Racist Love

In Racist Love Leslie Bow traces the ways in which Asian Americans become objects of anxiety and desire. Conceptualizing these feelings as “racist love,” she explores how race is abstracted and then projected onto Asianized objects. Bow shows how anthropomorphic objects and images such as cartoon animals in children’s books, home décor and cute tchotchkes, contemporary visual art, and artificially intelligent robots function as repositories of seemingly positive feelings and attachment to Asianness. At the same time, Bow demonstrates that these Asianized proxies reveal how fetishistic attraction and pleasure serve as a source of anti-Asian bias and violence. By outlining how attraction to popular representations of Asianness cloaks racial resentment and fears of globalization, Bow provides a new means of understanding the ambivalence surrounding Asians in the United States while offering a theory of the psychological, affective, and symbolic dynamics of racist love in contemporary America.

Knowledge management strategies and organizational improvisation: what changed after the emergence of technological turbulence caused by artificial intelligence?

PurposeThis paper aims to analyze the moderating effect of technological turbulence caused by artificial intelligence on the relationship between the traditional knowledge management strategies of personalization (tacit knowledge) and codification (explicit knowledge), and organizational improvisation, which refers to the firm's ability to generate ideas and respond to changes in the technological environment in real time. Until now, individuals have played a key and indispensable role in organizational improvisation since they are the owners of tacit knowledge and users of explicit knowledge.Design/methodology/approachThe research model was tested in a sample of firms from sectors in which the adoption of intelligent robots is growing.FindingsBoth personalization and codification have a positive and significant influence on improvisation, the former to a greater extent. Nevertheless, when technological turbulence caused by artificial intelligence occurs, the relationship between personalization and improvisation is weakened, whereas the link between codification and improvisation is strengthened.Originality/valueContrary to the pre-digital literature consensus, explicit knowledge is becoming the new major driver of organizational improvisation, while tacit knowledge sharing is losing strength and relevance. This finding may be a first indication that intelligent robots are the new exponents of improvisation for their ability to respond to changes in the environment in real time because of a combination of explicit knowledge, beyond being a mere support tool for humans.

A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sensors has also improved in accuracy and efficiency because of the development of deep learning. However, due to the insufficient usage of semantic information and precise texture information of underlying characteristics, tiny target recognition remains a difficulty. To address the aforementioned issues, we propose a target detection method based on a jump-connected pyramid model to improve the target detection performance of robots in complex scenarios. In order to verify the effectiveness of the algorithm, we designed and implemented a software system for target detection of intelligent robots and performed software integration of the proposed algorithm model with excellent experimental results. These experiments reveal that, when compared to other algorithms, our suggested algorithm’s characteristics have higher flexibility and robustness and can deliver a higher scene classification accuracy rate.

Dynamic path planning over CG-Space of 10DOF Rover with static and randomly moving obstacles using RRT* rewiring

Dynamic path planning is a core research content for intelligent robots. This paper presents a CG-Space-based dynamic path planning and obstacle avoidance algorithm for 10 DOF wheeled mobile robot (Rover) traversing over 3D uneven terrains. CG-Space is the locus of the center of gravity location of Rover while moving on a 3D terrain. A CG-Space-based modified RRT* samples a random space tree structure. Dynamic rewiring this tree can handle the randomly moving obstacles and target in real time. Simulations demonstrate that the Rover can obtain the target location in 3D uneven dynamic environments with fixed and randomly moving obstacles.

China’s innovation-based approach in the fight of Covid-19. An estimation of China’s impact for global health to come

When the infectious coronavirus SARS-CoV-2 broke out it resulted in a global crisis. In the fight of Covid-19, China’s government relied on its strength to apply new technologies, i.e. for controlling and containment of the virus by tracing and tracking Chinese citizens. Relying on the trajectory of industrialisation, China has pursued a path of innovation. While it is reasoned that China’s advantage might have origin in the experience of the SARS outbreak almost two decades ago, this article argues that mainly China’s innovation- driven climate has favoured the application of new technologies in combatting the current crisis. Based on the innovation-driven trajectory this article explores China’s pathway out the corona crisis and how this might strengthen China’s role in global health governance. In order to pursue this aim, this article explores several areas, in which the next generation of technologies, such as AI-based diagnostic or intelligent robots were applied and concludes with an outlook based on the formulated political agenda, strategic considerations and initial international cooperation regarding China’s impact for global health.

Reconfigurable Computing for Reactive Robotics Using Open-Source FPGAs

Reconfigurable computing provides a paradigm to create intelligent systems different from the classic software computing approach. Instead of using a processor with an instruction set, a full stack of middleware, and an application program running on top, the field-programmable gate arrays (FPGAs) integrate a cell set that can be configured in different ways. A few vendors have dominated this market with their proprietary tools, hardware devices, and boards, resulting in fragmented ecosystems with few standards and little interoperation. However, a new and complete toolchain for FPGAs with its associated open tools has recently emerged from the open-source community. Robotics is an expanding application field that may definitely benefit from this revolution, as fast speed and low power consumption are usual requirements. This paper hypothesizes that basic reactive robot behaviors may be easily designed following the reconfigurable computing approach and the state-of-the-art open FPGA toolchain. They provide new abstractions such as circuit blocks and wires for building intelligent robots. Visual programming and block libraries make such development painless and reliable. As experimental validation, two reactive behaviors have been created in a real robot involving common sensors, actuators, and in-between logic. They have been also implemented using classic software programming for comparison purposes. Results are discussed and show that the development of reactive robot behaviors using reconfigurable computing and open tools is feasible, also achieving a high degree of simplicity and reusability, and benefiting from FPGAs’ low power consumption and time-critical responsiveness.

Long-distance Localization of 4Cr13 Stainless Ball-pocket in Radiative Region

Radiation caused by high-energy particles would speed up the damage of accelerator equipment. The high residual radiation from equipment affects staff health as well. Intelligent robots receive various limits to replace human in completing complex and time-consuming maintenance in radiative region because of high sensitivity to radiation. The 4Cr13 stainless ball-pocket was designed in the study of localization in long distance with the advantages of the conical fit technology. Moreover, the 4Cr13 stainless ball-pocket and bearing ball combine and form a locating structure, which has good performance on automatic aligning, self-locking and rapid dismantling. The comprehensive mechanical properties of 4Cr13 stainless ball-pocket were studied and optimized based on three heat treatment methods of martensite steel containing chromium alloy. The study of machining conditions states that compared with the design accuracy of localization, the machining error retains definite allowance. The 4Cr13 stainless ball-pocket successfully exhibits sufficient supporting strength, wearing reducing and radiation resistance. This study shows that 4Cr13 stainless ball-pocket has better fitting precision than 0.2 mm in practice. This study could offer a reliable strategy and measure for long-distance localization in other dangerous regions.

Trajectory distributions: A new description of movement for trajectory prediction

Trajectory prediction is a fundamental and challenging task for numerous applications, such as autonomous driving and intelligent robots. Current works typically treat pedestrian trajectories as a series of 2D point coordinates. However, in real scenarios, the trajectory often exhibits randomness, and has its own probability distribution. Inspired by this observation and other movement characteristics of pedestrians, we propose a simple and intuitive movement description called a trajectory distribution, which maps the coordinates of the pedestrian trajectory to a 2D Gaussian distribution in space. Based on this novel description, we develop a new trajectory prediction method, which we call the social probability method. The method combines trajectory distributions and powerful convolutional recurrent neural networks. Both the input and output of our method are trajectory distributions, which provide the recurrent neural network with sufficient spatial and random information about moving pedestrians. Furthermore, the social probability method extracts spatio-temporal features directly from the new movement description to generate robust and accurate predictions. Experiments on public benchmark datasets show the effectiveness of the proposed method.