Review of the 22nd National Conference on the Theoretical Study of Science Popularization in China and the International Forum on Science Communication towards 2020

The 22nd National Conference on the Theoretical Study of Science Popularization in China and the International Forum on Science Communication towards 2020 was organised by the China Research Institute for Science Popularization (CRISP) in Beijing from October 17 to October 18, 2015. Nearly 200 international and national delegates from scientific research institutions, colleges and universities, local associations for science and technology from eight countries including America, Canada, Sweden, Australia, New Zealand, India, Japan and Korea participated in the Conference

Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms

Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of nodes for DR implementing. Current studies resort to intuitive yet naive approaches for DR targeting, as price is implicitly associated with demand, though optimality cannot be ensured. In this paper, we derive such a relationship in the security-constrained economic dispatch via the multi-parametric programming theory, based on which the DR targeting problem is rigorously formulated as a mixed-integer quadratic programming problem aiming at reducing the averaged price to a reference level by efficiently reducing targeted nodes' demand. A solution strategy is proposed to accelerate the computation. Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction. Besides, we empirically show that the proposed approach is immune to inaccurate system parameters, and can be generalized to variants of DR targeting tasks.Comment: submitted to IEEE Transactions on Power System

Household carbon and energy inequality in Latin American and Caribbean countries

Reducing inequality, eradicating poverty and achieving a carbon-neutral society are recognized as important components of the United Nations’ Sustainable Development Goals. In this study, we focus on carbon and energy inequality between and within ten Latin American and Caribbean (LAC) countries. Detailed carbon and energy footprint were estimated by combining the consumption profiles (2014) in ten LAC countries with environmental extended multi-regional input-output (MRIO) analysis. Our results show significant inequality of regional total and per capita carbon and energy footprint across the studied LAC countries in 2014. The top 10% income category was responsible for 29.1% and 26.3% of the regional total carbon and energy footprint, and their per capita carbon and energy footprint were 12.2 and 7.5 times of the bottom 10% earners in that region. The average carbon footprint of studied LAC countries varied between 0.53 and 2.21 t CO2e/cap (ton of CO2 equivalent, per capita), and the energy footprint ranged from 0.38 to 1.76 t SOE/cap (ton of Standard Oil Equivalent, per capita). The huge difference in total and per capita carbon emissions and energy consumption of different income groups suggests notable differences in climate change responsibility, and supports policies for achieving sustainable consumption in terms of carbon tax, renewable energy subsidy, and decarbonizing the consumption structure in different LAC countries

Impacts of poverty alleviation on national and global carbon emissions

Wealth and income are disproportionately distributed among the global population. This has direct consequences on consumption patterns and consumption-based carbon footprints, resulting in carbon inequality. Due to persistent inequality, millions of people still live in poverty today. On the basis of global expenditure data, we compute country- and expenditure-specific per capita carbon footprints with unprecedented details. We show that they can reach several hundred tons of CO2 per year, while the majority of people living below poverty lines have yearly carbon footprints of less than 1 tCO2. Reaching targets under United Nations Sustainable Development Goal 1, lifting more than one billion people out of poverty, leads to only small relative increases in global carbon emissions of 1.6–2.1% or less. Nevertheless, carbon emissions in low- and lower-middle-income countries in sub-Saharan Africa can more than double as an effect of poverty alleviation. To ensure global progress on poverty alleviation without overshooting climate targets, high-emitting countries need to reduce their emissions substantially

Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times

MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning

Prompt-based learning reformulates downstream tasks as cloze problems by combining the original input with a template. This technique is particularly useful in few-shot learning, where a model is trained on a limited amount of data. However, the limited templates and text used in few-shot prompt-based learning still leave significant room for performance improvement. Additionally, existing methods using model ensembles can constrain the model efficiency. To address these issues, we propose an augmentation method called MixPro, which augments both the vanilla input text and the templates through token-level, sentence-level, and epoch-level Mixup strategies. We conduct experiments on five few-shot datasets, and the results show that MixPro outperforms other augmentation baselines, improving model performance by an average of 5.08% compared to before augmentation.Comment: Under review at the Frontiers of Computer Science (https://www.springer.com/journal/11704/); 14 pages, 4 figures, 5 table

Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates

Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances. However, significant differences between CAVs and prior knowledge could severely reduce the evaluation efficiency. Towards addressing this issue, most existing studies focus on the adaptive design of testing scenarios during the CAV testing process, but so far they cannot be applied to high-dimensional scenarios. In this paper, we focus on the adaptive safety performance evaluation by leveraging the testing results, after the CAV testing process. It can significantly improve the evaluation efficiency and be applied to high-dimensional scenarios. Specifically, instead of directly evaluating the unknown quantity (e.g., crash rates) of CAV safety performances, we evaluate the differences between the unknown quantity and known quantity (i.e., control variates). By leveraging the testing results, the control variates could be well designed and optimized such that the differences are close to zero, so the evaluation variance could be dramatically reduced for different CAVs. To handle the high-dimensional scenarios, we propose the sparse control variates method, where the control variates are designed only for the sparse and critical variables of scenarios. According to the number of critical variables in each scenario, the control variates are stratified into strata and optimized within each stratum using multiple linear regression techniques. We justify the proposed method's effectiveness by rigorous theoretical analysis and empirical study of high-dimensional overtaking scenarios

Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities

With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accurate evaluation methods. Many existing studies adopt static evaluation, where they assess AI assistants' API call based on pre-defined dialogue histories. However, such evaluation method can be misleading, as an AI assistant might fail in generating API calls from preceding human interaction in real cases. Instead of the resource-intensive method of direct human-machine interactions, we propose Automated Dynamic Evaluation (AutoDE) to assess an assistant's API call capability without human involvement. In our framework, we endeavor to closely mirror genuine human conversation patterns in human-machine interactions, using a LLM-based user agent, equipped with a user script to ensure human alignment. Experimental results highlight that AutoDE uncovers errors overlooked by static evaluations, aligning more closely with human assessment. Testing four AI assistants using our crafted benchmark, our method further mirrored human evaluation compared to conventional static evaluations.Comment: Accepted at LREC-COLING 202

Implementation of carbon pricing in an aging world calls for targeted protection schemes

Understanding the impact of climate fiscal policies on vulnerable groups is a prerequisite for equitable climate mitigation. However, there has been a lack of attention to the impacts of such policies on the elderly, especially the low-income elderly, in existing climate policy literature. Here, we quantify and compare the distributional impacts of carbon pricing on different age-income groups in the United States, the United Kingdom, and Japan and then on different age groups in other 28 developed countries. We find that the elderly are more vulnerable to carbon pricing than younger groups in the same income group. In particular, the low-income elderly and elderly in less wealthy countries face greater challenges because carbon pricing lead to both higher rate of increase in living cost among low-income elderly and greater income inequality within the same age group. In addition, the low-income elderly would benefit less than the younger groups within the same income group in the commonly proposed carbon revenues recycling schemes. The high vulnerability of the low-income elderly to carbon pricing calls for targeted social protection along with climate mitigation polices toward an aging world

Global spillover effects of the European Green Deal and plausible mitigation options

Achieving European Green Deal (EGD) targets for carbon removal and ecological restoration would reduce agricultural and forestry production within the European Union yet simultaneously extend ecosystem impacts elsewhere. Here we quantify such spillover impacts by coupling an extended multi-regional input–output analysis with an agro-ecological zones model. We find that EGD’s agricultural and forestry targets set for 2030 could result in a 23.9 Mha increase in demand for agricultural land outside the European Union, which in turn would lead to an increase in land-use-related carbon emissions by 758.9 MtCO2-equivalent (244.8% of EGD’s carbon removal target in the land, land-use-change and forestry sectors) and a biodiversity loss of 3.86 million mean species abundance loss. Such spillover impacts far exceed the ecological benefits from EGD conservation-based import policies, such as promoting deforestation-free products and phasing out food-based biofuel. We then propose three options beyond the primary targets of the EGD with the aim to mitigate such spillover impacts. The assessment of these options reveals the critical role of reducing meat and dairy consumption, highlighting the impact of consumer behaviour on environmental outcomes. This raises questions about public awareness, willingness to change diets and the role of policy in influencing consumer behaviours.</p