Talk More Listen Less: Energy-Efficient Neighbor Discovery in Wireless Sensor Networks

Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.Comment: 9 pages, 14 figures, published in IEEE INFOCOM 201

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Empirical Analysis on the International Competitiveness Of Shannxi Agricultural Product

On the foundation of reviewing the trade status of Shannxi agricultural product, adopting Trade Competitive Index, Revealed Comparative Advantage Index and Competitive Advantage Index, this paper calculates and evaluates the international competitiveness of Shannxi agricultural product from 1997 to 2004. It is found that from TC and CA indexes, Shannxi agriculture product is in an advantage position of international competition, while from the RCA index, it doesn’t have a very strong international advantage. In actual application, according to the trade situation of Shannxi agriculture product, we should consider synthetically the calculation results of these three indexes and develop the competitive advantage on the basis of comparative advantage. The conclusion supplies actual mentalities for promoting the international competitiveness of Shannxi agricultural product. Key words: Empirical analysis, International competitiveness, Shannxi agricultural product Résumé: Sur la base de la rétrospection du statut commercial du produit agricole du Shannxi et en adoptant l’Index compétitif du commerce, l’Index de l’avantage comparatif révélé et l’Index de l’avantage compétitif, cet essai calcule et évalue la compétitivité internaionale du produit agricole du Shannxi de 1997 à 2004. On trouve que, selon le premier et le troisième indexs, le produit agricole du Shannxi occupe une position avantageuse dans la compétition internationale, alors que d’après le deuxième index, il ne possède pas un avantage international très solide. Dans l’application actuelle, conformément à la situation du commerce du produit agricole du Shannxi, on doit considérer synthétiquement les résultats de calculation des trois index et développer l’avantage compétitif sur la base de l’avantage comparatif. La conclusion justifie les mentalités actuelles qui insistent à promouvoir la compétitivité internationale du produit agricole du Shannxi. Mots-Clés: analyse empirique, compétitivité internationale, produit agricole du Shannx

MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods

Self-organization and phase transition in financial markets with multiple choices

Market confidence is essential for successful investing. By incorporating multi-market into the evolutionary minority game, we investigate the effects of investor beliefs on the evolution of collective behaviors and asset prices. When there exists another investment opportunity, market confidence, including overconfidence and under-confidence, is not always good or bad for investment. The roles of market confidence is closely related to market impact. For low market impact, overconfidence in a particular asset makes an investor become insensitive to losses and a delayed strategy adjustment leads to a decline in wealth, and thereafter, one's runaway from the market. For high market impact, under-confidence in a particular asset makes an investor over-sensitive to losses and one's too frequent strategy adjustment leads to a large fluctuation in asset prices, and thereafter, a decrease in the number of agents. At an intermediate market impact, the phase transition occurs. No matter what the market impact is, an equilibrium between different markets exists, which is reflected in the occurrence of similar price fluctuations in different markets. A theoretical analysis indicates that such an equilibrium results from the coupled effects of strategy updating and shift in investment. The runaway of the agents trading a specific asset will lead to a decline in the asset price volatility and such a decline will be inhibited by the clustering of the strategies. A uniform strategy distribution will lead to a large fluctuation in asset prices and such a fluctuation will be suppressed by the decrease in the number of agents in the market. A functional relationship between the price fluctuations and the numbers of agents is found

Coupled effects of local movement and global interaction on contagion

By incorporating segregated spatial domain and individual-based linkage into the SIS (susceptible-infected-susceptible) model, we investigate the coupled effects of random walk and intragroup interaction on contagion. Compared with the situation where only local movement or individual-based linkage exists, the coexistence of them leads to a wider spread of infectious disease. The roles of narrowing segregated spatial domain and reducing mobility in epidemic control are checked, these two measures are found to be conducive to curbing the spread of infectious disease. Considering heterogeneous time scales between local movement and global interaction, a log-log relation between the change in the number of infected individuals and the timescale $\tau$ is found. A theoretical analysis indicates that the evolutionary dynamics in the present model is related to the encounter probability and the encounter time. A functional relation between the epidemic threshold and the ratio of shortcuts, and a functional relation between the encounter time and the timescale $\tau$ are found

Sox10+ adult stem cells contribute to biomaterial encapsulation and microvascularization.

Implanted biomaterials and biomedical devices generally induce foreign body reaction and end up with encapsulation by a dense avascular fibrous layer enriched in extracellular matrix. Fibroblasts/myofibroblasts are thought to be the major cell type involved in encapsulation, but it is unclear whether and how stem cells contribute to this process. Here we show, for the first time, that Sox10+ adult stem cells contribute to both encapsulation and microvessel formation. Sox10+ adult stem cells were found sparsely in the stroma of subcutaneous loose connective tissues. Upon subcutaneous biomaterial implantation, Sox10+ stem cells were activated and recruited to the biomaterial scaffold, and differentiated into fibroblasts and then myofibroblasts. This differentiation process from Sox10+ stem cells to myofibroblasts could be recapitulated in vitro. On the other hand, Sox10+ stem cells could differentiate into perivascular cells to stabilize newly formed microvessels. Sox10+ stem cells and endothelial cells in three-dimensional co-culture self-assembled into microvessels, and platelet-derived growth factor had chemotactic effect on Sox10+ stem cells. Transplanted Sox10+ stem cells differentiated into smooth muscle cells to stabilize functional microvessels. These findings demonstrate the critical role of adult stem cells in tissue remodeling and unravel the complexity of stem cell fate determination