Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance

High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3D images remains a challenge due to their high spatial resolution and dimensionality in contrast to currently limited GPU memory. Therefore, most existing 3D image segmentation methods use patch-based models, which have low inference efficiency and ignore global contextual information. To address these problems, we propose a super-resolution (SR) based patch-free 3D image segmentation framework that can realize HR segmentation from a global-wise low-resolution (LR) input. The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input. To furthermore balance the information loss with the LR input, we propose a High-Frequency Guidance Module (HGM), and design an efficient selective cropping algorithm to crop an HR patch from the original image as restoration guidance for it. In addition, we also propose a Task-Fusion Module (TFM) to exploit the inter connections between segmentation and SR task, realizing joint optimization of the two tasks. When predicting, only the main segmentation task is needed, while other modules can be removed for acceleration. The experimental results on two different datasets show that our framework has a four times higher inference speed compared to traditional patch-based methods, while its performance also surpasses other patch-based and patch-free models.Comment: Version #2 uploaded in Jul 10, 202

3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods

JD.com (B): Culture Consolidation and Talent Review

In 2012, JD.com (hereafter “the company”) had emerged to be the biggest B2C (Business to Consumer) e-commerce retailer in China. From 2009 to 2012, the company’s headcount quickly grew from several thousand to nearly thirty thousand. Still, it was short of talents; and the human resources (HR) department’s role and business impact were perceived as insignificant. Its founder and CEO, Richard Liu (hereafter Richard), realized that he had to strengthen the company’s internal management in order to sustain rapid growth. In 2011 and 2012, he recruited several chief officers (CxOs), including Rain Long (hereafter Rain)—chief human resources officer (CHO) and general counsel. Case A describes the challenges she faced when joining the company in August 2012, and invites students to think about what her priorities should be when tackling these challenges. Case B describes what Rain decided to do and how she executed her priorities in the first year—primarily, two improvement projects—culture consolidation and talent review. It asks students to set priorities on tackling the remaining HR problems in the coming few years

An Energy Harvester with Temperature Threshold Triggered Cycling Generation for Thermal Event Autonomous Monitoring

This paper proposes a temperature threshold triggered energy harvester for potential application of heat-event monitoring. The proposed structure comprises an electricity generation cantilever and a bimetallic cantilever that magnetically attract together. When the structure is heated to a pre-set temperature threshold, the heat absorption induced bimetallic effect of the bimetallic cantilever will cause sufficient bending of the generation cantilever to get rid of the magnetic attraction. The action triggers the freed generation cantilever into resonance to piezoelectrically generate electricity, and the heated bimetallic cantilever dissipates heat to the environment. With the heat dissipated, the bimetallic cantilever will be restored to attract with the generation cantilever again and the structure returns to the original state. Under continual heating, the temperature threshold triggered cycle is repeated to intermittently generate electric power. In this paper, the temperature threshold of the harvester is modeled, and the harvester prototype is fabricated and tested. The test results indicate that, with the temperature threshold of 71 °C, the harvesting prototype is tested to generate 1.14 V peak-to-peak voltage and 1.077 μW instantaneous power within one cycle. The thermal harvesting scheme shows application potential in heat event-driven autonomous monitoring