The efficient extraction of spatial-spectral features is crucial for hyperspectral images (HSIs) classification, especially the spatial features are essential to improve the classification accuracy. Both the convolutional neural network (CNN) and transformers frameworks can effectively represent high-level global and local semantic features, which have significant potential for HSIs classification field. We proposed an enhanced high-frequency spatial feature (EH-FS2) method for HSIs classification. First, we connected two networks, CNN and transformer, as a shallow-deep feature extractor to rapidly extract multiscale spectral-spatial features by CAB attention module. This module not only realizes the efficient association of local and global features but also emphasizes the important spatial location information. Then, we added a local deep convolutional (L-D-ConvFFN) to the GLS2FFN module to effectively compensate for the loss of high-frequency spatial information by the self-attention mechanism as a low-pass filter and enhance the capability of perceiving high-frequency spatial details. The experiments were validated on the Salinas (SAs), Pavia University (PU), and WHU-Hi-LongKou datasets, and the classification performance outperforms several state-of-the-art approaches. The experiments have shown that the EH-FS2 method can better distinguish the features between different classes, especially for the enhancement and recovery of high-frequency spatial features with significant advantages. Furthermore, it can also be proved that the generalization of the HSI classification problem to the advanced semantic classification problem is validated.

Nonrigid deformation (NRD) and image noise in multimodal remote sensing images (MRSI) lead to abrupt changes in feature directions, resulting in sensitivity to rotational variation, sparse correct matches, and high false match rates. In order to address these challenges, this article proposes a second-order tensor orientation feature transformation (SOFT) method to improve the rotational invariance of MRSI matching and increase the number of correct matches (NCMs). The SOFT method has two main contributions: 1) a novel second-order tensor orientation descriptor is constructed by generating a tensor orientation feature map using a designed second-order tensor function, which is then combined with a gradient location and orientation histogram (GLOH)-like descriptor framework to achieve robust rotational invariance in multimodal image matching and 2) an error-removal global-local iterative optimization (EGIO) is introduced, employing a skewness of mixed pixel intensity (SMPI) function to automatically select matching seed points, followed by an iterative partition optimization strategy for refining corresponding points. Experiments on 744 groups of typical MRSIs demonstrate that the SOFT method significantly outperforms nine state-of-the-art methods, achieving an average 97% improvement in the NCMs, an average 25.51% improvement in the rate of correct matches (RCMs), and an average reduction in RMSE of 2.69 pixels. The proposed SOFT method, thus, offers robust MRSI matching with strong rotational invariance and precise identification of corresponding points, proving its effectiveness for complex remote sensing scenarios. Access to experiment-related data and codes will be provided athttps://skyearth.org/research/.

Indium selenide (InSe) is a typical layered metal-chalcogenide semiconductor that has potential for developing ultrafast optoelectronic devices. In this work, InSe-polyvinyl alcohol (InSe-PVA) film is employed as saturable absorber and prepared by mixing InSe nanosheets solution and polyvinyl alcohol solution. The nonlinear absorption properties of the InSe saturable absorber (InSe-SA) are investigated, showing that the nonsaturable absorption and modulation depth are 37.5% and 9.55%, respectively. Traditional soliton lasers are generated in erbium-doped fiber (EDF) laser-employed InSe as a mode-locker. The central wavelength and pulse duration of the traditional soliton pulse are 1568.73 nm and 2.06 ps, respectively, under a repetition rate of 1.731 MHz. The maximum average output power is 16.4 mW at the pump power of 413 mW. To the best of our knowledge, this is the first demonstration of a traditional soliton pulse with InSe as a mode-locker. The experimental results further demonstrate that InSe is an outstanding nonlinear absorption material in ultrafast fiber laser.

Optical networks-on-chips (NoCs) based on silicon photonics have been proposed as an emerging communication architecture for many-core chip multiprocessors. However, the thermal sensitivity of silicon photonics is one of the major challenges. Q-learning-based adaptive routing has been proposed in related work to mitigate the thermal issue. However, table overhead of the traditional table-based Q-routing would scale up quickly with the increase of network size. In this article, we propose a table-free approximate Q-learning-based thermal-aware adaptive routing to find optimal low-loss paths in the presence of on-chip temperature variations. The simulation results show that the proposed table-free approximate Q-learning-based adaptive routing can converge faster and it can achieve similar optimization effect as compared to the best optimization effect of the traditional table-based Q-routing. The performance gap between the proposed approximation method and the traditional table-based Q-routing expands when the network size increases.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has enabled the simultaneous transcriptomic profiling of individual cells under different biological conditions. scRNA-seq data have two unique challenges that can affect the sensitivity and specificity of single-cell differential expression analysis: a large proportion of expressed genes with zero or low read counts ('dropout' events) and multimodal data distributions. RESULTS: We have developed a zero-inflation-adjusted quantile (ZIAQ) algorithm, which is the first method to account for both dropout rates and complex scRNA-seq data distributions in the same model. ZIAQ demonstrates superior performance over several existing methods on simulated scRNA-seq datasets by finding more differentially expressed genes. When ZIAQ was applied to the comparison of neoplastic and non-neoplastic cells from a human glioblastoma dataset, the ranking of biologically relevant genes and pathways showed clear improvement over existing methods. AVAILABILITY AND IMPLEMENTATION: ZIAQ is implemented in the R language and available at https://github.com/gefeizhang/ZIAQ. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

For chip multiprocessors, one major challenge is to bridge the increasing speed gap between processor and the global on-chip interconnect delay. By integrating optical interconnects in network-on-chip (NoC) architectures, optical NoCs can overcome the power and bandwidth bottleneck of traditional electrical on-chip networks. However, while considering the thermal sensitivity of silicon photonic devices used in optical NoCs, optical interconnects may not have advantages in power efficiency as compared with their electrical counterparts. To tackle this problem, in this article, we propose a thermal-aware design and simulation approach for optical NoCs. Key techniques include thermal-sensitive optical power loss models from device level to network level, a thermal-aware adaptive routing mechanism, and a thermal-aware simulation platform. The thermal-aware simulation platform enables optical NoC simulation together with on-chip temperature simulation as well as optical thermal effect modeling. With the proposed thermal-aware simulation platform, we conducted a case study of an 8 x 8 mesh-based optical NoC under a set of synthetic traffic patterns as well as real applications at typical temperature scenarios. By comparing and analyzing different temperature distributions, we can conclude that it can achieves a better optimization effect for the temperature distributions where the hot spots are scattered across the chip.