Tensor-train methods for sequential state and parameter learning in state-space models

We consider sequential state and parameter learning in state-space models with intractable state transition and observation processes. By exploiting low-rank tensor-train (TT) decompositions, we propose new sequential learning methods for joint parameter and state estimation under the Bayesian framework. Our key innovation is the introduction of scalable function approximation tools such as TT for recursively learning the sequentially updated posterior distributions. The function approximation perspective of our methods offers tractable error analysis and potentially alleviates the particle degeneracy faced by many particle-based methods. In addition to the new insights into algorithmic design, our methods complement conventional particle-based methods. Our TT-based approximations naturally define conditional Knothe--Rosenblatt (KR) rearrangements that lead to filtering, smoothing and path estimation accompanying our sequential learning algorithms, which open the door to removing potential approximation bias. We also explore several preconditioning techniques based on either linear or nonlinear KR rearrangements to enhance the approximation power of TT for practical problems. We demonstrate the efficacy and efficiency of our proposed methods on several state-space models, in which our methods achieve state-of-the-art estimation accuracy and computational performance

Research on Relationships between the Pigment of Dunhuang Mogao Grottoes Frescoes and Ecological Microorganism

Dunhuang Mogao Grottoes is one of the largest art treasures in the world. She has a large number of murals, sculptures, beautiful and vivid; There are precious Buddhist scriptures, documents, noble and elegant. Spanning more than 1,600 years, the Mogao Grottoes show the world the extensive and profound Chinese culture with a long history. But over time, the murals in the Mogao Grottoes have also changed a lot. Thousands of years of wind and rain erosion, changes in the surrounding environment, and the influence of various biological communities have caused serious color changes and fading of murals in Mogao Grottoes. To slow down the color change of Dunhuang frescoes, protection measures should be taken from the perspective of ecological microorganisms. At present, Cladosporium, Planococcus, Phoma, Chaetomium, and other strains have caused serious discoloration or discoloration of murals to a certain extent. This paper studies the main color of Dunhuang frescoes, red, and summarizes the discoloration factors and mechanism of red lead. On this level, one should try to keep the murals and control the indoor temperature. Humidity, people, and other factors slow the fading of the murals. But these are often insufficient to protect the integrity of the murals, so we have conducted a review of the literature to provide an updated overview of the available evidence on the subject

Success factors for English as a second language university students' attainment in academic English language proficiency: exploring the roles of secondary school medium-of-instruction, motivation and language learning strategies

Displaying a strong competence in English as a second language (L2) is a major advantage for university graduates in personal development and career advancement. There are limited studies that have explored how the implementation of English-Medium-Instruction (EMI) in non-Anglophone universities can affect students' academic English proficiency. This mixed-method study explores how both the learners' variables (motivation and L2 learning strategies) and the medium-of-instruction policy implemented at secondary and university settings can contribute to students' success in academic English learning at a bilingual university in Hong Kong. The findings of a large-scale questionnaire reveal that the effect of medium-of-instruction in students' secondary school education is not a significant predictor of students' academic English language proficiency at university. The findings suggest that university students who previously attended secondary schools which adopt medium-of-instruction other than English tend not to be disadvantaged in improving their academic English proficiency. Institutional factor (i.e. provision of EMI lectures at university) and learners' variables, including students' L2 strategy use and motivation, are statistically significant predictors of the student's English language proficiency. The implications for higher education policy in Hong Kong and worldwide will be discussed

Protocols of Control in Chinese Online News Media: The Case of Wenzhou News

This thesis explores censorship and self-censorship in online news production in China. It presents an analysis drawn from observation in the online newsroom and interviews with online news workers and cyber police officers in China. In addition, it studies the mechanisms of online censorship and protocols of news censorship in an online newsroom context. This involves an analysis of journalistic activities in the process of online news production, self-censorship of online news workers, and power flows between the Chinese authorities and online news media in determining the output of online news content. Although the Chinese “free press” is enshrined in the Constitution of The People’s Republic of China as a right, the mechanism of online news censorship is shaped under the influence of an anti-liberal theory of limited freedom of speech. Confucius, the proclaimer of this theory, devalues individual liberty, advocating the ‘right to speak’ is a benefaction of the ruling class, and this “freedom” can be compromised for the welfare of the state. It is a view shared by Confucius’ successors. This theory, therefore, conceptually sets up a distinctive paternalistic protocol of online news censorship in China, as the online news workers are instructed to censor and self-censor online content under the influence of administrative interference. Through thematic analysis of field notes, which covers a four-week period of observation and recording in the online newsroom of Wenzhou News, a local online news organisation in China, the hierarchical structure and general workflow in this online newsroom are illustrated. By further analysing interviews conducted with online journalists, editors, web administrators and cyber police officers, this thesis draws on the perspectives of online news workers and censors towards the protocol of online news censorship, through which the power matrix between the Chinese government, the Communist Party of China, and online news media are triangulated. By analysing and constructing technological and social modes of censorship in the online environment, this thesis sets up a conceptual framework of the protocols of online news censorship in China, draws detailed processes of online news production under the pressure of censorship, and explores the concept of “harmonisation” within the online newsroom where specific ideological motivations and structural operationalisation influence the output of online news content

FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.Comment: Accepted by SenSys '1