Intellectual property comes with risks such as value evaluation and externalities. It is an intangible asset involving a large investment and a long return period; however, it plays an important role in enhancing the competitiveness of enterprises. Some companies see intellectual property as being a long-term development strategy, which makes evaluating the performance of corporate intellectual property particularly important. The TOPSIS method is widely used to evaluate the pros and cons, as well as the performance of various programs. It can make full use of the data to accurately reflect the gaps in various programs. This paper uses the TOPSIS method to evaluate the performance of the intellectual property value strategy of six typical technology companies based on three dimensions: financial performance, external performance, and value chain performance. The study found that the value chain performance was the most important in the performance evaluation of the intellectual property value strategy, followed by the external performance, and then by the financial performance. There were also significant differences in the intellectual property value strategy of the six technology companies.

In the current era of globalization, cross-listing literature has been growing as a tool to achieve sustainable growth and provide policy implications for multinationals, international investors, and regulators. This research explores the three themes—influential aspects, intellectual structure, and conceptual structure—that underpin the growing cross-listing-based literature published in the Web of Sciences until July 2020. This study used bibliometric coupling to segregate the research front of cross-listing and then studied each theme’s conceptual structure and influential aspects separately. The analysis revealed that the cross-listing literature could be divided into three clusters: (1) price discrepancies and stock returns related to asymmetric information and market efficiencies, (2) earnings quality, earnings management, and the adoption of accounting standards, and (3) cross-listing benefits covering the growth, informativeness, and liquidity. For instance, our analysis identifies the impact of cross-listing on local market developments regarding trading volume and liquidity, secondly the benefits of financial market liberalization for cross-listing, particularly regarding the cost of capital, and thirdly the variation in abnormal returns after cross-listing with changing risk exposure, shareholding base, and amount of money raised. This research also proposes a future research agenda for the advancement of each cluster of cross-listing identified. The outcomes of this literature review will provide valuable information to practitioners and researchers and help them to further understand the broad perspective and prospects of cross-listing.

Most semantic segmentation works have obtained accurate segmentation results through exploring the contextual dependencies. However, there are several major limitations that need further investigation. For example, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. Moreover, local convolutions are commonly used in deep learning models to learn attention and capture local patterns in the data. These convolutions operate on a small neighborhood of the input, focusing on nearby information and disregarding global structural patterns. To address these concerns, we propose a Global Domain Adaptation Attention with Data-Dependent Regulator (GDAAR) method to explore the contextual dependencies. Specifically, to effectively capture both the global distribution information and local appearance details, we suggest using a stacked relation approach. This involves incorporating the feature node itself and its pairwise affinities with all other feature nodes within the network, arranged in raster scan order. By doing so, we can learn a global domain adaptation attention mechanism. Meanwhile, to improve the features similarity belonging to the same segment region while keeping the discriminative power of features belonging to different segments, we design a data-dependent regulator to adjust the global domain adaptation attention on the feature map during inference. Extensive ablation studies demonstrate that our GDAAR better captures the global distribution information for the contextual dependencies and achieves the state-of-the-art performance on several popular benchmarks.