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Search Results: 1 - 10 of 2038 matches for " Vu Hoang "
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High-risk and multiple human papillomavirus infections among married women in Can Tho, Viet Nam
Lan Thi Hoang Vu
Western Pacific Surveillance and Response , 2012,
Abstract: Introduction: The two currently licensed human papillomavirus (HPV) vaccines are highly efficacious in preventing cervical pre-cancers related to HPV 6, 11, 16 and 18. Before implementing a large-scale HPV vaccine campaign in Viet Nam, information about the prevalence of infection with the HPV vaccine types is required. This study was done in Can Tho, the province with the highest prevalence of cervical cancer in the south of Viet Nam, to explore the distribution of other high-risk types of HPV among married women in this province.Method: The study employed a cross-sectional design with multistage sampling. A total of 1000 participants were randomly selected, interviewed and given gynaecological examinations. HPV infection status and HPV genotyping test were completed for all participants.Results: A broad spectrum of HPV types was reported in this study. The prevalence of cases infected with HPV 16 and/or 18 was 7%; the prevalence of cases infected with other high-risk HPV types was 6%. The highest prevalence for single and multiple infections, as well as for high-risk infections, was reported for the youngest age group (less than 30 years).Discussion: While it is relevant to implement an HPV vaccine campaign in Viet Nam due to the high prevalence of infection with HPV 16 and/or 18, it is important to note that one can be infected with multiple types of HPV. Vaccination does not protect against all types of high-risk HPV. Future vaccine campaigns should openly disclose this information to women receiving vaccines.
Absence of bound states for waveguides in 2D periodic structures
Vu Hoang,Maria Radosz
Mathematics , 2011, DOI: 10.1063/1.4868480
Abstract: We study a Helmholtz-type spectral problem in a two-dimensional medium consisting of a fully periodic background structure and a perturbation in form of a line defect. The defect is aligned along one of the coordinate axes, periodic in that direction (with the same periodicity as the background), and bounded in the other direction. This setting models a so-called "soft-wall" waveguide problem. We show that there are no bound states, i.e., the spectrum of the operator under study contains no point spectrum.
No Local Double Exponential Gradient Growth in Hyperbolic Flow for the Euler equation
Vu Hoang,Maria Radosz
Mathematics , 2014,
Abstract: We consider smooth, double-odd solutions of the two-dimensional Euler equation in $[-1, 1)^2$ with periodic boundary conditions. It is tempting to think that the symmetry in the flow induces possible double-exponential growth in time of the vorticity gradient at the origin, in particular when conditions are such that the flow is "hyperbolic". This is because examples of solutions with $C^{1, \gamma}$-regularity were already constructed with exponential gradient growth by A. Zlatos. We analyze the flow in a small box around the origin in a strongly hyperbolic regime and prove that the compression of the fluid induced by the hyperbolic flow alone is not sufficient to create double-exponential growth of the gradient.
Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series
Hoang-Vu Nguyen,Jilles Vreeken
Computer Science , 2015,
Abstract: Change detection in multivariate time series has applications in many domains, including health care and network monitoring. A common approach to detect changes is to compare the divergence between the distributions of a reference window and a test window. When the number of dimensions is very large, however, the naive approach has both quality and efficiency issues: to ensure robustness the window size needs to be large, which not only leads to missed alarms but also increases runtime. To this end, we propose LIGHT, a linear-time algorithm for robustly detecting non-linear changes in massively high dimensional time series. Importantly, LIGHT provides high flexibility in choosing the window size, allowing the domain expert to fit the level of details required. To do such, we 1) perform scalable PCA to reduce dimensionality, 2) perform scalable factorization of the joint distribution, and 3) scalably compute divergences between these lower dimensional distributions. Extensive empirical evaluation on both synthetic and real-world data show that LIGHT outperforms state of the art with up to 100% improvement in both quality and efficiency.
Flexibly Mining Better Subgroups
Hoang-Vu Nguyen,Jilles Vreeken
Computer Science , 2015,
Abstract: In subgroup discovery, also known as supervised pattern mining, discovering high quality one-dimensional subgroups and refinements of these is a crucial task. For nominal attributes, this is relatively straightforward, as we can consider individual attribute values as binary features. For numerical attributes, the task is more challenging as individual numeric values are not reliable statistics. Instead, we can consider combinations of adjacent values, i.e. bins. Existing binning strategies, however, are not tailored for subgroup discovery. That is, they do not directly optimize for the quality of subgroups, therewith potentially degrading the mining result. To address this issue, we propose FLEXI. In short, with FLEXI we propose to use optimal binning to find high quality binary features for both numeric and ordinal attributes. We instantiate FLEXI with various quality measures and show how to achieve efficiency accordingly. Experiments on both synthetic and real-world data sets show that FLEXI outperforms state of the art with up to 25 times improvement in subgroup quality.
Universal Dependency Analysis
Hoang-Vu Nguyen,Jilles Vreeken
Computer Science , 2015,
Abstract: Most data is multi-dimensional. Discovering whether any subset of dimensions, or subspaces, of such data is significantly correlated is a core task in data mining. To do so, we require a measure that quantifies how correlated a subspace is. For practical use, such a measure should be universal in the sense that it captures correlation in subspaces of any dimensionality and allows to meaningfully compare correlation scores across different subspaces, regardless how many dimensions they have and what specific statistical properties their dimensions possess. Further, it would be nice if the measure can non-parametrically and efficiently capture both linear and non-linear correlations. In this paper, we propose UDS, a multivariate correlation measure that fulfills all of these desiderata. In short, we define \uds based on cumulative entropy and propose a principled normalization scheme to bring its scores across different subspaces to the same domain, enabling universal correlation assessment. UDS is purely non-parametric as we make no assumption on data distributions nor types of correlation. To compute it on empirical data, we introduce an efficient and non-parametric method. Extensive experiments show that UDS outperforms state of the art.
Canonical Divergence Analysis
Hoang-Vu Nguyen,Jilles Vreeken
Computer Science , 2015,
Abstract: We aim to analyze the relation between two random vectors that may potentially have both different number of attributes as well as realizations, and which may even not have a joint distribution. This problem arises in many practical domains, including biology and architecture. Existing techniques assume the vectors to have the same domain or to be jointly distributed, and hence are not applicable. To address this, we propose Canonical Divergence Analysis (CDA). We introduce three instantiations, each of which permits practical implementation. Extensive empirical evaluation shows the potential of our method.
A Chaotic Pulse-Time Modulation Method for Digital Communication
Nguyen Xuan Quyen,Vu Van Yem,Thang Manh Hoang
Abstract and Applied Analysis , 2012, DOI: 10.1155/2012/835304
Abstract: We present and investigate a method of chaotic pulse-time modulation (PTM) named chaotic pulse-width-position modulation (CPWPM) which is the combination of pulse-position-modulation (PPM) and pulse-width modulation (PWM) with the inclusion of chaos technique for digital communications. CPWPM signal is in the pulse train format, in which binary information is modulated onto chaotically-varied intervals of position and width of pulses, and therefore two bits are encoded on a single pulse. The operation of the method is described and the theoretical evaluation of bit-error rate (BER) performance in the presence of additive white Gaussian noise (AWGN) is provided. In addition, the chaotic behavior with tent map and its effect on average parameters of the system are investigated.Theoretical estimation and numerical simulation of a CPWPM system with specific parameters are carried out in order to verify the performance of the proposed method.
Relaxin reduces xenograft tumour growth of human MDA-MB-231 breast cancer cells
Yvonne Radestock, Cuong Hoang-Vu, Sabine Hombach-Klonisch
Breast Cancer Research , 2008, DOI: 10.1186/bcr2136
Abstract: We have established stable transfectants of highly invasive oestrogen-receptor alpha-negative MDA-MB-231 human breast cancer cells with constitutive expression of bioactive H2-relaxin (MDA/RLN2). RLN2 secretion was determined by ELISA. Relaxin receptor RXFP1 (Relaxin-family-peptide) was detected by reverse transcription (RT) PCR and its activation was assessed by induction of cyclic adenosine monophosphate (cAMP). Stable MDA/RLN2 clones and RLN2 treated MDA-MB-231 cells were subjected to motility and in vitro-invasion assays. Proliferation was assessed in bromodeoxyuridine (BrdU) and MTT assays. S100A4 expression was determined by RT-PCR and Western blot. Specific small interfering RNA was employed to down-regulate relaxin receptor and S100A4. MDA/EGFP vector control and two MDA/RLN2 clones were injected subcutaneously in nude mice to determine tumour growth and cancer cell invasiveness in vivo. Xenograft tumour tissues were assessed by histology and immunohistochemistry and frozen tissues were used for the detection of S100A4 and RLN2.Short-term exposure to relaxin for 24 hours increased cell motility in a relaxin receptor-dependent manner. This increase in cell motility was mediated by S100A4. Long-term exposure to relaxin secreted from stable transfectants reduced cell motility and in vitro invasiveness. Relaxin decreased cell proliferation and down-regulated cellular S100A4 levels in MDA-MB-231 and T47D breast cancer cells. Stable MDA/RLN2 transfectants produced smaller xenograft tumours containing reduced S100A4 protein levels in vivo.Our results indicate that long-term exposure to relaxin confers growth inhibitory and anti-invasive properties in oestrogen-independent tumours in vivo, which may in part be mediated through a down-regulation of S100A4.The polypeptide hormone relaxin is increased in human breast carcinoma tissues [1]. In all human breast tumours investigated, immunoreactive H2 relaxin (RLN2) was localised to the cytoplasm of neoplastic epithelial
A Chaos-Based Secure Direct-Sequence/Spread-Spectrum Communication System
Nguyen Xuan Quyen,Vu Van Yem,Thang Manh Hoang
Abstract and Applied Analysis , 2013, DOI: 10.1155/2013/764341
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