nils-herrmann/clean_scirepeval huggingface dataset:
Batch-shaping for learning conditional channel gated networks

We present a method that trains large capacity neural networks with significantly improved accuracy and lower dynamic computational cost. We achieve this by gating the deep-learning architecture on a fine-grained-level. Individual convolutional maps are turned on/off conditionally on features in the network. To achieve this, we introduce a new residual block architecture that gates convolutional channels in a fine-grained manner. We also introduce a generally applicable tool $batch$-$shaping$ that matches the marginal aggregate posteriors of features in a neural network to a pre-specified prior distribution. We use this novel technique to force gates to be more conditional on the data. We present results on CIFAR-10 and ImageNet datasets for image classification, and Cityscapes for semantic segmentation. Our results show that our method can slim down large architectures conditionally, such that the average computational cost on the data is on par with a smaller architecture, but with higher accuracy. In particular, on ImageNet, our ResNet50 and ResNet34 gated networks obtain 74.60% and 72.55% top-1 accuracy compared to the 69.76% accuracy of the baseline ResNet18 model, for similar complexity. We also show that the resulting networks automatically learn to use more features for difficult examples and fewer features for simple examples.
Metamorphic Testing of a Deep Learning Based Forecaster

In this paper, we present the Metamorphic Testing of an in-use deep learning based forecasting application. The application looks at the past data of system characteristics (e.g. 'memory allocation') to predict outages in the future. We focus on two statistical / machine learning based components - a) detection of co-relation between system characteristics and b) estimating the future value of a system characteristic using an LSTM (a deep learning architecture). In total, 19 Metamorphic Relations have been developed and we provide proofs & algorithms where applicable. We evaluated our method through two settings. In the first, we executed the relations on the actual application and uncovered 8 issues not known before. Second, we generated hypothetical bugs, through Mutation Testing, on a reference implementation of the LSTM based forecaster and found that 65.9% of the bugs were caught through the relations.
Perturbation potentials to overcome order/disorder transitions in alchemical binding free energy calculations.

We investigate the role of order/disorder transitions in alchemical simulations of protein-ligand absolute binding free energies. We show, in the context of a potential of mean force description, that for a benchmarking system (the complex of the L99A mutant of T4 lysozyme with 3-iodotoluene) and for a more challenging system relevant for medicinal applications (the complex of the farnesoid X receptor with inhibitor 26 from a recent D3R challenge) that order/disorder transitions can significantly hamper Hamiltonian replica exchange sampling efficiency and slow down the rate of equilibration of binding free energy estimates. We further show that our analytical model of alchemical binding combined with the formalism developed by Straub et al. for the treatment of order/disorder transitions of molecular systems can be successfully employed to analyze the transitions and help design alchemical schedules and soft-core functions that avoid or reduce the adverse effects of rare binding/unbinding transitions. The results of this work pave the way for the application of these techniques to the alchemical estimation with explicit solvation of hydration free energies and absolute binding free energies of systems undergoing order/disorder transitions.
Approachable Music Composition with Machine Learning at Scale

To make music composition more approachable, we designed the first AI-powered Google Doodle, the Bach Doodle, where users can create their own melody and have it harmonized by a machine learning model Coconet (Huang et al., 2017) in the style of Bach. For users to input melodies, we designed a simplified sheet-music based interface. To support an interactive experience at scale, we re-implemented Coconet in TensorFlow.js (Smilkov et al., 2019) to run in the browser and reduced its runtime from 40s to 2s by adopting dilated depth-wise separable convolutions and fusing operations. We also reduced the model download size to approximately 400KB through post-training weight quantization. We calibrated a speed test based on partial model evaluation time to determine if the harmonization request should be performed locally or sent to remote TPU servers. In three days, people spent 350 years worth of time playing with the Bach Doodle, and Coconet received more than 55 million queries. Users could choose to rate their compositions and contribute them to a public dataset, which we are releasing with this paper. We hope that the community finds this dataset useful for applications ranging from ethnomusicological studies, to music education, to improving machine learning models.
Estimating the angular power spectrum of the gravitational-wave background in the presence of shot noise

There has been much recent interest in studying anisotropies in the astrophysical gravitational-wave (GW) background, as these could provide us with interesting new information about galaxy clustering and large-scale structure. However, this information is obscured by shot noise, caused by the finite number of GW sources that contribute to the background at any given time. We develop a new method for estimating the angular spectrum of anisotropies, based on the principle of combining statistically-independent data segments. We show that this gives an unbiased estimate of the true, astrophysical spectrum, removing the offset due to shot noise power, and that in the limit of many data segments, it is the most efficient (i.e. lowest-variance) estimator possible.
Environment from cross-correlations: connecting hot gas and the quenching of galaxies

The observable properties of galaxies depend on both internal processes and the external environment. In terms of the environmental role, we still do not have a clear picture of the processes driving the transformation of galaxies. The use of proxies for environment (e.g. host halo mass, distance to the Nth nearest neighbour, etc.), as opposed to the real physical conditions (e.g. hot-gas density) may bear some responsibility for this. Here, we propose a new method that directly links galaxies to their local environments, by using spatial cross-correlations of galaxy catalogues with maps from large-scale structure surveys [e.g. thermal Sunyaev–Zel’dovich (tSZ) effect, diffuse X-ray emission, weak lensing of galaxies, or the cosmic microwave background (CMB)]. We focus here on the quenching of galaxies and its link to local hot gas properties. Maps of galaxy overdensity and quenched fraction excess are constructed from volume-limited Sloan Digital Sky Survey (SDSS) catalogues, which are cross-correlated with tSZ effect and X-ray maps from Planck and ROSAT, respectively. Strong signals out to Mpc scales are detected for most cross-correlations and are compared to predictions from the Evolution and Assembly of GaLaxies and their Environments (EAGLE) and BAryons and Haloes of MAssive Systems (BAHAMAS) cosmological hydrodynamical simulations. The simulations successfully reproduce many, but not all, of the observed power spectra, with an indication that environmental quenching may be too efficient in the simulations. We demonstrate that the cross-correlations are sensitive to both the internal [e.g. active galactic nucleus (AGN) and stellar feedback] and external processes (e.g. ram pressure stripping, harassment, strangulation, etc.) responsible for quenching. The methods outlined in this paper can be adapted to other observables and, with upcoming surveys, will provide a stringent test of physical models for environmental transformation.
Cataloging accreted stars withinGaiaDR2 using deep learning

The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate theGaiaDR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion ofGaiaDR2.Methods.A method known as “transfer learning” is shown to be effective through extensive testing on a set of mockGaiacatalogs that are based on the FIREcosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matchedGaia/RAVE data set, which improves sensitivity to properties of the real Milky Way.The result is a catalog that identifies ∼767 000 accreted stars withinGaiaDR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.
Scaling relations and baryonic cycling in local star-forming galaxies

Assessments of the cold-gas reservoir in galaxies are a cornerstone for understanding star-formation processes and the role of feedback and baryonic cycling in galaxy evolution. Here we exploit a sample of 392 galaxies (dubbed MAGMA, Metallicity and Gas for Mass Assembly), presented in a recent paper, to quantify molecular and atomic gas properties across a broad range in stellar mass, Mstar, from ∼107 − 1011 M⊙. First, we find the metallicity (Z) dependence of the conversion factor for CO luminosity to molecular H2 mass αCO to be shallower than previous estimates, with αCO  ∝  (Z/Z⊙)−1.55. Second, molecular gas mass MH2 is found to be strongly correlated with Mstar and star-formation rate (SFR), enabling predictions of MH2 good to within ∼0.2 dex; analogous relations for atomic gas mass MHI and total gas mass Mgas are less accurate, ∼0.4 dex and ∼0.3 dex, respectively. Indeed, the behavior of atomic gas mass MHI in MAGMA scaling relations suggests that it may be a third, independent variable that encapsulates information about the circumgalactic environment and gas accretion. If Mgas is considered to depend on MHI, together with Mstar and SFR, we obtain a relation that predicts Mgas to within ∼0.05 dex. Finally, the analysis of depletion times and the scaling of MHI/Mstar and MH2/Mstar over three different mass bins suggests that the partition of gas and the regulation of star formation through gas content depends on the mass regime. Dwarf galaxies (Mstar  ≲  3 × 109 M⊙) tend to be overwhelmed by (H I) accretion, and despite short τH2 (and thus presumably high star-formation efficiency), star formation is unable to keep up with the gas supply. For galaxies in the intermediate Mstar “gas-equilibrium” bin (3 × 109 M⊙ ≲ Mstar ≲3 × 1010 M⊙), star formation proceeds apace with gas availability, and H I and H2 are both proportional to SFR. In the most massive “gas-poor, bimodality” regime (Mstar ≳ 3 × 1010 M⊙), H I does not apparently participate in star formation, although it generally dominates in mass over H2. Our results confirm that atomic gas plays a key role in baryonic cycling, and is a fundamental ingredient for current and future star formation, especially in dwarf galaxies.
A Substellar Companion to a Hot Star in K2's Campaign 0 Field

The K2 mission has enabled searches for transits in crowded stellar environments very different from the original Kepler mission field. We describe here the reduction and analysis of time series data from K2's Campaign 0 superstamp, which contains the 150 Myr open cluster M35. We report on the identification of a substellar transiting object orbiting an A star at the periphery of the superstamp. To investigate this transiting source, we performed ground based follow-up observations, including photometry with the Las Cumbres Observatory telescope network and high resolution spectroscopy with Keck/High Resolution Echelle Spectrometer. We confirm that the host star is a hot, rapidly rotating star, precluding precision radial velocity measurements. We nevertheless present a statistical validation of the planet or brown dwarf candidate using speckle interferometry from the WIYN telescope to rule out false positive stellar eclipsing binary scenarios. Based on parallax and proper motion data from Gaia Data Release 2 (DR2), we conclude that the star is not likely to be a member of M35, but instead is a background star around 100 pc behind the cluster. We present an updated ephemeris to enable future transit observations. We note that this is a rare system as a hot host star with a substellar companion. It has a high potential for future follow-up, including Doppler tomography and mid-infrared secondary transit observations.
Forays into the dark side of the swamp

Motivated by the swampland conjectures, we study the cosmological signatures of a quintessence potential which induces time variation in the low-energy effective field theory. After deriving the evolution of the quintessence field, we illustrate its possible ramifications by exploring putative imprints in a number of directions of particle phenomenology. We first show that a dark matter self-interaction rate increasing with time gives a novel way of reconciling the large self-interactions required to address small-scale structure issues with the constraint coming from clusters. Next, we study the effects of kinetic mixing variation during the radiation-dominated era on freeze-in dark matter production. Last, we elucidate quintessence effects on the restoration of the electroweak symmetry at finite temperature and the lifetime of the electroweak vacuum through a modification of the effective Higgs mass and quartic coupling.
Measuring neutrino masses with large-scale structure: Euclid forecast with controlled theoretical error

We present a Markov-Chain Monte-Carlo (MCMC) forecast for the precision of neutrino mass and cosmological parameter measurements with a Euclid-like galaxy clustering survey. We use a complete perturbation theory model for the galaxy one-loop power spectrum and tree-level bispectrum, which includes bias, redshift space distortions, IR resummation for baryon acoustic oscillations and UV counterterms. The latter encapsulate various effects of short-scale dynamics which cannot be modeled within perturbation theory. Our MCMC procedure consistently computes the non-linear power spectra and bispectra as we scan over different cosmologies. The second ingredient of our approach is the theoretical error covariance which captures uncertainties due to higher-order non-linear corrections omitted in our model. Having specified characteristics of a Euclid-like spectroscopic survey, we generate and fit mock galaxy power spectrum and bispectrum likelihoods. Our results suggest that even under very agnostic assumptions about non-linearities and short-scale physics a future Euclid-like survey will be able to measure the sum of neutrino masses with a standard deviation of 28 meV . When combined with the Planck cosmic microwave background likelihood, this uncertainty decreases to 13 meV . Over-optimistically reducing the theoretical error on the bispectrum down to the two-loop level marginally tightens this bound to 11 meV . Moreover, we show that the future large-scale structure (LSS) spectroscopic data will greatly improve constraints on the other cosmological parameters, e.g. reaching a percent (per mille) error on the Hubble constant with LSS alone (LSS + Planck).
Quant GANs: deep generation of financial time series

Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.
Atoms in Molecules From Alchemical Perturbation Density Functional Theory.

Based on thermodynamic integration we introduce atoms in molecules (AIM) using the orbital-free framework of alchemical perturbation density functional theory (APDFT). Within APDFT, atomic energies and electron densities in molecules are arbitrary because any reference system and integration path can be selected as long as it meets the boundary conditions. We choose the uniform electron gas (jellium) as reference, and linearly scale up all nuclear charges, situated at any query molecule's atomic coordinates. Within the approximations made when calculating one-particle electron densities, this universal choice affords unambiguous and exact definitions of energies and electron densities of AIMs. Numerical results are presented for neutral small molecules (CO, N2, BF, CO2), various small molecules with different electronic hybridisation states of carbon (CH4, C2H6, C2H4, C2H2, HCN), and for all the possible BN doped mutants connecting benzene to borazine (C2nB3-nN3-nH6, 0 ≦ n≦ 3). Our results, as well as comparison to atomic energy estimates resulting from DFT trained neural network models and atomic basis set overlap within CCSD, suggest that APDFT based AIMs enable meaningful, interesting, and counter-intuitive interpretations of chemical bonding and electron densities.
Remote dipole field reconstruction with dusty galaxies

The kinetic Sunyaev Zel'dovich (kSZ) effect, cosmic microwave background (CMB) temperature anisotropies induced by scattering of CMB photons from free electrons, forms the dominant blackbody component of the CMB on small angular scales. Future CMB experiments on the resolution/sensitivity frontier will measure the kSZ effect with high significance. Through the cross-correlation with tracers of structure, it will be possible to reconstruct the remote CMB dipole field (e.g. the CMB dipole observed at different locations in the Universe) using the technique of kSZ tomography. In this paper, we derive a quadratic estimator for the remote dipole field using the Cosmic Infrared Background (CIB) as a tracer of structure. We forecast the ability of current and next-generation measurements of the CMB and CIB to perform kSZ tomography. The total signal-to-noise of the reconstruction is order unity for current datasets, and can improve by a factor of up to $10^3$ for future datasets, based on statistical error only. The CIB-based reconstruction is highly correlated with a galaxy survey-based reconstruction of the remote dipole field, which can be exploited to improve constraints on cosmological parameters and models for the CIB and distribution of baryons.
Matrices of Optimal Tree-Depth and Row-Invariant Parameterized Algorithm for Integer Programming

A long line of research on fixed parameter tractability of integer programming culminated with showing that integer programs with n variables and a constraint matrix with tree-depth d and largest entry Δ are solvable in time g(d,Δ) poly(n) for some function g, i.e., fixed parameter tractable when parameterized by tree-depth d and Δ. However, the tree-depth of a constraint matrix depends on the positions of its non-zero entries and thus does not reflect its geometric structure. In particular, tree-depth of a constraint matrix is not preserved by row operations, i.e., a given integer program can be equivalent to another with a smaller dual tree-depth. 
We prove that the branch-depth of the matroid defined by the columns of the constraint matrix is equal to the minimum tree-depth of a row-equivalent matrix. We also design a fixed parameter algorithm parameterized by an integer d and the entry complexity of an input matrix that either outputs a matrix with the smallest dual tree-depth that is row-equivalent to the input matrix or outputs that there is no matrix with dual tree-depth at most d that is row-equivalent to the input matrix. Finally, we use these results to obtain a fixed parameter algorithm for integer programming parameterized by the branch-depth of the input constraint matrix and the entry complexity. The parameterization by branch-depth cannot be replaced by the more permissive notion of branch-width.
Technical Report: Partial Dependence through Stratification

Partial dependence curves (FPD) introduced by Friedman, are an important model interpretation tool, but are often not accessible to business analysts and scientists who typically lack the skills to choose, tune, and assess machine learning models. It is also common for the same partial dependence algorithm on the same data to give meaningfully different curves for different models, which calls into question their precision. Expertise is required to distinguish between model artifacts and true relationships in the data. 
In this paper, we contribute methods for computing partial dependence curves, for both numerical (StratPD) and categorical explanatory variables (CatStratPD), that work directly from training data rather than predictions of a model. Our methods provide a direct estimate of partial dependence, and rely on approximating the partial derivative of an unknown regression function without first fitting a model and then approximating its partial derivative. We investigate settings where contemporary partial dependence methods---including FPD, ALE, and SHAP methods---give biased results. Furthermore, we demonstrate that our approach works correctly on synthetic and plausibly on real data sets. Our goal is not to argue that model-based techniques are not useful. Rather, we hope to open a new line of inquiry into nonparametric partial dependence.
Almost non-negatively curved 4-manifolds with circle symmetry

We prove that if a closed smooth simply-connected 4-manifold with a circle action admits an almost non-negatively curved sequence of invariant Riemannian metrics, then it also admits a non-negatively curved Riemannian metric invariant with respect to the same action. This gives a classification of closed smooth simply-connected 4-manifolds of almost non-negative curvature under the assumption of continuous symmetry.
PPO Dash: Improving Generalization in Deep Reinforcement Learning

Deep reinforcement learning is prone to overfitting, and traditional benchmarks such as Atari 2600 benchmark can exacerbate this problem. The Obstacle Tower Challenge addresses this by using randomized environments and separate seeds for training, validation, and test runs. This paper examines various improvements and best practices to the PPO algorithm using the Obstacle Tower Challenge to empirically study their impact with regards to generalization. Our experiments show that the combination provides state-of-the-art performance on the Obstacle Tower Challenge.
MaskPlus: Improving Mask Generation for Instance Segmentation

Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts – a detection component and a mask generation branch, and mostly focus on the improvement of the detection part. In this paper, we present an approach that extends Mask R-CNN with five novel techniques for improving the mask generation branch and reducing the conflicts between the mask branch and the detection component in training. These five techniques are independent to each other and can be flexibly utilized in building various instance segmentation architectures for increasing the overall accuracy. We demonstrate the effectiveness of our approach with tests on the COCO dataset.