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Nonlinear optical processing of ambient natural light is highly desired for computational imaging and sensing. Strong optical nonlinear response under weak broadband incoherent light is essential for this purpose. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than achievable in most optical nonlinear materials. We fabricated a 10,000-pixel array of optoelectronic neurons, and experimentally demonstrated an intelligent imaging system that instantly attenuates intense glares while retaining the weaker-intensity objects captured by a cellphone camera. This intelligent glare-reduction is important for various imaging applications, including autonomous driving, machine vision, and security cameras. The rapid nonlinear processing of incoherent broadband light might also find applications in optical computing, where nonlinear activation functions for ambient light conditions are highly sought.

Semiconductor photoconductive switches are useful and versatile emitters of terahertz (THz) radiation with a broad range of applications in THz imaging and time-domain spectroscopy. One fundamental challenge for achieving efficient ultrafast switching, however, is the relatively long carrier lifetime in most common semiconductors. To obtain picosecond ultrafast pulses, especially when coupled with waveguides/transmission lines, semiconductors are typically engineered with high defect density to reduce the carrier lifetimes, which in turn lowers the overall power output of the photoconductive switches. To overcome this fundamental trade-off, here we present a new hybrid photoconductive switch design by engineering a hot-carrier fast lane using graphene on silicon. While photoexcited carriers are generated in the silicon layer, similar to a conventional switch, the hot carriers are transferred to the graphene layer for efficient collection at the contacts. As a result, the graphene-silicon hybrid photoconductive switch emits THz fields with up to 80 times amplitude enhancement compared to its graphene-free counterpart. These results both further the understanding of ultrafast hot carrier transport in such hybrid systems and lay the groundwork toward intrinsically more powerful THz devices based on 2D-3D hybrid heterostructures.

Miniaturized spectrometers are of considerable interest for their portability. Most designs to date employ a photodetector array with distinct spectral responses or require elaborated integration of micro & nano optic modules, typically with a centimeter-scale footprint. Here, we report a design of a micron-sized near-infrared ultra-miniaturized spectrometer based on two-dimensional van der Waals heterostructure (2D-vdWH). By introducing heavy metal atoms with delocalized electronic orbitals between 2D-vdWHs, we greatly enhance the interlayer coupling and realize electrically tunable infrared photoresponse (1.15 to 1.47 μm). Combining the gate-tunable photoresponse and regression algorithm, we achieve spectral reconstruction and spectral imaging in a device with an active footprint < 10 μm. Considering the ultra-small footprint and simple fabrication process, the 2D-vdWHs with designable bandgap energy and enhanced photoresponse offer an attractive solution for on-chip infrared spectroscopy.

Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.

ABSTRACT Apple leaf spot is one of the most devastating diseases in the apple industry, caused by Alternaria alternata f. sp mali (A. alternata). SET‐domain group (SDG) proteins function as the histone methyltransferases and participate in plant development and stress responses. However, whether SDG proteins are associated with A. alternata resistance is largely unclear. Here, we describe the pathogen‐inducible MdSDG26 gene in apple (Malus × domestica). MdSDG26 has two transcript variants that function similarly in catalyzing histone methylation and A. alternata resistance. Transient overexpression of MdSDG26 increased the global levels of H3K4me3 and H3K36me3, whereas knockdown of MdSDG26 only reduced the H3K36me3 level. Transcriptome analysis revealed that MdSDG26 affected the genome‐wide transcriptome changes in response to A. alternata infection. ChIP‐qPCR analysis demonstrated that MdSDG26 modulates the levels of H3K36me3 and H3K4me3 at both the promoter and exon regions of MdNTL9. As a negative regulator of A. alternata resistance in apples, MdNTL9 plays a pivotal role in MdSDG26‐mediated resistance to A. alternata. Therefore, our findings provide compelling evidence for the regulatory function of MdSDG26 in histone methylation and its molecular role in conferring resistance to A. alternata. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the ant colony algorithm is easy to fall into local optimum and the convergence speed is slow in traveling salesman problem (TSP), dynamic grouping ant colony algorithm combined with cat swarm optimization is proposed. Firstly, the ants are distributed manually in different cities evenly when the population is initialized. Secondly, based on the division of labor in the cat swarm optimization, a dynamic grouping mechanism is introduced into the ant colony system to classify the ants into two types: search ants and tracking ants. Search ants increase the algorithm??s diversity in the early stage by improving the path construction rules, tracking ants use pheromone diffusion mechanism to adaptively update local pheromone to highlight the role of superior sub-paths, and avoid the algorithm falling into local optimum. Finally, the convergence speed is accelerated by the pheromone global update mechanism. Simulation experiments are carried out on multiple sets of instances in TSPLIB through Matlab. The simulation results show that the improved algorithm balances the diversity and conver-gence speed, and effectively imporves the quality of the solutions.