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.     

Is external research assessment associated with convergence or divergence of research quality across universities and disciplines? Evidence from the PBRF process in New Zealand

ABSTRACT

Performance-based research evaluations have been adopted in several countries both to measure research quality in higher education institutions and as a basis for the allocation of funding across institutions. Much attention has been given to evaluating whether such schemes have increased the quality and quantity of research. This paper examines whether the introduction of the New Zealand Performance-Based Research Fund process produced convergence or divergence in measured research quality across universities and disciplines between the 2003 and 2012 assessments. Two convergence measures are obtained. One, referred to as β-convergence, relates to the relationship between changes in average quality and the initial quality level. The second concept, referred to as σ-convergence, relates to the changes in the dispersion in average research quality over time. Average quality scores by discipline and university were obtained from individual researcher data, revealing substantial β- and σ-convergence in research quality over the period. The hypothesis of uniform rates of convergence across almost all universities and disciplines is supported. The results provide insights into the incentives created by performance-based funding schemes.     

Depreciation of Business R&D Capital

We develop a forward-looking profit model to estimate the depreciation rates of business R&D capital. By using U.S. government data between 1987 and 2007, and the newly developed model, we estimate both constant and time-varying industry-specific R&D depreciation rates. The results comprise a set of R&D depreciation rates for major U.S. high-tech industries. They align with the main conclusions from recent studies that the rates are in general higher than the traditionally assumed 15 percent and vary across industries. The relative ranking of the constant R&D depreciation rates among industries is consistent with industry observations and the industry-specific time-varying rates are informative about the dynamics of technological change and the levels of competition across industries. Lastly, we also present a cross-country comparison of the R&D depreciation rates between the U.S. and Japan, and find that the results reflect the relative technological competitiveness in key industries.     

基于灰色关联分析的公路工程造价估算研究

目前分析公路工程造价相似性问题过多依赖于主观经验判断法,缺乏有效合理的定量方法,本文利用灰色关联度分析原理,提出一种分析公路工程造价相似性的定量方法。首先选取相似案例库,采用熵值法求出该案例库的重要属性特征权重值,再计算出相似案例库与待估案例的灰色关联度,最后得出待估案例的公路工程造价估算值,并通过实例证明该估算模型研究的可行性。     

How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level

Although significant progress has been made in China's basic research in recent years, there remains a wide gap between research in China and that from developed countries. How to optimize the allocative efficiency of research resources is of great importance for increasing research output. In this paper, using the fixed effect stochastic frontier model based on the translog production function, we estimate output and substitution elasticities of research and development (R&D) inputs at universities in China's provincial level during 2009–2016. We find that the R&D technical efficiency of China's universities, after a rapid growth, has tended to become relatively stable. Improvements of internationalization degree and exogenous R&D capabilities are conducive to promoting R&D technical efficiency, whereas expenditures from government grants inhibit the promotion of R&D technical efficiency; the effects of R&D capital deepening and internet penetration are not evident. The output elasticity of R&D capital is much higher than that of R&D personnel, suggesting that R&D capital is the main driving force of research output. The substitution elasticity between R&D capital and personnel has experienced a change from substitution to complementary since 2014. To realize sustained growth of research output, we should increase R&D input with positive output elasticity or reduce R&D input with negative output elasticity, making the necessary trade-offs according to the substitution relationship between the two R&D inputs.     

Risk aversion,public disclosure,and partially informed outsiders

In this paper, we examine the impact of public disclosure and partially informed outsiders on a risk-averse insider’s trading behavior, market efficiency, and market depth. In our model, under disclosure requirements, except for the final auction, market depth is the same at every auction. When informed outsiders are risk-neutral, in contrast to the case of a risk-averse insider with no informed outsiders, the insider is more concerned about the uncertainty about future price risk. When the number of informed outsiders increases, market liquidity improves, and the insider increases the variance of her random component to conceal her trading strategy. However, since the insider is relatively more risk-averse, she pays less attention to doing this on her own. Besides, the order flow provided by informed outsiders and randomly added by the insider injects additional liquidity into the market. When informed outsiders are risk-averse, compared to risk-neutral informed outsiders, an insider is most concerned about trading risks brought by informed outsiders at the beginning of trading. Furthermore, whether the trader is an insider or informed outsider, the more risk-averse trader has lower expected profits. Moreover, outsiders’ greater risk aversion leads to a smaller market depth.     

Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Bankruptcy prediction is still important topic receiving notable attention. Information about an imminent bankruptcy threat is a crucial aspect of the decision-making process of managers, financial institutions, and government agencies. In this paper, we utilize a newly acquired dataset comprising financial parameters derived from the annual reports of small- and medium-sized companies. The data, which reveal the true ratio between bankrupt and non-bankrupt companies, are severely imbalanced and only contain a small fraction of bankrupt companies. Our solution to overcome this challenging scenario of imbalanced learning was to adopt three one-class classification methods: a least-squares approach to anomaly detection, an isolation forest, and one-class support vector machines for comparison with conventional support vector machines. We provide a comprehensive analysis of the financial attributes and identify those that are most relevant to bankruptcy prediction. The highest prediction performance in terms of the geometric mean score is 91%. The results are validated on two datasets from the manufacturing and construction industries.     

Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network

Optimizing a portfolio of mean-reverting assets under transaction costs and a finite horizon is severely constrained by the curse of high dimensionality. To overcome the exponential barrier, we develop an efficient, scalable algorithm by employing a feedforward neural network. A novel concept is to apply HJB equations as an advanced start for the neural network. Empirical tests with several practical examples, including a portfolio of 48 correlated pair trades over 50 time steps, show the advantages of the approach in a high-dimensional setting. We conjecture that other financial optimization problems are amenable to similar approaches.     

Pitching research: ‘qualitative cousins’ and the ‘extended family’

This study has two related goals – one very specific and one more general. The specific goal is to constructively engage with Lodhia (2017) on the issue of whether and to what extent ‘qualitative cousins’ are well served by Faff's (2015) original pitching research (PR) template. Using this ‘cousins’ focus as a primer, the more general goal is to update the agenda created by Faff (2015) and in so doing, explore the ‘extended family’ of PR work/resources now available.     

Drivers of research impact: evidence from the top three finance journals

We study the characteristics of all published papers in the top three finance journals (JF, JFE and RFS), and how these paper characteristics affect the number of citations in Google Scholar and the Web of Science database. First, we find the characteristics in the universalist perspective remain constant while the characteristics in the constructivist and presentation perspectives increase over time. Second, some characteristics are significantly different between the high-impact and the low-impact papers. Third, paper quality, research method, journal placement and paper age are the most important drivers. Last, different drivers play different roles in different journals.