A Two-Stage Allocation Scheme for Delay-Sensitive Services in Dense Vehicular Networks

Driven by the rapid development of wireless communication system, more and more vehicular services can be efficiently supported via vehicle-to-everything (V2X) communications. In order to allocate radio resource with the reasonable implementation complexity in dense urban intersection, a two-stage allocation algorithm is proposed in this paper, whose main objective is to minimize delay and ensure reliability. In particular, as for the first stage, the allocation policy is based on traffic density information (TDI), which is different from utilizing channel state information (CSI) and queue state information (QSI) in the second stage. Moreover, in order to reflect the influence of TDI on delay, a macroscopic vehicular mobility model is employed in this paper. Simulation results show that the proposed algorithm can acquire an asymptotically optimal performance with the acceptable complexity

Early redox activities modulate Xenopus tail regeneration.

Redox state sustained by reactive oxygen species (ROS) is crucial for regeneration; however, the interplay between oxygen (O2), ROS and hypoxia-inducible factors (HIF) remains elusive. Here we observe, using an optic-based probe (optrode), an elevated and steady O2 influx immediately upon amputation. The spatiotemporal O2 influx profile correlates with the regeneration of Xenopus laevis tadpole tails. Inhibition of ROS production but not ROS scavenging decreases O2 influx. Inhibition of HIF-1α impairs regeneration and stabilization of HIF-1α induces regeneration in the refractory period. In the regeneration bud, hypoxia correlates with O2 influx, ROS production, and HIF-1α stabilization that modulate regeneration. Further analyses reveal that heat shock protein 90 is a putative downstream target of HIF-1α while electric current reversal is a de facto downstream target of HIF-1α. Collectively, the results show a mechanism for regeneration via the orchestration of O2 influx, ROS production, and HIF-1α stabilization

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and semantic search. In this paper, we present GENI, a method for tackling the problem of estimating node importance in KGs, which enables several downstream applications such as item recommendation and resource allocation. While a number of approaches have been developed to address this problem for general graphs, they do not fully utilize information available in KGs, or lack flexibility needed to model complex relationship between entities and their importance. To address these limitations, we explore supervised machine learning algorithms. In particular, building upon recent advancement of graph neural networks (GNNs), we develop GENI, a GNN-based method designed to deal with distinctive challenges involved with predicting node importance in KGs. Our method performs an aggregation of importance scores instead of aggregating node embeddings via predicate-aware attention mechanism and flexible centrality adjustment. In our evaluation of GENI and existing methods on predicting node importance in real-world KGs with different characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.Comment: KDD 2019 Research Track. 11 pages. Changelog: Type 3 font removed, and minor updates made in the Appendix (v2

Internet of Things Cloud: Architecture and Implementation

The Internet of Things (IoT), which enables common objects to be intelligent and interactive, is considered the next evolution of the Internet. Its pervasiveness and abilities to collect and analyze data which can be converted into information have motivated a plethora of IoT applications. For the successful deployment and management of these applications, cloud computing techniques are indispensable since they provide high computational capabilities as well as large storage capacity. This paper aims at providing insights about the architecture, implementation and performance of the IoT cloud. Several potential application scenarios of IoT cloud are studied, and an architecture is discussed regarding the functionality of each component. Moreover, the implementation details of the IoT cloud are presented along with the services that it offers. The main contributions of this paper lie in the combination of the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT) servers to offer IoT services in the architecture of the IoT cloud with various techniques to guarantee high performance. Finally, experimental results are given in order to demonstrate the service capabilities of the IoT cloud under certain conditions.Comment: 19pages, 4figures, IEEE Communications Magazin

Offset quantum-well method for tunable distributed Bragg reflector lasers and electro-absorption modulated distributed feedback lasers

A two-section offset quantum-well structure tunable laser with a tuning range of 7 nm was fabricated using offset quantum-well method. The distributed Bragg reflector (DBR) was realized just by selectively wet etching the multiquantum-well (MQW) layer above the quaternary lower waveguide. A threshold current of 32 mA and an output power of 9 mW at 100 mA were achieved. Furthermore, with this offset structure method, a distributed feedback (DFB) laser was integrated with an electro-absorption modulator (EAM), which was capable of producing 20 dB of optical extinction