ORCID Profile
0000-0002-0294-4561
Current Organisations
University of Oxford
,
Amazon
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Publisher: Springer Science and Business Media LLC
Date: 03-2015
Publisher: Elsevier BV
Date: 08-2010
Publisher: Springer Science and Business Media LLC
Date: 08-07-2008
DOI: 10.1038/MP.2008.34
Publisher: IEEE
Date: 06-2009
Publisher: IEEE
Date: 03-2016
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 12-2016
Publisher: IOP Publishing
Date: 09-2020
Abstract: Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimize this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 07-2017
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.35
Publisher: IEEE
Date: 12-2016
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/DSAA.2016.48
Publisher: MDPI AG
Date: 02-02-2023
DOI: 10.3390/PHARMACEUTICS15020495
Abstract: In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs—the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.
Publisher: Society for Neuroscience
Date: 18-02-2009
DOI: 10.1523/JNEUROSCI.4184-08.2009
Abstract: The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal ( a 2 = 0.55, p = 0.04, left a 2 = 0.74, p = 0.006, right), bilateral parietal ( a 2 = 0.85, p 0.001, left a 2 = 0.84, p 0.001, right), and left occipital ( a 2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto-occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata ( p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition.
Publisher: Public Library of Science (PLoS)
Date: 09-09-2021
DOI: 10.1371/JOURNAL.PCBI.1008886
Abstract: Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one in idual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different in iduals is costly, time-consuming and requires a large s le size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an in idual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.44
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 02-2012
Publisher: Springer Science and Business Media LLC
Date: 20-07-2018
Publisher: IEEE
Date: 1970
Publisher: Springer Science and Business Media LLC
Date: 10-07-2019
Publisher: IEEE
Date: 06-2019
Publisher: Elsevier BV
Date: 08-2009
Publisher: IEEE
Date: 11-2018
Location: United Kingdom of Great Britain and Northern Ireland
Location: Viet Nam
No related grants have been discovered for Vu Nguyen.