开放架构的数字视频管理系统iView研究与实现

iView是我们研制的基于多模态元数据提取,具有开放架构且支持无线移动存取的数字视频全内容管理系统。本文讨论了系统的需求,体系框架设计实现,描述了其中涉及的关键技术思想,并对存在的问题和研究方向做了简单探讨。     

Rent seeking and defence expenditure

A plethora of arguments from the ‘public choice school’ suggest that public goods are ‘under‐supplied’ by comparison with public sector provision of more private goods. The implication is that rent seeking by the ‘military‐industrial complex’ serves, in some measure, to offset potential allocative inefficiency. In this paper a comparison of rent seeking by producers of public goods and producers of private goods identifies a bias that favours public good supply. The bias results from the different way in which collective demand for public goods and for private goods is expressed. The prospect of larger rents to the ‘military‐industrial complex’ implies greater rent‐seeking expenditures in this sector. The extent to which public goods (and, in particular, defence) are ‘under supplied’ is much exaggerated.     

Optimal layered search

This article investigates the problem of planning an optimal layered search for a stationary target. A search is layered if it assigns each point in the search space an integer number of glimpses or looks. General necessary and sufficient conditions and an existence theorem are obtained. The special case in which the initial target distribution is bivariate normal is considered in detail.     

Equipment replacement under technological change

For infinite-horizon replacement economy problems it is common practice to truncate the problem at some finite horizon. We develop bounds on the error due to such a truncation. These bounds differ from previous results in that they include both revenues and costs. Bounds are illustrated through a numerical example from a real case in vehicle replacement. © 1994 John Wiley & Sons, Inc.     

Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources,with application to COVID-19

Testing provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.