基于标准数据的创新程度提升路径研究

创新程度在资源配置中的作用日趋明显，但目前创新程度常以专利作为测量值。随着标准在产业发展中引导性增强，作为创新重要产出的标准在创新程度提升中的地位日渐凸显。提出一个以标准数据为依据，通过对标准创新程度值的测量，结合国际国内创新程度四分图模型，研究具体领域创新程度提升路径。以信息技术应用领域标准为分析对象，结合国际标准化组织颁布标准和中国标准，确定信息技术在各应用领域创新现状。结果显示：信息技术在办公系统和银行业务两个领域应用的国际国内创新程度都很低，应在完成产业升级后逐渐淘汰；其在健康关怀领域应用的国际发展前景良好但国内创新程度很低，是重点研究领域。随后，对信息技术在健康关怀领域应用的国际和国内标准具体内容进行对比分析，发现中国与国际发展存在差距，并以国际国内创新程度为依据对位于四分图不同区域的信息技术应用领域创新程度提升提出合理建议。     

基于卷积神经网络的列车检测算法研究

列车检测作为列车自动驾驶的核心技术,可以有效地降低列车追尾等事故造成的人身危险和财产损失。为实现精准的列车检测,选用改进的卷积神经网络(PVANET)对输入图像进行特征提取,在此基础上,采用候选区域网络,从生成的特征图里滑动搜索,判断出图像中可能为列车的区域位置,并进一步采用快速区域卷积神经网络对每个候选区域进行分类,计算出其所属类别的置信度,同时精确定位列车。经验证,该方法适应范围广、鲁棒性高,可以有效地检测不同环境光强及不同朝向的列车,保障列车安全,为列车自动驾驶及辅助驾驶提供安全保障。     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

效率视角下"一带一路"沿线国家创新影响力研究

从效率差异视角构建国家创新影响力评价方法，揭示“一带一路”沿线国家创新要素流动倾向性以促进交流合作。运用DEA模型测算各国创新效率，根据DEA原理识别国家间的认可关系，构建创新网络并用节点特征向量中心度表示各国创新影响力，分析创新影响力类型、创新网络特征及创新投入冗余率与产出不足率。结果发现，各国创新效率存在显著差距;各国分别处于创新网络中心、核心和边缘位置，国家创新影响力依次递减;按照创新要素相对强度，沿线国家可分为6种创新类型;最后，提出沿线国家间开展创新交流与合作的必要性和途径。     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Interbank tiering and money center banks

This paper provides evidence that interbank markets are tiered rather than flat, in the sense that most banks do not lend to each other directly but through money center banks acting as intermediaries. We capture the concept of tiering by developing a core-periphery model, and devise a procedure for fitting the model to real-world networks. Using Bundesbank data on bilateral interbank exposures among 2000 banks from 1999 to 2012, we find strong evidence of tiering in the German banking system. This extent of tiering is unlikely to arise in standard random networks. Indeed, we show that bank specialization and balance sheet variables predict how banks position themselves in the interbank market. This link provides a promising avenue for understanding the formation of financial networks.     

演化视角下全产业链创新网络结构及核心企业行为对创新绩效的影响——以苹果公司网络为例

从理论上分析了全产业链创新网络结构特点以及核心企业行为模式与创新绩效的关系,提出相关假设;从开放性的2个维度,即开放广度和开放深度量化核心企业开放式创新的行为模式。依据偏最小二乘法,将结构和行为模式的表征变量作为自变量,创新绩效的4个表征变量作为因变量,进行交叉检验;最后选取1个主成分进行回归分析,检验理论假设。     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

The emergence of multi-sided platform MNEs: Internalization theory and networks

The rise of the digital economy provides firms across the globe with unique business opportunities. Companies such as Facebook, Alibaba, and Uber are competing in a new multi-sided platform world; the primary focus of these firms, from their inception, is to provide digital infrastructure, information and technology—intangible assets that enable direct interaction or value creation across platforms by linking different user group and complementors, often at the international level. Building on data drawn from multinational multisided Platform corporations (MMPCs) operating in China, we combine insights from internalization theory and network effects in understanding the value creation of such firms. We explore the boundaries of these new “breed” of MNEs in exploiting firm-specific advantages (FSAs) and in creating new knowledge between headquarters and subsidiaries. The findings suggest that internalization theory needs to shift its focus from the ‘boundaries of the firm’ to the ‘boundaries of the local network’. By integrating their internal and external networks of knowledge in adapting their business models in host markets, this new breed of MNEs is more likely than the traditional one to gain a sustainable competitive advantage in the new information age.     

Assessing the role of network topology in transportation network resilience

The abstract representation of a transportation system as a network of nodes and interconnecting links, whether that system involves roadways, railways, sea links, airspace, or intermodal combinations, defines a network topology. Among the most common in the context of transportation systems are the grid, ring, hub-and-spoke, complete, scale-free and small-world networks. This paper investigates the role of network topology, and the topology’s characteristics, in a transportation system’s ability to cope with disaster. Specifically, the paper hypothesizes that the topological attributes of a transportation system significantly affect its resilience to disaster events. Resilience accounts for not only the innate ability of the system to absorb externally induced changes, but also cost-effective and efficient, adaptive actions that can be taken to preserve or restore performance post-event. Comprehensive and systematically designed numerical experiments were conducted on 17 network structures with some relation to transportation system layout. Resilience of these network structures in terms of throughput, connectivity or compactness was quantified. Resilience is considered with and without the benefits of preparedness and recovery actions. The impact of component-level damage on system resilience is also investigated. A comprehensive, systematic analysis of results from these experiments provides a basis for the characterization of highly resilient network topologies and conversely identification of network attributes that might lead to poorly performing systems.