Patent Analytics Based on Feature Vector Space Model: A Case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

Exploring the unknown requires leveraging uncertainty: Two Essays on A Real Options Perspective on the Pattern and Decision-Making of Entrepreneurial Internationalization

Uncertainty is at the center of both entrepreneurship and international business research. One of the fundamental underlying assumptions of entrepreneurship and internationalization theories is that entrepreneurial organizations and entrepreneurs constantly operate in uncertain environments. Even more so in a cross-border context, increasing levels of host country uncertainty can drastically reshape firms’ entrepreneurial internationalization patterns and outcomes as well as entrepreneurs’ internationalization decision making process. Yet, the field of entrepreneurship and international business still lack theoretical explanations for the role of uncertainty in entrepreneurial internationalization. In this two essay dissertation, I applied real options theory, a theoretical perspective that emphasizes decision making at high levels of uncertainty as well as taking advantage of changing levels of uncertainty over time to achieve better organizational outcomes, to re-conceptualize the role of uncertainty in entrepreneurial firms’ internationalization process and entrepreneurs’ internationalization decision making. In Essay one, I compare Transaction Cost Economics (TCE) view with Real Options Theory (ROT) in predicting entrepreneurial firms’ internationalization patterns and outcomes. In particular, by merging several international trade and FDI databases, I empirically tested the impact of host country institutional and economic uncertainty on entrepreneurial firms’ choice of real options entry as well as the effect of real options entry on firms’ entry time, entry location, market exits, and post entry performance. In Essay two, I investigate the uncertainty conditions under which individual entrepreneurs apply real options reasoning in their internationalization decision making process. I theorize the uncertainty leveraging perspective by applying real option reasoning to entrepreneurs’ internationalization decision making. Empirically, I employed a 2 by 2 randomized between and within-subjects mixed design experiment on a representative sample of U.S. international entrepreneurs. Toking together, the two essays examine the role of uncertainty in entrepreneurial internationalization process and decision making. The dissertation contributes to both the entrepreneurship and internationalization literature by offering a real options perspective of uncertainty leveraging and by empirically testing the effects of both perceived and actual host country uncertainty in entrepreneurial firms’ internationalization process and entrepreneurs’ internationalization decision making

Precision-Recall Curve (PRC) Classification Trees

The classification of imbalanced data has presented a significant challenge for most well-known classification algorithms that were often designed for data with relatively balanced class distributions. Nevertheless skewed class distribution is a common feature in real world problems. It is especially prevalent in certain application domains with great need for machine learning and better predictive analysis such as disease diagnosis, fraud detection, bankruptcy prediction, and suspect identification. In this paper, we propose a novel tree-based algorithm based on the area under the precision-recall curve (AUPRC) for variable selection in the classification context. Our algorithm, named as the "Precision-Recall Curve classification tree", or simply the "PRC classification tree" modifies two crucial stages in tree building. The first stage is to maximize the area under the precision-recall curve in node variable selection. The second stage is to maximize the harmonic mean of recall and precision (F-measure) for threshold selection. We found the proposed PRC classification tree, and its subsequent extension, the PRC random forest, work well especially for class-imbalanced data sets. We have demonstrated that our methods outperform their classic counterparts, the usual CART and random forest for both synthetic and real data. Furthermore, the ROC classification tree proposed by our group previously has shown good performance in imbalanced data. The combination of them, the PRC-ROC tree, also shows great promise in identifying the minority class

Income inequality and human capital allocation

This study discusses the relationship between income inequality and human capital allocation in China. We categorise income inequality into intersectoral (state- versus non-state owned) and intergenerational income inequality. Based on relevant theoretical assumptions and empirical tests using existing regional data, we find that income inequality influences regional human capital allocation in China in three ways. First, intersectoral income inequality has a negative impact on regional human capital mismatch (i.e., inconsistency between job skill requirements and workers’ actual skills). Second, intergenerational income inequality positively affects regional human capital mismatch. Third, the interaction of intersectoral and intergenerational income inequality has a negative impact on human capital mismatch. Thus, we observe differences in the net impact of intersectoral and intergenerational income inequality on human capital mismatch in China. The net impact of intersectoral income inequality on human capital mismatch is persistently negative, while the impact of intergenerational income inequality on human capital mismatch is contingent upon the degree of regional intersectoral income inequality. However, the imbalance in China’s regional development creates discrepancies in the relationship between improvement in income equality across regions and optimisation of human capital allocation. Thus, the process of formulating relevant policies must be regional, long-term based, and phased

Universal R\'enyi Entropy of Quasiparticle Excitations

The R\'enyi entropies of quasiparticle excitations in the many-body gapped systems show a remarkable universal picture which can be understood partially by combination of a semiclassical argument with the quantum effect of (in)distinguishability. The universal R\'enyi entropies are independent of the model, the quasiparticle momenta, and the connectedness of the subsystem. In this letter we calculate exactly the single-interval and double-interval R\'enyi entropies of quasiparticle excitations in the many-body gapped fermions, bosons, and XY chains. We find additional contributions to the universal R\'enyi entropy in the excited states with quasiparticles of different momenta. The additional terms are different in the fermionic and bosonic chains, depend on the momentum differences of the quasiparticles, and are different for the single interval and the double interval. We derive the analytical R\'enyi entropy in the extremely gapped limit, matching perfectly the numerical results as long as either the intrinsic correlation length of the model or all the de Broglie wavelengths of the quasiparticles are small. When the momentum difference of any pair of distinct quasiparticles is small, the additional terms are non-negligible. On the contrary, when the difference of the momenta of each pair of distinct quasiparticles is large, the additional terms could be neglected. The universal single-interval R\'enyi entropy and its additional terms in the XY chain are the same as those in the fermionic chain, while the universal R\'enyi entropy of the double intervals and its additional terms are different, due to the fact that the local degrees of freedom of the XY chain are the Pauli matrices not the spinless fermions. We argue that the derived formulas have universal properties and can be applied for a wider range of models than those discussed here.Comment: published version: 7 pages, 4 figure