COMPREHENSIVE REVIEW ON THE EXPLOITATION OF ADVANCED MEMORY OPTIMIZATION STRATEGIES TO IMPROVE PERFORMANCE FOR CONVOLUTIONAL AND SPIKING NEURAL NETWORKS IN MEDICAL IMAGING USING HARDWARE ACCELERATORS

Comprehensive Review on the Exploitation of Advanced Memory Optimization Strategies to Improve Performance for Convolutional and Spiking Neural Networks in Medical Imaging Using Hardware Accelerators

Advanced memory optimization techniques are reviewed to enhance the performance of Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs) on hardware accelerators, addressing the real-world challenges in medical imaging.This review evaluates various platforms: In-Memory Computing (IMC), Field-Programmable Gate Array (FPGA), Python

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Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning

Abstract Background The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment.Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs

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Intrinsic surface p-wave superconductivity in layered AuSn4

Abstract The search for topological superconductivity (TSC) is currently an exciting pursuit, since non-trivial topological superconducting phases could host exotic Majorana modes.However, the difficulty in fabricating proximity-induced TSC heterostructures, the sensitivity to disorder and stringent topological restrictions of intrinsic TSC place s

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