Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnification, dust and time-delay constraints from the first resolved strongly lensed Type Ia supernova iPTF16geu

We report lensing magnifications, extinction, and time-delay estimates for the first resolved, multiply-imaged Type Ia supernova iPTF16geu, at z = 0.409, using Hubble Space Telescope (HST) observations in combination with supporting ground-based data. Multi-band photometry of the resolved images provides unique information about the differential dimming due to dust in the lensing galaxy. Using HST and Keck AO reference images taken after the SN faded, we obtain a total lensing magnification for iPTF16geu of $\mu = 67.8^{+2.6}_{-2.9}$, accounting for extinction in the host and lensing galaxy. As expected from the symmetry of the system, we measure very short time-delays for the three fainter images with respect to the brightest one: -0.23 ± 0.99, -1.43 ± 0.74 and 1.36 ± 1.07 days. Interestingly, we find large differences between the magnifications of the four supernova images, even after accounting for uncertainties in the extinction corrections: $\Delta m_1 = -3.88^{+0.07}_{-0.06}$, $\Delta m_2 = -2.99^{+0.09}_{-0.08}$, $\Delta m_3 = -2.19^{+0.14}_{-0.15}$ and $\Delta m_4 = -2.40^{+0.14}_{-0.12}$ mag, discrepant with model predictions suggesting similar image brightnesses. A possible explanation for the large differences is gravitational lensing by substructures, micro- or millilensing, in addition to the large scale lens causing the image separations. We find that the inferred magnification is insensitive to the assumptions about the dust properties in the host and lens galaxy.
Optimizing multi-objective decision making having qualitative evaluation

We develop a ranking process for multi-objective decision making. For optimizing the multi-objective problem having both quantitative and qualitative objectives, weight assessment is important to convert the problem into the corresponding single objective problem. Therefore, a ranking process is proposed to simultaneously obtain the objective weights and the evaluation of alternatives with multiple objectives. Several new concepts are developed to handle the dynamism in distance computation and ranking of decisions in a multi-objective model having qualitative evaluations. The proposed process is illustrated in a numerical example.
2nd Place Solution to the GQA Challenge 2019

We present a simple method that achieves unexpectedly superior performance for Complex Reasoning involved Visual Question Answering. Our solution collects statistical features from high-frequency words of all the questions asked about an image and use them as accurate knowledge for answering further questions of the same image. We are fully aware that this setting is not ubiquitously applicable, and in a more common setting one should assume the questions are asked separately and they cannot be gathered to obtain a knowledge base. Nonetheless, we use this method as an evidence to demonstrate our observation that the bottleneck effect is more severe on the feature extraction part than it is on the knowledge reasoning part. We show significant gaps when using the same reasoning model with 1) ground-truth features; 2) statistical features; 3) detected features from completely learned detectors, and analyze what these gaps mean to researches on visual reasoning topics. Our model with the statistical features achieves the 2nd place in the GQA Challenge 2019.
A Unified Framework for Problems on Guessing, Source Coding and Task Partitioning

We study four problems namely, Campbell's source coding problem, Arikan's guessing problem, Huieihel et al.'s memoryless guessing problem, and Bunte and Lapidoth's task partitioning problem. We observe a close relationship among these problems. In all these problems, the objective is to minimize moments of some functions of random variables, and R\'enyi entropy and Sundaresan's divergence arise as optimal solutions. This motivates us to establish a connection among these four problems. In this paper, we study a more general problem and show that R\'{e}nyi and Shannon entropies arise as its solution. We show that the problems on source coding, guessing and task partitioning are particular instances of this general optimization problem, and derive the lower bounds using this framework. We also refine some known results and present new results for mismatched version of these problems using a unified approach. We strongly feel that this generalization would, in addition to help in understanding the similarities and distinctiveness of these problems, also help to solve any new problem that falls in this framework.
Entropic considerations on the Universe and Universe-black hole systems

We study the entropic considerations on the Universe system and the Universe-black hole system, filled by cosmological constant or exotic quintessence-like and phantom-like fields having negative pressure, using their relevant entropic bounds. It turns out that for both systems these considerations single out the cosmological constant, among the negative pressure candidate fields, as the viable cosmological field.
Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data

We focus on the problem of unsupervised cell outlier detection in mixed type tabular datasets. Traditional methods for outlier detection are concerned only on detecting which rows in the dataset are outliers. However, identifying which cells in the dataset corrupt a speciﬁc row is an important problem in practice, especially in high-dimensional tables. We introduce the Robust Variational Autoencoder (RVAE), a deep generative model that learns the joint distribution of the clean data while identifying the outlier cells in the dataset. RVAE learns the probability of each cell in the dataset being an outlier, balancing the contributions of the different likelihood models in the row outlier score, making the method suitable for outlier detection in mixed type datasets. We show experimentally that the RVAE performs better than several state of the art methods in cell outlier detection for tabular datasets, while providing comparable or better results for row outlier detection.
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

Linear regression in $\ell_p$-norm is a canonical optimization problem that arises in several applications, including sparse recovery, semi-supervised learning, and signal processing. Generic convex optimization algorithms for solving $\ell_p$-regression are slow in practice. Iteratively Reweighted Least Squares (IRLS) is an easy to implement family of algorithms for solving these problems that has been studied for over 50 years. However, these algorithms often diverge for p > 3, and since the work of Osborne (1985), it has been an open problem whether there is an IRLS algorithm that is guaranteed to converge rapidly for p > 3. We propose p-IRLS, the first IRLS algorithm that provably converges geometrically for any $p \in [2,\infty). $ Our algorithm is simple to implement and is guaranteed to find a $(1+\varepsilon)$-approximate solution in $O(p^{3.5} m^{\frac{p-2}{2(p-1)}} \log \frac{m}{\varepsilon}) \le O_p(\sqrt{m} \log \frac{m}{\varepsilon} )$ iterations. Our experiments demonstrate that it performs even better than our theoretical bounds, beats the standard Matlab/CVX implementation for solving these problems by 10--50x, and is the fastest among available implementations in the high-accuracy regime.
A Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data

A tacit assumption in linear regression is that (response, predictor)-pairs correspond to identical observational units. A series of recent works have studied scenarios in which this assumption is violated under terms such as ``Unlabeled Sensing and ``Regression with Unknown Permutation''. In this paper, we study the setup of multiple response variables and a notion of mismatches that generalizes permutations in order to allow for missing matches as well as for one-to-many matches. A two-stage method is proposed under the assumption that most pairs are correctly matched. In the first stage, the regression parameter is estimated by handling mismatches as contaminations, and subsequently the generalized permutation is estimated by a basic variant of matching. The approach is both computationally convenient and equipped with favorable statistical guarantees. Specifically, it is shown that the conditions for permutation recovery become considerably less stringent as the number of responses $m$ per observation increase. Particularly, for $m = \Omega(\log n)$, the required signal-to-noise ratio no longer depends on the sample size $n$. Numerical results on synthetic and real data are presented to support the main findings of our analysis.
Exploiting visual search theory to infer social interactions

In this paper we propose a new method to infer human social interactions using typical techniques adopted in literature for visual search and information retrieval. The main piece of information we use to discriminate among different types of interactions is provided by proxemics cues acquired by a tracker, and used to distinguish between intentional and casual interactions. The proxemics information has been acquired through the analysis of two different metrics: on the one hand we observe the current distance between subjects, and on the other hand we measure the O-space synergy between subjects. The obtained values are taken at every time step over a temporal sliding window, and processed in the Discrete Fourier Transform (DFT) domain. The features are eventually merged into an unique array, and clustered using the K-means algorithm. The clusters are reorganized using a second larger temporal window into a Bag Of Words framework, so as to build the feature vector that will feed the SVM classifier.
Visualizing Situational Data: Applying Information Fusion for Detecting Social-Ecological Events

As anthropogenic and environmental behaviors rapidly evolve many ecosystems and communities, managers of natural resources, scientists, and other stakeholders increasingly need tools that can rapidly alert them to emerging events that can affect social well-being. Data detailing such behaviors may derive from textual sources with varying content, requiring an approach to merge multiple media sources and create linked relationships between relevant event terms. In addition, applied methods need to provide both quantitative capacity and qualitative functionality, which can rapidly display emerging trends and potentially significant individual events. This article presents an information fusion approach for conducting text searches on web-based sources to provide managers and scientists with rapid search capabilities identifying potentially significant social—ecological events. Along with general analytical utility, a network approach that links associated terms is used to show semantic relationships between social—ecological terms at different timescales.
Step-by-Step Community Detection for Volume-Regular Graphs

Spectral techniques have proved amongst the most eﬀective approaches to graph clustering. However, in general they require explicit computation of the main eigenvectors of a suitable matrix (usually the Laplacian matrix of the graph). Recent work (e.g., Becchetti et al., SODA 2017) suggests that observing the temporal evolution of the power method applied to an initial random vector may, at least in some cases, provide enough information on the space spanned by the ﬁrst two eigenvectors, so as to allow recovery of a hidden partition without explicit eigenvector computations. While the results of Becchetti et al. apply to perfectly balanced partitions and/or graphs that exhibit very strong forms of regularity, we extend their approach to graphs containing a hidden k partition and characterized by a milder form of volume-regularity. We show that the class of k - volume regular graphs is the largest class of undirected (possibly weighted) graphs whose transition matrix admits k “stepwise” eigenvectors (i.e., vectors that are constant over each set of the hidden partition). To obtain this result, we highlight a connection between volume regularity and lumpability of Markov chains. Moreover, we prove that if the stepwise eigenvectors are those associated to the ﬁrst k eigenvalues and the gap between the k -th and the ( k +1)-th eigenvalues is suﬃciently large, the Averaging dynamics of Becchetti
Security Smells in Infrastructure as Code Scripts

Context: Security smells are coding patterns in source code that are indicative of security weaknesses. As infrastructure as code (IaC) scripts are used to provision cloud-based servers and systems at scale, security smells in IaC scripts could be used to enable malicious users to exploit vulnerabilities in the provisioned systems. Goal: The goal of this paper is to help practitioners avoid insecure coding practices while developing infrastructure as code (IaC) scripts through an empirical study of security smells in IaC scripts. Methodology: We apply qualitative analysis with 3,339 IaC scripts to identify security smells for IaC scripts written in three languages: Ansible, Chef, and Puppet. We construct a static analysis tool called Security Linter for Infrastructure as Code scripts (SLIC) to automatically identify security smells in 61,097 scripts collected from 1,093 open source software repositories. We also submit bug reports for 1,500 randomly-selected smell occurrences identified from the 61,097 scripts. Results: We identify nine security smells for IaC scripts. By applying SLIC on 61,097 IaC scripts we identify 64,356 occurrences of security smells that included 9,092 hard-coded passwords. We observe agreement for 130 of the responded 187 bug reports, which suggests the relevance of security smells for IaC scripts amongst practitioners. Conclusion: We observe security smells to be prevalent in IaC scripts. We recommend practitioners to rigorously inspect the presence of the identified security smells in IaC scripts using (i) code review, and (ii) static analysis tools.
CupQ: A New Clinical Literature Search Engine

A new clinical literature search engine, called CupQ, is presented. It aims to help clinicians stay updated with medical knowledge. Although PubMed is currently one of the most widely used digital libraries for biomedical information, it frequently does not return clinically relevant results. CupQ utilizes a ranking algorithm that filters non-medical journals, compares semantic similarity between queries, and incorporates journal impact factor and publication date. It organizes search results into useful categories for medical practitioners: reviews, guidelines, and studies. Qualitative comparisons suggest that CupQ may return more clinically relevant information than PubMed. CupQ is available at this https URL.
Identities and estimations involving the least common multiple of strong divisibility sequences

In this paper, we first prove that for any strong divisibility sequences $\boldsymbol{a} = \left(a_n\right)_{n\geq 1}$, we have the identity: $\mathrm{lcm} \left\lbrace \binom{n}{0}_{\bf{a}}, \binom{n}{1}_{\bf{a}},\dots, \binom{n}{n}_{\bf{a}} \right\rbrace = \frac{\mathrm{lcm} \left(a_1,\dots , a_n , a_{n+1}\right)}{a_{n+1}}$ $\left(\forall n \geq 1\right)$, generalizing the identity of Farhi (obtained in 2009 for $a_n=n$). Then, we derive from this one some other interesting identities. Finally, we apply those identities to estimate the least common multiple of the consecutive terms of some Lucas sequences. Denoting by $\left(F_n\right)_n$ the usual Fibonacci sequence, we prove for example that for all $n \geq 1$, we have \[ \Phi^{\frac{n^2}{4}-\frac{9}{4}} \leq \mathrm{lcm}\left(F_1,\dots,F_n\right) \leq \Phi^{\frac{n^2}{3}+\frac{4n}{3}} , \] where $\Phi$ denotes the golden ratio.
Improving Semantic Segmentation via Dilated Affinity

Introducing explicit constraints on the structural predictions has been an effective way to improve the performance of semantic segmentation models. Existing methods are mainly based on insufficient hand-crafted rules that only partially capture the image structure, and some methods can also suffer from the efficiency issue. As a result, most of the state-of-the-art fully convolutional networks did not adopt these techniques. In this work, we propose a simple, fast yet effective method that exploits structural information through direct supervision with minor additional expense. To be specific, our method explicitly requires the network to predict semantic segmentation as well as dilated affinity, which is a sparse version of pair-wise pixel affinity. The capability of telling the relationships between pixels are directly built into the model and enhance the quality of segmentation in two stages. 1) Joint training with dilated affinity can provide robust feature representations and thus lead to finer segmentation results. 2) The extra output of affinity information can be further utilized to refine the original segmentation with a fast propagation process. Consistent improvements are observed on various benchmark datasets when applying our framework to the existing state-of-the-art model. Codes will be released soon.
CO (J = 1–0) Observations toward Filamentary Molecular Clouds in the Galactic Region with l = [169.°75, 174.°75], b = [−0.°75, 0.°5]

We present observations of the CO isotopologues (12CO, 13CO, and C18O) toward the Galactic region with 169. °75 ≤ l ≤ 174. °75 and −0. °75 ≤ b ≤ 0. °5 using the Purple Mountain Observatory 13.7 m millimeter-wavelength telescope. Based on the 13CO (J = 1 − 0) data, we find five molecular clouds within the velocity range between −25 and 8 km s−1 that are all characterized by conspicuous filamentary structures. We have identified eight filaments with a length of 6.38–28.45 pc, a mean H2 column density of 0.70 × 1021–6.53 × 1021 cm−2, and a line mass of 20.24–161.91 M☉ pc−1, assuming a distance of ∼1.7 kpc. Gaussian fittings to the inner parts of the radial density profiles lead to a mean FWHM width of 1.13 ± 0.01 pc. The velocity structures of most filaments present continuous distributions with slight velocity gradients. We find that turbulence is the dominant internal pressure to support the fragmentation of filaments instead of thermal pressure. Most filaments have virial parameters smaller than 2; thus, they are gravitationally bound. Four filaments have an LTE line mass close to the virial line mass. We further extract dense clumps using the 13CO data and find that 64% of the clumps are associated with the filaments. According to the complementary IR data, most filaments have associated Class II young stellar objects. Class I objects are mainly found to be located in the filaments with a virial parameter close to 1. Within two virialized filaments, 12CO outflows have been detected, indicating ongoing star-forming activity therein.
Labelings vs. Embeddings: On Distributed Representations of Distances

We investigate for which metric spaces the performance of distance labeling and of $\ell_\infty$-embeddings differ, and how significant can this difference be. Recall that a distance labeling is a distributed representation of distances in a metric space $(X,d)$, where each point $x\in X$ is assigned a succinct label, such that the distance between any two points $x,y \in X$ can be approximated given only their labels. A highly structured special case is an embedding into $\ell_\infty$, where each point $x\in X$ is assigned a vector $f(x)$ such that $\|f(x)-f(y)\|_\infty$ is approximately $d(x,y)$. The performance of a distance labeling or an $\ell_\infty$-embedding is measured via its distortion and its label-size/dimension. We also study the analogous question for the prioritized versions of these two measures. Here, a priority order $\pi=(x_1,\dots,x_n)$ of the point set $X$ is given, and higher-priority points should have shorter labels. Formally, a distance labeling has prioritized label-size $\alpha(.)$ if every $x_j$ has label size at most $\alpha(j)$. Similarly, an embedding $f: X \to \ell_\infty$ has prioritized dimension $\alpha(.)$ if $f(x_j)$ is non-zero only in the first $\alpha(j)$ coordinates. In addition, we compare these their prioritized measures to their classical (worst-case) versions. We answer these questions in several scenarios, uncovering a surprisingly diverse range of behaviors. First, in some cases labelings and embeddings have very similar worst-case performance, but in other cases there is a huge disparity. However in the prioritized setting, we most often find a strict separation between the performance of labelings and embeddings. And finally, when comparing the classical and prioritized settings, we find that the worst-case bound for label size often ``translates'' to a prioritized one, but also a surprising exception to this rule.
The nature of the soft excess and spectral variability in the Seyfert 1 galaxy Zw 229.015

We have carried out a systematic analysis of the nearby (z = 0.0279) active galaxy Zw 229.015 using multi-epoch, multi-instrument, and deep pointed observations with XMM–Newton, Suzaku, Swift,and NuSTAR. Spectral and temporal variability are examined in detail on both the long (weeks-to-years) and short (hours) time-scales. A deep Suzaku observation of the source shows two distinct spectral states; a bright-soft state and a dim-hard state in which changes in the power-law component account for the differences. Partial covering, blurred reflection, and soft Comptonization models describe the X-ray spectra comparably well, but the smooth, rather featureless, spectrum may be favouring the soft Comptonization scenario. Moreover, independent of the spectral model, the observed spectral variability is ascribed to the changes in the power-law continuum only and do not require changes in the properties of the absorber or blurred reflector incorporated in the other scenarios. The multi-epoch observations between 2009 and 2018 can be described in similar fashion. This could be understood if the primary emission is originating at a large distance from a standard accretion disc or if the disc is optically thin and geometrically thick as recently proposed for Zw 229.015. Our investigation shows that Zw 229.015 behaves similar to sources like Akn 120 and Mrk 530 that exhibit a strong soft excess, but weak Compton hump and Fe Kα emission.
A Scalable Framework for Multilevel Streaming Data Analytics using Deep Learning

The rapid growth of data in velocity, volume, value, variety, and veracity has enabled exciting new opportunities and presented big challenges for businesses of all types. Recently, there has been considerable interest in developing systems for processing continuous data streams with the increasing need for real-time analytics for decision support in the business, healthcare, manufacturing, and security. The analytics of streaming data usually relies on the output of offline analytics on static or archived data. However, businesses and organizations like our industry partner Gnowit, strive to provide their customers with real time market information and continuously look for a unified analytics framework that can integrate both streaming and offline analytics in a seamless fashion to extract knowledge from large volumes of hybrid streaming data. We present our study on designing a multilevel streaming text data analytics framework by comparing leading edge scalable open-source, distributed, and in-memory technologies. We demonstrate the functionality of the framework for a use case of multilevel text analytics using deep learning for language understanding and sentiment analysis including data indexing and query processing. Our framework combines Spark streaming for real time text processing, the Long Short Term Memory (LSTM) deep learning model for higher level sentiment analysis, and other tools for SQL-based analytical processing to provide a scalable solution for multilevel streaming text analytics.