3,482 research outputs found

    A wide field-of-view scanning endoscope for whole anal canal imaging

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    We report a novel wide field-of-view (FOV) scanning endoscope, the AnCam, which is based on contact image sensor (CIS) technology used in commercialized business card scanners. The AnCam can capture the whole image of the anal canal within 10 seconds with a resolution of 89 μm, a maximum FOV of 100 mm × 120 mm, and a depth-of-field (DOF) of 0.65 mm at 5.9 line pairs per mm (lp/mm). We demonstrate the performance of the AnCam by imaging the entire anal canal of pigs and tracking the dynamics of acetowhite testing. We believe the AnCam can potentially be a simple and convenient solution for screening of the anal canal for dysplasia and for surveillance in patients following treatment for anal cancer

    Indoor CO2 monitoring in a surgical intensive care unit under visitation restrictions during the COVID-19 pandemic

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    BackgroundIndoor CO2 concentration is an important metric of indoor air quality (IAQ). The dynamic temporal pattern of CO2 levels in intensive care units (ICUs), where healthcare providers experience high cognitive load and occupant numbers are frequently changing, has not been comprehensively characterized.ObjectiveWe attempted to describe the dynamic change in CO2 levels in the ICU using an Internet of Things-based (IoT-based) monitoring system. Specifically, given that the COVID-19 pandemic makes hospital visitation restrictions necessary worldwide, this study aimed to appraise the impact of visitation restrictions on CO2 levels in the ICU.MethodsSince February 2020, an IoT-based intelligent indoor environment monitoring system has been implemented in a 24-bed university hospital ICU, which is symmetrically divided into areas A and B. One sensor was placed at the workstation of each area for continuous monitoring. The data of CO2 and other pollutants (e.g., PM2.5) measured under standard and restricted visitation policies during the COVID-19 pandemic were retrieved for analysis. Additionally, the CO2 levels were compared between workdays and non-working days and between areas A and B.ResultsThe median CO2 level (interquartile range [IQR]) was 616 (524–682) ppm, and only 979 (0.34%) data points obtained in area A during standard visitation were ≥ 1,000 ppm. The CO2 concentrations were significantly lower during restricted visitation (median [IQR]: 576 [556–596] ppm) than during standard visitation (628 [602–663] ppm; p < 0.001). The PM2.5 concentrations were significantly lower during restricted visitation (median [IQR]: 1 [0–1] μg/m3) than during standard visitation (2 [1–3] μg/m3; p < 0.001). The daily CO2 and PM2.5 levels were relatively low at night and elevated as the occupant number increased during clinical handover and visitation. The CO2 concentrations were significantly higher in area A (median [IQR]: 681 [653–712] ppm) than in area B (524 [504–547] ppm; p < 0.001). The CO2 concentrations were significantly lower on non-working days (median [IQR]: 606 [587–671] ppm) than on workdays (583 [573–600] ppm; p < 0.001).ConclusionOur study suggests that visitation restrictions during the COVID-19 pandemic may affect CO2 levels in the ICU. Implantation of the IoT-based IAQ sensing network system may facilitate the monitoring of indoor CO2 levels

    Energy-resolved Photoconductivity Mapping in a Monolayer-bilayer WSe2 Lateral Heterostructure

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    Vertical and lateral heterostructures of van der Waals materials provide tremendous flexibility for band structure engineering. Since electronic bands are sensitively affected by defects, strain, and interlayer coupling, the edge and heterojunction of these two-dimensional (2D) systems may exhibit novel physical properties, which can be fully revealed only by spatially resolved probes. Here, we report the spatial mapping of photoconductivity in a monolayer-bilayer WSe2 lateral heterostructure under multiple excitation lasers. As the photon energy increases, the light-induced conductivity detected by microwave impedance microscopy first appears along the hetero-interface and bilayer edge, then along the monolayer edge, inside the bilayer area, and finally in the interior of the monolayer region. The sequential emergence of mobile carriers in different sections of the sample is consistent with the theoretical calculation of local energy gaps. Quantitative analysis of the microscopy and transport data also reveals the linear dependence of photoconductivity on the laser intensity and the influence of interlayer coupling on carrier recombination. Combining theoretical modeling, atomic scale imaging, mesoscale impedance microscopy, and device-level characterization, our work suggests an exciting perspective to control the intrinsic band-gap variation in 2D heterostructures down to the few-nanometer regime.Comment: 18 pages, 5 figures; Nano Lett., Just Accepted Manuscrip

    One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training

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    Deep neural networks (DNNs) are widely deployed on real-world devices. Concerns regarding their security have gained great attention from researchers. Recently, a new weight modification attack called bit flip attack (BFA) was proposed, which exploits memory fault inject techniques such as row hammer to attack quantized models in the deployment stage. With only a few bit flips, the target model can be rendered useless as a random guesser or even be implanted with malicious functionalities. In this work, we seek to further reduce the number of bit flips. We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release. This high-risk model, obtained coupled with a corresponding malicious model, behaves normally and can escape various detection methods. The results on benchmark datasets show that an adversary can easily convert this high-risk but normal model to a malicious one on victim's side by \textbf{flipping only one critical bit} on average in the deployment stage. Moreover, our attack still poses a significant threat even when defenses are employed. The codes for reproducing main experiments are available at \url{https://github.com/jianshuod/TBA}.Comment: This work is accepted by the ICCV 2023. 14 page

    Learning to Weight Samples for Dynamic Early-exiting Networks

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    Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks allow easy samples to be output at early exits, removing the need for executing deeper layers. While existing works mainly focus on the architectural design of multi-exit networks, the training strategies for such models are largely left unexplored. The current state-of-the-art models treat all samples the same during training. However, the early-exiting behavior during testing has been ignored, leading to a gap between training and testing. In this paper, we propose to bridge this gap by sample weighting. Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers. The training of hard samples (mostly exit from deeper layers), however, should be emphasized by the late classifiers. Our work proposes to adopt a weight prediction network to weight the loss of different training samples at each exit. This weight prediction network and the backbone model are jointly optimized under a meta-learning framework with a novel optimization objective. By bringing the adaptive behavior during inference into the training phase, we show that the proposed weighting mechanism consistently improves the trade-off between classification accuracy and inference efficiency. Code is available at https://github.com/LeapLabTHU/L2W-DEN.Comment: ECCV 202

    Fabrication of multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors by using CF4 plasma treatment

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    Multianalyte CeO2 biosensors have been demonstrated to detect pH, glucose, and urine concentrations. To enhance the multianalyte sensing capability of these biosensors, CF4 plasma treatment was applied to create nanograin structures on the CeO2 membrane surface and thereby increase the contact surface area. Multiple material analyses indicated that crystallization or grainization caused by the incorporation of flourine atoms during plasma treatment might be related to the formation of the nanograins. Because of the changes in surface morphology and crystalline structures, the multianalyte sensing performance was considerably enhanced. Multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors exhibit potential for use in future biomedical sensing device applications
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