A Dual-modality Smartphone Microendoscope for Quantifying the Physiological and Morphological Properties of Epithelial Tissues

We report a nonconcurrent dual-modality fiber-optic microendoscope (named SmartME) that integrates quantitative diffuse reflectance spectroscopy (DRS) and high-resolution fluorescence imaging (FLI) into a smartphone platform. The FLI module has a spatial resolution of ~3.5 µm, which allows the determination of the nuclear-cytoplasmic ratio (N/C) of epithelial tissues. The DRS has a spectral resolution of ~2 nm and can measure the total hemoglobin concentration (THC) and scattering properties of epithelial tissues with mean errors of 4.7% and 6.9%, respectively, which are comparable to the errors achieved with a benchtop spectrometer. Our preliminary in vivo studies from a single healthy human subject demonstrate that the SmartME can noninvasively quantify the tissue parameters of normal human oral mucosa tissues, including labial mucosa tissue, gingival tissue, and tongue dorsum tissue. The THCs of the three oral mucosa tissues are significantly different from each other (p ≤ 0.003). The reduced scattering coefficients of the gingival and labial tissues are significantly different from those of the tongue dorsum tissue (p \u3c 0.001) but are not significantly different from each other. The N/Cs for all three tissue types are similar. The SmartME has great potential to be used as a portable, cost-effective, and globally connected tool to quantify the THC and scattering properties of tissues in vivo

MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. However, there are still several challenges that may limit its large-scale application in the real world. To make the policy learned from a training scenario generalizable to new unseen scenarios, a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method is proposed to learn the decentralized policy for each intersection that considers neighbor information in a latent way. Specifically, we formulate the policy learning as a meta-learning problem over a set of related tasks, where each task corresponds to traffic signal control at an intersection whose neighbors are regarded as the unobserved part of the state. Then, a learned latent variable is introduced to represent the task's specific information and is further brought into the policy for learning. In addition, to make the policy learning stable, a novel intrinsic reward is designed to encourage each agent's received rewards and observation transition to be predictable only conditioned on its own history. Extensive experiments conducted on CityFlow demonstrate that the proposed method substantially outperforms existing approaches and shows superior generalizability

Design and implementation of an integrated surface texture information system for design, manufacture and measurement

The optimised design and reliable measurement of surface texture are essential to guarantee the functional performance of a geometric product. Current support tools are however often limited in functionality, integrity and efficiency. In this paper, an integrated surface texture information system for design, manufacture and measurement, called “CatSurf”, has been designed and developed, which aims to facilitate rapid and flexible manufacturing requirements. A category theory based knowledge acquisition and knowledge representation mechanism has been devised to retrieve and organize knowledge from various Geometrical Product Specifications (GPS) documents in surface texture. Two modules (for profile and areal surface texture) each with five components are developed in the CatSurf. It also focuses on integrating the surface texture information into a Computer-aided Technology (CAx) framework. Two test cases demonstrate design process of specifications for the profile and areal surface texture in AutoCAD and SolidWorks environments respectively