A novel base station antenna based on rectangular waveguide

© 2016 IEICE. A novel base station antenna element is proposed. It consists of a surface of parallel strips to rotate the polarization direction and a segment of a rectangular waveguide. The surface is designed on a single-sided substrate, which has the same area as the aperture of the waveguide. In assembling, the non-copper side of the substrate is placed in direct contact with the aperture of the waveguide antenna. To achieve the polarization rotation, the parallel strips on the surface are rotated by 45° with respect to the walls of the waveguide antenna. By adding the surface, the linear polarization direction of the rectangular waveguide antenna is rotated by 45° to comply with the requirements of cellular industry. SMA connector with a conical probe is used as the coaxial-to-waveguide adaptor. Results have shown that the proposed antenna has a fractional impedance bandwidth of 35%, and a stable radiation pattern is also achieved

Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR)

As models for nature language processing (NLP), computer vision (CV) and recommendation systems (RS) require surging computation, a large number of GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput. However, training such LB tasks often meets large generalization gap and downgrades final precision, which limits enlarging the batch size. In this work, we develop the variance reduced gradient descent technique (VRGD) based on the gradient signal to noise ratio (GSNR) and apply it onto popular optimizers such as SGD/Adam/LARS/LAMB. We carry out a theoretical analysis of convergence rate to explain its fast training dynamics, and a generalization analysis to demonstrate its smaller generalization gap on LB training. Comprehensive experiments demonstrate that VRGD can accelerate training ($1\sim 2 \times$), narrow generalization gap and improve final accuracy. We push the batch size limit of BERT pretraining up to 128k/64k and DLRM to 512k without noticeable accuracy loss. We improve ImageNet Top-1 accuracy at 96k by $0.52pp$ than LARS. The generalization gap of BERT and ImageNet training is significantly reduce by over $65\%$.Comment: 25 pages, 5 figure

Circulating MicroRNAs: Potential Biomarkers for Cancer

Cancer is the leading cause of death in the world. Development of minimally invasive biomarkers for early detection of cancer is urgently needed to reduce high morbidity and mortality associated with malignancy. MicroRNAs (miRNAs) are small regulatory RNAs that modulate the activity of specific mRNA targets and play important roles in a wide range of physiologic and pathologic processes. Recently, miRNAs were found to be dysregulated in a variety of diseases including cancer. Emerging evidence suggests that miRNAs are involved in tumor initiation and progression. Together, the different expression profiles of miRNAs in cancer, and the stability of circulating miRNAs, make them new potentially clinical biomarkers for cancer diagnosis, classification, therapeutic decisions, and prognosis