Miracle-dz/UniFAR-FTUs huggingface dataset:
SDSS-IV MaNGA: Internal mass distributions and orbital structures of early-type galaxies and their dependence on environment

In our earlier 2019 paper, we evaluated the reliability of Schwarzschild’s orbit-superposition dynamical modelling method in estimating the internal mass distribution, intrinsic stellar shapes and orbit distributions of early-type galaxies (ETGs) taken from the Illustris cosmological simulation. We now apply the same techniques to galaxies taken from the integral-field survey Mapping Nearby Galaxies with APO (MaNGA), using a sample of 149 ETGs in the mass range of 109.90 ∼ 1011.80M⊙ and made up of 105 central and 44 satellite galaxies. We find that low-mass ETGs with log (M*/M⊙) < 11.1 have an average dark matter fraction of ∼0.2 within one effective radius Re, tend to be oblate-like, and are dominated by rotation about their minor axis. High-mass ETGs with log (M*/M⊙) > 11.1 have an average dark matter fraction of ∼0.4 within one effective radius Re, tend to be prolate-like, and are dominated by rotation about their major axis and by centrophilic orbits. The changes of internal structures within one Re are dominated by the total stellar mass of the individual galaxies. We find no differences of internal structures between central and satellite ETGs for the same stellar masses. However, for similar stellar mass and colour distributions, we find that ETGs more prolate-like, or with more hot orbits, tend to have higher close neighbour counts at rp ∼ 40 kpc.
Improved BiGAN training with marginal likelihood equalization

We propose a novel training procedure for improving the performance of generative adversarial networks (GANs), especially to bidirectional GANs. First, we enforce that the empirical distribution of the inverse inference network matches the prior distribution, which favors the generator network reproducibility on the seen samples. Second, we have found that the marginal log-likelihood of the samples shows a severe overrepresentation of a certain type of samples. To address this issue, we propose to train the bidirectional GAN using a non-uniform sampling for the mini-batch selection, resulting in improved quality and variety in generated samples measured quantitatively and by visual inspection. We illustrate our new procedure with the well-known CIFAR10, Fashion MNIST and CelebA datasets.
Variance and Error in One-Step Phase-Retrieval

Time multiplexed approaches for high frame-rate holographic displays have been around since the invention of One-Step Phase-Retrieval (OSPR) in the early 2000s. When discovered, formulations were created for variance reduction but other image quality metrics were ignored. This work sets out statistical models for the mean squared error (MSE) and structural similarity index (SSIM) behaviour of OSPR for a range of image types in order to better understand the effect of time multiplexing on visible images. This finds that while observed variances converges to zero as the number of frames per second increases, MSE converges to a non-zero value while SSIM converges quadratically to a non-unitary value.
An FMOS Survey of Moderate-luminosity, Broad-line AGNs in COSMOS, SXDS, and E-CDF-S

We present near-IR spectroscopy in the J- and H-bands for a large sample of 243 X-ray-selected, moderate-luminosity Type 1 active galactic nuclei (AGNs) in the COSMOS, SXDS, and E-CDF-S survey fields using the multi-object spectrograph Subaru/FMOS. Our sample covers the redshift range 0.5 ≲ z ≲ 3.0 and X-ray luminosity range of 1043 ≲ ≲ 1045 erg s−1. We provide emission-line properties and derived virial black hole mass estimates, bolometric luminosities, and Eddington ratios, based on Hα (211), Hβ (63), and Mg ii (4). We compare line widths, luminosities, and black hole mass estimates from Hα and Hβ, and augment these with commensurate measurements of Mg ii and C iv detected in optical spectra. We demonstrate the robustness of using Hα, Hβ, and Mg ii as reliable black hole mass estimators for high-z moderate-luminosity AGNs, while the use of C iv is prone to large uncertainties (≳0.4 dex). We extend a recently proposed correction based on the C iv blueshift to lower luminosities and black hole masses. While our sample shows an improvement in their C iv black hole mass estimates, the deficit of high blueshift sources reduces its overall importance for moderate-luminosity AGNs compared to the most luminous quasars. In addition, we revisit luminosity correlations between Lbol, , L[O iii], L5100, and LHα and find them to be consistent with a simple empirical model, based on a small number of well-established scaling relations. Finally, we highlight our highest redshift AGN, CID 781, at z = 4.6, which has the lowest black hole mass (∼108 M⊙) among current near-IR samples at this redshift and is in a state of fast growth.
On the quantum complexity of closest pair and related problems

The closest pair problem is a fundamental problem of computational geometry: given a set of n points in a d-dimensional space, find a pair with the smallest distance. A classical algorithm taught in introductory courses solves this problem in O(n log n) time in constant dimensions (i.e., when d = O(1)). This paper asks and answers the question of the problem's quantum time complexity. Specifically, we give an Õ(n2/3) algorithm in constant dimensions, which is optimal up to a polylogarithmic factor by the lower bound on the quantum query complexity of element distinctness. The key to our algorithm is an efficient history-independent data structure that supports quantum interference. In polylog(n) dimensions, no known quantum algorithms perform better than brute force search, with a quadratic speedup provided by Grover's algorithm. To give evidence that the quadratic speedup is nearly optimal, we initiate the study of quantum fine-grained complexity and introduce the Quantum Strong Exponential Time Hypothesis (QSETH), which is based on the assumption that Grover's algorithm is optimal for CNF-SAT when the clause width is large. We show that the naïve Grover approach to closest pair in higher dimensions is optimal up to an no(1) factor unless QSETH is false. We also study the bichromatic closest pair problem and the orthogonal vectors problem, with broadly similar results.
Weakly Supervised Deep Learning Approach in Streaming Environments

The feasibility of existing data stream algorithms is often hindered by the weakly supervised condition of data streams. A self-evolving deep neural network, namely Parsimonious Network (ParsNet), is proposed as a solution to various weakly-supervised data stream problems. A self-labelling strategy with hedge (SLASH) is proposed in which its auto-correction mechanism copes with the accumulation of mistakes significantly affecting the model’s generalization. ParsNet is developed from a closed-loop configuration of the self-evolving generative and discriminative training processes exploiting shared parameters in which its structure flexibly grows and shrinks to overcome the issue of concept drift with/without labels. The numerical evaluation has been performed under two challenging problems, namely sporadic access to ground truth and infinitely delayed access to the ground truth. Our numerical study shows the advantage of ParsNet with a substantial margin from its counterparts in the high-dimensional data streams and infinite delay simulation protocol. To support the reproducible research initiative, the source code of ParsNet along with supplementary materials are made available at https://bit.ly/2qNW7p4.
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram.
Improved Detection of Adversarial Attacks via Penetration Distortion Maximization

This paper is concerned with the defense of deep models against adversarial attacks. We developan adversarial detection method, which is inspired by the certificate defense approach, and capturesthe idea of separating class clusters in the embedding space to increase the margin. The resultingdefense is intuitive, effective, scalable, and can be integrated into any given neural classificationmodel. Our method demonstrates state-of-the-art (detection) performance under all threat models.
Proof-of-Execution: Reaching Consensus through Fault-Tolerant Speculation

Since the introduction of blockchains, several new database systems and applications have tried to employ them. At the core of such blockchain designs are Byzantine Fault-Tolerant (BFT) consensus protocols that enable designing systems that are resilient to failures and malicious behavior. Unfortunately, existing BFT protocols seem unsuitable for usage in database systems due to their high computational costs, high communication costs, high client latencies, and/or reliance on trusted components and clients. In this paper, we present the Proof-of-Execution consensus protocol (PoE) that alleviates these challenges. At the core of PoE are out-of-order processing and speculative execution, which allow PoE to execute transactions before consensus is reached among the replicas. With these techniques, PoE manages to reduce the costs of BFT in normal cases, while still providing reliable consensus toward clients in all cases. We envision the use of PoE in high-performance resilient database systems. To validate this vision, we implement PoE in our efficient ResilientDB blockchain and database framework. ResilientDB helps us to implement and evaluate PoE against several state-of-the-art BFT protocols. Our evaluation shows that PoE achieves up to $86\%$ more throughput than existing BFT protocols.
Exact solution of the Schrodinger equation for photoemission from a metal

We solve rigorously the time dependent Schrodinger equation describing electron emission from a metal surface by a laser field perpendicular to the surface. We consider the system to be one-dimensional, with the half-line $x 0$ to the vacuum. The laser field is modeled as a classical electric field oscillating with frequency $\omega$, acting only at $x>0$. We consider an initial condition which is a stationary state of the system without a field, and, at time $t=0$, the field is switched on. We prove the existence of a solution $\psi(x,t)$ of the Schrodinger equation for $t>0$, and compute the surface current. The current exhibits a complex oscillatory behavior, which is not captured by the "simple" three step scenario. As $t\to\infty$, $\psi(x,t)$ converges with a rate $t^{-\frac32}$ to a time periodic function with period $\frac{2\pi}{\omega}$ which coincides with that found by Faisal, Kaminski and Saczuk (Phys Rev A 72, 023412, 2015). However, for realistic values of the parameters, we have found that it can take quite a long time (over 50 laser periods) for the system to converge to its asymptote. Of particular physical importance is the current averaged over a laser period $\frac{2\pi}\omega$, which exhibits a dramatic increase when $\hbar\omega$ becomes larger than the work function of the metal, which is consistent with the original photoelectric effect.
Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning

Generative adversarial imitation learning (GAIL) has attracted increasing attention in the field of robot learning. It enables robots to learn a policy to achieve a task demonstrated by an expert while simultaneously estimating the reward function behind the expert's behaviors. However, this framework is limited to learning a single task with a single reward function. This study proposes an extended framework called situated GAIL (S-GAIL), in which a task variable is introduced to both the discriminator and generator of the GAIL framework. The task variable has the roles of discriminating different contexts and making the framework learn different reward functions and policies for multiple tasks. To achieve the early convergence of learning and robustness during reward estimation, we introduce a term to adjust the entropy regularization coefficient in the generator's objective function. Our experiments using two setups (navigation in a discrete grid world and arm reaching in a continuous space) demonstrate that the proposed framework can acquire multiple reward functions and policies more effectively than existing frameworks. The task variable enables our framework to differentiate contexts while sharing common knowledge among multiple tasks.
Sub-critical cohesive crack propagation with hydro-mechanical coupling and friction

Looking at the long-time behaviour of a dam, it is necessary to assume that the water can penetrate a possible crack washing away some components of the concrete. This type of corrosion reduces the tensile strength and fracture energy of the concrete compared to the same parameters measured during a short-time laboratory test. This phenomenon causes the so called sub-critical crack propagation. That is the reason why the International Commission of Large Dams recommends to neglect the tensile strength of the joint between the dam and the foundation, which is the weakest point of a gravity dam. In these conditions a shear displacement discontinuity starts growing in a point, called Fictitious Crack Tip (shortened FCT), which is still subjected to a compression stress. In order to manage this problem, in this paper the cohesive crack model is re-formulated with the focus on the shear stress component. In this context, the classical Newton-Raphson method fails to converge to an equilibrium state. Therefore the approach used is based on two stages: (a) a global one in which the FCT is moved ahead of one increment; (b) a local one in which the non-linear conditions occurring in the Fracture Process Zone are taken into account. This two-stage approach, which is known in the literature as a Large Time Increment method, is able to model three different mechanical regimes occurring during the crack propagation between a dam and the foundation rock.
Prevalence, serodiversity and antibiogram of enterotoxigenic Escherichia coli (ETEC) in diarrhoeic calves and lambs of Kashmir valley (J&K), India

Enterotoxigenic E. coli (ETEC) is the major cause of diarrhoea in neonatal animals. This study determined the prevalence, serological diversity, virulence gene profile and in-vitro antibiogram of ETEC isolates from diarrhoeic faeces of calves and lambs. The prevalence rate of ETEC in lambs was recorded 18.46 % with O8 as predominant serotype. However, in calves the prevalence rate was recorded 8.57 % with O15 and O26 as predominant serotypes. The antibiogram screening showed differential susceptibility pattern among ETEC isolates with highest resistance to ampicillin and highest sensitivity to enrofloxacin. In the present study, for the first time it was reported that the diarrhoea in calves and lambs occur due to virulent gene est not due to elt gene, which was absent in all the isolates.
Operator and Manufacturer Independent D2D Private Link for Future 5G Networks

Direct Mobile-to-Mobile communication mode known also as Device-to-Device (D2D) communication is expected to be supported in the 5G mobile system. D2D communication aims to improve system spectrum efficiency, overall system throughput, energy efficiency and reduce the connection delay between devices. However, new security threats and challenges need to be considered regarding device and user authentication to avoid unauthorized access, abuse and attacks on the whole system. In this paper, a strong standalone authentication technique therefore is proposed. It is based on combining users’ biometric identities and a new clone-resistant device identity. The novel property of the proposal is that it is fully independent on both device manufacturer and mobile system operator. The biometric identity deploys user’s keystroke dynamics and accelerometer to generate user’s biometric identity by deploying a machine learning technique. The proposed mobile device clone-resistant identity is based on deploying a new concept of a pure digital clone-resistant structure which is both manufacturer and mobile operator independent. When combining both identities, a mutually authenticated D2D secured link between any two devices can be established in addition to a strong user-device authentication. Furthermore, the concept does not allow the managing trusted authority to intercept users’ private links. Being an independent and standalone system, the technique would offer a broad spectrum of attractive future smart applications over the 5G mobile system infrastructure.
Ordering Matters: Word Ordering Aware Unsupervised NMT

Denoising-based Unsupervised Neural Machine Translation (U-NMT) models typically employ denoising strategy at the encoder module to prevent the model from memorizing the input source sentence. Specifically, given an input sentence of length n, the model applies n/2 random swaps between consecutive words and trains the denoising-based U-NMT model. Though effective, applying denoising strategy on every sentence in the training data leads to uncertainty in the model thereby, limiting the benefits from the denoising-based U-NMT model. In this paper, we propose a simple fine-tuning strategy where we fine-tune the trained denoising-based U-NMT system without the denoising strategy. The input sentences are presented as is i.e., without any shuffling noise added. We observe significant improvements in translation performance on many language pairs from our fine-tuning strategy. Our analysis reveals that our proposed models lead to increase in higher n-gram BLEU score compared to the denoising U-NMT models.
TRENDS IN SELF-EMPLOYMENT AMONG DIFFERENT RACE GROUPS IN THE UNITED STATES OF AMERICA AMERİKA BİRLEŞİK DEVLETLERİNDE FARKLI ETNİK KÖKENLER ARASINDA SERBEST MESLEK ERBABLIĞI EĞİLİMİ

The main goal of this paper is to see the trends self-employment behavior of natives and immigrants living in the U.S. by focusing on different race and ethnic groups using the current population survey (CPS) data under a linear probability model framework. There are two main outcomes of this study. First, although the real income level of immigrants is decreasing over time, they still have the incentive to be self-employed as much as the natives. Second, the Oaxaca decomposition technique results show that there is a little difference between the average group characteristics of natives and immigrants.
Abstractions and Automated Algorithms for Mixed Domain Finite Element Methods

Mixed dimensional partial differential equations (PDEs) are equations coupling unknown fields defined over domains of differing topological dimension. Such equations naturally arise in a wide range of scientific fields including geology, physiology, biology, and fracture mechanics. Mixed dimensional PDEs are also commonly encountered when imposing non-standard conditions over a subspace of lower dimension, e.g., through a Lagrange multiplier. In this article, we present general abstractions and algorithms for finite element discretizations of mixed domain and mixed dimensional PDEs of codimension up to one (i.e., nD-mD with |n-m| ≤ 1). We introduce high-level mathematical software abstractions together with lower-level algorithms for expressing and efficiently solving such coupled systems. The concepts introduced here have also been implemented in the context of the FEniCS finite element software. We illustrate the new features through a range of examples, including a constrained Poisson problem, a set of Stokes-type flow models, and a model for ionic electrodiffusion.
Examining ECLS-B

Using the Early Childhood Longitudinal Study–Birth Cohort (ECLS-B), a nationally representative data set, we examined the extent to which mothers of preschool children with and without the diagnosis of an Autism Spectrum Disorder (ASD) reported stress and depressive symptoms prior to and following diagnosis of ASD. At 4 years, approximately 100 children were parent-identified as diagnosed with ASD. Mothers of children with ASD at 9 months and 4 years had significantly higher incidence of depressive symptoms and stress than mothers of typically developing children. Mothers of children with ASD experienced higher levels of depressive symptoms than mothers of children with disabilities, but the difference was not statistically significant. Using linear regression, a within-group comparison of depressive symptoms of mothers of children with ASD indicated no differences based on child gender, ethnicity, number of children in the family, or partnership status. Implications are presented.
The Use of Some of the Information Criterion in Determining the Best Model for Forecasting of Thalassemia Cases Depending on Iraqi Patient Data Using ARIMA Model

Thalassemia is a major health problem in Iraq, and despites a prevention programme. There has been no decrease in the prevalence of the disease, due to a lack of awareness, implying that genetic counseling was a failure. This failure has been attributed to a lack of recognition of problems related to Thalassemia, unorganised teamwork and services, lack of knowledge and insufficient numbers of extension workers, lack of Thalassemia support groups, and inadequate research in Thalassemia prevention and control. Autoregressive Integrated Moving Average (ARIMA) model and forecasting has become a major tool in different applications. The ARIMA model introduced by Box and Jenkins (1971) is among the most effective approaches for analysing time-series data. In this study, we used Box and Jenkins methodology to build an ARIMA model to forecast the number of people with Thalassemia, for the period from 2016-2018, from the data base from Maysan Health Center specific for Thalassemia the Maysan Provence, Iraq. After the model selection, the best model for forecasting was ARIMA (0, 1, 1) and of models were used for forecasting Thalassemia.
Percutaneous Coronary Intervention Contrast-Induced Nephropathy in Patients With ST Elevation Myocardial Infarction Treated With Primary

We assessed the relationship between contrast-induced nephropathy (CIN) and SYNTAX score (SS) and serum uric acid (SUA) levels in patients with ST elevation myocardial infarction (STEMI). A total of 835 STEMI patients in whom primary percutaneous coronary intervention was performed in our cardiology clinic were included in this study (615 male, 220 female; mean age 58.1 + 12.2 years). The patients were divided into 2 groups (CIN and non-CIN). Contrast-induced nephropathy was observed in 9.6% (80) of patients; SS (13.9 + 6.2/22.1 + 5.8) and SUA (5.1 + 0.9/6.2 + 0.9) values in the CIN group were higher compared with the non-CIN group (P < .001, for all). All SS (95% confidence interval [CI] 1⁄4 1.136-1.250, P 1⁄4 .001), SUA (95% CI 1⁄4 1.877-3.236, P 1⁄4 .002), and diabetes (95% CI 1⁄4 0.998-1.039, P 1⁄4 .026) were independent predictors of CIN in logistic regression analysis. Procedures that can prevent CIN may be beneficial in patients at high risk as identified by the SS and SUA levels.