A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

In this letter, we propose a novel offloading learning approach to compromise energy consumption and latency in a multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach

Analysis of HER2 Gene Amplification and Certain Prognostic Factors in Breast Cancer

Objective: The HER2 gene amplification and certain prognostic factors in breast cancer were analyzed. Method: The gene amplification and protein expression of human epidermal growth factor receptor in 100 breast cancer tissues detected by FISH and IHC detection method in the hospital from January 2020 to December 2021 were analyzed. To analyze some breast cancer prognostic factors. Result: 0 is 8 cases of HER-2 protein breast cancer, (1+) is 11 cases, (2+) is 49 cases, (3+) is 32 cases. The HER2 gene was amplified in 49 cases, of which 23 cases showed red signals in clusters, and 26 cases showed red signals in dots. 51 cases of HER-2 gene were not amplified. There are differences in the detection results of FISH and IHC detection methods (P＞0.05). ER, PR and polysomy of chromosome 17 are prognostic factors associated with HER2 gene amplification in certain breast cancers. (P＜0.05) Conclusion: To analyze the HER2 gene amplification in breast cancer and targeted select FISH and IHC detection methods can improve the therapeutic effect and prognostic factor, which deserves clinical attention

ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores

Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. PTCs can achieve ultra-fast and efficient tensor operations for neural network (NN) acceleration. Current PTC designs are either manually constructed or based on matrix decomposition theory, which lacks the adaptability to meet various hardware constraints and device specifications. To our best knowledge, automatic PTC design methodology is still unexplored. It will be promising to move beyond the manual design paradigm and "nurture" photonic neurocomputing with AI and design automation. Therefore, in this work, for the first time, we propose a fully differentiable framework, dubbed ADEPT, that can efficiently search PTC designs adaptive to various circuit footprint constraints and foundry PDKs. Extensive experiments show superior flexibility and effectiveness of the proposed ADEPT framework to explore a large PTC design space. On various NN models and benchmarks, our searched PTC topology outperforms prior manually-designed structures with competitive matrix representability, 2-30x higher footprint compactness, and better noise robustness, demonstrating a new paradigm in photonic neural chip design. The code of ADEPT is available at https://github.com/JeremieMelo/ADEPT using the https://github.com/JeremieMelo/pytorch-onn (TorchONN) library.Comment: Accepted to ACM/IEEE Design Automation Conference (DAC), 202