nils-herrmann/clean_scirepeval huggingface dataset:
Agent-based and macroscopic modeling of the complex socio-economic systems

The current economic crisis has provoked an active response from the interdisciplinary scientific community. As a result many papers suggesting what can be improved in understanding of the complex socio-economics systems were published. Some of the most prominent papers on the topic include (Bouchaud, 2009; Farmer and Foley, 2009; Farmer et al, 2012; Helbing, 2010; Pietronero, 2008). These papers share the idea that agent-based modeling is essential for the better understanding of the complex socio-economic systems and consequently better policy making. Yet in order for an agent-based model to be useful it should also be analytically tractable, possess a macroscopic treatment (Cristelli et al, 2012). In this work we shed a new light on our research group's contributions towards understanding of the correspondence between the inter-individual interactions and collective behavior. We also provide some new insights into the implications of the global and local interactions, the leadership and the predator-prey interactions in the complex socio-economic systems.
Measuring and Predicting Speed of Social Mobilization

Large-scale mobilization of individuals across social networks is becoming increasingly influential in society. However, little is known about what traits of recruiters and recruits and affect the speed at which one mobilizes the other. Here we identify and measure traits of individuals and their relationships that predict mobilization speed. We ran a global social mobilization contest and recorded personal traits of the participants and those they recruited. We identified how those traits corresponded with the speed of mobilization. Recruits mobilized faster when they first heard about the contest directly from the contest organization, and decreased in speed when hearing from less personal source types (e.g. family vs. media). Mobilization was faster when the recruiter and the recruit heard about the contest through the same source type, and slower when both individuals were in different countries. Females mobilized other females faster than males mobilized other males. Younger recruiters mobilized others faster, and older recruits mobilized slower. These findings suggest relevant factors for engineering social mobilization tasks for increased speed.
Modular dynamical systems on networks

We propose a new framework for the study of continuous time dynamical systems on networks. We view such dynamical systems as collections of interacting control systems. We show that a class of maps between graphs called graph fibrations give rise to maps between dynamical systems on networks. This allows us to produce conjugacy between dynamical systems out of combinatorial data. In particular we show that surjective graph fibrations lead to synchrony subspaces in networks. The injective graph fibrations, on the other hand, give rise to surjective maps from large dynamical systems to smaller ones. One can view these surjections as a kind of "fast/slow" variable decompositions or as "abstractions" in the computer science sense of the word.
Quantum mechanics from invariance principles

Quantum mechanics is an extremely successful theory of nature and yet it lacks an intuitive axiomatization. In contrast, the special theory of relativity is well understood and is rooted into natural or experimentally justified postulates. Here we introduce an axiomatization approach to quantum mechanics which is very similar to special theory of relativity derivation. The core idea is that a composed system obeys the same laws of nature as its components. This leads to a Jordan-Lie algebraic formulation of quantum mechanics. The starting assumptions are minimal: the laws of nature are invariant under time evolution, the laws of nature are invariant under tensor composition, the laws of nature are relational, together with the ability to define a physical state (positivity). Quantum mechanics is singled out by a fifth experimentally justified postulate: nature violates Bell's inequalities.
A Liability Tracking Approach to Long Term Management of Pension Funds

We propose a long term portfolio management method which takes into account a liability. Our approach is based on the LQG (Linear, Quadratic cost, Gaussian) control problem framework and then the optimal portfolio strategy hedges the liability by directly tracking a benchmark process which represents the liability. Two numerical results using empirical data published by Japanese organizations are served: simulations tracking an artificial liability and an estimated liability of Japanese organization. The latter one demonstrates that our optimal portfolio strategy can hedge his or her liability.
$l_{2,p}$ Matrix Norm and Its Application in Feature Selection

Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of $l_1$ vector norm, the mixed $l_{2,1}$ matrix norm is often used to find jointly sparse solutions. Moreover, an efficient iterative algorithm has been designed to solve $l_{2,1}$-norm involved minimizations. Actually, computational studies have showed that $l_p$-regularization ($0<p<1$) is sparser than $l_1$-regularization, but the extension to matrix norm has been seldom considered. This paper presents a definition of mixed $l_{2,p}$ $(p\in (0, 1])$ matrix pseudo norm which is thought as both generalizations of $l_p$ vector norm to matrix and $l_{2,1}$-norm to nonconvex cases $(0<p<1)$. Fortunately, an efficient unified algorithm is proposed to solve the induced $l_{2,p}$-norm $(p\in (0, 1])$ optimization problems. The convergence can also be uniformly demonstrated for all $p\in (0, 1]$. Typical $p\in (0,1]$ are applied to select features in computational biology and the experimental results show that some choices of $0<p<1$ do improve the sparse pattern of using $p=1$.
Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM

BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA-MEM shows better performance than several state-of-art read aligners to date. 
Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at this http URL. 
Contact: hengli@broadinstitute.org
On multi-class learning through the minimization of the confusion matrix norm

In imbalanced multi-class classification problems, the misclassification rate as an error measure may not be a relevant choice. Several methods have been developed where the performance measure retained richer information than the mere misclassification rate: misclassification costs, ROC-based information, etc. Following this idea of dealing with alternate measures of performance, we propose to address imbalanced classification problems by using a new measure to be optimized: the norm of the confusion matrix. Indeed, recent results show that using the norm of the confusion matrix as an error measure can be quite interesting due to the fine-grain informations contained in the matrix, especially in the case of imbalanced classes. Our first contribution then consists in showing that optimizing criterion based on the confusion matrix gives rise to a common background for cost-sensitive methods aimed at dealing with imbalanced classes learning problems. As our second contribution, we propose an extension of a recent multi-class boosting method --- namely AdaBoost.MM --- to the imbalanced class problem, by greedily minimizing the empirical norm of the confusion matrix. A theoretical analysis of the properties of the proposed method is presented, while experimental results illustrate the behavior of the algorithm and show the relevancy of the approach compared to other methods.
Hubble Space Telescope detection of oxygen in the atmosphere of exoplanet HD 189733b

Detecting heavy atoms in the inflated atmospheres of giant exoplanets that orbit close to their parent stars is a key factor for understanding their bulk composition, their evolution, and the processes that drive their expansion and interaction with the impinging stellar wind. Unfortunately, very few detections have been made thus far. Here, we use archive data obtained with the Cosmic Origins Spectrograph onboard the Hubble Space Telescope to report an absorption of ∼6.4%±1.8% by neutral oxygen during the HD 189733b transit. Using published results from a simple hydrodynamic model of HD 189733b, and assuming a mean temperature of ∼(8−12) × 10 3 Kf or the upper atmosphere of the exoplanet, a mean vertical integrated O I density column of ∼8 × 10 15 cm −2 produces only a 3.5% attenuation transit. Much like the case of the hot-Jupiter HD 209458b, super-solar abundances and/or super-thermal broadening of the absorption lines are required to fit the deep transit drop-off observed in most far-ultraviolet lines. We also report evidence of short-time variability in the measured stellar flux, a variability that we analyze using time series derived from the time-tagged exposures, which we then compare to solar flaring activity. In that frame, we find that non-statistical uncertainties in the measured fluxes are not negligible, which calls for caution when reporting transit absorptions. Despite cumulative uncertainties that originate from variability in the stellar and sky background signals and in the instrument response, we also show a possible detection for both a transit and early-ingress absorption in the ion C II 133.5 nm lines. If confirmed, this would be the second exoplanet for which an early ingress absorption is reported. In contrast, such an early ingress signature is not detected for neutral O I. Assuming the HD 189733b magnetosphere to be at the origin of the early absorption, we use the Parker model for the stellar wind and a particle-in-cell code for the magnetosphere to show that its orientation should be deflected ∼10−30 ◦ from the planet-star line, while its nose’s position should be at least ∼16.7 Rp upstream of the exoplanet in order to fit the C II transit light curve. The derived stand-off distance is consistent with a surface magnetic field strength of ∼5.3 Gauss for the exoplanet, and a supersonic stellar wind impinging at ∼250 km s −1 , with a temperature of 1.2 × 10 5 K and a density ∼6.3 × 10 6 cm −3 at the planetary orbit, yet the fit is not unique.
The Small-Maturity Heston Forward Smile

In this paper we investigate the asymptotics of forward-start options and the forward implied volatility smile in the Heston model as the maturity approaches zero. We prove that the forward smile for out-of-the-money options explodes and compute a closed-form high-order expansion detailing the rate of the explosion. Furthermore the result shows that the square-root behaviour of the variance process induces a singularity such that for certain parameter configurations one cannot obtain high-order out-of-the-money forward smile asymptotics. In the at-the-money case a separate model-independent analysis shows that the small-maturity limit is well defined for any Ito diffusion. The proofs rely on the theory of sharp large deviations (and refinements) and incidentally we provide an example of degenerate large deviations behaviour.
Modeling a Sensor to Improve Its Efficacy

Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when employed within a Bayesian inferential framework. As a test bed we employ a robotic arm that is designed to autonomously take its own measurements using an inexpensive LEGO light sensor to estimate the position and radius of a white circle on a black field. The light sensor integrates the light arriving from a spatially distributed region within its field of view weighted by its spatial sensitivity function (SSF). We demonstrate that by incorporating an accurate model of the light sensor SSF into the likelihood function of a Bayesian inference engine, an autonomous system can make improved inferences about its surroundings. The method presented here is data based, fairly general, and made with plug-and-play in mind so that it could be implemented in similar problems.
Modeling temporal networks using random itineraries

We propose a procedure to generate dynamical networks with bursty, possibly repetitive and correlated temporal behaviors. Regarding any weighted directed graph as being composed of the accumulation of paths between its nodes, our construction uses random walks of variable length to produce time-extended structures with adjustable features. The procedure is first described in a general framework. It is then illustrated in a case study inspired by a transportation system for which the resulting synthetic network is shown to accurately mimic the empirical phenomenology.
Limited Attention and Centrality in Social Networks

How does one find important or influential people in an online social network? Researchers have proposed a variety of centrality measures to identify individuals that are, for example, often visited by a random walk, infected in an epidemic, or receive many messages from friends. Recent research suggests that a social media users' capacity to respond to an incoming message is constrained by their finite attention, which they divide over all incoming information, i.e., information sent by users they follow. We propose a new measure of centrality - limited-attention version of Bonacich's Alpha-centrality - that models the effect of limited attention on epidemic diffusion. The new measure describes a process in which nodes broadcast messages to their out-neighbors, but the neighbors' ability to receive the message depends on the number of in-neighbors they have. We evaluate the proposed measure on real-world online social networks and show that it can better reproduce an empirical influence ranking of users than other popular centrality measures.
Is There A Real Estate Bubble in Switzerland? (Diagnostic as of 2012-Q4)

We have analyzed the risks of possible development of bubbles in Switzerland’s residential real estate market. The data employed in this work has been collected by comparis.ch, and carefully cleaned from duplicate records through a procedure based on supervised machine learning methods. The study uses the log periodic power law (LPPL) bubble model to analyze the development of asking prices of residential properties in all Swiss districts between 2005 and 2013. The results suggest that there are 11 critical districts that exhibit signatures of bubbles, and seven districts where bubbles have already burst. Despite these strong signatures, it is argued that, based on the current economic environment, a soft landing rather than a severe crash is expected.
Inferring Fitness in Finite Populations with Moran-like dynamics

Biological fitness is not an observable quantity and must be inferred from population dynamics. Bayesian inference applied to the Moran process and variants yields a robust inference method that can infer fitness in populations evolving via a Moran dynamic and generalizations. Information about fitness is derived solely from birth-events in birth-death and death-birth processes in which selection acts proportionally to fitness, which allows the method to be applied to populations on a network where the network itself may be changing in time. Populations may also be allowed to change size while still allowing estimates for fitness to be inferred.
Universal Doomsday: Analyzing Our Prospects for Survival

Given a sufficiently large universe, numerous civilizations almost surely exist. Some of these civilizations will be short-lived and die out relatively early in their development, i.e., before having the chance to spread to other planets. Others will be long-lived, potentially colonizing their galaxy and becoming enormous in size. What fraction of civilizations in the universe are long-lived? The ``universal doomsday'' argument states that long-lived civilizations must be rare because if they were not, we should find ourselves living in one. Furthermore, because long-lived civilizations are rare, our civilization's prospects for long-term survival are poor. Here, we develop the formalism required for universal doomsday calculations and show that while the argument has some force, our future is not as gloomy as the traditional doomsday argument would suggest, at least when the number of early existential threats is small.
MJ no more: using concurrent wikipedia edit spikes with social network plausibility checks for breaking news detection

We have developed an application called Wikipedia Live Monitor that monitors article edits on different language versions of Wikipedia--as they happen in realtime. Wikipedia articles in different languages are highly interlinked. For example, the English article "en:2013_Russian_meteor_event" on the topic of the February 15 meteoroid that exploded over the region of Chelyabinsk Oblast, Russia, is interlinked with "ru:ПaДehne_meteopnta_ha_Ypajie_B_2013_roДy?, the Russian article on the same topic. As we monitor multiple language versions of Wikipedia in parallel, we can exploit this fact to detect concurrent edit spikes of Wikipedia articles covering the same topics, both in only one, and in different languages. We treat such concurrent edit spikes as signals for potential breaking news events, whose plausibility we then check with full-text cross-language searches on multiple social networks. Unlike the reverse approach of monitoring social networks first, and potentially checking plausibility on Wikipedia second, the approach proposed in this paper has the advantage of being less prone to false-positive alerts, while being equally sensitive to true-positive events, however, at only a fraction of the processing cost. A live demo of our application is available online at the URL http://wikipedia-irc.herokuapp.com/, the source code is available under the terms of the Apache 2.0 license at https://github.com/tomayac/wikipedia-irc.
Topological properties of sets represented by an inequality involving distances

Consider a set represented by an inequality. An interesting phenomenon which occurs in various settings in mathematics is that the interior of this set is the subset where strict inequality holds, the boundary is the subset where equality holds, and the closure of the set is the closure of its interior. This paper discusses this phenomenon assuming the set is a Voronoi cell induced by given sites (subsets), a geometric object which appears in many fields of science and technology and has diverse applications. Simple counterexamples show that the discussed phenomenon does not hold in general, but it is established in a wide class of cases. More precisely, the setting is a (possibly infinite dimensional) uniformly convex normed space with arbitrary positively separated sites. An important ingredient in the proof is a strong version of the triangle inequality due to Clarkson (1936), an interesting inequality which has been almost totally forgotten.
Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models

We consider distributed estimation of the inverse covariance matrix, also called the concentration or precision matrix, in Gaussian graphical models. Traditional centralized estimation often requires global inference of the covariance matrix, which can be computationally intensive in large dimensions. Approximate inference based on message-passing algorithms, on the other hand, can lead to unstable and biased estimation in loopy graphical models. Here, we propose a general framework for distributed estimation based on a maximum marginal likelihood (MML) approach. This approach computes local parameter estimates by maximizing marginal likelihoods defined with respect to data collected from local neighborhoods. Due to the non-convexity of the MML problem, we introduce and solve a convex relaxation. The local estimates are then combined into a global estimate without the need for iterative message-passing between neighborhoods. The proposed algorithm is naturally parallelizable and computationally efficient, thereby making it suitable for high-dimensional problems. In the classical regime where the number of variables p is fixed and the number of samples T increases to infinity, the proposed estimator is shown to be asymptotically consistent and to improve monotonically as the local neighborhood size increases. In the high-dimensional scaling regime where both p and T increase to infinity, the convergence rate to the true parameters is derived and is seen to be comparable to centralized maximum-likelihood estimation. Extensive numerical experiments demonstrate the improved performance of the two-hop version of the proposed estimator, which suffices to almost close the gap to the centralized maximum likelihood estimator at a reduced computational cost.
Greedy feature selection for subspace clustering

Unions of subspaces provide a powerful generalization of single subspace models for collections of high-dimensional data; however, learning multiple subspaces from data is challenging due to the fact that segmentation--the identification of points that live in the same subspace--and subspace estimation must be performed simultaneously. Recently, sparse recovery methods were shown to provide a provable and robust strategy for exact feature selection (EFS)--recovering subsets of points from the ensemble that live in the same subspace. In parallel with recent studies of EFS with l1-minimization, in this paper, we develop sufficient conditions for EFS with a greedy method for sparse signal recovery known as orthogonal matching pursuit (OMP). Following our analysis, we provide an empirical study of feature selection strategies for signals living on unions of subspaces and characterize the gap between sparse recovery methods and nearest neighbor (NN)-based approaches. In particular, we demonstrate that sparse recovery methods provide significant advantages over NN methods and that the gap between the two approaches is particularly pronounced when the sampling of subspaces in the data set is sparse. Our results suggest that OMP may be employed to reliably recover exact feature sets in a number of regimes where NN approaches fail to reveal the subspace membership of points in the ensemble.
The RAppArmor Package: Enforcing Security Policies in R Using Dynamic Sandboxing on Linux

The increasing availability of cloud computing and scientific super computers brings great potential for making R accessible through public or shared resources. This allows us to efficiently run code requiring lots of cycles and memory, or embed R functionality into, e.g., systems and web services. However some important security concerns need to be addressed before this can be put in production. The prime use case in the design of R has always been a single statistician running R on the local machine through the interactive console. Therefore the execution environment of R is entirely unrestricted, which could result in malicious behavior or excessive use of hardware resources in a shared environment. Properly securing an R process turns out to be a complex problem. We describe various approaches and illustrate potential issues using some of our personal experiences in hosting public web services. Finally we introduce the RAppArmor package: a Linux based reference implementation for dynamic sandboxing in R on the level of the operating system.