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This technology is a method to target and eliminate the viral reservoir of latent HIV-1 using latency reversal agents facilitated by targeted delivery with monoclonal antibodies. Unmet Need: Method for efficient, specific delivery of HIV-1 latency reversal agents Current techniques to treat HIV involve the use of antiretroviral therapies to reduce HIV levels in the blood to undetectable levels. However, these therapies are unable to eliminate viral reservoir, which is established during primary HIV infection and comprised of subpopulations of CD4+ resting memory T cells. This technology is software and hardware for efficient initialization and optimization of deep neural network (DNN) parameters that can be used for training large-scale, optimally robust DNN models. Unmet Need: Resource-efficient method for training large-scale neural networks in deep learning To perform more complicated tasks, deep neural networks (DNNs) have been growing larger and more complex. With current randomization-based weight initialization and update methods, training the many parameters of these large-scale DNNs requires extensive amounts of computational resources, data, and time, which can hinder deep learning models under development from reaching target performance. This technology is an immunotherapy approach that targets the cell surface marker CD58 to enhance antitumor immunity and bypass immune evasion mechanisms, therefore improving treatment outcomes in patients. Unmet Need: Addressing cancer immunotherapy resistance and immune evasion Despite the success of immune checkpoint inhibitors in treating cancer, many patients develop resistance, diminishing the overall effectiveness of these therapies. Current treatments often fail to address immune evasion mechanisms and disruptions in critical pathways.