nils-herrmann/clean_scirepeval huggingface dataset:
Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia DR2 stars brighter than G = 18

Combining the precise parallaxes and optical photometry delivered by Gaia’s second data release with the photometric catalogues of Pan-STARRS1, 2MASS, and AllWISE, we derived Bayesian stellar parameters, distances, and extinctions for 265 million of the 285 million objects brighter than G = 18. Because of the wide wavelength range used, our results substantially improve the accuracy and precision of previous extinction and effective temperature estimates. After cleaning our results for both unreliable input and output data, we retain 137 million stars, for which we achieve a median precision of 5% in distance, 0.20 mag in V-band extinction, and 245 K in effective temperature for G ≤ 14, degrading towards fainter magnitudes (12%, 0.20 mag, and 245 K at G = 16; 16%, 0.23 mag, and 260 K at G = 17, respectively). We find a very good agreement with the asteroseismic surface gravities and distances of 7000 stars in the Kepler, K2-C3, and K2-C6 fields, with stellar parameters from the APOGEE survey, and with distances to star clusters. Our results are available through the ADQL query interface of the Gaia mirror at the Leibniz-Institut für Astrophysik Potsdam (gaia.aip.de) and as binary tables at data.aip.de. As a first application, we provide distance- and extinction-corrected colour-magnitude diagrams, extinction maps as a function of distance, and extensive density maps. These demonstrate the potential of our value-added dataset for mapping the three-dimensional structure of our Galaxy. In particular, we see a clear manifestation of the Galactic bar in the stellar density distributions, an observation that can almost be considered direct imaging of the Galactic bar.
Faster and More Accurate Trace-based Policy Evaluation via Overall Target Error Meta-Optimization

To improve the speed and accuracy of the trace based policy evaluation method TD(λ), under appropriate assumptions, we derive and propose an off-policy compatible method of meta-learning state-based λ’s online with efficient incremental updates. Furthermore, we prove the derived bias-variance tradeoff minimization method, with slight adjustments, is equivalent to minimizing the overall target error in terms of state based λ’s. In experiments, the method shows significantly better performance when compared to the existing method and the baselines.
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension

We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG—our most syntactic objective—performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation.
The Black-Scholes Equation in Presence of Arbitrage

We apply Geometric Arbitrage Theory to obtain results First, for a generic market dynamics given by a multidimensional Ito's process we specify and prove the equivalence between (NFLVR) and expected utility maximization. As a by-product we provide a geometric characterization of the (NUPBR) condition given by the zero curvature (ZC) condition. Finally, we extend the Black-Scholes PDE to markets allowing arbitrage.
A game theoretic setting of capitation versus fee-for-service payment systems

We aim to determine whether a game-theoretic model between an insurer and a healthcare practice yields a predictive equilibrium that incentivizes either player to deviate from a fee-for-service to capitation payment system. Using United States data from various primary care surveys, we find that non-extreme equilibria (i.e., shares of patients, or shares of patient visits, seen under a fee-for-service payment system) can be derived from a Stackelberg game if insurers award a non-linear bonus to practices based on performance. Overall, both insurers and practices can be incentivized to embrace capitation payments somewhat, but potentially at the expense of practice performance.
Supernovae sparked by dark matter in white dwarfs

It was recently demonstrated that asymmetric dark matter can ignite supernovae by collecting and collapsing inside lone sub-Chandrasekhar mass white dwarfs, and that this may be the cause of Type Ia supernovae. A ball of asymmetric dark matter accumulated inside a white dwarf and collapsing under its own weight, sheds enough gravitational potential energy through scattering with nuclei, to spark the fusion reactions that precede a Type Ia supernova explosion. In this article we elaborate on this mechanism and use it to place new bounds on interactions between nucleons and asymmetric dark matter for masses $m_{X} = 10^{6}-10^{16}$ GeV. Interestingly, we find that for dark matter more massive than $10^{11}$ GeV, Type Ia supernova ignition can proceed through the Hawking evaporation of a small black hole formed by the collapsed dark matter. We also identify how a cold white dwarf's Coulomb crystal structure substantially suppresses dark matter-nuclear scattering at low momentum transfers, which is crucial for calculating the time it takes dark matter to form a black hole. Higgs and vector portal dark matter models that ignite Type Ia supernovae are explored.
Authenticated key-value stores with hardware enclaves

Authenticated data storage on an untrusted platform is an important computing paradigm for cloud applications ranging from data outsourcing, to cryptocurrency and general transparency logs. These modern applications increasingly feature update-intensive workloads, whereas existing authenticated data structures (ADSs) designed with in-place updates are inefficient to handle such workloads. This work addresses the issue and presents a novel authenticated log-structured merge tree (eLSM) based key-value store built on Intel SGX. We present a system design that runs the code of eLSM store inside enclave. To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels. We design protocols to support data integrity, (range) query completeness, and freshness. Our protocol causes small proofs by including the Merkle proofs at selected levels. We implement eLSM on top of Google LevelDB and Facebook RocksDB with minimal code change and performance interference. We evaluate the performance of eLSM under the YCSB workload benchmark and show a performance advantage of up to 4.5X speedup.
A Deep Generative Model for Graph Layout

Different layouts can characterize different aspects of the same graph. Finding a “good” layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error process is often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoder architecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent space and the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent space for users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluations of the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graph visualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics.
Screened fifth forces in parity-breaking correlation functions

Cross-correlating two different types of galaxy gives rise to parity breaking in the correlation function that derives from differences in the galaxies' properties and environments. This is typically associated with a difference in galaxy bias, describing the relation between galaxy number density and dark matter density, although observational effects such as magnification bias also play a role. In this paper we show that the presence of a screened fifth force adds additional degrees of freedom to the correlation function, describing the effective coupling of the force to the two galaxy populations. These are also properties of the galaxies' environments, but with different dependence in general to galaxy bias. We derive the parity-breaking correlation function analytically as a function of fifth-force strength and the two populations' fifth-force charges, and explore the result numerically using Hu-Sawicki $f(R)$ as a toy model of chameleon screening. We find that screening gives rise to an octopole, which, in the absence of magnification bias, is not present in any gravity theory without screening and thus is a qualitatively distinct signature. The modification to the dipole and octopole can be $\mathcal{O}(10\%)$ and $\mathcal{O}(100\%)$ respectively at redshift $z \gtrsim 0.5$ due to screening, but decreases towards lower redshift. The change in the background power spectrum in $f(R)$ theories induces a change in the dipole of roughly the same size, but dominant to the effect of screening at low $z$. While current data is insufficient to measure the parity-breaking dipole or octopole to the precision required to test these models, future surveys such as DESI, Euclid and SKA have the potential to probe screened fifth forces through the dipole.
Teaching AI, Ethics, Law and Policy

The cyberspace and development of intelligent systems using Artificial Intelligence (AI) creates new challenges to computer professionals, data scientists, regulators and policy makers. For example, self-driving cars raise new technical, ethical, legal and public policy issues. This paper proposes a course named Computers, Ethics, Law, and Public Policy, and suggests a curriculum for such a course. This paper presents ethical, legal, and public policy issues relevant to building and using intelligent systems.
Context-Aware Zero-Shot Learning for Object Recognition

Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations. A limitation of previous approaches is that only intrinsic properties of objects, e.g. their visual appearance, are taken into account while their context, e.g. the surrounding objects in the image, is ignored. Following the intuitive principle that objects tend to be found in certain contexts but not others, we propose a new and challenging approach, context-aware ZSL, that leverages semantic representations in a new way to model the conditional likelihood of an object to appear in a given context. Finally, through extensive experiments conducted on Visual Genome, we show that contextual information can substantially improve the standard ZSL approach and is robust to unbalanced classes.
Unsupervised Data Augmentation for Consistency Training

Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL.
Morphology and star formation in IllustrisTNG: the build-up of spheroids and discs

Using the IllustrisTNG simulations, we investigate the connection between galaxy morphology and star formation in central galaxies with stellar masses in the range 109–1011.5 M⊙. We quantify galaxy morphology by a kinematical decomposition of the stellar component into a spheroidal and a disc component (spheroid-to-total ratio, S/T) and by the concentration of the stellar mass density profile (C82). S/T is correlated with stellar mass and star formation activity, while C82 correlates only with stellar mass. Overall, we find good agreement with observational estimates for both S/T and C82. Low- and high-mass galaxies are dominated by random stellar motion, while only intermediate-mass galaxies (M⋆ ≈ 1010–1010.5 M⊙) are dominated by ordered rotation. Whereas higher mass galaxies are typical spheroids with high concentrations, lower mass galaxies have low concentration, pointing to different formation channels. Although we find a correlation between S/T and star formation activity, in the TNG model galaxies do not necessarily change their morphology when they transition through the green valley or when they cease their star formation, this depending on galaxy stellar mass and morphological estimator. Instead, the morphology (S/T and C82) is generally set during the star-forming phase of galaxies. The apparent correlation between S/T and star formation arises because earlier forming galaxies had, on average, a higher S/T at a given stellar mass. Furthermore, we show that mergers drive in situ bulge formation in intermediate-mass galaxies and are responsible for the recent spheroidal mass assembly in the massive galaxies with M⋆ > 1011 M⊙. In particular, these massive galaxies assemble about half of the spheroidal mass while star-forming and the other half through mergers while quiescent.
Landmarks and frontiers in biological fluid dynamics

Biological systems are influenced by fluid mechanics at nearly all spatiotemporal scales. This broad relevance of fluid mechanics to biology has been increasingly appreciated by engineers and biologists alike, leading to continued expansion of research in the field of biological fluid dynamics. While this growth is exciting, it can present a barrier to researchers seeking a concise introduction to key challenges and opportunities for progress in the field. Rather than attempt a comprehensive review of the literature, this article highlights a limited selection of classic and recent work. In addition to motivating the study of biological fluid dynamics in general, the goal is to identify both longstanding and emerging conceptual questions that can guide future research. Answers to these fluid mechanics questions can lead to breakthroughs in our ability to predict, diagnose, and correct biological dysfunction, while also inspiring a host of new engineering technologies.
Facial Pose Estimation by Deep Learning from Label Distributions

Facial pose estimation has gained a lot of attentions in many practical applications, such as human-robot interaction, gaze estimation and driver monitoring. Meanwhile, end-to-end deep learning-based facial pose estimation is becoming more and more popular. However, facial pose estimation suffers from a key challenge: the lack of sufficient training data for many poses, especially for large poses. Inspired by the observation that the faces under close poses look similar, we reformulate the facial pose estimation as a label distribution learning problem, considering each face image as an example associated with a Gaussian label distribution rather than a single label, and construct a convolutional neural network which is trained with a multi-loss function on AFLW dataset and 300W-LP dataset to predict the facial poses directly from color image. Extensive experiments are conducted on several popular benchmarks, including AFLW2000, BIWI, AFLW and AFW, where our approach shows a significant advantage over other state-of-the-art methods.
Coevo: a collaborative design platform with artificial agents

We present Coevo, an online platform that allows both humans and artificial agents to design shapes that solve different tasks. Our goal is to explore common shared design tools that can be used by humans and artificial agents in a context of creation. This approach can provide a better knowledge transfer and interaction with artificial agents since a common language of design is defined. In this paper, we outline the main components of this platform and discuss the definition of a human-centered language to enhance human-AI collaboration in co-creation scenarios.
Cosmology with dropout selection: straw-man surveys & CMB lensing

We seek to prove the means, motive and opportunity of 'dropout' selected 2 ⩽ z ⩽ 5 galaxies for large-scale structure. Together with acquired low-z tracers, these samples would map practically every linear mode and facilitate a tomographic decomposition of the Cosmic Microwave Background (CMB) lensing kernel over an unprecedented volume. With this, one may infer (the time evolution of) matter density fluctuations and perform compelling tests of horizon-scale General Relativity, neutrino masses and Inflation—viz., curvature, running of the spectral index and a scale-dependent halo bias induced by (local) primordial non-Gaussianity. This is facilitated by the order-of-magnitude increase in sensitivity achieved by planned CMB, optical-to-near-infrared imaging and spectroscopy. Focusing on traditional color-color—rather than photometric redshift—selection, we estimate the expected completeness, contamination, and spectroscopic survey speed of tailored Lyman-break galaxy (LBG) samples. With these in hand, we forecast the potential of CMB lensing cross-correlation, `clustering redshifts' and Redshift-Space Distortions (RSD) analyses. In particular, we estimate: the depth dependence of interlopers based on CFHTLS-Archive-Research Survey (CARS) data and propagate this to biases in cosmology; a simple relation for the dependence of the linear galaxy bias on redshift and depth; new inferences of non-linear halo bias at these redshifts using legacy data; detailed forecasts of LBG spectra as would be observed by the Dark Energy Spectroscopic Instrument, Prime Focus Spectrograph, and their successors. We further assess the relative competitiveness of these spectroscopic facilities based on an intuitive figure-of-merit and define modern equivalents to traditional color selection criteria for the Large Synoptic Survey Telescope, where necessary. We confirm these science cases to be compelling for achievable facilities in the next decade by defining a LBG sample of increasing Lyman-α equivalent width with redshift, which delivers both percent-level RSD constraints on the growth rate at high-z and measurements of CMB lensing cross-correlation at z=3 and 4 with a significance measured in the hundreds, given sufficient area overlap. Finally, we discuss the limitations of this initial exploration and provide avenues for future investigation.
Starburst and post-starburst high-redshift protogalaxies

Quenching of star-formation has been identified in many starburst and post-starburst galaxies, indicating burst-like star-formation histories (SFH) in the primordial Universe. Galaxies undergoing violent episodes of star-formation are expected to be rich in high energy cosmic rays (CRs). We have investigated the role of these CRs in such environments, particularly how they could contribute to this burst-like SFH via quenching and feedback. These high energy particles interact with the baryon and radiation fields of their host via hadronic processes to produce secondary leptons. The secondary particles then also interact with ambient radiation fields to generate X-rays through inverse-Compton scattering. In addition, they can thermalise directly with the semi-ionised medium via Coulomb processes. Heating at a rate of ∼10−25 erg cm−3 s−1can be attained by Coulomb processes in a star-forming galaxy with one core-collapse SN event per decade, and this is sufficient to cause quenching of star-formation. At high-redshift, a substantial amount of CR secondary electron energy can be diverted into inverse-Compton X-ray emission. This yields an X-ray luminosity of above 1041 erg s−1by redshiftz = 7 which drives a further heating effect, operating over larger scales. This would be able to halt inflowing cold gas filaments, strangulating subsequent star-formation. We selected a sample of 16 starburst and post-starburst galaxies at 7 ≲ z ≲ 9 and determine the star-formation rates they could have sustained. We applied a model with CR injection, propagation and heating to calculate energy deposition rates in these 16 sources. Our calculations show that CR feedback cannot be neglected as it has the strength to suppress star-formation in these systems. We also show that their currently observed quiescence is consistent with the suffocation of cold inflows, probably by a combination of X-ray and CR heating.
Probing cosmic axions through resonant emission and absorption in atomic systems with superradiance

The $\mu$eV-mass axion is one of the most promising candidates for cold dark matter, and remains to be a well-motivated solution to the CP problem of Quantum Chromodynamics (QCD) via the Peccei-Quinn mechanism. In this paper, we propose a novel method to detect the dark-matter axions in our galaxy via the resonant emission $|{\rm e}\rangle \to |{\rm g}\rangle + \gamma + \gamma^{\prime}_{} + a$ (or absorption $a + |{\rm e}\rangle \to |{\rm g}\rangle + \gamma + \gamma^{\prime}_{}$) in an atomic system with superradiance, where $|{\rm e}\rangle$ and $|{\rm g}\rangle$ stand for the excited and ground energy levels of atoms, respectively. A similar process via $|{\rm e}\rangle \to |{\rm g}\rangle + \gamma + a$ (or $a + |{\rm e}\rangle \to |{\rm g}\rangle + \gamma$) is also put forward to probe the axion-electron coupling. For the nominal experimental setup assuming a background-free environment, most of the parameter space for typical QCD axion models can be covered with parahydrogen molecules or ytterbium atoms. However, the background in a realistic experimental setup remains to be a major issue that needs to be solved in future studies. Searching for better atomic or molecular candidates may be required for a bigger signal-to-noise ratio.