Miracle-dz/UniFAR-FTUs huggingface dataset:
From Playground to Boardroom, CEO’s Family Background and Managerial Style∗

By matching a CEO’s place of residence at her formative years with U.S. Census survey and the 1940 Census report, I obtain an estimate of the CEO’s family wealth and study the link between the CEO’s professional performance and her endowed social status. I find that CEOs born to poor families outperform those born to wealthy families, as measured by their firms’ operating return on assets. In addition, CEOs endowed with low social status tend to join highly levered firms, but firm leverage quickly decreases over CEO tenure. The results suggest that CEO’s endowed social status can be a valuable signal of her ability, but it is not recognized as such by the labor market. Conditional on the same performance, CEOs from poor backgrounds are less likely to get promoted than their peers from wealthy backgrounds.
Corruption Robust Exploration in Episodic Reinforcement Learning

We initiate the study of multi-stage episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system extending recent results for the special case of stochastic bandits. We provide a framework which modifies the aggressive exploration enjoyed by existing reinforcement learning approaches based on "optimism in the face of uncertainty", by complementing them with principles from "action elimination". Importantly, our framework circumvents the major challenges posed by naively applying action elimination in the RL setting, as formalized by a lower bound we demonstrate. Our framework yields efficient algorithms which (a) attain near-optimal regret in the absence of corruptions and (b) adapt to unknown levels corruption, enjoying regret guarantees which degrade gracefully in the total corruption encountered. To showcase the generality of our approach, we derive results for both tabular settings (where states and actions are finite) as well as linear-function-approximation settings (where the dynamics and rewards admit a linear underlying representation). Notably, our work provides the first sublinear regret guarantee which accommodates any deviation from purely i.i. d. transitions in the bandit-feedback model for episodic reinforcement learning.
Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control

This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments. The learning framework can extract spectral information from the full non-linear dynamics by learning the eigenvalues and eigenfunctions of the associated Koopman operator. We then exploit the learned Koopman eigenfunctions to learn a lifted linear state-space model. To the best of our knowledge, our method is the first to utilize Koopman eigenfunctions as lifting functions for EDMD-based methods. We demonstrate the performance of the framework in state prediction and closed loop trajectory tracking of a simulated cart pole system. Our method is able to significantly improve the controller performance while relying on linear control methods to do nonlinear control.
Evidence for an Additional Symmetry Breaking from Direct Observation of Band Splitting in the Nematic State of FeSe Superconductor

Cong Li, Xianxin Wu, Le Wang, Defa Liu, Yongqing Cai, Yang Wang, Qiang Gao, Chunyao Song, Jianwei Huang, Chenxiao Dong, Jing Liu, Ping Ai, Hailan Luo, ChaoHui Yin, Guodong Liu, Yuan Huang, Qingyan Wang, Xiaowen Jia, Fengfeng Zhang, Shenjin Zhang, Feng Yang, Zhimin Wang, Qinjun Peng, Zuyan Xu, Youguo Shi, Jiangping Hu, Tao Xiang, Lin Zhao1,∗ and X. J. Zhou1,2,7,8,∗ Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100049, China Institute for Theoretical Physics and Astrophysics, Julius-Maximilians University of Wurzburg, Am Hubland, D-97074 Wurzburg, Germany Max Planck Institute of Microstructure Physics, Weinberg 2, Halle 06120, Germany Military Transportation University, Tianjin 300161, China Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China Songshan Lake Materials Laboratory, Dongguan 523808, China Beijing Academy of Quantum Information Sciences, Beijing 100193, China Collaborative Innovation Center of Quantum Matter, Beijing 100190, China These people contributed equally to the present work. ∗Corresponding authors: lzhao@iphy.ac. cn and XJZhou@iphy.ac. cn
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation

Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.
Angular-Domain Selective Channel Tracking and Doppler Compensation for High-Mobility mmWave Massive MIMO

In this paper, we consider a mmWave massive multiple-input multiple-output (MIMO) communication system with one static base station (BS) serving a fast-moving user, both equipped with a very large array. The transmitted signal arrives at the user through multiple paths, each with a different angle-of-arrival (AoA) and hence Doppler frequency offset (DFO), thus resulting in a fast time-varying multipath fading MIMO channel. In order to mitigate the Doppler-induced channel aging for reduced pilot overhead, we propose a new angular-domain selective channel tracking and Doppler compensation scheme at the user side. Specifically, we formulate the joint estimation of partial angular–domain channel and DFO parameters as a dynamic compressive sensing (CS) problem. Then we propose a Doppler-aware-dynamic variational Bayesian inference (DD-VBI) algorithm to solve this problem efficiently. Finally, we propose a practical DFO compensation scheme which selects the dominant paths of the fast time-varying channel for DFO compensation and thereby converts it into a slow time-varying effective channel. Compared with the existing methods, the proposed scheme can enjoy the huge array gain provided by the massive MIMO and also balance the tradeoff between the CSI signaling overhead and spatial multiplexing gain. Simulation results verify the advantages of the proposed scheme over various baseline schemes.
Foldy-Lax approximation of the electromagnetic fields generated by anisotropic inhomogeneities in the mesoscale regime with complements for the perfectly conducting case

The Foldy-Lax (or the point-interaction) approximation of the electromagnetic fields generated by a cluster of small scaled inhomogeneities is derived in the mesoscale regime, i.e. when the minimum distance $\delta$ between the particles is proportional to their maximum radi $a$ in the form $\delta=c_r \; a$ with a positive constant $c_r$ that we call the dilution parameter. We consider two types of families of inhomogeneities. In the first one, the small particles are modeled by anisotropic electric permittivities and/or magnetic permeabilities with possibly complex values. In the second one, they are given as perfectly conductive inclusions. In both the cases, we provide the dominating field (the so-called Foldy-Lax field) with explicit error estimates in terms of the dilution parameter $c_r$. In the case of perfectly conductive inclusions, the results provided here improve sharply the ones derived recently in \cite{AB-SM:MMS2019}. Such approximations are key steps in different research areas as imaging and material sciences.
The Efficacy of Computed Tomographic Angiography in Identification of Intracranial Aneurysms

Objective: Computed tomographic angiography (CTA) has surfaced as a valuable non-invasive diagnostic modality in the management of intracranial aneurysms (IAs). In this study, the author reports the accuracy of CTA versus digital subtraction angiography (DSA) in the assessment of patients with IAs. Methods: A retrospective review was conducted for all patients investigated for IAs with both CTA and DSA using standard imaging protocols at king Abdulaziz university hospital between January 2008 and December 2013. Thirty-one patients with IAs underwent evaluation with CTA and DSA during the study period. Comparison between the two modalities included accuracy of detection of IAs was assessed. Results: Patient's age ranged from 17 and 70 years (average 42.8 ± 7.9 years), and 20 patients (64.5%) were females. SAH was the initial presentation in 20 patients (64.5%), five patients (16%) with headache and seizures disorder had a mass lesion on CT scans demonstrating a large IAs, and CT scans were normal in 6 patients (19.4%). Both of CTA and DSA studies detected 29 IAs in 28 patients (90.3%). Three patients had no IAs detected in both CTA and DSA examinations, in one patient operated for repeated SAH with intracerbral hematoma, a small internal carotid artery blister was detected intraoperatively and clipped. Twenty-two patients (78.6%) underwent craniotomy and microsurgical clipping of IAs, and endovascular coiling was performed in 6 patients. CTA was effective in the post-treatment follow up and evaluation of IAs; however, in two patients CTA was not accurate in assessing the recurrence of the aneurysms. Conclusion: CTA provides accurate and valuable information for patients with cerebral aneurysms. It can be used alone for the diagnosis, treatment planning, and post-treatment follow up of IAs. (Saleh S. Baeesa. The efficacy of Computed Tomographic Angiography in Identification of Intracranial
Unified Multifaceted Feature Learning for Person Re-Identification

Person re-identification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras, of which it is of great importance to learn multifaceted features expressed in different parts of a person, e.g., clothes, bags, and other accessories in the main body, appearance in the head, and shoes in the foot. To learn such features, existing methods are focused on the striping-based approach that builds multi-branch neural networks to learn local features in each part of the identities, with one-branch network dedicated to one part. This results in complex models with a large number of parameters. To address this issue, this paper proposes to learn the multifaceted features in a simple unified single-branch neural network. The Unified Multifaceted Feature Learning (UMFL) framework is introduced to fulfill this goal, which consists of two key collaborative modules: compound batch image erasing (including batch constant erasing and random erasing) and hierarchical structured loss. The loss structures the augmented images resulted by the two types of image erasing in a two-level hierarchy and enforces multifaceted attention to different parts. As we show in the extensive experimental results on four benchmark person ReID datasets, despite the use of significantly simplified network structure, our method performs substantially better than state-of-the-art competing methods. Our method can also effectively generalize to vehicle ReID, achieving similar improvement on two vehicle ReID datasets.
Sieving Fake News From Genuine: A Synopsis

With the rise of social media, it has become easier to disseminate fake news faster and cheaper, compared to traditional news media, such as television and newspapers. Recently this phenomenon has attracted lot of public attention, because it is causing significant social and financial impacts on their lives and businesses. Fake news are responsible for creating false, deceptive, misleading, and suspicious information that can greatly effect the outcome of an event. This paper presents a synopsis that explains what are fake news with examples and also discusses some of the current machine learning techniques, specifically natural language processing (NLP) and deep learning, for automatically predicting and detecting fake news. Based on this synopsis, we recommend that there is a potential of using NLP and deep learning to improve automatic detection of fake news, but with the right set of data and features.
Globular Cluster Systems and X-Ray Atmospheres in Galaxies

We compare the empirical relationships between the mass of a galaxy’s globular cluster system (GCS) MGCS, the gas mass in the hot X-ray atmosphere MX within a fiducial radius of 5re, the total gravitational mass Mgrav within 5re, and lastly the total halo mass Mh calibrated from weak lensing. We use a sample of 45 early-type galaxies for which both GCS and X-ray data are available; all the galaxies in our sample are relatively high-mass with Mh > 1012M⊙. We find that , similar to the previously known scaling relation . Both components scale much more steeply than the more well known dependence of total stellar mass for luminous galaxies. These results strengthen previous suggestions that feedback had little effect on formation of the GCS. The current data are also used to measure the relative mass fractions of baryonic matter and dark matter within 5re. We find a strikingly uniform mean of with few outliers and an rms scatter of ±0.07. This result is in good agreement with two recent suites of hydrodynamic galaxy formation models.
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Overparameterized neural networks can be highly accurate on average on an i.i. d. test set yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that hold on average but not in such groups). Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups. However, we find that naively applying group DRO to overparameterized neural networks fails: these models can perfectly fit the training data, and any model with vanishing average training loss also already has vanishing worst-case training loss. Instead, the poor worst-case performance arises from poor generalization on some groups. By coupling group DRO models with increased regularization---a stronger-than-typical L2 penalty or early stopping---we achieve substantially higher worst-group accuracies, with 10-40 percentage point improvements on a natural language inference task and two image tasks, while maintaining high average accuracies. Our results suggest that regularization is important for worst-group generalization in the overparameterized regime, even if it is not needed for average generalization. Finally, we introduce a stochastic optimization algorithm, with convergence guarantees, to efficiently train group DRO models.
Self-supervised Learning of 3D Objects from Natural Images

We present a method to learn single-view reconstruction of the 3D shape, pose, and texture of objects from categorized natural images in a self-supervised manner. Since this is a severely ill-posed problem, carefully designing a training method and introducing constraints are essential. To avoid the difficulty of training all elements at the same time, we propose training category-specific base shapes with fixed pose distribution and simple textures first, and subsequently training poses and textures using the obtained shapes. Another difficulty is that shapes and backgrounds sometimes become excessively complicated to mistakenly reconstruct textures on object surfaces. To suppress it, we propose using strong regularization and constraints on object surfaces and background images. With these two techniques, we demonstrate that we can use natural image collections such as CIFAR-10 and PASCAL objects for training, which indicates the possibility to realize 3D object reconstruction on diverse object categories beyond synthetic datasets.
Structural stability of infinite-order regression

We develop a class of tests for the structural stability of infinite-order models such as the infinite-order autoregressive model and the nonparametric sieve regression. When the number $ p $ of restrictions diverges, the traditional tests based on the suprema of Wald, LM and LR statistics or their exponentially weighted averages diverge as well. We introduce a suitable transformation of these tests and obtain proper weak limits under the condition that $p $ grows to infinity as the sample size $n $ goes to infinity. In general, this limit distribution is different from the sequential limit, which can be obtained by increasing the order of the standardized tied-down Bessel process in Andrews (1993). In particular, our joint asymptotic analysis discovers a nonlinear high order serial correlation, for which we provide a consistent estimator. Our Monte Carlo simulation illustrates the importance of robustifying the structural break test against the nonlinear serial correlation even when $ p $ is moderate. Furthermore, we also establish a weighted power optimality property of our tests under some regularity conditions. We examine finite-sample performance in a Monte Carlo study and illustrate the test with a number of empirical examples.
Int. J. Biosci

This study aimed to assess the antioxidant activity of the pineapple crude juice extracted from the whole fruit, and its effect in stimulating thyroid hormones synthesis in hypothyroid rats, as well as anticipating the concomitant hyperlipidemia. Male Swiss albino rats were divided into: normal (euthyroid) controls; normal rats orally administrated with pineapple juice (0.75 ml/100g body weight) for 8 consecutive weeks; hypothyroid rats administrated methimazole (0.02% w/v) in drinking water for 15 days; and treated hypothyroid rats orally administrated with methimazole then pineapple juice for 8 weeks. In vitro antioxidant studies demonstrated that pineapple juice has high scavenging activity for nitric oxide and α,α2-diphenyl-β-picrylhydrazyl radicals, a high ferric reducing antioxidant power and an inhibitory effect on lipid peroxidation. Supplementation of euthyroid rats with pineapple juice for 8 weeks reduced the body weight and elevated serum T3 and T4 levels, compared to normal controls. While, supplementing hypothyroid rats with the pineapple juice normalized serum levels of T3, free cholesterol and its percentage of esterification, LDL-C, HDL-C, atherogenic index and LCAT activity, as well as serum, heart and hepatic MDA concentration and GPX activity. The histological examination of thyroid tissue sections of hypothyroid supplemented rats revealed an improvement in the cellular architecture of the thyroid gland epithelium lining the follicles and partial filling of the follicular lumen with colloid. Pineapple juice attenuates the excessive methimazole induced oxidative stress and consequent hyperlipidemia, also, activates the thyroid gland functions, suggesting its benefit as therapeutic supplement or as an adjunct in hypothyroidism therapy. * Corresponding Author: Amr Y Esmat  amryesmat@yahoo.com International Journal of Biosciences | IJB | ISSN: 2220-6655 (Print), 2222-5234 (Online) http://www.innspub. net Vol. 10, No. 3, p. 173-184, 2017
EUROPEAN ACADEMIC RESEARCH, VOL

Common bread wheat (Triticum aestivum L.) is an allohexaploid species (2n = 6x = 42, having AABBDD genome). Globally, wheat is a staple food for human population due to its bread making quality. The bread making quality of wheat is controlled by seed storage proteins. In the present study water soluble seed storage proteins were extracted from ditelosomic and deletion lines of group 5 homoeologous chromosomes. Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE) was carried out using 12.5% resolving gel (3.0M Tris pH 9, 0.4% SDS and 4.5% stacking gel (0.4M Tris pH 7.0, 0.4% SDS). Each individual protein band was considered as a locus / allele. Alleles were scored as present (1), absent (0) and a bivariate (1-0) data matrix was generated. In a total, 69 alleles were scored in thirteen genotypes giving an average of 5.3 alleles per genotype. High amount of genetic diversity ranging from * Corresponding author. 1Department of Genetics, Hazara University, Mansehra, KPK, Pakistan. 2Department of Genetics, Karachi University, Karachi, Pakistan. 3Rice Program, Crop Science Institute, National Agricultural Research Centre, Islamabad, Pakistan. 4PARC Institute of Advanced Studies in Agriculture, National Agricultural Research Centre, Islamabad, Pakistan. Niaz Ali, Imtiaz Ahmed Khan, Muhammad Amir Zia, Tanweer Kumar, Muhammad Yousuf, Sohaib Roomi, Habib AhmadEstimation of Genetic Diversity in Genetic Stocks of Common Wheat (Triticum aestivum L.) Using SDS-PAGE EUROPEAN ACADEMIC RESEARCH, VOL. I, ISSUE 8/ NOVEMBER 2013 1862 0-100% was estimated in available wheat genotypes. One comparison (Del5AL12-Del 5AS-3) showed complete homozygosity (GD=0%). Two comparisons (Del 5AS10--Del 5DL-1 and Dit 5Bl--Del 5BL6) showed 100 % genetic distance. Bivariate data was also used to construct a dendrogram using computer program Popgene ver. 32. The genetic stocks of hexaploid wheat of different lines were clustered in 4 groups (A, B, C and D) comprising 4, 3, 3, and 3 genotypes, respectively. Genetic stocks Del5AL12 and Del 5DL-1 were most distantly related and hence it is recommended that these two lines should be crossed to create a breeding population with maximum genetic diversity which will be useful for the development of new improved varieties of wheat.
The diet of predatory fish in drinking water reservoirs – how can they contribute to biomanipulation efforts?

Abstract. Efforts to positively influence ecological processes and water quality by manipulating the fish community (biomanipulation) are of particular importance in drinking water reservoirs. One of the principle measures employed is to increase the abundance of predatory fish species as a means of reducing planktonophagous and benthophagous cyprinids. However, there is little information available on the effectiveness of different predatory fish in biomanipulation exercises. We examined the diet of the five dominant predatory species (pike Esox lucius, zander Sander lucioperca, asp Leuciscus aspius, European catfish Silurus glanis, and perch Perca fluviatilis) in five representative reservoirs in the Morava River drainage basin (Czech Republic). Fish prey made up 75 % of total food intake, with undesirable small cyprinids dominant by biomass (40 %). European catfish and asp were not taken as prey and showed no sign of cannibalism. On the other hand, predation on conspecific predatory species (including cannibalism) was relatively high in perch, pike and zander, thereby reducing their net benefit overall. This little-considered aspect of predatory feeding needs to be taken into consideration in future biomanipulation stocking strategies.