Review of ferrite radar absorbing metamaterials

Ferrite absorbing metamaterials designed with metamaterial structures can effectively broaden the absorption bandwidth of electromagnetic waves. The current mainstream approach is to compound ferrite with other materials to produce ferrite absorbing metamaterials with better performance

Does regional value chain participation affect global value chain positions? Evidence from China

Does participation in the ASEAN-China regional value chain (RVC) affect China’s manufacturing enterprises’ global value chain (GVC) positions in the context of the establishment of the ASEAN-China Free Trade Area (ACFTA)? In this paper, we discuss the theoretical mechanisms and impacts of RVC participation on GVC positions and use an input-output model to decompose the gross exports of China by different sources and destinations. The model measures China’s manufacturing industries’ total, upstream and downstream participation within the ASEAN-China regional value chain. Using panel data from the OECD for 2005 to 2015, the empirical results show that (1) the participation of China’s manufacturing industries in the RVC is conducive to improvement in their GVC positions, (2) moving to more upstream can indeed promote the GVC positions of enterprises, and (3) in contrast to labour-intensive and capitalintensive manufacturing, knowledge-intensive manufacturing in upstream activities of the RVC contributes the most to GVC positions. It is suggested that China should develop knowledge-oriented industries and move to more upstream of the ASEAN-China RVC to raise manufacturing industries’ positions in the GVC

Thrust: Adaptively Propels Large Language Models with External Knowledge

Although large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. However, the existing information retrieval techniques could be costly and may even introduce noisy and sometimes misleading knowledge. To address these challenges, we propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary. To achieve this goal, we propose measuring whether a PTLM contains enough knowledge to solve an instance with a novel metric, Thrust, which leverages the representation distribution of a small number of seen instances. Extensive experiments demonstrate that thrust is a good measurement of PTLM models' instance-level knowledgeability. Moreover, we can achieve significantly higher cost-efficiency with the Thrust score as the retrieval indicator than the naive usage of external knowledge on 88% of the evaluated tasks with 26% average performance improvement. Such findings shed light on the real-world practice of knowledge-enhanced LMs with a limited knowledge-seeking budget due to computation latency or costs.Comment: 13 pages, 6 figure

Angle-selective perfect absorption with two-dimensional materials

Two-dimensional (2D) materials have great potential in photonic and optoelectronic devices. However, the relatively weak light absorption in 2D materials hinders their application in practical devices. Here, we propose a general approach to achieve angle-selective perfect light absorption in 2D materials. As a demonstration of the concept, we experimentally show giant light absorption by placing large-area single-layer graphene on a structure consisting of a chalcogenide layer atop a mirror and achieving a total absorption of 77.6% in the mid-infrared wavelength range (~13 μm), where the graphene contributes a record-high 47.2% absorptivity of mid-infrared light. Construction of such an angle-selective thin optical element is important for solar and thermal energy harvesting, photo-detection and sensing applications. Our study points to a new opportunity to combine 2D materials with photonic structures to enable novel device applications

UAV first view landmark localization with active reinforcement learning

We present an active reinforcement learning framework for unmanned aerial vehicle (UAV) first view landmark localization. We formulate the problem of landmark localization as that of a Markov decision process and introduce an active landmark-localization network (ALLNet) to address it. The aim of the ALLNet is to locate a bounding box that surrounds the landmark in a first view image sequence. To this end, it is trained in a reinforcement learning fashion. Specifically, it employs support vector machine (SVM) scores on the bounding box patches as rewards and learns the bounding box transformations as actions. Furthermore, each SVM score indicates whether or not the landmark is detected by the bounding box such that it enables the ALLNet to have the capability of judging whether the landmark leaves or re-enters a first view image. Therefore, the operation of the ALLNet is not only dominated by the reinforcement learning process but also supplemented by an active learning motivated manner. Once the landmark is considered to leave the first view image, the ALLNet stops operating until the SVM detects its re-entry to the view. The active reinforcement learning model enables training a robust ALLNet for landmark localization. The experimental results validate the effectiveness of the proposed model for UAV first view landmark localization

Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications