In Cognitive Radio Networks, nodes can work on different frequency bands. Existing routing proposals help nodes select frequency bands without considering the effect of band switching and intra-band backoff. In this paper, We propose a joint interaction between on-demand routing and spectrum scheduling. A node analytical model is proposed to describe the scheduling-based channel assignment progress, which relief the inter-flow interference and frequent switching delay. We also use an on-demand interaction to derive a cumulative delay based routing protocol. Simulation results show that, comparing to other approaches, our protocol provides better adaptability to the multiflow environment and derives paths with much lower cumulative delay.

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Recently, researchers around the world in medical institutions and pharmaceutical companies are demanding a wider access to healthcare data for secondary use in order to provide enhanced and personalized medical services. For this purpose, healthcare information exchange between health authorities can be leveraged as a fundamental concept to meet these demands and enable the discovery of new insights and cures. However, health data are highly sensitive and private information that requires strong authentication and authorization procedures to manage the access to them. In this regard, the cloud paradigm has been used in these e-healthcare solutions, but they remain inefficient due to their inability to adapt to the expanding volume of data generated from body sensors and their vulnerability against cyberattacks. Hence, collaborative and distributed data governance supported by edge computing and blockchain promises enormous potentials in improving the performance and security of the whole system. In this paper, we present a secure and efficient data management framework, named "EdgeMediChain", for sharing health data. The proposed architecture leverages both edge computing and blockchain to facilitate and provide the necessary requirements for a healthcare ecosystem in terms of scalability, security, as well as privacy. The Ethereum-based testbed evaluations show the effectiveness of EdgeMediChain in terms of execution time with a reduction of nearly 84.75% for 2000 concurrent transactions, higher throughput compared to a traditional blockchain, and scalable ledger storage with a linear growth rate. INDEX TERMS Blockchain, data sharing, edge computing, electronic medical records, healthcare, internet of things, privacy, security, smart contracts.

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A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving. We present a panoptic driving perception network (you only look once for panoptic (YOLOP)) to perform traffic object detection, drivable area segmentation, and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. Our model performs extremely well on the challenging BDD100K dataset, achieving state-of-the-art on all three tasks in terms of accuracy and speed. Besides, we verify the effectiveness of our multi-task learning model for joint training via ablative studies. To our best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS), and maintain excellent accuracy. To facilitate further research, the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.

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Disease and Gene Annotations database (DGA, http://dga.nubic.northwestern.edu) is a collaborative effort aiming to provide a comprehensive and integrative annotation of the human genes in disease network context by integrating computable controlled vocabulary of the Disease Ontology (DO version 3 revision 2510, which has 8043 inherited, developmental and acquired human diseases), NCBI Gene Reference Into Function (GeneRIF) and molecular interaction network (MIN). DGA integrates these resources together using semantic mappings to build an integrative set of disease-to-gene and gene-to-gene relationships with excellent coverage based on current knowledge. DGA is kept current by periodically reparsing DO, GeneRIF, and MINs. DGA provides a user-friendly and interactive web interface system enabling users to efficiently query, download and visualize the DO tree structure and annotations as a tree, a network graph or a tabular list. To facilitate integrative analysis, DGA provides a web service Application Programming Interface for integration with external analytic tools.

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