nils-herrmann/clean_scirepeval huggingface dataset:
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox

To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to support training complex models on large datasets. Unfortunately, the design of these systems largely ignores a critical component of the overall analytics process: the deployment and serving of models at scale. In this work, we present Velox, a new component of the Berkeley Data Analytics Stack. Velox is a data management system for facilitating the next steps in real-world, large-scale analytics pipelines: online model management, maintenance, and serving. Velox provides end-user applications and services with a low-latency, intuitive interface to models, transforming the raw statistical models currently trained using existing offline large-scale compute frameworks into full-blown, end-to-end data products capable of recommending products, targeting advertisements, and personalizing web content. To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting). In this paper, we describe the challenges and architectural considerations required to achieve this functionality, including the abilities to span online and offline systems, to adaptively adjust model materialization strategies, and to exploit inherent statistical properties such as model error tolerance, all while operating at "Big Data" scale.
Hardness of parameter estimation in graphical models

We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are uniquely determined by the mean parameters, so the problem is feasible in principle. The goal of this paper is to investigate the computational feasibility of this statistical task. Our main result shows that parameter estimation is in general intractable: no algorithm can learn the canonical parameters of a generic pair-wise binary graphical model from the mean parameters in time bounded by a polynomial in the number of variables (unless RP = NP). Indeed, such a result has been believed to be true (see the monograph by Wainwright and Jordan (2008)) but no proof was known. 
Our proof gives a polynomial time reduction from approximating the partition function of the hard-core model, known to be hard, to learning approximate parameters. Our reduction entails showing that the marginal polytope boundary has an inherent repulsive property, which validates an optimization procedure over the polytope that does not use any knowledge of its structure (as required by the ellipsoid method and others).
Misaligned accretion on to supermassive black hole binaries

We present the results of high-resolution numerical simulations of gas clouds falling onto binary supermassive black holes to form circumbinary accretion discs, with both prograde and retrograde cloud orbits. We explore a range of clouds masses and cooling rates. We find that for low mass discs that cool fast enough to fragment, prograde discs are significantly shorter-lived than similar discs orbiting retrograde with respect to the binary. For fragmenting discs of all masses, we also find that prograde discs fragment across a narrower radial region. If the cooling is slow enough that the disc enters a self-regulating gravitoturbulent regime, we find that alignment between the disc and binary planes occurs on a timescale primarily dictated by the disc thickness. We estimate realistic cooling times for such discs, and find that in the majority of cases we expect fragmentation to occur. The longer lifetime of low-mass fragmenting retrograde discs allows them to drive significant binary evolution, and may provide a mechanism for solving the 'last parsec problem'.
GLOBAL PROPERTIES OF M31'S STELLAR HALO FROM THE SPLASH SURVEY. II. METALLICITY PROFILE

We present the metallicity distribution of red giant branch (RGB) stars in M31's stellar halo, derived from photometric metallicity estimates for over 1500 spectroscopically confirmed RGB halo stars. The stellar sample comes from 38 halo fields observed with the Keck/DEIMOS spectrograph, ranging from 9 to 175 kpc in projected distance from M31's center, and includes 52 confirmed M31 halo stars beyond 100 kpc. While a wide range of metallicities is seen throughout the halo, the metal-rich peak of the metallicity distribution function becomes significantly less prominent with increasing radius. The metallicity profile of M31's stellar halo shows a continuous gradient from 9 to ∼100 kpc, with a magnitude of ∼ − 0.01 dex kpc−1. The stellar velocity distributions in each field are used to identify stars that are likely associated with tidal debris features. The removal of tidal debris features does not significantly alter the metallicity gradient in M31's halo: a gradient is maintained in fields spanning 10–90 kpc. We analyze the halo metallicity profile, as well as the relative metallicities of stars associated with tidal debris features and the underlying halo population, in the context of current simulations of stellar halo formation. We argue that the large-scale gradient in M31's halo implies M31 accreted at least one relatively massive progenitor in the past, while the field to field variation seen in the metallicity profile indicates that multiple smaller progenitors are likely to have contributed substantially to M31's outer halo.
Quarkonia production in proton-lead collisions at LHCb

The production of $J/\psi$ and $\Upsilon$ mesons decaying into dimuon final state is studied at the LHCb experiment, with rapidity in the range of (1.5, 4.0) or (-5.0, -2.5) in proton-lead collisions at a nucleon-nucleon centre-of-mass energy $\sqrt{s_{NN}}$ = 5 TeV, based on a data sample corresponding to an integrated luminosity of 1.6 /fb. The nuclear modification factor and forward-backward production ratio are determined for prompt $J/\psi$, $J/\psi$ from b-hadron decay and $\Upsilon(1S)$ mesons in study of the cold nuclear matter effects.
The frustrated brain: from dynamics on motifs to communities and networks

Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems—resonance pairs—promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes.
Ideas for Citizen Science in Astronomy

We review the expanding, internet-enabled, and rapidly evolving field of citizen astronomy, focusing on research projects in stellar, extragalactic, and planetary science that have benefited from the participation of members of the public. These volunteers contribute in various ways: making and analyzing new observations, visually classifying features in images and light curves, exploring models constrained by astronomical data sets, and initiating new scientific enquiries. The most productive citizen astronomy projects involve close collaboration between the professionals and amateurs involved and occupy scientific niches not easily filled by great observatories or machine learning methods: Citizen astronomers are motivated by being of service to science, as well as by their interest in the subject. We expect participation and productivity in citizen astronomy to increase, as data sets get larger and citizen science platforms become more efficient. Opportunities include engaging citizens in ever-more adv...
Speckle Imaging with Hypertelescopes

Optical stellar interferometers have demonstrated milli-arcsecond resolution with fewapertures spaced hundreds of meters apart. To obtain rich direct images, many aper-tures will be needed, for a better sampling of the incoming wavefront. The coherentimaging thus achievable improves the sensitivity with respect to the incoherent com-bination of successive fringed exposures. Ecient use of highly diluted apertures forcoherent imaging can be done with pupil densi cation, a technique also called \hyper-telescope imaging". Although best done with adaptive phasing, concentrating mostenergy in a dominant interference peak for a rich direct image of a complex source,such imaging is also possible with random phase errors such as caused by ", using methods such as speckle imaging which uses several short-exposureimages to reconstruct the true image. We have simulated such observations using anaperture which changes through the night, as naturally happens on Earth with xedgrounded mirror elements, and nd that reconstructed images of star clusters and ex-tended objects are of high quality. As part of the study we also estimated the requiredphoton levels for achieving a good signal to noise ratio using such a technique.: high angular resolution, interferometers techniques:interferometric.
Outflows from accretion discs formed in neutron star mergers: effect of black hole spin

The accretion disk that forms after a neutron star merger is a source of neutron-rich ejecta. The ejected material contributes to a radioactively-powered electromagnetic transient, with properties that depend sensitively on the composition of the outflow. Here we investigate how the spin of the black hole remnant influences mass ejection on the thermal and viscous timescales. We carry out two-dimensional, time-dependent hydrodynamic simulations of merger remnant accretion disks including viscous angular momentum transport and approximate neutrino self-irradiation. The gravity of the spinning black hole is included via a pseudo-Newtonian potential. We find that a disk around a spinning black hole ejects more mass, up to a factor of several, relative to the non-spinning case. The enhanced mass loss is due to energy release by accretion occurring deeper in the gravitational potential, raising the disk temperature and hence the rate of viscous heating in regions where neutrino cooling is ineffective. The mean electron fraction of the outflow increases moderately with BH spin due to a highly-irradiated (though not neutrino-driven) wind component. While the bulk of the ejecta is still very neutron-rich, thus generating heavy r-process elements, the leading edge of the wind contains a small amount of Lanthanide-free material. This component can give rise to a ~1 day blue optical `bump' in a kilonova light curve, even in the case of prompt BH formation, which may facilitate its detection.
Gravitational-wave modes from precessing black-hole binaries

Gravitational waves from precessing black-hole binaries exhibit features that are absent in nonprecessing systems. The most prominent of these is a parity-violating asymmetry that beams energy and linear momentum preferentially along or opposite to the orbital angular momentum, leading to recoil of the binary. The asymmetry will appear as amplitude and phase modulations at the orbital frequency. For strongly precessing systems, it accounts for at least 3% amplitude modulation for binaries in the sensitivity band of ground-based gravitational-wave detectors, and can exceed 50% for massive systems. Such asymmetric features are also clearly visible when the waves are decomposed into modes of spin-weighted spherical harmonics, and are inherent in the waves themselves---rather than resulting from residual eccentricity in numerical simulations, or from mode-mixing due to precession. In particular, there is generically no instantaneous frame for which the mode decomposition will have any symmetry. We introduce a method to simplify the expressions for waveforms given in analytical relativity, which can be used to combine existing high-order waveforms for nonprecessing systems with expressions for the precessing contributions, leading to improved accuracy and a unified treatment of precessing and nonprecessing binaries. Using this method, it is possible to clarify the nature and the origins of the asymmetries and show the effects of asymmetry on recoils more clearly. We present post-Newtonian (PN) expressions for the waveform modes that include these terms, complete to the relative 2PN level in spin (proportional to $v^4/c^4$ times a certain combination of the spins). Comparing the results of those expressions to numerical results, we find good qualitative agreement. We also demonstrate how these expressions can be used to efficiently calculate waveforms for gravitational-wave astronomy.
Multivariate Comparison of Classification Algorithms

Statistical tests that compare classification algorithms are univariate and use a single performance measure, e.g., misclassification error, $F$ measure, AUC, and so on. In multivariate tests, comparison is done using multiple measures simultaneously. For example, error is the sum of false positives and false negatives and a univariate test on error cannot make a distinction between these two sources, but a 2-variate test can. Similarly, instead of combining precision and recall in $F$ measure, we can have a 2-variate test on (precision, recall). We use Hotelling's multivariate $T^2$ test for comparing two algorithms, and when we have three or more algorithms we use the multivariate analysis of variance (MANOVA) followed by pairwise post hoc tests. In our experiments, we see that multivariate tests have higher power than univariate tests, that is, they can detect differences that univariate tests cannot. We also discuss how multivariate analysis allows us to automatically extract performance measures that best distinguish the behavior of multiple algorithms.
Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia

An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the outbreak are underway. However, the efficacy of these interventions, and their likely impact on an Ebola epidemic of this size, is unknown. Forecasting and simulation of these interventions may inform public health efforts.  We use existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and use this model to forecast the progression of the epidemic, as well as the efficacy of several interventions, including increased contact tracing, improved infection control practices, the use of a hypothetical pharmaceutical intervention to improve survival in hospitalized patients.  Model forecasts until Dec. 31, 2014 show an increasingly severe epidemic with no sign of having reached a peak. Modeling results suggest that increased contact tracing, improved infection control, or a combination of the two can have a substantial impact on the number of Ebola cases, but these interventions are not sufficient to halt the progress of the epidemic. The hypothetical pharmaceutical intervention, while impacting mortality, had a smaller effect on the forecasted trajectory of the epidemic.  Near-term, practical interventions to address the ongoing Ebola epidemic may have a beneficial impact on public health, but they will not result in the immediate halting, or even obvious slowing of the epidemic. A long-term commitment of resources and support will be necessary to address the outbreak.
The Design Reference Asteroid for the OSIRIS-REx Mission Target (101955) Bennu

The Design Reference Asteroid (DRA) is a compilation of all that is known about the OSIRIS-REx mission target, asteroid (101955) Bennu. It contains our best knowledge of the properties of Bennu based on an extensive observational campaign that began shortly after its discovery, and has been used to inform mission plan development and flight system design. The DRA will also be compared with post-encounter science results to determine the accuracy of our Earth-based characterization efforts. The extensive observations of Bennu in 1999 has made it one of the best-characterized near-Earth asteroids. Many physical parameters are well determined, and span a number of categories: Orbital, Bulk, Rotational, Radar, Photometric, Spectroscopic, Thermal, Surface Analog, and Environment Properties. Some results described in the DRA have been published in peer-reviewed journals while others have been reviewed by OSIRIS-REx Science Team members and/or external reviewers. Some data, such as Surface Analog Properties, are based on our best knowledge of asteroid surfaces, in particular those of asteroids Eros and Itokawa. This public release of the OSIRIS-REx Design Reference Asteroid is a annotated version of the internal OSIRIS-REx document OREX-DOCS-04.00-00002, Rev 9 (accepted by the OSIRIS-REx project on 2014-April-14). The supplemental data products that accompany the official OSIRIS-REx version of the DRA are not included in this release. We are making this document available as a service to future mission planners in the hope that it will inform their efforts.
Collapsed Variational Bayes Inference of Infinite Relational Model

The Infinite Relational Model (IRM) is a probabilistic model for relational data clustering that partitions objects into clusters based on observed relationships. This paper presents Averaged CVB (ACVB) solutions for IRM, convergence-guaranteed and practically useful fast Collapsed Variational Bayes (CVB) inferences. We first derive ordinary CVB and CVB0 for IRM based on the lower bound maximization. CVB solutions yield deterministic iterative procedures for inferring IRM given the truncated number of clusters. Our proposal includes CVB0 updates of hyperparameters including the concentration parameter of the Dirichlet Process, which has not been studied in the literature. To make the CVB more practically useful, we further study the CVB inference in two aspects. First, we study the convergence issues and develop a convergence-guaranteed algorithm for any CVB-based inferences called ACVB, which enables automatic convergence detection and frees non-expert practitioners from difficult and costly manual monitoring of inference processes. Second, we present a few techniques for speeding up IRM inferences. In particular, we describe the linear time inference of CVB0, allowing the IRM for larger relational data uses. The ACVB solutions of IRM showed comparable or better performance compared to existing inference methods in experiments, and provide deterministic, faster, and easier convergence detection.
Barycentric Corrections at 1 cm s-1 for Precise Doppler Velocities

The goal of this paper is to establish the requirements of a barycentric correction with an rms of ≲1 cm s-1, which is an order of magnitude better than necessary for the Doppler detection of true Earth analogs (∼9 cm s-1). We describe the theory and implementation of accounting for the effects on precise Doppler measurements of motion of the telescope through space, primarily from rotational and orbital motion of the Earth, and the motion of the solar system with respect to target star (i.e., the “barycentric correction”). We describe the minimal algorithm necessary to accomplish this and how it differs from a naïve subtraction of velocities (i.e., a Galilean transformation). We demonstrate the validity of code we have developed from the California Planet Survey code via comparison with the pulsar timing package, TEMPO2. We estimate the magnitude of various terms and effects, including relativistic effects, and the errors associated with incomplete knowledge of telescope position, timing, and stellar position and motion. We note that chromatic aberration will create uncertainties in the time of observation, which will complicate efforts to detect true Earth analogs. Our code is available for public use and validation.
Production and elliptic flow of dileptons and photons in a matrix model of the quark-gluon plasma.

We consider a nonperturbative approach to the thermal production of dileptons and photons at temperatures near the critical temperature in QCD. The suppression of colored excitations at low temperature is modeled by including a small value of the Polyakov loop, in a "semi"-quark-gluon plasma (QGP). Comparing the semi-QGP to the perturbative QGP, we find a mild enhancement of thermal dileptons. In contrast, to leading logarithmic order in weak coupling there are far fewer hard photons from the semi-QGP than the usual QGP. To illustrate the possible effects on photon and dilepton production in heavy-ion collisions, we integrate the rate with a simulation using ideal hydrodynamics. Dileptons uniformly exhibit a small flow, but the strong suppression of photons in the semi-QGP tends to weight the elliptical flow of photons to that generated in the hadronic phase.
Identification of core-periphery structure in networks

Many networks can be usefully decomposed into a dense core plus an outlying, loosely connected periphery. Here we propose an algorithm for performing such a decomposition on empirical network data using methods of statistical inference. Our method fits a generative model of core-periphery structure to observed data using a combination of an expectation-maximization algorithm for calculating the parameters of the model and a belief propagation algorithm for calculating the decomposition itself. We find the method to be efficient, scaling easily to networks with a million or more nodes, and we test it on a range of networks, including real-world examples as well as computer-generated benchmarks, for which it successfully identifies known core-periphery structure with low error rate. We also demonstrate that the method is immune to the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak. There is no such transition for core-periphery structure, which is detectable, albeit with some statistical error, no matter how weak it is.
Numerical weather prediction in two dimensions with topography, using a finite volume method

We aim to study a finite volume scheme to solve the two dimensional inviscid primitive equations of the atmosphere with humidity and saturation, in presence of topography and subject to physically plausible boundary conditions to the system of equations. In that respect, a version of a projection method is introduced to enforce the compatibility condition on the horizontal velocity field, which comes from the boundary conditions. The resulting scheme allows for a significant reduction of the errors near the topography when compared to more standard finite volume schemes. In the numerical simulations, we first present the associated good convergence results that are satisfied by the solutions simulated by our scheme when compared to particular analytic solutions. We then report on numerical experiments using realistic parameters. Finally, the effects of a random small-scale forcing on the velocity equation is numerically investigated. The numerical results show that such a forcing is responsible for recurrent large-scale patterns to emerge in the temperature and velocity fields.