Multi-authority attribute-based keyword search over encrypted cloud data

National Research Foundation (NRF) Singapore; AXA Research Fun

A Bridge between Myriad Lands: The Ryukyu Kingdom and Ming China (1372-1526)

Master'sMASTER OF ART

Going Deeper with Convolutional Neural Network for Intelligent Transportation

Over last several decades, computer vision researchers have been devoted to find good feature to solve different tasks, object recognition, object detection, object segmentation, activity recognition and so forth. Ideal features transform raw pixel intensity values to a representation in which these computer vision problems are easier to solve. Recently, deep feature from covolutional neural network(CNN) have attracted many researchers to solve many problems in computer vision. In the supervised setting, these hierarchies are trained to solve specific problems by minimizing an objective function for different tasks. More recently, the feature learned from large scale image dataset have been proved to be very effective and generic for many computer vision task. The feature learned from recognition task can be used in the object detection task. This work aims to uncover the principles that lead to these generic feature representations in the transfer learning, which does not need to train the dataset again but transfer the rich feature from CNN learned from ImageNet dataset. This work aims to uncover the principles that lead to these generic feature representations in the transfer learning, which does not need to train the dataset again but transfer the rich feature from CNN learned from ImageNet dataset. We begin by summarize some related prior works, particularly the paper in object recognition, object detection and segmentation. We introduce the deep feature to computer vision task in intelligent transportation system. First, we apply deep feature in object detection task, especially in vehicle detection task. Second, to make fully use of objectness proposals, we apply proposal generator on road marking detection and recognition task. Third, to fully understand the transportation situation, we introduce the deep feature into scene understanding in road. We experiment each task for different public datasets, and prove our framework is robust

An experimental study of learned cardinality estimation

Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned models have the potential to replace existing cardinality estimators. In this thesis, we ask a forward-thinking question: Are we ready to deploy these learned cardinality models in production? Our study consists of three main parts. Firstly, we focus on the static environment (i.e., no data updates) and compare five new learned methods with eight traditional methods on four real-world datasets under a unified workload setting. The results show that learned models are indeed more accurate than traditional methods, but they often suffer from high training and inference costs. Secondly, we explore whether these learned models are ready for dynamic environments (i.e., frequent data updates). We find that they can- not catch up with fast data updates and return large errors for different reasons. For less frequent updates, they can perform better but there is no clear winner among themselves. Thirdly, we take a deeper look into learned models and explore when they may go wrong. Our results show that the performance of learned methods can be greatly affected by the changes in correlation, skewness, or domain size. More importantly, their behaviors are much harder to interpret and often unpredictable. Based on these findings, we identify two promising research directions (control the cost of learned models and make learned models trustworthy) and suggest a number of research opportunities. We hope that our study can guide researchers and practitioners to work together to eventually push learned cardinality estimators into real database systems

2016 Commencement Program

The commencement program of the 2016 Columbia College Chicago graduation ceremonies.https://digitalcommons.colum.edu/commencement/1052/thumbnail.jp

Efficient processing of XML documents

Ph.DDOCTOR OF PHILOSOPH

Chinese Mine Warfare: A PLA Navy \u27Assassin\u27s Mace\u27 Capability

After a lengthy hiatus-lasting nearly six centuries—China is reemerging as a maritime power, this time with an emphasis on undersea warfare. Between 1996 and 2006, the Chinese navy took delivery of more than thirty submarines. These vessels include two new classes of nuclear submarines-the advanced Song-class diesel submarines and the Yuan class of diesel boats which, according to some reports, was a surprise for U.S. intelligence. Above and beyond this ambitious naval construction program, the People\u27s Republic of China (PRC) received during 2005-06 an additional eight formidable Kilo-class submarines (and associated weaponry), which were purchased in 2002, to add to the four it already operated. A new nuclear submarine base on Hainan Island may well herald a new era of more extended Chinese submarine operations.https://digital-commons.usnwc.edu/cmsi-red-books/1002/thumbnail.jp

Assembling the Cure: Materia Medica and the Culture of Healing in Late Imperial China

This dissertation examines the intersection between the culture of knowledge and socio-economic conditions of late Ming and Qing China (1550-1800) through the lens of materia medica. I argue that medicine in China during this time developed new characteristics that emphasized the centrality of drugs as objects of pharmacological knowledge, commodities valued by authenticity and efficacy, and embodiment of medical skills and expertise. My inquiry contributes to a deeper understanding of the materiality of healing as a basic condition in early modern societies: on the one hand, textual knowledge about drugs and the substances themselves became increasingly available via the commoditization of texts and goods; on the other hand, anxiety arose out of the unruly nature of potent substances, whose promise to cure remained difficult to grasp in social practice of medicine.History of Scienc