Carbon Border Adjustment Mechanism Impact Assessment Report for Vietnam

The EU’s Carbon Border Adjustment Mechanism (CBAM) is evolving rapidly, with many uncertainties remaining regarding its long-term scope, embedded emissions calculation, and reactions of EU-trade partners. In its current form, the CBAM can affect Vietnamese enterprises exporting to EU although its direct impacts on Vietnam’s GDP are unlikely significant. If the CBAM is expanded to other trade-intensive sectors of Vietnam or taken up by other key trading partners of Vietnam, the impacts may grow quickly. Therefore, Vietnam should engage proactively with the CBAM and prepare for mitigation of potential impacts. One of the pro-active approaches is to accelerate and deepen the adoption of carbon pricing. This will facilitate energy transition, support achievement of Vietnam’s climate change mitigation target (NDC) under the Paris Agreement and long-term net-zero targets and would allow to harness co-benefits. It is also advisable for Vietnam to engage in constructive dialogues with the EU in order to negotiate a fair implementation of CBAM that takes into account Vietnam’s efforts. A key demand here should be the use of emissions credits instead of having to buy CBAM certificates

Efficient Human Vision Inspired Action Recognition using Adaptive Spatiotemporal Sampling

Adaptive sampling that exploits the spatiotemporal redundancy in videos is critical for always-on action recognition on wearable devices with limited computing and battery resources. The commonly used fixed sampling strategy is not context-aware and may under-sample the visual content, and thus adversely impacts both computation efficiency and accuracy. Inspired by the concepts of foveal vision and pre-attentive processing from the human visual perception mechanism, we introduce a novel adaptive spatiotemporal sampling scheme for efficient action recognition. Our system pre-scans the global scene context at low-resolution and decides to skip or request high-resolution features at salient regions for further processing. We validate the system on EPIC-KITCHENS and UCF-101 datasets for action recognition, and show that our proposed approach can greatly speed up inference with a tolerable loss of accuracy compared with those from state-of-the-art baselines. Source code is available in https://github.com/knmac/adaptive_spatiotemporal

Design an Intelligent System to automatically Tutor the Method for Solving Problems

Nowadays, intelligent systems have been applied in many real-word domains. The Intelligent chatbot is an intelligent system, it can interact with the human to tutor how to work some activities. In this work, we design an architecture to build an intelligent chatbot, which can tutor to solve problems, and construct scripts for automatically tutoring. The knowledge base of the intelligent tutoring chatbot is designed by using the requirements of an Intelligent Problem Solver. It is the combination between the knowledge model of relations and operators, and the structures of hint questions and sample problems, which are practical cases. Based on the knowledge base and tutoring scripts, a tutoring engine is designed. The tutoring chatbot plays as an instructor for solving real-world problems. It simulates the working of the instructor to tutor the user for solving problems. By utilizing the knowledge base and reasoning, the architecture of the intelligent chatbot are emerging to apply in the real-world. It is used to build an intelligent chatbot to support the learning of high-school mathematics and a consultant system in public administration. The experimental results show the effectiveness of the proposed method in comparison with the existing systems

Security risks from climate change and environmental degradation: implications for sustainable land use transformation in the Global South

Climate change and environmental degradation remain the most complex challenges that present and future generations of humankind face and raise several security risks that have received relatively little attention in the literature. This paper aims to review the evidence of security risks arising from these challenges in the Global South and to provide forward-looking perspectives on how to increase the resilience of affected individuals and communities. We see diverse land use strategies as a key element to drive a transformation towards greater sustainability and resilience. We propose that rural land use in the Global South should be geared towards the promotion of resource and biodiversity conservation, the development of agroforestry, tree-based farming systems, the diversification of crops, and the utilization of climate-resilient cultivars, and neglected and under-utilized plants. These actions would contribute to addressing the security risks stemming from the interconnected challenges of climate change and environmental degradation

On the Performance of a Wireless Powered Communication System Using a Helping Relay

This paper studies the outage performance and system throughput of a bidirectional wireless information and power transfer system with a helping relay. The relay helps forward wireless power from the access point (AP) to the user, and also the information from the user to the AP in the reverse direction. We assume that the relay uses time switching based energy harvesting protocol. The analytical results provide theoretical insights into the effect of various system parameters, such as time switching factor, source transmission rate, transmitting-power-to-noise ratio to system performance for both amplify-and-forward and decode-and-forward relaying protocols. The optimal time switching ratio is determined in each case to maximize the information throughput from the user to the AP subject to the energy harvesting and consumption balance constraints at both the relay and the user. All of the above analyses are confirmed by Monte-Carlo simulation

Sublinear time algorithms for earth mover's distance

We study the problem of estimating the Earth Mover’s Distance (EMD) between probability distributions when given access only to samples of the distributions. We give closeness testers and additive-error estimators over domains in [0, 1][superscript d], with sample complexities independent of domain size – permitting the testability even of continuous distributions over infinite domains. Instead, our algorithms depend on other parameters, such as the diameter of the domain space, which may be significantly smaller. We also prove lower bounds showing the dependencies on these parameters to be essentially optimal. Additionally, we consider whether natural classes of distributions exist for which there are algorithms with better dependence on the dimension, and show that for highly clusterable data, this is indeed the case. Lastly, we consider a variant of the EMD, defined over tree metrics instead of the usual l 1 metric, and give tight upper and lower bounds