Miracle-dz/UniFAR-FTUs huggingface dataset:
A SCUBA-2 selected Herschel-SPIRE dropout and the nature of this population

Dusty star-forming galaxies (DSFGs) detected at z > 4 provide important examples of the first generations of massive galaxies. However, few examples with spectroscopic confirmation are currently known, with Hershel struggling to detect significant numbers of z > 6 DSFGs. NGP6_D1 is a bright 850 $\mathrm{ \mu}$m source (12.3 ± 2.5 mJy) with no counterparts at shorter wavelengths (a SPIRE dropout). Interferometric observations confirm it is a single source, with no evidence for any optical or NIR emission, or nearby likely foreground lensing sources. No >3σ detected lines are seen in both LMT Redshift Search Receiver and IRAM 30 m EMIR spectra of NGP6_D1 across 32 GHz of bandwidth despite reaching detection limits of $\sim 1\, \mathrm{mJy}/500 \, \mathrm{km~s}^{-1}$, so the redshift remains unknown. Template fitting suggests that NGP6_D1 is most likely between z = 5.8 and 8.3. SED analysis finds that NGP6_D1 is a ULIRG, with a dust mass ∼108–109 M⊙ and a star-formation rate of ∼500 M⊙ yr−1. We place upper limits on the gas mass of NGP6_D1 of MH2 <(1.1 ± 3.5) × 1011 M⊙, consistent with a gas-to-dust ratio of ∼100–1000. We discuss the nature of NGP6_D1 in the context of the broader sub-mm population, and find that comparable SPIRE dropouts account for ∼20 per cent of all SCUBA-2 detected sources, but with a similar flux density distribution to the general population.
Unblocking of stellar electron capture for neutron-rich 
N=50
 nuclei at finite temperature

We have calculated electron-capture rates for neutron-rich $N=50$ nuclei ($^{78}\mathrm{Ni}, ^{82}\mathrm{Ge}, ^{86}\mathrm{Kr}, ^{88}\mathrm{Sr}$) within the thermal quasiparticle random-phase approximation approach at temperatures $T=0$, corresponding to capture on the ground state, and at $T=10\phantom{\rule{4pt}{0ex}}\mathrm{GK}$ (0.86 MeV), which is a typical temperature at which the $N=50$ nuclei are abundant during a supernova collapse. In agreement with recent experiments, we find no Gamow-Teller (${\mathrm{GT}}_{+}$) strength at low excitation energies, $El7\phantom{\rule{4pt}{0ex}}\mathrm{MeV}$, caused by Pauli blocking induced by the $N=50$ shell gap. At the astrophysically relevant temperatures, this Pauli blocking of the ${\mathrm{GT}}_{+}$ strength is overcome by thermal excitations across the $Z=40$ proton and $N=50$ neutron shell gaps, leading to a sizable GT contribution to the electron capture. At the high densities, at which the $N=50$ nuclei are important for stellar electron capture, forbidden transitions contribute noticeably to the capture rate. Our results indicate that the neutron-rich $N=50$ nuclei do not serve as an obstacle of electron capture during supernova collapse.
Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX, an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.
Four (Super)luminous Supernovae from the First Months of the ZTF Survey

We present photometry and spectroscopy of four hydrogen-poor luminous supernovae discovered during the 2-month long science commissioning and early operations of the Zwicky Transient Facility (ZTF) survey. Three of these objects, SN 2018bym (ZTF18aapgrxo), SN 2018avk (ZTF18aaisyyp), and SN 2018bgv (ZTF18aavrmcg), resemble typical SLSN-I spectroscopically, while SN 2018don (ZTF18aajqcue) may be an object similar to SN 2007bi experiencing considerable host galaxy reddening, or an intrinsically long-lived, luminous, and red SN Ic. We analyze the light curves, spectra, and host galaxy properties of these four objects and put them in context of the population of SLSN-I. SN 2018bgv stands out as the fastest-rising SLSN-I observed to date, with a rest-frame g-band rise time of just 10 days from explosion to peak—if it is powered by magnetar spin-down, the implied ejecta mass is only ≃1 M⊙. SN 2018don also displays unusual properties—in addition to its red colors and comparatively massive host galaxy, the light curve undergoes some of the strongest light-curve undulations postpeak seen in an SLSN-I, which we speculate may be due to interaction with circumstellar material. We discuss the promises and challenges of finding SLSNe in large-scale surveys like ZTF given the observed diversity in the population.
Object-centric Forward Modeling for Model Predictive Control

We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals. We propose to model a scene as a collection of objects, each with an explicit spatial location and implicit visual feature, and learn to model the effects of actions using random interaction data. Our model allows capturing the robot-object and object-object interactions, and leads to more sample-efficient and accurate predictions. We show that this learned model can be leveraged to search for action sequences that lead to desired goal configurations, and that in conjunction with a learned correction module, this allows for robust closed loop execution. We present experiments both in simulation and the real world, and show that our approach improves over alternate implicit or pixel-space forward models. Please see our project page (this https URL) for result videos.
Limit theorems for wobbly interval intermittent maps

We consider perturbations of interval maps with indifferent fixed points, which we refer to as wobbly interval intermittent maps, for which stable laws for general Holder observables fail. We obtain limit laws for such maps and Holder observables. These limit laws are similar to the classical semistable laws previously established for deterministic processes, but certain limitations imposed by the current dynamical set up are reflected in the main result. One of the considered examples is an interval map with a countable number of discontinuities, and to analyse it we need to construct a Markov/Young tower.
Azimuthal variations of oxygen abundance profiles in star-forming regions of disc galaxies in EAGLE simulations

The exploration of the spatial distribution of chemical abundances in star-forming regions of galactic discs can help us to understand the complex interplay of physical processes that regulate the star formation activity and the chemical enrichment across a galaxy. We study the azimuthal variations of the normalized oxygen abundance profiles in the highest numerical resolution run of the Evolution and Assembly of GaLaxies and their Environments (EAGLE) Project at $z$ = 0. We use young stellar populations to trace the abundances of star-forming regions. Oxygen profiles are estimated along different line of sights from a centrally located observer. The mean azimuthal variation in the EAGLE discs are ∼0.12 ± 0.03 dex $R_{\rm eff}^{-1}$ for slopes and ∼0.12 ± 0.03 dex for the zero-points, in agreement with previous works. Metallicity gradients measured along random directions correlate with those determined by averaging over the whole discs, although with a large dispersion. We find a slight trend for higher azimuthal variations in the disc components of low star-forming and bulge-dominated galaxies. We also investigate the metallicity profiles of stellar populations with higher and lower levels of enrichment than the average metallicity profiles, and we find that high star-forming regions with high metallicity tend to have slightly shallower metallicity slopes compared with the overall metallicity gradient. The simulated azimuthal variations in the EAGLE discs are in agreement with observations, although the large variety of metallicity gradients would encourage further exploration of the metal mixing in numerical simulations.
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?

Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning? This question has largely been studied only with respect to (worst-case) approximation error, in the more classical approximate dynamic programming literature. With regards to the statistical viewpoint, this question is largely unexplored, and the extant body of literature mainly focuses on conditions which permit sample efficient reinforcement learning with little understanding of what are necessary conditions for efficient reinforcement learning. This work shows that, from the statistical viewpoint, the situation is far subtler than suggested by the more traditional approximation viewpoint, where the requirements on the representation that suffice for sample efficient RL are even more stringent. Our main results provide sharp thresholds for reinforcement learning methods, showing that there are hard limitations on what constitutes good function approximation (in terms of the dimensionality of the representation), where we focus on natural representational conditions relevant to value-based, model-based, and policy-based learning. These lower bounds highlight that having a good (value-based, model-based, or policy-based) representation in and of itself is insufficient for efficient reinforcement learning, unless the quality of this approximation passes certain hard thresholds. Furthermore, our lower bounds also imply exponential separations on the sample complexity between 1) value-based learning with perfect representation and value-based learning with a good-but-not-perfect representation, 2) value-based learning and policy-based learning, 3) policy-based learning and supervised learning and 4) reinforcement learning and imitation learning.
Regularization of sideways problem for a time fractional diffusion equation with nonlinear source.

In this paper, we consider an inverse problem for a time-fractional diffusion equation with a nonlinear source. We prove that the considered problem is ill-posed, i.e. the solution does not depend continuously on the data. The problem is ill-posed in the sense of Hadamard. Under some weak {\color{black} a} priori assumptions on the sought solution, we propose a new regularization method for stabili{\color{black}z}ing the ill-posed problem. We also provide a numerical example to illustrate our results.
A Distributed Ledger Based Infrastructure for Smart Transportation System and Social Good

This paper presents a system architecture to promote the development of smart transportation systems. Thanks to the use of distributed ledgers and related technologies, it is possible to create, store and share data generated by users through their sensors, while moving. In particular, IOTA and IPFS are used to store and certify data (and their related metadata) coming from sensors or by the users themselves. Ethereum is exploited as the smart contract platform that coordinates the data sharing and provisioning. The necessary privacy guarantees are provided by the usage of Zero Knowledge Proof. We show some results obtained from some use case scenarios that demonstrate how such technologies can be integrated to build novel smart services and to promote social good in user mobility.
TorchBeast: A PyTorch Platform for Distributed RL

TorchBeast is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular IMPALA algorithm for fast, asynchronous, parallel training of RL agents. Additionally, TorchBeast has simplicity as an explicit design goal: We provide both a pure-Python implementation ("MonoBeast") as well as a multi-machine high-performance version ("PolyBeast"). In the latter, parts of the implementation are written in C++, but all parts pertaining to machine learning are kept in simple Python using PyTorch, with the environments provided using the OpenAI Gym interface. This enables researchers to conduct scalable RL research using TorchBeast without any programming knowledge beyond Python and PyTorch. In this paper, we describe the TorchBeast design principles and implementation and demonstrate that it performs on-par with IMPALA on Atari. TorchBeast is released as an open-source package under the Apache 2.0 license and is available at \url{this https URL}.
Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring

Estimating population‐level effects of a vaccine is challenging because there may be interference, that is, the outcome of one individual may depend on the vaccination status of another individual. Partial interference occurs when individuals can be partitioned into groups such that interference occurs only within groups. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to draw inference about causal effects of an exposure or treatment. Tchetgen Tchetgen and VanderWeele proposed a modified IPW estimator for causal effects in the presence of partial interference. Motivated by a cholera vaccine study in Bangladesh, this paper considers an extension of the Tchetgen Tchetgen and VanderWeele IPW estimator to the setting where the outcome is subject to right censoring using inverse probability of censoring weights (IPCW). Censoring weights are estimated using proportional hazards frailty models. The large sample properties of the IPCW estimators are derived, and simulation studies are presented demonstrating the estimators' performance in finite samples. The methods are then used to analyze data from the cholera vaccine study.
Area of scalar isosurfaces in homogeneous isotropic turbulence as a function of Reynolds and Schmidt numbers

A fundamental effect of fluid turbulence is turbulent mixing, which results in the stretching and wrinkling of scalar isosurfaces. Thus, the area of isosurfaces is of interest in understanding turbulence in general, with specific applications in, for example, combustion and the identification of turbulent/non-turbulent interfaces. We report measurements of isosurface areas in 28 direct numerical simulations (DNS) of homogeneous isotropic turbulence with a mean scalar gradient resolved on up to $14\,256^{3}$ grid points with Taylor Reynolds number $Re_{\unicode[STIX]{x1D706}}$ ranging from 24 to 633 and Schmidt number $Sc$ ranging from 0.1 to 7. More precisely, we measure layers with very small but finite thickness. The continuous equation we evaluate converges exactly to the area in the limit of zero layer thickness. We demonstrate a method for numerically integrating this equation that, for a test case with an analytical solution, converges linearly towards the exact solution with decreasing layer width. By applying the technique to DNS data and testing for convergence with resolution of the simulations, we verify the resolution requirements for DNS recently proposed by Yeung et al. (Phys. Rev. Fluids, vol. 3 (6), 2018, 064603). We conclude that isosurface areas scale with the square root of the Taylor Péclet number $Pe_{\unicode[STIX]{x1D706}}$ between approximately 50 and 4429, with some departure from power-law scaling evident for $2.4<Pe_{\unicode[STIX]{x1D706}}<50$. No independent effect of either $Re_{\unicode[STIX]{x1D706}}$ or $Sc$ is observed. The excellent scaling of area with $Pe_{\unicode[STIX]{x1D706}}^{1/2}$ occurs even though the probability density function of the scalar gradient is very close to exponential for $Re_{\unicode[STIX]{x1D706}}=98$ but approximately lognormal when $Re_{\unicode[STIX]{x1D706}}=633$.
A new continuum model for general relativistic viscous heat-conducting media

The lack of formulation of macroscopic equations for irreversible dynamics of viscous heat-conducting media compatible with the causality principle of Einstein’s special relativity and the Euler–Lagrange structure of general relativity is a long-lasting problem. In this paper, we propose a possible solution to this problem in the framework of SHTC equations. The approach does not rely on postulates of equilibrium irreversible thermodynamics but treats irreversible processes from the non-equilibrium point of view. Thus, each transfer process is characterized by a characteristic velocity of perturbation propagation in the non-equilibrium state, as well as by an intrinsic time/length scale of the dissipative dynamics. The resulting system of governing equations is formulated as a first-order system of hyperbolic equations with relaxation-type irreversible terms. Via a formal asymptotic analysis, we demonstrate that classical transport coefficients such as viscosity, heat conductivity, etc., are recovered in leading terms of our theory as effective transport coefficients. Some numerical examples are presented in order to demonstrate the viability of the approach. This article is part of the theme issue ‘Fundamental aspects of nonequilibrium thermodynamics’.
Entropy Decay Rates for Conservative Spectral Schemes Modeling Fokker-Planck-Landau Type Flows in the Mean Field Limit

The focus of this work is to create benchmark simulations of decay rates to statistical equilibrium in transport plasma models for Coulomb particle interactions given by a coupled Vlasov-Poisson Fokker-Planck-Landau equation, as well as with Maxwell type and hard sphere interactions. The qualitative decay to the equilibrium Maxwell-Boltzmann distribution through relative entropy is studied in detail for all three types of particle interactions by means of a conservative hybrid spectral and discontinuous Galerkin scheme adapted from previous work. More precisely, the Coulomb case shows that there is a degenerate spectrum, with a decay rate close to the law of two thirds predicted by upper estimates in a work of Strain and Guo in 2006, while the Maxwell type and hard sphere examples both exhibit a spectral gap as predicted by Desvillettes and Villani in 2000. Such decay rate behavior indicates that the analytical estimates for the Coulomb case is sharp while, still to this date, there is no analytical proof of sharp degenerate spectral behaviour for the Fokker-Planck-Landau operator. Simulations are presented, both for the space-homogeneous case of just particle potential interactions and the space-inhomogeneous case for the mean field coupling through the Poisson equation for total charges in periodic domains. New explicit derivations of spectral collisional weights are presented in the case of Maxwell type and hard sphere interactions and the stability of all three scenarios, including Coulomb interactions, is investigated.
Homomorphism obstructions for satellite maps

A knot in a solid torus defines a map on the set of (smooth or topological) concordance classes of knots in S 3 S^3 . This set admits a group structure, but a conjecture of Hedden suggests that satellite maps never induce interesting homomorphisms: we give new evidence for this conjecture in both categories. First, we use Casson-Gordon signatures to give the first obstruction to a slice pattern inducing a homomorphism on the topological concordance group, constructing examples with every winding number besides ± 1 \pm 1 . We then provide subtle examples of satellite maps which map arbitrarily deep into the n n -solvable filtration of Cochran, Orr, and Teichner [Ann. of Math. (2) 157 (2003), pp. 433–519], act like homomorphisms on arbitrary finite sets of knots, and yet which still do not induce homomorphisms. Finally, we verify Hedden’s conjecture in the smooth category for all small crossing number satellite operators but one.
On the environment of low surface brightness galaxies at different scales

We select a volume-limited sample of galaxies derived from the SDSS DR7 to study the environment of low surface brightness (LSB) galaxies at different scales, as well as several physical properties of the dark matter haloes where the LSB galaxies of the sample are embedded. To characterize the environment, we make use of a number of publicly available value-added galaxy catalogues. We find a slight preference for LSB galaxies to be found in filaments instead of clusters, with their mean distance to the nearest filament typically larger than for high surface brightness (HSB) galaxies. The fraction of isolated central LSB galaxies is higher than the same fraction for HSB ones, and the density of their local environment lower. The stellar-to-halo mass ratio using four different estimates is up to ∼20 per cent for HSB galaxies. LSB central galaxies present more recent assembly times when compared with their HSB counterparts. Regarding the λ spin parameter, using six different proxies for its estimation, we find that LSB galaxies present systematically larger values of λ than the HSB galaxy sample, and constructing a control sample with direct kinematic information drawn from ALFALFA, we confirm that the spin parameter of LSB galaxies is 1.6–2 times larger than the one estimated for their HSB counterparts.
High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning

An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, computability, transferability and extensibility of the methods discussed. They include empirical force fields, representations based on reproducing kernels, using permutationally invariant polynomials, neural network-learned representations and combinations thereof. Future directions and potential improvements are discussed primarily from a practical, application-oriented perspective.
Single-word speech recognition with Convolutional Neural Networks on raw waveforms

This work focuses on single-word speech recognition, where the end goal is to accurately recognize a set of predefined words from short audio clips. It uses the Speech Commands dataset, which consists of 65 000 one-second long utterances of 30 short words of which we learn to classify 10 words, along with classes for “unknown” words as well as “silence”. Single-word speech recognition can be used in voice interfaces for applications with keyword detection, which can be useful on mobile and embedded devices. These devices often have strict requirements in terms of computing power and memory, which is recognized in the design of the speech recognition model. To classify samples, we use a Convolutional Neural Network (CNN) with one-dimensional convolutions on the raw audio waveform. As opposed to more traditional methods where feature-engineering is crucial, we leverage the power of deep learning to learn the feature representation during training. To effectively train the model we use data augmentation, weighted random sampling, Stochastic Gradient Descent with Warm Restarts (SGDR) and pseudo-labeling. The model achieves 97.4% accuracy on the validation set, 88.7% and 89.4% on the two test sets. The results show that the model can predict samples of words it has seen during training with high accuracy, but it somewhat struggles to generalize to words outside of the scope of the training data and extremely noisy samples.