views, since taking your −90 ∘ , 90 ∘ , and full back views is not feasible in a selfie capture.In the core of our system, in addition to the automatization components, hair strands are estimated and deformed in 3D (rather than 2D as in state of the art) thus enabling superior results. We provide qualitative, quantitative, and Mechanical Turk human studies that support the proposed system, and show results on a diverse variety of videos (8 different celebrity videos, 9 selfie mobile videos, spanning age, gender, hair length, type, and styling).
With the development of smart homes, more and more smart home devices are equipped with networking features, and networking technology has been widely applied in smart devices.In order to meet the networking requirements of small, low power consumption, low cost and short distance portable terminal equipment, the short distance wireless technology has been developed rapidly [1] . Currently, the short distance wireless networking technologies used in smart homes in the field of whole-house intelligence mainly include star wireless network technologies, represented by WiFi and bluetooth, and wireless mesh network technologies, represented by Bluetooth (BLE) mesh and ZigBee [2] . Compared to the star wireless network technologies such as WiFi and Bluetooth , the wireless mesh network technologies have the advantage that the device itself can act as a route node for other devices,the communication range is extended by means of multiple hops [3] , then all the nodes can constitute a mesh so that the communication signals can well cover the entire house, avoiding the problem of weak signals or connection failures incurred by the fact that the WiFi device is located in remote corners. However, the BLE mesh and ZigBee wireless mesh network technologies still have limitations. BLE mesh networks use the flooding algorithm to transmit data from the source node to the destination node from near to far layer by layer, which is like flooding. But due to the use of the flooding routing algorithm, all devices need to perform broadcast forwarding in each communication process, resulting in low communication efficiency and high wireless bandwidth occupancy. ZigBee uses a wireless routing algorithm based on an Ad hoc On-Demand Distance Vector (AODV) standard, which is more efficient than BLE mesh, but it is still a bit inadequate in the case of multi-node networks, reflected in not ideal stability and communication delay.In this paper, the O-Mesh mesh routing algorithm with low network overhead and high efficiency was proposed, which effectively improves the performance of the entire wireless mesh network through mechanisms such as central node routing, neighbor node management, multiple node reuse for route discovery, alternate path caching, and central node data forwarding, resulting in a significant increase in total network throughput and average delay. Telink's chip TLSR8258 supporting multiple protocols (ZigBee, BLE mesh) was used for testing and verification, and the results showed that the average communication delay was reduced by 28% compared to ZigBee.
To improve the performance of multi-agent reinforcement learning under the constraint of wireless resources, we propose a message importance metric and design an importance-aware scheduling policy to effectively exchange messages. The key insight is spending the precious communication resources on important messages. The message importance depends not only on the messages themselves, but also on the needs of agents who receive them. Accordingly, we propose a query-message-based architecture, called QMNet. Agents generate queries and messages with the environment observation. Sharing queries can help calculate message importance. Exchanging messages can help agents cooperate better. Besides, we exploit the message importance to deal with random access collisions in decentralized systems. Furthermore, a message prediction mechanism is proposed to compensate for messages that are not transmitted. Finally, we evaluate the proposed schemes in a traffic junction environment, where only a fraction of agents can send messages due to limited wireless resources. Results show that QMNet can extract valuable information to guarantee the system performance even when only 30% of agents can share messages. By exploiting message prediction, the system can further save 40% of wireless resources. The importance-aware decentralized multi-access mechanism can effectively avoid collisions, achieving almost the same performance as centralized scheduling.
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