Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive and Uniform Sample of Broad-line Active Galactic Nuclei from the SDSS DR7

A new, complete sample of 14,584 broad-line active galactic nuclei (AGNs) at z < 0.35 is presented, which are uncovered homogeneously from the complete database of galaxies and quasars observed spectroscopically in the Sloan Digital Sky Survey Seventh Data Release. The stellar continuum is properly removed for each spectrum with significant host absorption line features, and careful analyses of the emission line spectra, particularly in the Hα and Hβ wavebands, are carried out. The broad Balmer emission line, particularly Hα, is used to indicate the presence of an AGN. The broad Hα lines have luminosities in a range of 1038.5– 1044.3 , and line widths (FWHMs) of 500–34,000 . The virial black hole masses, estimated from the broad-line measurements, span a range of 105.1– 1010.3 , and the Eddington ratios vary from −3.3 to 1.3 in logarithmic scale. Other quantities such as multiwavelength photometric properties and flags denoting peculiar line profiles are also included in this catalog. We describe the construction of this catalog and briefly discuss its properties. The catalog is publicly available online. This homogeneously selected AGN catalog, along with the accurately measured spectral parameters, provides the most updated, largest AGN sample data, which will enable further comprehensive investigations of the properties of the AGN population in the low-redshift universe.
Competing Bandits in Matching Markets

Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting.
Contrasting Meteorological Drivers of the Glacier Mass Balance Between the Karakoram and Central Himalaya

There is strong variation in glacier mass balances in High Mountain Asia. Particularly interesting is the fact that glaciers are in equilibrium or even gaining mass in the Karakoram and Kunlun Shan ranges, which is in sharp contrast with the negative mass balances in the rest of High Mountain Asia. To understand this difference, an in-depth understanding of the meteorological drivers of the glacier mass balance is required. In this study, two catchments in contrasting climatic regions, one in the central Himalaya (Langtang) and one in the Karakoram (Shimshal), are modeled at 1 km grid spacing with the numerical atmospheric model WRF for the period of 2011–2013. Our results show that the accumulation and melt dynamics of both regions differ due to contrasting meteorological conditions. In Shimshal, 92% of the annual precipitation falls in the form of snow, in contrast with 42% in Langtang. In addition, 80% of the total snow falls above an altitude of 5000 m a.s. l, compared with 35% in Langtang. Another prominent contrast is that most of the annual snowfall falls between December and May (71%), compared with 52% in Langtang. The melt regimes are also different, with 41% less energy available for melt in Shimshal. The melt in the Karakoram is controlled by net shortwave radiation (r = 0.79 ± 0.01) through the relatively low glacier albedo in summer, while net longwave radiation (clouds) dominates the energy balance in the Langtang region (r = 0.76 ± 0.02). High amounts of snowfall and low melt rates result in a simulated positive glacier surface mass balance in Shimshal (+0.31 ± 0.06 m w.e. year−1) for the study period, while little snowfall, and high melt rates lead to a negative mass balance in Langtang (−0.40 ± 0.09 m w.e. year−1). The melt in Shimshal is highly variable between years, and is especially sensitive to summer snow events that reset the surface albedo. We conclude that understanding glacier mass balance anomalies requires quantification and insight into subtle shifts in the energy balance and accumulation regimes at high altitude and that the sensitivity of glaciers to climate change is regionally variable.
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation

Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then ‘translating’ the resulting pseudo-translation, or ‘Translationese’ into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.
Deep Network Approximation Characterized by Number of Neurons

This paper quantitatively characterizes the approximation power of deep feed-forward neural networks (FNNs) in terms of the number of neurons, i.e., the product of the network width and depth. It is shown by construction that ReLU FNNs with width $\mywidth$ and depth $9L+12$ can approximate an arbitrary Holder continuous function of order $\alpha$ with a Lipschitz constant $\nu$ on $[0,1]^d$ with a tight approximation rate $5(8\sqrt{d})^\alpha\nu N^{-2\alpha/d}L^{-2\alpha/d}$ for any given $N,L\in \N^+$. The constructive approximation is a corollary of a more general result for an arbitrary continuous function $f$ in terms of its modulus of continuity $\omega_f(\cdot)$. In particular, the approximation rate of ReLU FNNs with width $\mywidth$ and depth $9L+12$ for a general continuous function $f$ is $5\omega_f(8\sqrt{d} N^{-2/d}L^{-2/d})$. We also extend our analysis to the case when the domain of $f$ is irregular or localized in an $\epsilon$-neighborhood of a $d_{\mathcal{M}}$-dimensional smooth manifold $\mathcal{M}\subseteq [0,1]^d$ with $d_{\mathcal{M}}\ll d$. Especially, in the case of an essentially low-dimensional domain, we show an approximation rate $3\omega_f\big(\tfrac{4\epsilon}{1-\delta}\sqrt{\tfrac{d}{d_\delta}}\big)+5\omega_f\big(\tfrac{16d}{(1-\delta)\sqrt{d_\delta}}N^{-2/d_\delta}L^{-2/d_\delta }\big)$ for ReLU FNNs to approximate $f$ in the $\epsilon$-neighborhood, where $d_\delta=\OO\big(d_{\mathcal{M}}\tfrac{\ln (d/\delta)}{\delta^2}\big)$ for any given $\delta\in(0,1)$. Our analysis provides a general guide for selecting the width and the depth of ReLU FNNs to approximate continuous functions especially in parallel computing.
Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.
Stopped and stationary light at the single-photon level inside a hollow-core fiber

An experimental platform operating at the level of individual quanta and providing strong light-matter coupling is a key requirement for quantum information processing. We report on narrowband light storage and retrieval as well as stationary light, based on electromagnetically induced transparency, for weak coherent light pulses down to the single-photon level with a signal-to-noise ratio of 59. The experiments were carried out with laser-cooled atoms loaded into a hollow-core photonic crystal fiber to provide strong light-matter coupling, thereby demonstrating the prospects for future quantum networks of such a platform.
Influence of dietary replacement of sunflower oil with milk thistle ( Silybum marianum ) oil on chicken meat quality and antioxidant status of liver

The research focused on the effects of dietary replacement of 3% sunflower oil (SO group) with 3% milk thistle oil (MTO group) on the technological quality of meat, such as pH value, colour (CIE L*, CIE a*, CIE b*), drip loss (%), shear force (N), and cooking loss (%), as well as on the content of fatty acid lipids in broiler breast and thigh muscles. Significant difference (P < 0.05) was determined for pHi, pHu, CIE a*, CIE b* values between groups, although the values for the stated indicators were within the standard range. Lipids of breast meat of the MTO group contained more arachidic acid (P<0.001), octadecenoic acid isomer B (P=0.047) and eicosatrienoic acid (P=0.041), and less α-linolenic acid (P<0.001) and Σn-3 PUFA. Lipids of thigh meat of the MTO group contained more ΣSFA, myristoleic acid, eicosatrienoic acid (P<0.05) and eicosenoic acid (P<0.001), and less α-linolenic acid, and had narrower Σn-3/n-6 PUFA ratio than the SO group. According to the antioxidant status of broiler liver, there was significantly higher catalase activity determined in the MTO group.
Unsupervised Monocular Depth and Ego-Motion Learning With Structure and Semantics

We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically we model the motions of individual objects and learn their 3D motion vector jointly with depth and ego-motion. We obtain more accurate results, especially for challenging dynamic scenes not addressed by previous approaches. This is an extended version of Casser et al. Code and models have been open sourced at: https://sites.google. com/corp/view/struct2depth.
Type IIn supernova light-curve properties measured from an untargeted survey sample

The evolution of a Type IIn supernova (SN IIn) is governed by the interaction between the SN ejecta and a hydrogen-rich circumstellar medium. The SNe IIn thus allow us to probe the late-time mass-loss history of their progenitor stars. We present a sample of SNe IIn from the untargeted, magnitude-limited surveys of the Palomar Transient Factory (PTF) and its successor, the intermediate PTF (iPTF). To date, statistics on SN IIn optical light-curve properties have generally been based on small (≲10 SNe) samples from targeted SN surveys. The SNe IIn found and followed by the PTF/iPTF were used to select a sample of 42 events with useful constraints on the rise times as well as with available post-peak photometry. The sample SNe were discovered in 2009−2016 and have at least one low-resolution classification spectrum, as well as photometry from the P48 and P60 telescopes at Palomar Observatory. We study the light-curve properties of these SNe IIn using spline fits (for the peak and the declining portion) and template matching (for the rising portion). We study the peak-magnitude distribution, rise times, decline rates, colour evolution, host galaxies, and K-corrections of the SNe in our sample. We find that the typical rise times are divided into fast and slow risers at 20 ± 6 d and 50 ± 11 d, respectively. The decline rates are possibly divided into two clusters (with slopes 0.013 ± 0.006 mag d−1 and 0.040 ± 0.010 mag d−1), but this division has weak statistical significance. We find no significant correlation between the peak luminosity of SNe IIn and their rise times, but the more luminous SNe IIn are generally found to be more long-lasting. Slowly rising SNe IIn are generally found to decline slowly. The SNe in our sample were hosted by galaxies of absolute magnitude −22 ≲ Mg ≲ −13 mag. The K-corrections at light-curve peak of the SNe IIn in our sample are found to be within 0.2 mag for the observer’s frame r-band, for SNe at redshifts z < 0.25. By applying K-corrections and also including ostensibly “superluminous” SNe IIn, we find that the peak magnitudes are Mrpeak = −19.18 ± 1.32 mag. We conclude that the occurrence of conspicuous light-curve bumps in SNe IIn, such as in iPTF13z, are limited to 1.4+14.6−1.0 % of the SNe IIn. We also investigate a possible sub-type of SNe IIn with a fast rise to a ≳50 d plateau followed by a slow, linear decline.
GRAVITATIONAL WAVES FROM MASSIVE MAGNETARS FORMED IN BINARY NEUTRON STAR MERGERS

Binary neutron star (NS) mergers are among the most promising sources of gravitational waves (GWs), as well as candidate progenitors for short gamma-ray bursts (SGRBs). Depending on the total initial mass of the system and the NS equation of state (EOS), the post-merger phase can be characterized by a prompt collapse to a black hole or by the formation of a supramassive NS, or even a stable NS. In the latter cases of post-merger NS (PMNS) formation, magnetic field amplification during the merger will produce a magnetar and induce a mass quadrupole moment in the newly formed NS. If the timescale for orthogonalization of the magnetic symmetry axis with the spin axis is smaller than the spindown time, the NS will radiate its spin down energy primarily via GWs. Here we study this scenario for the various outcomes of NS formation: we generalize the set of equilibrium states for a twisted torus magnetic configuration to include solutions that, for the same external dipolar field, carry a larger magnetic energy reservoir; we hence compute the magnetic ellipticity for such configurations, and the corresponding strength of the expected GW signal as a function of the relative magnitude of the dipolar and toroidal field components. The relative number of GW detections from PMNSs and from binary NSs is a very strong function of the NS EOS, being higher (∼1%) for the stiffest EOSs and negligibly small for the softest ones. For intermediate-stiffness EOSs, such as the n = 4/7 polytrope recently used by Giacomazzo and Perna or the GM1 used by Lasky et al., the relative fraction is ∼0.3%; correspondingly, we estimate a GW detection rate from stable PMNSs of ∼0.1–1 yr−1 with advanced detectors, and of ∼100–1000 yr−1 with detectors of third generation such as the Einstein Telescope. Measurement of such GW signals would provide constraints on the NS EOS and, in connection with an SGRB, on the nature of the binary progenitors giving rise to these events.
Lattice Transformer for Speech Translation

Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results. However, depending on the up-stream systems, e.g., speech recognition, or word segmentation, the input to translation system can vary greatly. The goal of this work is to extend the attention mechanism of the transformer to naturally consume the lattice in addition to the traditional sequential input. We first propose a general lattice transformer for speech translation where the input is the output of the automatic speech recognition (ASR) which contains multiple paths and posterior scores. To leverage the extra information from the lattice structure, we develop a novel controllable lattice attention mechanism to obtain latent representations. On the LDC Spanish-English speech translation corpus, our experiments show that lattice transformer generalizes significantly better and outperforms both a transformer baseline and a lattice LSTM. Additionally, we validate our approach on the WMT 2017 Chinese-English translation task with lattice inputs from different BPE segmentations. In this task, we also observe the improvements over strong baselines.
Training Neural Networks for and by Interpolation

The majority of modern deep learning models are able to interpolate the data: the empirical loss can be driven near zero on all samples simultaneously. In this work, we explicitly exploit this interpolation property for the design of a new optimization algorithm for deep learning. Specifically, we use it to compute an adaptive learning-rate given a stochastic gradient direction. This results in the Adaptive Learning-rates for Interpolation with Gradients (ALI-G) algorithm. ALI-G retains the advantages of SGD, which are low computational cost and provable convergence in the convex setting. But unlike SGD, the learning-rate of ALI-G can be computed inexpensively in closed-form and does not require a manual schedule. We provide a detailed analysis of ALI-G in the stochastic convex setting with explicit convergence rates. In order to obtain good empirical performance in deep learning, we extend the algorithm to use a maximal learning-rate, which gives a single hyper-parameter to tune. We show that employing such a maximal learning-rate has an intuitive proximal interpretation and preserves all convergence guarantees. We provide experiments on a variety of architectures and tasks: (i) learning a differentiable neural computer; (ii) training a wide residual network on the SVHN data set; (iii) training a Bi-LSTM on the SNLI data set; and (iv) training wide residual networks and densely connected networks on the CIFAR data sets. We empirically show that ALI-G outperforms adaptive gradient methods such as Adam, and provides comparable performance with SGD, although SGD benefits from manual learning rate schedules. We release PyTorch and Tensorflow implementations of ALI-G as standalone optimizers that can be used as a drop-in replacement in existing code (code available at this https URL ).
Perturbation theory and higher order $\mathcal{S}^{\!p}$-differentiability of operator functions

We establish, for $1 < p < \infty$, higher order $\mathcal{S}^p$-differentiability results of the function $\varphi : t\in \mathbb{R} \mapsto f(A+tK) - f(A)$ for selfadjoint operators $A$ and $K$ on a separable Hilbert space $\mathcal{H}$ with $K$ element of the Schatten class $\mathcal{S}^p(\mathcal{H})$ and $f$ $n$-times differentiable on $\mathbb{R}$. We prove that if either $A$ and $f^{(n)}$ are bounded or $f^{(i)}, 1 \leq i \leq n$ are bounded, $\varphi$ is $n$-times differentiable on $\mathbb{R}$ in the $\mathcal{S}^p$-norm with bounded $n$th derivative. If $f\in C^n(\mathbb{R})$ with bounded $f^{(n)}$, we prove that $\varphi$ is $n$-times continuously differentiable on $\mathbb{R}$. We give explicit formulas for the derivatives of $\varphi$, in terms of multiple operator integrals. As for application, we establish a formula and $\mathcal{S}^p$-estimates for operator Taylor remainders for a more extensive class of functions. These results are the $n$th order analogue of the results of \cite{KPSS}. They also extend the results of \cite{CLSS} from $\mathcal{S}^2(\mathcal{H})$ to $\mathcal{S}^p(\mathcal{H})$ and the results of \cite{LMS} from $n$-times continuously differentiable functions to $n$-times differentiable functions $f$.
COMPACT EBG STRUCTURE FOR ALLEVIATING MUTUAL COUPLING BETWEEN PATCH ANTENNA ARRAY ELEMENTS

The periodic structure like electromagnetic band gap (EBG) is a hot research topic in the academia and RF-microwave industry due to their extraordinary surface wave suppression property. This study involved in designing a compact uni-planar type EBG structure for a 2.4 GHz resonant frequency band. Double folded bend metallic connecting lines are successfully utilized to realize a low frequency structure while a size reduction of 61% is achieved compared to the theoretically calculated size. From the transmission response, the surface wave band gap (SWBG) is found to be 1.2 GHz (1.91-3.11 GHz) whereas the artificial magnetic conductor (AMC) characteristic is observed at 3.3 GHz. The FEM based EM simulator HFSS is used to characterize the EBG structure. The SWBG property is utilized for alleviation of mutual coupling between elements of a microstrip antenna array. A 2 x 5 EBG lattice is inserted between the E-plane coupled array which reduced the coupling level by 17 dB without any adverse effect on the radiation performances.
Differentiation and the Relationship between Product Market Competition and Price Discrimination

We investigate how the effect of competition on price discrimination varies depending on the level of quality provided by companies in the hospitality industry. Our findings reconcile conflicting results of previous literature on this topic. Namely, we provide strong empirical evidence that competition affects differently the price of single and double rooms of hotels with greater quality versus those with lower quality. In the presence (absence) of differentiation, competition increases (decreases) price discrimination. Our findings are robust to the use of econometric techniques that alleviate endogeneity concerns.
Black hole magnetosphere with small-scale flux tubes – II. Stability and dynamics

In some Seyfert galaxies, the hard X-rays that produce fluorescent emission lines are thought to be generated in a hot corona that is compact and located at only a few gravitational radii above the supermassive black hole. We consider the possibility that this X-ray source may be powered by small-scale magnetic flux tubes attached to the accretion disc near the black hole. We use three-dimensional, time-dependent, special relativistic, force-free simulations in a simplified setting to study the dynamics of such flux tubes as they get continuously twisted by the central compact star/black hole. We find that the dynamical evolution of the flux tubes connecting the central compact object and the accretion disc is strongly influenced by the confinement of the surrounding field. Although differential rotation between the central object and the disc tends to inflate the flux tubes, strong confinement from surrounding field quenches the formation of a jet-like outflow, as the inflated flux tube becomes kink unstable and dissipates most of the extracted rotational energy relatively close to the central object. Such a process may be able to heat up the plasma and produce strong X-ray emission. We estimate the energy dissipation rate and discuss its astrophysical implications.