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This paper contributes to open innovation (OI) and boundary spanning literatures by providing a first understanding of industry-based boundary spanners in university-industry (U-I) collaboration through case studies of Chinese multinational corporations (MNCs) and their international OI platforms. Organisational roles created by these platforms within the MNCs and their activity of bridging between organisations are examined. The analysis of 25 in-depth interviews in two MNCs located in eight countries, along with internal documents, sheds light on U-I collaboration practices implemented by Chinese MNCs. Two new boundary spanning roles are identified: Dual Cultural Bridger – as these OI platforms bridge organisational and national cultural gaps to prevent and solve problems in the collaborative process; and International Network Enhancer – as these OI platforms act as trust building and local knowledge listening posts for the MNC’s global network. Managerial and policy implications are provided.

Using firm- and industry-level panel data, this study investigates the impact of the Ghana-China trade on labour productivity of Ghanaian manufacturing firms and compares it to the impact induced by the trade of Ghana with the OECD. The main findings suggest that the productivity effect in Ghanaian manufacturing firms triggered by engaging in international trade activities is contingent upon the industrial competitive advantage and the trading partners. The empirical results show that trading with China creates greater potentials for Ghanaian manufacturing firms to raise productivity in comparison to trading with OECD countries. Higher intensities of imports from China stimulate productivity gains while more exports to China only enhance productivity in industries in which Ghana has a comparative advantage.

The global roll-out of vaccines is crucial to defeating the pandemic, but the lack of technical know-how and manufacturing infrastructure for production are constraining the speedy provision of vaccines globally. This will remain true even if the intellectual property rights of COVID-19 vaccines are waived temporarily. To accelerate technology transfer and global vaccine production, this article calls for the promotion of joint ventures between global COVID-19 vaccine manufacturers and local pharmaceutical companies so as to create regional manufacturing hubs. These joint ventures must be supported financially by global North countries, international organisations, and host-country governments. This approach will incentivize pharmaceutical companies to share not only their patents but also tacit production knowledge because the risk and cost of setting up new facilities will be shared. In this paper, we show how the joint venture approach will have benefits for all the actors involve and generate positive impact on Sub-Saharan Africa and other regions over the long run. Following these policy proposals will not only help protect the global community from COVID-19 but will also present a rare window of opportunity to stimulate the life science industry in the global South, supporting sustainable economic and technological development.

Micro-expression spotting is a fundamental step in the micro-expression analysis. This paper proposes a novel network based convolutional neural network (CNN) for spotting multi-scale spontaneous micro-expression intervals in long videos. We named the network as Micro-Expression Spotting Network (MESNet). It is composed of three modules. The first module is a 2+1D Spatiotemporal Convolutional Network, which uses 2D convolution to extract spatial features and 1D convolution to extract temporal features. The second module is a Clip Proposal Network, which gives some proposed micro-expression clips. The last module is a Classification Regression Network, which classifies the proposed clips to micro-expression or not, and further regresses their temporal boundaries. We also propose a novel evaluation metric for spotting micro-expression. Extensive experiments have been conducted on the two long video datasets: CAS(ME)2 and SAMM, and the leave-one-subject-out cross-validation is used to evaluate the spotting performance. Results show that the proposed MESNet effectively enhances the F1-score metric. And comparative results show the proposed MESNet has achieved a good performance, which outperforms other state-of-the-art methods, especially in the SAMM dataset.

This article investigates how a leading Chinese multinational enterprise in a core sector of the fourth industrial revolution manages research collaborations within and beyond its sectoral systems of innovation to create novel innovations and push the technological frontier of the industry. We find the firm expanded its technology boundaries through collaborations, particularly multidisciplinary collaborations in both core and noncore technological areas of the industry to establish and maintain its leadership in this dynamic sector. High disciplinary diversity in the company’s collaborative research portfolio is positively associated with the novelty of its innovation outputs. Promising noncore technologies have been integrated into its production through collaborative research, thus promoting technological convergence at both the firm and the sectoral levels. As a result, the boundary of the industry is expanded.

The world is in the early stages of a paradigm transition toward a global green economy. In this article, we propose the notion of green windows of opportunity, highlighting the importance of institutional changes in the creation of new opportunities for latecomer development. We emphasize how demand and mission-guided technical change influence the directionality of latecomer development and highlight the important role emerging economies may attain in the global green transformation. We provide important insights regarding opportunities for green development in emerging economies, how these opportunities emerge in different renewable energy sectors and their implications for the global green economy.

This perspective paper examines the impact of the COVID-19 pandemic on global production and trade from the perspective of global value chains. Particular attention is paid to the transmission mechanisms and the role of digital technology in sustainable post-pandemic economic recovery. It argues that emerging technologies will be a driver of the global economic recovery, while the challenge to sustainable and inclusive global development will be significant, especially with regard to inequality and job creation. Also discussed are policy implications, to ensure this recovery is inclusive and sustainable, not leaving any country or people behind.

Research Summary While there is a wealth of literature on the benefits of open innovation (OI), little is known about when do latecomer firms undertake international open innovation (IOI) given the related benefits and challenges. This study examines how the characteristics of firms and their surrounding environment affect their engagement with international collaboration. Demand side factors appear to be more significant drivers of IOI in latecomer firms than the pursuit of technology leadership. Market expansion-oriented innovation strategy, international orientation, previous collaboration experiences, and technology intensity of the industry are found to be associated with a high degree of IOI; firms with stronger R&D capacity tend to be less open to international collaboration. Findings from this research are helpful for managers interested in using IOI to promote their companies' innovation performance, and to managers of MNEs or policy makers who would like to understand the international innovation strategy of Chinese firms. Managerial Summary Little is known about when do latecomer firms undertake IOI. This study uses a national survey data set of manufacturing firms in China, examining how the characteristics of firms and their surrounding environment affect their degrees of international openness in innovation. Heckman's two-step model is used to test the hypotheses, supplemented with robustness checks. Demand side factors appear to be more significant drivers of IOI in latecomer firms than the pursuit of technology leadership. Market expansion-oriented innovation strategy, international orientation, previous collaboration experiences, and technology intensity of the industry are found to be associated with a high degree of IOI; firms with stronger R&D capacity tend to be less open to international collaboration. Managerial and policy implications are discussed.

The digital economy has led to significant socio-economic transformations in all aspects of our society and livelihoods. While there is a growing literature on the effect of digital platforms on development, none consolidates the available evidence from a development perspective. In this systematic literature review, we grouped the available literature into three main themes and critically synthesize and analyse the role of digital technology and digital platforms on the development of the modern economy. To enable the inclusion of a wide array of published papers, we allowed for relevant quantitative, qualitative, and mixed methods studies globally. This approach allows us to reflect on the role of digital platforms for development more broadly as well as discuss opportunities for future research.

In this final chapter, the editors present a set of ideas arising from the analysis in the Handbook and other research, drawing implications and suggesting policies oriented toward positive developments in innovation for China and other countries. Implications for government and business are discussed and areas for future research are identified. The chapter groups the implications of the research under three headings that identify key groups of stakeholders.

Pursuing the “Go Global” strategy launched in 1999, China has recently become one of the major outbound investors worldwide. In the first stage, Chinese outward foreign direct investments (OFDIs) were directed to developing countries, mainly driven by resource-seeking motives; afterward, they started targeting advanced economies, searching for new markets and new technologies. This chapter provides some descriptive evidence of dynamic trends and spatial/sectoral distribution of the Chinese OFDIs that are more likely to affect investors’ innovation capabilities: greenfield investments in research and development activities and cross-border acquisitions of medium-high-tech companies located in technologically advanced countries. Moreover, it discusses the impact of such OFDIs by providing a critical review of the existing literature about the moderating factors that enhance the chances of positive outcomes and the learning mechanisms though which investors source new knowledge from foreign subsidiaries. It concludes with suggestions for future research.

The Oxford Handbook of China Innovation provides a contemporary and authoritative view of the role of innovation in China’s extraordinary emergence. The Handbook consists of chapters written by over sixty experts from universities and research institutions worldwide, who describe and analyze this phenomenon with criticism, discussion of policy issues, and views about further development. It focuses on the microeconomic factors in China’s growth, of which the critical force has been the steady drive for innovation. It identifies the many factors instrumental in the development of innovation and evaluates those that are specific to China’s context, and those applicable to other nations. The scope of topics is comprehensive, covering China's development policies, the place of innovation in national priorities, the components of the national innovation system, and the resources required for their effective deployment. These include the institutions and policies that provide incentives and support to technological development, including people, financial mechanisms, private ownership, rule of law, and culture. The issue of foreign influence is also addressed, including the evolution of policy toward inward foreign direct investment and knowledge transfer and China’s goals for outward foreign direct investment. The chapters include discussion of the capabilities and strategies of world-class Chinese innovators, together with emerging issues such as environmental remediation, green energy, digital innovation, open innovation, mass innovation, and China’s future science and technology policy. As China emerges as a contender for global leadership in many fields, this Handbook provides a foundation for informed conjecture regarding the challenges ahead.

In this Introduction to our Special Issue, we provide detailed and critical comments and summaries of the excellent papers. The papers are important contributions to the China-U.S. and China-Europe...

Background Maresin-1 (MaR1) is an anti-inflammatory pro-resolving mediator and is considered a potential regulator of metabolic diseases. Non-alcoholic fatty liver disease (NAFLD) is a very common metabolic liver disease. However, little information is available on the relationship between MaR1 and NAFLD in humans. Therefore, the study explored the association between serum MaR1 levels and NAFLD. Methods A cross-sectional study was conducted in 240 Chinese people, including 116 non-NAFLD subjects and 124 NAFLD patients. Serum MaR1 levels were determined by enzyme-linked immunosorbent assay (ELISA). The association between MaR1 and NAFLD was assessed. Results Circulating MaR1 levels in NAFLD patients were markedly lower than those in non-NAFLD subjects (63.63 [59.87–73.93] vs 73.11 [65.12–84.50] pg/mL, P = 0.000). The percentages of patients with NAFLD gradually decreased with the increase of MaR1 quartiles (P P P Conclusions Circulating MaR1 levels were decreased in patients with NAFLD, and a negative correlation was identified between NAFLD and serum MaR1 concentrations. Decreased MaR1 might be involved in the development of NAFLD.

Although many studies have provided evidence that abstract knowledge can be acquired in artificial grammar learning, it remains unclear how abstract knowledge can be attained in sequence learning. To address this issue, we proposed a dual simple recurrent network (DSRN) model that includes a surface SRN encoding and predicting the surface properties of stimuli and an abstract SRN encoding and predicting the abstract properties of stimuli. The results of Simulations 1 and 2 showed that the DSRN model can account for learning effects in the serial reaction time (SRT) task under different conditions, and the manipulation of the contribution weight of each SRN accounted for the contribution of conscious and unconscious processes in inclusion and exclusion tests in previous studies. The results of human performance in Simulation 3 provided further evidence that people can implicitly learn both chunking and abstract knowledge in sequence learning, and the results of Simulation 3 confirmed that the DSRN model can account for how people implicitly acquire the two types of knowledge in sequence learning. These findings extend the learning ability of the SRN model and help understand how different types of knowledge can be acquired implicitly in sequence learning.

Green technology innovation (GTI) has become a basic force for promoting energy development transformation. China must play the role of the first driving force of GTI to achieve low-carbon sustainable development. Based on the relevant panel data of 30 Mainland Chinese provinces from 2007 to 2017, this paper calculates the GTI level in each province through the Slack Based Model- Global Malmquist-Luenberger (SBM-GML) model. This paper uses the Dynamic Spatial Dobbin Model (DSDM) to analyze the spatial spillover of GTI on regional energy intensity (EI) from the time and space dimensions. This study finds that GTI has achieved positive quarter-on-quarter growth each year, but innovation efficiency is low, resulting in a small overall annual innovation improvement rate. Moreover, the level of GTI in the eastern region is much higher than that in the central and western regions, and regional heterogeneity is gradually increasing. China's regional EI shows significant positive agglomeration characteristics. High EI agglomeration is mainly concentrated in major energy provinces such as those in Northwest and Northeast China, while low EI agglomeration mainly occurs in developed provinces in East and Central China with complete infrastructure and strong technological innovation.

Using qualitative case study, and unique firm-level survey data in Ghana and Tanzania collected between 2013 and 2015, this chapter analyses the nature and the sources of innovation in both formal and informal sectors. Also, the chapter explores the learning processes underlying innovations as well the various institutional constraints underlying these innovations in Ghana and Tanzania. Our analyses reveal that innovation occurs just about anywhere in Ghana and Tanzania, and innovation is widespread across all sectors, including formal and informal sectors. Our results also show that firms engage in multiple incremental innovations at the same time, enabling firms to gain complementary effects. Knowledge spillover, imitation and adaptation were identified as the main mechanisms through which knowledge is transferred for innovation activities in Ghana and Tanzania.

Using survey data collected in 2013, and multivariate probit estimation, this chapter examines the effect of ICT on innovation performance of manufacturing firms in Ghana. The estimation results suggest that ICT not only leads to a higher likelihood of firms to innovate but also directly enhances the growth of sales in new innovative products. In particular, our results show that the internet serves as the most important source of information, enabling local firms to transcend borders and integrate and take advantage of existing and developed infrastructure existing elsewhere for local innovation activities. The results suggest that knowledge is now a click away and policy must encourage firms to participate and take advantage of the knowledge economy by engaging and interacting with frontier firms.

This article describes the data analyzed in the paper “Implicit sequence learning of chunking and abstract structures” (Fu et al., 2018) [1]. It includes reaction times in the serial reaction time task and generation proformance for each confidence rating or attribution under the inclusion and exclusion tests from three experiments. For the serial reaction time task, the independent varialbles were type of stimuli and blocks or type of deviants; for the generation tests, the independent varialbles were type of stimuli, instructions, and confidence ratings or attribution tests. The data can be used to examine wether a computor model can account for what type of knowledge is acquried in implicit sequence learning.

Micro-expression is one of important clues for detecting lies. Its most outstanding characteristics include short duration and low intensity of movement. Therefore, video clips of high spatial-temporal resolution are much more desired than still images to provide sufficient details. On the other hand, owing to the difficulties to collect and encode micro-expression data, it is small sample size. In this paper, we use only 560 micro-expression video clips to evaluate the proposed network model: Transferring Long-term Convolutional Neural Network (TLCNN). TLCNN uses Deep CNN to extract features from each frame of micro-expression video clips, then feeds them to Long Short Term Memory (LSTM) which learn the temporal sequence information of micro-expression. Due to the small sample size of micro-expression data, TLCNN uses two steps of transfer learning: (1) transferring from expression data and (2) transferring from single frame of micro-expression video clips, which can be regarded as “big data”. Evaluation on 560 micro-expression video clips collected from three spontaneous databases is performed. The results show that the proposed TLCNN is better than some state-of-the-art algorithms.

Facial expression is an important nonverbal act of human to express their emotion. It is the most prevalent indicator of emotion in human’s emotional study. Recently, how depressive people process the emotional information have received much interest. Previous studies have found that the ability to recognize facial expression in those people with major depressive disorder (MDD) is damaged. For example, they recognize negative expression slower, and show a negative bias. Furthermore, patients with depression have structural or functional abnormalities related to emotion-related brain regions. As a result, depressive patients may not accurately identify subtle changes on facial expression in social interactions. As a special form of subtle change, micro-expression is difficult to detect for healthy people. This is due to the short duration and weakened muscle movements of facial expressions. The duration of the micro-expression is between 1/25 and 1/5 s, and only part of the muscle movements are occurring, although sometimes it contains all muscle movements of the macro expression. Considering the damaged ability of depressive people to recognize facial expressions, this study aimed to examine whether micro-expression could be detected by depressive patients and how the recognition performance of micro-expression differs from that of macro expressions. According to the previous results of depressive people on the recognition performance of macro expression and brief expression, we speculate that their ability to identify micro-expression is also likely to be lower than healthy people, and this difference may be amplified due to the increased difficulty of recognition. In this study, macro expression recognition test, brief affect recognition test and micro-facial recognition test were used to examine the performance difference in identifying macro expression, brief expression and micro-expression between depressive patients and healthy people. The results showed that depressive patients recognized expression and micro-expression worse than healthy people, and both depressive patients and healthy people performed worse in micro-expression recognition than that in the macro expression recognition. It also showed that the HAMD score was negative correlated with the accuracy of expression and micro-expression recognition. Previous studies have focused on the recognition of common facial expression (i.e., macro-expression) in depressive patients, and this study had extended this line work to micro-expression recognition. These results indicated that the expression recognition deficiency of depressive patients is independent from the duration of facial expression, reflecting the severity degree of depression to some extent.

There is an increasing interests in micro-expression researches. Spotting micro-expressions in long-term videos is very important, not only for providing clues for lie detection, but also for reducing the labor required to collect micro-expression data. However, little progress has been made in spotting micro-expressions. In this paper, we propose a Main Directional Maximal Difference (MDMD) Analysis for micro-expression spotting. MDMD uses the magnitude maximal difference in the main direction of optical flow features to spot facial movements, including micro-expressions. Using block structured facial regions, MDMD obtains more accurate features of movement of expressions for automatically spotting micro-expressions and macro-expressions from videos. This method involves both the temporal and spatial locations of face movements. Evaluations using the CAS(ME)2 database containing micro-expressions and macro-expressions show that MDMD is more robust than some state-of-the-art algorithms.

Purpose The purpose of this paper is to focus on “how to create value from collaborative innovation,” which is a core question when companies plan open-innovation initiatives. China’s Huawei Technologies is taken as the main case study, with other companies’ practices as further examples to elaborate and validate a new yet practical model. Design/methodology/approach This paper is based on the direct experience over many years of two Huawei technology executives – the very recent head of the technology cooperation department and the current manager of the same unit. Findings This study provides a spiral four-stage model, named SWIM, with each stage being a decision guided by a 2 × 2 matrix. These stages, named scope, weave, identify and modularize, try to balance resource allocation toward a foreseeable value, though it might be long term. Research limitations/implications The research is primarily based on one company. The validity of its recommended model can be tested only after other companies have applied it. Practical implications The research offers a practical framework for how companies can improve their open innovation. Social implications Large companies are important players in innovation networks. Improving the ability of large companies to operate open innovation will help many other companies and the society as a whole. Originality/value The proposed model is original and provides insights from China and is not a traditional source of management innovation. The paper will also help Western readers get a better understanding of management in what will soon be the world’s largest economy.

This chapter analyses how the gender of the entrepreneur is associated with firm-level innovation performance, both directly and in conjunction with other firm and manager attributes. Using a unique survey data set collected in 2013 from the DILIC project in Ghana, and formulating a two-stage model, the chapter examines if gender differences exist in firm-level innovation activities in Ghana. Our analyses show significant differences in innovation behaviour between women’s and men’s firms, suggesting that women are less likely to introduce technological and new-to-market innovations, and also sell less of innovative products. However, the results show that women are more active in adopting non-technological, especially marketing, innovation. For policy, the chapter offers new insights into gender differences, and the role of informal firms in the innovation system of Ghana, and suggests that there is a need for new policy redirection towards informality on the one hand and the need for specific institutional arrangements to address this gender gap on the other hand.

Normative image sets are widely used in memory, perception, and language studies. Following the pioneering work of Snodgrass and Vanderwart (1980), a number of normalized image sets with various language norms have been created. However, original image sets that are carefully selected to accommodate Chinese culture and language are still in short supply. In the present study, we provided the China Image Set (CIS), a new set of photo stimuli with Chinese norms. The CIS consists of 551 high-quality colored photo stimuli that cover 21 categories and are normalized on 12 different variables, including name agreement, category agreement, familiarity, visual complexity, object manipulability, manipulation experience, color diagnosticity, shape diagnosticity, image variability, age of acquisition, image agreement, and within-category typicality. Of the 12 variables, shape diagnosticity and manipulation experience with the object depicted in a stimulus are the two newly introduced and normalized variables. Multiple regression analysis reveals that name agreement, age of acquisition, image agreement, shape diagnosticity, and image variability are the most robust determinants of picture naming latency. Our normative dataset of the high-quality photo stimuli offers an ecologically more valid tool to study object recognition and language processing within Chinese culture than has previously been available.

Associative recognition requires discriminating between old items and conjunction lures constructed by recombining elements from two different study items. This task can be solved not only by recollection but also by familiarity if the to-be-remembered stimuli are perceived as a unitized representation. In two event-related potential (ERP) studies, we provide evidence for the integration of internal and external facial features by showing that the early frontal old-new effect (considered a correlate of familiarity) is modulated by the specific combination of facial features. Participants studied faces consisting of internal features (eyes, eyebrows, nose, and mouth) paired with external features (hair, head shape, and ears). During the testing phase, intact, recombined, and new faces were presented. Recombined faces consisted of internal and external features taken from two different studied faces. The results showed that at the frontal sites, during the time window from 300 to 500 ms, ERPs to intact faces were more positive than those to new and recombined faces; the latter two did not differ from one another. The late parietal effect was observed only after a more extended study phase in Experiment 2. We take the modulation of the early frontal old-new effect as evidence for the contribution of familiarity to associative recognition for combinations of internal and external facial features.

The growth of digital financial innovations in emerging economies varies across countries with Kenya’s mobile money transactions leading in Africa. Empirical evidence is lacking to describe from a system perspective the major factors attributing to this. Among the major money mobile transfer innovations, M-Pesa has recorded a tremendous impact since its launch in 2007. Consequently, this paper uses M-Pesa as a case study to understand the determinants of the success of digital financial innovations in an emerging economy. The analysis is applied at the level of technological innovation system (TIS), a framework that has provided useful insights into the functions that need to be stimulated for the successful deployment of innovative technologies. The analysis exposes key systemic functions that characterize M-Pesa rapid diffusion in Kenya. The study finds that TIS framework can be applied to explain the diffusion and uptake of a new technology. However, local adaptation process attracts critical coordination aspects, significant learning and localized capabilities attributed to M-Pesa rapid expansion. These aspects are not explicitly articulated in the TIS literature. The study concludes with policy recommendations towards stimulation of key functions that may support diffusion of digital financial innovations in emerging economies.

This paper contributes to the literature by examining the impact of different forms of state ownership on corporate innovation and the moderating effects of environmental, social, and governance (ESG) practices, economic policy uncertainty (EPU), and corruption in this ownership–innovation nexus. Building on both agency theory and institutional theory, we identify and divide the ultimate controlling shareholders into three types: central government, local government, and private shareholders. This study draws on data from 2629 listed firms in China between 2007 and 2015. Our results suggest that state-owned enterprises (SOEs) controlled by the central government show the strongest innovation performance in all scenarios. In addition, private firms outperform local SOEs in terms of patent quantity in both manufacturing and nonmanufacturing sectors and in high-economic-development regions, whereas local SOEs outperform their private peers with respect to patent quality, mainly in the manufacturing sector and high-economic-development regions. Such an ownership–innovation nexus is then found to be more pronounced for firms engaging in more ESG practices, during periods of higher EPU, and when less corruption is present. These findings demonstrate the value of diversity in state capitalism in guiding SOEs' heterogeneous innovation activities in emerging economies.

A micro-expression is considered a fast facial movement that indicates genuine emotions and thus provides a cue for deception detection. Due to its promising applications in various fields, psychologists and computer scientists, particularly those focus on computer vision and pattern recognition, have shown interest and conducted research on this topic. However, micro-expression recognition accuracy is still low. To improve the accuracy of such recognition, in this study, micro-expression data and their corresponding Local Binary Pattern (LBP) (Ojala et al., 2002) 1 code data are fused by correlation analysis. Here, we propose Sparse Tensor Canonical Correlation Analysis (STCCA) for micro-expression characteristics. A sparse solution is obtained by the regularized low rank matrix approximation. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that STCCA performs better than the Three-dimensional Canonical Correlation Analysis (3D-CCA) without sparse resolution. The experimental results also show that STCCA performs better than three-order Discriminant Tensor Subspace Analysis (DTSA3) with discriminant information, smaller projected dimensions and a larger training set sample size. The experiments also showed that Multi-linear Principal Component Analysis (MPCA) is not suitable for micro-expression recognition because the eigenvectors corresponding to smaller eigenvectors are discarded, and those eigenvectors include brief and subtle motion information.

Micro-expressions are brief facial movements characterized by short duration, involuntariness and low intensity. Recognition of spontaneous facial micro-expressions is a great challenge. In this paper, we propose a simple yet effective Main Directional Mean Optical-flow (MDMO) feature for micro-expression recognition. We apply a robust optical flow method on micro-expression video clips and partition the facial area into regions of interest (ROIs) based partially on action units. The MDMO is a ROI-based, normalized statistic feature that considers both local statistic motion information and its spatial location. One of the significant characteristics of MDMO is that its feature dimension is small. The length of a MDMO feature vector is $36 \times 2=72$ , where $36$ is the number of ROIs. Furthermore, to reduce the influence of noise due to head movements, we propose an optical-flow-driven method to align all frames of a micro-expression video clip. Finally, a SVM classifier with the proposed MDMO feature is adopted for micro-expression recognition. Experimental results on three spontaneous micro-expression databases, namely SMIC, CASME and CASME II, show that the MDMO can achieve better performance than two state-of-the-art baseline features, i.e., LBP-TOP and HOOF.

A majority of video summarization systems use linear representations, such as rectangular storyboards and timelines at linear scales. In this paper, we propose a novel nonlinear dynamic representation called SpiralTape that summarizes a video in a smooth spiral pattern. SpiralTape provides an unusual and fresh activity suitable for stimulating environments such as science and technology museums, in which children or young individuals can have enjoyable experiences that create meaningful learning outcomes. In addition, SpiralTape provides an uninterrupted overall structure of video content and takes design principles including compactness, continuity, efficient overview, and interactivity into consideration. A working SpiralTape system was developed and deployed in pilot applications and exhibitions. Elaborate user studies with evaluation benchmarks on multiple metrics were conducted to compare SpiralTape with two representative linear video summarization methods and a state-of-the-art radial video visualization. The evaluation results demonstrate the effectiveness and natural interaction performance of SpiralTape.

The intentional binding effect refers to a subjective compression over a temporal interval between the start point initialized by a voluntary action and the endpoint signaled by an external sensory (visual or audio) feedback. The present study aimed to explore the influence of tactile sensory feedback on this binding effect by comparing voluntary key-press actions with voluntary key-release actions. In experiment 1, each participant was instructed to report the perceived interval (in ms) between an action and the subsequent visual sensory feedback. In this task, either the action (key-press or key-release) was voluntarily performed by the participant or a kinematically identical movement was passively applied to the left index finger of the participant. In experiment 2, we explored whether the difference in the perception of time was affected by the direction of action. In experiment 3, we developed an apparatus in which two parallel laser beams were generated by a laser emission unit and detected by a laser receiver unit; this allowed the movement of the left index finger to be detected without it touching a keyboard (i.e., without any tactile sensory feedback). Convergent results from all of the experiments showed that the temporal binding effect was only observed when the action was both voluntary and involved physical contact with the key, suggesting that the combination of intention and tactile sensory feedback, as a form of top-down processing, likely distracted attention from temporal events and caused the different binding effects.

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.

Based on case studies of leading Chinese MNEs' international operations in developed countries, this study develops a reverse knowledge acquisition model of emerging market MNEs through subsidiary-led reverse learning, knowledge sharing and integration processes. It unpacks MNEs' external learning process and contributes to the literature by exploring three mechanisms of learning, sharing and integration. It finds three reverse learning channels, a multi-level hub-spoke type of knowledge acquiring mechanism, and a two-tier three-step integration mechanism. The learning mechanism confirms knowledge acquisition driven by reverse learning behaviours; the sharing mechanism enriches the community perspective of capability building and sharing; the integration mechanism provides an effective way of knowledge integration within the MNEs. Refereed/Peer-reviewed

Background: Mild cognitive impairment (MCI) frequently occurs in Parkinson's disease (PD). Neurovascular changes interact with neurodegenerative processes in PD. However, the deficits of cerebral blood flow (CBF) perfusion and the associated functional connectivity (FC) in PD patients with MCI (PD-MCI) remain unclear. Purpose: This study aimed to explore the specific neurovascular perfusion alterations in PD-MCI compared to PD with normal cognition (PD-NC) and healthy controls (HCs), and to further examine the resultant whole brain FC changes in the abnormal perfusion regions. Methods: Relative CBF (rCBF) was calculated using arterial spin labeling (ASL) in 54 patients with PD (27 patients with PD-NC and 27 patients with PD-MCI) and 25 HCs matched for age and gender ratio, who also underwent the structural MRI, resting-state functional MRI (rs-fMRI) and neuropsychological examinations. The gray matter (GM) changes in PD patients were analyzed using voxel-based morphometry (VBM). The alterations in rCBF perfusion and FC among groups were then analyzed respectively. Additionally, correlations between these alterations and neuropsychological performances were further examined. Results: Compared to HC, left caudate atrophy was detected in patients with PD. In comparison to both PD-NC and HC, patients with PD-MCI specifically exhibited hypoperfusion in the parietal memory network (PMN) in the precuneus (PCu) and decreased PCu-FC in the right striatum. Moreover, PCu perfusion and PCu-FC strengths in the right striatum were positively associated with memory performance in PD-MCI. Conclusions: These findings suggest that the posterior PMN dysfunction underlies memory deficits in PD-MCI.

China has been transforming its development strategy from an investment-driven to an innovation-led one. Recent government policies (e.g., "Made in China 2025") have shown the country’s ambition to upgrade its manufacturing industry. Meanwhile, the technological environments and market conditions in China have also been changing rapidly. All these changes in terms of government policies, technological environments, and market conditions bring both opportunities and challenges for multinational enterprises (MNEs) in China. How should MNEs react to such new realities? As one leading MNE with a long history in the Chinese market, Siemens has been adapting its global R&D strategies, establishing its R&D centers of industrial robots in China. This represents an interesting case to academic researchers, industry practitioners, as well as policy makers. To better understand the co-evolution of MNEs and China’s innovation systems, we conducted this case study project on the Siemens innovation about industrial robots. The research shows that, on the one hand, Siemens’ decision of conducting R&D on industrial robots in China is driven by China’s evolving host-country environments. On the other hand, the changing R&D strategies of Siemens in China also have impacts on China’s innovation system, helping domestic manufacturers to make more advanced robots.