nils-herrmann/clean_scirepeval huggingface dataset:
Seismic Earth Pressure Development in Sheet Pile Retaining Walls: A Numerical Study

The design of retaining walls requires the complete knowledge of the earth pressure distribution behind the wall. Due to the complex soil-structure effect, the estimation of earth pressure is not an easy task; even in the static case. The problem becomes even more complex for the dynamic (i.e., seismic) analysis and design of retaining walls. Several earth pressure models have been developed over the years to integrate the dynamic earth pressure with the static earth pressure and to improve the design of retaining wall in seismic regions. Among all the models, MononobeOkabe (M-O) method is commonly used to estimate the magnitude of seismic earth pressures in retaining walls and is adopted in design practices around the world (e.g., EuroCode and Australian Standards). However, the M-O method has several drawbacks and does not provide reliable estimate of the earth pressure in many instances. This study investigates the accuracy of the M-O method to predict the dynamic earth pressure in sheet pile wall. A 2D plane strain finite element model of the wall-soil system was developed in DIANA. The backfill soil was modelled with Mohr-Coulomb failure criterion while the wall was assumed behave elastically. The numerically predicted dynamic earth pressure was compared with the M-O model prediction. Further, the point of application of total dynamic force was determined and compared with the static case. Finally, the applicability of M-O methods to compute the seismic earth pressure was discussed.
The fluctuation energy balance in non-suspended fluid-mediated particle transport

Here we compare two extreme regimes of non-suspended fluid-mediated particle transport, transport in light and heavy fluids ("saltation" and "bedload", respectively), regarding their particle fluctuation energy balance. From direct numerical simulations, we surprisingly find that the ratio between collisional and fluid drag dissipation of fluctuation energy is significantly larger in saltation than in bedload, even though the contribution of interparticle collisions to transport of momentum and energy is much smaller in saltation due to the low concentration of particles in the transport layer. We conclude that the much higher frequency of high-energy particle-bed impacts ("splash") in saltation is the cause for this counter-intuitive behavior. Moreover, from a comparison of these simulations to Particle Tracking Velocimetry measurements which we performed in a wind tunnel under steady transport of fine and coarse sand, we find that turbulent fluctuations of the flow produce particle fluctuation energy at an unexpectedly high rate in saltation even under conditions for which the effects of turbulence are usually believed to be small.
Stochastic Local Interaction (SLI) Model: Interfacing Machine Learning and Geostatistics

Machine learning and geostatistics are two powerful approaches used to model spatial data. Each approach has advantages and disadvantages, but they both suffer from poor scaling of the computational load with data size due to the inversion of large covariance matrices. We present a new method for the modeling of spatial data that combines ideas from statistical physics, computational geometry, and machine learning. The proposed Stochastic Local Interaction (SLI) model is based on an explicit precision (inverse covariance) matrix and can be applied to data in d-dimensional spaces. The SLI model is defined by means of a Gaussian joint probability density function, which is expressed in terms of energy functionals that involve local interaction constraints implemented by means of kernel functions. The variability of the sampling density is accounted by means of local kernel bandwidths. The SLI model leads to a semi-analytical expression for interpolation (prediction), which is valid in any number of dimensions and does not require the inversion of a covariance matrix.
Differentially Private Bayesian Optimization

Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems. As machine learning becomes commonplace, Bayesian optimization becomes an attractive method for practitioners to automate the process of classifier hyper-parameter tuning. A key observation is that the data used for tuning models in these settings is often sensitive. Certain data such as genetic predisposition, personal email statistics, and car accident history, if not properly private, may be at risk of being inferred from Bayesian optimization outputs. To address this, we introduce methods for releasing the best hyper-parameters and classifier accuracy privately. Leveraging the strong theoretical guarantees of differential privacy and known Bayesian optimization convergence bounds, we prove that under a GP assumption these private quantities are often near-optimal. Finally, even if this assumption is not satisfied, we can use different smoothness guarantees to protect privacy.
A hierarchy of linear threshold models for the spread of political revolutions on social networks

We study a linear threshold agent-based model (ABM) for the spread of political revolutions on social networks using empirical network data. We propose new techniques for building a hierarchy of simplified ordinary differential equation (ODE) based models that aim to capture essential features of the ABM, including effects of the actual networks, and give insight in the parameter regime transitions of the ABM. We relate the ABM and the hierarchy of models to a population-level compartmental ODE model that we proposed previously for the spread of political revolutions , which is shown to be mathematically consistent with the proposed ABM and provides a way to analyze the global behaviour of the ABM. This consistency with the linear threshold ABM also provides further justification a posteriori for the compartmental model of . Extending concepts from epidemiological modelling, we define a basic reproduction number $R_0$ for the linear threshold ABM and apply it to predict ABM behaviour on empirical networks. In small-scale numerical tests we investigate experimentally the differences in spreading behaviour that occur under the linear threshold ABM model when applied to some empirical online and offline social networks, searching for quantitative evidence that political revolutions may be facilitated by the modern online social networks of social media.
Topological phase transitions in superradiance lattices

Topological phases of matter are of fundamental interest and have promising applications. Fascinating topological properties of light have been unveiled in classical optical materials. However, the manifestation of topological physics in quantum optics has not been discovered. Here we study the topological phases in a two-dimensional momentum-space superradiance lattice composed of timed Dicke states (TDSs) in electromagnetically induced transparency (EIT). By periodically modulating the three EIT coupling fields, we can create a Haldane model with in situ tunable topological properties, which manifest themselves in the contrast between diffraction signals emitted by superradiant TDSs. The topological superradiance lattices provide a controllable platform for simulating exotic phenomena in condensed matter physics and offer a basis of topological quantum optics and novel photonic devices.
Quantization of the conformal arclength functional on space curves

By a conformal string in Euclidean space is meant a closed critical curve with non-constant conformal curvatures of the conformal arclength functional. We prove that (1) the set of conformal classes of conformal strings is in 1-1 correspondence with the rational points of the complex domain $\{q\in \mathbb{C} \,:\, 1/2 0,\,\, |q| < 1/\sqrt{2}\}$ and (2) any conformal class has a model conformal string, called symmetrical configuration, which is determined by three phenomenological invariants: the order of its symmetry group and its linking numbers with the two conformal circles representing the rotational axes of the symmetry group. This amounts to the quantization of closed trajectories of the contact dynamical system associated to the conformal arclength functional via Griffiths' formalism of the calculus of variations.
Bulk Flows and End of the Dark Ages with the SKA

The early Universe is a precious probe of the birth of primordial objects, first star formation events and consequent production of photons and heavy elements. Higher-order corrections to the cosmological linear perturbation theory predicts the formation of coherent supersonic gaseous streaming motions at decoupling time. These bulk flows impact the gas cooling process and determine a cascade effect on the whole baryon evolution. By analytical estimates and N-body hydrodynamical chemistry numerical simulations including atomic and molecular evolution, gas cooling, star formation, feedback effects and metal spreading for individual species from different stellar populations according to the proper yields and lifetimes, we discuss the role of these primordial bulk flows at the end of the dark ages and their detectable impacts during the first Gyr in view of the upcoming SKA mission. Early bulk flows can inhibit molecular gas cooling capabilities, suppressing star formation, metal spreading and the abundance of small primordial galaxies in the infant Universe. This can determine a delay in the re-ionization process and in the heating of neutral hydrogen making the observable HI signal during cosmic evolution patchier and noisier. The planned SKA mission will represent a major advance over existing instruments, since it will be able to probe the effects on HI 21cm at z ~ 6-20 and on molecular line emissions from first collapsing sites at z ~ 20-40. Therefore, it will be optimal to address the effects of primordial streaming motions on early baryon evolution and to give constraints on structure formation in the first Gyr.
Precise determination of $\alpha_s$ from the $C$-parameter distribution

nal states coming from the underlying partons created in the collisions appear as boosted and collimated groups of hadrons known as jets. Event shapes have proven to be very successful to study these collisions quantitatively. They combine the energy and momenta of all of the measured hadrons into an infraredand collinear-safe parameter which describes the geometric properties of the whole event by a single variable distribution. Due to their global nature event shapes have nice theoretical properties, making it possible to obtain very accurate theoretical predictions using QCD.  Because the dijet limit involves restrictions that only allow collinear and soft degrees of freedom for the nal-state radiation, such
Project of the underwater system for chemical threat detection

In this article we describe a novel method for the detection of explosives and other hazardous substances in the marine environment using neutron activation. Unlike the other considered methods based on this technique we propose to use guides forneutron and gamma quanta which speeds up and simplifies identification. Moreover, it may provide a determination of the density distribution of a dangerous substance. First preliminary results of Monte Carlo simulations dedicated for design of a device exploiting this method are also presented.
Canonical, stable, general mapping using context schemes

Sequence mapping is the cornerstone of modern genomics. However, most existing sequence mapping algorithms are insufficiently general.



We introduce context schemes: a method that allows the unambiguous recognition of a reference base in a query sequence by testing the query for substrings from an algorithmically defined set. Context schemes only map when there is a unique best mapping, and define this criterion uniformly for all reference bases. Mappings under context schemes can also be made stable, so that extension of the query string (e.g. by increasing read length) will not alter the mapping of previously mapped positions. Context schemes are general in several senses. They natively support the detection of arbitrary complex, novel rearrangements relative to the reference. They can scale over orders of magnitude in query sequence length. Finally, they are trivially extensible to more complex reference structures, such as graphs, that incorporate additional variation. We demonstrate empirically the existence of high-performance context schemes, and present efficient context scheme mapping algorithms.



The software test framework created for this study is available from https://registry.hub.docker.com/u/adamnovak/sequence-graphs/.


CONTACT
anovak@soe.ucsc.edu


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Gravitational collapse and formation of universal horizons

The invariance under the Lorentz symmetry group is a cornerstone of modern physics, and is strongly supported by observations. In fact, all the experiments carried out so far are consistent with it , and no evidence to show that such a symmetry needs to be broken at certain energy scales, although it is arguable that such constraints in the gravitational sector are much weaker than those in the matter sector . Nevertheless, there are various reasons to construct gravitational theories with broken Lorentz invariance (LI) . In particular, our understanding of space-times at Plank scale is still highly limited, and the renomalizability and unitarity of gravity often lead to the violation of LI . One concrete example is the Ho
Ricci curvature of the Internet topology

Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's “thin triangle condition”, which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier , Lin et al. , etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.
Nuclear energy density functionals: What we can learn about/from their global performance?

A short review of recent results on the global performance of covariant energy density functionals is presented. It is focused on an analysis of the accuracy of the description of physical observables of ground and excited states as well as to related theoretical uncertainties. In addition, a global analysis of pairing properties is presented and the impact of pairing on the position of two-neutron drip line is discussed.
Photon upconversion with hot carriers in plasmonic systems

We propose a scheme of photon upconversion based on harnessing the energy of plasmonic hot carriers. Low-energy photons excite hot electrons and hot holes in a plasmonic nanoparticle, which are then injected into an adjacent semiconductor quantum well where they radiatively recombine to emit a photon of higher energy. We theoretically study the proposed upconversion scheme using Fermi-liquid theory and determine the internal quantum efficiency of upconversion to be as high as 25% in 5 nm silver nanocubes. This upconversion scheme is linear in its operation, does not require coherent illumination, offers spectral tunability, and is more efficient than conventional upconverters.
Remote focusing of a light beam

The remote focusing of light in a graded-index medium via mode interference is demonstrated using exact analytical solutions of the wave equation. Strong focusing of a light beam occurs at extremely long distances and this repeats periodically with distance. The high efficiency transfer of a strongly focused subwavelength spot through an optical waveguide over large distances takes place with a period of revival. The simulated intensity distributions of a focused light beam and focusing efficiency (power in a central spot/total power) are presented. The far-field super-resolution imaging capabilities of a graded-index waveguide are also analyzed.
Second International Nurse Rostering Competition (INRC-II) - Problem Description and Rules -

In this paper, we provide all information to participate to the Second International Nurse Rostering Competition (INRC-II). First, we describe the problem formulation, which, differently from INRC-I, is a multi-stage procedure. Second, we illustrate all the necessary infrastructure do be used together with the participant's solver, including the testbed, the file formats, and the validation/simulation tools. Finally, we state the rules of the competition. All update-to-date information about the competition is available at this http URL
Cosmology from EoR/Cosmic Dawn with the SKA

The SKA will build upon early detections of the EoR by precursor instruments, such as MWA, PAPER, and LOFAR, and planned instruments, such as HERA, to make the first high signal-to-noise measurements of fluctuations in the 21 cm brightness temperature from both reionization and the cosmic dawn. This will allow both imaging and statistical maps of the 21cm signal at redshifts z = 6 - 27 and constrain the underlying cosmology and evolution of the density field. This era includes nearly 60% of the (in principle) observable volume of the Universe and many more linear modes than the CMB, presenting an opportunity for SKA to usher in a new level of precision cosmology. This optimistic picture is complicated by the need to understand and remove the effect of astrophysics, so that systematics rather than statistics will limit constraints. This chapter describes the cosmological, as opposed to astrophysical, information available to SKA. Key areas for discussion include: cosmological parameters constraints using 21cm fluctuations as a tracer of the density field; lensing of the 21cm signal, constraints on heating via exotic physics such as decaying or annihilating dark matter; impact of fundamental physics such as non-Gaussianity or warm dark matter on the source population; and constraints on the bulk flows arising from the decoupling of baryons and photons at z = 1000. The chapter explores the path to separating cosmology from astrophysics, for example via velocity space distortions and separation in redshift. We discuss new opportunities for extracting cosmology made possible by the sensitivity of SKA Phase 1 and explores the advances achievable with SKA2.
A Generalized Affinity Propagation Clustering Algorithm for Nonspherical Cluster Discovery

Clustering analysis aims to discover the underlying clusters in the data points according to their similarities. It has wide applications ranging from bioinformatics to astronomy. Here, we proposed a Generalized Affinity Propagation (G-AP) clustering algorithm. Data points are first organized in a sparsely connected in-tree (IT) structure by a physically inspired strategy. Then, additional edges are added to the IT structure for those reachable nodes. This expanded structure is subsequently trimmed by affinity propagation method. Consequently, the underlying cluster structure, with separate clusters, emerges. In contrast to other IT-based methods, G-AP is fully automatic and takes as input the pairs of similarities between data points only. Unlike affinity propagation, G-AP is capable of discovering nonspherical clusters.
Deep Belief Nets for Topic Modeling

Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the other hand, is attractive because textual content is often very informative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets (DBN) that both find low-dimensional latent representations for documents. Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an online publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance evaluation of the DBN model and comparisons to the LDA model.
Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling

We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior of any feature. We theoretically prove good properties of our estimators and empirically show that our estimators considerably outperform the estimators from the previous state-of-the-art methods.
A patient-specific scatter artifacts correction method

This paper provides a fast and patient-specific scatter artifact correction method for cone-beam computed tomography (CBCT) used in image-guided interventional procedures. Due to increased irradiated volume of interest in CBCT imaging, scatter radiation has increased dramatically compared to 2D imaging, leading to a degradation of image quality. In this study, we propose a scatter artifact correction strategy using an analytical convolution-based model whose free parameters are estimated using a rough estimation of scatter profiles from the acquired cone-beam projections. It was evaluated using Monte Carlo simulations with both monochromatic and polychromatic X-ray sources. The results demonstrated that the proposed method significantly reduced the scatter-induced shading artifacts and recovered CT numbers.
Statistical-mechanical analysis of pre-training and fine tuning in deep learning

In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning — pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to lear...