nils-herrmann/clean_scirepeval huggingface dataset:
Acceleration of evolutionary spread by long-range dispersal

Pathogens, invasive species, rumors, or innovations spread much more quickly around the world nowadays than in previous centuries. The speedup is caused by more frequent long-range dispersal, for example via air traffic. These jumps are crucial because they can generate satellite “outbreaks” at many distant locations, thus rapidly increasing the total rate of spread. We present a simple intuitive argument that captures the resulting spreading patterns. We show that even rare long-range jumps can transform the spread of simple epidemics from wave-like to a very fast type of “metastatic” growth. More generally, our approach can be used to describe how new evolutionary variants spread and thus improves our predictive understanding of the speed of Darwinian adaptation. The spreading of evolutionary novelties across populations is the central element of adaptation. Unless populations are well mixed (like bacteria in a shaken test tube), the spreading dynamics depend not only on fitness differences but also on the dispersal behavior of the species. Spreading at a constant speed is generally predicted when dispersal is sufficiently short ranged, specifically when the dispersal kernel falls off exponentially or faster. However, the case of long-range dispersal is unresolved: Although it is clear that even rare long-range jumps can lead to a drastic speedup—as air-traffic–mediated epidemics show—it has been difficult to quantify the ensuing stochastic dynamical process. However, such knowledge is indispensable for a predictive understanding of many spreading processes in natural populations. We present a simple iterative scaling approximation supported by simulations and rigorous bounds that accurately predicts evolutionary spread, which is determined by a trade-off between frequency and potential effectiveness of long-distance jumps. In contrast to the exponential laws predicted by deterministic “mean-field” approximations, we show that the asymptotic spatial growth is according to either a power law or a stretched exponential, depending on the tails of the dispersal kernel. More importantly, we provide a full time-dependent description of the convergence to the asymptotic behavior, which can be anomalously slow and is relevant even for long times. Our results also apply to spreading dynamics on networks with a spectrum of long-range links under certain conditions on the probabilities of long-distance travel: These are relevant for the spread of epidemics.
Communication Communities in MOOCs

Massive Open Online Courses (MOOCs) bring together thousands of people from different geographies and demographic backgrounds -- but to date, little is known about how they learn or communicate. We introduce a new content-analysed MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to extract communities of learners based on the nature of their online forum posts. We see that BNMF yields a superior probabilistic generative model for online discussions when compared to other models, and that the communities it learns are differentiated by their composite students' demographic and course performance indicators. These findings suggest that computationally efficient probabilistic generative modelling of MOOCs can reveal important insights for educational researchers and practitioners and help to develop more intelligent and responsive online learning environments.
Is Higgs inflation ruled out

We consider the status of Higgs inflation in light of the recently announced detection of B modes in the polarization of the cosmic microwave background radiation by the BICEP2 Collaboration. In order for the primordial B-mode signal to be observable by BICEP2, the energy scale of inflation must be high: V inf ≈ 2 × 1 0 16 GeV . Higgs inflation generally predicts a small amplitude of tensor perturbations, and therefore it is natural to ask if Higgs inflation might accommodate this new measurement. We find that the answer is essentially no, unless one considers either extreme fine-tuning or possibly adding new beyond the Standard Model fields, which remove some of the more attractive features of the original idea. We also explore the possible importance of a factor that has not previously been explicitly incorporated, namely the gauge dependence of the effective potential used in calculating inflationary observables (e.g., n S and r ), to see if this might provide additional wiggle room. Such gauge effects are comparable to the effects of Higgs mass uncertainties and other observables already considered in the analysis, and therefore they are relevant for constraining models. However, they are therefore too small to remove the apparent incompatibility between the BICEP2 observation and the predictions of Higgs inflation.
Colloquium : Majorana fermions in nuclear, particle, and solid-state physics

Ettore Majorana (1906-1938) disappeared while traveling by ship from Palermo to Naples in 1938. His fate has never been fully resolved and several articles have been written that explore the mystery itself. His demise intrigues us still today because of his seminal work, published the previous year, that established symmetric solutions to the Dirac equation that describe a fermionic particle that is its own anti-particle. This work has long had a significant impact in neutrino physics, where this fundamental question regarding the particle remains unanswered. But the formalism he developed has found many uses as there are now a number of candidate spin-1/2 neutral particles that may be truly neutral with no quantum number to distinguish them from their anti-particles. If such particles exist, they will influence many areas of nuclear and particle physics. Most notably the process of neutrinoless double beta decay can only exist if neutrinos are massive Majorana particles. Hence, many efforts to search for this process are underway. Majorana's influence doesn't stop with particle physics, however, even though that was his original consideration. The equations he derived also arise in solid state physics where they describe electronic states in materials with superconducting order. Of special interest here is the class of solutions of the Majorana equation in one and two spatial dimensions at exactly zero energy. These Majorana zero modes are endowed with some remarkable physical properties that may lead to advances in quantum computing and, in fact, there is evidence that they have been experimentally observed. This review first summarizes the basics of Majorana's theory and its implications. It then provides an overview of the rich experimental programs trying to find a fermion that is its own anti-particle in nuclear, particle, and solid state physics.
Semiconductor phonon and charge transport Monte Carlo simulation using Geant4

A phonon and charge transport simulation based on the Geant4 Monte Carlo toolkit is presented. The transport code is capable of propagating acoustic phonons, electrons and holes in cryogenic crystals. Anisotropic phonon propagation, oblique carrier propagation and phonon emission by accelerated carriers are all taken into account. The simulation successfully reproduces theoretical predictions and experimental observations such as phonon caustics, heat pulse propagation times and mean carrier drift velocities. Implementation of the transport code using the Geant4 toolkit ensures availability to the wider scientific community.
Noble gas excimer scintillation following neutron capture in boron thin films

Far-ultraviolet scintillation signals have been measured in heavy noble gases (argon, krypton, xenon) following boron-neutron capture (10B(n,α)7Li) in 10B thin films. The observed scintillation yields are comparable to the yields from some liquid and solid neutron scintillators. At noble gas pressures of 107 kPa, the number of photons produced per neutron absorbed following irradiation of a 1200 nm thick 10B film was 14 000 for xenon, 11 000 for krypton, and 6000 for argon. The absolute scintillation yields from the experimental configuration were calculated using data from (1) experimental irradiations, (2) thin-film characterizations, (3) photomultiplier tube calibrations, and (4) photon collection modeling. Both the boron films and the photomultiplier tube were characterized at the National Institute of Standards and Technology. Monte Carlo modeling of the reaction cell provided estimates of the photon collection efficiency and the transport behavior of 10B(n,α)7Li reaction products escaping the thin f...
Matroid Bandits: Fast Combinatorial Optimization with Learning

A matroid is a notion of independence in combinatorial optimization which is closely related to computational efficiency. In particular, it is well known that the maximum of a constrained modular function can be found greedily if and only if the constraints are associated with a matroid. In this paper, we bring together the ideas of bandits and matroids, and propose a new class of combinatorial bandits, matroid bandits. The objective in these problems is to learn how to maximize a modular function on a matroid. This function is stochastic and initially unknown. We propose a practical algorithm for solving our problem, Optimistic Matroid Maximization (OMM); and prove two upper bounds, gap-dependent and gap-free, on its regret. Both bounds are sublinear in time and at most linear in all other quantities of interest. The gap-dependent upper bound is tight and we prove a matching lower bound on a partition matroid bandit. Finally, we evaluate our method on three real-world problems and show that it is practical.
Interactive Debugging of ASP Programs

Broad application of answer set programming (ASP) for declarative problem solving requires the development of tools supporting the coding process. Program debugging is one of the crucial activities within this process. Recently suggested ASP debugging approaches allow efficient computation of possible explanations of a fault. However, even for a small program a debugger might return a large number of possible explanations and selection of the correct one must be done manually. In this paper we present an interactive query-based ASP debugging method which extends previous approaches and finds a preferred explanation by means of observations. The system queries a programmer whether a set of ground atoms must be true in all (cautiously) or some (bravely) answer sets of the program. Since some queries can be more informative than the others, we discuss query selection strategies which, given user's preferences for an explanation, can find the best query. That is, the query an answer of which reduces the overall number of queries required for the identification of a preferred explanation.
Synergy and redundancy in the Granger causal analysis of dynamical networks

We analyze, by means of Granger causality (GC), the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. While we show that fully conditioned GC (CGC) is not affected by synergy, the pairwise analysis fails to prove synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned GC (PCGC) is an effective approach if the set of conditioning variables is properly chosen. Here we consider two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for PCGC and show that, depending on the data structure, either one or the other might be equally valid. On the other hand, we observe that fully conditioned approaches do not work well in the presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the CGC (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in the presence of redundancy. Finally we apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy.
Collective behaviours in the stock market: a maximum entropy approach

Scale invariance, collective behaviours and structural reorganization are crucial for portfolio management (portfolio composition, hedging, alternative definition of risk, etc.). This lack of any characteristic scale and such elaborated behaviours find their origin in the theory of complex systems. There are several mechanisms which generate scale invariance but maximum entropy models are able to explain both scale invariance and collective behaviours. The study of the structure and collective modes of financial markets attracts more and more attention. It has been shown that some agent based models are able to reproduce some stylized facts. Despite their partial success, there is still the problem of rules design. In this work, we used a statistical inverse approach to model the structure and co-movements in financial markets. Inverse models restrict the number of assumptions. We found that a pairwise maximum entropy model is consistent with the data and is able to describe the complex structure of financial systems. We considered the existence of a critical state which is linked to how the market processes information, how it responds to exogenous inputs and how its structure changes. The considered data sets did not reveal a persistent critical state but rather oscillations between order and disorder. In this framework, we also showed that the collective modes are mostly dominated by pairwise co-movements and that univariate models are not good candidates to model crashes. The analysis also suggests a genuine adaptive process since both the maximum variance of the log-likelihood and the accuracy of the predictive scheme vary through time. This approach may provide some clue to crash precursors and may provide highlights on how a shock spreads in a financial network and if it will lead to a crash. The natural continuation of the present work could be the study of such a mechanism.
What is Tumblr: a statistical overview and comparison

Tumblr, as one of the most popular microblogging platforms, has gained momentum recently. It is reported to have 166.4 millions of users and 73.4 billions of posts by January 2014. While many articles about Tumblr have been published in major press, there is not much scholar work so far. In this paper, we provide some pioneer analysis on Tumblr from a variety of aspects. We study the social network structure among Tumblr users, analyze its user generated content, and describe reblogging patterns to analyze its user behavior. We aim to provide a comprehensive statistical overview of Tumblr and compare it with other popular social services, including blogosphere, Twitter and Facebook, in answering a couple of key questions: What is Tumblr? How is Tumblr different from other social media networks? In short, we find Tumblr has more rich content than other microblogging platforms, and it contains hybrid characteristics of social networking, traditional blogosphere, and social media. This work serves as an early snapshot of Tumblr that later work can leverage.
Branching-ratio approximation for the self-exciting Hawkes process.

We introduce a model-independent approximation for the branching ratio of Hawkes self-exciting point processes. Our estimator requires knowing only the mean and variance of the event count in a sufficiently large time window, statistics that are readily obtained from empirical data. The method we propose greatly simplifies the estimation of the Hawkes branching ratio, recently proposed as a proxy for market endogeneity and formerly estimated using numerical likelihood maximization. We employ our method to support recent theoretical and experimental results indicating that the best fitting Hawkes model to describe S&P futures price changes is in fact critical (now and in the recent past) in light of the long memory of financial market activity.
Multilevel Monte Carlo For Exponential L\'{e}vy Models

We apply multilevel Monte Carlo for option pricing problems using exponential L\'{e}vy models with a uniform timestep discretisation to monitor the running maximum required for lookback and barrier options. The numerical results demonstrate the computational efficiency of this approach. We derive estimates of the convergence rate for the error introduced by the discrete monitoring of the running supremum of a broad class of L\'{e}vy processes. We use these to obtain upper bounds on the multilevel Monte Carlo variance convergence rate for the Variance Gamma, NIG and $\alpha$-stable processes used in the numerical experiments. We also show numerical results and analysis of a trapezoidal approximation for Asian options.
Parameter Estimation of Social Forces in Crowd Dynamics Models via a Probabilistic Method

Focusing on a specific crowd dynamics situation, including real life experiments and measurements, our paper targets a twofold aim: (1) we present a Bayesian probabilistic method to estimate the value and the uncertainty (in the form of a probability density function) of parameters in crowd dynamic models from the experimental data; and (2) we introduce a fitness measure for the models to classify a couple of model structures (forces) according to their fitness to the experimental data, preparing the stage for a more general model-selection and validation strategy inspired by probabilistic data analysis. Finally, we review the essential aspects of our experimental setup and measurement technique.
Using n-grams Models for Visual Semantic Place Recognition

The aim of this paper is to present a new method for visual place recognition. Our system combines global image characterization and visual words, which allows to use efficient Bayesian filtering methods to integrate several images. More precisely, we extend the classical HMM model with techniques inspired by the field of Natural Language Processing. This paper presents our system and the Bayesian filtering algorithm. The performance of our system and the influence of the main parameters are evaluated on a standard database. The discussion highlights the interest of using such models and proposes improvements.
Breathing dynamics based parameter sensitivity analysis of hetero-polymeric DNA.

We study the parameter sensitivity of hetero-polymeric DNA within the purview of DNA breathing dynamics. The degree of correlation between the mean bubble size and the model parameters is estimated for this purpose for three different DNA sequences. The analysis leads us to a better understanding of the sequence dependent nature of the breathing dynamics of hetero-polymeric DNA. Out of the 14 model parameters for DNA stability in the statistical Poland-Scheraga approach, the hydrogen bond interaction ε(hb)(AT) for an AT base pair and the ring factor ξ turn out to be the most sensitive parameters. In addition, the stacking interaction ε(st)(TA-TA) for an TA-TA nearest neighbor pair of base-pairs is found to be the most sensitive one among all stacking interactions. Moreover, we also establish that the nature of stacking interaction has a deciding effect on the DNA breathing dynamics, not the number of times a particular stacking interaction appears in a sequence. We show that the sensitivity analysis can be used as an effective measure to guide a stochastic optimization technique to find the kinetic rate constants related to the dynamics as opposed to the case where the rate constants are measured using the conventional unbiased way of optimization.
On the state dependency of fast feedback processes in (paleo) climate sensitivity

Paleo data have been frequently used to determine the equilibrium (Charney) climate sensitivity Sa, and—if slow feedback processes (e.g., land‐ice albedo) are adequately taken into account—they indicate a similar range as estimates based on instrumental data and climate model results. Many studies assume the (fast) feedback processes to be independent of the background climate state, e.g., equally strong during warm and cold periods. Here we assess the dependency of the fast feedback processes on the background climate state using data of the last 800 kyr and a box model of the climate system for interpretation. Applying a new method to account for background state dependency, we find Sa=0.61±0.07 K (W m−2)−1(±1σ) using a reconstruction of Last Glacial Maximum (LGM) cooling of −4.0 K and significantly lower climate sensitivity during glacial climates. Due to uncertainties in reconstructing the LGM temperature anomaly, Sa is estimated in the range Sa = 0.54–0.95 K (W m−2)−1.
PROSPECTING IN ULTRACOOL DWARFS: MEASURING THE METALLICITIES OF MID- AND LATE-M DWARFS

Metallicity is a fundamental parameter that contributes to the physical characteristics of a star. The low temperatures and complex molecules present in M dwarf atmospheres make it difficult to measure their metallicities using techniques that have been commonly used for Sun-like stars. Although there has been significant progress in developing empirical methods to measure M dwarf metallicities over the last few years, these techniques have been developed primarily for early- to mid-M dwarfs. We present a method to measure the metallicity of mid- to late-M dwarfs from moderate resolution (R ∼ 2000) K-band (≃ 2.2 μm) spectra. We calibrate our formula using 44 wide binaries containing an F, G, K, or early-M primary of known metallicity and a mid- to late-M dwarf companion. We show that similar features and techniques used for early-M dwarfs are still effective for late-M dwarfs. Our revised calibration is accurate to ∼0.07 dex for M4.5–M9.5 dwarfs with −0.58 < [Fe/H] < +0.56 and shows no systematic trends with spectral type, metallicity, or the method used to determine the primary star metallicity. We show that our method gives consistent metallicities for the components of M+M wide binaries. We verify that our new formula works for unresolved binaries by combining spectra of single stars. Lastly, we show that our calibration gives consistent metallicities with the Mann et al. study for overlapping (M4–M5) stars, establishing that the two calibrations can be used in combination to determine metallicities across the entire M dwarf sequence.
Dependence of US hurricane economic loss on maximum wind speed and storm size

Many empirical hurricane economic loss models consider only wind speed and neglect storm size. These models may be inadequate in accurately predicting the losses of super-sized storms, such as Hurricane Sandy in 2012. In this study, we examined the dependences of normalized US hurricane loss on both wind speed and storm size for 73 tropical cyclones that made landfall in the US from 1988 through 2012. A multi-variate least squares regression is used to construct a hurricane loss model using both wind speed and size as predictors. Using maximum wind speed and size together captures more variance of losses than using wind speed or size alone. It is found that normalized hurricane loss (L) approximately follows a power law relation with maximum wind speed (V max) and size (R), L = 10c V maxa R b , with c determining an overall scaling factor and the exponents a and b generally ranging between 4–12 and 2–4 respectively. Both a and b tend to increase with stronger wind speed. Hurricane Sandy’s size was about three times of the average size of all hurricanes analyzed. Based on the bi-variate regression model that explains the most variance for hurricanes, Hurricane Sandy’s loss would be approximately 20 times smaller if its size were of the average size with maximum wind speed unchanged. It is important to revise conventional empirical hurricane loss models that are only dependent on maximum wind speed to include both maximum wind speed and size as predictors.