nils-herrmann/clean_scirepeval huggingface dataset:
Kitaev honeycomb and other exotic spin models with polar molecules

We show that ultracold polar molecules pinned in an optical lattice can be used to access a variety of exotic spin models, including the Kitaev honeycomb model. Treating each molecule as a rigid rotor, we use DC electric and microwave fields to define superpositions of rotational levels as effective spin degrees of freedom, while dipole–dipole interactions give rise to interactions between the spins. In particular, we show that, with sufficient microwave control, the interaction between two spins can be written as a sum of five independently controllable Hamiltonian terms proportional to the five rank-2 spherical harmonics Y 2, q (θ, φ), where (θ, φ) are the spherical coordinates of the vector connecting the two molecules. To demonstrate the potential of this approach beyond the simplest examples studied in [S.R. Manmana et al.,  (2013)], we focus on the realisation of the Kitaev honeycomb model, which can support exotic non-Abelian anyonic excitations. We also discuss the possibility of generating spin Hamiltonians with arbitrary spin S, including those exhibiting SU(N=2S+1) symmetry.
Ecosystems perspective on financial networks: Diagnostic tools

The world economy consists of highly interconnected and interdependent commercial and financial networks. Here, we develop temporal and structural network tools to analyze the state of the economy and the financial markets. Our analysis indicates that a strong clustering can be a warning sign. Reduction in diversity, which was an essential aspect of the dynamics surrounding the financial markets crisis of 2008, is seen as a key emergent feature arising naturally from the evolutionary and adaptive dynamics inherent to the financial markets. Similarly, collusion amongst construction firms in a number of regions in Japan in the 2000s can be identified with the formation of clusters of anomalous highly connected companies.
Canalization and Control in Automata Networks: Body Segmentation in Drosophila melanogaster

We present schema redescription as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. In our formulation, canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level). This way, our approach provides a method to link micro- to macro-level dynamics – a crux of complexity. Several new results ensue from this methodology: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks), identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. We exemplify our methodology with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster. We use this model to show that our analysis does not contradict any previous findings. But we also obtain new knowledge about its behaviour: a better understanding of the size of its wild-type attractor basin (larger than previously thought), the identification of novel minimal conditions and critical nodes that control wild-type behaviour, and the resilience of these to stochastic interventions. Our methodology is applicable to any complex network that can be modelled using automata, but we focus on biochemical regulation and signalling, towards a better understanding of the (decentralized) control that orchestrates cellular activity – with the ultimate goal of explaining how do cells and tissues ‘compute’.
Pricing Using a Homogeneously Saturated Equation

A homogeneously saturated equation for the time development of the price of a financial asset is presented and investigated for the pricing of European call options using noise that is distributed as a Student's t-distribution. In the limit that the saturation parameter of the equation equals zero, the standard model of geometric motion for the price of an asset is obtained. The homogeneously saturated equation for the price of an asset is similar to a simple equation for the output of a homogeneously broadened laser. The homogeneously saturated equation tends to limit the range of returns and thus seems to be realistic. Fits to linear returns obtained from the adjusted closing values for the S&P 500 index were used to obtain best-fit parameters for Student's t-distributions and for normal distributions, and these fits were used to price options, and to compare approaches to modelling prices. This work has value in understanding the pricing of assets and of European call options.
2 theories from cluster algebras

b partition function of the theory coincides with the cluster partition function defined from the pair (Q,m). Our formalism includes the case where 3d N = 2 theories arise from the compact- ification of the 6d (2,0) AN−1 theory on a large class of 3-manifolds M, including complements of arbitrary links in S 3 . In this case the quiver is defined from a 2d ideal triangulation, the mutation sequence represents an element of the mapping class group, and the 3-manifold is equipped with a canonical ideal triangulation. Our partition func- tion then coincides with that of the holomorphic part of the SL(N) Chern-Simons partition function on M.
The hypothesis of urban scaling: formalization, implications and challenges

There is strong expectation that cities, across time, culture and level of development, share much in common in terms of their form and function. Recently, attempts to formalize mathematically these expectations have led to the hypothesis of urban scaling, namely that certain properties of all cities change, on average, with their size in predictable scale-invariant ways. The emergence of these scaling relations depends on a few general properties of cities as social networks, co-located in space and time, that conceivably apply to a wide range of human settlements. Here, we discuss the present evidence for the hypothesis of urban scaling, some of the methodological issues dealing with proxy measurements and units of analysis and place these ndings in the context of other theories of cities and urban systems. We show that a large body of evidence about the scaling properties of cities indicates, in analogy to other complex systems, that they cannot be treated as extensive systems and discuss the consequences of these results for an emerging statistical theory of cities.
Conservation Laws, Financial Entropy and the Eurozone Crisis

The article starts by examining the idea of conservation laws as applied to market economies. It formulates a measure of financial entropy and gives numerical simula-tions indicating that this tends to rise. We discuss an analogue for free energy released during this process. The concepts of real and symbolic appropriation are introduced as a means to analyse debt and taxation. We then examine the conflict between the conservation laws that apply to commodity exchange with the exponential growth implied by capital accumulation and how these have necessitated a sequence of evolutionary forms for money, and go on to present a simple stochastic model for the formation of rates of interest and a model for the time evolution of the rate of profit.
Understanding metropolitan patterns of daily encounters

Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters’ bounded nature. An individual’s encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of “familiar strangers” in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or “structure of co-presence” across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and—particularly—disclosing the impact of human behavior on various diffusion/spreading processes.
Neutrino textures and charged lepton flavour violation in light of $\theta_{13}$, MEG and LHC data

In light of recent results from the LHC, MEG and neutrino experiments, we revisit the issue of charged lepton flavour violation (LFV) in supersymmetric theories with massive neutrinos, where flavour-violating soft supersymmetry-breaking masses for sleptons are induced naturally by radiative corrections. We link our results to the expectations for neutrino mixing angles in SU(5) enhanced by an abelian flavour symmetry, with particular focus on $\theta_{13}$. We focus on the radiative decays $\ell_i \rightarrow \ell_j \gamma$ and on detection prospects at the LHC and a linear collider (LC). We use supersymmetric parameters consistent with cosmological considerations and with LHC searches for supersymmetry and the Higgs mass. We find a class of scenarios where the LHC may be sensitive to LFV sparticle decays and LFV processes could be detectable at a LC with centre-of-mass energy above 1 TeV, whereas LFV lepton decays may be suppressed by cancellations in the decay amplitudes.
Transfer matrix for treating stratified media including birefringent crystals.

Birefringent crystals are extensively used to manipulate polarized light. The generalized transfer matrix developed allows efficient calculation of the full polarization state of light transmitted through and reflected by a stack of arbitrarily many discrete layers of isotropic and birefringent materials at any frequency and angle of incidence. The matrix of a uniaxial birefringent crystal with arbitrary rotation is calculated, along with its reduction to the matrix of an isotropic medium. This method is of great practical importance where tight control of systematic effects is needed in optical systems employing birefringent crystals, one example being wave plates used by cosmic microwave background polarimetry with wide field-of-view telescopes.
Hyperboloidal Einstein-matter evolution and tails for scalar and Yang–Mills fields

We show how matter can be included in a constrained ADM-like formulation of the Einstein equations on constant mean curvature surfaces. Previous results on the regularity of the equations at future null infinity are unaffected by the addition of matter with a trace-free energy–momentum tensor. Two examples are studied in detail, namely a conformally coupled scalar field and a Yang–Mills field. We first derive the equations under no symmetry assumptions and then reduce them to spherical symmetry. Both sectors (gravitational and sphaleron) of the spherically symmetric Yang–Mills field are included. We implement this scheme numerically in order to study late-time tails of scalar and Yang–Mills fields coupled to the Einstein equations. We are able to evolve spacetimes that disperse to flat space, accrete onto a given black hole or collapse to a black hole from regular initial data. The sphaleron sector of Yang–Mills is found to exhibit some nontrivial gauge dynamics.
Increased Network Interdependency Leads to Aging

Although species longevity is subject to a diverse range of selective forces, the mortality curves of a wide variety of organisms are rather similar. We argue that aging and its universal characteristics may have evolved by means of a gradual increase in the systemic interdependence between a large collection of biochemical or mechanical components. Modeling the organism as a dependency network which we create using a constructive evolutionary process, we age it by allowing nodes to be broken or repaired according to a probabilistic algorithm that accounts for random failures/repairs and dependencies. Our simulations show that the network slowly accumulates damage and then catastrophically collapses. We use our simulations to fit experimental data for the time dependent mortality rates of a variety of multicellular organisms and even complex machines such as automobiles. Our study suggests that aging is an emergent finite-size effect in networks with dynamical dependencies and that the qualitative and quantitative features of aging are not sensitively dependent on the details of system structure.
A joint analysis of the Drake equation and the Fermi paradox

I propose a unified framework for a joint analysis of the Drake equation and the Fermi paradox, which enables a simultaneous, quantitative study of both of them. The analysis is based on a simplified form of the Drake equation and on a fairly simple scheme for the colonization of the Milky Way. It appears that for sufficiently long-lived civilizations, colonization of the Galaxy is the only reasonable option to gain knowledge about other life forms. This argument allows one to define a region in the parameter space of the Drake equation, where the Fermi paradox definitely holds (‘Strong Fermi paradox’).
Stock Price Fluctuations in an Agent-Based Model with Market Liquidity

We study an agent-based stock market model with heterogeneous agents and friction. Our model is based on that of Foellmer-Schweizer(1993): The process of a stock price in a discrete-time framework is determined by temporary equilibria via agents' excess demand functions, and the diffusion approximation approach is applied to characterize the continuous-time limit (as transaction intervals shorten) as a solution of the corresponding stochastic differential equation (SDE). In this paper we further make the assumption that some of the agents are bound by either short sale constraints or budget constraints. Then we show that the continuous-time process of the stock price can be derived from a certain SDE with oblique reflection. Moreover we find that the short sale (respectively, budget) constraint causes overpricing (respectively, underpricing).
Poisson-Newton formulas and Dirichlet series

We prove that a Poisson-Newton formula, in a broad sense, is associated to each Dirichlet series with a meromorphic extension to the whole complex plane. These formulas simultaneously generalize the classical Poisson formula and Newton formulas for Newton sums. Classical Poisson formulas in Fourier analysis, classical summation formulas as Euler-McLaurin or Abel-Plana formulas, explicit formulas in number theory and Selberg trace formulas in Riemannian geometry appear as special cases of our general Poisson-Newton formula. We also associate to finite order meromorphic functions general Poisson-Newton formulas that yield many classical integral formulas.
An alternative text representation to TF-IDF and Bag-of-Words

In text mining, information retrieval, and machine learning, text documents are commonly represented through variants of sparse Bag of Words (sBoW) vectors (e.g. TF-IDF). Although simple and intuitive, sBoW style representations suffer from their inherent over-sparsity and fail to capture word-level synonymy and polysemy. Especially when labeled data is limited (e.g. in document classification), or the text documents are short (e.g. emails or abstracts), many features are rarely observed within the training corpus. This leads to overfitting and reduced generalization accuracy. In this paper we propose Dense Cohort of Terms (dCoT), an unsupervised algorithm to learn improved sBoW document features. dCoT explicitly models absent words by removing and reconstructing random sub-sets of words in the unlabeled corpus. With this approach, dCoT learns to reconstruct frequent words from co-occurring infrequent words and maps the high dimensional sparse sBoW vectors into a low-dimensional dense representation. We show that the feature removal can be marginalized out and that the reconstruction can be solved for in closed-form. We demonstrate empirically, on several benchmark datasets, that dCoT features significantly improve the classification accuracy across several document classification tasks.
The semiclassical theory of discontinuous systems and ray-splitting billiards

We analyze the semiclassical limit of spectral theory on manifolds whose metrics have jump-like discontinuities. Such systems are quite different from manifolds with smooth Riemannian metrics because the semiclassical limit does not relate to a classical flow but rather to branching (ray-splitting) billiard dynamics. In order to describe this system we introduce a dynamical system on the space of functions on phase space. To identify the quantum dynamics in the semiclassical limit we compute the principal symbols of the Fourier integral operators associated to reflected and refracted geodesic rays and identify the relation between classical and quantum dynamics. In particular we prove a quantum ergodicity theorem for discontinuous systems. In order to do this we introduce a new notion of ergodicity for the ray-splitting dynamics.
Studying User Footprints in Different Online Social Networks

With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, Linked In, Twitter and You  Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users' online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, Friend Feed, and Profilactic, we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler similarity, Word net based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and Linked In. In this paper, we present the analysis and results from applying automated classifiers for disambiguating profiles belonging to the same user from different social networks. User ID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.
Modified-gravity wormholes without exotic matter

TEX-ed March 22, 2013)A fundamental ingredient in wormhole physics is the ﬂaring-out condition at the throat which,in classical general relativity, entails the violation of the null energy condition. In this work, wepresent the most general conditions in the context of modiﬁed gravity, in which the matter threadingthe wormhole throat satisﬁes all of the energy conditions, and it is the higher order curvatureterms, which may be interpreted as a gravitational ﬂuid, that support these nonstandard wormholegeometries. Thus, we explicitly show that wormhole geometries can be theoretically constructedwithout the presence of exotic matter, but are sustained in the context of modiﬁed gravity.
A Unified Community Detection, Visualization and Analysis Method

Community detection in social graphs has attracted researchers' interest for a long time. With the widespread of social networks on the Internet it has recently become an important research domain. Most contributions focus upon the definition of algorithms for optimizing the so-called modularity function. In the first place interest was limited to unipartite graph inputs and partitioned community outputs. Recently bipartite graphs, directed graphs and overlapping communities have been investigated. Few contributions embrace at the same time the three types of nodes. In this paper we present a method which unifies commmunity detection for the three types of nodes and at the same time merges partitionned and overlapping communities. Moreover results are visualized in such a way that they can be analyzed and semantically interpreted. For validation we experiment this method on well known simple benchmarks. It is then applied to real data in three cases. In two examples of photos sets with tagged people we reveal social networks. A second type of application is of particularly interest. After applying our method to Human Brain Tractography Data provided by a team of neurologists, we produce clusters of white fibers in accordance with other well known clustering methods. Moreover our approach for visualizing overlapping clusters allows better understanding of the results by the neurologist team. These last results open up the possibility of applying community detection methods in other domains such as data analysis with original enhanced performances.
Delayed Twin Observables Are They a Fundamental Concept in Quantum Mechanics

Opposite-subsystem twin events and twin observables, studied previously in the context of distant correlations, are first generalized to pure states of not-necessarily-composite systems, and afterwards they are further generalized to delayed twins that are due to unitary evolution of the quantum system. The versatile aspects of delayed twin observables are studied in terms of necessary and sufficient conditions to make possible various applications. Three of these are sketched: Preparation of some quantum experiments, easy solution of a puzzle in an important Scully et al. real experiment, and, finally, it is shown that exact measurement in quantum mechanics is an example of opposite-subsystem delayed twins in bipartite pure states.
Algebraically special perturbations of the Schwarzschild solution in higher dimensions

We study algebraically special perturbations of a generalized Schwarzschild solution in any number of dimensions. There are two motivations. First, to learn whether there exist interesting higher-dimensional algebraically special solutions beyond the known ones. Second, algebraically special perturbations present an obstruction to the unique reconstruction of general metric perturbations from gauge-invariant variables analogous to the Teukolsky scalars and it is desirable to know the extent of this non-uniqueness. In four dimensions, our results generalize those of Couch and Newman, who found infinite families of time-dependent algebraically special perturbations. In higher dimensions, we find that the only regular algebraically special perturbations are those corresponding to deformations within the Myers–Perry family. Our results are relevant for several inequivalent definitions of ‘algebraically special’.