nils-herrmann/clean_scirepeval huggingface dataset:
Heat kernel based community detection

The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank diffusion, is useful in identifying a community nearby a starting seed node. We present the first deterministic, local algorithm to compute this diffusion and use that algorithm to study the communities that it produces. Our algorithm is formally a relaxation method for solving a linear system to estimate the matrix exponential in a degree-weighted norm. We prove that this algorithm stays localized in a large graph and has a worst-case constant runtime that depends only on the parameters of the diffusion, not the size of the graph. On large graphs, our experiments indicate that the communities produced by this method have better conductance than those produced by PageRank, although they take slightly longer to compute. On a real-world community identification task, the heat kernel communities perform better than those from the PageRank diffusion.
Teleparallel gravity in five-dimensional theories

We study teleparallel gravity in five-dimensional spacetime with particular discussions on Kaluza–Klein (KK) and braneworld theories. We directly perform the dimensional reduction by differential forms. In the braneworld theory, the teleparallel gravity formalism in the Friedmann–Lemaître–Robertson–Walker cosmology is equivalent to general relativity (GR) due to the same Friedmann equation, whereas in the KK case the reduction of our formulation does not recover the effect as GR of four-dimensional spacetime.
Higher curvature gravity at LHC

We investigate brane-world models in different viable $F(R)$ gravity theories where the Lagrangian is an arbitrary function of the curvature scalar. Deriving the warped metric for this model, resembling Randal-Sundrum (RS) like solutions, we determine the graviton KK modes. The recent observations at the LHC, which constrain the RS graviton KK modes to a mass range greater than 3 TeV, are incompatible to RS model predictions. It is shown that the models with $F(R)$ gravity in the bulk address the issue which in turn constrains the $F(R)$ model itself.
Sentiment Analysis by Using Fuzzy Logic

How could a product or service is reasonably evaluated by anyone in the shortest time? A million dollar question but it is having a simple answer: Sentiment analysis. Sentiment analysis is consumers review on products and services which helps both the producers and consumers (stakeholders) to take effective and efficient decision within a shortest period of time. Producers can have better knowledge of their products and services through the sentiment analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know their products status (ex. product limitations or market status). Consumers can have better knowledge of their interested products and services through the sentiment analysis (ex. positive and negative comments or consumers likes and dislikes) which will help them to know their deserving products status (ex. product limitations or market status). For more specification of the sentiment values, fuzzy logic could be introduced. Therefore, sentiment analysis with the help of fuzzy logic (deals with reasoning and gives closer views to the exact sentiment values) will help the producers or consumers or any interested person for taking the effective decision according to their product or service interest.
Naturalness in Higgs inflation in a frame independent formalism

We make use of the frame and gauge independent formalism for scalar and tensor cosmological perturbations developed in Ref.  to show that the physical cutoff for 2-to-2 tree level scatterings in Higgs inflation is above the Planck scale M_P throughout inflation. More precisely, we found that in the Jordan frame, the physical cutoff scale is $({\Lambda}/a)_J \gtrsim \sqrt{M_P^2+{\xi}{\phi}^2}$, while in the Einstein frame it is $({\Lambda}/a)_J \gtrsim M_P$, where $\xi$ is the nonminimal coupling and $\phi$ denotes the Higgs vev during inflation. The dimensionless ratio of the physical cutoff to the relevant Planck scale is equal to one in both frames, thus demonstrating the physical equivalence of the two frames. Our analysis implies that Higgs inflation is unitary up to the Planck scale, and hence there is no naturalness problem in Higgs inflation. In this paper we only consider the graviton and scalar interactions.
Electromagnetism in Quark-Antiquark Bound States

Non-perturbative proof is presented of a unique version of Goldstone theorem, that electromagnetism contributes to the masses of spinless particles of quark-antiquark bound states in the form of a commutator of the quark electric matrix and a coefficient factor which may be expressed simply in terms of a three-body Bethe-Salpeter amplitude, in the case where the Lagrangian conserves an approximate $SU(3)$ chiral symmetry. The coupled Bethe-Salpeter equation and Dyson-Schwinger equation in the ladder-rainbow approximation are layed out with every ingredient renormalized, which are applied to the problem of evaluating the mass differences of some charged and neutral pseudoscalar and vector mesons. The numerical results yield satisfactory agreement with experimental observations for the mass spectrum and meson decay constants. Estimates of the light quark masses are also given in the Bethe-Salpeter formalism.
Simple Model for Identifying Critical Regions in Atrial Fibrillation

Atrial fibrillation (AF) is the most common abnormal heart rhythm and the single biggest cause of stroke. Ablation, destroying regions of the atria, is applied largely empirically and can be curative but with a disappointing clinical success rate. We design a simple model of activation wave front propagation on an anisotropic structure mimicking the branching network of heart muscle cells. This integration of phenomenological dynamics and pertinent structure shows how AF emerges spontaneously when the transverse cell-to-cell coupling decreases, as occurs with age, beyond a threshold value. We identify critical regions responsible for the initiation and maintenance of AF, the ablation of which terminates AF. The simplicity of the model allows us to calculate analytically the risk of arrhythmia and express the threshold value of transversal cell-to-cell coupling as a function of the model parameters. This threshold value decreases with increasing refractory period by reducing the number of critical regions which can initiate and sustain microreentrant circuits. These biologically testable predictions might inform ablation therapies and arrhythmic risk assessment.
Coherent Chaos Interest Rate Models

The Wiener chaos approach to interest rate modelling arises from the observation that the pricing kernel admits a representation in terms of the conditional variance of a square-integrable random variable, which in turn admits a chaos expansion. When the expansion coefficients factorise into multiple copies of a single function, then the resulting interest rate model is called coherent, whereas a generic interest rate model will necessarily be incoherent. Coherent representations are nevertheless of fundamental importance because incoherent ones can always be expressed as a linear superposition of coherent elements. This property is exploited to derive general expressions for the pricing kernel and the associated bond price and short rate processes in the case of an n-th order chaos model for each $n$. The pricing formulae for bond options and swaptions are obtained in closed forms for a number of examples. An explicit representation for the pricing kernel of a generic---incoherent---model is then obtained by use of the underlying coherent elements. Finally, finite-dimensional realisations of the coherent chaos models are investigated in detail. In particular, it is shown that a class of highly tractable models can be constructed having the characteristic feature that the discount bond price is given by a piecewise flat (simple) process.
Warped Graviton"Z + Missing Energy"Signal at Hadron Colliders

We examine the reach at hadron colliders for the lightest warped Kaluza-Klein (KK) graviton G1 in the Z(→ ll)Z(→ νν̄) channel, l = e, μ, within Randall-Sundrummodels of hierarchy and flavor where the Standard Model gauge fields and fermions propagate in the 5D bulk. The reconstructed Z and the accompanying large missing energy allow for an efficient suppression of backgrounds. For reasonable parameters, a ∼ 2 (2.6) TeV G1 can be discovered at 5σ with 300 fb (3 ab) of 14 TeV LHC data via our “Z + missing energy” signal. Using this signal, the discovery reach for G1 at a future 100 TeV pp collider is estimated to be as high as ∼ 10 TeV. We discuss mass determination of the singly produced G1, using the energy distribution of its visible (Z → ll) decay product, by adapting a recently proposed method. Based on our analysis, a mass measurement at the ∼ 5% level with ∼ 3 ab of the 14 TeV LHC data can be feasible.
Neighborhood selection for thresholding-based subspace clustering

Subspace clustering refers to the problem of clustering high-dimensional data points into a union of low-dimensional linear subspaces, where the number of subspaces, their dimensions and orientations are all unknown. In this paper, we propose a variation of the recently introduced thresholding-based subspace clustering (TSC) algorithm, which applies spectral clustering to an adjacency matrix constructed from the nearest neighbors of each data point with respect to the spherical distance measure. The new element resides in an individual and data-driven choice of the number of nearest neighbors. Previous performance results for TSC, as well as for other subspace clustering algorithms based on spectral clustering, come in terms of an intermediate performance measure, which does not address the clustering error directly. Our main analytical contribution is a performance analysis of the modified TSC algorithm (as well as the original TSC algorithm) in terms of the clustering error directly.
Genetic Influences on Translation in Yeast

Heritable differences in gene expression between individuals are an important source of phenotypic variation. The question of how closely the effects of genetic variation on protein levels mirror those on mRNA levels remains open. Here, we addressed this question by using ribosome profiling to examine how genetic differences between two strains of the yeast S. cerevisiae affect translation. Strain differences in translation were observed for hundreds of genes. Allele specific measurements in the diploid hybrid between the two strains revealed roughly half as many cis-acting effects on translation as were observed for mRNA levels. In both the parents and the hybrid, most effects on translation were of small magnitude, such that the direction of an mRNA difference was typically reflected in a concordant footprint difference. The relative importance of cis and trans acting variation on footprint levels was similar to that for mRNA levels. There was a tendency for translation to cause larger footprint differences than expected given the respective mRNA differences. This is in contrast to translational differences between yeast species that have been reported to more often oppose than reinforce mRNA differences. Finally, we catalogued instances of premature translation termination in the two yeast strains and also found several instances where erroneous reference gene annotations lead to apparent nonsense mutations that in fact reside outside of the translated gene body. Overall, genetic influences on translation subtly modulate gene expression differences, and translation does not create strong discrepancies between genetic influences on mRNA and protein levels.  Individuals in a species differ from each other in many ways. For many traits, a fraction of this variation is genetic – it is caused by DNA sequence variants in the genome of each individual. Some of these variants influence traits by altering how much certain genes are expressed, i.e. how many mRNA and protein molecules are made in different individuals. Surprisingly, earlier work has found that the effects of genetic variants on mRNA and protein levels for the same genes appear to be very different. Many variants appeared to influence only mRNA (but not protein) levels, and vice versa. In this paper, we studied this question by using a technique called “ribosome profiling” to measure translation (the cellular process of reading mRNA molecules and synthesizing protein molecules) in two yeast strains. We found that the genetic differences between these two strains influence translation for hundreds of genes. Because most of these effects were small in magnitude, they explain at most a small fraction of the discrepancies between the effects of genetic variants on mRNA and protein levels.
Structure Conditions under Progressively Added Information

It has been understood that the "local" existence of the Markowitz' optimal portfolio or the solution to the local-risk minimization problem is guaranteed by some specific mathematical structures on the underlying assets price processes known in the literature as "{\it Structure Conditions}". In this paper, we consider a semi-martingale market model, and an arbitrary random time that is not adapted to the information flow of the market model. This random time may model the default time of a firm, the death time of an insured, or any the occurrence time of an event that might impact the market model somehow. By adding additional uncertainty to the market model, via this random time, the {\it structures conditions} may fail and hence the Markowitz's optimal portfolio and other quadratic-optimal portfolios might fail to exist. Our aim is to investigate the impact of this random time on the structures conditions from different perspectives. Our analysis allows us to conclude that under some mild assumptions on the market model and the random time, these structures conditions will remain valid on the one hand. Furthermore, we provide two examples illustrating the importance of these assumptions. On the other hand, we describe the random time models for which these structure conditions are preserved for any market model. These results are elaborated separately for the two contexts of stopping with the random time and incorporating totally a specific class of random times respectively.
Scalable and Robust Construction of Topical Hierarchies

Automated generation of high-quality topical hierarchies for a text collection is a dream problem in knowledge engineering with many valuable applications. In this paper a scalable and robust algorithm is proposed for constructing a hierarchy of topics from a text collection. We divide and conquer the problem using a top-down recursive framework, based on a tensor orthogonal decomposition technique. We solve a critical challenge to perform scalable inference for our newly designed hierarchical topic model. Experiments with various real-world datasets illustrate its ability to generate robust, high-quality hierarchies efficiently. Our method reduces the time of construction by several orders of magnitude, and its robust feature renders it possible for users to interactively revise the hierarchy.
Curr\'iculo interdisciplinar para licenciatura em ci\^encias da natureza

Descreve-se neste trabalho uma proposta de curr\'iculo interdisciplinar para a forma\c{c}\~ao de professores de ci\^encias da natureza. O curso permite a obten\c{c}\~ao de quatro diplomas: professor de ci\^encias para o ensino fundamental (nomenclatura brasileira), professor de biologia, f\'isica e qu\'imica para o ensino m\'edio. O diploma de professor de ci\^encias \'e obtido com a integraliza\c{c}\~ao de cr\'editos oferecidos ao longo dos tr\^es primeiros anos do curso. Para cada ano subsequente \'e poss\'ivel obter os diplomas de professor do ensino m\'edio. Os componentes curriculares pertinentes \`as ci\^encias da natureza s\~ao inteiramente interdisciplinares nos tr\^es primeiros anos. No quarto ano s\~ao oferecidas disciplinas espec\'ificas de biologia,  
An interdisciplinary curriculum for science teaching undergraduate course will be described. The curriculum allows four degrees according the Brazilian educational legislation: science teacher for the middle school, biology, chemistry and physics teacher for the high school. The science teacher degree is obtained by accomplishing the three initial years syllabus. For each subsequent year it will be possible to obtain the other degrees. The components of the curriculum for the three initial years are radically interdisciplinary, with a pedagogical organization in such ways to prepare students for the subsequent year dedicated to a specific discipline (biology, chemistry or physics).
Reflection and transmission of ocean wave spectra by a band of randomly distributed ice floes

A new ocean wave/sea-ice interaction model is proposed that simulates how a directional wave spectrum evolves as it travels through an arbitrary finite array of circular ice floes, where wave/ ice dynamics are entirely governed by wave-scattering effects. The model is applied to characterize the wave reflection and transmission properties of a strip of ice floes, such as an ice edge band. A method is devised to extract the reflected and transmitted directional wave spectra produced by the array. The method builds upon an integral mapping from polar to Cartesian coordinates of the scattered wave components. Sensitivity tests are conducted for a row of floes randomly perturbed from a regular arrangement. Results for random arrays are generated using ensemble averaging. A realistic ice edge band is then reconstructed from field experiment data. Simulations show good qualitative agreement with the data in terms of transmitted wave energy and directional spreading. In particular, it is observed that short waves become isotropic quickly after penetrating the ice field.
Analyzing Large Biological Datasets with an Improved Algorithm for MIC

The computational framework used the traditional similarity measures to find out the significant relationships in biological annotations. But its prerequisites that the biological annotations do not cooccur with each other is particular. To overcome it, in this paper a new method Improved Algorithm for Maximal Information Coefficient (IAMIC) is suggested to discover the hidden regularities between biological annotations. IAMIC approximates a novel similarity coefficient on maximal information coefficient with generality and equitability, by bettering axis partition through quadratic optimisation instead of violence search. The experimental results show that IAMIC is more appropriate for identifying the associations between biological annotations, and further extracting the novel associations hidden in collected data sets than other similarity measures.
Holographic patterning of graphene-oxide films by light-driven reduction.

We report on the patterning and reduction of graphene-oxide films by holographic lithography. Light reduction can be used to engineer low-cost graphene-based devices by performing a local conversion of insulating oxide into the conductive graphene. In this work, computer-generated holograms have been exploited to realize complex graphene patterns in a single shot, different from serial laser writing or mask-based photolithographic processes. The technique has been further improved by achieving speckle noise reduction: submicron and diffraction-limited features have been obtained. In addition we have also demonstrated that the gray-scale lithography capability can be used to obtain different reduction levels in a single exposure.
Learning the Latent State Space of Time-Varying Graphs

From social networks to Internet applications, a wide variety of electronic communication tools are producing streams of graph data; where the nodes represent users and the edges represent the contacts between them over time. This has led to an increased interest in mechanisms to model the dynamic structure of time-varying graphs. In this work, we develop a framework for learning the latent state space of a time-varying email graph. We show how the framework can be used to find subsequences that correspond to global real-time events in the Email graph (e.g. vacations, breaks, ...etc.). These events impact the underlying graph process to make its characteristics non-stationary. Within the framework, we compare two different representations of the temporal relationships; discrete vs. probabilistic. We use the two representations as inputs to a mixture model to learn the latent state transitions that correspond to important changes in the Email graph structure over time.
Interplay between microdynamics and macrorheology in vesicle suspensions.

The microscopic dynamics of objects suspended in a fluid determines the macroscopic rheology of a suspension. For example, as shown by Danker and Misbah [ 2007, 98, 088104], the viscosity of a dilute suspension of fluid-filled vesicles is a non-monotonic function of the viscosity contrast (the ratio between the viscosities of the internal encapsulated and the external suspending fluids) and exhibits a minimum at the critical point of the tank-treading-to-tumbling transition. By performing numerical simulations, we recover this effect and demonstrate that it persists for a wide range of vesicle parameters such as the concentration, membrane deformability, or swelling degree. We also explain why other numerical and experimental studies lead to contradicting results. Furthermore, our simulations show that this effect even persists in non-dilute and confined suspensions, but that it becomes less pronounced at higher concentrations and for more swollen vesicles. For dense suspensions and for spherical (circular in 2D) vesicles, the intrinsic viscosity tends to depend weakly on the viscosity contrast.
Near-optimal Reinforcement Learning in Factored MDPs

Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will suffer Ω(SAT) regret on some MDP, where T is the elapsed time and S and A are the cardinalities of the state and action spaces. This implies T = Ω(SA) time to guarantee a near-optimal policy. In many settings of practical interest, due to the curse of dimensionality, S and A can be so enormous that this learning time is unacceptable. We establish that, if the system is known to be a factored MDP, it is possible to achieve regret that scales polynomially in the number of parameters encoding the factored MDP, which may be exponentially smaller than S or A. We provide two algorithms that satisfy near-optimal regret bounds in this context: posterior sampling reinforcement learning PSRL) and an upper confidence bound algorithm (UCRL-Factored).
A fast Fourier transform method for Mellin-type option pricing

Analytical pricing formulas and Greeks are obtained for European and American basket put options using Mellin transforms. We assume assets are driven by geometric Brownian motion which exhibit correlation and pay a continuous dividend rate. A novel approach to numerical Mellin inversion is achieved via the fast Fourier transform, enabling the computation of option values at equidistant log asset prices. Numerical accuracy is verified among existing methods for American call options.
Big data analytics - Retour vers le futur 3. De statisticien à data scientist

The rapid evolution of information systems managing more and more voluminous data has caused profound paradigm shifts in the job of statistician, becoming successively data miner, bioinformatician and now data scientist. Without the sake of completeness and after having illustrated these successive mutations, this article briefly introduced the new research issues that quickly rise in Statistics, and more generally in Mathematics, in order to integrate the characteristics: volume, variety and velocity, of big data.