nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Goldstone modes with entanglement entropy

In the face of mounting numerical evidence, Metlitski and Grover  have given compelling analytical arguments that systems with spontaneous broken continuous symmetry contain a subleading contribution to the entanglement entropy that diverges logarithmically with system size. They predict that the coefficient of this log is a universal quantity that depends on the number of Goldstone modes. In this paper, we confirm the presence of this log term through quantum Monte Carlo calculations of the second R\'enyi entropy on the spin-1/2 $XY$ model. Devising an algorithm to facilitate convergence of entropy data at extremely low temperatures, we demonstrate that the single Goldstone mode in the ground state can be identified through the coefficient of the log term. Furthermore, our simulation accuracy allows us to obtain an additional geometric constant additive to the R\'enyi entropy, that matches a predicted fully universal form obtained from a free bosonic field theory with no adjustable parameters.
DETECTION OF QUASAR FEEDBACK FROM THE THERMAL SUNYAEV–ZEL’DOVICH EFFECT IN PLANCK

Poorly understood feedback processes associated with highly luminous black hole accretion in quasars may dramatically affect the properties of their host galaxies. We search for the effect of quasar feedback on surrounding gas using Planck maps of the thermal Sunyaev–Zel’dovich effect (tSZ). By stacking tSZ Compton-y maps centered on the locations of 26,686 spectroscopic quasars from the Sloan Digital Sky Survey, we detect a strong but unresolved tSZ Compton-y signal at >5σ significance that likely originates from a combination of virialized halo atmosphere gas and quasar feedback effects. We show that the feedback contribution to our detected quasar tSZ signal is likely to dominate over virialized halo gas by isolating the feedback tSZ component for high- and low-redshift quasars. We find that this quasar tSZ signal also scales with black hole mass and bolometric luminosity, all consistent with general expectations of quasar feedback. We estimate the mean angularly integrated Compton-y of quasars at z ≃ 1.5 to be 3.5 × 10−6 Mpc2, corresponding to mean total thermal energies in feedback and virialized halo gas of 1.1 ( ± 0.2 ) × 10 62 ?> erg, and discuss the implications for quasar feedback. If confirmed, the large total thermal feedback energetics we estimate to be 5% (±1% statistical uncertainty) of the black hole mass will have important implications for the effects of quasar feedback on the host galaxy, as well as the surrounding intergalactic medium.
Direct photon production at RHIC and LHC energies

Direct photon spectra and elliptic flow v2 in heavy-ion collisions at RHIC and LHC energies are investigated within a relativistic transport approach incorporating both hadronic and partonic phases - the Parton-Hadron-String Dynamics (PHSD). The results suggest that a large v2 of the direct photons - as observed by the PHENIX Collaboration - signals a significant contribution of photons produced in interactions of secondary mesons and baryons in the late stages of the collision. In order to further differentiate the origin of the direct photon azimuthal asymmetry, we compare our predictions for the centrality dependence of the direct photon yield to the recent measurements by the PHENIX Collaboration and provide predictions for Pb+Pb collisions at LHC energies with respect to the direct photon spectra and v2(pT) for 0-40% centrality.
NEW OBSERVATIONS OF THE SOLAR 0.5–5 KEV SOFT X-RAY SPECTRUM

The solar corona is orders of magnitude hotter than the underlying photosphere, but how the corona attains such high temperatures is still not understood. Soft X-ray (SXR) emission provides important diagnostics for thermal processes in the high-temperature corona, and is also an important driver of ionospheric dynamics at Earth. There is a crucial observational gap between ∼0.2 and ∼4 keV, outside the ranges of existing spectrometers. We present observations from a new SXR spectrometer, the Amptek X123-SDD, which measured the spatially integrated solar spectral irradiance from ∼0.5 to ∼5 keV, with ∼0.15 keV FWHM resolution, during sounding rocket flights on 2012 June 23 and 2013 October 21. These measurements show that the highly variable SXR emission is orders of magnitude greater than that during the deep minimum of 2009, even with only weak activity. The observed spectra show significant high-temperature (5–10 MK) emission and are well fit by simple power-law temperature distributions with indices of ∼6, close to the predictions of nanoflare models of coronal heating. Observations during the more active 2013 flight indicate an enrichment of low first-ionization potential elements of only ∼1.6, below the usually observed value of ∼4, suggesting that abundance variations may be related to coronal heating processes. The XUV Photometer System Level 4 data product, a spectral irradiance model derived from integrated broadband measurements, significantly overestimates the spectra from both flights, suggesting a need for revision of its non-flare reference spectra, with important implications for studies of Earth ionospheric dynamics driven by solar SXRs.
On the breakdown of the curvature perturbation ζ during reheating

It is known that in single scalar field inflationary models the standard curvature perturbation ζ, which is supposedly conserved at superhorizon scales, diverges during reheating at times 0ϕ̇=, i.e. when the time derivative of the background inflaton field vanishes. This happens because the comoving gauge 0φ=, where φ denotes the inflaton perturbation, breaks down when 0ϕ̇=. The issue is usually bypassed by averaging out the inflaton oscillations but strictly speaking the evolution of ζ is ill posed mathematically. We solve this problem in the free theory by introducing a family of smooth gauges that still eliminates the inflaton fluctuation φ in the Hamiltonian formalism and gives a well behaved curvature perturbation ζ, which is now rigorously conserved at superhorizon scales. At the linearized level, this conserved variable can be used to unambiguously propagate the inflationary perturbations from the end of inflation to subsequent epochs. We discuss the implications of our results for the inflationary predictions.
Convex Duality with Transaction Costs

Convex duality for two different super-replication problems in a continuous time financial market with proportional transaction cost is proved. In this market, static hedging in a finite number of options, in addition to usual dynamic hedging with the underlying stock, are allowed. The first one of the problems considered is the model-independent hedging that requires the super-replication to hold for every continuous path. In the second one the market model is given through a probability measure ℙ and the inequalities are understood the probability measure almost surely. The main result, using the convex duality, proves that the two super-replication problems have the same value provided that the probability measure satisfies the conditional full support property. Hence, the transaction costs prevents one from using the structure of a specific model to reduce the super-replication cost.
Monitoring term drift based on semantic consistency in an evolving vector field

Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent changes in a vector space model by a combination of random indexing and evolving self-organizing maps (ESOM). To monitor semantic drifts within the observation period, an experiment was carried out on the term space of a collection of 12.8 million Amazon book reviews. For evaluation, the semantic consistency of ESOM term clusters was compared with their respective neighbourhoods in WordNet, and contrasted with distances among term vectors by random indexing. We found that at 0.05 level of significance, the terms in the clusters showed a high level of semantic consistency. Tracking the drift of distributional patterns in the term space across time periods, we found that consistency decreased, but not at a statistically significant level. Our method is highly scalable, with interpretations in philosophy.
Electrical plasmon detection in graphene waveguides

We present a simple device architecture that allows all-electrical detection of plasmons in a graphene waveguide. The key principle of our electrical plasmon detection scheme is the nonlinear nature of the hydrodynamic equations of motion that describe transport in graphene at room temperature and in a wide range of carrier densities. These nonlinearities yield a dc voltage in response to the oscillating field of a propagating plasmon. For illustrative purposes, we calculate the dc voltage arising from the propagation of the lowest-energy modes in a fully analytical fashion. Our device architecture for all-electrical plasmon detection paves the way for the integration of graphene plasmonic waveguides in electronic circuits.
Learning Efficient Anomaly Detectors from K-NN Graphs

We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average $K$-NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly at $\alpha$-false alarm level if the predicted score is in the $\alpha$-percentile. The resulting anomaly detector is shown to be asymptotically optimal in that for any false alarm rate $\alpha$, its decision region converges to the $\alpha$-percentile minimum volume level set of the unknown underlying density. In addition, we test both the statistical performance and computational efficiency of our algorithm on a number of synthetic and real-data experiments. Our results demonstrate the superiority of our algorithm over existing $K$-NN based anomaly detection algorithms, with significant computational savings.
Indexing Arbitrary-Length k-Mers in Sequencing Reads

We propose a lightweight data structure for indexing and querying collections of NGS reads data in main memory. The data structure supports the interface proposed in the pioneering work by Philippe et al. for counting and locating k-mers in sequencing reads. Our solution, PgSA (pseudogenome suffix array), based on finding overlapping reads, is competitive to the existing algorithms in the space use, query times, or both. The main applications of our index include variant calling, error correction and analysis of reads from RNA-seq experiments.
Marginalizing Gaussian process hyperparameters using sequential Monte Carlo

Gaussian process regression is a popular method for non-parametric probabilistic modeling of functions. The Gaussian process prior is characterized by so-called hyperparameters, which often have a large influence on the posterior model and can be difficult to tune. This work provides a method for numerical marginalization of the hyperparameters, relying on the rigorous framework of sequential Monte Carlo. Our method is well suited for online problems, and we demonstrate its ability to handle real-world problems with several dimensions and compare it to other marginalization methods. We also conclude that our proposed method is a competitive alternative to the commonly used point estimates maximizing the likelihood, both in terms of computational load and its ability to handle multimodal posteriors.
Stochastic recursive inclusions with two timescales

A framework is presented to analyze the asymptotic behavior of two timescale stochastic approximation algorithms to include situations where the mean fields are set-valued. The framework is a natural generalization of the one developed by Borkar. Perkins and Leslie have developed a framework for asynchronous coupled stochastic approximation algorithms with set-valued mean fields. Our framework is however more general as compared to the synchronous version of the Perkins and Leslie framework.
Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix

The interplay between computational efficiency and statistical accuracy in high-dimensional inference has drawn increasing attention in the literature. In this paper, we study computational and statistical boundaries for submatrix localization. Given one observation of (one or multiple non-overlapping) signal submatrix (of magnitude $\lambda$ and size $k_m \times k_n$) contaminated with a noise matrix (of size $m \times n$), we establish two transition thresholds for the signal to noise $\lambda/\sigma$ ratio in terms of $m$, $n$, $k_m$, and $k_n$. The first threshold, $\sf SNR_c$, corresponds to the computational boundary. Below this threshold, it is shown that no polynomial time algorithm can succeed in identifying the submatrix, under the \textit{hidden clique hypothesis}. We introduce adaptive linear time spectral algorithms that identify the submatrix with high probability when the signal strength is above the threshold $\sf SNR_c$. The second threshold, $\sf SNR_s$, captures the statistical boundary, below which no method can succeed with probability going to one in the minimax sense. The exhaustive search method successfully finds the submatrix above this threshold. The results show an interesting phenomenon that $\sf SNR_c$ is always significantly larger than $\sf SNR_s$, which implies an essential gap between statistical optimality and computational efficiency for submatrix localization.
COSMIC REIONIZATION AND EARLY STAR-FORMING GALAXIES: A JOINT ANALYSIS OF NEW CONSTRAINTS FROM PLANCK AND THE HUBBLE SPACE TELESCOPE

We discuss new constraints on the epoch of cosmic reionization and test the assumption that most of the ionizing photons responsible arose from high-redshift star-forming galaxies. Good progress has been made in charting the end of reionization through spectroscopic studies of z ≃ ?> 6–8 QSOs, gamma-ray bursts, and galaxies expected to host Lyα emission. However, the most stringent constraints on its duration have come from the integrated optical depth, τ, of Thomson scattering to the cosmic microwave background. Using the latest data on the abundance and luminosity distribution of distant galaxies from Hubble Space Telescope imaging, we simultaneously match the reduced value τ = 0.066 ± 0.012 ?> recently reported by the Planck collaboration and the evolving neutrality of the intergalactic medium with a reionization history within 6 ≲ z ≲ 10 ?> , thereby reducing the requirement for a significant population of very high redshift ( z ≫ 10 ?> ) galaxies. Our analysis strengthens the conclusion that star-forming galaxies dominated the reionization process and has important implications for upcoming 21 cm experiments and searches for early galaxies with the James Webb Space Telescope.
DISCOVERY OF RESOLVED DEBRIS DISK AROUND HD 131835

We report the discovery of the resolved disk around HD 131835 and present the analysis and modeling of its thermal emission. HD 131835 is a ∼15 Myr A2 star in the Scorpius–Centaurus OB association at a distance of 122.7 − 12.8 + 16.2 ?> pc . The extended disk has been detected to ∼ 1 &farcs; 5 ?> (200 AU) at 11.7 and 18.3 μm with T-ReCS on Gemini South. The disk is inclined at an angle of ∼75° with the position angle of ∼61°. The flux of the HD 131835 system is 49.3 ± 7.6 and 84 ± 45 mJy at 11.7 and 18.3 μm, respectively. A model with three grain populations gives a satisfactory fit to both the spectral energy distribution and the images simultaneously. This best-fit model is composed of a hot continuous power-law disk and two rings. We characterized the grain temperature profile and found that the grains in all three populations are emitting at temperatures higher than blackbodies. In particular, the grains in the continuous disk are unusually warm, even when considering small graphite particles as the composition.
Patterns in Illinois Educational School Data

We examine Illinois educational data from standardized exams and analyze primary factors affecting the achievement of public school students. We focus on the simplest possible models: representation of data through visualizations and regressions on single variables. Exam scores are shown to depend on school type, location, and poverty concentration. For most schools in Illinois, student test scores decline linearly with poverty concentration. However Chicago must be treated separately. Selective schools in Chicago, as well as some traditional and charter schools, deviate from this pattern based on poverty. For any poverty level, Chicago schools perform better than those in the rest of Illinois. Selective programs for gifted students show high performance at each grade level, most notably at the high school level, when compared to other Illinois school types. The case of Chicago charter schools is more complex. In the last six years, their students' scores overtook those of students in traditional Chicago high schools.
Naive Quantum Gravity

A possible alternative route to a quantum theory of gravity is presented. The usual path is to quantize the gravitational field in order to introduce the statistical structure characteristic of quantum mechanics. The procedure followed here instead is to remove the statistical element of quantum theory by introducing final boundary conditions as well as initial. The relevant quantum formalism then becomes compatible with the non-statistical nature of general relativity and a viable theory can be constructed without difficulty. This approach also provides a simple method of avoiding the configuration space description of quantum mechanics and allows the formulation to be carried out entirely within the four dimensions of spacetime. These advantages are made possible by the inherent retrocausal nature of the final boundary conditions.
Massively Multitask Networks for Drug Discovery

Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
Information and Trading Targets in a Dynamic Market Equilibrium

This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an unconstrained strategic insider who trades on long-lived information. We consider cases in which the constrained rebalancer is partially informed as well as the special case in which the rebalancer is ex ante uninformed. We derive a linear Bayesian Nash equilibrium, describe an algorithm for computing such equilibria, and present numerical results on properties of these equilibria.
Techniques for multiboson interferometry

The quantum statistics (QS) correlations of identical bosons are well known to be sensitive to the space-time extent and dynamics of the particle emitting source in high-energy collisions. While two-pion correlations are most often experimentally measured, the QS correlations of three pions and higher are rarely explored. A set of techniques to isolate and analyze three- and four-pion QS correlations is presented. In particular, the technique of built correlation functions allows one to more easily study the effects of quantum coherence at finite relative momenta instead of at the unmeasured intercept of correlation functions.
Hyperparameter Search in Machine Learning

We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based on given data. Most common learning algorithms feature a set of hyperparameters that must be determined before training commences. The choice of hyperparameters can significantly affect the resulting model's performance, but determining good values can be complex; hence a disciplined, theoretically sound search strategy is essential.
Learning to Search Better than Your Teacher

Methods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference. This is unsatisfactory in many applications where the reference policy is suboptimal and the goal of learning is to improve upon it. Can learning to search work even when the reference is poor? 
 
We provide a new learning to search algorithm, LOLS, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee. Consequently, LOLS can improve upon the reference policy, unlike previous algorithms. This enables us to develop structured contextual bandits, a partial information structured prediction setting with many potential applications.
Transforming a 4th year Modern Optics Course Using a Deliberate Practice Framework

We present a study of active learning pedagogies in an upper division physics course. This work was guided by the principle of deliberate practice for the development of expertise, and this principle was used in the design of the materials and the orchestration of the classroom activities of the students. We present our process for efficiently converting a traditional lecture course based on instructor notes into activities for such a course with active learning methods. Ninety percent of the same material was covered and scores on common exam problems showed a 15 % improvement with an effect size greater than 1 after the transformation. We observe that the improvement and the associated effect size is sustained after handing off the materials to a second instructor. Because the improvement on exam questions was independent of specific problem topics and because the material tested was so mathematically advanced and broad (including linear algebra, Fourier Transforms, partial differential equations, vector calculus), we expect the transformation process could be applied to most upper division physics courses having a similar mathematical base.