons and catinos) and we discuss its evolution: either to a classical fight or to a quantum entanglement. We also propose the $BSV\Psi$ scenario, which implements the Complete Wave Function as well as the Big Bang and the String Landscape in a very (super)natural way. Then we test the gravitational decoherence of the entangled system applying an experimental setting due to Galileo. We also discuss the Information Loss paradox. For this purpose we consider a massless black cat falling inside a massive black hole. After that we outline a method to compute the contribution of black cats to the dark matter of the universe. Finally, in the spirit of Schroedinger, we propose that next generation double-slit experiments should use cats as projectiles. Cat interferometry will inevitably lead to the `Many Cats' interpretation of Quantum Mechanics, allowing to shed new light on old mysteries and paradoxes. For example, according to this interpretation, conservative estimates show that decision making of a single domestic cat will create about 550 billion whole universes every day, with as many replicas of itself.
Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual’s covariates on his or her behavior or other measurable responses. The authors show that generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular the authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual’s enduring traits and his or her choices, even when there is no intrinsic affinity between them. The authors also suggest some possible constructive responses to these results.
Social Network Sensors for Early Detection of Contagious Outbreaks

Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. It is known that individuals near the center of a social network are likely to be infected sooner during the course of an outbreak, on average, than those at the periphery. Unfortunately, mapping a whole network to identify central individuals who might be monitored for infection is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students who were either members of a group of randomly chosen individuals or a group of their friends. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9–16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p<0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.
Generating-function approach for bond percolation in hierarchical networks.

We study bond percolations on hierarchical scale-free networks with the open bond probability of the shortcuts p and that of the ordinary bonds p. The system has a critical phase in which the percolating probability P takes an intermediate value 0 < P < 1. Using generating function approach, we calculate the fractal exponent ψ of the root clusters to show that ψ varies continuously with p in the critical phase. We confirm numerically that the distribution n(s) of cluster size s in the critical phase obeys a power law n(s) ∝ s(-τ), where τ satisfies the scaling relation τ=1+ψ(-1). In addition the critical exponent β(p) of the order parameter varies as p, from β ≃ 0.164694 at p=0 to infinity at p=p(c)=5/32.
Modes of collaboration in modern science: Beyond power laws and preferential attachment

The goal of the study was to determine the underlying processes leading to the observed collaborator distribution in modern scientific fields, with special attention to nonpower-law behavior. Nanoscience is used as a case study of a modern interdisciplinary field and its coauthorship network for 2000–2004 period is constructed from the NanoBank database. We find three collaboration modes that correspond to three distinct ranges in the distribution of collaborators: (1) for authors with fewer than 20 collaborators (the majority) preferential attachment does not hold and they form a log-normal “hook” instead of a power law; (2) authors with more than 20 collaborators benefit from preferential attachment and form a power law tail; and (3) authors with between 250 and 800 collaborators are more frequent than expected because of the hyperauthorship practices in certain subfields.
The cause of universality in growth fluctuations

Phenomena as diverse as breeding bird populations, the size of U.S. firms, money invested in mutual funds, the GDP of individual countries and the scientific output of universities all show unusual but remarkably similar growth fluctuations. The fluctuations display characteristic features, including double exponential scaling in the body of the distribution and power law scaling of the standard deviation as a function of size. To explain this we propose a remarkably simple additive replication model: At each step each individual is replaced by a new number of individuals drawn from the same replication distribution. If the replication distribution is sufficiently heavy tailed then the growth fluctuations are Levy distributed. We analyze the data from bird populations, firms, and mutual funds and show that our predictions match the data well, in several respects: Our theory results in a much better collapse of the individual distributions onto a single curve and also correctly predicts the scaling of the standard deviation with size. To illustrate how this can emerge from a collective microscopic dynamics we propose a model based on stochastic influence dynamics over a scale-free contact network and show that it produces results similar to those observed. We also extend the model to deal with correlations between individual elements. Our main conclusion is that the universality of growth fluctuations is driven by the additivity of growth processes and the action of the generalized central limit theorem.
Does it matter if you answer slowly

In this paper, we have analyzed item response times measured at a large scale unspeeded low stakes test for primary-school students. We have demonstrated the existence of significant difference in the response time for boys and girls as well as difference in response time of correct and incorrect answers on this test. We have also demonstrated existence of the warm up effect for this test. The results show that responses given by girls exhibit much greater warm up effect and that difference appears to be the most important cause of the difference on the test level.
Comments on "First Dark Matter Results from the XENON100 Experiment"

The XENON100 collaboration has recently released new dark matter limits, placing particular emphasis on their impact on searches known to be sensitive to light-mass (below 10 GeV/c^2) Weakly Interacting Massive Particles (WIMPs), such as DAMA and CoGeNT. We describe here several sources of uncertainty and bias in their analysis that make their new claimed sensitivity presently untenable. In particular, we point out additional work in this field and simple kinematic arguments that indicate that liquid xenon (LXe) may be a relatively insensitive detection medium for the recoil energies (few keV_r) expected from such low mass WIMPs.
Adaptive networks: Coevolution of disease and topology.

Adaptive networks have been recently introduced in the context of disease propagation on complex networks. They account for the mutual interaction between the network topology and the states of the nodes. Until now, existing models have been analyzed using low complexity analytical formalisms, revealing nevertheless some novel dynamical features. However, current methods have failed to reproduce with accuracy the simultaneous time evolution of the disease and the underlying network topology. In the framework of the adaptive susceptible-infectious-susceptible (SIS) model of Gross [ 208701 (2006)]10.1103/PhysRevLett.96.208701, we introduce an improved compartmental formalism able to handle this coevolutionary task successfully. With this approach, we analyze the interplay and outcomes of both dynamical elements, process and structure, on adaptive networks featuring different degree distributions at the initial stage.
Propagation dynamics on networks featuring complex topologies.

Analytical description of propagation phenomena on random networks has flourished in recent years, yet more complex systems have mainly been studied through numerical means. In this paper, a mean-field description is used to coherently couple the dynamics of the network elements (such as nodes, vertices, individuals, etc.) on the one hand and their recurrent topological patterns (such as subgraphs, groups, etc.) on the other hand. In a susceptible-infectious-susceptible (SIS) model of epidemic spread on social networks with community structure, this approach yields a set of ordinary differential equations for the time evolution of the system, as well as analytical solutions for the epidemic threshold and equilibria. The results obtained are in good agreement with numerical simulations and reproduce the behavior of random networks in the appropriate limits which highlights the influence of topology on the processes. Finally, it is demonstrated that our model predicts higher epidemic thresholds for clustered structures than for equivalent random topologies in the case of networks with zero degree correlation.
Random graphs containing arbitrary distributions of subgraphs

Traditional random graph models of networks generate networks that are locally treelike, meaning that all local neighborhoods take the form of trees. In this respect such models are highly unrealistic, most real networks having strongly nontreelike neighborhoods that contain short loops, cliques, or other biconnected subgraphs. In this paper we propose and analyze a class of random graph models that incorporates general subgraphs, allowing for nontreelike neighborhoods while still remaining solvable for many fundamental network properties. Among other things we give solutions for the size of the giant component, the position of the phase transition at which the giant component appears, and percolation properties for both site and bond percolation on networks generated by the model.
Nothing but relativity, redux

A previous paper demonstrated that spacetime transformations consistent with the principle of relativity could be derived without assuming explicitly the constancy of the speed of light. Here, we correct an error in the earlier paper while showing that this derivation can be done under weaker assumptions, in particular, without an implicit assumption of differentiability or even continuity for the spacetime mapping. Hence, these historic results could have been derived centuries ago, even before the advent of calculus.
Closed timelike curves via postselection: theory and experimental test of consistency.

Closed timelike curves (CTCs) are trajectories in spacetime that effectively travel backwards in time: a test particle following a CTC can interact with its former self in the past. A widely accepted quantum theory of CTCs was proposed by Deutsch. Here we analyze an alternative quantum formulation of CTCs based on teleportation and postselection, and show that it is inequivalent to Deutsch's. The predictions or retrodictions of our theory can be simulated experimentally: we report the results of an experiment illustrating how in our particular theory the "grandfather paradox" is resolved.
Slime mould logical gates: exploring ballistic approach

University of the West of England, Bristol, United Kingdomandrew.adamatzky@uwe.ac.ukAbstractPlasmodium of Physarum polycephalum is a single cell visible by unaided eye. On anon-nutrient substrate the plasmodium propagates as a traveling localization, as acompact wave-fragment of protoplasm. The plasmodium-localization travels in itsoriginally predetermined direction for a substantial period of time even when nogradient of chemo-attractants is present. We utilize this property of Physarum lo-calizations to design a two-input two-output Boolean logic gates hx;yi!hxy;x+yiand hx;yi!hx;xyi. We verify the designs in laboratory experiments and computersimulations. We cascade the logical gates into one-bit half-adder and simulate itsfunctionality. Physarum polycephalum, logical gate, adder, unconventional computer,chemical computer, biological computer
Entropic dynamics, time and quantum theory

Quantum mechanics is derived as an application of the method of maximum entropy. No appeal is made to any underlying classical action principle whether deterministic or stochastic. Instead, the basic assumption is that in addition to the particles of interest x there exist extra variables y whose entropy S(x) depends on x. The Schrödinger equation follows from their coupled dynamics: the entropy S(x) drives the dynamics of the particles x while they in their turn determine the evolution of S(x). In this ‘entropic dynamics’ time is introduced as a device to keep track of change. A welcome feature of such an entropic time is that it naturally incorporates an arrow of time. Both the magnitude and the phase of the wavefunction are given statistical interpretations: the magnitude gives the distribution of x in agreement with the usual Born rule and the phase carries information about the entropy S(x) of the extra variables. Extending the model to include external electromagnetic fields yields further insight into the nature of the quantum phase.
Reply to the Comments on the XENON100 First Dark Matter Results

The recently submitted preprint on the first results from the XENON100 dark matter experiment (arxiv:1005.0380) was followed by a criticism by J.I. Collar and D.N. McKinsey (arxiv:1005.0838), focused on our extrapolation of the scintillation efficiency L_eff to the lowest nuclear recoil energies, where no data and no theoretical model exist. Here we add clarifications on our analysis and comment on their criticism.
Voter model on a directed network: role of bidirectional opinion exchanges.

The voter model with the node update rule is numerically investigated on a directed network. We start from a directed hierarchical tree, and split and rewire each incoming arc at the probability p . In order to discriminate the better and worse opinions, we break the Z2 symmetry (σ=±1) by giving a little more preference to the opinion σ=1 . It is found that as p becomes larger, introducing more complicated pattern of information flow channels, and as the network size N becomes larger, the system eventually evolves to the state in which more voters agree on the better opinion, even though the voter at the top of the hierarchy keeps the worse opinion. We also find that the pure hierarchical tree makes opinion agreement very fast, while the final absorbing state can easily be influenced by voters at the higher ranks. On the other hand, although the ordering occurs much slower, the existence of complicated pattern of bidirectional information flow allows the system to agree on the better opinion.
The weight of a falling chain, revisited

A vertically hanging chain is released from rest and falls due to gravity on a scale pan. We discuss the various experimental and theoretical aspects of this classic problem. Careful time-resolved force measurements allow us to determine the differences between the idealized problem and its implementation in the laboratory. We observe that, in spite of the upward force exerted by the pan on the chain, the free end at the top falls faster than a freely falling body. Because a real chain exhibits a finite minimum radius of curvature, the contact at the bottom results in a tensional force, which pulls the falling part downward.