nils-herrmann/clean_scirepeval huggingface dataset:
Ross recovery with recurrent and transient processes

Recently, Ross showed that it is possible to recover an objective measure from a risk-neutral measure. His model assumes that there is a finite-state Markov process X that drives the economy in discrete time. Many authors extended his model to a continuous-time setting with a Markov diffusion process X with state space R. Unfortunately, the continuous-time model fails to recover an objective measure from a risk-neutral measure. We determine under which information recovery is possible in the continuous-time model. It was proven that if X is recurrent under the objective measure, then recovery is possible. In this article, when X is transient under the objective measure, we investigate what information is sufficient to recover.
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual data and extracts a bilingual signal from a smaller set of raw-text sentence-aligned data. This is achieved using a novel sampled bag-of-words cross-lingual objective, which is used to regularize two noise-contrastive language models for efficient cross-lingual feature learning. We show that bilingual embeddings learned using the proposed model outperform state-of-the-art methods on a cross-lingual document classification task as well as a lexical translation task on WMT11 data.
Stringlike structures in the real and complex Kerr-Schild geometry

Four-dimensional Kerr-Schild (KS) geometry displays remarkable relationships with quantum world and theory of superstrings. In particular, the Kerr-Newman (KN) solution has gyromagnetic ratio g = 2, as that of the Dirac electron and represents a consistent background for gravitational and electromagnetic field of the electron. As a consequence of very big spin/mass ratio, black hole horizons disappear, exposing the naked Kerr singular ring. We consider four-decade history of development of this structure which took finally the form of a point-string-membrane-bubble complex which is reminiscent of the enhancon model of string/M-theory. A complex string obtained in the complex structure of the Kerr geometry gives an extra dimension to the world-sheet of the real Kerr string, forming a membrane by analogue with the string/M-theory unification. By analysis of the orientifold parity of the complex Kerr string, we obtain that the determined by the Kerr theorem principal null congruence of the Kerr geometry is described by a quartic equation in projective twistor space CP3, and therefore, it creates the known Calabi- Yau twofold (K3 surface) in twistor space of the 4d KS geometry. We connect it with N=2 superstring which has (complex) critical dimension two and may be embedded into complex KS geometry.
Positioning the neutron drip line and the r-process paths in the nuclear landscape

Exploring nucleon drip lines and astrophysical rapid neutron capture process (r-process) paths in the nuclear landscape is extremely challenging in nuclear physics and astrophysics. While various models predict similar proton drip line, their predictions for neutron drip line and the r-process paths involving heavy neutron-rich nuclei exhibit a significant variation which hampers our accurate understanding of the r-process nucleosynthesis mechanism. Using microscopic density functional theory with a representative set of non-relativistic and relativistic interactions, we demonstrate for the first time that this variation is mainly due to the uncertainty of nuclear matter symmetry energy $E_{\rm{sym}}(\rho_{\rm{sc}})$ at the subsaturation cross density $\rho_{\rm{sc}}=0.11/0.16\times\rho_0$ ($\rho_0$ is saturation density), which reflects the symmetry energy of heavy nuclei. Using the recent accurate constraint on $E_{\rm{sym}}(\rho_{\rm{sc}})$ from the binding energy difference of heavy isotope pairs, we obtain quite precise predictions for the location of the neutron drip line, the r-process paths and the number of bound nuclei in the nuclear landscape. Our results have important implications on extrapolating the properties of unknown neutron-rich rare isotopes from the data on known nuclei.
Validation of k-Nearest Neighbor Classifiers Using Inclusion and Exclusion

This paper presents a series of PAC exponential error bounds for $k$-nearest neighbors classifiers, with O($n^{-\frac{r}{2r+1}}\sqrt{k \ln n}$) error bound range for each integer $r>0$, where $n$ is the number of in-sample examples. This shows that $k$-nn classifiers, in spite of their famously fractured decision boundaries, come close to having Gaussian-style exponential error bounds with O($n^{-\frac{1}{2}}$) bound ranges.
Propagation of Systemic Risk in Interbank Networks

This work explores the characteristics of financial contagion in networks whose links distributions approaches a power law, using a model that defines banks balance sheets from information of network connectivity. By varying the parameters for the creation of the network, several interbank networks are built, in which the concentrations of debts and credits are obtained from links distributions during the creation networks process. Three main types of interbank network are analyzed for their resilience to contagion: i) concentration of debts is greater than concentration of credits, ii) concentration of credits is greater than concentration of debts and iii) concentrations of debts and credits are similar. We also tested the effect of a variation in connectivity in conjunction with variation in concentration of links. The results suggest that more connected networks with high concentration of credits (featuring nodes that are large creditors of the system) present greater resilience to contagion when compared with the others networks analyzed. Evaluating some topological indices of systemic risk suggested by the literature we have verified the ability of these indices to explain the impact on the system caused by the failure of a node. There is a clear positive correlation between the topological indices and the magnitude of losses in the case of networks with high concentration of debts. This correlation is smaller for more resilient networks.
Communication impacting financial markets

Since the attribution of the Nobel prize in 2002 to Kahneman for prospect theory, behavioral finance has become an increasingly important subfield of finance. However the main parts of behavioral finance, prospect theory included, understand financial markets through individual investment behavior. Behavioral finance thereby ignores any interaction between participants. We introduce a socio-financial model (Vitting Andersen J. and Nowak A., An Introduction to Socio-Finance (Springer, Berlin) 2013) that studies the impact of communication on the pricing in financial markets. Considering the simplest possible case where each market participant has either a positive (bullish) or negative (bearish) sentiment with respect to the market, we model the evolution of the sentiment in the population due to communication in subgroups of different sizes. Nonlinear feedback effects between the market performance and changes in sentiments are taken into account by assuming that the market performance is dependent on changes in sentiments (e.g., a large sudden positive change in bullishness would lead to more buying). The market performance in turn has an impact on the sentiment through the transition probabilities to change an opinion in a group of a given size. The idea is that if for example the market has observed a recent downturn, it will be easier for even a bearish minority to convince a bullish majority to change opinion compared to the case where the meeting takes place in a bullish upturn of the market. Within the framework of our proposed model, financial markets stylized facts such as volatility clustering and extreme events may be perceived as arising due to abrupt sentiment changes via ongoing communication of the market participants. The model introduces a new volatility measure which is apt of capturing volatility clustering and from maximum-likelihood analysis we are able to apply the model to real data and give additional long term insight into where a market is heading.
A class of warm Jupiters with mutually inclined, apsidally misaligned close friends

The orbits of giant extrasolar planets often have surprisingly small semimajor axes, large eccentricities, or severe misalignments between their orbit normals and their host stars' spin axes. In some formation scenarios invoking Kozai-Lidov oscillations, an external planetary companion drives a planet onto an orbit having these properties. The mutual inclinations for Kozai-Lidov oscillations can be large and have not been confirmed observationally. Here we present evidence that observed eccentric warm Jupiters with eccentric giant companions have mutual inclinations that oscillate between 35° and 65°. Our inference is based on the pairs’ observed apsidal separations, which cluster near 90°. The near-orthogonality of periapse directions is effected by the outer companion's quadrupolar and octupolar potentials. These systems may be undergoing a stalled version of tidal migration that produces warm Jupiters over hot Jupiters, and they provide evidence for a population of multiplanet systems that are not flat and have been sculpted by Kozai-Lidov oscillations. Numerical simulations of stalled nondisk migration replicate the observed lack of coplanarity for giant exoplanet pairs. Close planet friends get out of line Some “warm” giant exoplanets orbit much closer to their star than do similar planets in our solar system. Dawson and Chiang argue that understanding how such orbits have evolved can answer an outstanding question: How do “hot” giant planets (which are more common than these “warm” ones) get so close to their host star? These planets frequently have giant companions. Numerical simulations revealed that planets with an eccentric giant companion may have just the right mutual inclination for the inner planet to be pushed slowly toward the star. This process could then produce systems with “warm” rather than “hot” Jupiters. Science, this issue p. 212
Seeing asymptotic freedom in an exact correlator of a large-$N$ matrix field theory

Exact expressions for correlation functions are known for the large-$N$ (planar) limit of the $(1+1)$-dimensional ${\rm SU}(N)\times {\rm SU}(N)$ principal chiral sigma model. These were obtained with the form-factor bootstrap, an entirely nonperturbative method. The large-$N$ solution of this asymptotically-free model is far less trivial than that of O($N$) sigma model (or other isovector models). Here we study the Euclidean two-point correlation function $N^{-1} $, where $\Phi(x)\sim Z^{-1/2}U(x)$ is the scaling field and $U(x)\in SU(N)$ is the bare field. We express the two-point function in terms of the spectrum of the operator $\sqrt{-d^{2}/du^{2}}$, where $u\in (-1,1)$. At short distances, this expression perfectly matches the result from the perturbative renormalization group.
Distributed Estimation, Information Loss and Exponential Families

Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learning framework that first calculates the local maximum likelihood estimates (MLE) based on the data subsets, and then combines the local MLEs to achieve the best possible approximation to the global MLE given the whole dataset. We study this framework's statistical properties, showing that the efficiency loss compared to the global setting relates to how much the underlying distribution families deviate from full exponential families, drawing connection to the theory of information loss by Fisher, Rao and Efron. We show that the "full-exponential-family-ness" represents the lower bound of the error rate of arbitrary combinations of local MLEs, and is achieved by a KL-divergence-based combination method but not by a more common linear combination method. We also study the empirical properties of both methods, showing that the KL method significantly outperforms linear combination in practical settings with issues such as model misspecification, non-convexity, and heterogeneous data partitions.
Compressed sensing with side information: Geometrical interpretation and performance bounds

We address the problem of Compressed Sensing (CS) with side information. Namely, when reconstructing a target CS signal, we assume access to a similar signal. This additional knowledge, the side information, is integrated into CS via ℓ<sub>1</sub>-ℓ<sub>1</sub> and ℓ<sub>1</sub>-ℓ<sub>2</sub> minimization. We then provide lower bounds on the number of measurements that these problems require for successful reconstruction of the target signal. If the side information has good quality, the number of measurements is significantly reduced via ℓ<sub>1</sub>-ℓ<sub>1</sub> minimization, but not so much via ℓ<sub>1</sub>-ℓ<sub>2</sub> minimization. We provide geometrical interpretations and experimental results illustrating our findings.
Increasing the active surface of titanium islands on graphene by nitrogen sputtering

Titanium-island formation on graphene as a function of defect density is investigated. When depositing titanium on pristine graphene, titanium atoms cluster and form islands with an average diameter of about 10nm and an average height of a few atomic layers. We show that if defects are introduced in the graphene by ion bombardment, the mobility of the deposited titanium atoms is reduced and the average diameter of the islands decreases to 5nm with monoatomic height. This results in an optimized coverage for hydrogen storage applications since the actual titanium surface available per unit graphene area is significantly increased.
Cross-correlations of American baby names

Societal and cultural transformations are very general and debated topics, both by scientists (e.g., sociologists) and by public opinion (e.g., artists, music producers, brand manufacturers, and advertising agencies). Although almost everyone would be able to express a position on such arguments, it is much more difficult to support such an opinion based on scientific evidence. In this work we analyze the case of American baby names and describe the evolution of tastes of parents regarding the choice of the name during the years of the last century. Using quantitative methods we find that a deep transformation occurred at the end of the 20th century and suggest that this might be studied from a quantitative sociological point of view. The quantitative description of cultural evolution is a challenging task. The most difficult part of the problem is probably to find the appropriate measurable quantities that can make more quantitative such evasive concepts as, for example, dynamics of cultural movements, behavioral patterns, and traditions of the people. A strategy to tackle this issue is to observe particular features of human activities, i.e., cultural traits, such as names given to newborns. We study the names of babies born in the United States from 1910 to 2012. Our analysis shows that groups of different correlated states naturally emerge in different epochs, and we are able to follow and decrypt their evolution. Although these groups of states are stable across many decades, a sudden reorganization occurs in the last part of the 20th century. We unambiguously demonstrate that cultural evolution of society can be observed and quantified by looking at cultural traits. We think that this kind of quantitative analysis can be possibly extended to other cultural traits: Although databases covering more than one century (such as the one we used) are rare, the cultural evolution on shorter timescales can be studied due to the fact that many human activities are usually recorded in the present digital era.
Nearly linear light cones in long-range interacting quantum systems.

In nonrelativistic quantum theories with short-range Hamiltonians, a velocity v can be chosen such that the influence of any local perturbation is approximately confined to within a distance r until a time t∼r/v, thereby defining a linear light cone and giving rise to an emergent notion of locality. In systems with power-law (1/r^{α}) interactions, when α exceeds the dimension D, an analogous bound confines influences to within a distance r only until a time t∼(α/v)logr, suggesting that the velocity, as calculated from the slope of the light cone, may grow exponentially in time. We rule out this possibility; light cones of power-law interacting systems are bounded by a polynomial for α>2D and become linear as α→∞. Our results impose strong new constraints on the growth of correlations and the production of entangled states in a variety of rapidly emerging, long-range interacting atomic, molecular, and optical systems.
Long-term laser frequency stabilization using fiber interferometers.

We report long-term laser frequency stabilization using only the target laser and a pair of 5 m fiber interferometers, one as a frequency reference and the second as a sensitive thermometer to stabilize the frequency reference. When used to stabilize a distributed feedback laser at 795 nm, the frequency Allan deviation at 1000 s drops from 5.6 × 10(-8) to 6.9 × 10(-10). The performance equals that of an offset lock employing a second, atom-stabilized laser in the temperature control.
Social Diffusion and Global Drift on Networks

We study a mathematical model of social diffusion on a symmetric weighted network where individual nodes' states gradually assimilate to local social norms made by their neighbors' average states. Unlike physical diffusion, this process is not state conservational and thus the global state of the network (i.e., sum of node states) will drift. The asymptotic average node state will be the average of initial node states weighted by their strengths. Here we show that, while the global state is not conserved in this process, the inner product of strength and state vectors is conserved instead, and perfect positive correlation between node states and local averages of their self-neighbor strength ratios always results in upward (or at least neutral) global drift. We also show that the strength assortativity negatively affects the speed of homogenization. Based on these findings, we propose an adaptive link weight adjustment method to achieve the highest upward global drift by increasing the strength-state correlation. The effectiveness of the method was confirmed through numerical simulations and implications for real-world social applications are discussed.
Convex Modeling of Interactions With Strong Heredity

We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity. We propose FAMILY, a very general framework for this task. Our proposal is a generalization of several existing methods, such as VANISH, hierNet, the all-pairs lasso, and the lasso using only main effects. It can be formulated as the solution to a convex optimization problem, which we solve using an efficient alternating directions method of multipliers (ADMM) algorithm. This algorithm has guaranteed convergence to the global optimum, can be easily specialized to any convex penalty function of interest, and allows for a straightforward extension to the setting of generalized linear models. We derive an unbiased estimator of the degrees of freedom of FAMILY, and explore its performance in a simulation study and on an HIV sequence dataset.
Coulomb string tension, asymptotic string tension, and the gluon chain

We compute, via numerical simulations, the non-perturbative Coulomb potential and position-space ghost propagator in pure SU(3) gauge theory in Coulomb gauge. We find that that the Coulomb potential scales nicely in accordance with asymptotic freedom, that the Coulomb potential is linear in the infrared, and that the Coulomb string tension is about four times larger than the asymptotic string tension. We explain how it is possible that the asymptotic string tension can be lower than the Coulomb string tension by a factor of four.
Detection of cheating by decimation algorithm

We expand the item response theory to study the case of "cheating students" for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data. Nevertheless, the greedy algorithm that we employed in the present study exhibits good performance with a few number of training data. The key point is the sparseness of the interactions in our problem in the context of the Boltzmann machine learning: the existence of cheating students is expected to be very rare (possibly even in real world). We compare a standard approach to infer the sparse interactions in the Boltzmann machine learning to our greedy algorithm and we find the latter to be superior in several aspects.
Applications of statistical physics distributions to several types of income

This paper explores several types of income which have not been explored so far by authors who tackled income and wealth distribution using Statistical Physics. The main types of income we plan to analyze are income before redistribution (or gross income), income of retired people (or pensions), and income of active people (mostly wages). The distributions used to analyze income distributions are Fermi-Dirac distribution and polynomial distribution (as this is present in describing the behavior of dynamic systems in certain aspects). The data we utilize for our analysis are from France and the UK. We find that both distributions are robust in describing these varieties of income. The main finding we consider to be the applicability of these distributions to pensions, which are not regulated entirely by market mechanisms.
Sculpting the stellar cusp in the galactic center

Observations of the innermost parsec surrounding Sgr A*—the supermassive black hole in the center of our Galaxy—have revealed a diversity of structures whose existence and characteristics apparently defy the fundamental principles of dynamics. In this article, we review the challenges to the dynamics theories that have been brought forth in the past two decades by the observations of the galactic center. We outline the theoretical framework that has been developed to reconcile the discrepancies between the theoretical predictions and the observational results. In particular, we highlight the role of the recently discovered sub-parsec stellar disk in determining the dynamics and resolving the inconsistencies. We also discuss the implications for the recent activity of Sgr A*.
Flip-flopping binary black holes.

We study binary spinning black holes to display the long term individual spin dynamics. We perform a full numerical simulation starting at an initial proper separation of d≈25M between equal mass holes and evolve them down to merger for nearly 48 orbits, 3 precession cycles, and half of a flip-flop cycle. The simulation lasts for t=20 000M and displays a total change in the orientation of the spin of one of the black holes from an initial alignment with the orbital angular momentum to a complete antialignment after half of a flip-flop cycle. We compare this evolution with an integration of the 3.5 post-Newtonian equations of motion and spin evolution to show that this process continuously flip flops the spin during the lifetime of the binary until merger. We also provide lower order analytic expressions for the maximum flip-flop angle and frequency. We discuss the effects this dynamics may have on spin growth in accreting binaries and on the observational consequences for galactic and supermassive binary black holes.