Complementary Value of Transthoracic Echocardiography and Cinefluoroscopic Evaluation of Mechanical Heart Prosthetic Valves

Cianciulli, Tomás Francisco; Lax, Jorge A.; Cerruti, Fausto E; Gigena, Gerardo; Redruello, Héctor Jorge; Orsi,; Gagliardi, Juan; Dorelle, Adriana N.; Riccitelli,; Prezioso, Horacio A.

doi:10.1111/j.0742-2822.2004.t01-22-20040211.x

Cited by 2 publications

(1 citation statement)

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“…IBM [14] built a 5.4-billion transistor chip with 4096 neurosynaptic cores called True North, a fully functional digital chip. To provide the extreme complexity of the human cerebral cortex, Cianciulli et al [15] combined complementary metal oxide semiconductors (CMOS) and two-terminal resistive device with electric circuits. Spintransfer torque magnetic memory [16] (STTMRAM), with nonvolatility, high speed, and high endurance, is suitable as a stochastic memristive device, considering the functional implication of synaptic neuronal plasticity.…”

Section: Introductionmentioning

confidence: 99%

Multitask Learning by Multiwave Optical Diffractive Network

Yuan

Liu

et al. 2020

Mathematical Problems in Engineering

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Recently, there has been tremendous research studies in optical neural networks that could complete comparatively complex computation by optical characteristic with much more fewer dissipation than electrical networks. Existed neural networks based on the optical circuit are structured with an optical grating platform with different diffractive phases at different diffractive points (Chen and Zhu, 2019 and Mo et al., 2018). In this study, it proposed a multiwave deep diffractive network with approximately 106 synapses, and it is easy to make hardware implementation of neuromorphic networks. In the optical architecture, it can utilize optical diffractive characteristic and different wavelengths to perform different tasks. Different wavelengths and different tasks inputs are independent of each other. Moreover, we can utilize the characteristic of them to inference several tasks, simultaneously. The results of experiments were demonstrated that the network could get a comparable performance to single-wavelength single-task. Compared to the multinetwork, single network can save the cost of fabrication with lithography. We train the network on MNIST and MNIST-FASHION which are two different datasets to perform classification of 32∗32 inputs with 10 classes. Our method achieves competitive results across both of them. In particular, on the complex task MNIST_FASION, our framework obtains an excellent accuracy improvement with 3.2%. In the meanwhile, MNSIT also has the improvement with 1.15%.

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Section: Introductionmentioning

confidence: 99%