nils-herrmann/clean_scirepeval huggingface dataset:
Dark matter from hidden forces

We examine the possibility that dark matter may be the manifestation of dark forces of a hidden sector, i.e. "Dark Force = Dark Matter." As an illustrative and minimal example we consider the hidden SU(2)_h x U(1)_h gauge group. The hidden dynamics is indirectly coupled to the Standard Model (SM) through kinetic mixing of U(1)_h with the U(1)_Y of hypercharge. We assume a hidden symmetry breaking pattern analogous to that of the SM electroweak symmetry, augmented with an extra scalar that allows both the "hidden Z boson" Z_h and the "hidden photon" \gamma_h to be massive. The "hidden W" bosons W_h are dark matter in this scenario. This setup can readily accommodate a potential direct detection signal for dark matter at ~10 GeV from CDMSII-Si data. For some choices of parameters, the model can lead to signals both in "dark matter beam" experiments, from Z_h\to W_h W_h, as well as in experiments that look for visible signals of dark photons, mediated by \gamma_h. Other possible phenomenological consequences are also briefly discussed.
Effects of thermal gradients on total internal reflection corner cubes.

Uncoated corner cube retroreflectors (CCRs) operating via total internal reflection (TIR) are less susceptible to internal heating than their metal-coated analogs, lacking an absorber on the rear surface. Even so, environments that induce differential heating within the CCR will result in thermal lensing of the incident wavefront, introducing aberrations that will generally reduce the central irradiance of the polarization-sensitive far-field diffraction pattern (FFDP). In this paper, we characterize the sensitivity of TIR CCRs to axial and radial thermal gradients. We present simulated FFDPs for key input polarizations and incidence angles and provide a generalized analytic model that approximates the behavior of the central irradiance as temperature differences are introduced.
Intensity Frontier Instrumentation

The fundamental origin of flavor in the Standard Model (SM) remains a mystery. Despite the roughly eighty years since Rabi asked “Who ordered that?” upon learning of the discovery of the muon, we have not understood the reason that there are three generations or, more recently, why the quark and neutrino mixing matrices and masses are so different. The solution to the flavor problem would give profound insights into physics beyond the Standard Model (BSM) and tell us about the couplings and the mass scale at which the next level of insight can be found. The SM fails to explain all observed phenomena: new interactions and yet unseen particles must exist. They may manifest themselves by causing SM reactions to differ from often very precise predictions. The Intensity Frontier (1) explores these fundamental questions by searching for new physics in extremely rare processes or those forbidden in the SM. This often requires massive and/or extremely finely tuned detectors.
A Spin-Light Polarimeter for Multi-GeV Longitudinally Polarized Electron Beams

The physics program at the upgraded Jefferson Lab (JLab) and the physics program envisioned for the proposed electron-ion collider (EIC) include large efforts to search for interactions beyond the Standard Model (SM) using parity violation in electroweak interactions. These experiments require precision electron polarimetry with an uncertainty of <; 0.5%. The spin dependent Synchrotron radiation (SR), called “spin-light,” can be used to monitor the electron beam polarization. In this article we develop a conceptual design for a “spin-light” polarimeter that can be used at a high intensity, multi-GeV electron accelerator. We have also built a Geant4 based simulation for a prototype device and report some of the results from these simulations.
Kelvin wake pattern at large Froude numbers

Gravity waves generated by an object moving at constant speed at the water surface form a specific pattern commonly known as the Kelvin wake. It was proved by Lord Kelvin that such a wake is delimited by a constant angle ${\simeq }19. 4{7}^{\circ } $ . However a recent study by Rabaud and Moisy based on the observation of airborne images showed that the wake angle seems to decrease as the Froude number $Fr$ increases, scaling as $F{r}^{- 1} $ for large Froude numbers. To explain such observations they make the strong hypothesis that an object of size $b$ cannot generate wavelengths larger than $b$ . Without the need of such an assumption and modelling the moving object by an axisymmetric pressure field, we analytically show that the angle corresponding to the maximum amplitude of the waves scales as $F{r}^{- 1} $ for large Froude numbers, whereas the angle delimiting the wake region outside which the surface is essentially flat remains constant and equal to the Kelvin angle for all $Fr$ .
Using river locks to teach hydrodynamic concepts

In this work, the use of a river lock as a non-formal setting for teaching hydrodynamical concepts is proposed. In particular, we describe the operation of a river lock situated at the Sobradinho dam, on the São Francisco River (Brazil). A model to represent and to analyse the dynamics of river lock operation is presented and we derive the dynamical equations for the rising of the water column as an example to understand the Euler equation. Furthermore, with this activity, we enable the integration of content initially introduced in the classroom with practical applications, thereby allowing the association of physical themes to content relevant in disciplines such as history and geography. In addition, experiences of this kind enable teachers to talk about the environmental and social impacts caused by the construction of a dam and, consequently, a crossover of concepts has been made possible, leading to more meaningful learning for the students.
Generative Multiple-Instance Learning Models For Quantitative Electromyography

We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task of facilitating the diagnosis of neuromuscular disorders using sets of motor unit potential trains (MUPTs) detected within a muscle which can be cast as a MIL problem. Our approach leads to a state-of-the-art solution to the problem of muscle classification. By introducing and analyzing generative models for MIL in a general framework and examining a variety of model structures and components, our work also serves as a methodological guide to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets and on the MUSK1 dataset, one of the most widely used benchmarks for MIL.
The Bregman Variational Dual-Tree Framework

Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly in-feasible for moderate to large scale datasets. A significant effort to find more efficient solutions to the problem has been made in the literature. One of the state-of-the-art methods that has been recently introduced is the Variational Dual-Tree (VDT) framework. Despite some of its unique features, VDT is currently restricted only to Euclidean spaces where the Euclidean distance quantifies the similarity. In this paper, we extend the VDT framework beyond the Euclidean distance to more general Bregman divergences that include the Euclidean distance as a special case. By exploiting the properties of the general Bregman divergence, we show how the new framework can maintain all the pivotal features of the VDT framework and yet significantly improve its performance in non-Euclidean domains. We apply the proposed framework to different text categorization problems and demonstrate its benefits over the original VDT.
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HL-MRFs are general tools for fast and accurate structured prediction. We introduce the first inference algorithm that is both scalable and applicable to the full class of HL-MRFs, and show how to train HL-MRFs with several learning algorithms. Our experiments show that HL-MRFs match or surpass the predictive performance of state-of-the-art methods, including discrete models, in four application domains.
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning

Joint sparsity regularization in multi-task learning has attracted much attention in recent years. The traditional convex formulation employs the group Lasso relaxation to achieve joint sparsity across tasks. Although this approach leads to a simple convex formulation, it suffers from several issues due to the looseness of the relaxation. To remedy this problem, we view jointly sparse multi-task learning as a specialized random effects model, and derive a convex relaxation approach that involves two steps. The first step learns the covariance matrix of the coefficients using a convex formulation which we refer to as sparse covariance coding; the second step solves a ridge regression problem with a sparse quadratic regularizer based on the covariance matrix obtained in the first step. It is shown that this approach produces an asymptotically optimal quadratic regularizer in the multitask learning setting when the number of tasks approaches infinity. Experimental results demonstrate that the convex formulation obtained via the proposed model significantly outperforms group Lasso (and related multi-stage formulations
Modeling Documents with Deep Boltzmann Machines

We introduce a Deep Boltzmann Machine model suitable for modeling and extracting latent semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This parameter tying enables an efficient pretraining algorithm and a state initialization scheme that aids inference. The model can be trained just as efficiently as a standard Restricted Boltzmann Machine. Our experiments show that the model assigns better log probability to unseen data than the Replicated Softmax model. Features extracted from our model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks.
Observational issues in loop quantum cosmology

Quantum gravity is sometimes considered as a kind of metaphysical speculation. In this review, we show that, although still extremely difficult to reach, observational signatures can in fact be expected. The early universe is an invaluable laboratory to probe ‘Planck scale physics’. Focusing on loop quantum gravity as one of the best candidate for a non-perturbative and background-independent quantization of gravity, we detail some expected features.
Structure and dynamics of core/periphery networks

Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways ensuring cooperation and providing multiple options of network flow re-channelling greatly contribute to the high robustness of complex systems. Core processes enable a coordinated response to various stimuli, decrease noise, and evolve slowly. The integrative function of network cores is an important step in the development of a large variety of complex organisms and organizations. In addition to these important features and several decades of research interest, studies on core/periphery networks still have a number of unexplored areas.
A new type of nonsingular black-hole solution in general relativity

Certain exact solutions of the Einstein field equations over nonsimply-connected manifolds are reviewed. These solutions are spherically symmetric and have no curvature singularity. They provide a regularization of the standard Schwarzschild solution with a curvature singularity at the center. Spherically symmetric collapse of matter in $\mathbb{R}^4$ may result in these nonsingular black-hole solutions, if quantum-gravity effects allow for topology change near the center or if nontrivial topology is already present as a remnant from a quantum spacetime foam.
Neutrino physics with multi-ton scale liquid xenon detectors

We study the sensitivity of large-scale xenon detectors to low-energy solar neutrinos, to coherent neutrino-nucleus scattering and to neutrinoless double beta decay. As a concrete example, we consider the xenon part of the proposed DARWIN (Dark Matter WIMP Search with Noble Liquids) experiment. We perform detailed Monte Carlo simulations of the expected backgrounds, considering realistic energy resolutions and thresholds in the detector. In a low-energy window of 2–30 keV, where the sensitivity to solar pp and 7Be-neutrinos is highest, an integrated pp-neutrino rate of 5900 events can be reached in a fiducial mass of 14 tons of natural xenon, after 5 years of data. The pp-neutrino flux could thus be measured with a statistical uncertainty around 1%, reaching the precision of solar model predictions. These low-energy solar neutrinos will be the limiting background to the dark matter search channel for WIMP-nucleon cross sections below ∼ 2 × 10−48 cm2 and WIMP masses around 50 GeV⋅c−2, for an assumed 99.5% rejection of electronic recoils due to elastic neutrino-electron scatters. Nuclear recoils from coherent scattering of solar neutrinos will limit the sensitivity to WIMP masses below ∼ 6 GeV⋅c−2 to cross sections above ∼ 4 × 10−45cm2. DARWIN could reach a competitive half-life sensitivity of 5.6 × 1026 y to the neutrinoless double beta decay of 136Xe after 5 years of data, using 6 tons of natural xenon in the central detector region.
Lattice Model for Fermionic Toric Code

The Z_2 topological order in Z_2 spin liquid and in exactly soluble Kitaev toric code model is the simplest topological order for 2+1D bosonic systems. More general 2+1D bosonic topologically ordered states can be constructed via exact soluble string-net models. However, the most important topologically ordered phases of matter are arguably the fermionic fractional quantum Hall states. Topological phases of matter for fermion systems are strictly richer than their bosonic counterparts because locality has different meanings for the two kinds of systems. In this paper, we describe a simple fermionic version of the toric code model to illustrate many salient features of fermionic exactly soluble models and fermionic topologically ordered states.
Maximum spectral luminous efficacy of white light

As lighting efficiency improves, it is useful to understand the theoretical limits to luminous efficacy for light that we perceive as white. Independent of the efficiency with which photons are generated, there exists a spectrally imposed limit to the luminous efficacy of any source of photons. We find that, depending on the acceptable bandpass and—to a lesser extent—the color temperature of the light, the ideal white light source achieves a spectral luminous efficacy of 250–370 lm/W. This is consistent with previous calculations, but here we explore the maximum luminous efficacy as a function of photopic sensitivity threshold, color temperature, and color rendering index; deriving peak performance as a function of all three parameters. We also present example experimental spectra from a variety of light sources, quantifying the intrinsic efficacy of their spectral distributions.
Anisotropic two-dimensional electron gas at SrTiO3(110)

Although still in its infancy, electronics based on all-oxide materials is a rapidly developing field, and strontium titanate is its key player. For this area to thrive, an atomic-scale control and understanding of the materials’ surfaces and interfaces needs to be achieved. A SrTiO3 crystal with (110) orientation automatically forms an overlayer that is more insulating than the bulk and chemically less reactive, akin to the native SiO2 on conventional wafer. With appropriate doping a two-dimensional electron gas (2DEG) forms underneath the SrTiO3(110) surface. This (110) 2DEG is very different from (001): The effective mass here depends on the quantum number, and a completely flat band can be realized. Such a flat band bears good prospects for, among others, magnetism and thermoelectricity. Two-dimensional electron gases (2DEGs) at oxide heterostructures are attracting considerable attention, as these might one day substitute conventional semiconductors at least for some functionalities. Here we present a minimal setup for such a 2DEG––the SrTiO3(110)-(4 × 1) surface, natively terminated with one monolayer of tetrahedrally coordinated titania. Oxygen vacancies induced by synchrotron radiation migrate underneath this overlayer; this leads to a confining potential and electron doping such that a 2DEG develops. Our angle-resolved photoemission spectroscopy and theoretical results show that confinement along (110) is strikingly different from the (001) crystal orientation. In particular, the quantized subbands show a surprising “semiheavy” band, in contrast with the analog in the bulk, and a high electronic anisotropy. This anisotropy and even the effective mass of the (110) 2DEG is tunable by doping, offering a high flexibility to engineer the properties of this system.
Anomalous Fermi-liquid phase in metallic skyrmion crystals

In non-centrosymmetric crystals such as MnSi, magnetic order can take the form of a skyrmion crystal (SkX) . In this phase, conduction electrons coupled to the local magnetic moments acquire a Berry's phase, leading to an emergent electromagnetism. Motivated by experimental reports of a non-Fermi liquid phase in MnSi, in which resistivity is observed to scale as $\Delta \rho \sim T^{3/2}$, here we examine the effect of coupling phonons of an incommensurate SkX to electrons. Despite the formal similarity to a system consisting of a Fermi surface coupled to an electromagnetic field, the Berry phase fluctuations do not lead to non-Fermi liquid behavior. Instead, we propose a different mechanism in which electrons scatter off columnar fluctuation in a three-dimensional SkX. When the effects of lattice induced anisotropy are neglected, these fluctuations are ultra-soft and induce an `anomalous Fermi liquid' in which Landau quasiparticles survive but with an anomalous $\Delta \rho(T)\sim T^{7/4}$ resistivity perpendicular to the columns, and a Fermi liquid resistivity along them.
Continuous compliance: a proxy-based monitoring framework

Within the Own Risk and Solvency Assessment framework, the Solvency II directive introduces the need for insurance undertakings to have efficient tools enabling the companies to assess the continuous compliance with regulatory solvency requirements. Because of the great operational complexity resulting from each complete evaluation of the Solvency Ratio, this monitoring is often complicated to implement in practice. This issue is particularly important for life insurance companies due to the high complexity to project life insurance liabilities. It appears relevant in such a context to use parametric tools, such as Curve Fitting and Least Squares Monte Carlo in order to estimate, on a regular basis, the impact on the economic own funds and on the regulatory capital of the company of any change over time of its underlying risk factors. In this article, we first outline the principles of the continuous compliance requirement then we propose and implement a possible monitoring tool enabling to approximate the eligible elements and the regulatory capital over time. In a final section we compare the use of the Curve Fitting and the Least Squares Monte Carlo methodologies in a standard empirical finite sample framework, and stress adapted advices for future proxies users.
Multilayer networks

The chapter “Multilayer Networks” introduces the topic of interconnected multilayer networks, analyzing them from a few fronts: types of multilayer networks, their mathematical description, the dynamics of random walks, and the centrality (versatility) of nodes. Multilayer networks appear naturally in real data as, in many cases, the relationships (links) between the elements (nodes) can be of different kinds. For example, people can be connected through friendship, family relations, or work relations. This structure can be represented as a network formed by three layers, one for each type of relationship, with the same nodes repeated in all layers. Thus, there are different behaviors in each layer, and interactions between them, which result in more realistic but, at the same time, more complex dynamics. This chapter shows how interconnected multilayer topology describes such networks more accurately than edge coloring does and introduces the tensor formalism used to construct them.
Analemmatic and Horizontal Sundials of the Bronze Age (Northern Black Sea Coast)

The paper presents the results of a study of unique plate with images of Srubna burial of tumuli group Popov Yar-2 (Ukraine) and plate of Srubna burial of tumulus field Tavriya-1 (Russia). A distinctive feature of the images is the orderliness and symmetry of the composition, as well as the location of elliptical cupped depressions. With the help of mathematical and astronomical methods we prove in this paper that the plates with the images are the ancient sundials. At the Popov Yar-2 plate located two sundials, which worked at the same time: the analemmatic sundial and the horizontal sundial with two gnomons and the linear scale. At the Tavriya-1 plate located analemmatic sundial. On the basis of the reconstruction of the linear parameters of the gnomon of both Popov Yar-2 plate sundials and given the scale value of horizontal sundial, in the article that the potential indirect impact protoscientific knowledge of ancient Egypt to the Srubna population in the Northern Black Sea coast. 
Srubna burial, plate, wells, cupped depressions, analemmatic, horizontal sundial, gnomon, archaeoastronomy, archeology