nils-herrmann/clean_scirepeval huggingface dataset:
The path most travelled: Mining road usage patterns from massive call data

Rapid urbanization places increasing stress on already burdened transportation systems, resulting in delays and poor levels of service. Billions of spatiotemporal call detail records (CDRs) collected from mobile devices create new opportunities to quantify and solve these problems. However, there is a need for tools to map new data onto existing transportation infrastructure. In this work, we propose a system that leverages this data to identify patterns in road usage. First, we develop an algorithm to mine billions of calls and learn location transition probabilities of callers. These transition probabilities are then upscaled with demographic data to estimate origin-destination (OD) flows of residents between any two intersections of a city. Next, we implement a distributed incremental traffic assignment algorithm to route these flows on road networks and estimate congestion and level of service for each roadway. From this assignment, we construct a bipartite usage network by connecting census tracts to the roads used by their inhabitants. Comparing the topologies of the physical road network and bipartite usage network allows us to classify each road's role in a city's transportation network and detect causes of local bottlenecks. Finally, we demonstrate an interactive, web-based visualization platform that allows researchers, policymakers, and drivers to explore road congestion and usage in a new dimension. To demonstrate the flexibility of this system, we perform these analyses in multiple cities across the globe with diverse geographical and sociodemographic qualities. This platform provides a foundation to build congestion mitigation solutions and generate new insights into urban mobility.
Multi-period Trading Prediction Markets with Connections to Machine Learning

We present a new model for prediction markets, in which we use risk measures to model agents and introduce a market maker to describe the trading process. This specific choice on modelling tools brings us mathematical convenience. The analysis shows that the whole market effectively approaches a global objective, despite that the market is designed such that each agent only cares about its own goal. Additionally, the market dynamics provides a sensible algorithm for optimising the global objective. An intimate connection between machine learning and our markets is thus established, such that we could 1) analyse a market by applying machine learning methods to the global objective, and 2) solve machine learning problems by setting up and running certain markets.
GraphChi-DB: Simple Design for a Scalable Graph Database System - on Just a PC

We propose a new data structure, Parallel Adjacency Lists (PAL), for efficiently managing graphs with billions of edges on disk. The PAL structure is based on the graph storage model of GraphChi (Kyrola et. al., OSDI 2012), but we extend it to enable online database features such as queries and fast insertions. In addition, we extend the model with edge and vertex attributes. Compared to previous data structures, PAL can store graphs more compactly while allowing fast access to both the incoming and the outgoing edges of a vertex, without duplicating data. Based on PAL, we design a graph database management system, GraphChi-DB, which can also execute powerful analytical graph computation. 
We evaluate our design experimentally and demonstrate that GraphChi-DB achieves state-of-the-art performance on graphs that are much larger than the available memory. GraphChi-DB enables anyone with just a laptop or a PC to work with extremely large graphs.
Fast Prediction with SVM Models Containing RBF Kernels

We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner products between support vectors and test instances. The approximation is applicable to all kernel methods featuring sums of kernel evaluations and makes no assumptions regarding data normalization. The prediction speed of approximated models no longer relates to the amount of support vectors but is quadratic in terms of the number of input dimensions. If the number of input dimensions is small compared to the amount of support vectors, the approximated model is significantly faster in prediction and has a smaller memory footprint. An optimized C++ implementation was made to assess the gain in prediction speed in a set of practical tests. We additionally provide a method to verify the approximation accuracy, prior to training models or during run-time, to ensure the loss in accuracy remains acceptable and within known bounds.
First experimental constraints on the disformally coupled Galileon model

The Galileon model is a modified gravity model that can explain 
 the late-time accelerated expansion of the Universe. In a previous work, we derived 
 experimental constraints on the Galileon model with no explicit coupling to matter and 
 showed that this model agrees with the most recent cosmological data. In the context of 
 braneworld constructions or massive gravity, the Galileon model exhibits a disformal 
 coupling to matter, which we study in this paper. After comparing our constraints on the uncoupled model with 
 recent studies, we extend the analysis framework to the disformally coupled Galileon model 
 and derive the first experimental constraints on that coupling, using precise measurements 
 of cosmological distances and the growth rate of cosmic structures. In the uncoupled case, with updated data, we still observe a 
 low tension between the constraints set by growth data and those from distances. In the 
 disformally coupled Galileon model, we obtain better agreement with data and favour a 
 non-zero disformal coupling to matter at the 2.5σ level. This gives an interesting hint of the 
 possible braneworld origin of Galileon theory.
Black hole Meissner effect and Blandford-Znajek jets

Spinning black holes tend to expel magnetic fields. In this way they are similar to superconductors. It has been a persistent concern that this black hole "Meissner effect" could quench jet power at high spins. This would make it impossible for the rapidly rotating black holes in Cyg X-1 and GRS 1915+105 to drive Blandford-Znajek jets. We give a simple geometrical argument why fields which become entirely radial near the horizon are not expelled by the Meissner effect and may continue to power jets up to the extremal limit. A simple and natural example is a split-monopole field. We stress that ordinary Blandford-Znajek jets are impossible if the Meissner effect operates and expels the field. Finally, we note that in our general relativistic magnetohydrodynamic simulations of black hole jets, there is no evidence that jets are quenched by the Meissner effect. The simulated jets develop a large split monopole component spontaneously which supports our proposal for how the Meissner effect is evaded and jets from rapidly rotating black holes are powered in nature.
Detecting Change Points in the Large-Scale Structure of Evolving Networks

Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the large-scale pattern of interactions changes fundamentally and quantify how large and what kind of change occurred. Here, we formalize for the first time the network change-point detection problem within an online probabilistic learning framework and introduce a method that can reliably solve it. This method combines a generalized hierarchical random graph model with a Bayesian hypothesis test to quantitatively determine if, when, and precisely how a change point has occurred. We analyze the detectability of our method using synthetic data with known change points of different types and magnitudes, and show that this method is more accurate than several previously used alternatives. Applied to two high-resolution evolving social networks, this method identifies a sequence of change points that align with known external ``shocks'' to these networks.
On the Hawkes Process with Different Exciting Functions

The Hawkes process is a simple point process, whose intensity function depends on the entire past history and is self-exciting and has the clustering property. The Hawkes process is in general non-Markovian. The linear Hawkes process has immigration-birth representation. Based on that, Fierro et al. recently introduced a generalized linear Hawkes model with different exciting functions. In this paper, we study the convergence to equilibrium, large deviation principle, and moderate deviation principle for this generalized model. This model also has connections to the multivariate linear Hawkes process. Some applications to finance are also discussed.
Breathing solitary-pulse pairs in a linearly coupled system.

It is shown that pairs of solitary pulses (SPs) in a linearly coupled system with opposite group velocity dispersions form robust breathing bound states. The system can be realized by temporal-modulation coupling of SPs with different carrier frequencies propagating in the same medium, or by coupling of SPs in a dual-core waveguide. Broad SP pairs are produced in a virtually exact form by means of the variational approximation. Strong nonlinearity tends to destroy the periodic evolution of the SP pairs.
Network structures between strategies in iterated prisoners’ dilemma games

We use replicator dynamics to study an iterated prisoners’ dilemma game with memory. In this study, we investigate the characteristics of all 32 possible strategies with a single-step memory by observing the results when each strategy encounters another one. Based on these results, we define similarity measures between the 32 strategies and perform a network analysis of the relationship between the strategies by constructing a strategies network. Interestingly, we find that a win-lose circulation, like rock-paper-scissors, exists between strategies and that the circulation results from one unusual strategy.
Optimized photonic gauge of extreme high vacuum with Petawatt lasers

One of the latest proposed applications of ultra-intense laser pulses is their possible use to gauge extreme high vacuum by measuring the photon radiation resulting from nonlinear Thomson scattering within a vacuum tube. Here, we provide a complete analysis of the process, computing the expected rates and spectra, both for linear and circular polarizations of the laser pulses, taking into account the effect of the time envelope in a slowly varying envelope approximation. We also design a realistic experimental configuration allowing for the implementation of the idea and compute the corresponding geometric efficiencies. Finally, we develop an optimization procedure for this photonic gauge of extreme high vacuum at high repetition rate Petawatt and multi-Petawatt laser facilities, such as VEGA, JuSPARC and ELI.
Is Intelligence Artificial?

Our understanding of intelligence is directed primarily at the level of human beings. This paper attempts to give a more unifying definition that can be applied to the natural world in general. The definition would be used more to verify a degree of intelligence, not to quantify it and might help when making judgements on the matter. A version of an accepted test for AI is then put forward as the 'acid test' for Artificial Intelligence itself. It might be what a free-thinking program or robot would try to achieve. Recent work by the author on AI has been more from a direction of mechanical processes, or ones that might operate automatically. This paper will not try to question the idea of intelligence, in the sense of a pro-active or conscious event, but try to put it into a more passive, automatic and mechanical context. The paper also suggests looking at intelligence and consciousness as being slightly different.
A Network Centrality Method for the Rating Problem

We propose a new method for aggregating the information of multiple users rating multiple items. Our approach is based on the network relations induced between items by the rating activity of the users. Our method correlates better than the simple average with respect to the original rankings of the users, and besides, it is computationally more efficient than other methods proposed in the literature. Moreover, our method is able to discount the information that would be obtained adding to the system additional users with a systematically biased rating activity.
Recovering from Derivatives Funding: A Consistent Approach to DVA, FVA and Hedging

The inclusion of DVA in the fair-value of derivative transactions has now become standard accounting practice in most parts of the world. Furthermore, some sophisticated banks are including an FVA (Funding Valuation Adjustment), but since DVA can be interpreted as a funding benefit the oft-debated issue regarding a possible double-counting of funding benefits arises, with little consensus as to its resolution. One possibility is to price the derivative by replication, guaranteeing a consistent inclusion of costs and benefits. However, as has recently been noted, DVA is (at least partially) unhedgeable, having no exact market hedge. Furthermore, current frameworks shed little light on the controversial question, raised by Hull (2012), of whether the effect a derivative has on the riskiness of an institution's debt should be taken into account when calculating FVA. In this paper we propose a solution to these two problems by identifying an instrument, a fictitious CDS written on the hedging counterparty which is implicitly contained in any given derivatives transaction. This allows us to show that the hedger's unhedged jump-to-default risk has, despite not being actively managed, a well defined value associated to a funding benefit. Carrying out the replication including such a CDS, we obtain a price for the derivative consisting of its collateralized equivalent, a contingent CVA, a contingent DVA, and an FVA, coupled to the price via the hedger's short-term bond-CDS basis. The resulting funding cost is non-zero, but substantially smaller than what is obtained in alternative approaches due to the effect the derivative has on the recovery of the hedger's liabilities. Also, price agreement is possible for two sophisticated counterparties entering a deal if their bond-CDS bases obey a certain relationship, similar to what was first obtained by Morini and Prampolini (2010).
Reassortment between Influenza B Lineages and the Emergence of a Coadapted PB1–PB2–HA Gene Complex

Influenza B viruses make a considerable contribution to morbidity attributed to seasonal influenza. Currently circulating influenza B isolates are known to belong to two antigenically distinct lineages referred to as B/Victoria and B/Yamagata. Frequent exchange of genomic segments of these two lineages has been noted in the past, but the observed patterns of reassortment have not been formalized in detail. We investigate interlineage reassortments by comparing phylogenetic trees across genomic segments. Our analyses indicate that of the eight segments of influenza B viruses only segments coding for polymerase basic 1 and 2 (PB1 and PB2) and hemagglutinin (HA) proteins have maintained separate Victoria and Yamagata lineages and that currently circulating strains possess PB1, PB2, and HA segments derived entirely from one or the other lineage; other segments have repeatedly reassorted between lineages thereby reducing genetic diversity. We argue that this difference between segments is due to selection against reassortant viruses with mixed-lineage PB1, PB2, and HA segments. Given sufficient time and continued recruitment to the reassortment-isolated PB1–PB2–HA gene complex, we expect influenza B viruses to eventually undergo sympatric speciation.
On the Frequency of Drawdowns for Brownian Motion Processes

Drawdowns measuring the decline in value from the historical running maxima over a given period of time are considered as extremal events from the standpoint of risk management. To date, research on the topic has mainly focused on the side of severity by studying the first drawdown over a certain prespecified size. In this paper we extend the discussion by investigating the frequency of drawdowns and some of their inherent characteristics. We consider two types of drawdown time sequences depending on whether a historical running maximum is reset or not. For each type we study the frequency rate of drawdowns, the Laplace transform of the nth drawdown time, the distribution of the running maximum, and the value process at the nth drawdown time, as well as some other quantities of interest. Interesting relationships between these two drawdown time sequences are also established. Finally, insurance policies protecting against the risk of frequent drawdowns are also proposed and priced.
Foldscope: Origami-Based Paper Microscope

Here we describe an ultra-low-cost origami-based approach for large-scale manufacturing of microscopes, specifically demonstrating brightfield, darkfield, and fluorescence microscopes. Merging principles of optical design with origami enables high-volume fabrication of microscopes from 2D media. Flexure mechanisms created via folding enable a flat compact design. Structural loops in folded paper provide kinematic constraints as a means for passive self-alignment. This light, rugged instrument can survive harsh field conditions while providing a diversity of imaging capabilities, thus serving wide-ranging applications for cost-effective, portable microscopes in science and education.
Topological implications of negative curvature for biological and social networks

Network measures that reflect the most salient properties of complex large-scale networks are in high demand in the network research community. In this paper we adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parametrized finite networks, and show that a variety of biological and social networks are hyperbolic. This hyperbolicity property has strong implications on the higher-order connectivity and other topological properties of these networks. Specifically, we derive and prove bounds on the distance among shortest or approximately shortest paths in hyperbolic networks. We describe two implications of these bounds to crosstalk in biological networks, and to the existence of central, influential neighborhoods in both biological and social networks.
Temperature dependence of the chiral vortical effects

We discuss the origins of temperature dependence of the axial vortical effect, i.e. generation of an axial current in a rotating chiral medium along the rotation axis. We show that the corresponding transport coefficient depends, in general, on the number of light weakly interacting degrees of freedom, rather than on the gravitational anomaly. We also comment on the role of low-dimensional defects in the rotating medium, and appearance of the chiral vortical effect due to them.
Measuring the growth of matter fluctuations with third-order galaxy correlations

Measurements of the linear growth factor $D$ at different redshifts $z$ are key to distinguish among cosmological models. One can estimate the derivative $dD(z)/d\ln(1+z)$ from redshift space measurements of the 3D anisotropic galaxy two-point correlation $\xi(z)$, but the degeneracy of its transverse (or projected) component with galaxy bias $b$, i.e. $\xi_{\perp}(z) \propto\ D^2(z) b^2(z)$, introduces large errors in the growth measurement. Here we present a comparison between two methods which break this degeneracy by combining second- and third-order statistics. One uses the shape of the reduced three-point correlation and the other a combination of third-order one- and two-point cumulants. These methods use the fact that, for Gaussian initial conditions and scales larger than $20$ $h^{-1}$Mpc, the reduced third-order matter correlations are independent of redshift (and therefore of the growth factor) while the third-order galaxy correlations depend on $b$. We use matter and halo catalogs from the MICE-GC simulation to test how well we can recover $b(z)$ and therefore $D(z)$ with these methods in 3D real space. We also present a new approach, which enables us to measure $D$ directly from the redshift evolution of second- and third-order galaxy correlations without the need of modelling matter correlations. For haloes with masses lower than $10^{14}$ $h^{-1}$M$_\odot$, we find $10%$ deviations between the different estimates of $D$, which are comparable to current observational errors. At higher masses we find larger differences that can probably be attributed to the breakdown of the bias model and non-Poissonian shot noise.
SCoPE: an efficient method of Cosmological Parameter Estimation

Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological parameter estimation from CMB and other data. However, due to the intrinsic serial nature of the MCMC sampler, convergence is often very slow. Here we present a fast and independently written Monte Carlo method for cosmological parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE), that employs delayed rejection to increase the acceptance rate of a chain, and pre-fetching that helps an individual chain to run on parallel CPUs. An inter-chain covariance update is also incorporated to prevent clustering of the chains allowing faster and better mixing of the chains. We use an adaptive method for covariance calculation to calculate and update the covariance automatically as the chains progress. Our analysis shows that the acceptance probability of each step in SCoPE is more than 95% and the convergence of the chains are faster. Using SCoPE, we carry out some cosmological parameter estimations with different cosmological models using WMAP-9 and Planck results. One of the current research interests in cosmology is quantifying the nature of dark energy. We analyze the cosmological parameters from two illustrative commonly used parameterisations of dark energy models. We also asses primordial helium fraction in the universe can be constrained by the present CMB data from WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us to understand the workability of the SCoPE better, on the other hand it provides a completely independent estimation of cosmological parameters from WMAP-9 and Planck data.
AntiPlag: Plagiarism detection on electronic submissions of text based assignments

Plagiarism is one of the growing issues in academia and is always a concern in Universities and other academic institutions. The situation is becoming even worse with the availability of ample resources on the web. This paper focuses on creating an effective and fast tool for plagiarism detection for text based electronic assignments. Our plagiarism detection tool named AntiPlag is developed using the tri-gram sequence matching technique. Three sets of text based assignments were tested by AntiPlag and the results were compared against an existing commercial plagiarism detection tool. AntiPlag showed better results in terms of false positives compared to the commercial tool due to the pre-processing steps performed in AntiPlag. In addition, to improve the detection latency, AntiPlag applies a data clustering technique making it four times faster than the commercial tool considered. AntiPlag could be used to isolate plagiarized text based assignments from non-plagiarised assignments easily. Therefore, we present AntiPlag, a fast and effective tool for plagiarism detection on text based electronic assignments.