nils-herrmann/clean_scirepeval huggingface dataset:
Generalizing from a few environments in safety-critical reinforcement learning

Before deploying autonomous agents in the real world, we need to be confident they will perform safely in novel situations. Ideally, we would expose agents to a very wide range of situations during training, allowing them to learn about every possible danger, but this is often impractical. This paper investigates safety and generalization from a limited number of training environments in deep reinforcement learning (RL). We find RL algorithms can fail dangerously on unseen test environments even when performing perfectly on training environments. Firstly, in a gridworld setting, we show that catastrophes can be significantly reduced with simple modifications, including ensemble model averaging and the use of a blocking classifier. In the more challenging CoinRun environment we find similar methods do not significantly reduce catastrophes. However, we do find that the uncertainty information from the ensemble is useful for predicting whether a catastrophe will occur within a few steps and hence whether human intervention should be requested.
Experimental Investigation of Multiplicity Correlation Between Prompt Neutrons and Photons in the Spontaneous Fission of $^{252}$Cf

Excited nuclear fragments are emitted during nuclear fission. The de-excitation of fission fragments takes place as sequential emission of neutrons followed by photons. Particle emission dissipates the intrinsic and rotational excitation energies of the fragment until a long-lived state is reached. Both the number of neutrons and the number of photons emitted during de-excitation are dependent on the initial excitation energy, angular momentum, and parity of the radiating fission fragment. A correlation between neutron and photon multiplicities following fission is thus expected. We perform linear regression analysis of the data collected by the Chi-Nu array at the LANSCE facility in Los Alamos National Laboratory. We have developed analytic expressions to determine the bias introduced by background sources and the misclassification of particles by our pulse shape discrimination capable organic scintillation detectors. Additionally, we have developed perturbative techniques to derive corrections to the binomial-response model. We use the perturbative techniques to correct the experimentally-determined neutron-photon correlations for the effects of photon-neutron pileup. We have experimentally determined an event-by-event covariance, $\text{cov}(N_n,N_\gamma)$, of $-0.33 \pm 0.05$ between the neutron and photon multiplicities emitted following the spontaneous fission of $^{252}$Cf. The result suggests the existence of a small event-by-event competition of neutron and photon emission in fission. We compare our result with previous analyses and model calculations.
"Machine LLRning": Learning to Softly Demodulate

Soft demodulation, or demapping, of received symbols back into their conveyed soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any modern receiver. In this paper, a trainable universal neural network-based demodulator architecture, dubbed "LLRnet", is introduced. LLRnet facilitates an improved performance with significantly reduced overall computational complexity. For instance for the commonly used quadrature amplitude modulation (QAM), LLRnet demonstrates LLR estimates approaching the optimal log maximum a-posteriori inference with an order of magnitude less operations than that of the straightforward exact implementation. Link-level simulation examples for the application of LLRnet to 5G-NR and DVB-S.2 are provided. LLRnet is a (yet another) powerful example for the usefulness of applying machine learning to physical layer design.
Learning to Rank Broad and Narrow Queries in E-Commerce

Search is a prominent channel for discovering products on an e-commerce platform. Ranking products retrieved from search becomes crucial to address customer's need and optimize for business metrics. While learning to Rank (LETOR) models have been extensively studied and have demonstrated efficacy in the context of web search; it is a relatively new research area to be explored in the e-commerce. In this paper, we present a framework for building LETOR model for an e-commerce platform. We analyze user queries and propose a mechanism to segment queries between broad and narrow based on user's intent. We discuss different types of features - query, product and query-product and discuss challenges in using them. We show that sparsity in product features can be tackled through a denoising auto-encoder while skip-gram based word embeddings help solve the query-product sparsity issues. We also present various target metrics that can be employed for evaluating search results and compare their robustness. Further, we build and compare performances of both pointwise and pairwise LETOR models on fashion category data set. We also build and compare distinct models for broad and narrow queries, analyze feature importance across these and show that these specialized models perform better than a combined model in the fashion world.
Neutron star mergers and rare core-collapse supernovae as sources of r-process enrichment in simulated galaxies

We use cosmological, magnetohydrodynamical simulations of Milky Way-mass galaxies from the Auriga project to study their enrichment with rapid neutron capture (r-process) elements. We implement a variety of enrichment models from both binary neutron star mergers and rare core-collapse supernovae. We focus on the abundances of (extremely) metal-poor stars, most of which were formed during the first ~Gyr of the Universe in external galaxies and later accreted onto the main galaxy. We find that the majority of metal-poor stars are r-process enriched in all our enrichment models. Neutron star merger models result in a median r-process abundance ratio which increases with metallicity, whereas the median trend in rare core-collapse supernova models is approximately flat. The scatter in r-process abundance increases for models with longer delay times or lower rates of r-process producing events. Our results are nearly perfectly converged, in part due to the mixing of gas between mesh cells in the simulations. Additionally, different Milky Way-mass galaxies show only small variation in their respective r-process abundance ratios. Current (sparse and potentially biased) observations of metal-poor stars in the Milky Way seem to prefer rare core-collapse supernovae over neutron star mergers as the dominant source of r-process elements at low metallicity, but we discuss possible caveats to our models. Dwarf galaxies which experience a single r-process event early in their history show highly enhanced r-process abundances at low metallicity, which is seen both in observations and in our simulations. We also find that the elements produced in a single event are mixed with ~10^8 Msun of gas relatively quickly, distributing the r-process elements over a large region.
The Fundamental Relation between Halo Mass and Galaxy Group Properties

We explore the interrelationships between the galaxy group halo mass and various observable group properties. We propose a simple scenario that describes the evolution of the central galaxies and their host dark matter halos. Star formation quenching is one key process in this scenario, which leads to the different assembly histories of blue groups (group with a blue central) and red groups (group with a red central). For blue groups, both the central galaxy and the halo continue to grow their mass. For red groups, the central galaxy has been quenched and its stellar mass remains about constant, while its halo continues to grow by merging smaller halos. From this simple scenario, we speculate about the driving properties that should strongly correlate with the group halo mass. We then apply the machine learning algorithm the Random Forest (RF) regressor to blue groups and red groups separately in the semianalytical model L-GALAXIES to explore these nonlinear multicorrelations and to verify the scenario as proposed above. Remarkably, the results given by the RF regressor are fully consistent with the prediction from our simple scenario and hence provide strong support for it. As a consequence, the group halo mass can be more accurately determined from observable galaxy properties by the RF regressor with a 50% reduction in error. A halo mass more accurately determined in this way also enables more accurate investigations on the galaxy–halo connection and other important related issues, including galactic conformity and the effect of halo assembly bias on galaxy assembly.
The dependence of AGN activity on environment in SDSS

Environment is one of the key external drivers of the galaxies, while active galactic nucleus (AGN) is one of the key internal drivers. Both of them play fundamental roles in regulating the formation and evolution of galaxies. We explore the interrelationship between environment and AGN in SDSS. At a given stellar mass, the specific star formation rate distribution of the AGN host galaxies remains unchanged with overdensity, with the peak of the distribution around the Green Valley. We show that, at a given stellar mass, the AGN fraction that has been commonly used in previous studies (defined as the number of AGNs relative to all galaxies including passive and star forming ones) does decrease with increasing overdensity for satellites. This is largely due to the fact that the fraction of passive galaxies strongly depends on environment. In order to investigate the intrinsic correlation between AGN and environment, especially under the assumption that AGN feedback is responsible for star formation quenching, the AGN fraction should be defined as the number of AGNs relative to the star-forming galaxies only. With the new definition, we find little dependence of AGN fraction on overdensity, central/satellite, and group halo mass. There is only marginal evidence that AGN may prefer denser regions, which is possibly due to more frequent interaction of galaxies or higher merger rate in groups. Our results support the scenario that internal secular evolution is the predominant mechanism of triggering AGN activity, while external environment related processes only play a minor role.
Bayesian hierarchical inference of asteroseismic inclination angles

The stellar inclination angle – the angle between the rotation axis of a star and our line of sight – provides valuable information in many different areas, from the characterization of the geometry of exoplanetary and eclipsing binary systems to the formation and evolution of those systems. We propose a method based on asteroseismology and a Bayesian hierarchical scheme for extracting the inclination angle of a single star. This hierarchical method therefore provides a means to both accurately and robustly extract inclination angles from red giant stars. We successfully apply this technique to an artificial data set with an underlying isotropic inclination angle distribution to verify the method. We also apply this technique to 123 red giant stars observed with Kepler. We also show the need for a selection function to account for possible population-level biases, which are not present in individual star-by-star cases, in order to extend the hierarchical method towards inferring underlying population inclination angle distributions.
The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies, and redshifts with 12 optical filters

The Southern Photometric Local Universe Survey (S-PLUS) is imaging ∼9300 deg2 of the celestial sphere in 12 optical bands using a dedicated 0.8 m robotic telescope, the T80-South, at the Cerro Tololo Inter-american Observatory, Chile. The telescope is equipped with a 9.2k × 9.2k e2v detector with 10 $\rm {\mu m}$ pixels, resulting in a field of view of 2 deg2 with a plate scale of 0.55 arcsec pixel−1. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (|b| > 30°, 8000 deg2) and two areas of the Galactic Disc and Bulge (for an additional 1300 deg2). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 ugriz broad-band filters and 7 narrow-band filters centred on prominent stellar spectral features: the Balmer jump/[OII], Ca H + K, H δ, G band, Mg b triplet, H α, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (δz/(1 + z) = 0.02 or better) for galaxies with r < 19.7 AB mag and z < 0.4, thus producing a 3D map of the local Universe over a volume of more than $1\, (\mathrm{Gpc}/h)^3$. The final S-PLUS catalogue will also enable the study of star formation and stellar populations in and around the Milky Way and nearby galaxies, as well as searches for quasars, variable sources, and low-metallicity stars. In this paper we introduce the main characteristics of the survey, illustrated with science verification data highlighting the unique capabilities of S-PLUS. We also present the first public data release of ∼336 deg2 of the Stripe 82 area, in 12 bands, to a limiting magnitude of r = 21, available at datalab.noao.edu/splus.
Locality and entanglement in table-top testing of the quantum nature of linearized gravity

This paper points out the importance of the assumption of locality of physical interactions, and the concomitant necessity of propagation of an entity (in this case, off-shell quanta - virtual gravitons) between two nonrelativistic test masses in unveiling the quantum nature of linearized gravity through a laboratory experiment. At the outset, we will argue that observing the quantum nature of a system is not limited to evidencing Oℏ corrections to a classical theory: it instead hinges upon verifying tasks that a classical system cannot accomplish. We explain the background concepts needed from quantum field theory and quantum information theory to fully appreciate the previously proposed table-top experiments, namely forces arising through the exchange of virtual (off-shell) quanta, as well as local operations and classical communication (LOCC) and entanglement witnesses. We clarify the key assumption inherent in our evidencing experiment, namely the locality of physical interactions, which is a generic feature of interacting systems of quantum fields around us, and naturally incorporate microcausality in the description of our experiment. We also present the types of states the matter field must inhabit, putting the experiment on firm relativistic quantum-field-theoretic grounds. At the end, we use a nonlocal theory of gravity to illustrate how our mechanism may still be used to detect the qualitatively quantum nature of a force when the scale of nonlocality is finite. We find that the scale of nonlocality, including the entanglement entropy production in local and nonlocal gravity, may be revealed from the results of our experiment.
Using machine learning to understand physics graduate school admissions

Among all of the first-year graduate students enrolled in doctoral-granting physics departments, the percentage of female and racial minority students has remained unchanged for the past 20 years. The current graduate program admissions process can create challenges for achieving diversity goals in physics. In this paper, we will investigate how the various aspects of a prospective student's application to a physics doctoral program affect the likelihood the applicant will be admitted. Admissions data was collected from a large, Midwestern public research university that has a decentralized admissions process and included applicants' undergraduate GPAs and institutions, research interests, and GRE scores. Because the collected data varied in scale, we used supervised machine learning algorithms to create models that predict who was admitted into the PhD program. We find that using only the applicant's undergraduate GPA and physics GRE score, we are able to predict with 75% accuracy who will be admitted to the program.
Interplay between SM precision, BSM physics, and the measurements of 
αhad
 in 
μ‐e
 scattering

Muon electron scattering experiments such as the proposed MUonE experiment, offer an opportunity for an improved measurement of the Leading Order hadronic running of $\alpha$, denoted $\Delta \alpha_{\rm{had}}$. Such a measurement could be utilized to reduce the theoretical uncertainty on the prediction of the anomalous magnetic moment of the muon, $g-2$. Currently there is a discrepancy between theory and data for this observable which could potentially be explained by Beyond the Standard Model (BSM) physics. Here we investigate the possible impact of missing Standard Model (SM) higher order corrections and BSM physics on the proposed measurement of $\Delta \alpha_{\rm{had}}$. In principle either could be indirectly fitted into $\Delta \alpha_{\rm{had}}$, causing inconsistencies if used in a $g-2$ application. The literature suggests a target of 10 ppm on the cross section for the theoretical accuracy. We assess the validity of this target in detail using a variety of methods, finding that a 1 ppm target is a more conservative estimate to ensure missing higher orders do not dominate the theoretical uncertainty. For the potential BSM contributions we study various models which contribute first at tree- and loop-level. Of particular interest is the impact from dark photon models, which can potentially affect the measurement of $\Delta \alpha_{\rm{had}}$ at the desired accuracy. At loop-level there exists in general a kinematic suppression adequate to reduce the BSM contributions to a level which can be neglected for the extraction of $\Delta \alpha_{\rm{had}}$.
Election predictions are arbitrage-free: response to Taleb

Taleb [Election predictions as martingales: An arbitrage approach.  2018, 18, 1–5] claimed a novel approach to evaluating the quality of probabilistic election forecasts via no-arbitrage pricing techniques and argued that popular forecasts of the 2016 U.S. Presidential election had violated arbitrage boundaries. We show that under mild assumptions all such political forecasts are arbitrage-free and that the heuristic that Taleb's argument was based on is false.
Minimum Power to Maintain a Nonequilibrium Distribution of a Markov Chain

Biological systems use energy to maintain non-equilibrium distributions for long times, e.g. of chemical concentrations or protein conformations. What are the fundamental limits of the power used to "hold" a stochastic system in a desired distribution over states? We study the setting of an uncontrolled Markov chain $Q$ altered into a controlled chain $P$ having a desired stationary distribution. Thermodynamics considerations lead to an appropriately defined Kullback-Leibler (KL) divergence rate $D(P||Q)$ as the cost of control, a setting introduced by Todorov, corresponding to a Markov decision process with mean log loss action cost. 
The optimal controlled chain $P^*$ minimizes the KL divergence rate $D(\cdot||Q)$ subject to a stationary distribution constraint, and the minimal KL divergence rate lower bounds the power used. While this optimization problem is familiar from the large deviations literature, we offer a novel interpretation as a minimum "holding cost" and compute the minimizer $P^*$ more explicitly than previously available. We state a version of our results for both discrete- and continuous-time Markov chains, and find nice expressions for the important case of a reversible uncontrolled chain $Q$, for a two-state chain, and for birth-and-death processes.
Protecting Privacy of Users in Brain-Computer Interface Applications

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e., such that each individual’s EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations.
Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EM

Cryo-electron microscopy (cryo-EM), the subject of the 2017 Nobel Prize in Chemistry, is a technology for obtaining 3D reconstructions of macromolecules from many noisy 2D projections of instances of these macromolecules, whose orientations and positions are unknown. These molecules are not rigid objects, but flexible objects involved in dynamical processes. The different conformations are exhibited by different instances of the macromolecule observed in a cryo-EM experiment, each of which is recorded as a particle image. The range of conformations and the conformation of each particle are not known a priori; one of the great promises of cryo-EM is to map this conformation space. Remarkable progress has been made in reconstructing rigid molecules based on homogeneous samples of molecules in spite of the unknown orientation of each particle image and significant progress has been made in recovering a few distinct states from mixtures of rather distinct conformations, but more complex heterogeneous samples remain a major challenge. We introduce the ‘hyper-molecule’ theoretical framework for modeling structures across different states of heterogeneous molecules, including continuums of states. The key idea behind this framework is representing heterogeneous macromolecules as high-dimensional objects, with the additional dimensions representing the conformation space. This idea is then refined to model properties such as localized heterogeneity. In addition, we introduce an algorithmic framework for reconstructing such heterogeneous objects from experimental data using a Bayesian formulation of the problem and Markov chain Monte Carlo (MCMC) algorithms to address the computational challenges in recovering these high dimensional hyper-molecules. We demonstrate these ideas in a preliminary prototype implementation, applied to synthetic data.