203 research outputs found
The economics of Kyoto flexible mechanisms: a survey
Scientific study has revealed that man-made greenhouse gas emissions are causing global warming. A milestone of international effort for combating global warming was the establishment of the Kyoto Protocol to the United Nations Framework Convention on Climate Change in 1997. The Protocol sets out a 5% emission reduction target from 1990 level for Annex I region in 2010. Economic research has foreseen that it will be very costly for some industrial countries to comply the target individually and therefore suggested international co-operation through flexible mechanisms - Emissions Trading, Joint Implementation and Clean Development Mechanism. Because main greenhouse gases have the uniform-mixing feature, the use of the flexible mechanisms could be both environmentally and economically effective. Recently emerged a number of theoretical and empirical studies to explore the economics of the mechanisms. This paper is intended to survey the existing research results with the mechanisms, including conceptual clarifications, cost and benefit analysis, potential problems, empirical findings, and perspectives for future research.global warming; Kyoto Protocol; flexible mechanisms
Global value, full value and societal costs:capturing the true cost of destroying marine ecosystems
Incremental Learning Method for Data with Delayed Labels
Most research on machine learning tasks relies on the availability of true labels immediately after making a prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. In general, delayed labels create two problems. First, labelled data is insufficient because the label for each data chunk will be obtained multiple times. Second, there remains a problem of concept drift due to the long period of data. In this work, we propose a novel incremental ensemble learning when delayed labels occur. First, we build a sliding time window to preserve the historical data. Then we train an adaptive classifier by labelled data in the sliding time window. It is worth noting that we improve the TrAdaBoost to expand the data of the latest moment when building an adaptive classifier. It can correctly distinguish the wrong types of source domain sample classification. Finally, we integrate the various classifiers to make predictions. We apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labelling setting
An integrated socio-economic and ecological framework for evaluating the societal costs and benefits of fishing activities in the Pearl River Delta
Agent as Cerebrum, Controller as Cerebellum: Implementing an Embodied LMM-based Agent on Drones
In this study, we present a novel paradigm for industrial robotic embodied
agents, encapsulating an 'agent as cerebrum, controller as cerebellum'
architecture. Our approach harnesses the power of Large Multimodal Models
(LMMs) within an agent framework known as AeroAgent, tailored for drone
technology in industrial settings. To facilitate seamless integration with
robotic systems, we introduce ROSchain, a bespoke linkage framework connecting
LMM-based agents to the Robot Operating System (ROS). We report findings from
extensive empirical research, including simulated experiments on the Airgen and
real-world case study, particularly in individual search and rescue operations.
The results demonstrate AeroAgent's superior performance in comparison to
existing Deep Reinforcement Learning (DRL)-based agents, highlighting the
advantages of the embodied LMM in complex, real-world scenarios.Comment: 17 pages, 12 figure
FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction
We present a large-scale facial UV-texture dataset that contains over 50,000
high-quality texture UV-maps with even illuminations, neutral expressions, and
cleaned facial regions, which are desired characteristics for rendering
realistic 3D face models under different lighting conditions. The dataset is
derived from a large-scale face image dataset namely FFHQ, with the help of our
fully automatic and robust UV-texture production pipeline. Our pipeline
utilizes the recent advances in StyleGAN-based facial image editing approaches
to generate multi-view normalized face images from single-image inputs. An
elaborated UV-texture extraction, correction, and completion procedure is then
applied to produce high-quality UV-maps from the normalized face images.
Compared with existing UV-texture datasets, our dataset has more diverse and
higher-quality texture maps. We further train a GAN-based texture decoder as
the nonlinear texture basis for parametric fitting based 3D face
reconstruction. Experiments show that our method improves the reconstruction
accuracy over state-of-the-art approaches, and more importantly, produces
high-quality texture maps that are ready for realistic renderings. The dataset,
code, and pre-trained texture decoder are publicly available at
https://github.com/csbhr/FFHQ-UV.Comment: The dataset, code, and pre-trained texture decoder are publicly
available at https://github.com/csbhr/FFHQ-U
Inference of nonlinear causal effects with GWAS summary data
Large-scale genome-wide association studies (GWAS) have offered an exciting
opportunity to discover putative causal genes or risk factors associated with
diseases by using SNPs as instrumental variables (IVs). However, conventional
approaches assume linear causal relations partly for simplicity and partly for
the only availability of GWAS summary data. In this work, we propose a novel
model {for transcriptome-wide association studies (TWAS)} to incorporate
nonlinear relationships across IVs, an exposure, and an outcome, which is
robust against violations of the valid IV assumptions and permits the use of
GWAS summary data. We decouple the estimation of a marginal causal effect and a
nonlinear transformation, where the former is estimated via sliced inverse
regression and a sparse instrumental variable regression, and the latter is
estimated by a ratio-adjusted inverse regression. On this ground, we propose an
inferential procedure. An application of the proposed method to the ADNI gene
expression data and the IGAP GWAS summary data identifies 18 causal genes
associated with Alzheimer's disease, including APOE and TOMM40, in addition to
7 other genes missed by two-stage least squares considering only linear
relationships. Our findings suggest that nonlinear modeling is required to
unleash the power of IV regression for identifying potentially nonlinear
gene-trait associations. Accompanying this paper is our Python library
nl-causal(https://github.com/nl-causal/nonlinear-causal) that implements the
proposed method.Comment: 36 pages, 8 figure
China's trade-off between economic benefits and sulfur dioxide emissions in changing global trade
China has been suffering from air quality degradation since its ascension into the World Trade Organization in 2001. The unequal exchange that occurs with international tradeâthat is, developed countries obtaining larger shares of tradeârelated value added relative to the shares of tradeârelated air pollution incurred locallyâmay obstruct the greening of global supply chains. In this study, we conduct a multiâregional inputâoutput analysis to examine the change in the distribution of economic benefits and sulfur dioxide emissions underlying China's international trade from 2002 to 2015. The results show that both net tradeârelated economic benefits and SO2 emissions in China rapidly increased from 2002 to 2007 and then decelerated after 2007 due to changes in China's green development strategy. In the past 13 years, China has suffered from economicâenvironmental inequality due to trade with most developed countries, for example, the United States, the European Union, East Asia, and Canada. East Asia, particularly Japan and South Korea, became both an economic and environmental winner while trading with China in 2015. China has also outsourced emissions to less developed regions, such as SubâSaharan Africa. We propose policy implications to further reduce the economicâenvironmental inequality underlying China's international trade
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