The 28th Conference of Parties(COP 28)of the United Nations Framework Convention on Climate Change(UNFCCC)is being held in Dubai,United Arab Emirates,from November 30 to December 12,2023,with the participation of over...The 28th Conference of Parties(COP 28)of the United Nations Framework Convention on Climate Change(UNFCCC)is being held in Dubai,United Arab Emirates,from November 30 to December 12,2023,with the participation of over 160 world leaders(Figure 1).The tone of the COP 28 is set by the observation that,clearly,the nationally determined contributions related to CO_(2) emission reduction are not on track to curb the temperature rise as per the Paris Agreement.In order to hold the increase in global mean temperature to well below 2℃ above the pre-industrial level and to pursue efforts to limit the warming to 1.5℃,there is a need for ratcheting up ambition for near-term climate action.展开更多
Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However,they did n...Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However,they did not pay enough attention to the decision support for performing arts which is a special category unlike movies. For performing arts with high-dimensional categorical attributes and limit samples, determining ticket prices in different levels is still a challenge job faced by the producers and distributors. In terms of these difficulties, factorization machine(FM), which can handle huge sparse categorical attributes, is used in this work first. Adaptive stochastic gradient descent(ASGD) and Markov chain Monte Carlo(MCMC) are both explored to estimate the model parameters of FM. FM with ASGD(FM-ASGD) and FM with MCMC(FM-MCMC) both can achieve a better prediction accuracy, compared with a traditional algorithm. In addition, the multi-output model is proposed to determine the price in multiple price levels simultaneously, which avoids the trouble of the models' repeating training. The results also confirm the prediction accuracy of the multi-output model, compared with those from the general single-output model.展开更多
基金This study was supported by the International Cooperation Program between the National Science Foundation of China(NSFC)the United Nations Environment Program(UNEP)(grant no.42261144002)the Top-Notch Young Talents Program of China.
文摘The 28th Conference of Parties(COP 28)of the United Nations Framework Convention on Climate Change(UNFCCC)is being held in Dubai,United Arab Emirates,from November 30 to December 12,2023,with the participation of over 160 world leaders(Figure 1).The tone of the COP 28 is set by the observation that,clearly,the nationally determined contributions related to CO_(2) emission reduction are not on track to curb the temperature rise as per the Paris Agreement.In order to hold the increase in global mean temperature to well below 2℃ above the pre-industrial level and to pursue efforts to limit the warming to 1.5℃,there is a need for ratcheting up ambition for near-term climate action.
基金the Fund of the Science and Technology Commission of Shanghai Municipality(No.13511506402)
文摘Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However,they did not pay enough attention to the decision support for performing arts which is a special category unlike movies. For performing arts with high-dimensional categorical attributes and limit samples, determining ticket prices in different levels is still a challenge job faced by the producers and distributors. In terms of these difficulties, factorization machine(FM), which can handle huge sparse categorical attributes, is used in this work first. Adaptive stochastic gradient descent(ASGD) and Markov chain Monte Carlo(MCMC) are both explored to estimate the model parameters of FM. FM with ASGD(FM-ASGD) and FM with MCMC(FM-MCMC) both can achieve a better prediction accuracy, compared with a traditional algorithm. In addition, the multi-output model is proposed to determine the price in multiple price levels simultaneously, which avoids the trouble of the models' repeating training. The results also confirm the prediction accuracy of the multi-output model, compared with those from the general single-output model.
