Bayesian Estimation of White Matter Atlas from High Angular Resolution Diffusion Imaging

We present a Bayesian probabilistic model to estimate the brain white matter atlas from high angular resolution diffusion imaging (HARDI) data. This model incorporates a shape prior of the white matter anatomy and the likelihood of individual observed HARDI datasets. We first assume that the atlas is generated from a known hyperatlas through a flow of diffeomorphisms and its shape prior can be constructed based on the framework of large deformation diffeomorphic metric mapping (LDDMM). LDDMM characterizes a nonlinear diffeomorphic shape space in a linear space of initial momentum uniquely determining diffeomorphic geodesic flows from the hyperatlas. Therefore, the shape prior of the HARDI atlas can be modeled using a centered Gaussian random field (GRF) model of the initial momentum. In order to construct the likelihood of observed HARDI datasets, it is necessary to study the diffeomorphic transformation of individual observations relative to the atlas and the probabilistic distribution of orientation distribution functions (ODFs). To this end, we construct the likelihood related to the transformation using the same construction as discussed for the shape prior of the atlas. The probabilistic distribution of ODFs is then constructed based on the ODF Riemannian manifold. We assume that the observed ODFs are generated by an exponential map of random tangent vectors at the deformed atlas ODF. Hence, the likelihood of the ODFs can be modeled using a GRF of their tangent vectors in the ODF Riemannian manifold. We solve for the maximum a posteriori using the Expectation-Maximization algorithm and derive the corresponding update equations. Finally, we illustrate the HARDI atlas constructed based on a Chinese aging cohort of 94 adults and compare it with that generated by averaging the coefficients of spherical harmonics of the ODF across subjects

Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging based on Riemannian Structure of Orientation Distribution Functions

In this paper, we propose a novel large deformation diffeomorphic registration algorithm to align high angular resolution diffusion images (HARDI) characterized by orientation distribution functions (ODFs). Our proposed algorithm seeks an optimal diffeomorphism of large deformation between two ODF fields in a spatial volume domain and at the same time, locally reorients an ODF in a manner such that it remains consistent with the surrounding anatomical structure. To this end, we first review the Riemannian manifold of ODFs. We then define the reorientation of an ODF when an affine transformation is applied and subsequently, define the diffeomorphic group action to be applied on the ODF based on this reorientation. We incorporate the Riemannian metric of ODFs for quantifying the similarity of two HARDI images into a variational problem defined under the large deformation diffeomorphic metric mapping (LDDMM) framework. We finally derive the gradient of the cost function in both Riemannian spaces of diffeomorphisms and the ODFs, and present its numerical implementation. Both synthetic and real brain HARDI data are used to illustrate the performance of our registration algorithm

Interaction Between Traditional Media and Social Media Coverage on Social Issues in China: A Content Analysis

Professional project report submitted in partial fulfillment of the requirements for the degree of Masters of Arts in Journalism from the School of Journalism, University of Missouri--Columbia.To what extent does online public opinion and traditional media coverage interact with each other on social issues in China? This research employs a content analysis of 524 Weibo posts and 327 news articles regarding a social incident in China. The researcher uses Chi-square tests to compare the use of alternative media and the frame selection of social media and traditional media in different phases. Social media and traditional media react differently when covering social issues. Social media have a better interaction with traditional media while traditional media make less reference to social media. Additionally, social media and traditional media play different social roles when covering public affairs by selecting different frames. Even if the traditional media are partially free and under the government control, social media can hardly substitute the role of social responsibility of traditional media in defining the problem and issue treatment. Noticeably, the choices of frame in both social media and traditional media are not influenced by their interactions, but instead by different time frames. Discussion focuses on the changes in the roles played by media, government, and Chinese citizens.Includes bibliographic references

The impact of the Covid-19 Pandemic on earnings management - Evidence from listed companies in China

The Covid-19 pandemic and the ensuing recession have caught many firms off-guard, posing an existential threat to the viability of some businesses, which in turn has had a significant impact on the financial reports of enterprises. The aim of this study is to determine the impact of the Covid-19 pandemic on earnings management practices. This study uses a sample including 1,832 listed companies in China for a period from 2015 to 2020. To capture earnings management, this paper uses the Modified Jones model to measure accrual-based earnings management (Dechow et al., 1995), and real activity-based earnings management is measured by the sum of the absolute values of the abnormal production costs, the abnormal cash flow from operations, and the abnormal discretionary expenditures (Cohen et al., 2008; Roychowdhury, 2006). The results show that the sample companies are more inclined to manipulate earnings during the pandemic period than in the prior time. This finding implies that financial reporting is less reliable during the Covid-19 pandemic. The further investigation provides proof of significant income-increasing earnings management during the pandemic period. This finding shows that companies manage earnings upward by mitigating reported losses to a level acceptable to stakeholders in order to attract investors without losing existing ones, thereby supporting economic recovery. In addition, this article finds that financially distressed firms are more inclined to adopt earnings manipulation, especially accrual-based earnings management. Keywords Earnings management, the Covid-19 pandemic crisis, Chinese listed companie

Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis

We propose a large deformation diffeomorphic metric mapping algorithm to align multiple b-value diffusion weighted imaging (mDWI) data, specifically acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then propose a Bayesian model for estimating the white matter atlas from HYDIs. We adopt the work given in Hosseinbor et al. (2012) and represent the q-space diffusion signal with the Bessel Fourier orientation reconstruction (BFOR) signal basis. The BFOR framework provides the representation of mDWI in the q-space and thus reduces memory requirement. In addition, since the BFOR signal basis is orthonormal, the L2 norm that quantifies the differences in the q-space signals of any two mDWI datasets can be easily computed as the sum of the squared differences in the BFOR expansion coefficients. In this work, we show that the reorientation of the $q$-space signal due to spatial transformation can be easily defined on the BFOR signal basis. We incorporate the BFOR signal basis into the LDDMM framework and derive the gradient descent algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally, we extend the previous Bayesian atlas estimation framework for scalar-valued images to HYDIs and derive the expectation-maximization algorithm for solving the HYDI atlas estimation problem. Using real HYDI datasets, we show the Bayesian model generates the white matter atlas with anatomical details. Moreover, we show that it is important to consider the variation of mDWI reorientation due to a small change in diffeomorphic transformation in the LDDMM-HYDI optimization and to incorporate the full information of HYDI for aligning mDWI

Commodity market stability and sustainable development: The effect of public health policies

This study explores the influence of public health policies on commodity market volatility during public health emergencies, such as pandemics, using data from China and the US. We investigate how stringent public health measures can mitigate the effects of pandemics on the stability of commodity markets by stabilizing domestic demand and supply of natural resources. Our findings highlight the interconnectedness between commodity market stability and oil production, showing that firms increase their oil inventories in response to oil market volatility as a precautionary measure. This action, in turn, affects the amount of oil available for production, impacting oil consumption and extraction rates. We demonstrate that stability in the oil market significantly influences not only oil consumption but also has broader implications for sustainable development, green asset markets, and carbon emissions

Shop The Look: Building a Large Scale Visual Shopping System at Pinterest

As online content becomes ever more visual, the demand for searching by visual queries grows correspondingly stronger. Shop The Look is an online shopping discovery service at Pinterest, leveraging visual search to enable users to find and buy products within an image. In this work, we provide a holistic view of how we built Shop The Look, a shopping oriented visual search system, along with lessons learned from addressing shopping needs. We discuss topics including core technology across object detection and visual embeddings, serving infrastructure for realtime inference, and data labeling methodology for training/evaluation data collection and human evaluation. The user-facing impacts of our system design choices are measured through offline evaluations, human relevance judgements, and online A/B experiments. The collective improvements amount to cumulative relative gains of over 160% in end-to-end human relevance judgements and over 80% in engagement. Shop The Look is deployed in production at Pinterest.Comment: 10 pages, 7 figures, Accepted to KDD'2

The impact of the Covid-19 Pandemic on earnings management - Evidence from listed companies in China

The Covid-19 pandemic and the ensuing recession have caught many firms off-guard, posing an existential threat to the viability of some businesses, which in turn has had a significant impact on the financial reports of enterprises. The aim of this study is to determine the impact of the Covid-19 pandemic on earnings management practices. This study uses a sample including 1,832 listed companies in China for a period from 2015 to 2020. To capture earnings management, this paper uses the Modified Jones model to measure accrual-based earnings management (Dechow et al., 1995), and real activity-based earnings management is measured by the sum of the absolute values of the abnormal production costs, the abnormal cash flow from operations, and the abnormal discretionary expenditures (Cohen et al., 2008; Roychowdhury, 2006). The results show that the sample companies are more inclined to manipulate earnings during the pandemic period than in the prior time. This finding implies that financial reporting is less reliable during the Covid-19 pandemic. The further investigation provides proof of significant income-increasing earnings management during the pandemic period. This finding shows that companies manage earnings upward by mitigating reported losses to a level acceptable to stakeholders in order to attract investors without losing existing ones, thereby supporting economic recovery. In addition, this article finds that financially distressed firms are more inclined to adopt earnings manipulation, especially accrual-based earnings management. Keywords Earnings management, the Covid-19 pandemic crisis, Chinese listed companie