Statistical Modeling of Data From Smoking Cessation Clinical Trials

In smoking cessation clinical trials, subjects commonly experience a series of lapse and recovery episodes of varying lengths. Any quit episode may become permanent, in the sense that the subject stops smoking for good, and any lapse may also become permanent, in the sense that the subject abandons the quit attempt entirely. Individual quit patterns may reflect the effects of treatment and measured and unmeasured covariates. To describe this complex data structure, we propose a multivariate time-to-event model that i) incorporates alternating recurrent events of two types, each with the possibility of cure , ii) allows for the modifying effects of treatment and covariates, and iii) reflects within-subject correlation via frailties. Specifically, we introduce a novel cure-mixture frailty model in which the cure probability follows a binary regression and the time to event given not cured is determined by a proportional hazard model. We then extend it to data with recurring events of two alternating types, where we assume that each type of event has a gamma frailty, and we link the frailties by means of a Clayton copula. In my first project, I fit this model to data from a smoking cessation drug trial. In my second project, I developed a Bayesian method to predict individual long-term smoking behavior from observed short-term quit/relapse patterns. In my third project, I investigated the theoretical properties of the survival distribution, evidently not previously described, that arises from our cure-mixture frailty model

Data Compression in Multi-Hop Large-Scale Wireless Sensor Networks

Data collection from a multi-hop large-scale outdoor WSN deployment for environmental monitoring is full of challenges due to the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity) and the highly dynamic wireless link conditions in an outdoor communication environment. We present a compressed sensing approach which can recover the sensing data at the sink with good accuracy when very few packets are collected, thus leading to a significant reduction of the network traffic and an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is efficient and simple to implement on the resource-constrained motes without motes storing of a part of random measurement matrix, as opposed to other existing compressed sensing based schemes. We provide a systematic method via machine learning to find a suitable representation basis, for the given WSN deployment and data field, which is both sparse and incoherent with the measurement matrix in the compressed sensing. We validate our approach and evaluate its performance using our real-world multi-hop WSN testbed deployment in situ in collecting the humidity and soil moisture data. The results show that our approach significantly outperforms three other compressed sensing based algorithms regarding the data recovery accuracy for the entire WSN observation field under drastically reduced communication costs. For some WSN scenarios, compressed sensing may not be applicable. Therefore we also design a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for variouSs data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for wireless sensor networks such as LEC, S-LZW and LTC using various real-world sensor data sets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs

Compressed Sensing in Multi-Hop Large-Scale Wireless Sensor Networks Based on Routing Topology Tomography

Data acquisition from multi-hop large-scale outdoor wireless sensor network (WSN) deployments for environmental monitoring is full of challenges. This is because of the severe resource constraints on tiny battery-operated motes (e.g., bandwidth, memory, power, and computing capacity), the data acquisition volume from large-scale WSNs, and the highly dynamic wireless link conditions in outdoor harsh communication environments. We present a novel compressed sensing approach, which can recover the sensing data at the sink with high fidelity when a very few data packets need to be collected, leading to a significant reduction of the network transmissions and thus an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is both efficient and simple to implement on the resource-constrained motes without motes' storing of any part of the random projection matrix, as opposed to other existing compressed sensing-based schemes. We further propose a systematic method via machine learning to find a suitable representation basis, for any given WSN deployment and data field, which is both sparse and incoherent with the random projection matrix in compressed sensing for data collection. We validate our approach and evaluate its performance using a real-world outdoor multihop WSN testbed deployment in situ. The results demonstrate that our approach significantly outperforms existing compressed sensing approaches by reducing data recovery errors by an order of magnitude for the entire WSN observation field while drastically reducing wireless communication costs at the same time

Exploration and practice of talent training mode in Sino-foreign cooperative schools -- taking Software engineering as an example

With the deepening of globalization, Sino-foreign cooperation in running schools plays an important role in China’s higher education. Sino-foreign cooperation in running schools refers to the degree education program set up by Chinese higher education institutions and foreign higher education institutions. This mode of cooperation can not only provide Chinese students with broader learning opportunities and international educational resources, but also promote educational exchanges and cooperation between China and foreign countries. As an important subject in the field of information technology, software engineering has broad employment prospects and development potential, so it has been widely used and promoted in Chinese-foreign cooperative education. This paper will take the software engineering major as an example to explore and practice the talent training model of Sino-foreign cooperation in running schools, aiming to provide reference for other majors and Chinese-foreign cooperation in running schools