Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the paired clear and synthesized foggy images of the same scenes. In addition, they have limitations in preserving vivid color and rich textual details in defogging. To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging. QPC-Net consists of a Fog2Fogfree block and a Fogfree2Fog block. In each block, there are three learning-based modules, namely, fog removal, color-texture recovery, and fog synthetic, which sequentially compose dual-path that constrain each other to generate high quality images. Specifically, the color-texture recovery model is designed to exploit the self-similarity of texture and structure information by learning the holistic channel-spatial feature correlations between the foggy image with its several derived images. Moreover, in the fog synthetic module, we utilize the atmospheric scattering model to guide it to improve the generative quality by focusing on an atmospheric light optimization with a novel sky segmentation network. Extensive experiments on both synthetic and real-world datasets show that QPC-Net outperforms state-of-the-art defogging methods in terms of quantitative accuracy and subjective visual quality.
Since the ‘Open door policy’ was implemented, competition between cities to attract foreign investment was becoming more and more crucial. Driven by this trend, local authorities prioritise their city development agenda on attraction of foreign firms to meet the demand from the global economic system. Through the circulation of knowledge flows, technology flows, and capital flows, cities acquire the nutrition from ‘Global pipelines’ and exchange the local knowledge with MNEs. Hence, economic activities and location attributes to some extent determine the spatial agglomeration of multinationals. The agglomeration of multinationals has meaningful implications for local development, because of the huge amount of foreign direct investment (FDI) operation undertaken by MNEs in all industries and services.
This study suggests to combine geographic proximity from FDI location theory and traditional location theory with agglomeration theory to explain the foreign firms’ agglomeration in the sector-region topic. There are some prerequisites need to clarify here. Knowledge flows are assumed to be acquired by co-located firms. Each cluster provides ‘open membership’ (knowledge sharing is transparent and noticed) to each firm. Firms that have higher degree of connectivity (many firms surrounding them) receive more knowledge from the network. In some models, firm size is not regarded as atomistic, and turnover will be used to capture firm-size. Geographically weighted measures are introduced to capture the effect of local clusters on MNE’s agglomeration. Through the use of different analytical techniques, the study examines the effect of diverse local sectors and proximity on the location choice of foreign firms. Furthermore, the study tests the effect in different conditions, with different bandwidths and firms’ turnover. Location factors are also included in the research framework.
This study provide an integrated location perspective on foreign and local firms, hopefully trigger the further discussion on co-agglomeration issues between different disciplines. Specifically, the discussion of logic behind foreign firm’s spatial decision is a contribution to existing knowledge body, such as whether relatedness of technology and different geographical proximity are the determinants to their spatial agglomeration. It also provides an empirical evidence from China to illustrate the emerging phenomena since China has already been the biggest FDI receiver in the world since 2014. The research identified several findings: (1) significant county clusters are identified based on co-agglomeration of foreign and local firms. There are three significant clusters: Shanghai cluster, Suzhou cluster and Hangzhou clusters. The outlier clusters are different in Jiangsu and Zhengjiang province, there are several isolated significant clusters in northern Jiangsu, but a connected economic block exits in southern Zhejiang. (2) Spatial concentration of relatedness (within sectors) and unrelatedness (between sectors) Foreign firms prefer to locate in local clusters who own the similarity of technology and knowledge with them. What’s more, HT foreign firms tend to locate in HT and medium HT local clusters. (3) One firm’s medicine is another firms’ poison in attracting foreign firms. In the diversity agglomeration, some firms get benefit by co-locating with other firms, but some might be harmed by it. This argument is supported by the empirical researches in Netherlands, the heterogeneity of agglomerations on firm performance are strongly moderated by firms characteristics (Knoben, et al., 2015). (4) The U-relationship between foreign manufactures and local KI services agglomeration and proximity. (5)High speed railway station is strongly related to the locality of foreign firms.
The findings of the thesis will provide policy makers a clear picture of co-agglomeration patterns in Yangtze River Delta. In addition, local governments who adopts policy of encouraging FDI by foreign firms has a reference to conduct their spatial plan in their territories.
Path planning is a key task in mobile robots, and the application of Deep Q Network (DQN) algorithm for mobile robot path planning has become a hotspot and challenge in current research. In order to solve the obstacle avoidance limitations faced by the DQN algorithm in indoor robot path planning, this paper proposes a solution based on an improved DQN algorithm. In view of the low learning efficiency of the DQN algorithm, the Duel DQN structure is introduced to enhance the performance and combined with a Prioritized Experience Replay (PER) mechanism to ensure the stability of the robot during the learning process. In addition, the idea of Munchausen Deep Q Network (M-DQN) is incorporated to guide the robot to learn the optimal policy more effectively. Based on the above improvements, the PER-D2MQN algorithm is proposed in this paper. In order to validate the effectiveness of the proposed algorithm, we conducted multidimensional simulation comparison experiments of the PER-D2MQN algorithm with DQN, Duel DQN, and the existing methodology PMR-DQN in the Gazebo simulation environment and examined the cumulative and average rewards for reaching the goal point, the number of convergent execution steps, and the time consumed by the robot in reaching the goal point. The simulation results show that the PER-D2MQN algorithm obtains the highest reward in both static and complex environments, exhibits the best convergence, and finds the goal point with the lowest average number of steps and the shortest elapsed time.
For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT) imaging system has been a critical component for complex vascular and nonvascular interventional procedures. While it can significantly improve multiplanar soft tissue imaging and provide pre-treatment target lesion roadmapping and guidance, the traditional workflow can be cumbersome and time-consuming, especially for less experienced users. To streamline this process and enhance procedural efficiency overall, we proposed a visual perception system, namely AutoCBCT, seamlessly integrated with an angiography suite. This system dynamically models both the patient's body and the surgical environment in real-time. AutoCBCT enables a novel workflow with automated positioning, navigation and simulated test-runs, eliminating the need for manual operations and interactions. The proposed system has been successfully deployed and studied in both lab and clinical settings, demonstrating significantly improved workflow efficiency.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Demand for screen content videos that contain computer generated text and graphics is growing. They are very different from natural videos, because they include much sharper edge transitions and very repetitive patterns. On this type of material, the efficacy of the conventional discrete cosine transform (DCT) is questionable because it relies on the assumption that a Gauss-Markov model leads to a base-band signal. However, the assumption may not hold true for screen content material. This work exploits a class of staircase transforms. Unlike the DCT whose bases are samplings of sinusoidal functions, the staircase transforms have their bases sampled from staircase functions, which better approximate the sharp transitions often encountered in the context of screen content. The staircase transform is integrated into a hybrid transform coding scheme, in conjunction with DCT. It is experimentally shown that the proposed approach provides an average of 2.9% compression performance gains in terms of BD-rate reduction. A perceptual comparison further demonstrates that the use of staircase transform achieves substantial reduction in ringing artifact due to the Gibbs phenomenon.
Optically-sectioned structured illumination microscopy (OS-SIM) is an important tool for biological imaging and engineering surface measurements. However, in the current OS-SIM systems, the dependence of the sectioning strength on illumination pattern frequency hinders the achievement of consistent high axial resolution for various surface topography measurements. In this paper, we develop a parallel multi-slit modulation and decoding technique for OS-SIM, called PMMD-OS-SIM, to solve the existing dependence problem. Specifically, a set of high-contrast parallel multi-slit illumination patterns are projected onto the sample to modulate the surface height information. And then, a specially-designed decoding algorithm is applied to the modulated patterns for high-quality optical sectioning. By effectively combining the above modulation and decoding techniques, the optical-sectioning strength of PMMD-OS-SIM is decoupled from the illumination pattern frequency, thereby facilitating consistent high-resolution measurements for a wide range of surface topographies. The validity of the proposed method is demonstrated by measurement experiments performed on various test samples.
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight. We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a convolutional neural network supplemented with some heuristic rules.
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