Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function. We also presented a scalable state representation method to model the prediction problem as a Markov decision process (MDP) and use reinforcement learning (RL) to solve the problem. Additionally, we built a dataset called SmallCity based on the crowdsourcing method to conduct the research. As far as we know, this is the first time the IRL approach has been introduced to the urban safety perception and planning field to help experts quantitatively analyze perceptual features. Our results showed that IRL has promising prospects in this field. We will later open-source the crowdsourcing data collection site and the model proposed in this paper.Comment: ACML2022 Camera-ready Versio

Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification

Person re-identification (re-ID) continues to pose a significant challenge, particularly in scenarios involving occlusions. Prior approaches aimed at tackling occlusions have predominantly focused on aligning physical body features through the utilization of external semantic cues. However, these methods tend to be intricate and susceptible to noise. To address the aforementioned challenges, we present an innovative end-to-end solution known as the Dynamic Patch-aware Enrichment Transformer (DPEFormer). This model effectively distinguishes human body information from occlusions automatically and dynamically, eliminating the need for external detectors or precise image alignment. Specifically, we introduce a dynamic patch token selection module (DPSM). DPSM utilizes a label-guided proxy token as an intermediary to identify informative occlusion-free tokens. These tokens are then selected for deriving subsequent local part features. To facilitate the seamless integration of global classification features with the finely detailed local features selected by DPSM, we introduce a novel feature blending module (FBM). FBM enhances feature representation through the complementary nature of information and the exploitation of part diversity. Furthermore, to ensure that DPSM and the entire DPEFormer can effectively learn with only identity labels, we also propose a Realistic Occlusion Augmentation (ROA) strategy. This strategy leverages the recent advances in the Segment Anything Model (SAM). As a result, it generates occlusion images that closely resemble real-world occlusions, greatly enhancing the subsequent contrastive learning process. Experiments on occluded and holistic re-ID benchmarks signify a substantial advancement of DPEFormer over existing state-of-the-art approaches. The code will be made publicly available.Comment: 12 pages, 6 figure

Numerical analysis of aerodynamic characteristics of iced rotor in forward flight

Based on a 3-D rotor icing model and the CLORNS code, aerodynamic characteristics of iced rotors in forward flight are calculated and analyzed. At first, ice accretion on the UH-1H rotor in hover, and ice accretion on the SRB rotor in forward flight are calculated. The results are used to validate the employed numerical simulation method through comparisons with experimental data. Then, the degradation of the aerodynamic characteristics of the iced SRB rotor is analyzed, and the variation of the pressure coefficients on the rotor blades is discussed in detail. Finally, parameters, such as the icing time, the temperature, and the icing position, are quantified, and conclusions are obtained. The influence of the ice accretion on the sectional aerodynamic characteristics increases along the spanwise direction, and deicing near the 0.7R blade section should be preferred at the beginning of the ice accretion. Finally, it is concluded that ice will not be removed in time if the deicer is activated based solely on the variation of the rotor aerodynamics

Initial public offering (IPO) underpricing in China A -share market

Research in literature illustrates that initial public offerings (IPO) price are underestimated in the primary market. This paper investigates 270 new issue shares from 10th January 2011 to 30th December 2014 excluding data from 2013 because CSRC postponed IPO; I find that the average market-adjusted return is 60.72%. The cross-sectional analysis is used to measure the proxy variables of hypothesis. I find strong evidence for speculation effect hypothesis and inequality supply and demand of new share hypothesis and weak evidence for information asymmetry hypothesis, moreover, split share structure hypothesis is failed to explain IPO underpricing in China A-share stock market. Generally, IPO underpricing is caused by current issue system and irrational investment behavior