The COVID-19 pandemic has highlighted the importance of diagnosis and monitoring as early and accurately as possible. However, the reverse-transcription polymerase chain reaction (RT-PCR) test results in two issues: (1) protracted turnaround time from sample collection to testing result and (2) compromised test accuracy, as low as 67%, due to when and how the samples are collected, packaged, and delivered to the lab to conduct the RT-PCR test. Thus, we present ComputeCOVID19+, our computed tomography-based framework to improve the testing speed and accuracy of COVID-19 (plus its variants) via a deep learning-based network for CT image enhancement called DDnet, short for DenseNet and Deconvolution network. To demonstrate its speed and accuracy, we evaluate Com-puteCOVID19+ across several sources of computed tomography (CT) images and on many heterogeneous platforms, including multicore CPU, many-core GPU, and even FPGA. Our results show that ComputeCOVID19+ can significantly shorten the turnaround time from days to minutes and improve the testing accuracy to 91%.
The continuous development of the aquaculture industry is accompanied by a large amount of aquaculture wastewater discharge, which brings a series of environmental protection problems. With the call for the construction of high-quality modern marine pastures, it is imperative to enhance the research and development capabilities of marine observation/monitoring equipment and pollution reduction/monitoring equipment. At present, underwater submersibles have the characteristics of complex structure and high concealment requirements. As a typical combination of robotics and bionics, the bionic robotic fish has the characteristics of high swimming efficiency, strong mobility, and strong concealment. This project will apply the bionic robotic fish to the field of pasture construction and monitoring in oceans and other waters. A bionic fish with coordinated pectoral and caudal fins.
Cricothyrotomy serves as one of the most efficient surgical interventions when a patient is enduring a Can't Intubate Can't Oxygenate (CICO) scenario. However, medical background and professional training are required for the provider to establish a patent airway successfully. Motivated by robotics applications in search and rescue, this work focuses on applying artificial intelligence techniques on the precise localization of the incision site, the cricothyroid membrane (CTM), of the injured using an RGB-D camera, and the manipulation of a robot arm with reinforcement learning to reach the detected CTM keypoint. In this paper, we further improved the success rate of our previously proposed Hybrid Neural Network (HNNet) in detecting the CTM from 84.3% to 96.6%, yielding an error of less than 5mm in real-world coordinates. In addition, a separate neural network was trained to manipulate a robotic arm for reaching a waypoint with an error of less than 5mm. An integrated system that combines both the perception and the control techniques was built and experimentally validated using a human-size manikin to validate the overall concept of autonomous cricothyrotomy with an RGB-D camera and a robotic manipulator using artificial intelligence.
Similarities between natural languages and programming languages have prompted researchers to apply neural network models to software problems, such as code generation and repair. However, program-specific characteristics pose unique prediction challenges that require the design of new and specialized neural network solutions. In this work, we identify new prediction challenges in application programming interface (API) completion tasks and find that existing solutions are unable to capture complex program dependencies in program semantics and structures. We design a new neural network model Multi-HyLSTM to overcome the newly identified challenges and comprehend complex dependencies between API calls. Our neural network is empowered with a specialized dataflow analysis to extract multiple global API dependence paths for neural network predictions. We evaluate Multi-HyLSTM on 64,478 Android Apps and predict 774,460 Java cryptographic API calls that are usually challenging for developers to use correctly. Our Multi-HyLSTM achieves an excellent top-1 API completion accuracy at 98.99%. Moreover, we show the effectiveness of our design choices through an ablation study and have released our dataset.
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