Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

Community dynamics generates complex epidemiology through self-induced amplification and suppression

The development of quantitative models of outbreaks is key to their eventual control, from human and computer viruses through to social (and antisocial) activities. Standard epidemiological models can reproduce many general features of outbreaks. Unfortunately, the large temporal fluctuations which often dominate real-world data are thought to require more complicated, system-specific models involving super-spreaders, specific social network topologies and rewirings, and birth-death processes. However we show here that these large fluctuations have a generic explanation in terms of underlying community dynamics. Communities increasing (or decreasing) in size, act as instantaneous amplifiers (or suppressors) yielding a complex temporal evolution whose features vary dramatically according to the relative timescales of the community dynamics. We uncover, and provide an analytic theory for, a novel epidemiological phase transition driven by the population's response to an outbreak. An imminent epidemic will be suppressed if individual communities start to break up more frequently or join together less frequently, but will be amplified if the reverse is true

Inventory Sharing and Demand-Side Underweighting

Problem definition: Transshipment/inventory sharing has been used in practice because of its risk-pooling potential. However, human decision makers play a critical role in making inventory decisions in an inventory sharing system, which may affect its benefits. We investigate whether the opportunity to transship inventory influences decision makers’ inventory decisions and whether, as a result, the intended risk-pooling benefits materialize. Academic/practical relevance: Previous research in transshipment, which is focused on finding optimal stocking and sharing decisions, assumes rational decision making without any systematic bias. As one of the first to study inventory sharing from a behavioral perspective, we demonstrate a persistent stocking-decision bias relevant for inventory sharing systems. Methodology: We develop a behavioral model of a multilocation inventory system with transshipments. Using four behavioral studies, we identify, test, estimate, and mitigate a demand-side underweighting bias: although inventory sharing brings both a supply-side benefit and a demand-side benefit, players underestimate the latter. We show analytically that such bias leads to underordering. We also explore whether reframing the inventory sharing decision reduces this bias. Results: Our results show that subjects persistently reduce their order quantities when transshipments are allowed. This underordering, which persists even when a decision-support system suggests optimal quantities, causes insufficient inventory in the system, in turn reducing the risk-pooling benefits of inventory sharing. Underordering is evidently caused by an underweighting bias; although players correctly estimate the supply-side potential from transshipment, they only estimate 20% of the demand-side potential. Managerial implications: Although inventory sharing can profitably reduce inventory, too much underordering undermines its intended risk-pooling benefits. The demand-side benefits of transshipment need to be emphasized when implementing inventory sharing systems

Energy-Efficient Non-Orthogonal Transmission under Reliability and Finite Blocklength Constraints

This paper investigates an energy-efficient non-orthogonal transmission design problem for two downlink receivers that have strict reliability and finite blocklength (latency) constraints. The Shannon capacity formula widely used in traditional designs needs the assumption of infinite blocklength and thus is no longer appropriate. We adopt the newly finite blocklength coding capacity formula for explicitly specifying the trade-off between reliability and code blocklength. However, conventional successive interference cancellation (SIC) may become infeasible due to heterogeneous blocklengths. We thus consider several scenarios with different channel conditions and with/without SIC. By carefully examining the problem structure, we present in closed-form the optimal power and code blocklength for energy-efficient transmissions. Simulation results provide interesting insights into conditions for which non-orthogonal transmission is more energy efficient than the orthogonal transmission such as TDMA.Comment: accepted by IEEE GlobeCom workshop on URLLC, 201

Inducing Compliance with Post-Market Studies for Drugs under FDA’s Accelerated Approval Pathway

Problem definition: In 1992, FDA instituted the accelerated approval pathway (AP) to allow promising drugs to enter the market based on limited evidence of efficacy, thereby permitting manufacturers to verify true clinical benefits through post-market studies. However, most postmarket studies have not been completed as promised. We address this non-compliance problem. Academic/Practical Relevance: The prevalence of this non-compliance problem poses considerable public health risk, thus compromising the original purpose of a well-intentioned AP initiative. We provide an internally consistent and implementable solution to the problem through a comprehensive analysis of the myriad complicating factors and tradeoffs facing FDA. Methodology: We adopt a Stackelberg framework in which the regulator, which cannot observe the manufacturer’s private cost information or level of effort, leads by imposing a post-market study deadline. The profit-maximizing manufacturer then follows by establishing its level of effort to invest in its post-market study. In establishing its deadline, the regulator optimizes the tradeoff between providing public access to potentially effective drugs and mitigating public health risks from ineffective drugs. Results: We develop a deadline-dependent user fee menu as a screening mechanism that establishes an incentive for manufacturer compliance. We show that its effectiveness in inducing compliance depends fundamentally on the enforceability of sanction, a drug-specific measure that indicates how difficult it is to withdraw an unproven drug from the market, and the drug’s success probability: The higher is either, the higher is the probability that the mechanism induces compliance. Managerial Implications: We synthesize and distill the salient tradeoffs and nuances facing FDA’s non-compliance problem and provide an implementable solution. We quantify the value of the solution as a function of a drug’s success probability and enforceability. From public policy perspective, we provide guidance for FDA to increase the viability and effectiveness of AP