Green Biased Technical Change in Terms of Industrial Water Resources in China’s Yangtze River Economic Belt

As a significant ecological corridor from west to east across China, the Yangtze River Economical Belt (YREB) is in great need of green development and transformation. Rather than only focusing on the overall growth of green productivity, it is important to identify whether the technical change is biased towards economic performance or green performance in promoting green productivity. By employing the biased technical change theory and Malmquist index decomposition method, we analyze the green biased technical change in terms of industrial water resources in YREB at the output side and the input side respectively. We find that the green biased technical change varies during 2006–2015 at both the input side and output side in YREB. At the input side, water-saving biased technical change is generally dominant compared to water-using biased technical change during 2006–2015, presenting the substitution effects of non-water production factors. At the output side, the economy-growth biased technical change is the main force to promote green productivity, whereas the role of water-conservation biased technical change is insufficient. The green performance at the output side needs to be strengthened compared to the economic performance in YREB. A series of water-related environmental policies introduced in China since 2008 have promoted the green biased technical change both at the input side and the output side in YREB, but the policy effects at the output side is still inadequate compared to that at the input side. The technological innovation in sewage treatment and control need to catch up with the economic growth in YREB. Our research gives insights to enable a deeper understanding of the green biased technical change in YREB and will benefit more focused policy-making of green innovation

When to trust AI: advances and challenges for certification of neural networks

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges

When to Trust AI: Advances and Challenges for Certification of Neural Networks

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges

Dynamics of Resolved Polar Clouds

The polar regions have been experiencing rapid warming and ice loss as greenhouse gas concentrations have risen. The projected warming in the Arctic varies significantly across climate models, part of which is attributed to polar cloud feedbacks. This thesis addresses the question of what drives the changes in polar clouds as the climate warms, using a large eddy simulation (LES) model. LES is a powerful high-resolution model that resolves the most energetic turbulence relevant for clouds. First, we focus on the Arctic boundary layer clouds through three observation based case studies. The cloud and boundary layer characteristics simulated by the LES agree reasonably well with observations and model intercomparisons. We found that during polar night over sea ice, cloud water path increases with temperature and free-tropospheric relative humidity, but it decreases with inversion strength across the cloud top. Most of these changes can be explained by a mixed-layer model. The strength of the estimated positive cloud longwave feedback largely depends on the cloud top inversion strength. Next, we extend the LES domain to cover the entire polar troposphere, and use output from an idealized GCM as forcing to drive the LES. This novel framework allows changes in the large-scale circulation to be parameterized in the LES. The simulated seasonal cycle of liquid clouds resembles observations. In a warmer climate, there is a significant decrease of the low-level liquid clouds during summer and autumn. In spring and winter, liquid clouds increase at all levels. Both the liquid and ice cloud tops rise as the climate warms. Offline radiative transfer calculations estimate a positive cloud feedback that is dominated by longwave feedback

Provable Preimage Under-Approximation for Neural Networks (Full Version)

Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set. However, often it is important to know whether a given property holds globally for the input domain, and if not then for what proportion of the input the property is true. To analyze such properties requires computing preimage abstractions of neural networks. In this work, we propose an efficient anytime algorithm for generating symbolic under-approximations of the preimage of any polyhedron output set for neural networks. Our algorithm combines a novel technique for cheaply computing polytope preimage under-approximations using linear relaxation, with a carefully-designed refinement procedure that iteratively partitions the input region into subregions using input and ReLU splitting in order to improve the approximation. Empirically, we validate the efficacy of our method across a range of domains, including a high-dimensional MNIST classification task beyond the reach of existing preimage computation methods. Finally, as use cases, we showcase the application to quantitative verification and robustness analysis. We present a sound and complete algorithm for the former, which exploits our disjoint union of polytopes representation to provide formal guarantees. For the latter, we find that our method can provide useful quantitative information even when standard verifiers cannot verify a robustness property

Provable preimage under-approximation for neural networks

Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set. However, often it is important to know whether a given property holds globally for the input domain, and if not then for what proportion of the input the property is true. To analyze such properties requires computing preimage abstractions of neural networks. In this work, we propose an efficient anytime algorithm for generating symbolic under-approximations of the preimage of any polyhedron output set for neural networks. Our algorithm combines a novel technique for cheaply computing polytope preimage under-approximations using linear relaxation, with a carefully-designed refinement procedure that iteratively partitions the input region into subregions using input and ReLU splitting in order to improve the approximation. Empirically, we validate the efficacy of our method across a range of domains, including a high-dimensional MNIST classification task beyond the reach of existing preimage computation methods. Finally, as use cases, we showcase the application to quantitative verification and robustness analysis. We present a sound and complete algorithm for the former, which exploits our disjoint union of polytopes representation to provide formal guarantees. For the latter, we find that our method can provide useful quantitative information even when standard verifiers cannot verify a robustness property

investor sentiment and mutual fund performance

Investor sentiment, reflecting investors' expectations of the market, is closely related to investors' trading behavior. This paper focuses on the impact of market investor sentiment on risk excess returns of 4288 mutual funds. We found that when investor sentiment rises, profit-oriented funds are more sensitive to investor sentiment, while profit-oriented funds obtain lower risk excess returns. At the same time, loss-making mutual funds obtain significantly higher alphas when investor sentiment rises. High sentiment sensitivity mutual funds invest more in small stocks. We also find that when investor sentiment is extremely high, there will be many noise traders, and the impact of sentiment on fund excess returns is not significant. But during periods of extremely low investor sentiment, increased sentiment has a differential impact on mutual fund wins and losses, resulting in lower alphas for winners and higher alphas for losers. At the same time, skilled mutual fund managers have the ability to exploit investor sentiment to generate excess returns. This paper uses AAII investor sentiment as a robustness test indicator and finds that AAII sentiment indicator and BW investor sentiment indicator have highly consistent effects on mutual fund excess returns

Sensitivity of idealized mixed-phase stratocumulus to climate perturbations

Large eddy simulations (LES) that explicitly resolve boundary layer (BL) turbulence and clouds are used to explore the sensitivity of idealized Arctic BL clouds to climate perturbations. The LES focus on conditions resembling springtime, when surface heat fluxes over sea ice are weak, and the cloud radiative effect is dominated by the longwave effect. In the LES, the condensed water path increases with BL temperature and free‐tropospheric relative humidity, but it decreases with inversion strength. The dependencies of cloud properties on environmental variables exhibited by the LES can largely be reproduced by a mixed‐layer model. Mixed‐layer model analysis shows that the liquid water path increases with warming because the liquid water gradient increase under warming overcompensates for geometric cloud thinning. This response contrasts with the response of subtropical stratocumulus to warming, whose liquid water path decreases as the clouds thin geometrically under warming. The results suggest that methods used to explain the response of lower‐latitude BL clouds to climate change can also elucidate changes in idealized Arctic BL clouds, although subtropical and Arctic clouds occupy different thermodynamic regimes

Sensitivity of idealized mixed-phase stratocumulus to climate perturbations

Large eddy simulations (LES) that explicitly resolve boundary layer (BL) turbulence and clouds are used to explore the sensitivity of idealized Arctic BL clouds to climate perturbations. The LES focus on conditions resembling springtime, when surface heat fluxes over sea ice are weak, and the cloud radiative effect is dominated by the longwave effect. In the LES, the condensed water path increases with BL temperature and free‐tropospheric relative humidity, but it decreases with inversion strength. The dependencies of cloud properties on environmental variables exhibited by the LES can largely be reproduced by a mixed‐layer model. Mixed‐layer model analysis shows that the liquid water path increases with warming because the liquid water gradient increase under warming overcompensates for geometric cloud thinning. This response contrasts with the response of subtropical stratocumulus to warming, whose liquid water path decreases as the clouds thin geometrically under warming. The results suggest that methods used to explain the response of lower‐latitude BL clouds to climate change can also elucidate changes in idealized Arctic BL clouds, although subtropical and Arctic clouds occupy different thermodynamic regimes

Top-of-atmosphere albedo bias from neglecting three-dimensional radiative transfer through clouds

Clouds cover on average nearly 70% of Earth’s surface and are important for the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and 3D structure. Earth system models are unable to perform 3D radiative transfer calculations and thus partially neglect the effect of cloud morphology on albedo. We show how the resulting radiative flux bias depends on cloud morphology and solar zenith angle. Using large-eddy simulations to produce 3D cloud fields, a Monte Carlo code for 3D radiative transfer, and observations of cloud climatology, we estimate the effect of this flux bias on global climate. The flux bias is largest at small zenith angles and for deeper clouds, while the albedo bias is largest (and negative) for large zenith angles. Globally, the radiative flux bias is estimated to be 1.6 W m⁻² and locally can be on the order of 5 W m⁻²