Miracle-dz/UniFAR-FTUs huggingface dataset:
Obscuring Fraction of Active Galactic Nuclei Implied by Supernova and Radiative Feedbacks

We study the obscuring structure of circumnuclear disks (CNDs) by considering supernova (SN) feedbacks from nuclear starburst and the effect of anisotropic radiative pressure from active galactic nuclei (AGNs). We suppose that the mass accretion onto a central supermassive black hole (SMBH) is triggered by SN-driven turbulence within CNDs, and we explore how the structures of CNDs depend on the BH mass (MBH) and AGN luminosity (LAGN). We find that the obscuring fraction (fobs) peaks at ∼10% of the Eddington luminosity (LEdd), and its maximal value is fobs ∼ 0.6 for less massive SMBHs (e.g., MBH < 108 M⊙). This is because the scale height of CNDs is determined by the SN-driven accretion for a smaller LAGN, while the dusty molecular gas in CNDs is blown away by the radiation pressure from AGNs beyond the critical luminosity. On the other hand, for massive SMBHs (e.g., MBH > 108 M⊙), fobs is always smaller than 0.2, and it is almost independent of LAGN because the scale height of CNDs is mainly controlled by the maximal star formation efficiency ( ) in CNDs. In comparison with the obscuring fractions suggested from the mid-infrared observations of nearby AGNs, the SN plus radiative feedback model with reproduces the observations for MBH = 108 M⊙ well. We also find that the intense starburst or the existence of dust-free absorbers inside CNDs are necessary for explaining X-ray observations.
A HARPS RV search for planets around young nearby stars

Context. Young nearby stars are good candidates in the search for planets with both radial velocity (RV) and direct imaging techniques. This, in turn, allows for the computation of the giant planet occurrence rates at all separations. The RV search around young stars is a challenge as they are generally faster rotators than older stars of similar spectral types and they exhibit signatures of magnetic activity (spots) or pulsation in their RV time series. Specific analyses are necessary to characterize, and possibly correct for, this activity. Aims. Our aim is to search for planets around young nearby stars and to estimate the giant planet (GP) occurrence rates for periods up to 1000 days. Methods. We used the HARPS spectrograph on the 3.6 m telescope at La Silla Observatory to observe 89 A−M young (<600 Myr) stars. We used our SAFIR (Spectroscopic data via Analysis of the Fourier Interspectrum Radial velocities) software to compute the RV and other spectroscopic observables. Then, we computed the companion occurrence rates on this sample. Results. We confirm the binary nature of HD 177171, HD 181321 and HD 186704. We report the detection of a close low mass stellar companion for HIP 36985. No planetary companion was detected. We obtain upper limits on the GP (<13 MJup) and BD (∈ [13;80] MJup) occurrence rates based on 83 young stars for periods less than 1000 days, which are set, 2−2+3 and 1−1+3%.
Predominant Musical Instrument Classification based on Spectral Features

This work aims to examine one of the cornerstone problems of Musical Instrument Retrieval (MIR), in particular, instrument classification. IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen for this purpose. The data includes musical clips recorded from various sources in the last century, thus having a wide variety of audio quality. We have presented a very concise summary of past work in this domain. Having implemented various supervised learning algorithms for this classification task, SVM classifier has outperformed the other state-of-the-art models with an accuracy of 79%. We also implemented Unsupervised techniques out of which Hierarchical Clustering has performed well.
Electric field fluctuations in the two-dimensional Coulomb fluid

The structure factor for electric field correlations in the two dimensional Coulomb fluid is simulated and compared to theories of the dielectric function. Singular changes in the structure factor occur at the Berezinskii–Kosterlitz–Thouless insulator to conductor transition, as well as at a higher temperature correlation transition between a poor electrolyte and perturbed Debye–Hückel fluid. Structure factors are found to differ in the canonical and grand canonical ensembles, with the poor electrolyte showing full ensemble inequivalence. We identify mechanisms of ‘underscreening’ and ‘pinch point’ scattering that are relevant to experiments on ionic liquids and artificial spin ice respectively.
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects

Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for both training and prediction, which restricts them to opaque, lambertian objects that produce good returns in an RGBD sensor. In this paper we forgo using a depth sensor in favor of raw stereo input. We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called KeyPose, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects. To evaluate the performance of our method, we create a dataset of 15 clear objects in five classes, with 48K 3D-keypoint labeled images. We train both instance and category models, and show generalization to new textures, poses, and objects. KeyPose surpasses state-of-the-art performance in 3D pose estimation on this dataset by factors of 1.5 to 3.5, even in cases where the competing method is provided with ground-truth depth. Stereo input is essential for this performance as it improves results compared to using monocular input by a factor of 2. We will release a public version of the data capture and labeling pipeline, the transparent object database, and the KeyPose models and evaluation code. Project website: https://sites.google. com/corp/view/keypose.
Elemental Abundances in M31: The Kinematics and Chemical Evolution of Dwarf Spheroidal Satellite Galaxies

We present deep spectroscopy from Keck/DEIMOS of Andromeda I, III, V, VII, and X, all of which are dwarf spheroidal satellites of M31. The sample includes 256 spectroscopic members across all five dSphs. We confirm previous measurements of the velocity dispersions and dynamical masses, and we provide upper limits on bulk rotation. Our measurements confirm that M31 satellites obey the same relation between stellar mass and stellar metallicity as Milky Way (MW) satellites and other dwarf galaxies in the Local Group. The metallicity distributions show trends with stellar mass that are similar to those of MW satellites, including evidence in massive satellites for external influence, like pre-enrichment or gas accretion. We present the first measurements of individual element ratios, like [Si/Fe], in the M31 system, as well as measurements of the average [α/Fe] ratio. The trends of [α/Fe] with [Fe/H] also follow the same galaxy mass–dependent patterns as MW satellites. Less massive galaxies have more steeply declining slopes of [α/Fe] that begin at lower [Fe/H]. Finally, we compare the chemical evolution of M31 satellites to M31's Giant Stellar Stream and smooth halo. The properties of the M31 system support the theoretical prediction that the inner halo is composed primarily of massive galaxies that were accreted early. As a result, the inner halo exhibits higher [Fe/H] and [α/Fe] than surviving satellite galaxies.
Spectral library of age-benchmark low-mass stars and brown dwarfs

In recent years, some extremely red brown dwarfs have been discovered. They were believed to have a low surface gravity, but many of their spectral characteristics are similar to those of high-surface-gravity brown dwarfs, showing that the spectral characteristics of young brown dwarfs are poorly understood. We aim to test surface-gravity indicators in late-M and early-L brown dwarf spectra using data obtained with the X-shooter spectrograph at the Very Large Telescope. We select a benchmark sample of brown dwarf members of Chamaeleon I (∼2 Myr), Upper Scorpius (5−10 Myr), the Pleiades (132 ± 27 Myr) and Praesepe (590−790 Myr) with well-constrained ages and similar metallicities. We provide a consistent spectral classification of the sample in the optical and in the near-infrared. We measure the equivalent widths of their alkali lines, finding that they have a moderate correlation with age, especially for objects with spectral types M8 and later. We use spectral indices defined in the literature to estimate surface gravity, finding that their gravity assignment is accurate for 75 per cent of our sample. We investigate the correlation between red colour and age, finding that after ∼10 Myr, the colour does not change significantly for our sample with spectral types M6.0–L3.0. In this case, the red colours might be associated with circumstellar discs, ring structures, extinction, or viewing angle. Finally, we calculate the bolometric luminosity, and J and K bolometric corrections for our sample. We find that six objects are overluminous compared with other members of the same association. These objects are flagged as binary candidates by the Gaia survey.
Deformed General Relativity and Quantum Black Holes Interior

Effective models of black holes interior have led to several proposals for regular black holes. In the so-called polymer models, based on effective deformations of the phase space of spherically symmetric general relativity in vacuum, one considers a deformed Hamiltonian constraint while keeping a non-deformed vectorial constraint, leading under some conditions to a notion of deformed covariance. In this article, we revisit and study further the question of covariance in these deformed gravity models. In particular, we propose a Lagrangian formulation for these deformed gravity models where polymer-like deformations are introduced at the level of the full theory prior to the symmetry reduction and prior to the Legendre transformation. This enables us to test whether the concept of deformed covariance found in spherically symmetric vacuum gravity can be extended to the full theory, and we show that, in the large class of models we are considering, the deformed covariance cannot be realized beyond spherical symmetry in the sense that the only deformed theory which leads to a closed constraints algebra is general relativity. Hence, we focus on the spherically symmetric sector, where there exist non-trivial deformed but closed constraints algebras. We investigate the possibility to deform the vectorial constraint as well and we prove that non-trivial deformations of the vectorial constraint with the condition that the constraints algebra remains closed do not exist. Then, we compute the most general deformed Hamiltonian constraint which admits a closed constraints algebra and thus leads to a well-defined effective theory associated with a notion of deformed covariance. Finally, we study static solutions of these effective theories and, remarkably, we solve explicitly and in full generality the corresponding modified Einstein equations, even for the effective theories which do not satisfy the closeness condition. In particular, we give the expressions of the components of the effective metric (for spherically symmetric black holes interior) in terms of the functions that govern the deformations of the theory.
Scratch that! An Evolution-based Adversarial Attack against Neural Networks

Recent research has shown that Deep Neural Networks (DNNs) for image classification are vulnerable to adversarial attacks. However, most works on adversarial samples utilize sub-perceptual noise that, while invisible or slightly visible to humans, often covers the entire image. Additionally, most of these attacks often require knowledge of the neural network architecture and its parameters, and the ability to calculate the gradients of the parameters with respect to the inputs. In this work, we show that it is possible to attack neural networks in a highly restricted threat setting, where attackers have no knowledge of the neural network (i.e., in a black-box setting) and can only modify highly localized adversarial noise in the form of randomly chosen straight lines or scratches. Our Adversarial Scratches attack method covers only 1-2% of the image pixels and are generated using the Covariance Matrix Adaptation Evolutionary Strategy, a purely black-box method that does not require knowledge of the neural network architecture and its gradients. Against ImageNet models, Adversarial Scratches requires 3 times fewer queries than GenAttack (without any optimizations) and 73 times fewer queries than ZOO, both prior state-of-the-art black-box attacks. We successfully deceive state-of-the-art Inception-v3, ResNet-50 and VGG-19 models trained on ImageNet with deceiving rates of 75.8%, 62.7%, and 45% respectively, with fewer queries than several state-of-the-art black-box attacks, while modifying less than 2% of the image pixels. Additionally, we provide a new threat scenario for neural networks, demonstrate a new attack surface that can be used to perform adversarial attacks, and discuss its potential implications.
Intrinsic Topological Transforms via the Distance Kernel Embedding

Topological transforms are parametrized families of topological invariants, which, by analogy with transforms in signal processing, are much more discriminative than single measurements. The first two topological transforms to be defined were the Persistent Homology Transform and Euler Characteristic Transform, both of which apply to shapes embedded in Euclidean space. The contribution of this paper is to define topological transforms that depend only on the intrinsic geometry of a shape, and hence are invariant to the choice of embedding. To that end, given an abstract metric measure space, we define an integral operator whose eigenfunctions are used to compute sublevel set persistent homology. We demonstrate that this operator, which we call the distance kernel operator, enjoys desirable stability properties, and that its spectrum and eigenfunctions concisely encode the large-scale geometry of our metric measure space. We then define a number of topological transforms using the eigenfunctions of this operator, and observe that these transforms inherit many of the stability and injectivity properties of the distance kernel operator.
Neural Tangents: Fast and Easy Infinite Neural Networks in Python

Neural Tangents is a library for working with infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network architectures. These networks can then be trained and evaluated either at finite-width as usual, or in their infinite-width limit. For the infinite-width networks, Neural Tangents performs exact inference either via Bayes' rule or gradient descent, and generates the corresponding Neural Network Gaussian Process and Neural Tangent kernels. Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks. The entire library runs out-of-the-box on CPU, GPU, or TPU. All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. In addition to the repository below, we provide an accompanying interactive Colab notebook at https://colab.sandbox. google.com/github/neural-tangents/neural-tangents/blob/master/notebooks/neural_tangents_cookbook. ipynb
GPU Computing with Python: Performance, Energy Efficiency and Usability

In this work, we examine the performance, energy efficiency, and usability when using Python for developing high-performance computing codes running on the graphics processing unit (GPU). We investigate the portability of performance and energy efficiency between Compute Unified Device Architecture (CUDA) and Open Compute Language (OpenCL); between GPU generations; and between low-end, mid-range, and high-end GPUs. Our findings showed that the impact of using Python is negligible for our applications, and furthermore, CUDA and OpenCL applications tuned to an equivalent level can in many cases obtain the same computational performance. Our experiments showed that performance in general varies more between different GPUs than between using CUDA and OpenCL. We also show that tuning for performance is a good way of tuning for energy efficiency, but that specific tuning is needed to obtain optimal energy efficiency.
Non-asymptotic quantum metrology

The number of times that we can access a system to extract information via quantum metrology is always finite, and possibly small, and realistic amounts of prior knowledge tend to be moderate. Thus theoretical consistency demands a methodology that departs from asymptotic approximations and restricted parameter locations, while practical convenience requires that it is also flexible and easy to use in applications with limited data. We submit that this methodology can and should be built on a Bayesian framework, and in this thesis we propose, construct, explore and exploit a new non-asymptotic quantum metrology. First we show the consistency of taking those solutions that are optimal in the asymptotic regime of many trials as a guide to calculate a Bayesian measure of uncertainty. This provides an approximate but useful way of studying the non-asymptotic regime whenever an exact optimisation is intractable, and it avoids the non-physical results that can arise when only the asymptotic theory is used. Secondly, we construct a new non-asymptotic Bayesian bound without relying on the previous approximation by first selecting a single-shot optimal quantum strategy, and then simulating a sequence of repetitions of this scheme. These methods are then applied to a single-parameter Mach-Zehnder interferometer, and to multi-parameter qubit and optical sensing networks. Our results provide a detailed characterisation of how the interplay between prior information, correlations and a limited amount of data affects the performance of quantum metrology protocols, which opens the door to a vast set of unexplored possibilities to enhance non-asymptotic schemes. Finally, we provide practical researchers with a numerical toolbox for Bayesian metrology, while theoretical workers will benefit from the broader and more fundamental perspective that arises from the unified character of our methodology.
Tail universality of critical Gaussian multiplicative chaos

In this article we study the tail probability of the mass of critical Gaussian multiplicative chaos (GMC) associated to a general class of log-correlated Gaussian fields in any dimension, including the Gaussian free field (GFF) in dimension two. More precisely, we derive a fully explicit formula for the leading order asymptotics for the tail probability and demonstrate a new universality phenomenon. Our analysis here shares similar philosophy with the subcritical case but requires a different approach due to complications in the analogous localisation step, and we also employ techniques from recent studies of fusion estimates in GMC theory.
Reproductive Coercion by an Intimate Partner: Occurrence, Associations, and Interference with Sexual Health Decision Making

Reproductive coercion is a form of intimate partner violence (IPV) in which a variety of tactics can be used to force pregnancy on an intimate partner. This study investigated the occurrence of male-perpetrated reproductive coercion, its relationship to other forms of IPV, and interference with women's sexual health decision making. Two data sets were collected, by conducting client-practitioner screenings (n = 2,108) and anonymous surveys (n = 84), from women seeking reproductive health services in a southwestern state and analyzed separately. Prevalence and correlates of reproductive coercion varied according to data-collection method, indicating that behaviorally based screening tools and anonymous screening may increase disclosure rates. In the screening sample, reproductive coercion was associated with forced sex, threats of physical harm, and verbal abuse. In the survey sample, reproductive coercion was associated with being nonwhite, fear of asking one's partner to use a condom, and fear of refusing sex. Social workers should be aware that reproductive coercion is a form of coercive control that may be associated with increased fear and IPV and may interfere with sexual health decision making; however, disclosure of reproductive coercion or IPV is not necessary for intervention. The article concludes with a discussion of screening and intervention strategies. Language: en
PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models

Abstract Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC algorithm is computationally expensive due to costly matrix operations. In addition, the MCMC algorithm needs to be run for more iterations because the strong cross-correlations among the spatial latent variables result in slow mixing Markov chains. To address these computational challenges, we propose a projection-based intrinsic conditional autoregression (PICAR) approach, which is a discretized and dimension-reduced representation of the underlying spatial random field using empirical basis functions on a triangular mesh. Our approach exhibits fast mixing as well as a considerable reduction in computational cost per iteration. PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models. We show, via simulation studies, that our approach performs well in terms of parameter inference and prediction. We provide several examples to illustrate the applicability of our method, including (i) a high-dimensional cloud cover dataset that showcases its computational efficiency, (ii) a spatially varying coefficient model that demonstrates the ease of implementation of PICAR in the probabilistic programming languages stan and nimble, and (iii) a watershed survey example that illustrates how PICAR applies to models that are not amenable to efficient inference via existing methods.
Probing the cosmic opacity from future gravitational wave standard sirens

In this work, using the Gaussian Process, we explore the potentiality of future gravitational wave (GW) measurement to probe cosmic opacity through comparing its opacity-free luminosity distance (LD) with the opacity-dependent one from type Ia supernovae (SNIa). GW data points are simulated from the third generation Einstein Telescope, and SNIa data are taken from the Joint Light Analysis (JLA) or Pantheon compilation. The advantages of using Gaussian Process are that one may match SNIa data with GW data at the same redshift and use all available data to probe cosmic opacity. We obtain that the error bar of the constraint on cosmic opacity can be reduced to $\sigma_{\epsilon}\sim 0.011$ and $0.006$ at $1\sigma$ confidence level (CL) for JLA and Pantheon respectively in a cosmological-independent way. Thus, the future GW measurements can give competitive results on the cosmic opacity test. Furthermore, we propose a method to probe the spatial homogeneity of the cosmic transparency through comparing the reconstructed LD from the mock GW with the reconstructed one from SNIa data in a flat $\Lambda$CDM with the Gaussian Process. The result shows that a transparent universe is favored at $1\sigma$ CL, although the best-fit value of cosmic opacity is redshift-dependent.