Geo-tagging and privacy-preservation in mobile cloud computing

With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile cloud computing, with the synergy between the cloud and mobile technologies, has brought us new opportunities to develop novel and practical systems such as mobile multimedia systems and cloud systems that provide collaborative data-mining services for data from disparate owners (e.g., mobile users). However, it also creates new challenges, e.g., the algorithms deployed in the computationally weak mobile device require higher efficiency, and introduces new problems such as the privacy concern when the private data is shared in the cloud for collaborative data-mining. The main objectives of this dissertation are: 1. to develop practical systems based on the unique features of mobile devices (i.e., all-in-one computing platform and sensors) and the powerful computing capability of the cloud; 2. to propose solutions protecting the data privacy when the data from disparate owners are shared in the cloud for collaborative data-mining. We first propose a mobile geo-tagging system. It is a novel, accurate and efficient image and video based remote target localization and tracking system using the Android smartphone. To cope with the smartphones' computational limitation, we design light-weight image/video processing algorithms to achieve a good balance between estimation accuracy and computational complexity. Our system is first of its kind and we provide first hand real-world experimental results, which demonstrate that our system is feasible and practicable. To address the privacy concern when data from disparate owners are shared in the cloud for collaborative data-mining, we then propose a generic compressive sensing (CS) based secure multiparty computation (MPC) framework for privacy-preserving collaborative data-mining in which data mining is performed in the CS domain. We perform the CS transformation and reconstruction processes with MPC protocols. We modify the original orthogonal matching pursuit algorithm and develop new MPC protocols so that the CS reconstruction process can be implemented using MPC. Our analysis and experimental results show that our generic framework is capable of enabling privacy preserving collaborative data-mining. The proposed framework can be applied to many privacy preserving collaborative data-mining and signal processing applications in the cloud. We identify an application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We further propose a privacy preserving storage and secure watermark detection framework by adopting our generic framework to address such a requirement. In our secure watermark detection framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. We also give mathematical and statistical analysis to derive the expected watermark detection performance in the compressive sensing domain, based on the target image, watermark pattern and the size of the compressive sensing matrix (but without the actual CS matrix), which means that the watermark detection performance in the CS domain can be estimated during the watermark embedding process. The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. By taking advantage of our mobile geo-tagging system and compressive sensing based privacy preserving data-mining framework, we develop a mobile privacy preserving collaborative filtering system. In our system, mobile users can share their personal data with each other in the cloud and get daily activity recommendations based on the data-mining results generated by the cloud, without leaking the privacy and secrecy of the data to other parties. Experimental results demonstrate that the proposed system is effective in enabling efficient mobile privacy preserving collaborative filtering services.Includes bibliographical references (pages 126-133)

Split-Bregman iteration for framelet based image inpainting

AbstractImage inpainting plays a significant role in image processing and has many applications. Framelet based inpainting methods were introduced recently by Cai et al. (2007, 2009) [6,7,9] under an assumption that images can be sparsely approximated in the framelet domain. By analyzing these methods, we present a framelet based inpainting model in which the cost functional is the weighted ℓ1 norm of the framelet coefficients of the underlying image. The split-Bregman iteration is exploited to derive an iterative algorithm for the model. The resulting algorithm assimilates advantages while avoiding limitations of the framelet based inpainting approaches in Cai et al. (2007, 2009) [6,7,9]. The convergence analysis of the proposed algorithm is presented. Our numerical experiments show that the algorithm proposed here performs favorably

Smoothing algorithms for nonsmooth and nonconvex minimization over the stiefel manifold

We consider a class of nonsmooth and nonconvex optimization problems over the Stiefel manifold where the objective function is the summation of a nonconvex smooth function and a nonsmooth Lipschitz continuous convex function composed with an linear mapping. We propose three numerical algorithms for solving this problem, by combining smoothing methods and some existing algorithms for smooth optimization over the Stiefel manifold. In particular, we approximate the aforementioned nonsmooth convex function by its Moreau envelope in our smoothing methods, and prove that the Moreau envelope has many favorable properties. Thanks to this and the scheme for updating the smoothing parameter, we show that any accumulation point of the solution sequence generated by the proposed algorithms is a stationary point of the original optimization problem. Numerical experiments on building graph Fourier basis are conducted to demonstrate the efficiency of the proposed algorithms.Comment: 22 page

Comparative Transcriptomics of Strawberries (Fragaria spp.) Provides Insights into Evolutionary Patterns

Multiple closely related species with genomic sequences provide an ideal system for studies on comparative and evolutionary genomics, as well as the mechanism of speciation. The whole genome sequences of six strawberry species (Fragaria spp.) have been released, which provide one of the richest genomic resources of any plant genus. In this study, we first generated seven transcriptome sequences of Fragaria species de novo, with a total of 48,557–82,537 unigenes per species. Combined with 13 other species genomes in Rosales, we reconstructed a phylogenetic tree at the genomic level. The phylogenic tree shows that Fragaria closed grouped with Rubus and the Fragaria clade is divided into three subclades. East Asian species appeared in every subclade, suggesting that the genus originated in this area at ∼7.99 Mya. Four species found in mountains of Southwest China originated at ∼3.98 Mya, suggesting that rapid speciation occurred to adapt to changing environments following the uplift of the Qinghai–Tibet Plateau. Moreover, we identified 510 very significantly positively selected genes in the cultivated species F. × ananassa genome. This set of genes was enriched in functions related to specific agronomic traits, such as carbon metabolism and plant hormone signal transduction processes, which are directly related to fruit quality and flavor. These findings illustrate comprehensive evolutionary patterns in Fragaria and the genetic basis of fruit domestication of cultivated strawberry at the genomic/transcriptomic level

Effect on the canine Eck fistula liver of intraportal TGF‐β alone or with hepatic growth factors

Transforming growth factor‐β canceled the hepatocyte proliferation caused by transforming growth factor‐α when the two substances were mixed and administered through a disconnected central portal vein branch after creation of an Eck fistula. In contrast, transforming growth factor‐β had no antidotal action on the stimulatory effects of insulin or full test doses of insulinlike factor‐2, hepatocyte growth factor, epidermal growth factor or triiodothymanine. A minor antidotal effect on hepatic stimulatory substance activity could be detected, but only with hepatic stimulatory substance was given in doses smaller than those known to cause maximum stimulatory response. These results suggest a highly specific pharmacological and physiological interaction between transforming growth factor‐α and transforming growth factor‐α in the modulation of liver growth control. (HEPATOLOGY 1992;16:1267–1270.) Copyright © 1992 American Association for the Study of Liver Disease

Transcriptome sequencing of Crucihimalaya himalaica (Brassicaceae) reveals how Arabidopsis close relative adapt to the Qinghai-Tibet Plateau

The extreme environment of the Qinghai-Tibet Plateau (QTP) provides an ideal natural laboratory for studies on adaptive evolution. Few genome/transcriptome based studies have been conducted on how plants adapt to the environments of QTP compared to numerous studies on vertebrates. Crucihimalaya himalaica is a close relative of Arabidopsis with typical QTP distribution, and is hoped to be a new model system to study speciation and ecological adaptation in extreme environment. In this study, we de novo generated a transcriptome sequence of C. himalaica, with a total of 49,438 unigenes. Compared to five relatives, 10,487 orthogroups were shared by all six species, and 4,286 orthogroups contain putative single copy gene. Further analysis identified 487 extremely significantly positively selected genes (PSGs) in C. himalaica transcriptome. Theses PSGs were enriched in functions related to specific adaptation traits, such as response to radiation, DNA repair, nitrogen metabolism, and stabilization of membrane. These functions are responsible for the adaptation of C. himalaica to the high radiation, soil depletion and low temperature environments on QTP. Our findings indicate that C. himalaica has evolved complex strategies for adapting to the extreme environments on QTP and provide novel insights into genetic mechanisms of highland adaptation in plants

Genome of Crucihimalaya himalaica, a close relative of Arabidopsis, shows ecological adaptation to high altitude

Crucihimalaya himalaica is a close relative of Arabidopsis with typical Qinghai–Tibet Plateau (QTP) distribution. Here, by combining short- and long-read sequencing technologies, we provide a de novo genome sequence of C. himalaica. Our results suggest that the quick uplifting of the QTP coincided with the expansion of repeat elements. Gene families showing dramatic contractions and expansions, as well as genes showing clear signs of natural selection, were likely responsible for C. himalaica’s specific adaptation to the harsh environment of the QTP. We also show that the transition to self-pollination of C. himalaica might have enabled its occupation of the QTP. This study provides insights into how plants might adapt to extreme environmental conditions