Quantitative Molecular MRI of Intervertebral Disc Degeneration

Degeneration of the intervertebral disc (IVD) is the most common cause of back-related disability among North American adults. Low-back-pain and associated disability costs the United States more than 100 billion dollars annually in health care expenditures and reduced productivity. The mechanism of IVD degeneration, especially its biomolecular aspect, is poorly understood in an in vivo setting. Thus there is increasingly a need for the non-invasive diagnosis and quantification of IVD degeneration. MRI is a non-invasive imaging modality capable of producing contrast sensitive to biomolecules. Therefore, the primary objective of this dissertation research project is to develop MRI techniques capable of non-invasive quantification of IVD biomolecular composition in vivo. We further developed three MRI techniques specifically for IVD imaging. Magnetization transfer (MT) MRI, T1ρ MRI and sodium MRI were first separately validated of their specificities for IVD biomolecular components. In doing so, we concluded that MT MRI is sensitive to IVD collagen content, T1ρ MRI is indicative of IVD osmotic pressure, and sodium MRI is sensitive to IVD proteoglycan (PG) content. Next, we applied all three techniques to human subjects in vivo. Due to the inherently low signal-to-noise ratio (SNR) efficiency of sodium MRI, we engineered a custom radio-frequency (RF) surface coil for sodium MRI of human lumbar spine on a 7 T MRI scanner. Cross-correlation of the MT MRI, T1ρ MRI and sodium MRI data with the corresponding Pfirrmann grade revealed that the relative collagen density of IVD increases with degeneration, the IVD osmotic pressure decreases with degeneration, and the IVD PG content decreases with degeneration. By establishing that in vivo MT MRI, T1ρ MRI and sodium MRI can be used to quantify multiple IVD biomolecular characteristics non-invasively, we open up the possibility to conduct longitudinal studies on human subjects as they undergo IVD degeneration. The combination of MT MRI, T1ρ MRI and sodium MRI provides scientists and clinicians with the diagnostic tool to improve our understanding of IVD degeneration, which could benefit future treatment and prognosis of IVD degeneration

Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search

Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 \% in terms of the top-1 metric.Comment: published in AAAI2020(oral

Multi-epoch analysis of the X-ray spectrum of the active galactic nucleus in NGC 5506

We present a multi-epoch X-ray spectroscopy analysis of the nearby narrow-line Seyfert I galaxy NGC 5506. For the first time, spectra taken by Chandra, XMM-Newton, Suzaku, and NuSTAR - covering the 2000-2014 time span - are analyzed simultaneously, using state-of-the-art models to describe reprocessing of the primary continuum by optical thick matter in the AGN environment. The main goal of our study is determining the spin of the supermassive black hole (SMBH). The nuclear X-ray spectrum is photoelectrically absorbed by matter with column density $\simeq 3 \times 10^{22}$ cm$^{-2}$. A soft excess is present at energies lower than the photoelectric cut-off. Both photo-ionized and collisionally ionized components are required to fit it. This component is constant over the time-scales probed by our data. The spectrum at energies higher than 2 keV is variable. We propose that its evolution could be driven by flux-dependent changes in the geometry of the innermost regions of the accretion disk. The black hole spin in NGC 5506 is constrained to be 0.93$\pm _{ 0.04 }^{0.04}$ at 90% confidence level for one interesting parameter.Comment: 13 pages, 9 figures. v2: refereed versio

DEUCON: Distributed End-to-End Utilization Control for Real-Time Systems

This paper presents the Distributed End-to-end Utiization CONtrol (DEUCON) algorithm. DEUCON can dynamically enforce desired CPU utilizations on all processors in a dis-tributed real-time system despite uncertainties in the system workload. In contrast to earlier centralized control schemes, DEUCON is a distributed control algorithm that is system-atically designed based on the Distributed Model Predictive Control theory. We decompose the global multi-processor utilization control problem into a set of localized subprob-lems, and design a peer-to-peer control structure where each local controller only needs to coordinate with a small number of neighbor processors. DEUCON can provide utilization guarantees similar to a centralized control algorithm, while significantly reducing the per-controller run-time overhead in terms of both computation and communication. Further-more, it can tolerate considerable network delay and indi-vidual processor failures. Consequently, DEUCON can pro-vide scalable and robust utilization control services for large distributed real-time systems that operate in unpredictable environments

Understanding User Engagement in Online Communities during COVID-19 Pandemic: Evidence from Sentiment and Semantic Analysis on YouTube

Since the outbreak of COVID-19, the pandemic has changed the lives of many people and brought dramatic motional experiences. Among many social media platforms, YouTube saw the most significant growth of any social media app among American users during the pandemic, according to the Pew Research Center on 7th April 2021. Exposure to COVID-19 related news can have a significant impact on user engagement on social networks. Different news may trigger different emotions (i.e., anger, anticipation, disgust, fear, joy, sadness, surprise, or trust), and a user may engage differently in response to the news. On YouTube, user engagement is manifested through actions such as liking, disliking, commenting, or sharing videos. During the pandemic, many users provide constructive comments that are encouraging, respectful, and informative to support each other. We applied sentiment analysis in the study to investigate different emotions and applied semantic analysis to investigate positive appraisal (i.e., encouraging, respectful, and informative) to identify salient factors that can motivate user engagement. The findings of the work shed light on how social network platforms could encourage constructive comments to help people provide emotional support to each other during pandemics through using positive appraisal in online news comments. The first research objective is to study the impact of sentiment valence of different emotions on people’s liking of news comments. News about COVID-19 on social networks may provide valuable information but also bring about public panic. In response to this COVID-19 related news, reviewers expressed their feelings by clicking the like, dislike buttons to the video and comments, or writing some comments under the video on YouTube. Some positive news was followed by comments expressing their anticipation, joy, and trust, while negative news might trigger sadness, fear, disgust, or anger. Our research focuses on sentiment analysis of news titles and the comments following each video. News title provides important information about the video, showing the summary of the video and allowing people to get a first glimpse of the content of the video. Through sentiment analysis of title and comments, correlations could be found between title/comments sentiment and user engagement. The second research objective is to investigate the impact of comments’ positive appraisal (i.e., encouraging, respectful, and informative content) on user engagement. The informative comments under the negative news have significant implications for the audience. They can be considered as a complement or judgment of the video content. Encouraging and respectful comments also help people build good conversations online. Our research focuses on semantic analysis of news titles and comments based on the three dimensions of positive appraisal and analyzes their impacts on user engagement to like the corresponding comment. We discuss the correlation between video title sentiment and the positive appraisal followed in the comments of the video to provide good conversations on the platform. A group of 38,085 online comments was collected from more than 400 different publishers from January 1st to January 30th, 2021, on YouTube. The dataset contains the most-viewed videos that were related to at least one of the following search queries: coronavirus, COVID-19, pandemic, or vaccine. NRC lexicon is adopted in the sentiment analysis to identify different emotions in titles and comments of the video. We adopt the topic modeling method and build a classifier from the Yahoo News Annotated Comments Corpus to identify constructive online comments for specific topics. We also measure inter-annotator agreements and compare the reliability of manual annotation and the classifier. We find that longer titles and sad emotions can obtain more likes on the comments of COVID-19 related news. During the pandemic, people tend to show their support when they find others are quite sad. We also expect to see correlations between some positive appraisals and user engagement