Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images.

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss from removed bands. To tackle these issues, in this article, we propose a novel spatial-spectral feature extraction framework, multiscale 2-D singular spectrum analysis (2-D-SSA) with principal component analysis (PCA) (2-D-MSSP), for noise-robust feature extraction and data classification of HSI. First, multiscale 2-D-SSA is applied to exploit the multiscale spatial features in each spectral band of HSI via extracting the varying trends within defined windows. Taking the extracted trend signals at each scale level as the input features, the PCA is employed to the spectral domain for dimensionality reduction and spatial-spectral feature extraction. The derived spatial-spectral features in each scale are separately classified and then fused at decision-level for efficacy. As our 2-D-MSSP method can extract features and simultaneously remove noise in both spatial and spectral domains, which ensures it to be noise-robust for classification of HSI, even the uncorrected dataset. Experiments on three publicly available datasets have fully validated the efficacy and robustness of the proposed approach, when benchmarked with 10 state-of-the-art classifiers, including six spatial-spectral methods and four deep learning classifiers. In addition, both quantitative and qualitative assessment has validated the efficacy of our approach in noise-robust classification of HSI even with limited training samples, especially in classifying uncorrected data without filtering noisy bands

Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos

BACKGROUND INTRODUCTION In this paper, we present an efficient framework to cognitively detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, the thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, cognitive fusion of these two modalities provides an effective solution to tackle this problem. METHODS First, a background model is extracted followed by two stage background-subtraction for foreground detection in visible and thermal images. To deal with cases of occlusion or overlap, knowledge based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. RESULTS To evaluate the proposed method, a publicly available color-thermal benchmark dataset OTCBVS is employed here. For our foreground detection evaluation, objective and subjective analysis against several state-of-the-art methods have been done on our manually segmented ground truth. For our object tracking evaluation, comprehensive qualitative experiments have also been done on all video sequences. CONCLUSIONS Promising results have shown that the proposed fusion based approach can successfully detect and track multiple human objects in most scenes regardless of any light change or occlusion problem

PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices

The success of transformers in computer vision has led to several attempts to adapt them for mobile devices, but their performance remains unsatisfactory in some real-world applications. To address this issue, we propose PP-MobileSeg, a semantic segmentation model that achieves state-of-the-art performance on mobile devices. PP-MobileSeg comprises three novel parts: the StrideFormer backbone, the Aggregated Attention Module (AAM), and the Valid Interpolate Module (VIM). The four-stage StrideFormer backbone is built with MV3 blocks and strided SEA attention, and it is able to extract rich semantic and detailed features with minimal parameter overhead. The AAM first filters the detailed features through semantic feature ensemble voting and then combines them with semantic features to enhance the semantic information. Furthermore, we proposed VIM to upsample the downsampled feature to the resolution of the input image. It significantly reduces model latency by only interpolating classes present in the final prediction, which is the most significant contributor to overall model latency. Extensive experiments show that PP-MobileSeg achieves a superior tradeoff between accuracy, model size, and latency compared to other methods. On the ADE20K dataset, PP-MobileSeg achieves 1.57% higher accuracy in mIoU than SeaFormer-Base with 32.9% fewer parameters and 42.3% faster acceleration on Qualcomm Snapdragon 855. Source codes are available at https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.8.Comment: 8 pages, 3 figure

Band Structure Engineering of Interfacial Semiconductors Based on Atomically Thin Lead Iodide Crystals

To explore new constituents in two-dimensional materials and to combine their best in van der Waals heterostructures, are in great demand as being unique platform to discover new physical phenomena and to design novel functionalities in interface-based devices. Herein, PbI2 crystals as thin as few-layers are first synthesized, particularly through a facile low-temperature solution approach with the crystals of large size, regular shape, different thicknesses and high-yields. As a prototypical demonstration of flexible band engineering of PbI2-based interfacial semiconductors, these PbI2 crystals are subsequently assembled with several transition metal dichalcogenide monolayers. The photoluminescence of MoS2 is strongly enhanced in MoS2/PbI2 stacks, while a dramatic photoluminescence quenching of WS2 and WSe2 is revealed in WS2/PbI2 and WSe2/PbI2 stacks. This is attributed to the effective heterojunction formation between PbI2 and these monolayers, but type I band alignment in MoS2/PbI2 stacks where fast-transferred charge carriers accumulate in MoS2 with high emission efficiency, and type II in WS2/PbI2 and WSe2/PbI2 stacks with separated electrons and holes suitable for light harvesting. Our results demonstrate that MoS2, WS2, WSe2 monolayers with very similar electronic structures themselves, show completely distinct light-matter interactions when interfacing with PbI2, providing unprecedent capabilities to engineer the device performance of two-dimensional heterostructures.Comment: 36 pages, 5 figure

Preventing intimal thickening of vein grafts in vein artery bypass using STAT-3 siRNA

<p>Abstract</p> <p>Background</p> <p>Proliferation and migration of vascular smooth muscle cells (VSMCs) play a key role in neointimal formation which leads to restenosis of vein graft in venous bypass. STAT-3 is a transcription factor associated with cell proliferation. We hypothesized that silencing of STAT-3 by siRNA will inhibit proliferation of VSMCs and attenuate intimal thickening.</p> <p>Methods</p> <p>Rat VSMCs were isolated and cultured in vitro by applying tissue piece inoculation methods. VSMCs were transfected with STAT 3 siRNA using lipofectamine 2000. In vitro proliferation of VSMC was quantified by the MTT assay, while in vivo assessment was performed in a venous transplantation model. In vivo delivery of STAT-3 siRNA plasmid or scramble plasmid was performed by admixing with liposomes 2000 and transfected into the vein graft by bioprotein gel applied onto the adventitia. Rat jugular vein-carotid artery bypass was performed. On day 3 and7 after grafting, the vein grafts were extracted, and analyzed morphologically by haematoxylin eosin (H&E), and assessed by immunohistochemistry for expression of Ki-67 and proliferating cell nuclear antigen (PCNA). Western-blot and reverse transcriptase polymerase chain reaction (RT-PCR) were used to detect the protein and mRNA expression in vivo and in vitro. Cell apoptosis in vein grafts was detected by TUNEL assay.</p> <p>Results</p> <p>MTT assay shows that the proliferation of VSMCs in the STAT-3 siRNA treated group was inhibited. On day 7 after operation, a reduced number of Ki-67 and PCNA positive cells were observed in the neointima of the vein graft in the STAT-3 siRNA treated group as compared to the scramble control. The PCNA index in the control group (31.3 ± 4.7) was higher than that in the STAT-3 siRNA treated group (23.3 ± 2.8) (P < 0.05) on 7d. The neointima in the experimental group(0.45 ± 0.04 μm) was thinner than that in the control group(0.86 ± 0.05 μm) (P < 0.05).Compared with the control group, the protein and mRNA levels in the experimental group in vivo and in vitro decreased significantly. Down regulation of STAT-3 with siRNA resulted in a reduced expression of Bcl-2 and cyclin D1. However, apoptotic cells were not obviously found in all grafts on day 3 and 7 post surgery.</p> <p>Conclusions</p> <p>The STAT-3 siRNA can inhibit the proliferation of VSMCs in vivo and in vitro and attenuate neointimal formation.</p

Whole Exome Sequencing Identifies a Novel Pathogenic RET Variant in Hirschsprung Disease

Hirschsprung disease is a birth defect characterized by complete absence of neuronal ganglion cells from a portion of the intestinal tract. To uncover genetic variants contributing to HSCR, we performed whole exome sequencing on seven members of an HSCR family. With the minor allele frequency (MAF) calculated by gnomAD, we finally filtered a total of 1,059 rare variants in this family (MAF &lt; 0.1%). With the mode of inheritance and pathogenicity scores by bioinformatics tools, we identified an in-frameshift variant p.Phe147del in RET as the disease-causing variant. Further analysis revealed that the in-frameshift variant may function by disrupting the glycosylation of RET protein. To our knowledge, this is the first study to report the in-frameshift variant p.Phe147del in RET responsible for heritable HSCR

SR-POD : sample rotation based on principal-axis orientation distribution for data augmentation in deep object detection

Convolutional neural networks (CNNs) have outperformed most state-of-the-art methods in object detection. However, CNNs suffer the difficulty of detecting objects with rotation, because the dataset used to train the CCNs often does not contain sufficient samples with various angles of orientation. In this paper, we propose a novel data-augmentation approach to handle samples with rotation, which utilizes the distribution of the object's orientation without the time-consuming process of rotating the sample images. Firstly, we present an orientation descriptor, named as "principal-axis orientation" to describe the orientation of the object's principal axis in an image and estimate the distribution of objects’ principal-axis orientations (PODs) of the whole dataset. Secondly, we define a similarity metric to calculate the POD similarity between the training set and an additional dataset, which is built by randomly selecting images from the benchmark ImageNet ILSVRC2012 dataset. Finally, we optimize a cost function to obtain an optimal rotation angle, which indicates the highest POD similarity between the two aforementioned data sets. In order to evaluate our data augmentation method for object detection, experiments, conducted on the benchmark PASCAL VOC2007 dataset, show that with the training set augmented using our method, the average precision (AP) of the Faster RCNN in the TV-monitor is improved by 7.5%. In addition, our experimental results also demonstrate that new samples generated by random rotation are more likely to result in poor performance of object detection