Application-specific optical processors have been considered disruptive technologies for modern computing that can fundamentally accelerate the development of artificial intelligence (AI) by offering substantially improved computing performance. Recent advancements in optical neural network architectures for neural information processing have been applied to perform various machine learning tasks. However, the existing architectures have limited complexity and performance; and each of them requires its own dedicated design that cannot be reconfigured to switch between different neural network models for different applications after deployment. Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons. It allocates almost all of its computational operations optically and achieves extremely high speed of data modulation and large-scale network parameter updating by dynamically programming optical modulators and photodetectors. We demonstrated the reconfiguration of the DPU to implement various diffractive feedforward and recurrent neural networks and developed a novel adaptive training approach to circumvent the system imperfections. We applied the trained networks for high-speed classifying of handwritten digit images and human action videos over benchmark datasets, and the experimental results revealed a comparable classification accuracy to the electronic computing approaches. Furthermore, our prototype system built with off-the-shelf optoelectronic components surpasses the performance of state-of-the-art graphics processing units (GPUs) by several times on computing speed and more than an order of magnitude on system energy efficiency. We believe the proposed reconfigurable DPU is a remarkable step towards high-performance neuromorphic optoelectronic computing processors that can achieve real-time dynamic architecture configurations according to software and will facilitate a broad range of AI applications, e.g., autonomous driving, robotics, and edge computing.Computing processors driven by electronics have evolved dramatically over the past decade, from general-purpose central processing units (CPUs) 1 to custom computing platforms, e.g., GPUs 2 , FPGAs 3 , and ASICs 4,5 , to meet the ubiquitously increasing demand of computing resources. The progress of these silicon computing hardware platforms has greatly contributed to the resurgence of artificial intelligence (AI) by allowing the training of larger-scale and more complicated models 6,7 . We have witnessed the extensive applications of various neural computing architectures, e.g., convolutional neural networks (CNNs) 2,7 , recurrent neural networks (RNNs) 8 , spiking neural networks (SNNs) 9 , and reservoir computing (RC) 10 , in a broad range of fields. However, electronic hardware implementations have reached unsustainable performance growth as the exponential scaling of electr...

The medial prefrontal cortex (mPFC) is a crucial cortical region that integrates information from numerous cortical and subcortical areas and converges updated information to output structures. It plays essential roles in the cognitive process, regulation of emotion, motivation, and sociability. Dysfunction of the mPFC has been found in various neurological and psychiatric disorders, such as depression, anxiety disorders, schizophrenia, autism spectrum disorders, Alzheimer’s disease, Parkinson’s disease, and addiction. In the present review, we summarize the preclinical and clinical studies to illustrate the role of the mPFC in these neurological diseases.

The concept of ideal binary time-frequency masks has received attention recently in monaural and binaural sound separation. Although often assumed, the optimality of ideal binary masks in terms of signal-to-noise ratio has not been rigorously addressed. In this paper we give a formal treatment on this issue and clarify the conditions for ideal binary masks to be optimal. We also experimentally compare the performance of ideal binary masks to that of ideal ratio masks on a speech mixture database and a music database. The results show that ideal binary masks are close in performance to ideal ratio masks which are closely related to the Wiener filter, the theoretically optimal linear filter.

The new coronavirus SARS-CoV-2 pandemic of early 2020 poses an enormous challenge to global public health. Coronavirus Disease 2019 (COVID-19) caused by the virus has spread rapidly throughout the world, taking thousands of lives in just over 2 months. It is critical to refine the incidence and mortality risks of COVID-19 for the effective management of the general public and patients during the outbreak. In this report, we investigate the incidence and mortality risks of the infection by analyzing the age composition of 5,319 infected patients, 76 fatal cases, and 1,144,648 individuals of the general public in China. Our results show a relatively low incidence risk for young people but a very high mortality risk for seniors. Notably, mortality risk could be as high as 0.48 for people older than 80 years. Furthermore, our study suggests that a good medical service can effectively reduce the mortality rate of the viral infection to 1% or less.

Separating singing voice from music accompaniment is very useful in many applications, such as lyrics recognition and alignment, singer identification, and music information retrieval. Although speech separation has been extensively studied for decades, singing voice separation has been little investigated. We propose a system to separate singing voice from music accompaniment for monaural recordings. Our system consists of three stages. The singing voice detection stage partitions and classifies an input into vocal and nonvocal portions. For vocal portions, the predominant pitch detection stage detects the pitch of the singing voice and then the separation stage uses the detected pitch to group the time-frequency segments of the singing voice. Quantitative results show that the system performs the separation task successfully.