The Chinese government\u27s response to drug use and HIV/AIDS: A review of policies and programs

Illicit drug use has become popular in China. Acknowledging the challenge of illicit drug use, China has adopted several new policies on the management of illicit drug use in recent years. This study reviews the current policies on drug use and assesses the harm reduction interventions among drug users in China. The review documents that the new policies on drug use provide a variety of choices of detoxification treatment for drug users. The methadone maintenance treatment and needle exchange programs have been adopted as harm reduction models in China. Most of the reviewed harm reduction programs have been successfully implemented and yielded positive effects in reducing drug related risk behaviors among drug users. Although there remain barriers to the effective implementation of policies on drug use and harm reduction programs, Chinese government has shown their commitment to support the expansion of harm reduction interventions for drug users throughout the country

Autonomous Vehicles Operating Collaboratively to Avoid Debris and Obstructions

The purpose of this project is to demonstrate the safety and increased fuel efficiency of an automated collision avoidance system in collaborative vehicle platooning. This project was cosponsored by Daimler Trucks North America headquartered in Portland, Oregon, as well as Dr. Birdsong, and Dr. DeBruhl of Cal Poly. The mechanical engineering team consists of Cole Oppenheim, James Gildart, Toan Le, and Kyle Bybee who worked in coordination with a team of computer engineers. Vehicle platooning is a driving technique to increase the fuel efficiency of a group of vehicles by following a lead vehicle closely to reduce the drag experienced by the group. Specifically, large tractor trailer trucks could become more efficient utilizing vehicle platooning. To implement this system most effectively would require an automatic system for collision avoidance. The goal for the mechanical engineering team working on this project was build and design two scale model vehicles, a test track, and dynamic models of the vehicles. These were then interface with computer vision software and hardware (created in collaboration of a team of computer engineers) that allows the vehicles to autonomously platoon and avoid objects that would otherwise cause a collision. Interactions with the computer engineering team occurred at minimum on a weekly basis and more whenever necessary. Interactions between the team’s original occurred as meetings to determine each team individual progress until integration could be accomplished. When the systems were being integrated, meetings occurred regularly (2-3 times a week) to ensure the vehicles could properly execute their design function. The goal of this project is to demonstrate how this system could be implemented in truck platooning safely and to demonstrate the advantages of platooning with system developed. This project was intended and will be presented to compete at the Enhanced Safety of Vehicles conference in the Netherlands in June of 2019. This report covers the scope of work of this project, the preliminary design direction, and the final design direction, and the final design for the assembly of the two 1/10 scale cars, the track design, and the controls strategy to interface with the CPE’s software

Chemical constituents and antibacterial activity of essential oils in <i>Amomum longiligulare</i> from Vietnam

This paper reports the chemical constituents and the antibacterial activity of essential oils from the leaves, rhizomes, and fruits of Amomum longiligulare T.L. Wu (Zingiberaceae) obtained by microwave-assisted hydrodistillation. The essential oils were analyzed by gas chromatography–mass spectrometry techniques. The minimum inhibitory concentration (MIC) values were measured by the broth microdilution assay. The oil yields of leaves, rhizomes and fruits from A. longiligulare were 0.23%, 0.27% and 1.93% (v/w), respectively, calculated on a dry weight basis. The leaf essential oil comprised mainly α-humulene (28.4%), α-pinene (24.9%), β-caryophyllene (17.3%), humulene epoxide II (7.3%), and β-pinene (4.7%). The major compounds of the rhizome essential oil were β-caryophyllene (28.7%), bicyclogermacrene (17.1%), humulene epoxide II (10.5%), camphene (7.9%), and α-pinene (5.7%). Camphor (40.7%) and bornyl acetate (34.2%) were the main constituents of the fruit oil. The essential oils demonstrated antimicrobial activities against Staphylococcus aureus, Bacillus cereus, Escherichia coli, and Pseudomonas aeruginosa with the MIC values ranging from 200 to 400 μg/mL. In summary, the A. longiligulare essential oils are a source of promising antibacterial agents. This is the first report on the chemical composition and antibacterial activity of A. longiligulare essential oil obtained by microwave-assisted hydrodistillation

Conditional Support Alignment for Domain Adaptation with Label Shift

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under the label distribution shift between source and target domains. In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASA outperforms other state-of-the-art methods on different UDA benchmark tasks under label shift conditions

KL Guided Domain Adaptation

Domain adaptation is an important problem and often needed for real-world applications. In this problem, instead of i.i.d. datapoints, we assume that the source (training) data and the target (testing) data have different distributions. With that setting, the empirical risk minimization training procedure often does not perform well, since it does not account for the change in the distribution. A common approach in the domain adaptation literature is to learn a representation of the input that has the same distributions over the source and the target domain. However, these approaches often require additional networks and/or optimizing an adversarial (minimax) objective, which can be very expensive or unstable in practice. To tackle this problem, we first derive a generalization bound for the target loss based on the training loss and the reverse Kullback-Leibler (KL) divergence between the source and the target representation distributions. Based on this bound, we derive an algorithm that minimizes the KL term to obtain a better generalization to the target domain. We show that with a probabilistic representation network, the KL term can be estimated efficiently via minibatch samples without any additional network or a minimax objective. This leads to a theoretically sound alignment method which is also very efficient and stable in practice. Experimental results also suggest that our method outperforms other representation-alignment approaches