nils-herrmann/clean_scirepeval huggingface dataset:
Typical physics Ph.D. admissions criteria limit access to underrepresented groups but fail to predict doctoral completion

Graduate admissions tests cannot identify the students most likely to complete a Ph.D. in physics. This study aims to understand the effectiveness of typical admissions criteria in identifying students who will complete the Physics Ph.D. Multivariate statistical analysis of roughly one in eight physics Ph.D. students from 2000 to 2010 indicates that the traditional admissions metrics of undergraduate grade point average (GPA) and the Graduate Record Examination (GRE) Quantitative, Verbal, and Physics Subject Tests do not predict completion as effectively admissions committees presume. Significant associations with completion were found for undergraduate GPA in all models and for GRE Quantitative in two of four studied models; GRE Physics and GRE Verbal were not significant in any model. It is notable that completion changed by less than 10% for U.S. physics major test takers scoring in the 10th versus 90th percentile on the Quantitative test. Aside from these limitations in predicting Ph.D. completion overall, overreliance on GRE scores in admissions processes also selects against underrepresented groups.
A Survey on GANs for Anomaly Detection

Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years. 
Generative Adversarial Networks (GANs) and the adversarial training process have been recently employed to face this task yielding remarkable results. In this paper we survey the principal GAN-based anomaly detection methods, highlighting their pros and cons. Our contributions are the empirical validation of the main GAN models for anomaly detection, the increase of the experimental results on different datasets and the public release of a complete Open Source toolbox for Anomaly Detection using GANs.
ORRB - OpenAI Remote Rendering Backend

We present the OpenAI Remote Rendering Backend (ORRB), a system that allows fast and customizable rendering of robotics environments. It is based on the Unity3d game engine and interfaces with the MuJoCo physics simulation library. ORRB was designed with visual domain randomization in mind. It is optimized for cloud deployment and high throughput operation. We are releasing it to the public under a liberal MIT license: this https URL .
Uncertainty Estimates for Ordinal Embeddings

To investigate objects without a describable notion of distance, one can gather ordinal information by asking triplet comparisons of the form "Is object $x$ closer to $y$ or is $x$ closer to $z$?" In order to learn from such data, the objects are typically embedded in a Euclidean space while satisfying as many triplet comparisons as possible. In this paper, we introduce empirical uncertainty estimates for standard embedding algorithms when few noisy triplets are available, using a bootstrap and a Bayesian approach. In particular, simulations show that these estimates are well calibrated and can serve to select embedding parameters or to quantify uncertainty in scientific applications.
Reduced-Order Quasilinear Model of Ocean Boundary-Layer Turbulence

The combined effectiveness of model reduction and the quasilinear approximation for the reproduction of the low-order statistics of oceanic surface boundary layer turbulence is investigated. Idealized horizontally homogeneous problems of surface-forced thermal convection and Langmuir turbulence are studied in detail. Model reduction is achieved with a Galerkin projection of the governing equations onto a subset of modes determined by proper orthogonal decomposition (POD). When applied to boundary layers that are horizontally homogeneous, POD and a horizontally averaged quasilinear approximation both assume flow features that are horizontally wavelike, making the pairing very efficient. For less than 0.2% of the modes retained, the reduced quasilinear model is able to reproduce vertical profiles of horizontal mean fields as well as certain energetically important second-order turbulent transport statistics and energies to within 30% error. Reduced-basis quasilinear statistics must approach the full-basis statistics as the basis size approaches completion; however, some quasilinear statistics resemble those found in the fully nonlinear simulations at smaller basis truncations. Thus, model reduction could possibly improve upon the accuracy of quasilinear dynamics.
Dark matter capture by the Sun: revisiting velocity distribution uncertainties

Among the different strategies aiming to detect WIMP dark matter (DM), a neutrino signal coming from the Sun would be a smoking gun. This possibility relies on the DM capture by the Sun driven by the local DM distribution assumptions: the local mass density and the velocity distribution. In this context, we revisit those astrophysical hypotheses (also relevant for direct detection). We focus especially on the DM velocity distribution considering different possibilities beyond the popular Maxwellian distribution. Namely, some alternatives can be considered through analytical approaches and by looking into cosmological simulations of spiral galaxies. Most of the fitting formulas used to constrain the local velocity distribution function fail to describe the peak and the high velocity tail of the velocity distribution observed in simulations, the latter being improved when adding the local escape velocity of DM into the benchmark fitting models. In addition we test the predictions by the Eddington inversion method and also illustrate the importance of the galactic dynamical history. We estimate the resulting uncertainties on the DM capture rate by the Sun and conclude that different velocity distributions will affect the capture rate of DM by the Sun up to a 15−20%. On top of that, the calculation of the intrinsic variance of the capture rate leads to poorly controlled uncertainties especially for high WIMP masses (>30 GeV) raising concerns about the capture scenario. ar X iv :1 90 6. 11 67 4v 1 [ as tr oph .G A ] 2 7 Ju n 20 19
Turbulent effects through quasi-rectification

This article introduces a physically realistic model for explaining how electromagnetic waves can be internally generated, propagate and interact in strongly magnetized plasmas or in nuclear magnetic resonance experiments. It studies high frequency solutions of nonlinear hyperbolic equations for time scales at which dispersive and nonlinear effects can be present in the leading term of the solutions. It explains how the produced waves can accumulate during long times to produce constructive and destructive interferences which, in the above contexts, are part of turbulent effects.
The minimal observable clade size of exchangeable coalescents

For $\Lambda$-$n$-coalescents with mutation, we analyse the size $O_n$ of the partition block of $i\in\{1,\ldots,n\}$ at the time where the first mutation appears on the tree that affects $i$ and is shared with any other $j\in\{1,\ldots,n\}$. We provide asymptotics of $O_n$ for $n\to\infty$ and a recursion for all moments of $O_n$ for finite $n$. This variable gives an upper bound for the minimal clade size [2], which is not observable in real data. In applications to genetics, it has been shown to be useful to lower classification errors in genealogical model selection [10].
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation

We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning1, and allows us to explore many combinations of different linkage criteria and cannot-link constraints. We prove the equivalence of existing clustering methods to some of those combinations and introduce new algorithms for combinations that have not been studied before. We study both theoretical and empirical properties of these combinations and prove that some of these define an ultrametric on the graph. We conduct a systematic comparison of various instantiations of GASP on a large variety of both synthetic and existing signed clustering problems, in terms of accuracy but also efficiency and robustness to noise. Lastly, we show that some of the algorithms included in our framework, when combined with the predictions from a CNN model, result in a simple bottom-up instance segmentation pipeline. Going all the way from pixels to final segments with a simple procedure, we achieve state-of-theart accuracy on the CREMI 2016 EM segmentation benchmark without requiring domain-specific superpixels.
Tuning-Free Disentanglement via Projection

In representation learning and non-linear dimension reduction, there is a huge interest to learn the 'disentangled' latent variables, where each sub-coordinate almost uniquely controls a facet of the observed data. While many regularization approaches have been proposed on variational autoencoders, heuristic tuning is required to balance between disentanglement and loss in reconstruction accuracy -- due to the unsupervised nature, there is no principled way to find an optimal weight for regularization. Motivated to completely bypass regularization, we consider a projection strategy: modifying the canonical Gaussian encoder, we add a layer of scaling and rotation to the Gaussian mean, such that the marginal correlations among latent sub-coordinates become exactly zero. This achieves a theoretically maximal disentanglement, as guaranteed by zero cross-correlation between one latent sub-coordinate and the observed varying with the rest. Unlike regularizations, the extra projection layer does not impact the flexibility of the previous encoder layers, leading to almost no loss in expressiveness. This approach is simple to implement in practice. Our numerical experiments demonstrate very good performance, with no tuning required.
DVP: Data Visualization Platform

We identify two major steps in data analysis, data exploration for understanding and observing patterns/relationships in data; and construction, design and assessment of various models to formalize these relationships. For each step, there exists a large set of tools and software. For the first step, many visualization tools exist, such as, GGobi, Parallax, and Crystal Vision, and most recently tableau and plottly. For the second step, many Scientific Computing Environments (SCEs) exist, such as, Matlab, Mathematica, R and Python. However, there does not exist a tool which allows for seamless two-way interaction between visualization tools and SCEs. We have designed and implemented a data visualization platform (DVP) with an architecture and design that attempts to bridge this gap. DVP connects seamlessly to SCEs to bring the computational capabilities to the visualization methods in a single coherent platform. DVP is designed with two interfaces, the desktop stand alone version and the online interface. DVP with its structure and planned features is a unique software that serves a great deal of parties, including university research, governmental decision support and country's economy modeling, traffic analysis and control, financial sector and companies, and any other party interested in data analysis and interpretation. A free demo for the online interface of DVP is available \citep{DVP}. Since DVP was launched, circa 2012, the present manuscript was not published since today for commercialization and patent considerations.
The Racah Algebra as a Subalgebra of the Bannai-Ito Algebra

Assume that ${\mathbb F}$ is a field with $\operatorname{char}{\mathbb F}\not=2$. The Racah algebra $\Re$ is a unital associative ${\mathbb F}$-algebra defined by generators and relations. The generators are $A$, $B$, $C$, $D$ and the relations assert that $[A,B]=[B,C]=[C,A]=2D$ and each of $[A,D]+AC-BA$, $[B,D]+BA-CB$, $[C,D]+CB-AC$ is central in $\Re$. The Bannai-Ito algebra $\mathfrak{BI}$ is a unital associative ${\mathbb F}$-algebra generated by $X$, $Y$, $Z$ and the relations assert that each of $\{X,Y\}-Z$, $\{Y,Z\}-X$, $\{Z,X\}-Y$ is central in $\mathfrak{BI}$. It was discovered that there exists an ${\mathbb F}$-algebra homomorphism $\zeta\colon \Re\to \mathfrak{BI}$ that sends $A \mapsto \frac{(2X-3)(2X+1)}{16}$, $B \mapsto \frac{(2Y-3)(2Y+1)}{16}$, $C \mapsto \frac{(2Z-3)(2Z+1)}{16}$. We show that $\zeta$ is injective and therefore $\Re$ can be considered as an ${\mathbb F}$-subalgebra of $\mathfrak{BI}$. Moreover we show that any Casimir element of $\Re$ can be uniquely expressed as a polynomial in $\{X,Y\}-Z$, $\{Y,Z\}-X$, $\{Z,X\}-Y$ and $X+Y+Z$ with coefficients in ${\mathbb F}$.
Compositional Semantic Parsing across Graphbanks

Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.
T-equivariant disc potential and SYZ mirror construction

We develop a $G$-equivariant Lagrangian Floer theory and obtain a curved $A_\infty$ algebra, and in particular a $G$-equivariant disc potential. We construct a Morse model, which counts pearly trees in the Borel construction $L_G$. When applied to a smooth moment map fiber of a semi-Fano toric manifold, our construction recovers the $T$-equivariant toric Landau-Ginzburg mirror of Givental. We also study the $\bS^1$-equivariant Floer theory of a typical singular SYZ fiber (i.e. a pinched torus) and compute its $\bS^1$-equivariant disc potential via the gluing technique developed in \cite{CHL18,HKL}.
Overcast on Osiris: 3D radiative-hydrodynamical simulations of a cloudy hot Jupiter using the parametrized, phase-equilibrium cloud formation code EddySed

We present results from 3D radiative-hydrodynamical simulations of HD 209458b with a fully coupled treatment of clouds using the EddySed code, critically, including cloud radiative feedback via absorption and scattering. We demonstrate that the thermal and optical structure of the simulated atmosphere is markedly different, for the majority of our simulations, when including cloud radiative effects, suggesting this important mechanism cannot be neglected. Additionally, we further demonstrate that the cloud structure is sensitive to not only the cloud sedimentation efficiency (termed fsed in EddySed), but also the temperature–pressure profile of the deeper atmosphere. We briefly discuss the large difference between the resolved cloud structures of this work, adopting a phase-equilibrium and parametrized cloud model, and our previous work incorporating a cloud microphysical model, although a fairer comparison where, for example, the same list of constituent condensates is included in both treatments is reserved for a future work. Our results underline the importance of further study into the potential condensate size distributions and vertical structures, as both strongly influence the radiative impact of clouds on the atmosphere. Finally, we present synthetic observations from our simulations reporting an improved match, over our previous cloud-free simulations, to the observed transmission, HST WFC3 emission, and 4.5 μm Spitzer phase curve of HD 209458b. Additionally, we find all our cloudy simulations have an apparent albedo consistent with observations.
Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers

When translating natural language questions into SQL queries to answer questions from a database, we would like our methods to generalize to domains and database schemas outside of the training set. To handle complex questions and database schemas with a neural encoder-decoder paradigm, it is critical to properly encode the schema as part of the input with the question. In this paper, we use relation-aware self-attention within the encoder so that it can reason about how the tables and columns in the provided schema relate to each other and use this information in interpreting the question. We achieve significant gains on the recently-released Spider dataset with 42.94% exact match accuracy, compared to the 18.96% reported in published work.
Modeling Dark Sector in Horndeski Gravity at First-Order Formalism

We investigate a cosmological scenario by finding solutions using first-order formalism in the Horndeski gravity that constrains the superpotential and implies that no free choice of scalar potential is allowed. Despite this, we show that a de Sitter phase at late-time cosmology can be realized, where the dark energy sector can be identified. The scalar field equation of state tends to the cosmological scenario at present time and allows us to conclude that it can simulate the dark energy in the Horndeski gravity.
Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities

Inverse modeling for the estimation of non‐Gaussian hydraulic conductivity fields in subsurface flow and solute transport models remains a challenging problem. This is mainly due to the non‐Gaussian property, the nonlinear physics, and the fact that many repeated evaluations of the forward model are often required. In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non‐Gaussian conductivity fields with heterogeneous conductivity within each facies using a low‐dimensional latent representation. In addition, a deep residual dense convolutional network (DRDCN) is proposed for surrogate modeling of forward models with high‐dimensional and highly complex mappings. The two networks are both based on a multilevel residual learning architecture called residual‐in‐residual dense block. The multilevel residual learning strategy and the dense connection structure ease the training of deep networks, enabling us to efficiently build deeper networks that have an essentially increased capacity for approximating mappings of very high complexity. The CAAE and DRDCN networks are incorporated into an iterative ensemble smoother to formulate an inversion framework. The numerical experiments performed using 2‐D and 3‐D solute transport models illustrate the performance of the integrated method. The obtained results indicate that the CAAE is a robust parameterization method for non‐Gaussian conductivity fields with different heterogeneity patterns. The DRDCN is able to obtain accurate approximations of the forward models with high‐dimensional and highly complex mappings using relatively limited training data. The CAAE and DRDCN methods together significantly reduce the computation time required to achieve accurate inversion results.
Selection Via Proxy: Efficient Data Selection For Deep Learning

Data selection methods such as active learning and core-set selection are useful tools for machine learning on large datasets, but they can be prohibitively expensive to apply in deep learning. Unlike in other areas of machine learning, the feature representations that these techniques depend on are learned in deep learning rather than given, which takes a substantial amount of training time. In this work, we show that we can significantly improve the computational efficiency of data selection in deep learning by using a much smaller proxy model to perform data selection for tasks that will eventually require a large target model (e.g., selecting data points to label for active learning). In deep learning, we can scale down models by removing hidden layers or reducing their dimension to create proxies that are an order of magnitude faster. Although these small proxy models have significantly higher error, we find that they empirically provide useful rankings for data selection that have a high correlation with those of larger models. We evaluate this "selection via proxy" (SVP) approach on several data selection tasks. For active learning, applying SVP to Sener and Savarese [2018]'s recent method for active learning in deep learning gives a 4x improvement in execution time while yielding the same model accuracy. For core-set selection, we show that a proxy model that trains 10x faster than a target ResNet164 model on CIFAR10 can be used to remove 50% of the training data without compromising the accuracy of the target model, making end-to-end training time improvements via core-set selection possible.
A Portfolio Choice Problem in the Framework of Expected Utility Operators

Possibilistic risk theory starts from the hypothesis that risk is modeled by fuzzy numbers. In particular, in a possibilistic portfolio choice problem, the return of a risky asset will be a fuzzy number. The expected utility operators have been introduced in a previous paper to build an abstract theory of possibilistic risk aversion. To each expected utility operator, one can associate the notion of possibilistic expected utility. Using this notion, we will formulate in this very general context a possibilistic portfolio choice problem. The main results of the paper are two approximate calculation formulas for the corresponding optimization problem. The first formula approximates the optimal allocation with respect to risk aversion and investor’s prudence, as well as the first three possibilistic moments. Besides these parameters, in the second formula, the temperance index of the utility function and the fourth possibilistic moment appear.
Axion dark matter from Higgs inflation with an intermediate H*

In order to accommodate the QCD axion as the dark matter (DM) in a model in which the Peccei-Quinn (PQ) symmetry is broken before the end of inflation, a relatively low scale of inflation has to be invoked in order to avoid bounds from DM isocurvature fluctuations, H* ≲ 𝒪(109) GeV . We construct a simple model in which the Standard Model Higgs field is non-minimally coupled to Palatini gravity and acts as the inflaton, leading to a scale of inflation H* ∼ 108 GeV . When the energy scale at which the PQ symmetry breaks is much larger than the scale of inflation, we find that in this scenario the required axion mass for which the axion constitutes all DM is m0 ≲ 0.05 μ eV for a quartic Higgs self-coupling λϕ = 0.1, which correspond to the PQ breaking scale vσ ≳ 1014 GeV and tensor-to-scalar ratio r ∼ 10−12. Future experiments sensitive to the relevant QCD axion mass scale can therefore shed light on the physics of the Universe before the end of inflation.
Improved axion emissivity from a supernova via nucleon-nucleon bremsstrahlung

The most efficient axion production mechanism in a supernova (SN) core is the nucleon-nucleon bremsstrahlung. This process has been often modeled at the level of the vacuum one-pion exchange (OPE) approximation. Starting from this naive recipe, we revise the calculation including systematically different effects, namely a non-vanishing mass for the exchanged pion, the contribution from the two-pions exchange, effective in-medium nucleon masses and multiple nucleon scatterings. Moreover, we allow for an arbitrary degree of nucleon degeneracy. A self consistent treatment of the axion emission rate including all these effects is currently missing. The aim of this work is to provide such an analysis. Furthermore, we demonstrate that the OPE potential with all the previous corrections gives rise to similar results as the on-shell T-matrix, and is \pagebreak therefore well justified for our and similar studies. We find that the axion emissivity is reduced by over an order of magnitude with respect to the basic OPE calculation, after all these effects are accounted for. The implications for the axion mass bound and the impact for the next generation experimental axion searches is also discussed.