We study the problem of object reconstruction in a multi-agent collaboration scenario. Specifically, we focus on the reconstruction of specific goals through several cooperative agents equipped with vision sensors to achieve higher efficiency than single agents. Our main insight is that a complete 3D object can be split into several local 3D models and assigned to different agents. In addition, we can use the salient characteristics of the collaboration agent itself to help realize the integration of local models. We develop a novel pipeline that first restores local 3D models from the images obtained from different agents, then the relative poses between collaborative agents are estimated by aligning intrinsic features. After that, all local models are integrated using the estimated parameters. Extensive experiments show that our proposed method is capable of accurately reconstructing 3D objects in the real world in a multi-agent collaborative manner. The full reconstruction pipeline is released to the public as an open-source project.

Customers' concerns about inappropriate use of personal information can create potential threats that jeopardize the proliferation of emerging markets. This study aims at investigating how the adaptive and maladaptive responses of consumers are driven by perceived threat and perceived efficacy of external cues theoretically and how consumers react to the reported misuse of personal data in privacy regulation in the emerging online market. Online experiments were conducted to test the research model. This study contributes to the extension of extended parallel process model by theoretically examining the relationships between perceived threat and perceived efficacy and offers insights into the improvement of privacy regulation from the consumer perspective.

Summary: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects produced large-scale RNA sequencing data, which provides an opportunity for performing integrated expression analysis for all genes across tens of thousands of tumor and normal tissue specimens. Rapid access to and easy visualization of such valuable data could facilitate research in a wide biological area. Here, we present the GE-mini APP for smart phones, a mobile visualization tool for integrated gene expression data based on both TCGA and GTEx. This gene-centric expression viewer provides a convenient method for displaying expression profiles of all available tumor and tissue types, while allowing drilling down to detailed views for specific tissue types. Availability and Implementation: Both the iOS and Android APPs are freely available to all non-commercial users in App Store and Google Play. The QR codes of App store and Google play are also provided for scanning and download. The GE-mini web server is also available at http://gemini.cancer-pku.cn/ . Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.