Sparse selection in Cox models with functional predictors

This thesis investigates sparse selection in the Cox regression models with functional predictors. Interest in sparse selection with functional predictors (Lindquist and McKeague, 2009; McKeague and Sen, 2010) can arise in biomedical studies. A functional predictor is a predictor with a trajectory which is usually indexed by time, location or other factors. When the trajectory of a covariate is observed for each subject, and we need to identify a common "sensitive" point of these trajectories which drives outcome, the problem can be formulated as sparse selection with functional predictors. For example, we may locate a gene that is associated to cancer risk along a chromosome. The functional linear regression method is widely used for the analysis of functional covariates. However, it could lack interpretability. The method we develop in this thesis has straightforward interpretation since it relates the hazard to some sensitive components of functional covariates. The Cox regression model has been extensively studied in the analysis of time-to-event data. In this thesis, we extend it to allow for sparse selection with functional predictors. Using the partial likelihood as the criterion function, and following the 3-step procedure for M-estimators established in van der Vaart and Wellner (1996), the consistency, rate of convergence and asymptotic distribution are obtained for M-estimators of the sensitive point and the regression coefficients. In this thesis, to study these large sample properties of the estimators, the fractional Brownian motion assumption is posed for the trajectories for mathematical tractability. Simulations are conducted to evaluate the finite sample performance of the methods, and a way to construct the confidence interval for the location parameter, i.e., the sensitive point, is proposed. The proposed method is applied to an adult brain cancer study and a breast cancer study to find the sensitive point, here the locus of a chromosome, which is closely related to cancer mortality. Since the breast cancer data set has missing values, we investigate the impact of varying proportions of missingness in the data on the accuracy of our estimator as well

Sentiment analysis on Chinese web forums using elastic nets: Features, classification and interpretation: Working paper series--11-11

Consumer opinion has always been of great concern for businesses and others in the commercial sector. Among all social media which contain opinion-rich content, Web forums have become influential due to the large volume of discussions and high levels of interactivity. The Chinese market has now emerged as one of the largest ones over the world, therefore understanding the opinions and sentiments expressed by Chinese consumers has become increasingly important. In this study, we proposed a generic framework to analyze sentiment in Chinese Web forums. To detect online sentiment, we developed a classification method using Elastic Nets with rich feature representation. The proposed sentiment analysis framework was evaluated on two of the most famous Chinese forums with topics on Chinese stock market and laptop. Findings about interesting features were discussed

LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the main network of in-vehicle networks, the controller area network (CAN) has many potential security hazards, resulting in higher requirements for intrusion detection systems to ensure safety. Among intrusion detection technologies, methods based on deep learning work best without prior expert knowledge. However, they all have a large model size and rely on cloud computing, and are therefore not suitable to be installed on the in-vehicle network. Therefore, we propose a lightweight parallel neural network structure, LiPar, to allocate task loads to multiple electronic control units (ECU). The LiPar model consists of multi-dimensional branch convolution networks, spatial and temporal feature fusion learning, and a resource adaptation algorithm. Through experiments, we prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.Comment: 13 pages, 13 figures, 6 tables, 51 referenc

Understanding Avatar Sentiments Using Verbal and Non-Verbal Cues

With the increased popularity of virtual worlds, hundreds of thousands of people from different physical locations can join virtual worlds. In this computer-based simulated 3D environment, avatars can both interact with each other and the environment. This new type of world has important implications for business, education, and society at large. In order to fully use the benefits of virtual worlds, it is important to know how the residents (i.e., avatars) behave, such as how they express sentiments. This research in progress seeks to study avatar sentiments in virtual worlds to examine whether and how sentiments are conveyed by avatars. Both verbal and non-verbal cues will be utilized in the sentiment analysis. To conduct the study, an advanced data collection method is leveraged to obtain various types of avatar data from a large number of real virtual world residents in Second Life in an effective and efficient way

Adoption of Social Media Search Systems: An IS Success Model Perspective

The social media search system aims at providing an organized and integrated access and search support to a massive amount of unstructured, multilingual, user-generated content in an effective and efficient manner. Previous research on social media analytics mainly focuses on developing and applying advanced analysis methods and/or tools to make sense of the large amount of user-generated data over the Internet. Relatively little effort has been put to specifically examine the social media search system. In this study, we utilize and apply the DeLone and McLean IS Success Model to examine this type of systems. To do it, a lab experiment was conducted, and the results showed that all causal relationships, except for satisfaction to social benefit, specified in the DeLone and McLean IS Success Model hold in the context of the large-scale, social media search system. Specifically, we found that information quality and system quality associated with the system could significantly influence both users’ intention to use and satisfaction toward it, both of which, in turn, had significant impacts on users’ perceived individual benefit and social benefit. In addition, satisfaction could significantly influence intention to use the system. Available at: https://aisel.aisnet.org/pajais/vol10/iss2/4

Effect of Crystallization Time on Behaviors of Glass-ceramic Produced from Sludge Incineration Ash

AbstractIncineration has become a significant treatment method for municipal sewage sludge because of the rising difficulty to find suitable sites for traditional landfill. However, a large amount of sludge incineration ash containing high levels of heavy metals is remained. In order to achieve resource utilization, glass–ceramics have been produced using sludge incineration ash. The optimum heat treatment was identified as Tn = 837°C for 1.0 h and Tc = 977°C for 2.0 h, respectively. The major crystalline phase identified from X-ray diffraction (XRD) and scanning electron microscopy (SEM) was wollastonite (CaSiO3) and the products displayed good performances. The results indicated that it was a feasible attempt to produce glass-ceramics from sludge incineration ash as decorative materials