Combined modulation format identification and optical signal-to-noise ratio monitoring with high accuracy and generalizability based on a proposed fused module few-shot learning algorithm in dynamic coherent optical transmissions.

Modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring are important portions of optical performance monitoring (OPM) for future dynamic optical networks. In this paper, we proposed a fusion module few-shot learning (FMFSL) algorithm as an improvement upon the ordinary few-shot learning algorithms for image recognition with the specialty in adopting a combination of a dilated convolutional group and an asymmetric convolutional group to advance the feature extraction. FMFSL algorithm is applied in MFI and OSNR monitoring in coherent optical communication systems with its performance investigated in both back-to-back and fiber transmission scenarios using small-scale constellation diagrams. The results show that FMFSL algorithm can achieve 100% accuracy in MFI and higher OSNR monitoring accuracy compared to the few-shot learning algorithms Deep Nearest Neighbor Neural Network (DN4) and Prototypical Nets (PN) with 2.14% and 4.28% for 64QAM and 3.38% and 8.06% for 128QAM, respectively, without much increase in time consumption. Furthermore, the trained FMFSL algorithm remains excellent in MFI and OSNR monitoring without retraining while employed in back-to-back transmission scenarios with smaller OSNR intervals and fiber transmission scenarios with different amounts of Kerr nonlinearity, demonstrating its high capabilities in generalization and robustness. FMFSL algorithm provides a potential solution for OPM in future dynamic optical networks as a novel machine learning tool.

Construction of photorefractive photonic quasicrystal microstructures by twisted square lattices.

A convenient method to fabricate two-dimensional photonic quasicrystal microstructures was experimentally demonstrated by using a rotatable four-wedge prism. Two-dimensional eightfold symmetric quasicrystal microstructures are formed by two groups of twisted square lattices in a photorefractive crystal. The experimental devices of this method are simple and stable without complicated optical adjustment equipment. Optical-induced quasicrystal microstructures are analyzed and verified by magnified imaging and far-field diffraction pattern imaging. The method can be extended to fabricate more complex quasicrystal and moiré lattice microstructures. We numerically demonstrate that this method can be used to fabricate other complex photonic microstructures by using different multi-wedge prisms and adjusting the rotation angle properly.

[In situ investigation to three dimensional structures of Chinese medicines seeds].

This paper is aimed to microscopic identification of traditional Chinese medicines (TCMs) using an in situ imaging method. In this study, two kinds of Zingiberaceae seeds, Amomi Rotundus Fructus and Alpiniae Katsumadai Semen, were investigated by synchrotron radiation in-line X-ray phase-contrast computed tomography (IXPCT) imaging method. The results showed that the microstructures of these Zingiberaceae seeds could be clearly obtained from the virtual slices information in different observing angles. It proves that IXPCT is an effective imaging method, which can provide the imaging information for the microscopic identification of the intact TCMs in situ and non-destructively.

The Neural Correlates of Effortful Cognitive Processing Deficits in Schizophrenia: An ERP Study.

Background: Individuals' information processing includes automatic and effortful processes and the latter require sustained concentration or attention and larger amounts of cognitive "capacity." Event-related potentials (ERPs) reflect all neural activities that are related to a certain stimulus. Investigating ERP characteristics of effortful cognitive processing in people with schizophrenia would be helpful in further understanding the neural mechanism of schizophrenia. Methods: Both schizophrenia patients (SCZ, n = 33) and health controls (HC, n = 33) completed ERP measurements during the performance of the basic facial emotion identification test (BFEIT) and the face-vignette task (FVT). Data of ERP components (N100, P200, and N250), BFEIT and FVT performances were analyzed. Results: Schizophrenia patients' accuracies of face emotion detection in the BFEIT and vignette emotion detection in the FVT were both significantly worse than the performance of the HC group. Repeated-measures ANOVAs performed on mean amplitudes and latencies revealed that the interaction effect for group × experiment × site (prefrontal, frontal, central, parietal, and occipital site) was significant for N250 amplitude. In FVT experiment, N250 amplitudes at prefrontal and frontal sites in schizophrenia group were larger than those of HC group; the maximum N250 amplitude was present at the prefrontal site in both the groups. For N250 latency, the interaction effect for group × experiment was significant; N250 latencies in the schizophrenia group were longer than those of the HC group. Conclusion: Schizophrenia patients present effortful cognitive processing dysfunctions which reflect in abnormal ERP components, especially N250 at prefrontal cortex and frontal cortex sites. These findings have important implications for further clarifying the neural mechanism of effortful cognitive processing deficits in schizophrenia.

Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by using SR-PCT.

Tissue micro-morphological abnormalities and interrelated quantitative data can provide immediate evidences for tumorigenesis and metastasis in microenvironment. However, the multiscale three-dimensional nondestructive pathological visualization, measurement, and quantitative analysis are still a challenging for the medical imaging and diagnosis. In this work, we employed the synchrotron-based X-ray phase-contrast tomography (SR-PCT) combined with phase-and-attenuation duality phase retrieval to reconstruct and extract the volumetric inner-structural characteristics of tumors in digesting system, helpful for tumor typing and statistic calculation of different tumor specimens. On the basis of the feature set including eight types of tumor micro-lesions presented by our SR-PCT reconstruction with high density resolution, the AlexNet-based deep convolutional neural network model was trained and obtained the 94.21% of average accuracy of auto-classification for the eight types of tumors in digesting system. The micro-pathomophological relationship of liver tumor angiogenesis and progression were revealed by quantitatively analyzing the microscopic changes of texture and grayscale features screened by a machine learning method of area under curve and principal component analysis. The results showed the specific path and clinical manifestations of tumor evolution and indicated that these progressions of tumor lesions rely on its inflammation microenvironment. Hence, this high phase-contrast 3D pathological characteristics and automatic analysis methods exhibited excellent recognizable and classifiable for micro tumor lesions.