Micro/Nano Liquid Crystal Layer–Based Tunable Optical Fiber Interferometers

Miniaturization and integration are the main trends in modern photonic technology. In this chapter, two kinds of micro-/nano liquid crystal (LC) layer–based tunable optical fiber interferometers are proposed. One fiber interferometer is the optical fiber gratings (LPGs), and the other one is the locally bent microfiber taper (LBMT). The working principles of the devices are theoretically analyzed. The preparation process and the functional properties of the devices are experimentally investigated as well

Propagation Characteristics of Explosive Waves in Layered Media Numerical Analysis

The layered media under one-dimensional strain with different wave-impedance materials have been studied. The three typical prototypes have been analysized, including steel plate, aluminum foam, and concrete as the middle layer, and the upper and lower layers are concrete material. The attenuation of the amplitude of stress at different positions, the peak stress and the duration at the dissimilar material interface, and the absorbing energy distribution in different layers for different models have been obtained by numerical simulation. The material of the middle layer with lower impedance can effectively reduce the amplitude of stress, increase the duration of explosive wave, and change the distribution of energy in different layers. But the influence of the middle layer with higher impedance material on layered media is contrary. The middle layer with soft material is the better matching of wave impedance to explosive wave propagation. The analytical conclusions are of great significance for the design of protective structures against the explosion-induced hazards and minesafety protection from outburst and explosion.Defence Science Journal, 2009, 59(5), pp.499-504, DOI:http://dx.doi.org/10.14429/dsj.59.155

A developmental approach to robotic pointing via human–robot interaction

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/)The ability of pointing is recognised as an essential skill of a robot in its communication and social interaction. This paper introduces a developmental learning approach to robotic pointing, by exploiting the interactions between a human and a robot. The approach is inspired through observing the process of human infant development. It works by first applying a reinforcement learning algorithm to guide the robot to create attempt movements towards a salient object that is out of the robot's initial reachable space. Through such movements, a human demonstrator is able to understand the robot desires to touch the target and consequently, to assist the robot to eventually reach the object successfully. The human-robot interaction helps establish the understanding of pointing gestures in the perception of both the human and the robot. From this, the robot can collect the successful pointing gestures in an effort to learn how to interact with humans. Developmental constraints are utilised to drive the entire learning procedure. The work is supported by experimental evaluation, demonstrating that the proposed approach can lead the robot to gradually gain the desirable pointing ability. It also allows that the resulting robot system exhibits similar developmental progress and features as with human infants

Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain

API recommendation methods have evolved from literal and semantic keyword matching to query expansion and query clarification. The latest query clarification method is knowledge graph (KG)-based, but limitations include out-of-vocabulary (OOV) failures and rigid question templates. To address these limitations, we propose a novel knowledge-guided query clarification approach for API recommendation that leverages a large language model (LLM) guided by KG. We utilize the LLM as a neural knowledge base to overcome OOV failures, generating fluent and appropriate clarification questions and options. We also leverage the structured API knowledge and entity relationships stored in the KG to filter out noise, and transfer the optimal clarification path from KG to the LLM, increasing the efficiency of the clarification process. Our approach is designed as an AI chain that consists of five steps, each handled by a separate LLM call, to improve accuracy, efficiency, and fluency for query clarification in API recommendation. We verify the usefulness of each unit in our AI chain, which all received high scores close to a perfect 5. When compared to the baselines, our approach shows a significant improvement in MRR, with a maximum increase of 63.9% higher when the query statement is covered in KG and 37.2% when it is not. Ablation experiments reveal that the guidance of knowledge in the KG and the knowledge-guided pathfinding strategy are crucial for our approach's performance, resulting in a 19.0% and 22.2% increase in MAP, respectively. Our approach demonstrates a way to bridge the gap between KG and LLM, effectively compensating for the strengths and weaknesses of both.Comment: Accepted on ASE'202