Consequence-based vs. Ethic-based Evaluations? Re-thinking Travel Decision-making amid a Global Pandemic

The global pandemic has put the idea of “travel shaming” under the spotlight—travelers are concerned of being criticized for traveling irresponsibly during the pandemic, hence hesitant to take nonessential travel. However, travel shaming, conceptualized as a type of ethic-based evaluation, has not drawn much attention as consequence-based evaluation (e.g., perceived risks and benefits) in travel-related risk research. This study aims to reveal how different dimensions of risk evaluation influence attitudes and intentions to travel through the lens of the COVID-19 pandemic. Our results show that both consequence-based and ethic-based evaluations play an important role in predicting travelers’ attitudes and intentions to travel during the pandemic. In addition, this study emphasizes that social trust and self-efficacy can exert a significant influence on both consequence-based and ethic-based risk evaluations. Contributions and discussions of this study are provided in closing

Alternating Direction Method of Multipliers for Separable Convex Optimization of Real Functions in Complex Variables

The alternating direction method of multipliers (ADMM) has been widely explored due to its broad applications, and its convergence has been gotten in the real field. In this paper, an ADMM is presented for separable convex optimization of real functions in complex variables. First, the convergence of the proposed method in the complex domain is established by using the Wirtinger Calculus technique. Second, the basis pursuit (BP) algorithm is given in the form of ADMM in which the projection algorithm and the soft thresholding formula are generalized from the real case. The numerical simulations on the reconstruction of electroencephalogram (EEG) signal are provided to show that our new ADMM has better behavior than the classic ADMM for solving separable convex optimization of real functions in complex variables

Stakeholder Analysis of Community Planning in Shanghai: A Case Study of Caoyang New Village

Community planning is a rather new concept in China that did not really arise until the beginning of the 2010s. Shanghai, in recent years, launched its “ ” which institutionalized community planning and provides us with a channel to understand how this concept is localized and implemented in China. This study strengthens knowledge of community planning by selecting Caoyang New Village as a case study and conducting a stakeholder analysis of the planning process. Interviews are made with different stakeholders, which help identify the stakeholders involved, examine their roles and positions, and investigate their interactions and dynamics. The results show that although having an intention to practice community-based planning and engage multiple entities, the current approach to community planning in Shanghai is dominantly top-down with centralized power, and is short of communication and collaboration channels. This has led to failure to meet the community’s most practical demands. Meanwhile, community planners are found at a central position in the stakeholder network, yet are not given the space to assist negotiation among entities. The author thus recommends power decentralization, collaboration establishment, and transformation of planners’ role as guidance for the future

Locality Preserving Multiview Graph Hashing for Large Scale Remote Sensing Image Search

Hashing is very popular for remote sensing image search. This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset. Existing methods always neglect that real-world remote sensing data lies on a low-dimensional manifold embedded in high-dimensional ambient space. Unlike previous methods, this article proposes to learn the consensus compact codes in a view-specific low-dimensional subspace. Furthermore, we have added a hyperparameter learnable module to avoid complex parameter tuning. In order to prove the effectiveness of our method, we carried out experiments on three widely used remote sensing data sets and compared them with seven state-of-the-art methods. Extensive experiments show that the proposed method can achieve competitive results compared to the other method.Comment: 5 pages,icassp accepte

Risk Controlled Image Retrieval

Most image retrieval research focuses on improving predictive performance, but they may fall short in scenarios where the reliability of the prediction is crucial. Though uncertainty quantification can help by assessing uncertainty for query and database images, this method can provide only a heuristic estimate rather than an guarantee. To address these limitations, we present Risk Controlled Image Retrieval (RCIR), which generates retrieval sets that are guaranteed to contain the ground truth samples with a predefined probability. RCIR can be easily plugged into any image retrieval method, agnostic to data distribution and model selection. To the best of our knowledge, this is the first work that provides coverage guarantees for image retrieval. The validity and efficiency of RCIR is demonstrated on four real-world image retrieval datasets, including the Stanford CAR-196 (Krause et al. 2013), CUB-200 (Wah et al. 2011), the Pittsburgh dataset (Torii et al. 2013) and the ChestX-Det dataset (Lian et al. 2021)