Context-aware Cluster Based Device-to-Device Communication to Serve Machine Type Communications

Billions of Machine Type Communication (MTC) devices are foreseen to be deployed in next ten years and therefore potentially open a new market for next generation wireless network. However, MTC applications have different characteristics and requirements compared with the services provided by legacy cellular networks. For instance, an MTC device sporadically requires to transmit a small data packet containing information generated by sensors. At the same time, due to the massive deployment of MTC devices, it is inefficient to charge their batteries manually and thus a long battery life is required for MTC devices. In this sense, legacy networks designed to serve human-driven traffics in real time can not support MTC efficiently. In order to improve the availability and battery life of MTC devices, context-aware device-to-device (D2D) communication is exploited in this paper. By applying D2D communication, some MTC users can serve as relays for other MTC users who experience bad channel conditions. Moreover, signaling schemes are also designed to enable the collection of context information and support the proposed D2D communication scheme. Last but not least, a system level simulator is implemented to evaluate the system performance of the proposed technologies and a large performance gain is shown by the numerical results

Quantum discord amplification induced by quantum phase transition via a cavity-Bose-Einstein-condensate system

We propose a theoretical scheme to realize a sensitive amplification of quantum discord (QD) between two atomic qubits via a cavity-Bose-Einstein condensate (BEC) system which was used to firstly realize the Dicke quantum phase transition (QPT) [Nature 464, 1301 (2010)]. It is shown that the influence of the cavity-BEC system upon the two qubits is equivalent to a phase decoherence environment. It is found that QPT in the cavity-BEC system is the physical mechanism of the sensitive QD amplification.Comment: 5 pages, 3 figure

Optical Impression in Restorative Dentistry

Intraoral scanners are responsible for data acquisition in digital workflow, which represents the first step in restorative dentistry. The present chapter aimed to investigate the various methods for acquiring oral information, diverse clinical applications based on optical impression technique, use of intraoral scan data according to the need for model, and the various considerations regarding the selection of intraoral scanners suitable for clinical goals. The acquired optical impression data can be sent anywhere in the world, which offers the advantage of overcoming any temporal or spatial constraints. The purpose of this chapter is to understand digital workflow using optical impression and to learn how to use it effectively in clinical practice

Generating CCG Categories

Previous CCG supertaggers usually predict categories using multi-class classification. Despite their simplicity, internal structures of categories are usually ignored. The rich semantics inside these structures may help us to better handle relations among categories and bring more robustness into existing supertaggers. In this work, we propose to generate categories rather than classify them: each category is decomposed into a sequence of smaller atomic tags, and the tagger aims to generate the correct sequence. We show that with this finer view on categories, annotations of different categories could be shared and interactions with sentence contexts could be enhanced. The proposed category generator is able to achieve state-of-the-art tagging (95.5% accuracy) and parsing (89.8% labeled F1) performances on the standard CCGBank. Furthermore, its performances on infrequent (even unseen) categories, out-of-domain texts and low resource language give promising results on introducing generation models to the general CCG analyses.Comment: Accepted by AAAI 202