Commuting Mode Choice Behaviour Study and Policy Suggestions for Low-Carbon Emission Transportation in Xi’an (China)

This study established the commuting mode choice models in the typical Chinese city of Xi’an by using the Logistic regression method. Results show that commuters will transfer from the walking, bicycle, electric-bicycle/motor or bus to the car if the commuting distance, household income or the car availability increase; commuters will transfer from the walking, bicycle, electric-bicycle/motor to the car and transit if the commuting distance increases; compared with transit, the shorter driving time is the significant factor for the commuters choosing cars. The findings indicate that there is the necessity of great investment in the public transit with high-quality services to shorten the traveling time, combined with measures of car restriction, parking control, road congestion charging and transit priority lanes for the low-carbon emission transportation development in Chinese cities. The findings in the typical city of Xi’an will provide reference value for other cities in the world

Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

A key challenge in fine-grained recognition is how to find and represent discriminative local regions. Recent attention models are capable of learning discriminative region localizers only from category labels with reinforcement learning. However, not utilizing any explicit part information, they are not able to accurately find multiple distinctive regions. In this work, we introduce an attribute-guided attention localization scheme where the local region localizers are learned under the guidance of part attribute descriptions. By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm. The attribute labeling requirement of the scheme is more amenable than the accurate part location annotation required by traditional part-based fine-grained recognition methods. Experimental results on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme on both fine-grained recognition and attribute recognition

A Comparative Study on the Theories of Highway Planning

On the con text of traffic planning development and its application, the author analyses the theories of highway network planning and its practicalities in China. The highway network planning to conduct a comprehensive comparison of the two schools, points out the characteristics of each theory, the operation characteristics of adaptability and cost planning of regional circumstances in different sizes for easy selection of applications in the future depending on the purpose and conditions of the plan

End-to-End Differentiable Molecular Mechanics Force Field Construction

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to molecules or biopolymers, making them difficult to optimize to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph nets to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using a feed-forward neural network. Since all stages are built using smooth functions, the entire process of chemical perception and parameter assignment is differentiable end-to-end with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach has the capacity to reproduce legacy atom types and can be fit to MM and QM energies and forces, among other targets