Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional S...
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— We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot’s "opinion" for which way and by how much to pass human movers crossing its path. T...
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Synchronization is a widespread phenomenon observed across natural and artificial networked systems. It often manifests itself by clusters of units exhibiting coincident dynamics. These clusters are a direct consequen...
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An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin *** process is cumbersome and time-consuming,often leading to unnecessary biopsies and *** noninvasive optical t...
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An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin *** process is cumbersome and time-consuming,often leading to unnecessary biopsies and *** noninvasive optical technologies such as reflectance confocal microscopy(RCM)can provide label-free,cellular-level resolution,in vivo images of skin without performing a *** RCM is a useful diagnostic tool,it requires specialized training because the acquired images are grayscale,lack nuclear features,and are difficult to correlate with tissue ***,we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution,enabling visualization of the epidermis,dermal-epidermal junction,and superficial dermis *** network was trained under an adversarial learning scheme,which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground *** show that this trained neural network can be used to rapidly perform virtual histology of in vivo,label-free RCM images of normal skin structure,basal cell carcinoma,and melanocytic nevi with pigmented melanocytes,demonstrating similar histological features to traditional histology from the same excised *** application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.
Aligned carbon nanotube films make an excellent hyperbolic material platform in the infrared. Here, we experimentally demonstrate the presence of high-k modes in aligned carbon nanotube films and outcouple them to fre...
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Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detectin...
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Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of- Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n3) and space complexity of O(n2), mainly due to a Cholesky factorization operation for an n × n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on largescale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the
In this study, selective area growth (SAG)s of AlGaN/GaN nanowires (NWs) on miscut N-polar GaN templates were studied and compared with that grown on Ga-polar templates using plasma-assisted molecular beam epitaxy (MB...
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This work introduces an approach to compute periodic phase diagram of micromagnetic systems by solving a periodic linearized Landau-Lifshitz-Gilbert (LLG) equation using an eigenvalue solver with the Finite Element Me...
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We investigate the impact of quantum well (QW) thickness on efficiency loss in c-plane InGaN/GaN LEDs using a small-signal electroluminescence (SSEL) technique. Multiple mechanisms related to efficiency loss are indep...
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Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides...
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