Fast spontaneous emission and high F\"orster resonance energy transfer rate in hybrid organic/inorganic plasmonic nanostructures

We report an experimental study of the plasmon-assisted spontaneous emission and the F\"orster resonance energy transfer between organic molecules and semiconductor colloidal quantum dots. The localized plasmonic field in the nanogap between a gold nano-popcorn's tips and a 5-nm separated gold film supports high photonic density of states and provides pathways for the light-matter interaction mechanisms. We demonstrate that, besides the total spontaneous emission rate enhancement factor up to 66 for quantum dots and molecules, the F\"orster resonance energy transfer efficiency and rate constant are simultaneously modified. While the energy transfer efficiency is reduced from 84 to 35 per cent due to the non-radiative quenching effect and fast donor spontaneous emission rate, the energy transfer rate constant is significantly increased from 4 to 20 ns-1. Our results have quantitatively elucidated decay mechanisms that are important toward understanding and controlling of the light-matter interaction at the nanoscale.Comment: 12 pages, 4 Figures, Submitte

ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline

Machine Learning as a service (MLaaS) permits resource-limited clients to access powerful data analytics services ubiquitously. Despite its merits, MLaaS poses significant concerns regarding the integrity of delegated computation and the privacy of the server's model parameters. To address this issue, Zhang et al. (CCS'20) initiated the study of zero-knowledge Machine Learning (zkML). Few zkML schemes have been proposed afterward; however, they focus on sole ML classification algorithms that may not offer satisfactory accuracy or require large-scale training data and model parameters, which may not be desirable for some applications. We propose ezDPS, a new efficient and zero-knowledge ML inference scheme. Unlike prior works, ezDPS is a zkML pipeline in which the data is processed in multiple stages for high accuracy. Each stage of ezDPS is harnessed with an established ML algorithm that is shown to be effective in various applications, including Discrete Wavelet Transformation, Principal Components Analysis, and Support Vector Machine. We design new gadgets to prove ML operations effectively. We fully implemented ezDPS and assessed its performance on real datasets. Experimental results showed that ezDPS achieves one-to-three orders of magnitude more efficient than the generic circuit-based approach in all metrics while maintaining more desirable accuracy than single ML classification approaches.Comment: This paper is to appear in Privacy-Enhancing Technologies Symposium (PETS) 202

Temperature dependent dynamics of photoexcited carriers of Si2Te3 nanowires

We report an optical study of the dynamics of photoexcited carriers in Si2Te3 nanowires at various temperatures and excitation powers. Si2Te3 nanowires were synthesized, by using gold as a catalyst, on a silicon substrate by the chemical vapor deposition method. The photoluminescence spectrum of Si2Te3 nanowires was primary dominated by defect and surface states related emission at both low and room temperatures. We observed that the decay time of photoexcited carries was very long (> 10 ns) at low temperatures and became shorter (< 2 ns) at room temperature. Further, the carrier decay time became faster at high excitation rates. The acceleration of the photoexcited carrier decay rates indicate the thermal quenching along with the non-radiative recombination at high temperature and excitation power. Our results have quantitatively elucidated decay mechanisms that are important towards understanding and controlling of the electronic states in Si2Te3 nanostructures for optoelectronic applications.Comment: 12 pages, 4 figures, submitte

BET bromodomain proteins control breast cancer aggressiveness promoted by adipocyte-derived exosomes

Cells can release lipid bilayer vesicles of endosomal and plasma membrane origin, which are known as exosomes or extracellular vesicles (EVs). EVs contain diverse shuttling lipids, RNA and transmembrane proteins, and play an important role in communicating between neighboring or distant cells. Breast cancer is the most commonly diagnosed malignancy, with over 2 million new cases in 2018, and is the leading cause of cancer mortality in women all over the world. Some observational studies have suggested that breast cancer is more likely to develop among women who have type 2 diabetes; the association is clear in postmenopausal women. Moreover, women with type 2 diabetes diagnosed before, at the same time, or after breast cancer diagnosis, have decreased overall survival compared to women without diabetes. The most recent medical studies provide more clues as to why breast cancer is more common and has poorer prognosis in type 2 diabetes patients, by pointing out the role of insulin-resistant adipocytes in the etiopathology. Here, we demonstrate how insulin-resistant adipocytes engage crosstalk with breast cancer cells through EVs in the microenvironment and drive the tumor cells to be more metastatic and aggressive. These progression mechanisms and the effects of insulin-resistant adipocytes on breast cancer cells require Bromodomain and ExtraTerminal (BET) proteins – an important epigenetic pathway. Targeting this pathway may help reduce morbidity and mortality of women with breast cancer and type 2 diabetes

Harpocrates: Privacy-Preserving and Immutable Audit Log for Sensitive Data Operations

The audit log is a crucial component to monitor fine-grained operations over sensitive data (e.g., personal, health) for security inspection and assurance. Since such data operations can be highly sensitive, it is vital to ensure that the audit log achieves not only validity and immutability, but also confidentiality against active threats to standard data regulations (e.g., HIPAA) compliance. Despite its critical needs, state-of-the-art privacy-preserving audit log schemes (e.g., Ghostor (NSDI '20), Calypso (VLDB '19)) do not fully obtain a high level of privacy, integrity, and immutability simultaneously, in which certain information (e.g., user identities) is still leaked in the log. In this paper, we propose Harpocrates, a new privacy-preserving and immutable audit log scheme. Harpocrates permits data store, share, and access operations to be recorded in the audit log without leaking sensitive information (e.g., data identifier, user identity), while permitting the validity of data operations to be publicly verifiable. Harpocrates makes use of blockchain techniques to achieve immutability and avoid a single point of failure, while cryptographic zero-knowledge proofs are harnessed for confidentiality and public verifiability. We analyze the security of our proposed technique and prove that it achieves non-malleability and indistinguishability. We fully implemented Harpocrates and evaluated its performance on a real blockchain system (i.e., Hyperledger Fabric) deployed on a commodity platform (i.e., Amazon EC2). Experimental results demonstrated that Harpocrates is highly scalable and achieves practical performance.Comment: To appear at IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA) 202

Environmental Economic Hydrothermal System Dispatch by Using a Novel Differential Evolution

This paper proposes the Novel Differential Evolution (NDE) method for solving the environmental economic hydrothermal system dispatch (EEHTSD) problem with the aim to reduce electricity generation fuel costs and emissions of thermal units. The EEHTSD problem is constrained by limitations on generations, active power balance, and amount of available water. NDE applies two modified techniques. The first one is modified mutation, which is used to balance global and local search. The second one is modified selection, which is used to keep the best solutions. When performing this modified selection, the proposed method completely reduces the impact of crossover by setting it to one. Moreover, the task of tuning this factor can be canceled. Original Differential Evolution (ODE), ODE with the first modification (MMDE), and ODE with the second modification (MSDE), and NDE were tested on two different hydrothermal systems for comparison and evaluation purposes. The performance of NDE was also compared to existing methods. It was indicated that the proposed NDE is a very promising method for solving the EEHTSD problem

An exploratory study of predictors of vocabulary knowledge of Vietnamese preschool-age children in a city

This study explores the effects of child-external and child-internal factors on vocabulary skills of Vietnamese pre-schoolers. Thirty-nine Vietnamese children (54–77 months) were tested on vocabulary and cognition skills. Their parents completed a questionnaire on background information. Correlation and regression analyses were performed to explore the contribution of multiple factors to the variability in vocabulary skills. Results showed that the effects of multiple factors varied across modality and domain. Productive vocabulary was individually sensitive to more factors than receptive vocabulary; and phonologically-based vocabulary was more sensitive than semantically-based vocabulary. The strongest predictor of receptive vocabulary, productive vocabulary, semantically-based vocabulary and phonologically-based vocabulary was child intelligence, child pre-schooling length, household income and child age, respectively. The findings seem to support the multidimensional views of language with evidence that different domains or modalities of vocabulary skills respond to the effects of multiple factors differently; and components of verbal ability should be examined separately