Results 201 to 210 of about 1,465,298 (295)
Detection of Remaining Feed in the Feed Troughs of Flat-Fed Meat Ducks Based on the RGB-D Sensor and YOLO V8. [PDF]
Tan X, Yuan J, Ying S, Wang J.
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In NF2–wild‐type meningiomas, loss of the epigenetic regulator KMT2C suppresses NF2 transcription and inactivates Hippo signaling, driving tumor progression and increasing ferroptosis sensitivity. Restoration of histone acetylation reverses these effects and inhibits tumor growth, identifying KMT2C as a key regulator linking epigenetic control, NF2 ...
Liuchao Zhang +13 more
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Radiomics Results for Adrenal Mass Characterization Are Stable and Reproducible Under Different Software. [PDF]
Feliciani G +9 more
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BreakNet: discontinuity-resilient multi-scale transformer segmentation of retinal layers. [PDF]
Ganjee R +5 more
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Bounded‐error estimation using dead zone and bounding ellipsoid
International Journal of Adaptive Control and Signal Processing, 1994AbstractThe use of a dead zone and a bounding ellipsoid for parameter estimation when measurement errors are bounded is discussed. the size of the dead zone is set to be exactly equal to the assumed noise bound. The algorithm retains the properties of computing parameter point estimates and allows a bounding ellipsoid to be computed at each iterative ...
Evans, R. J., Zhang, C., Soh, Y. C.
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Multiweight optimization in optimal bounding ellipsoid algorithms
IEEE Transactions on Signal Processing, 2006Optimal Bounding Ellipsoid (OBE) algorithms offer an attractive alternative to traditional least-squares methods for identification and filtering problems involving affine-in-parameters signal and system models. The benefits-including low computational efficiency, superior tracking ability, and selective updating that permits processor multi-tasking ...
D. Joachim, J.R. Deller
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Recurrent Neural Networks Training With Stable Bounding Ellipsoid Algorithm
IEEE Transactions on Neural Networks, 2009Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed.
Wen, Yu, José, de Jesús Rubio
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