The distribution of payment for forest environmental services (PFES) in Vietnam: Research evidence to inform payment guidelines

Peer reviewe

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme

REDD+ and Green Growth : Synergies or discord in Vietnam and Indonesia

Shifting global development discourses – Implications for forests and livelihoods: Special Issue (Supplement 1, December 2017)Green Growth (GG) has emerged as a global narrative, replacing to some extent and integrating earlier sustainable development narratives, while Reducing Emissions through avoiding Deforestation and forest Degradation (REDD+) has developed as major item in climate change negotiations. GG and REDD+ are both considered important strategies and are often seen as synergistic in achieving major changes in economic, regulatory and governance frameworks. Of concern, however, is that GG is sometimes seen as greenwashing of economic activities (which could include forest conversion to other land uses) by an oversimplified presentation of win-win solutions without challenging the actual root causes of unsustainable growth. How GG and REDD+ can contribute to transformational change in policy and practice depends on the relationship between these narratives, especially whether their adoption in national level policies manifests synergies or discord. In this paper, we will answer this question through analysing: (1) how the two narratives have unfolded in Vietnam and Indonesia and to what extent REDD+ and GG rhetoric include concrete policy objectives; (2) what issues policy actors perceive as challenges for their implementation. A comparative, mixed methods approach was employed to analyze how REDD+ and GG are framed in national policy documents. This analysis was supported by data from interviews with policy actors in both countries in two points of time, 2011/12 and 2015/16. The findings highlight the challenges for implementation of both REDD+ and GG as individual policy programmes, and the dilution of the REDD+ agenda and decision makers’ confusion about a GG strategy when these narratives are joined and translated by decision makers. Actors still perceive development and environmental objectives as a zero-sum struggle, favouring a development narrative that might lead to neither REDD+ nor green policy action. We conclude that REDD+ and GG can go hand in hand, if there is action to tackle deforestation and degradation.Peer reviewe

Gravity terrain correction for mainland territory of Vietnam

Terrain corrections for gravity data are a critical concern in rugged topography, because the magnitude of the corrections may be largely relative to the anomalies of interest. That is also important to determine the inner and outer radii beyond which the terrain effect can be neglected. Classical methods such as Lucaptrenco, Beriozkin and Prisivanco are indeed too slow with radius correction and are not extended while methods based on the Nagy’s and Kane’s are usually too approximate for the required accuracy. In order to achieve 0.1 mGal accuracy in terrain correction for mainland territory of Vietnam and reduce the computing time, the best inner and outer radii for terrain correction computation are 2 km and 70 km respectively. The results show that in nearly a half of the Vietnam territory, the terrain correction values ≥ 10 mGal, the corrections are smaller in the plain areas (less than 2 mGal) and higher in the mountainous region, in particular the correction reaches approximately 21 mGal in some locations of northern mountainous region. The complete Bouguer gravity map of mainland territory of Vietnam is reproduced based on the full terrain correction introduced in this paper

An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane

The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Pro-poor intervention strategies in irrigated agriculture in Asia: poverty in irrigated agriculture: issues and options: Vietnam

Irrigated farming / Poverty / Farm income / Irrigation management / Institutions / Legal aspects / Water rates / User charges / Participatory management / Privatization / Participatory rural appraisal / Performance indexes / Irrigation programs / Irrigation systems / Pumping / Irrigation canals / Social aspects / Economic aspects / Rivers / Hydrology / Dams / Households / Income / Regression analysis / Drainage / Cooperatives / Water delivery / Water distribution / Rice / Financing / Drought / Vietnam / Red River Delta / Nam Duong Irrigation System / Nam Thach Han Irrigation System / Han River

Broadcast Gossip Based Distributed Hypothesis Testing in Wireless Sensor Networks

We consider the scenario that N sensors collaborate to observe a single event. The sensors are distributed and can only exchange messages through a network to reach a consensus about the observed event. In this paper, we propose a very robust and simple method using broadcast gossip algorithm to solve the distributed hypothesis testing problem. The simulation result shows that our method has good performance and is very energy efficient comparing to existing methods