基于FPGA芯片的机载波形动态重构设计

针对机载综合射频传感器系统高度综合化的实际需求，基于部分可重构技术提出了一种机载传感器功能波形重构设计方法，以实现在现场可编程门阵列（Field Programmable Gate Array，FPGA）芯片局部区域上时分复用机载功能波形。该方法引入一种便于功能波形移植部署的FPGA平台设计，并在此平台上完成机载功能波形在FPGA芯片局部区域的可重构具体设计。工程应用表明，该设计能够灵活有效复用可编程逻辑器件资源，提高了综合射频传感器系统的功能波形集成度，具有较好的实践意义。     

Which manufacturing industries and sectors are most vulnerable to Brexit?

When the UK leaves the EU, trade arrangements between the UK and EU will change. Most of the options for future UK‐EU relationships currently under discussion imply increased trade barriers, which will reduce trade and also have effects on output and prices. In this paper, we use a multi‐market partial equilibrium model to analyse the vulnerability of 122 manufacturing industries to Brexit. In all five Brexit scenarios we model, there is an overall reduction in UK manufacturing output. Output grows in some industries but at the expense of higher consumer and intermediate goods prices. High tech and medium–high tech sectors are more at risk of a decline in domestic production than lower tech sectors. In most areas of the country, demand for high‐skilled workers falls more than for medium and low‐skilled workers.     

Backward SDEs for control with partial information

This paper considers a non‐Markov control problem arising in a financial market where asset returns depend on hidden factors. The problem is non‐Markov because nonlinear filtering is required to make inference on these factors, and hence the associated dynamic program effectively takes the filtering distribution as one of its state variables. This is of significant difficulty because the filtering distribution is a stochastic probability measure of infinite dimension, and therefore the dynamic program has a state that cannot be differentiated in the traditional sense. This lack of differentiability means that the problem cannot be solved using a Hamilton–Jacobi–Bellman equation. This paper will show how the problem can be analyzed and solved using backward stochastic differential equations, with a key tool being the problem's dual formulation.     

一类非单调二元算子方程解的存在唯一性

利用锥理论和非对称迭代方法,讨论了一类非单调二元算子方程解的存在唯一性,给出了迭代序列收敛于解的误差估计,所得结果是某些已知结果的改进和推广。     

地域相近性对大宗股权协议转让公告效应研究

本文从企业并购的信息角度入手,采用事件研究方法,考察了我国大宗股权协议转让过程中,相关公司的地理位置相近性对大宗股权协议转让公告效应的影响。论证了大宗股权收购的收购方对于同城的目标公司有强烈的偏好;同城收购的目标公司相对于异城收购的目标公司,可以在首次公告日的事件窗内取得统计上显著更高的异常收益;并且当目标公司规模小、风险大、高管持股比例高、收购方在收购后成为第一大股东、财务杠杆高等情况出现时,异常收益会更加显著。     

A sufficient and necessary condition for arbitrage-free pricing

This paper derives a sufficient and necessary condition for arbitrage-free pricing, by the mathematical definition of linear dependency. It states that any pricing function that can be expressed as a linear combination of some of its partial derivatives inherently possesses the arbitrage-free property. This condition can serve as a quick ‘reality check’ to help search for arbitrage-free asset pricing.     

二元函数微分学两个定理的推广

本文对二元函数可微性及高阶混合偏导数与求导次序无关的充分条件进行了推广并加以证明.     

Missing information,inferences, and evaluations

Research has shown consumers either form inferences to fill in missing information or adapt processing to accommodate missing values. This article illustrates that processing may influence which attributes are deemed necessary for the choice task and consequently inferred and that attribute-based processing,leads to inference use. The article also tests a framework that incorporates missing information, processing, inference use, and evaluation variables into a single parsimonious model that shows (1) the effects of missing information on processing and inference use and (2) the effects of inference use on evaluations. The model incorporates a link between processing and inference use that may help explain mixed results regarding the degree and direction of impact of missing information on evaluations. Finally, it explores other variables such as missing information overlap, product familiarity and importance, and attribute type that may also influence inference use.     

偏最小二乘神经网络在建筑造价预测中的应用

偏最小二乘回归方法在提取主成分方面具有很好的解释性,而且可以避免因素之间的多重相关性,提取的成分作为输入变量再用神经网络进行预测仿真,影响因素考虑周全,不用计算工程量,计算速度快,克服了普通神经网络运算量大,样本有限情况下易出现问题的弊端。     

OPTIMAL SELLING RULES FOR MONETARY INVARIANT CRITERIA: TRACKING THE MAXIMUM OF A PORTFOLIO WITH NEGATIVE DRIFT

Considering a positive portfolio diffusion X with negative drift, we investigate optimal stopping problems of the form where f is a nonincreasing function, τ is the next random time where the portfolio X crosses zero and θ is any stopping time smaller than τ. Hereby, our motivation is the obtention of an optimal selling strategy minimizing the relative distance between the liquidation value of the portfolio and its highest possible value before it reaches zero. This paper unifies optimal selling rules observed for the quadratic absolute distance criteria in this stationary framework with bang–bang type ones observed for monetary invariant criteria but in finite horizon. More precisely, we provide a verification result for the general stopping problem of interest and derive the exact solution for two classical criteria f of the literature. For the power utility criterion with , instantaneous selling is always optimal, which is consistent with previous observations for the Black‐Scholes model in finite observation. On the contrary, for a relative quadratic error criterion, , selling is optimal as soon as the process X crosses a specified function φ of its running maximum . These results reinforce the idea that optimal stopping problems of similar type lead easily to selling rules of very different nature. Nevertheless, our numerical experiments suggest that the practical optimal selling rule for the relative quadratic error criterion is in fact very close to immediate selling.