Improving the deconvolution of spectra at finite temperatures by replacing spectrum with a neural network.

In condensed matter physics studies, spectral information plays an important role in understanding the composition of materials. However, it is difficult to obtain a material's spectrum information directly through experiments or simulations. For example, the spectral information deconvoluted by scanning tunneling spectroscopy suffers from the temperature broadening effect, which is a known ill-posed problem and makes the deconvolution results unstable.

Weighted Large Margin Nearest Center Distance-Based Human Depth Recovery With Limited Bandwidth Consumption.

This paper proposes a weighted large margin nearest center (WLMNC) distance-based human depth recovery method for tele-immersive video interaction systems with limited bandwidth consumption. In the remote stage, the proposed method highly compresses the depth data of the remote human into skeletal block structures by learning the WLMNC distance, which is equivalent to downsampling the human depth map at $64{\times}$ the sampling rate. In the local stage, the method first recovers a rough human depth map based on a WLMNC distance augmented clustering approach and then obtains a fine depth map based on a rough depth-guided autoregressive model to preserve the depth discontinuities and suppress texture copy artifacts.

Understanding inhibition of viral proteins on type I IFN signaling pathways with modeling and optimization.

The interferon system provides a powerful and universal intracellular defense mechanism against viruses. As one part of their survival strategies, many viruses have evolved mechanisms to counteract the host type I interferon (IFN-alpha/beta) responses. In this study, we attempt to investigate virus- and double-strand RNA (dsRNA)-triggered type I IFN signaling pathways and understand the inhibition of IFN-alpha/beta induction by viral proteins using mathematical modeling and quantitative analysis.