Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

Pricing,greening, and transparency decisions considering the impact of government subsidies and CSR behavior in supply chain decisions

In this paper, we analytically model different government subsidy strategies in a supply chain manufacturing and selling a green product. We model the interaction between greening degree and transparency level set by a manufacturer and its impact on not only the supply chain, but also consumers and the government. The supply chain is composed of a manufacturer and a retailer. The manufacturer can choose two different strategies. First, he only cares about his production profit; and second, he concerns with CSR in addition to his production profit. We develop a new transparency-based index of consumer satisfaction to model how the market reacts to manufacturer CSR decisions. The government decide three different subsidy strategies. A three-stage Stackelberg game model is developed and solved to analytically derive managerial insights. As a result, if the transparency cost coefficient is sufficiently high, the greening degree and transparency level in CSR concerns strategy are higher than when the manufacturer is not concerned with corporate social responsibility. In addition, when the transparency cost coefficient is sufficiently high, the profit of supply chain members and government are equal in both strategies. We give a real-world example of Iranian brick industry.     

Home purchase restriction,real estate investment,and corporate innovation

It is ubiquitous for non-real estate firms to conduct real estate business in China. Home purchase restriction (HPR) affects corporate innovation by dampening the real estate investment of non-real estate firms. The extant literature has examined the impact of HPR on corporate innovation, but it has not focused on the expectation of HPR and the endogeneity problem. Employing a dataset of 1830 listed non-real estate firms over the period 2009–2016, this research explores the expectation of HPR on corporate innovation based on the motivations for real estate investment in non-real estate firms. We demonstrate that HPR facilitates the enhancement of research and development (R&D) investment in non-real estate listed firms by hindering real estate investment, particularly for non-high-tech firms. The effects of HPR arrive at the crest in the third implementation year and remain steady thereafter. The real estate investment of non-real estate firms rebounds and the R&D investment declines along with the cancellation of HPR. Tackling the selection bias and endogeneity problems, the baseline results are also robust. Hence, HPR should serve as a long-term vehicle to improving corporate innovation, in addition to preventing housing speculation.     

无穷范数松弛的低复杂度超奈奎斯特检测

超奈奎斯特(Faster-than-Nyquist,FTN)速率传输可以有效提高频谱效率，但这种非正交传输方式引入的严重码间串扰相应提高了接收端的处理难度。针对该问题，设计了一种基于循环成块传输的低复杂度检测算法。最优检测被建模为无约束的二元二次规划(Boolean Quadratic Program,BQP)问题，为了求解该NP-hard问题，采用无穷范数约束松弛原问题的非凸可行解集，并基于次梯度下降法提出松弛问题的有效优化算法。数值仿真结果表明，所提算法在误比特率(Bit Error Rate,BER)性能上优于频域均衡，且在可接受的性能损失范围内算法执行效率远高于理论最优的最大似然序列估计(Maximum Likelihood Sequence Estimation,MLSE)。     

The Importance of Tax Adjustments When Evaluating Wage Expectations*

Using elicited expectations of future gross salaries, we evaluate characteristics causing German students to make larger or smaller estimation errors. While students seem to underestimate actual salaries by 18 percent, we show that these errors are highly attributable to misconceptions of the progressive income tax. Developing a suitable adjustment procedure, we correct students’ estimates and find that errors decline by 12 percentage points. Conducting regression analyses, we reveal strong connections with students’ age, gender, work experience, secondary school track, and knowledge about student loans. These results change notably if not controlling for students’ misconceptions of the tax system.     

双选信道下一种基于混沌交织的FBMC系统

高速移动环境下，无线信道具有时频双选性衰落的特性，使得滤波器组多载波(Filter Bank Multi-carrier，FBMC)系统产生长突发差错。将一种基于Baker映射的混沌交织算法应用在滤波器组多载波系统中，根据混沌密钥对发送数据进行分块和重新排列，按照Baker映射规则完成数据交织。此方法可以将长突发差错变为单突发差错，结合卷积编码能有效地纠正双选信道产生的长突发差错。仿真结果表明，在双选择信道中，基于混沌交织的滤波器组多载波系统误比特率性能优于传统基于块交织的滤波器组多载波系统。     

Value relevance of multinational directorship and cross-listing on MNEs national governance disclosure practices in Sub-Saharan Africa: Evidence from Nigeria

We draw on institutional isomorphism literature to develop a conceptual framework which uncovers how emerging market MNEs manage institutional tensions and complexity in corporate governance (CG) regulations within and across economic environments. Using a sample of 400 firm-year observations (2011–2015) from Nigeria, we show foreign directorship and cross-listing as significant avenues for governance isomorphism. MNEs employ these mechanisms to manage and reconcile foreign and Nigerian CG regulations whilst overcoming institutional weaknesses at home. Specifically, governance isomorphism leads to improvement of home country CG disclosures practices because of associated linkages with international CG systems through cross-listing and employment of multinational directors.     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

Service-Oriented Computing for intelligent train maintenance

The purpose of this work is to apply the servicization of enterprise information systems in maintenance, particularly in the management of the maintenance process of the component parts of trains. Service Oriented Architecture (SOA) is an architectural approach that permits servicization since it provides a flexible set of design principles used during the modeling practices (abstraction and realization). With a view to supporting the model-driven engineering of software systems, Mode Driven Architecture (MDA) is a design approach delivering a set of guidelines for the configuring of specifications in systems development. Therefore, the combination of these two approaches can be fruitful to address the challenging issues the enterprise information system is facing today. Our study is based on a methodological approach using the MDA models for the automatic generation of web service. The case study concerns a Railways Maintenance Workshop (RMW) at Sidi Bel Abbes (Algeria). Finally, the information system for the management of maintenance of the component parts of passengers and baggage railcars, using the generated solution, is realized and deployed. This software helps to have better management of the RMW by the effective planning of interventions, improve performance by increasing reliability, traceability, and availability of the equipment (parts).     

不完备信息系统辐射源威胁等级判定

针对不完备信息系统条件下的辐射源威胁等级判定问题，提出了一种基于改进容差关系粗糙集（ITR-RS）的不完备信息系统辐射源威胁等级判定方法。该方法将粗糙集中的不完备信息系统理论引入辐射源威胁等级判定中，并构建一套完备的决策规则提取模型。此外，利用辨识矩阵实现对属性的约简，在降低系统所需处理数据量的同时，提高了算法的实时性；在容差关系粗糙集的基础上，提出一种改进的容差关系，在属性主、客权重相结合的基础上引入阈值来划分加权阈值容差类，得到更为合理、准确的决策规则。仿真试验及分析表明，与经典RS算法相比，所提算法的威胁等级判定正确率提高了23%，可用于信息系统不完备条件下的辐射源威胁等级判定。