Lead halide perovskites with good optoelectronic properties and high attenuation of high-energy radiation are great candidates for X-ray radiation detectors. Large area, dense, and thick films or wafers are a prerequisite for these applications. In this paper, a one-step heat-assisted high-pressure press method is developed to directly prepare a large (the largest has a diameter of 80 mm) and thickness- and shape-controlled phase-pure organic–inorganic hybrid CH3NH3PbI3 wafer of densely packed large microcrystals from raw powder materials. Meanwhile, this method uses no solvent to achieve essentially 100% material utilization. The obtained wafers show good ambipolar carrier mobilities of ∼20 cm2 V–1 s–1 and a μτ product as high as 3.84 × 10–4 cm2 V–1. Under an X-ray source using an acceleration voltage of 40 kV, the perovskite wafer-based X-ray detector shows an X-ray sensitivity as large as 1.22 × 105 μC Gyair –1 cm–2 under a 10 V bias, the highest reported for any perovskite material. The method provides a convenient strategy for producing large perovskite wafers with good optoelectronic properties, which will facilitate the development of large perovskite devices.

Neural coding is one of the central questions in systems neuroscience for understanding of how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding incoming stimulus is highly demanded for better performance of physical devices. Traditionally researchers have focused on functional magnetic resonance imaging (fMRI) data as the neural signals of interest for decoding visual scenes. However, our visual perception operates in a fast time scale of millisecond in terms of an event termed neural spike.There are few studies of decoding by using spikes. Here we fulfill this aim by developing a novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes, including static images and dynamic videos, from experimentally recorded spikes of a population of retinal ganglion cells. The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion. Our SID also outperforms on the reconstruction of visual stimulus compared to existing fMRI decoding models. In addition, with the aid of a spike encoder, we show that SID can be generalized to arbitrary visual scenes by using the image datasets of MNIST, CIFAR10, and CIFAR100. Furthermore, with a pre-trained SID, one can decode any dynamic videos to achieve real-time encoding and decoding of visual scenes by spikes. Altogether, our results shed new lights on neu-romorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses.

Frequent subgraph mining (FSM) plays an important role in graph mining, attracting a great deal of attention in many areas, such as bioinformatics, web data mining and social networks. In this paper, we propose SSIGRAM (Spark based Single Graph Mining), a Spark based parallel frequent subgraph mining algorithm in a single large graph. Aiming to approach the two computational challenges of FSM, we conduct the subgraph extension and support evaluation parallel across all the distributed cluster worker nodes. In addition, we also employ a heuristic search strategy and three novel optimizations: load balancing, pre-search pruning and top-down pruning in the support evaluation process, which significantly improve the performance. Extensive experiments with four different real-world datasets demonstrate that the proposed algorithm outperforms the existing GRAMI (Graph Mining) algorithm by an order of magnitude for all datasets and can work with a lower support threshold.

Neuroprosthesis, as one type of precision medicine device, is aiming for manipulating neuronal signals of the brain in a closed-loop fashion, together with receiving stimulus from the environment and controlling some part of our brain/body. In terms of vision, incoming information can be processed by the brain in millisecond interval. The retina computes visual scenes and then sends its output as neuronal spikes to the cortex for further computation. Therefore, the neuronal signal of interest for retinal neuroprosthesis is spike. Closed-loop computation in neuroprosthesis includes two stages: encoding stimulus to neuronal signal, and decoding it into stimulus. Here we review some of the recent progress about visual computation models that use spikes for analyzing natural scenes, including static images and dynamic movies. We hypothesize that for a better understanding of computational principles in the retina, one needs a hypercircuit view of the retina, in which different functional network motifs revealed in the cortex neuronal network should be taken into consideration for the retina. Different building blocks of the retina, including a diversity of cell types and synaptic connections, either chemical synapses or electrical synapses (gap junctions), make the retina an ideal neuronal network to adapt the computational techniques developed in artificial intelligence for modeling of encoding/decoding visual scenes. Altogether, one needs a systems approach of visual computation with spikes to advance the next generation of retinal neuroprosthesis as an artificial visual system.