nils-herrmann/clean_scirepeval huggingface dataset:
An accurate perturbative approach to redshift space clustering of biased tracers in modified gravity

We extend the scale-dependent Gaussian Streaming Model (GSM) to produce analytical predictions for the anisotropic redshift-space correlation function for biased tracers in modified gravity models. Employing the Convolution Lagrangian Perturbation Theory (CLPT) re-summation scheme, with a local Lagrangian bias schema provided by the peak-background split formalism, we predict the necessary ingredients that enter the GSM, the real-space halo pairwise velocity and the pairwise velocity dispersion. We further consider effective field theory contributions to the pairwise velocity dispersion in order to model correctly its large scale behavior. We apply our method on two widely-considered modified gravity models, the chameleon-screened f(R) Hu-Sawicki model and the nDGP Vainshtein model and compare our predictions against state-of-the-art N-body simulations for these models. We demonstrate that the GSM approach to predict the monopole and the quadrupole of the redshift-space correlation function for halos, gives very good agreement with the simulation data, for a wide range of screening mechanisms, levels of screening and halo masses at z=0.5 and z=1. Our work shows the applicability of the GSM, for cosmologies beyond GR, demonstrating that it can serve as a powerful predictive tool for the next stage of cosmological surveys like DESI, Euclid, LSST and WFIRST.
JINGLE V: Dust properties of nearby galaxies derived from hierarchical Bayesian SED fitting

We study the dust properties of 192 nearby galaxies from the JINGLE survey using photometric data in the 22-850$\mu$m range. We derive the total dust mass, temperature T and emissivity index β of the galaxies through the fitting of their spectral energy distribution (SED) using a single modified black-body model (SMBB). We apply a hierarchical Bayesian approach that reduces the known degeneracy between T and β. Applying the hierarchical approach, the strength of the T-β anti-correlation is reduced from a Pearson correlation coefficient R = −0.79 to R = −0.52. For the JINGLE galaxies we measure dust temperatures in the range 17 − 30 K and dust emissivity indices β in the range 0.6 − 2.2. We compare the SMBB model with the broken emissivity modified black-body (BMBB) and the two modified black-bodies (TMBB) models. The results derived with the SMBB and TMBB are in good agreement, thus applying the SMBB, which comes with fewer free parameters, does not penalize the measurement of the cold dust properties in the JINGLE sample. We investigate the relation between T and β and other global galaxy properties in the JINGLE and Herschel Reference Survey (HRS) sample. We find that β correlates with the stellar mass surface density (R = 0.62) and anti-correlates with the HI mass fraction (MHI/M*, R = −0.65), whereas the dust temperature correlates strongly with the SFR normalized by the dust mass (R = 0.73). These relations can be used to estimate T and β in galaxies with insufficient photometric data available to measure them directly through SED fitting.
First Gaia dynamical model of the Milky Way disc with six phase space coordinates: a test for galaxy dynamics

We construct the first comprehensive dynamical model for the high-quality subset of stellar kinematics of the Milky Way disc, with full 6D phase-space coordinates, provided by the Gaia Data Release 2. We adopt an axisymmetric approximation and use an updated Jeans Anisotropic Modelling (JAM) method, which allows for a generic shape and radial orientation of the velocity ellipsoid, as indicated by the Gaia data, to fit the mean velocities and all three components of the intrinsic velocity dispersion tensor. The Milky Way is the first galaxy for which all intrinsic phase space coordinates are available, and the kinematics are superior to the best integral-field kinematics of external galaxies. This situation removes the long-standing dynamical degeneracies and makes this the first dynamical model highly overconstrained by the kinematics. For these reasons, our ability to fit the data provides a fundamental test for both galaxy dynamics and the mass distribution in the Milky Way disc. We tightly constrain the volume average total density logarithmic slope, in the radial range 3.6–12 kpc, to be αtot = −2.149 ± 0.055 and find that the dark halo slope must be significantly steeper than αDM = −1 (NFW). The dark halo shape is close to spherical and its density is ρDM(R⊙) = 0.0115 ± 0.0020 M⊙ pc−3 (0.437 ± 0.076 GeV cm−3), in agreement with previous estimates. The circular velocity at the solar position vcirc(R⊙) = 236.5 ± 3.1 km s−1 (including systematics) and its gently declining radial trends are also consistent with recent determinations.
The cosmological analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure

The Effective Field Theory of Large-Scale Structure is a formalism that allows us to predict the clustering of Cosmological Large-Scale Structure in the mildly non-linear regime in an accurate and reliable way. After validating our technique against several sets of numerical simulations, we perform the analysis for the cosmological parameters of the DR12 BOSS data. We assume ΛCDM, a fixed value of the baryon/dark-matter ratio, Ωb/Ωc, and of the tilt of the primordial power spectrum, ns, and no significant input from numerical simulations. By using the one-loop power spectrum multipoles, we measure the primordial amplitude of the power spectrum, As, the abundance of matter, Ωm, and the Hubble parameter, H0, {to about 13%, 3.2% and 3.2% respectively, obtaining ln (1010As)=2.72± 0.13, 0Ωm=0.309± 0.01, H0=68.5± 2.2 km/(s Mpc) at 68% confidence level. If we then add a CMB prior on the sound horizon, the error bar on H0 is reduced to 1.6%.} These results are a substantial qualitative and quantitative improvement with respect to former analyses, and suggest that the EFTofLSS is a powerful instrument to extract cosmological information from Large-Scale Structure.
The Quijote Simulations

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 each simulation follows the evolution of 2563, 5123, or 10243 particles in a box of 1 h−1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions.
Cosmological parameters from the BOSS galaxy power spectrum

We present cosmological parameter measurements from the publicly available Baryon Oscillation Spectroscopic Survey (BOSS) data on anisotropic galaxy clustering in Fourier space. Compared to previous studies, our analysis has two main novel features. First, we use a complete perturbation theory model that properly takes into account the non-linear effects of dark matter clustering, short-scale physics, galaxy bias, redshift-space distortions, and large-scale bulk flows. Second, we employ a Markov-Chain Monte-Carlo technique and consistently reevaluate the full power spectrum likelihood as we scan over different cosmologies. Our baseline analysis assumes minimal ΛCDM, varies the neutrino masses within a reasonably tight range, fixes the primordial power spectrum tilt, and uses the big bang nucleosynthesis prior on the physical baryon density ωb. In this setup, we find the following late-Universe parameters: Hubble constant H0=(67.9± 1.1) km s−1Mpc−1, matter density fraction Ωm=0.295± 0.010, and the mass fluctuation amplitude σ8=0.721± 0.043. These parameters were measured directly from the BOSS data and independently of the Planck cosmic microwave background observations. Scanning over the power spectrum tilt or relaxing the other priors do not significantly alter our main conclusions. Finally, we discuss the information content of the BOSS power spectrum and show that it is dominated by the location of the baryon acoustic oscillations and the power spectrum shape. We argue that the contribution of the Alcock-Paczynski effect is marginal in ΛCDM, but becomes important for non-minimal cosmological models.
Frustratingly Easy Natural Question Answering

Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa. Additionally, a lot of systems on the QA leaderboards do not have associated research documentation in order to successfully replicate their experiments. In this paper, we outline these algorithmic components such as Attention-over-Attention, coupled with data augmentation and ensembling strategies that have shown to yield state-of-the-art results on benchmark datasets like SQuAD, even achieving super-human performance. Contrary to these prior results, when we evaluate on the recently proposed Natural Questions benchmark dataset, we find that an incredibly simple approach of transfer learning from BERT outperforms the previous state-of-the-art system trained on 4 million more examples than ours by 1.9 F1 points. Adding ensembling strategies further improves that number by 2.3 F1 points.
Deep Prediction of Investor Interest: a Supervised Clustering Approach

We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a synthetic scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.1,2
Counterfactual Cross-Validation: Effective Causal Model Selection from Observational Data

What is the most effective way to select the best causal model among potential candidates? In this paper, we propose a method to effectively select the best individual-level treatment effect (ITE) predictors from a set of candidates using only an observational validation set. In model selection or hyperparameter tuning, we are interested in choosing the best model or the value of hyperparameter from potential candidates. Thus, we focus on accurately preserving the rank order of the ITE prediction performance of candidate causal models. The proposed evaluation metric is theoretically proved to preserve the true ranking of the model performance in expectation and to minimize the upper bound of the finite sample uncertainty in model selection. Consistent with the theoretical result, empirical experiments demonstrate that our proposed method is more likely to select the best model and set of hyperparameter in both model selection and hyperparameter tuning.
Privacy-Aware Cost-Effective Scheduling Considering Non-Schedulable Appliances in Smart Home

A Smart home provides integrating and electronic information services to help residential users manage their energy usage and bill cost, but also exposes users to significant privacy risks due to fine-grained information collected by smart meters. Taking account of users' privacy concerns, this paper focuses on cost-effective runtime scheduling designed for schedulable and non-schedulable appliances. To alleviate the influence of operation uncertainties introduced by non-schedulable appliances, we formulate the problem by minimizing the expected sum of electricity cost under the worst privacy situation. Inventing the iterative alternative algorithm, we effectively schedule the appliances and rechargeable battery in a cost-effective way while satisfying users' privacy requirement. Experimental evaluation based on real-world data demonstrates the effectiveness of the proposed algorithm.
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees

We present a model-free reinforcement learning algorithm to synthesize control policies that maximize the probability of satisfying high-level control objectives given as Linear Temporal Logic (LTL) formulas. Uncertainty is considered in the workspace properties, the structure of the workspace, and the agent actions, giving rise to a Probabilistically-Labeled Markov Decision Process (PL-MDP) with unknown graph structure and stochastic behaviour, which is even more general than a fully unknown MDP. We first translate the LTL specification into a Limit Deterministic Büchi Automaton (LDBA), which is then used in an on-the-fly product with the PL-MDP. Thereafter, we define a synchronous reward function based on the acceptance condition of the LDBA. Finally, we show that the RL algorithm delivers a policy that maximizes the satisfaction probability asymptotically. We provide experimental results that showcase the efficiency of the proposed method.
Geometric Graph Convolutional Neural Networks

Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds. However, GCNs lack the ability to take into account the ordering of node neighbors, even when there is a geometric interpretation of the graph vertices that provides an order based on their spatial positions. To remedy this issue, we propose Geometric Graph Convolutional Network (geo-GCN) which uses spatial features to efficiently learn from graphs that can be naturally located in space. Our contribution is threefold: we propose a GCN-inspired architecture which (i) leverages node positions, (ii) is a proper generalisation of both GCNs and Convolutional Neural Networks (CNNs), (iii) benefits from augmentation which further improves the performance and assures invariance with respect to the desired properties. Empirically, geo-GCN outperforms state-of-the-art graph-based methods on image classification and chemical tasks.
Prime Torsion in the Brauer Group of an Elliptic Curve

We determine generators and relations in the $d$-torsion of the Brauer group of an elliptic curve, provided that the $d$-torsion of the elliptic curve itself is rational over the base field, for any integer $d \geq 2$. For $q$ any odd prime, we further give an algorithm to explicitly calculate generators and relations of the $q$-torsion of the Brauer group of an elliptic curve over any base field of characteristic different from two, three, and $q$, containing a primitive $q$-th root of unity. These generators are symbol algebras and the relations arise as their tensor products. We deduce an upper bound on the symbol length of the prime torsion of $\text{Br}(E)/\text{Br}(k)$.
DAMA/LIBRA annual modulation and axion quark nugget dark matter model

The DAMA/LIBRA (DL) experiment shows 9.5σ evidence for an annual modulation in the (1–6) keV energy range, strongly suggesting that the observed modulation has a dark matter origin. However, the conventional interpretation in terms of weakly interacting massive particle–nucleon interaction is excluded by other experiments. We propose an alternative source of modulation in the form of neutrons, which have been liberated from surrounding material. Our computations are based on the so-called axion quark nugget (AQN) dark matter model, which was originally invented long ago to explain the similarity between the dark and visible cosmological matter densities, i.e., Ωdark∼Ωvisible. In our proposal, the annual modulation is shown to be generated in the keV energy range, which is consistent with the DL observation in (1–6) keV range. This keV energy scale in our proposal is mostly determined by spectral properties of the neutrinos emitted by the AQN dark matter particles, while the absence of the modulation with energies above 6 keV is explained by a sharp cutoff in the neutrino’s energy spectrum at ∼15  MeV. This proposal can be directly tested by COSINE-100, ANAIS-112, CYGNO, and other similar experiments. It can be also tested by studying the correlations between the signals from these experiments and the signatures from drastically different detectors designed for studies of infrasonic or seismic events using such instruments as distributed acoustic sensing.
World quantum gravity: quantum test objects and Synge’s world function

function Ding Jia ( ∗ 1Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada 2Perimeter Institute for Theoretical Physics, Waterloo, Ontario, N2L 2Y5, Canada 3Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada Abstract A new path integral approach of quantum gravity based on relational variables and quantum test objects is presented. We take as a basic variables the squared invariant distance. This invariant quantity could be technically simpler to work with than variant quantities such as the metric tensor. It also could facilitate the studies of matter coupling and quantum spacetime causal structures. In contrast to approaches based on piecewise linear geometries, here gravity is captured by its effects on quantum test particles and fields. By an observation of Parker, under a Feynman sum a gravitational phase can be traded into a Van Vleck-Morette determinant term. This leads to a new candidate path integral for gravity, which in certain special cases can be computed efficient in the Lorentzian signature. We discuss some ambiguities left in the path integral measure, which invite further clarifications.
Tracking the untracked

The issue of seamless identification of users previously tracked using existing real-time optical position tracking system such as the OptiTrack system and maintaining continuous tracking state (history) of each of those users is a hard problem. In this article, we present a theoretical framework to integrate existing tracking systems with features such as user identification and history of up to `n' person activity. In our approach, we assume no direct communication with the tracking system, but access to all data it collects. Also, there are no guarantees that 1) the order of each tracked retro-reflective sphere reported is the same, and 2) that there will be any particular number of spheres in the room at any given time. We describe how the data is fused with existing tracking data to provide a seamless transition between other forms of position tracking.
Robot Capability and Intention in Trust-Based Decisions Across Tasks

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots—inferred capability and intention—and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.
Learning-based Model Predictive Control for Smart Building Thermal Management

This paper proposes a learning-based model predictive control (MPC) approach for the thermal control of a four-zone smart building. The objectives are to minimize energy consumption and maintain the residents’ comfort. The proposed control scheme incorporates learning with the model-based control. The occupancy profile in the building zones are estimated in a long-term horizon through the artificial neural network (ANN), and this data is fed into the model-based predictor to get the indoor temperature predictions. The Energy Plus software is utilized as the actual dataset provider (weather data, indoor temperature, energy consumption). The optimization problem, including the actual and predicted data, is solved in each step of the simulation and the input setpoint temperature for the heating/cooling system, is generated. Comparing the results of the proposed approach with the conventional MPC results proved the significantly better performance of the proposed method in energy savings (40.56% less cooling power consumption and 16.73% less heating power consumption), and residents’ comfort.
Sterile neutrinos in astrophysical neutrino flavor

In this paper, we study the effect of active-neutrino-sterile-neutrino mixing in the expected high-energy astrophysical neutrino flavor content. Non-unitarity in the measurement of the three-active neutrinos can be due to the existence of sterile neutrino states. We introduce the concept of the four-flavor tetrahedron in order to visualize the lack of unitarity in the astrophysical neutrino three-flavor triangle. We demonstrate that active-sterile neutrino mixings modify the allowed region of the astrophysical flavor ratio from the standard case. However, a projection of the four-flavor tetrahedron has restrictions of phase space similar to the three-flavor triangle. On the other hand, the initial presence of astrophysical sterile neutrinos drastically changes the scenario, and it allows an apparent unitarity violation in the three-flavor triangle space. Using current global fit constraints including the non-unitarity case, we also illustrate the allowed astrophysical neutrino flavor ratios. Thus, the measurement of the high-energy astrophyscal neutrino flavor content allows us to explore sterile neutrinos independently of the sterile neutrino mass scale. These are topics of investigation for current and future neutrino telescopes.
Prescribed non positive scalar curvature on asymptotically hyperbolic manifolds with application to the Lichnerowicz equation

We study the prescribed scalar curvature problem, namely finding which function can be obtained as the scalar curvature of a metric in a given conformal class. We deal with the case of asymptotically hyperbolic manifolds and restrict ourselves to non positive prescribed scalar curvature. Following earlier results, we obtain a necessary and sufficient condition on the zero set of the prescribed scalar curvature so that the problem admits a (unique) solution.
Reconciling Predictability and Coherent Caching

Real-time systems are required to respond to their physical environment within predictable time. While multi-core platforms provide incredible computational power and throughput, they also introduce new sources of unpredictability. For parallel applications with data shared across multiple cores, overhead to maintain data coherence is a major cause of execution time variability. This source of variability can be eliminated by application level control for limiting data caching at different levels of the cache hierarchy. This removes the requirement of explicit coherence machinery for selected data. We show that such control can reduce the worst case write request latency on shared data by 52%. Benchmark evaluations show that proposed technique has a minimal impact on average performance.