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Varying density of point clouds increases the difficulty of 3D detection. In this paper, we present a context-aware dynamic network (CADNet) to capture the variance of density by considering both point context and semantic context. Point-level contexts are generated from original point clouds to enlarge the effective receptive filed. They are extracted around the voxelized pillars based on our extended voxelization method and processed with the context encoder in parallel with the pillar features. With a large perception range, we are able to capture the variance of features for potential objects and generate attentive spatial guidance to help adjust the strengths for different regions. In the region proposal network, considering the limited representation ability of traditional convolution where same kernels are shared among different samples and positions, we propose a decomposable dynamic convolutional layer to adapt to the variance of input features by learning from the local semantic context. It adaptively generates the position-dependent coefficients for multiple fixed kernels and combines them to convolve with local features. Based on our dynamic convolution, we design a dual-path convolution block to further improve the representation ability. We conduct experiments on KITTI dataset and the proposed CADNet has achieved superior performance of 3D detection outperforming SECOND and PointPillars by a large margin at the speed of 30 FPS.

To develop and test semi-supervised generative adversarial networks (GANs) that detect retinal disorders on optical coherence tomography (OCT) images using a small-labeled dataset.From a public database, we randomly chose a small supervised dataset with 400 OCT images (100 choroidal neovascularization, 100 diabetic macular edema, 100 drusen, and 100 normal) and assigned all other OCT images to unsupervised dataset (107,912 images without labeling). We adopted a semi-supervised GAN and a supervised deep learning (DL) model for automatically detecting retinal disorders from OCT images. The performance of the 2 models was compared in 3 testing datasets with different OCT devices. The evaluation metrics included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves.The local validation dataset included 1000 images with 250 from each category. The independent clinical dataset included 366 OCT images using Cirrus OCT Shanghai Shibei Hospital and 511 OCT images using RTVue OCT from Xinhua Hospital respectively. The semi-supervised GANs classifier achieved better accuracy than supervised DL model (0.91 vs 0.86 for local cell validation dataset, 0.91 vs 0.86 in the Shanghai Shibei Hospital testing dataset, and 0.93 vs 0.92 in Xinhua Hospital testing dataset). For detecting urgent referrals (choroidal neo-vascularization and diabetic macular edema) from nonurgent referrals (drusen and normal) on OCT images, the semi-supervised GANs classifier also achieved better area under the receiver operating characteristic curves than supervised DL model (0.99 vs 0.97, 0.97 vs 0.96, and 0.99 vs 0.99, respectively).A semi-supervised GAN can achieve better performance than that of a supervised DL model when the labeled dataset is limited. The current study offers utility to various research and clinical studies using DL with relatively small datasets. Semi-supervised GANs can detect retinal disorders from OCT images using relatively small dataset.

Due to their high photoelectric conversion efficiency (PCE) and low-cost fabrication process, n-type silicon (n-Si)/poly(3, 4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) hybrid heter...

Discovering diverse significant regions (e.g., beaks and wings for some bird species) and extracting discriminative features from them is vitally important in fine-grained image recognition. Currently, the attention-based approaches present promising performance, which generally extract the fine-grained features by cropping or sampling significant parts. However, the cropping methods usually suffer from a fixed number of parts and difficulty to highlight irregular regions, and existing sampling methods may produce extremely distorted images. To effectively capture the fine-grained features, we propose an end-to-end separated smooth sampling network (SSSNet) in this paper. Specifically, we propose a separated smooth sampling module to highlight diverse significant regions of an image. Different from previous methods, we adopt smooth sampling on two separated coordinates to process images, which can effectively highlight discriminative contents and meanwhile avoid extreme distortion. We further propose an iterative masking method to embed into SSSNet, which can produce multiple attention maps without overlap to represent different significant regions. We conduct extensive experiments on CUB-200–2011, Stanford-Cars, and FGVC-Aircraft datasets. The results show the effectiveness of separated smooth sampling, and our SSSNet achieves better performance against previous state-of-the-art approaches under the same settings.

Recent research on single image super-resolution (SISR) has achieved great success due to the development of deep convolutional neural networks. However, most existing SISR methods merely focus on super-resolution of a single fixed integer scale factor. This simplified assumption does not meet the complex conditions for real-world images which often suffer from various blur kernels or various levels of noise. More importantly, previous methods lack the ability to cope with arbitrary degradation parameters (scale factors, blur kernels, and noise levels) with a single model. A few methods can handle multiple degradation factors, e.g., noninteger scale factors, blurring, and noise, simultaneously within a single SISR model. In this work, we propose a simple yet powerful method termed meta-USR which is the first unified super-resolution network for arbitrary degradation parameters with meta-learning. In Meta-USR, a meta-restoration module (MRM) is proposed to enhance the traditional upscale module with the capability to adaptively predict the weights of the convolution filters for various combinations of degradation parameters. Thus, the MRM can not only upscale the feature maps with arbitrary scale factors but also restore the SR image with different blur kernels and noise levels. Moreover, the lightweight MRM can be placed at the end of the network, which makes it very efficient for iteratively/repeatedly searching the various degradation factors. We evaluate the proposed method through extensive experiments on several widely used benchmark data sets on SISR. The qualitative and quantitative experimental results show the superiority of our Meta-USR.

Traditional convolutional neural networks (CNNs) share their kernels among all positions of the input, which may constrain the representation ability in feature extraction. Dynamic convolution proposes to generate different kernels for different inputs to improve the model capacity. However, the total parameters of the dynamic network can be significantly huge. In this article, we propose a lightweight dynamic convolution method to strengthen traditional CNNs with an affordable increase of total parameters and multiply-adds. Instead of generating the whole kernels directly or combining several static kernels, we choose to "look inside", learning the attention within convolutional kernels. An extra network is used to adjust the weights of kernels for every feature aggregation operation. By combining local and global contexts, the proposed approach can capture the variance among different samples, the variance in different positions of the feature maps, and the variance in different positions inside sliding windows. With a minor increase in the number of model parameters, remarkable improvements in image classification on CIFAR and ImageNet with multiple backbones have been obtained. Experiments on object detection also verify the effectiveness of the proposed method.

Programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1), the research focus in immune checkpoint regulation, play an important role in tumor immunotherapy. Inhibitors of this pathway are also the focus of tumor immunotherapy research. The PD-1/PD-L1 pathway can be blocked by selective binding to PD-L1. Clinical trials have been conducted in a variety of patients with advanced solid tumors. CS1001 is a high-affinity humanized full-length anti-PD-L1 monoclonal antibody with great clinical significance. We constructed a PD-L1-targeted radioactive molecular probe

Few-shot classification aims to categorize the samples from unseen classes with only few labeled samples. To address such a challenge, many methods exploit a base set consisting of massive labeled samples to learn an instance embedding function, i.e., image feature extractor, and it is expected to possess good transferability among different tasks. Such characteristics of few-shot learning are essentially different from that of traditional image classification only pursuing to get discriminative image representations. In this paper, we propose to learn intact features by erasing-inpainting for few-shot classification. Specifically, we argue that extracting intact features of target objects is more transferable, and then propose a novel cross-set erasing-inpainting (CSEI) method. CSEI processes the images in the support set using erasing and inpainting, and then uses them to augment the query set of the same task. Consequently, the feature embedding produced by our proposed method can contain more complete information of target objects. In addition, we propose task-specific feature modulation to make the features adaptive to the current task. The extensive experiments on two widely used benchmarks well demonstrates the effectiveness of our proposed method, which can consistently get considerable performance gains for different baseline methods.

License plate recognition (LPR) is a fundamental component of various intelligent transportation systems, and is always expected to be accurate and efficient enough in real-world applications. Nowadays, recognition of single character has been sophisticated benefiting from the power of deep learning, and extracting position information for forming a character sequence becomes the main bottleneck of LPR. To tackle this issue, we propose a novel holistic position attention (HPA) in this paper that consists of position network and shared classifier. Specifically, the position network explicitly encodes the character position into the maps of HPA, and then the shared classifier performs the character recognition in a unified and parallel way. Here the extracted features are modulated by the attention maps before feeding into the classifier to yield the final recognition results. Note that our proposed method is end-to-end trainable, character recognition can be concurrently performed, and no post-processing is needed. Thus our LPR system can achieve good effectiveness and efficiency simultaneously. The experimental results on four public datasets, including AOLP, Media Lab, CCPD, and CLPD, well demonstrate the superiority of our method to previous state-of-the-art methods in both accuracy and speed.

Carrier-selective contacts, especially in electron-selective contact (ESC), between the metal electrodes and the crystalline silicon (c-Si) substrates play a pivotal section in promoting the power ...

In unsupervised domain adaptive (UDA) semantic segmentation, the distillation based methods are currently dominant in performance. However, the distillation technique requires complicate multi-stage process and many training tricks. In this paper, we propose a simple yet effective method that can achieve competitive performance to the advanced distillation methods. Our core idea is to fully explore the target-domain information from the views of boundaries and features. First, we propose a novel mix-up strategy to generate high-quality target-domain boundaries with ground-truth labels. Different from the source-domain boundaries in previous works, we select the high-confidence target-domain areas and then paste them to the source-domain images. Such a strategy can generate the object boundaries in target domain (edge of target-domain object areas) with the correct labels. Consequently, the boundary information of target domain can be effectively captured by learning on the mixed-up samples. Second, we design a multi-level contrastive loss to improve the representation of target-domain data, including pixel-level and prototype-level contrastive learning. By combining two proposed methods, more discriminative features can be extracted and hard object boundaries can be better addressed for the target domain. The experimental results on two commonly adopted benchmarks (\textit{i.e.}, GTA5 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes) show that our method achieves competitive performance to complicated distillation methods. Notably, for the SYNTHIA$\rightarrow$ Cityscapes scenario, our method achieves the state-of-the-art performance with $57.8\%$ mIoU and $64.6\%$ mIoU on 16 classes and 13 classes. Code is available at https://github.com/ljjcoder/EHTDI.

Mesothelin (MSLN) is a molecular biomarker of many types of solid tumors, such as mesothelioma, pancreatic cancer, and colon cancer. Owing to the significant difference in expression between cancer cells and normal cells, mesothelin has been widely used as a key target in cancer immunotherapy. In this study, we used iodine isotope (

Temporal action detection is a challenging task due to vagueness of action boundaries. To tackle this issue, we propose an end-to-end progressive boundary refinement network (PBRNet) in this paper. PBRNet belongs to the family of one-stage detectors and is equipped with three cascaded detection modules for localizing action boundary more and more precisely. Specifically, PBRNet mainly consists of coarse pyramidal detection, refined pyramidal detection, and fine-grained detection. The first two modules build two feature pyramids to perform the anchor-based detection, and the third one explores the frame-level features to refine the boundaries of each action instance. In the fined-grained detection module, three frame-level classification branches are proposed to augment the frame-level features and update the confidence scores of action instances. Evidently, PBRNet integrates the anchor-based and frame-level methods. We experimentally evaluate the proposed PBRNet and comprehensively investigate the effect of the main components. The results show PBRNet achieves the state-of-the-art detection performances on two popular benchmarks: THUMOS'14 and ActivityNet, and meanwhile possesses a high inference speed.

Unsupervised domain adaptation in semantic segmentation is to exploit the pixel-level annotated samples in the source domain to aid the segmentation of unlabeled samples in the target domain. For such a task, the key point is to learn domain-invariant representations and adversarial learning is usually used, in which the discriminator is to distinguish which domain the input comes from, and the segmentation model targets to deceive the domain discriminator. In this work, we first propose a novel joint adversarial learning (JAL) to boost the domain discriminator in output space by introducing the information of domain discriminator from low-level features. Consequently, the training of the high-level decoder would be enhanced. Then we propose a weight transfer module (WTM) to alleviate the inherent bias of the trained decoder towards source domain. Specifically, WTM changes the original decoder into a new decoder, which is learned only under the supervision of adversarial loss and thus mainly focuses on reducing domain divergence. The extensive experiments on two widely used benchmarks show that our method can bring considerable performance improvement over different baseline methods, which well demonstrates the effectiveness of our method in the output space adaptation.

Combining electron- and hole-selective materials in one crystalline silicon (Si) solar cell, thereby avoiding any dopants, is not considered for application to photovoltaic industry until only comparable efficiency and stable performance are achievable. Here, it is demonstrated how a conventionally unstable electron-selective contact (ESC) is optimized with huge boost in stability as well as improved electron transport. With the introduction of a Ti thin film between a-Si:H(i)/LiF and Al electrode, high-level passivation (S

Despite the tremendous advances of electrocatalysts for the hydrogen and oxygen evolution reactions (HER/OER), there are few reports on bifunctional catalysts for the HER and plastic electroreforming. Herein, we present a facile hydrothermal and selenization treatment to fabricate cobalt selenide nanosheets on nickel foam (0.1-CoSe2/NF) as a bifunctional catalyst for plastic-electroreforming assisted water electrolysis. Benefiting from its large specific surface area, abundant active sites, high conductivity and 3D porous structure, 0.1-CoSe2/NF exhibits superior electrocatalytic performance and durability for both the HER and electrooxidation of plastic waste polylactic acid (PLA). Overpotentials of 202 mV (cathodic) and 288 mV (anodic) are observed at a current density of 100 mA cm−2 in an alkaline electrolyte. Moreover, PLA oxidation that suppresses the OER also addresses the safety concern of gas crossover in water electrolysis. Our work thus provides a promising pathway for low-cost, high efficiency, and stable production of green hydrogen assisted by electroreforming of plastic waste. Nanyang Technological University Submitted/Accepted version This work was supported by Nanyang Technological University (Grant No. NTU-ACE2021-02).

Angiotensin-converting enzyme 2 (ACE2), a transmembrane protein, is the main entry point for certain coronaviruses including the new coronavirus SARS-CoV-2 to enter cells. Synthesizing the PET imaging probe Al18F-DX600-BCH which is high-affinity ACE2 is aim to detect the expression of ACE2 in body and monitor the therapeutic effect. The Al18F-DX600-BCH was obtained manually with a 20.4% ± 5.2% radiochemical yield without attenuation correction and an over 99% purified radiochemical purity, being stable in vitro within 4 hours and cleared rapidly in blood (the half-lives of the distribution phase and clearance phase were 2.12 min and 25.31 min, respectively). Results of both biodistribution and PET imaging showed that Al18F-DX600-BCH was highly accumulated in the kidney (SUVkidney/normal > 50), and specific uptake in testis (SUVtestis/normal > 10) was observed in rat images. The kidney (++), gastrointestinal (++) and bronchial (+++) cells were evidenced of ACE2 positive by IHC staining of rats. A total of 10 volunteers were enrolled and received PET/CT 1 hour and 2 hours after injection or dynamic PET/CT during 0-330 seconds (NCT04542863), from which strong radioactivity accumulation was mostly observed in the genitourinary system (SUVrenal cortex = 32.00, SUVtestis = 4.56), and moderate accumulation in conjunctiva and nasal mucosa for several cases. This work firstly reported the probe Al18F-DX600-BCH targeting ACE2, conducting preliminary preclinical experiments and a total of 10 clinical transformations, which demonstrated the potential and possibility of non-invasive mapping of ACE2. Trial registration: ClinicalTrials.gov NCT04542863. Registered 9 September 2020.

Multi-view properties of light field (LF) imaging enable exciting applications such as auto-refocusing, depth estimation and 3D reconstruction. However, limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards more practical vision applications. Existing view synthesis methods mainly break the task into two steps, i.e. depth estimating and view warping, which are usually inefficient and produce artifacts over depth ambiguities. We have proposed an end-to-end deep learning framework named Pseudo 4DCNN to solve these problems in a conference paper. Rethinking on the overall paradigm, we further extend pseudo 4DCNN and propose a novel loss function which is applicable for all tasks of light field reconstruction i.e. EPI Structure Preserving (ESP) loss function. This loss function is proposed to attenuate the blurry edges and artifacts caused by averaging effect of ${L_2}$ norm based loss function. Furthermore, the extended Pseudo 4DCNN is compared with recent state-of-the-art (SOTA) approaches on more publicly available light field databases, as well as self-captured light field biometrics and microscopy datasets. Experimental results demonstrate that the proposed framework can achieve better performances than vanilla Pseudo 4DCNN and other SOTA methods, especially in the terms of visual quality under occlusions. The source codes and self-collected datasets for reproducibility will be available online soon.

Electron-transport layer (ETL) that promotes charge carrier separation and electron extraction is a key component of perovskite solar cells (PSCs). Here, we report a simple approach for improving c...

Irrespective of the success on reduction of contact resistivity, lack of chemical passivation of evaporated metal oxides heavily hinders their applications as passivating contacts, such contacts can be an alternative route for high efficiency and cost effective silicon solar cells. Here, we demonstrate that electron beam evaporated magnesium oxide (MgOx) thin film can work as a promising electron-selective passivating contact for n-Si solar cells after a post-annealing treatment and an alumina-initiated atomic hydrogenation. 10 nm MgOx on n-Si provided a surface recombination velocity down to 14.9 cm/s while 1 nm MgOx showed a low contact resistivity of 14 mΩ cm2. Comprehensive characterizations revealed the formation of Si–O–Mg bonds and the activation of atomic hydrogens were the main reasons for such high-level passivation. A PERC-like dopant-free rear contact was formed by using the 1 nm-MgOx as electron-collector and the 10 nm-MgOx as passivating layer, the resultant solar cells achieved 27% increment in efficiency and 51 mV increase in open-circuit voltage in comparison with reference devices. The ways of improving passivation quality of MgOx and novel design of contact structure open up the possibility of using evaporation-processed metal oxides as effective and low-cost carrier-selective passivating contacts for n-Si photovoltaic devices.

In this work, we propose a novel method named Weighted Channel Dropout (WCD) for the regularization of deep Convolutional Neural Network (CNN). Different from Dropout which randomly selects the neurons to set to zero in the fully-connected layers, WCD operates on the channels in the stack of convolutional layers. Specifically, WCD consists of two steps, i.e., Rating Channels and Selecting Channels, and three modules, i.e., Global Average Pooling, Weighted Random Selection and Random Number Generator. It filters the channels according to their activation status and can be plugged into any two consecutive layers, which unifies the original Dropout and Channel-Wise Dropout. WCD is totally parameter-free and deployed only in training phase with very slight computation cost. The network in test phase remains unchanged and thus the inference cost is not added at all. Besides, when combining with the existing networks, it requires no re-pretraining on ImageNet and thus is well-suited for the application on small datasets. Finally, WCD with VGGNet-16, ResNet-101, Inception-V3 are experimentally evaluated on multiple datasets. The extensive results demonstrate that WCD can bring consistent improvements over the baselines.

Specific contact resistance $(\rho _{c})$ , commonly extracted by Cox and Strack method (CSM) and transfer length method (TLM), is one of the most important properties of carrier-selective contacts. However, in most cases, the hole-selective contacts (HSCs) deposited on n-type silicon (n-Si) substrate are Schottky heterojunction other than Ohmic contact, impeding the accurate extraction of $\rho _{c}$ by CSM and TLM. In this paper, an expanded CSM is proposed to precisely extract the $\rho _{c}$ of MoOx/n-Si heterojunction, achieving a generally lower coefficient of variation. The current transport characteristic and the $\rho _{c}$ value of MoOx/n-Si heterocontact are further verified by technology computer aided design (TCAD) simulation. The results demonstrate that the expanded CSM enables a more precise $\rho _{c}$ extraction, a better preparation technology compatibility, and a wider range of application, compared to TLM.

Feedback is a fundamental mechanism existing in the human visual system, but has not been explored deeply in designing computer vision algorithms. In this paper, we claim that feedback plays a critical role in understanding convolutional neural networks (CNNs), e.g., how a neuron in CNNs describes an object's pattern, and how a collection of neurons form comprehensive perception to an object. To model the feedback in CNNs, we propose a novel model named Feedback CNN and develop two new processing algorithms, i.e., neural pathway pruning and pattern recovering. We mathematically prove that the proposed method can reach local optimum. Note that Feedback CNN belongs to weakly supervised methods and can be trained only using category-level labels. But it possesses a powerful capability to accurately localize and segment category-specific objects. We conduct extensive visualization analysis, and the results reveal the close relationship between neurons and object parts in Feedback CNN. Finally, we evaluate the proposed Feedback CNN over the tasks of weakly supervised object localization and segmentation, and the experimental results on ImageNet and Pascal VOC show that our method remarkably outperforms the state-of-the-art ones.

At present, it is a significant challenge to design electrode materials with both desirable electrochemical performance and high mass loading for supercapacitors by using a simple, efficient and safe method. With the introduction of Ni2+ ions, the hollow Ni-decorated Co9S8 nanospheres meeting the above requirements have been successfully synthesized by one-step electrodeposition method under a two-electrode system. As electrodes for supercapacitors, such Ni-decorated Co9S8 electrodes deliver an ultrahigh areal and volumetric specific capacitance of 5.64 F cm−2 and 171.85 F cm−3 at a current density of 1 mA cm−2, superior rate capability (88.9% retention at a current density of 20 mA cm−2) and remarkable cycling stability with a capacitance retention of 89% after 8500 cycles. Also, the as-fabricated flexible quasi-solid-state asymmetric supercapacitors based on Ni-decorated Co9S8 nanospheres and active carbon electrodes present high energy density. The practicability and operability of the device were successfully demonstrated by lighting up the LEDs with five different colors.

Black silicon (b-Si) with nanotextures is a promising light-trapping scheme for potentially achieving high conversion efficiency at reduced manufacturing cost in crystalline-silicon solar cells. However, the inherently high aspect-ratio and tiny feature size of the nanostructures are subject to severe surface (large surface areas) and Auger recombination (worse doping profile). These will abate the cost values of b-Si since one has to adopt a comprise strategy of applying shallow nanotextures with antireflection and passivation layers. Here, we show that silicon microwire solar cells featuring well-defined radial junctions can extensively suppress both surface and Auger recombination by providing excellent all-around electrical field. The radially doped silicon micropillar devices even show an internal quantum efficiency as good as that of planar substrate and their measured minority carrier lifetimes become nearly independent of total surface area. A great reduction in short-circuit current density loss was further identified as the junction abruptly changed from a fully diffused to a core-shell configuration, manifesting the powerful effectiveness of radial p-n+ junction on the suppression of Auger recombination. Furthermore, silicon microwire solar cell with a radial junction demonstrates 37% increase in efficiency compared with the reference cell, suggesting a feasible strategy towards high-efficiency solar devices.

Recent years have witnessed great progress of object detection. However, due to the domain shift problem, applying the knowledge of an object detector learned from one specific domain to another one often suffers severe performance degradation. Most existing methods adopt feature alignment either on the backbone network or instance classifier to increase the transferability of object detector. Differently, we propose to perform feature alignment in the RPN stage such that the foreground and background RPN proposals in target domain can be effectively distinguished. Specifically, we first construct one set of learnable RPN prototpyes, and then enforce the RPN features to align with the prototypes for both source and target domains. It essentially cooperates the learning of RPN prototypes and features to align the source and target RPN features. Particularly, we propose a simple yet effective method suitable for RPN feature alignment to generate high-quality pseudo label of proposals in target domain, i.e., using the filtered detection results with IoU. Furthermore, we adopt Grad CAM to find the discriminative region within a foreground proposal and use it to increase the discriminability of RPN features for alignment. We conduct extensive experiments on multiple cross-domain detection scenarios, and the results show the effectiveness of our proposed method against previous state-of-the-art methods.

Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias via meta-learning on similar tasks. In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel part-based self-supervised representation learning scheme to learn transferable representations by maximizing the similarity of an image to its discriminative part. To mitigate the overfitting in few-shot classification caused by data scarcity, we further propose a part augmentation strategy by retrieving extra images from a base dataset. We conduct systematic studies on miniImageNet and tieredImageNet benchmarks. Remarkably, our method yields impressive results, outperforming the previous best unsupervised methods by 7.74% and 9.24% under 5-way 1-shot and 5-way 5-shot settings, which are comparable with state-of-the-art supervised methods., Accepted by IJCAI 2021

Hepatocellular carcinoma (HCC) is characterized by a high rate of incidence and recurrence, and resistance to chemotherapy may aggravate the poor prognosis of HCC patients. Sorafenib resistance is a conundrum to the treatment of advanced/recurrent HCC. Therefore, studies on the molecular pathogenesis of HCC and the resistance to sorafenib are of great interest. Here, we report that GINS1 was highly expressed in HCC tumors, associated with tumor grades, and predicted poor patient survival using Gene Expression Omnibus (GEO) databases exploration. Cell cycle, cell proliferation assay and in vivo xenograft mouse model indicated that knocking down GINS1 induced in G1/S phase cell cycle arrest and decreased tumor cells proliferation in vitro and in vivo. Spheroid formation assay results showed that GINS1 promoted the stem cell activity of HCC tumor cells. Furthermore, GEO database (GSE17112) analysis showed that HRAS oncogenic gene set was enriched in GINS1 high-expressed cancer cells, and quantitative real-time PCR, and Western blot results proved that GINS1 enhanced HCC progression through regulating HRAS signaling pathway. Moreover, knocking down endogenous GINS1 with shGINS1 increased the sensitivity of HCC cells to sorafenib, and restoring HRAS or stem associated pathway partly recovered the sorafenib resistance. Overall, the collective findings highlight GINS1 functions in hepatocarcinogenesis and sorafenib resistance, and indicate its potential use of GINS1 in drug-resistant HCC.

Purpose To develop generative adversarial networks (GANs) that synthesize realistic anterior segment optical coherence tomography (AS-OCT) images and evaluate deep learning (DL) models that are trained on real and synthetic datasets for detecting angle closure. Methods The GAN architecture was adopted and trained on the dataset with AS-OCT images collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, synthesizing open- and closed-angle AS-OCT images. A visual Turing test with two glaucoma specialists was performed to assess the image quality of real and synthetic images. DL models, trained on either real or synthetic datasets, were developed. Using the clinicians' grading of the AS-OCT images as the reference standard, we compared the diagnostic performance of open-angle vs. closed-angle detection of DL models and the AS-OCT parameter, defined as a trabecular-iris space area 750 µm anterior to the scleral spur (TISA750), in a small independent validation dataset. Results The GAN training included 28,643 AS-OCT anterior chamber angle (ACA) images. The real and synthetic datasets for DL model training have an equal distribution of open- and closed-angle images (all with 10,000 images each). The independent validation dataset included 238 open-angle and 243 closed-angle AS-OCT ACA images. The image quality of real versus synthetic AS-OCT images was similar, as assessed by the two glaucoma specialists, except for the scleral spur visibility. For the independent validation dataset, both DL models achieved higher areas under the curve compared with TISA750. Two DL models had areas under the curve of 0.97 (95% confidence interval, 0.96-0.99) and 0.94 (95% confidence interval, 0.92-0.96). Conclusions The GAN synthetic AS-OCT images appeared to be of good quality, according to the glaucoma specialists. The DL models, trained on all-synthetic AS-OCT images, can achieve high diagnostic performance. Translational relevance The GANs can generate realistic AS-OCT images, which can also be used to train DL models.

Image inpainting faces the challenging issue of the requirements on structure reasonableness and texture coherence. In this paper, we propose a two-stage inpainting framework to address this issue. The basic idea is to address the two requirements in two separate stages. Completed segmentation of the corrupted image is firstly predicted through segmentation reconstruction network, while fine-grained image details are restored in the second stage through an image generator. The two stages are connected in series as the image details are generated under the guidance of completed segmentation map that predicted in the first stage. Specifically, in the second stage, we propose a novel graph-based relation network to model the relationship existed in corrupted image. In relation network, both intra-relationship for pixels in the same semantic region and inter-relationship between different semantic parts are considered, improving the consistency and compatibility of image textures. Besides, contrastive loss is designed to facilitate the relation network training. Such a framework not only simplifies the inpainting problem directly, but also exploits the relationship in corrupted image explicitly. Extensive experiments on various public datasets quantitatively and qualitatively demonstrate the superiority of our approach compared with the state-of-the-art.

Recent image inpainting works have shown promising results thanks to great advances of generative adversarial networks (GANs). However, these methods would still generate distorted structures or blurry textures for the situation of large missing area, which is mainly due to the inherent difficulty to train GANs. In this paper, we propose a novel multi-level discriminator (MLD) and wavelet loss (WT) to improve the learning of image inpainting generators. Our method does not change the structure of generator and only works in the training phase, which thus can be easily embedded into sophisticated inpainting networks and would not increase the inference time. Specifically, MLD divides the mask into multiple subregions and then imposes an independent discriminator to each subregion. It essentially increases the distribution overlap between the real images and generated images. Consequently, MLD improves the optimization of GANs by providing more effective gradients to generators. In addition, WT builds a reconstruction loss in the frequency domain, which can facilitate the training of image inpainting networks as a regularization term. Consequently, WT can enforce the generated contents to be more consistent and sharper than the traditional pixel-wise reconstruction loss. We integrate WLD and WT into off-the-shelf image inpainting networks, and conduct extensive experiments on CelebA-HQ, Paris StreetView, and Places2. The results well demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance and generates higher-quality images than the baselines.

The requirement to recognize the iris image of low-quality is rapidly increasing with the practical application of iris recognition, especially the urgent need for high-throughput or applications in covert situations. The appr-circle fitting can not meet the needs due to the high time cost and non-accurate boundary estimation during the normalization process. Furthermore, the appr-circular hypothesis of iris and pupil is not entirely established due to the squint and occlusion in non-cooperative environments. To mitigate this problem, a multi-mask normalization without appr-circular parameter estimation is proposed to make full use of the segmented masks, which provide robust pixel-level iris boundaries. It bridges the segmentation and feature extraction to recognize the low-quality iris, which is thrown directly by the traditional methods. Thus, the complex samples with no appr-circular iris or massive occlusions can be recognized correctly. The extensive experiments are conducted on the representative and challenging databases to verify the generalization and the accuracy of the proposed iris normalization method. Besides, the throughput rate is significantly improved.

Explosive growth in video traffic volumes incurs a high percentage of redundancy in today's Internet, following the 80–20 rule. Fortunately, the advanced in-network cache is considered as an effective scheme for eliminating the repetitive traffic by caching the popular content in network nodes. Besides, the emerging software-defined networking (SDN) enables centralized control and management, as well as the collaboration between network devices and upper applications. Moreover, the Network Functions Virtualization is also developed to support for customized network functions, including caching and streaming. This inspires us to design an SDN-assisted multimedia streaming Video-on-Demand system, integrating in-network cache, to improve the quality of service. The designed architecture is capable of reducing the redundant traffic via the reusable duplications. In particular, it can achieve greater performance gains by deploying specific scheduling policy. We further propose a variable-length interval cache strategy for RTP streaming, which can realize the self-adaptive adjustment of the size of cached video segments based on their access patterns. Our goal is to efficiently utilize the limited storage resources and increase the cache hit ratio. We present the theoretical analysis to demonstrate the attainable performance of the proposed algorithm; furthermore, the integrated system design is implemented as a prototype to show its feasibility and applicability. Ultimately, emulation experiments are conducted to evaluate the achievable performance improvement more comprehensively.