Determining key research areas for healthier diets and sustainable food systems in Viet Nam

Vietnamese food systems are undergoing rapid transformation, with important implications for human and environmental health and economic development. Poverty has decreased, and diet quality and undernutrition have improved significantly since the end of the Doi Moi reform period (1986-1993) as a result of Viet Nam opening its economy and increasing its regional and global trade. Yet poor diet quality is still contributing the triple burden of malnutrition, with 25 percent stunting among children under age 5, 26 percent and 29 percent of women and children, respectively, anemic, and 21 percent of adults overweight. Agricultural production systems have shifted from predominantly diverse smallholder systems to larger more commercialized and specialized systems, especially for crops, while the ‘meatification’ of the Vietnamese diet is generating serious trade-offs between improved nutrition and sustainability of the Vietnamese food systems. The food processing industry has developed rapidly, together with food imports, resulting in new and processed food products penetrating the food retail outlets, trending towards an increase in the Westernized consumption patterns that are shifting nutrition-related problems towards overweight and obesity and, with it, an increase of non-communicable disease-related health risks. While regulatory policies exist across the food system, these are not systematically implemented, making food safety a major concern for consumers and policy makers alike. Where data exists, it is not easy to aggregate with data from across food system dimensions, making it difficult for Viet Nam to make an informed analysis of current and potential food system trade-offs. In our research, we reviewed existing literature and data, and applied a food systems framework to develop an initial food systems profile for Viet Nam and to identify a comprehensive set a of research questions to fill current data gaps identified through the review. Insights on these would provide the comprehensive evidence needed to inform policy makers on how to develop new food systems policies for Viet Nam, and further refine and improve existing policies to achieve better quality diets and more sustainable food systems in Viet Nam. Based on these, we then engaged with stakeholders to develop research priorities in the Viet Nam context and identified 25 priority research questions. This paper aims to stimulate such reflections by clearly outlining key areas for research, government policy, and development programs on priority investment to build the evidence base around inclusive food systems interventions that aim to result in healthier diets and more sustainable food systems for Viet Nam

Dynamic state estimation and prediction for real-time control and operation

Real-time control and operation are crucial to deal with increasing complexity of modern power systems. To effectively enable those functions, it is required a Dynamic State Estimation (DSE) function to provide accurate network state variables at the right moment and predict their trends ahead. This paper addresses the important role of DSE over the conventional static State Estimation in such new context of smart grids. DSE approaches normally based on Extended Kalman Filter (EKF) need to collect recursively time-historic data, to update covariance vectors, and to treat heavy computation matrices. Computation burden mitigates the state-of-the-art utilizations of DSE in real large-scale networks although DSE was introduced several decades ago. In this paper, an improvement of DSE by using Unscented Kalman Filter (UKF) to alleviate computation burden will be discussed. The UKF-based approach avoids using linearization procedure thus outperforms the EKF-based approach to cope with non-linear models. Performance of the method is investigated with a simulation on a 18-bus test network. Preliminary results have been gained through a case study that motivate further research on this approach

Dynamic state estimation for distribution networks with renewable energy integration

The massive integration of variable and unpredictable Renewable Energy Sources (RES) and new types of load consumptions increases the dynamic and uncertain nature of the electricity grid. Emerging interests have focused on improving the monitoring capabilities of network operators so that they can have accurate insight into a network’s status at the right moment and predict its future trends. Though state estimation is crucial for this purpose to trigger control functions, it has been used mainly for steady-state analysis. The need for dynamic state estimation (DSE), however, is increasing for real-time control and operation. This paper addresses the important role of DSE over conventional static-state estimation in this new distribution network context. Computational burden mitigates the state-of-the-art utilizations of DSE in real large-scale networks, although DSE was introduced several decades ago. This paper the unscented Kalman filter (UKF) to alleviate computational burden with DSE. The UKF-based approach does not use a linearization procedure and thus outperforms the conventional Extended Kalman Filter based approach to cope with non-linear models. The performance of the UKF method is investigated with a simulation of an 18-bus distribution network on the real-time digital simulator (RTDS) platform. A distribution network with considerable integration of renewable energy production is used to evaluate the UKF-based DSE approach under different types of events

Application of the Graph Theory in Managing Power Flows in Future Electric Networks

No abstract

Comparison of machine learning methods for estimating energy consumption in buildings

The increasing number of decentralized renewable energy sources together with the grow in overall electricity consumption introduce many new challenges related to dimensioning of grid assets and supply-demand balancing. Approximately 40% of the total energy consumption is used to cover the needs of commercial and office buildings. To improve the design of the energy infrastructure and the efficient deployment of resources, new paradigms have to be thought up. Such new paradigms need automated methods to dynamically predict the energy consumption in buildings. At the same time these methods should be easily expandable to higher levels of aggregation such as neighbourhoods and the power distribution grid. Predicting energy consumption for a building is complex due to many influencing factors, such as weather conditions, performance and settings of heating and cooling systems, and the number of people present. In this paper, we investigate a newly developed stochastic model for time series prediction of energy consumption, namely the Conditional Restricted Boltzmann Machine (CRBM), and evaluate its performance in the context of building automation systems. The assessment is made on a real dataset consisting of 7 weeks of hourly resolution electricity consumption collected from a Dutch office building. The results showed that for the energy prediction problem solved here, CRBMs outperform Artificial Neural Networks (ANNs), and Hidden Markov Models (HMMs)

Generalizing DP-SGD with shuffling and batch clipping

Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible first order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shuffling. Our DP analysis follows the f -DP approach and introduces a new proof technique based on a slightly stronger adversarial model which allows us to derive simple closed form expressions and to also analyse group privacy. In particular, for E epochs work and groups of size g, we show a√gE DP dependency for batch clipping with shuffling

On the Tightness of the Moment Accountant for DP-SGD

In order to provide differential privacy, Gaussian noise with standard deviation σ is added to local SGD updates after performing a clipping operation in Differential Private SGD (DP-SGD). By non-trivially improving the moment account method we prove a closed form (ϵ, δ)-DP guarantee: DP-SGD is (ϵ ≤ 1/2, δ = 1/N )-DP if σ = p2(ϵ + ln(1/δ))/ϵ with T at least ≈ 2k2/ϵ and (2/e)2k2 − 1/2 ≥ ln(N ), where T is the total number of rounds, and K = kN is the total number of gradient computations where k measures K in number of epochs of size N of the local data set. We prove that our expression is close to tight in that if T is more than a constant factor ≈ 8 smaller than the lower bound ≈ 2k2/ϵ, then the (ϵ, δ)-DP guarantee is violated. Choosing the smallest possible value T ≈ 2k2/ϵ not only leads to a close to tight DP guarantee, but also minimizes the total number of communicated updates and this means that the least amount of noise is aggregated into the global model and in addition accuracy is optimized as confirmed by simulations

Besting the black-box: Barrier zones for adversarial example defense

Adversarial machine learning defenses have primarily been focused on mitigating static, white-box attacks. However, it remains an open question whether such defenses are robust under an adaptive black-box adversary. In this paper, we specifically focus on the black-box threat model and make the following contributions: First we develop an enhanced adaptive black-box attack which is experimentally shown to be ≥ 30% more effective than the original adaptive black-box attack proposed by Papernot et al. For our second contribution, we test 10 recent defenses using our new attack and propose our own black-box defense (barrier zones). We show that our defense based on barrier zones offers significant improvements in security over state-of-the-art defenses. This improvement includes greater than 85% robust accuracy against black-box boundary attacks, transfer attacks and our new adaptive black-box attack, for the datasets we study. For completeness, we verify our claims through extensive experimentation with 10 other defenses using three adversarial models (14 different black-box attacks) on two datasets (CIFAR-10 and Fashion-MNIST)