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Tsai Z.T.-Y.,Academia Sinica, Taiwan | Tsai Z.T.-Y.,National Yang Ming University | Huang G.T.-W.,Academia Sinica, Taiwan | Huang G.T.-W.,Carnegie Mellon University | And 3 more authors.
Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While traditional approaches can be used to discover binding motifs of a group of co-regulated genes, they often fail to accurately assign motifs to the corresponding TFs. Here, we consider two TFs together to infer their TFBSs and their synergistic relationship simultaneously. The basic idea is that if two TFs interact, their TFBSs, if distinct, would be conserved across species and coincided in the promoter regions of the genes they co-regulated. Applying our method to Saccharomyces cerevisiae chromatin immunoprecipitation data, we predicted 110 TF pairs with statistically significant motif assignments. A majority of these TF pairs have literature support to be synergistic, and the designated motifs to TFs match well with their known consensus. We further examined the synergism of predicted TF pairs in seven experimental conditions using ANOVA, and identified significant interactions. © 2011 IEEE. Source

Hung S.-H.,Application Security | Hung S.-H.,Research Center for Information Technology Innovation | Tzeng T.-T.,Application Security | Wu G.-D.,Application Security | Shieh J.-P.,Application Security
Software - Practice and Experience

Summary Smart mobile devices and wireless networks are changing the way people execute applications and access information. In the meantime, more and more personal data are thus spread around different data silos in the Internet. One day, you will find that you may want to access your own data, but the service providers may not allow or the vendors do not provide any application for your need. It means your personal data are locked in by the service vendors. While the advances in the hardware/software technology of the smart mobile devices enable more complicated application than the user can image a couple of years ago, those devices still fall short of big storage, powerful computing and more battery capacity for better user experience. The user may expect a device with secure and unlimited storage for his personal data and run his application as he wishes without incurring more energy consumption. So far, no simple solutions have been proposed to enable compute cloud for code offloading Android application and trusted storage cloud for personal data. The work described in this paper enhances current Android application framework to address the aforementioned issues. We introduce the MobileFBP framework to augment the compute part for the Android device and propose to leverage the central storage part with personal data store, that is, PDS, for trusted usage. In addition to the design and implementation of the enhanced framework, our preliminary experimental results are illustrated as well. Copyright © 2014 John Wiley & Sons, Ltd. Source

Lee C.-S.,National Chiao Tung University | Chung W.-H.,Research Center for Information Technology Innovation | Lee T.-S.,National Chiao Tung University
2015 IEEE Wireless Communications and Networking Conference, WCNC 2015

In this paper, the distributed channel access schemes for ALOHA-based cognitive radio networks are considered. In the considered system, time is divided into frames, which are further divided into sensing phase and transmission phase. We derive channel sensing policy in the sensing phase, and channel access policy in the transmission phase for a secondary user (SU) to maximize its throughput. To mitigate high complexities of the above scheme, we propose a threshold-based channel access scheme. In this scheme, an appropriate threshold is set based on channel occupancy information and channel state information, and only channels providing potentially high throughput will be sensed and accessed. The proposed schemes are fully distributed, i.e., no extra information exchange is needed among SUs, which is a highly desired and beneficial property. Simulation results confirm that the proposed schemes outperform prior random access schemes. © 2015 IEEE. Source

Lu X.,Japan National Institute of Information and Communications Technology | Tsao Y.,Research Center for Information Technology Innovation | Shen P.,Japan National Institute of Information and Communications Technology | Hori C.,Japan National Institute of Information and Communications Technology
Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014

In most algorithms for acoustic event detection (AED), frame based acoustic representations are used in acoustic modeling. Due to lack of context information in feature representation, large model confusions may occur during modeling. We have proposed a feature learning and representation algorithm to explore context information from temporal-frequency patches of signal for AED. With the algorithm, a sparse feature was extracted based on an acoustic dictionary composed of a bag of spectral patches. In our previous algorithm, the feature was obtained based on a definition of Euclidian distance between input signal and acoustic dictionary. In this study, we formulate the sparse feature extraction as l1 regularization in signal reconstruction. The sparsity of the representation is efficiently controlled via varying a regularization parameter. A support vector machine (SVM) classifier was built on the extracted sparse feature for AED. Our experimental results showed that the spectral patch based sparse representation effectively improved the performance by incorporating temporal-frequency context information in modeling. © 2014 IEEE. Source

Su C.-H.,Academia Sinica, Taiwan | Su C.-H.,National Taiwan University | Shih C.-H.,Academia Sinica, Taiwan | Chang T.-H.,Ohio State University | And 3 more authors.

In budding yeast, approximately a quarter of adjacent genes are divergently transcribed (divergent gene pairs). Whether genes in a divergent pair share the same regulatory system is still unknown. By examining transcription factor (TF) knockout experiments, we found that most TF knockout only altered the expression of one gene in a divergent pair. This prompted us to conduct a comprehensive analysis in silico to estimate how many divergent pairs are regulated by common sets of TFs (cis-regulatory modules, CRMs) using TF binding sites and expression data. Analyses of ten expression datasets show that only a limited number of divergent gene pairs share CRMs in any single dataset. However, around half of divergent pairs do share a regulatory system in at least one dataset. Our analysis suggests that genes in a divergent pair tend to be co-regulated in at least one condition; however, in most conditions, they may not be co-regulated. © 2010 Elsevier Inc. Source

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