Holomorphic Poisson Cohomology

A holomorphic Poisson structure induces a deformation of the complex structure as Hitchin's generalized geometry. Its associated cohomology naturally appears as the limit of a spectral sequence of a double complex. The first sheet of this spectral sequence is the Dolbeault cohomology with coefficients in the exterior algebra of the holomorphic tangent bundle. We identify various necessary conditions on compact complex manifolds on which this spectral sequence degenerates on the level of the second sheet. The manifolds to our concern include all compact complex surfaces, K\"ahler manifolds, and nilmanifolds with abelian complex structures or complex parallelizable manifolds

A Method and Tool for Finding Concurrency Bugs Involving Multiple Variables with Application to Modern Distributed Systems

Concurrency bugs are extremely hard to detect due to huge interleaving space. They are happening in the real world more often because of the prevalence of multi-threaded programs taking advantage of multi-core hardware, and microservice based distributed systems moving more and more applications to the cloud. As the most common non-deadlock concurrency bugs, atomicity violations are studied in many recent works, however, those methods are applicable only to single-variable atomicity violation, and don\u27t consider the specific challenge in distributed systems that have both pessimistic and optimistic concurrency control. This dissertation presents a tool using model checking to predict atomicity violation concurrency bugs involving two shared variables or shared resources. We developed a unique method inferring correlation between shared variables in multi-threaded programs and shared resources in microservice based distributed systems, that is based on dynamic analysis and is able to detect the correlation that would be missed by static analysis. For multi-threaded programs, we use a binary instrumentation tool to capture runtime information about shared variables and synchronization events, and for microservice based distributed systems, we use a web proxy to capture HTTP based traffic about API calls and the shared resources they access including distributed locks. Based on the detected correlation and runtime trace, the tool is powerful and can explore a vast interleaving space of a multi-threaded program or a microservice based distributed system given a small set of captured test runs. It is applicable to large real-world systems and can predict atomicity violations missed by other related works for multi-threaded programs and a couple of previous unknown atomicity violation in real world open source microservice based systems. A limitation is that redundant model checking may be performed if two recorded interleaved traces yield the same partial order model

US media and foreign policy making: the case study of the US media coverage on Taiwan

How does the American press view Taiwan? It is my contention that in foreign policy, the press supports the government, and therefore, the US press echoes the US government. The acknowledgment of the media\u27s political linkage provides a foundation for discussions on the relationship between the media and policy making. In order to answer the question of role of the press in foreign policy making in US with respect to Taiwan, there are three sub-questions I wish to examine: (1) what was American foreign policy toward Taiwan between the years of 1995-2005? (2) What was the attitude of the American press towards any topic related to Taiwan during that period of time? (3) Did the press support or oppose the president\u27s policy during this time

Transfer learning in Monte Carlo Methods and Beyond

From computational statistics to machine learning, many methods have already achieved excellent performance for one single task given a moderate or large number of data points, e.g., kernel methods and deep learning. A task is usually a regression task, a classification task or a Monte Carlo integration task in statistical learning. However, the performance of those methods is likely to degrade when the sample size of the training data is small; when latent information across tasks is ignored; when computational cost is prohibitively expensive. In this thesis, we focus on transfer learning for Monte Carlo methods and beyond via the scope of multi-task learning and meta-learning. It is well known that supervised learning aims to solve a specific task and often requires to train a model on some labeled data points (also known as training set). Monte Carlo methods provide us with estimators of expectations of functions of random variables with respect to some distributions. In this context, it is desirable to design novel algorithms or methods to explore and exploit transferable information across related tasks for both Monte Carlo methods and supervised learning. This thesis includes three novel transfer learning methods. In the first work, we extend existing control variates, a powerful kind of post-processing tools for Monte Carlo methods, and propose a general framework called vector-valued control variates for multiple related integrals. In the second work, inspired by gradient-based meta-learning, we further generalise existing control variates to meta-learning control variates. In the third work, we extend gradient-based meta-learning to be a gradient-based probabilistic learning algorithm for few-shot image classification by introducing latent class prototypes

Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours

In recent years, free-floating bike-sharing systems (FFBSSs) have been considerably developed in China. As there is no requirement to construct bike stations, this system can substantially reduce the cost when compared to the traditional bike-sharing systems. However, FFBSSs have also become a critical cause of parking disorder, especially during the morning and evening rush hours. To address this issue, the local governments stipulated that FFBSSs are required to deploy virtual stations near public transit stations and major establishments. Therefore, the location assignment of virtual stations is sufficiently considered in the FFBSSs, which is required to solve the parking disorder and satisfy the user demand, simultaneously. The purpose of this study is to optimize the location assignment of virtual stations that can meet the growing demand of users by analyzing the usage data of their shared bikes. This optimization problem is generally formulated as a mixed-integer linear programming (MILP) model to maximize the user demand. As an alternative solution, this article proposes a clustering algorithm, which can solve this problem in real time. The experimental results demonstrate that the MILP model and the proposed method are superior to the K-means method. Our method not only provides a solution for maximizing the user demand but also gives an optimized design scheme of the FFBSSs that represents the characteristics of virtual stations. Document type: Articl

Thermal properties of highly porous fibrous ceramics

Highly porous fibrous ceramics were fabricated by vacuum-molding the fiber slurry and sintering the dried felt. The materials comprised of a random network of ceramic fibers and air, with the pore sizes on micron scale. The effects of binder content and porosity on the microstructure and room-temperature thermal conductivity of fibrous ceramics were investigated. It was found that the room-temperature thermal conductivity increased with increasing binder content. In addition, the thermal conductivity decreased from 0.18 to 0.06 W/(m·K) when porosity increased from 73% to 90%, showing nearly a linear relationship. The high-temperature thermal conductivity in the range of 200-1200℃ for three different porosities were also investigated. The thermal conductivity increased as temperature and density increased. Furthermore, the porous ceramics were impregnated with silica aerogel to further lower the thermal conductivity. The room-temperature thermal conductivity decreased from 0.049 to 0.040 W/(m·K), and the back temperature decreased from 870℃ to 750℃ after the aerogel impregnation, showing better high-temperature insulation performance