nils-herrmann/clean_scirepeval huggingface dataset:
Macroscopic aspects of the Unruh effect

Macroscopic concepts pertaining to the Unruh effect are elaborated and used to clarify its physical manifestations. Based on a description of the motion of accelerated, spatially extended laboratories in Minkowski space in terms of Poincaré transformations, it is shown that, from a macroscopic perspective, an accelerated observer will not register with his measuring instruments any global thermal effects of acceleration in the inertial (Minkowskian) vacuum state. As is explained, this result is not in conflict with the well-known fact that microscopic probes used as thermometers respond non-trivially to acceleration if coupled to the vacuum. But this response cannot be interpreted as the effect of some exchange of thermal energy with a gas surrounding the observer; in fact, it is induced by the measuring process itself. It is also shown that genuine equilibrium states in a uniformly accelerated laboratory cannot be spatially homogeneous. In particular, these states coincide with the homogeneous inertial vacuum at sufficiently large distances from the horizon of the observer and consequently have the same (zero) temperature there. The analysis is carried out in the theory of a free massless scalar field; however the conclusion that the Unruh effect is not of a macroscopic thermal origin is generally valid.
On the Stability of Deep Networks

In this work we study the properties of deep neural networks (DNN) with random weights. We formally prove that these networks perform a distance-preserving embedding of the data. Based on this we then draw conclusions on the size of the training data and the networks' structure. A longer version of this paper with more results and details can be found in (Giryes et al., 2015). In particular, we formally prove in the longer version that DNN with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data.
CP Violation in Kaon Decays (II)

Major progress has been made in kaon physics in the past 50 years. The number of KL->pi+pi- events has increased by 6 orders of magnitude, and the observed CP violation was experimentally proven to be caused by a complex phase in the CKM matrix. Recent kaon experiments are now searching for new physics beyond the standard model with the K->pi nu nu-bar decays. The branching ratio of K->pi nu nu-bar decays are 7-8 orders of magnitude smaller than the branching ratio of KL->pi+ pi-. 
This paper reviews the progress of kaon experiments in the US and Japan, and how the 6-7 orders of magnitude improvements were possible in the past 50 years.
Do dark matter axions form a condensate with long-range correlation?

The authors investigate the formation of Bose-Einstein condensates from axion dark matter, and it is shown that a homogeneous condensate with long range correlations is unstable when $a\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}v\phantom{\rule{0}{0ex}}e$ interactions like gravity are taken into account. Instead many Bose-Einstein condensates in form of Bose stars with correlation lengths much below galactic scales are created.
A SEMI-COHERENT SEARCH FOR WEAK PULSATIONS IN AQUILA X–1

Non-pulsating neutron stars in low mass X-ray binaries largely outnumber those that show pulsations. The lack of detectable pulses represents a big open problem for two important reasons. The first is that the structure of the accretion flow in the region closest to the neutron star is not well understood and it is therefore unclear what is the mechanism that prevents the pulse formation. The second is that the detection of pulsations would immediately reveal the spin of the neutron star. AQUILA X–1 is a special source among low mass X-ray binaries because it has showed the unique property of pulsating for only ∼150 s out of a total observing time of more than 1.5 million seconds. However, the existing upper limits on the pulsed fraction leave open two alternatives. Either AQUILA X–1 has very weak pulses which have been undetected, or it has genuinely pulsed only for a tiny amount of the observed time. Understanding which of the two scenarios is the correct one is fundamental to increase our knowledge about the pulse formation process and understand the chances we have to detect weak pulses in other low-mass X-ray binaries. In this paper we perform a semi-coherent search on the entire X-ray data available for AQUILA X–1. We find no evidence for (new) weak pulsations with the most stringent upper limits being of the order of 0.3% in the 7–25 keV energy band.
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics

Machine learning offers novel ways and means to design personalized learning systems wherein each student's educational experience is customized in real time depending on their background, learning goals, and performance to date. SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner's knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts. SPARFA jointly learns the associations among the questions and the concepts, learner concept knowledge profiles, and the underlying question difficulties, solely based on the correct/incorrect graded responses of a population of learners to a collection of questions. In this paper, we extend the SPARFA framework significantly to enable: (i) the analysis of graded responses on an ordinal scale (partial credit) rather than a binary scale (correct/incorrect); (ii) the exploitation of tags/labels for questions that partially describe the question{concept associations. The resulting Ordinal SPARFA-Tag framework greatly enhances the interpretability of the estimated concepts. We demonstrate using real educational data that Ordinal SPARFA-Tag outperforms both SPARFA and existing collaborative filtering techniques in predicting missing learner responses.
Quantized Matrix Completion for Personalized Learning

The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e.g., correct/incorrect) graded learner responses to questions. The underlying factors are termed "concepts" (or knowledge components) and are used for learning analytics (LA), the estimation of learner concept-knowledge profiles, and for content analytics (CA), the estimation of question-concept associations and question difficulties. While SPARFA is a powerful tool for LA and CA, it requires a number of algorithm parameters (including the number of concepts), which are difficult to determine in practice. In this paper, we propose SPARFA-Lite, a convex optimization-based method for LA that builds on matrix completion, which only requires a single algorithm parameter and enables us to automatically identify the required number of concepts. Using a variety of educational datasets, we demonstrate that SPARFALite (i) achieves comparable performance in predicting unobserved learner responses to existing methods, including item response theory (IRT) and SPARFA, and (ii) is computationally more efficient.
Fast and accurate approximate inference of transcript expression from RNA-seq data

Assigning RNA-seq reads to their transcript of origin is a fundamental task in transcript expression estimation. Where ambiguities in assignments exist due to transcripts sharing sequence, e.g. alternative isoforms or alleles, the problem can be solved through probabilistic inference. Bayesian methods have been shown to provide accurate transcript abundance estimates compared with competing methods. However, exact Bayesian inference is intractable and approximate methods such as Markov chain Monte Carlo and Variational Bayes (VB) are typically used. While providing a high degree of accuracy and modelling flexibility, standard implementations can be prohibitively slow for large datasets and complex transcriptome annotations.  We propose a novel approximate inference scheme based on VB and apply it to an existing model of transcript expression inference from RNA-seq data. Recent advances in VB algorithmics are used to improve the convergence of the algorithm beyond the standard Variational Bayes Expectation Maximization algorithm. We apply our algorithm to simulated and biological datasets, demonstrating a significant increase in speed with only very small loss in accuracy of expression level estimation. We carry out a comparative study against seven popular alternative methods and demonstrate that our new algorithm provides excellent accuracy and inter-replicate consistency while remaining competitive in computation time. Availability and implementation: The methods were implemented in R and C++, and are available as part of the BitSeq project at github.com/BitSeq. The method is also available through the BitSeq Bioconductor package. The source code to reproduce all simulation results can be accessed via github.com/BitSeq/BitSeqVB_benchmarking.  james.hensman@sheffield.ac.uk or panagiotis.papastamoulis@manchester.ac.uk or Magnus.Rattray@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Thermodynamics of Asymptotically Conical Geometries.

We study the thermodynamical properties of a class of asymptotically conical geometries known as "subtracted geometries." We derive the mass and angular momentum from the regulated Komar integral and the Hawking-Horowitz prescription and show that they are equivalent. By deriving the asymptotic charges, we show that the Smarr formula and the first law of thermodynamics hold. We also propose an analog of Christodulou-Ruffini inequality. The analysis can be generalized to other asymptotically conical geometries.
Generative Deep Deconvolutional Learning

A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up and top-down probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via deconvolutional inference. To speed up this inference, a new statistical approach is proposed to project the top-layer dictionary elements to the data level. Following this, only one layer of deconvolution is required during testing. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images. Excellent classification results are obtained on both the MNIST and Caltech 101 datasets.
Chiral imprint of a cosmic gauge field on primordial gravitational waves

A cosmological gauge field with isotropic stress-energy introduces parity violation into the behavior of gravitational waves. We show that a primordial spectrum of inflationary gravitational waves develops a preferred handedness, left- or right-circularly polarized, depending on the abundance and coupling of the gauge field during the radiation era. A modest abundance of the gauge field would induce parity-violating correlations of the cosmic microwave background temperature and polarization patterns that could be detected by current and future experiments.
Universality of Plasmon Excitations in Dirac Semimetals.

We investigate the properties of the collective plasmon excitations in Dirac semimetals by using the methods of relativistic field theory. We find a strong and narrow plasmon excitation whose frequency is in the terahertz (THz) range which may be important for practical applications. The properties of the plasmon appear universal for all Dirac semimetals, due to the large degeneracy of the quasiparticles and the small Fermi velocity, v_{F}≪c. This universality is closely analogous to the phenomenon of "dimensional transmutation" that is responsible for the emergence of dimensionful scales in relativistic field theories such as quantum chromodynamics.
Light propagation in periodically modulated complex waveguides

Light propagation in optical waveguides with periodically modulated index of refraction and alternating gain and loss are investigated for linear and nonlinear systems. Based on a multiscale perturbation analysis, it is shown that for many non-parity-time ($\mathcal{PT}$) symmetric waveguides, their linear spectrum is partially complex, thus light exponentially grows or decays upon propagation, and this growth or delay is not altered by nonlinearity. However, several classes of non-$\mathcal{PT}$-symmetric waveguides are also identified to possess all-real linear spectrum. In the nonlinear regime longitudinally periodic and transversely quasi-localized modes are found for $\mathcal{PT}$-symmetric waveguides both above and below phase transition. These nonlinear modes are stable under evolution and can develop from initially weak initial conditions.
The status of modern five-dimensional gravity (A short review: Why physics needs the fifth dimension)

Recent criticism of higher-dimensional extensions of Einstein's theory is considered. This may have some justification in regard to string theory, but is misguided as applied to five-dimensional (5D) theories with a large extra dimension. Such theories smoothly embed general relativity, ensuring recovery of the latter's observational support. When the embedding of spacetime is carried out in accordance with Campbell's theorem, the resulting 5D theory naturally explains the origin of classical matter and vacuum energy. Also, constraints on the equations of motion near a high-energy surface or membrane in the 5D manifold lead to quantization and quantum uncertainty. These are major returns on the modest investment of one extra dimension. Instead of fruitless bickering about whether it is possible to "see" the fifth dimension, it is suggested that it be treated on par with other concepts of physics, such as time. The main criterion for the acceptance of a fifth dimension (or not) should be its usefulness.
Efficient decision-making by volume-conserving physical object

Decision-making is one of the most important intellectual abilities of not only humans but also other biological organisms, helping their survival. This ability, however, may not be limited to biological systems and may be exhibited by physical systems. Here we demonstrate that any physical object, as long as its volume is conserved when coupled with suitable operations, provides a sophisticated decision-making capability. We consider the multi-armed bandit problem (MBP), the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. Efficient MBP solvers are useful for many practical applications, because MBP abstracts a variety of decision-making problems in real-world situations in which an efficient trial-and-error is required. These decisions are made as dictated by a physical object, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war (TOW) game. This method, called ‘TOW dynamics’, exhibits higher efficiency than conventional reinforcement learning algorithms. We show analytical calculations that validate statistical reasons for TOW dynamics to produce the high performance despite its simplicity. These results imply that various physical systems in which some conservation law holds can be used to implement an efficient ‘decision-making object’. The proposed scheme will provide a new perspective to open up a physics-based analog computing paradigm and to understanding the biological information-processing principles that exploit their underlying physics.
Phonon-tuned bright single-photon source

Pure and bright single photon sources have recently been obtained by inserting solid-state emitters in photonic nanowires or microcavities. The cavity approach presents the attractive possibility to greatly increase the source operation frequency. However, it is perceived as technologically demanding because the emitter resonance must match the cavity resonance. Here we show that the spectral matching requirement is actually strongly lifted by the intrinsic coupling of the emitter to its environment. A single photon source consisting of a single InGaAs quantum dot inserted in a micropillar cavity is studied. Phonon coupling results in a large Purcell effect even when the quantum dot is detuned from the cavity resonance. The phonon-assisted cavity enhanced emission is shown to be a good single-photon source, with a brightness exceeding $40$ \% for a detuning range covering 15 cavity linewidths.
Human-Data Interaction: The Human Face of the Data-Driven Society

The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems.
Example Selection For Dictionary Learning

In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data. In this work, we explore whether active example selection strategies - algorithms that select which examples to use, based on the current estimate of the features - can accelerate learning. Specifically, we investigate effects of heuristic and saliency-inspired selection algorithms on the dictionary learning task with sparse activations. We show that some selection algorithms do improve the speed of learning, and we speculate on why they might work.
Active patterning and asymmetric transport in a model actomyosin network.

Cytoskeletal networks, which are essentially motor-filament assemblies, play a major role in many developmental processes involving structural remodeling and shape changes. These are achieved by nonequilibrium self-organization processes that generate functional patterns and drive intracellular transport. We construct a minimal physical model that incorporates the coupling between nonlinear elastic responses of individual filaments and force-dependent motor action. By performing stochastic simulations we show that the interplay of motor processes, described as driving anti-correlated motion of the network vertices, and the network connectivity, which determines the percolation character of the structure, can indeed capture the dynamical and structural cooperativity which gives rise to diverse patterns observed experimentally. The buckling instability of individual filaments is found to play a key role in localizing collapse events due to local force imbalance. Motor-driven buckling-induced node aggregation provides a dynamic mechanism that stabilizes the two-dimensional patterns below the apparent static percolation limit. Coordinated motor action is also shown to suppress random thermal noise on large time scales, the two-dimensional configuration that the system starts with thus remaining planar during the structural development. By carrying out similar simulations on a three-dimensional anchored network, we find that the myosin-driven isotropic contraction of a well-connected actin network, when combined with mechanical anchoring that confers directionality to the collective motion, may represent a novel mechanism of intracellular transport, as revealed by chromosome translocation in the starfish oocyte.
Scaling study of an improved fermion action on quenched lattices

We present scaling studies for heavy-quark observables calculated with an $O(a^2)$-improved fermion action on tree-level Symanzik improved gauge configurations. Lattices of $1/a = $ 2.0-3.8 GeV with an equal physical volume 1.6 fm are used. The results are compared with the standard domain-wall and naive Wilson fermions.
Information-Theoretic Methods for Identifying Relationships among Climate Variables

Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extremely challenging. We have developed a set of computational techniques that allow one to accurately compute marginal and joint entropies. These algorithms are probabilistic in nature and thus provide information on the uncertainty in our estimates, which enable us to establish statistical significance of our findings. We demonstrate these methods by identifying relations between cloud data from the International Satellite Cloud Climatology Project (ISCCP) and data from other sources, such as equatorial pacific sea surface temperatures (SST).
Nucleon resonances in the gamma p -> phi K+ Lambda reaction near threshold

We investigate the γ p → ϕ K + Λ reaction near threshold within an effective Lagrangian approach and the isobar model. Various nucleon resonances caused by the π and η meson exchanges and background contributions are considered. It is shown that the contribution from the N * ( 1535 ) resonance caused by the η meson exchange plays the predominant role. Hence, this reaction provides a good new platform to study the N * ( 1535 ) resonance. The predicted total cross section and specific features of the angular distributions can be tested by future experiments.
From dependency to causality: a machine learning approach

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with $n>2$ variables. The approach relies on the asymmetry of some conditional (in)dependence relations between the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for $n>2$ variate distributions.
Distributed Decision Trees

Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous parameter space, and therefore a budding tree can be jointly trained with backpropagation, like a neural network. Even though this continuity allows it to be used in hierarchical representation learning, the learned representations are local: Activation makes a soft selection among all root-to-leaf paths in a tree. In this work we extend the budding tree and propose the distributed tree where the children use different and independent splits and hence multiple paths in a tree can be traversed at the same time. This ability to combine multiple paths gives the power of a distributed representation, as in a traditional perceptron layer. We show that distributed trees perform comparably or better than budding and traditional hard trees on classification and regression tasks.