A reconfigurable memristor diode based on a CuInPS/graphene lateral heterojunction.

The conventional reconfiguration of transistors requires an additional independent terminal for controllable gate input, which complicates the device structure and makes circuit integration difficult. In this work, we propose a reconfigurable diode based on a 2D layered copper indium thiophosphate (CIPS) and graphene (Gr) van der Waals lateral heterojunction, exhibiting distinctively bias-dependent reconfiguration. The reconfiguration characteristics include reversible memristive behaviors with a current on/off ratio of about 10 and switchable rectifying polarity with a rectifying ratio of up to 3 × 10.

Optoelectronic Reconfigurable Logic Gates Based on Two-Dimensional Vertical Field-Effect Transistors.

Optoelectronic reconfigurable logic gates are promising candidates to meet the multifunctional and energy-efficient requirements of integrated circuits. However, complex device architectures need more power and hinder multifunctional device applications. Here, we design vertical field-effect transistors (VFET) based on the two-dimensional (2D) graphene/MoS/WSe/graphene van der Waals heterojunction forming ohmic and Schottky contact.

Framework for denoising Monte Carlo photon transport simulations using deep learning.

Significance: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resulting in high computational burdens.

Aim: We aim to develop an effective image denoising technique using deep learning (DL) to dramatically improve the low-photon MC simulation result quality, equivalently bringing further acceleration to the MC method.

Approach: We developed a cascade-network combining DnCNN with UNet, while extending a range of established image denoising neural-network architectures, including DnCNN, UNet, DRUNet, and deep residual-learning for denoising MC renderings (ResMCNet), in handling three-dimensional MC data and compared their performances against model-based denoising algorithms.

Graphics processing units-accelerated adaptive nonlocal means filter for denoising three-dimensional Monte Carlo photon transport simulations.

The Monte Carlo (MC) method is widely recognized as the gold standard for modeling light propagation inside turbid media. Due to the stochastic nature of this method, MC simulations suffer from inherent stochastic noise. Launching large numbers of photons can reduce noise but results in significantly greater computation times, even with graphics processing units (GPU)-based acceleration.

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms.

We present a highly scalable Monte Carlo (MC) three-dimensional photon transport simulation platform designed for heterogeneous computing systems. Through the development of a massively parallel MC algorithm using the Open Computing Language framework, this research extends our existing graphics processing unit (GPU)-accelerated MC technique to a highly scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware.

Determination of Other Related Carotenoids Substances in Astaxanthin Crystals Extracted from Adonis amurensis.

Astaxanthin is a kind of important carotenoids with powerful antioxidation capacity and other health functions. Extracting from Adonis amurensis is a promising way to obtain natural astaxanthin. However, how to ensure the high purity and to investigate related substances in astaxanthin crystals are necessary issues.

Big Data Analysis on Puerto Rico Testsite for Exploring Contamination Threats.

In this paper, we present the use of Principal Component Analysis and customized software, to accelerate the spectral analysis of biological samples. The work is part of the mission of the National Institute of Environmental Health Sciences sponsored Puerto Rico Testsite for Exploring Contamination Threats Center, establishing linkages between environmental pollutants and preterm birth. This paper provides an overview of the data repository developed for the Center, and presents a use case analysis of biological sample data maintained in the database system.