A Variant of Multi-task n-vehicle Exploration Problem: Maximizing Every Processor’s Average Profit

We discuss a variant of the multi-task n-vehicle exploration problem. Instead of requiring an optimal permutation of vehicles in every group, the new problem requires all vehicles in a group to arrive at the same destination. Given n tasks with assigned consume-time and profit, it may also be viewed as a maximization of every processor’s average profit. Further, we propose a new kind of partition problem in fractional form and analyze its computational complexity. By regarding fractional partition as a special case, we prove that the average profit maximization problem is NP-hard when the number of processors is fixed and it is strongly NP- hard in general. At last, a pseudo-polynomial time algorithm for the average profit maximization problem and the fractional partition problem is presented, using the idea of the pseudo-polynomial time algorithm for the classical partition problem.     

绩效度量的一致性、敏感性及准确性关系研究

将环境的波动性和度量噪音看作是影响绩效度量的两类不同的随机误差,在Linear-exponential-normal框架下,建立了以价值绩效度量和补偿绩效度量的线性组合为基础的收益激励模型,并分析了激励强度与绩效度量的一致性、敏感性和准确性之间的关系,以及环境波动性对上述关系的影响.研究发现,在确定性环境条件下,激励强度与绩效度量的"信号噪音比"成正比关系,但绩效度量的一致性的提高并不必然增加该度量指标在激励契约中的权重,而要视敏感性或噪音相对于一致性的变化幅度来定.特别地,在绩效度量的敏感性与一致性之间并不存在权衡取舍关系.研究还发现,波动性与激励强度之间存在负向关系,且波动性的存在降低了绩效度量的一致性和敏感性,但波动性对绩效度量的准确性的影响则呈非单调性变化.     

多任务委托-代理关系中电信服务提供商的激励机制研究

由于电信运营商和电信服务提供商(SP)之间存在利益冲突和信息不对称,很容易导致SP的违规行为。电信运营商和SP之间的关系属于多任务的委托-代理关系。在设计委托-代理激励合同时,从三个任务的角度考虑了电信运营商对SP的激励,并通过该模型分析了服务质量和信誉等级对SP的重要性。     

Part-Aware Mask-Guided Attention for Thorax Disease Classification

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.     

基于Windows XPE的水下航行器航行操纵系统多任务软件设计

航行操纵系统是水下航行器控制系统的重要组成部分。为了保证航行操纵系统的实时性与可靠性,鉴于其硬件板卡驱动支持Windows平台,故软件采用了一种基于Windows XPE实时嵌入式系统的设计方法,根据操纵要求对航行操纵系统实时多任务进行了划分,诸如手柄信息处理任务、自动控制任务和网络通信任务等,介绍了多任务执行实体—线程(thread)的优先级的设置和线程同步工具的选取,并分析了Windows的多线程调度策略,然后详细阐述了航行操纵系统各任务的执行流程以及实现方法。最后,对航行操纵系统进行了实验室仿真测试,结果表明该系统实时性和控制精度达到了设计要求,操纵系统性能良好。     

Standardization of near infrared spectra based on multi-task learning

In order to model the near infrared spectral difference between two instruments, this paper presents an approach based on multi-task learning for multivariate instrument standardization. A multi-task learning approach using trace norm regularization is employed to estimate the transformation matrix in direct standardization, and then is extended to the construction of the nonlinear transformation. The proposed approach is compared with the piecewise direct standardization (PDS) on two real data sets. Experimental results show that the proposed approach sometimes outperforms the conventional PDS method, and the multi-task learning method can be a promising way to overcome the over-fitting problem existing in direct standardization.     

代理人在职消费对动态多任务激励契约的影响

将代理人的在职消费行为引入到动态多任务委托代理框架中,构造了代理人在职消费行为下的两阶段多任务模型,分析了代理人在职消费行为对动态多任务激励契约的影响.研究结果表明:一是任务为两阶段时,无论代理人有无在职消费行为,代理人的努力程度随着时间均呈上升趋势,这就表明当委托人在设计契约时,如果委托人期望代理人在第一阶段的努力水平不低于第二阶段的努力水平,就需要适当提高第一阶段的业绩薪酬系数;二是代理人在职消费自利行为并不一定会提高自身的努力程度,需要依据在职消费行为对绩效的影响情形来具体分析;三是在两阶段内,代理人存在在职消费时,委托人可适当降低业绩薪酬系数.     

动态多任务双边道德风险契约研究

针对管理活动的动态性与多任务性的特点,将解聘补偿与解聘倾向引入动态多任务契约设计中,构建了基于解聘补偿的动态多任务双边道德风险契约。通过数理推导分析的方法给出了最优契约设计,声誉效应和棘轮效应的度量,探讨了解聘倾向对于契约设计的影响。结果表明解聘倾向的引入对于委托人的道德风险约束是有效的,但是对于代理人的道德风险约束则取决于声誉效应与棘轮效应的大小。在第2期契约中,解聘倾向对固定支付的影响取决于代理人保留收入与解聘补偿的差额。而第1期的契约设计要受到解聘补偿,声誉效应与棘轮效应三者的综合影响。任务关联性对契约设计影响以及相应的实证分析是未来的研究方向。