A Data Matrix Code Recognition Method Based on L-Shaped Dashed Edge Localization Using Central Prior.

The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.

Fertility in young-onset colorectal patients with cancer: a review.

Although the overall incidence and mortality of colorectal cancer have declined, diagnosed cases of young-onset colorectal cancer have increased significantly. Concerns about future fertility are second only to concerns about survival and may significantly affect the quality of life of young cancer survivors. Fertility preservation is an important issue in young-onset colorectal patients with cancer undergoing oncotherapy. Here, we discussed the effects of different treatments on fertility, common options for fertility preservation, factors affecting fertility preservation and improvement measures, and the relationship between fertility and pregnancy outcomes in young-onset colorectal patients with cancer.

Phylogeny and Taxonomic Revision of the Genus Melanosciadium (Apiaceae), Based on Plastid Genomes and Morphological Evidence.

Melanosciadium is considered a monotypic genus and is also endemic to the southwest of China. No detailed phylogenetic studies or plastid genomes have been identified in Melanosciadium. In this study, the plastid genome sequence and nrDNA sequence were used for the phylogenetic analysis of Melanosciadium and its related groups. Angelica tsinlingensis was previously considered a synonym of Hansenia forbesii. Similarly, Ligusticum angelicifolium was previously thought to be the genus Angelica or Ligusticopsis. Through field observations and morphological evidence, we believe that the two species are more similar to M. pimpinelloideum in leaves, umbel rays, and fruits. Meanwhile, we found a new species from Anhui Province (eastern China) that is similar to M. pimpinelloideum and have named it M. Jinzhaiensis. We sequenced and assembled the complete plastid genomes of these species and another three Angelica species. The genome comparison results show that M. pimpinelloideum, A. tsinlingensis, Ligusticum angelicifolium, and M. jinzhaiensis have similarities to each other in the plastid genome size, gene number, and length of the LSC and IR regions; the plastid genomes of these species are distinct from those of the Angelica species. In addition, we reconstruct the phylogenetic relationships using both plastid genome sequences and nrDNA sequences. The phylogenetic analysis revealed that A. tsinlingensis, M. pimpinelloideum, L. angelicifolium, and M. jinzhaiensis are closely related to each other and form a monophyletic group with strong support within the Selineae clade. Consequently, A. tsinlingensis and L. angelicifolium should be classified as members of the genus Melanosciadium, and suitable taxonomical treatments have been proposed. Meanwhile, a comprehensive description of the new species, M. jinzhaiensis, is presented, encompassing its habitat environment and detailed morphological traits.

[Corrigendum] Azurocidin 1 inhibits the aberrant proliferation of triple­negative breast cancer through the regulation of pyroptosis.

Following the publication of the above article, the authors drew to our attention that they had made a couple of inadvertent errors in assembling Figs. 4 and 5; first, for the BT­549 cell line, the data shown for the Pro­caspase­1/Cleaved caspase­1 in Fig. 5 and the GSDMD­F/GSDMD­N data in Fig. 4B were identical, and had been derived from the same original source; secondly, in Fig. 4A, the data shown correctly for the GSDMD BT­549 cell line had also inadvertently been included in this figure to represent the MDA­MB­231 cell line. The revised and corrected versions of Figs. 4 and 5, showing the correct western blotting data for the GSDMD experiment in Fig. 4A and the Pro­caspase­1/Cleaved caspase­1 data for the BT­549 cell line in Fig. 5, are shown in the next two pages. The authors regret that these errors in the assembly of Figs. 4 and 5 went unnoticed before the article was published, and thank the Editor of Oncology Reports for granting them the opportunity to publish this corrigendum. All the authors agree with the publication of this corrigendum; furthermore, they apologize to the readership of the journal for any inconvenience caused.[Oncology Reports 50: 188, 2023; DOI: 10.3892/or.2023.8625].

Adaptive Cross-Feature Fusion Network With Inconsistency Guidance for Multi-Modal Brain Tumor Segmentation.

In the context of contemporary artificial intelligence, increasing deep learning (DL) based segmentation methods have been recently proposed for brain tumor segmentation (BraTS) via analysis of multi-modal MRI. However, known DL-based works usually directly fuse the information of different modalities at multiple stages without considering the gap between modalities, leaving much room for performance improvement. In this paper, we introduce a novel deep neural network, termed ACFNet, for accurately segmenting brain tumor in multi-modal MRI. Specifically, ACFNet has a parallel structure with three encoder-decoder streams. The upper and lower streams generate coarse predictions from individual modality, while the middle stream integrates the complementary knowledge of different modalities and bridges the gap between them to yield fine prediction. To effectively integrate the complementary information, we propose an adaptive cross-feature fusion (ACF) module at the encoder that first explores the correlation information between the feature representations from upper and lower streams and then refines the fused correlation information. To bridge the gap between the information from multi-modal data, we propose a prediction inconsistency guidance (PIG) module at the decoder that helps the network focus more on error-prone regions through a guidance strategy when incorporating the features from the encoder. The guidance is obtained by calculating the prediction inconsistency between upper and lower streams and highlights the gap between multi-modal data. Extensive experiments on the BraTS 2020 dataset show that ACFNet is competent for the BraTS task with promising results and outperforms six mainstream competing methods.

Azurocidin 1 inhibits the aberrant proliferation of triple­negative breast cancer through the regulation of pyroptosis.

Azurocidin 1 (AZU1) is a heparin­binding protein which has been reported to be aberrantly expressed in various tumors, but its definite role in breast cancer (BC) has not been clarified. The aim of the present study was to explore the associations between AZU1 and BC. In the present study, bioinformatics and western blot analyses were applied to detect the expression level of AZU1 in BC tissues. The effect of AZU1 on cell proliferation and apoptosis was analyzed using Cell Counting Kit­8 assay, colony formation assay and flow cytometry. Based on bioinformatics analysis, AZU1 exhibited low expression in tissues and was negatively associated with the survival rate of patients with triple­negative BC (TNBC). Exogenous AZU1 stimuli significantly inhibited the proliferation and colony formation of TNBC cell lines. Furthermore, the data of flow cytometry revealed that exogenous AZU1 stimuli enhanced apoptosis in MDA­231 and BT­549 cells. As pyroptosis is a new type of cell death, the effects AZU1 played on the expression of gasdermin D (GSDMD), a specific biomarker of pyroptosis, were also investigated. The findings of the present study revealed that GSDMD, as well as its upstream regulators [NF­κB, NLR family pyrin domain containing 3 (NLRP3) and caspase­1], were significantly increased in TNBC cell lines when treated with exogenous AZU1, indicating that AZU1 contributed to the inhibition of pyroptosis of TNBC cell lines through the NF­κB/NLRP3/caspase­1 axis. Collectively, it was revealed for the first time, that AZU1 exposure promoted pyroptosis through the modulation of the pNF­κB/NLRP3/caspase­1/GSDMD axis in TNBC in vitro. The findings of the present study unveiled a novel mechanism of AZU1­induced pyroptosis in TNBC, which may aid in developing new strategies for therapeutic interventions in TNBC. breast cancer is the most commone form of cancer in women and is second only to lung cancer in terms of cancer­related mortality.

Collaborative Learning and Style-Adaptive Pooling Network for Perceptual Evaluation of Arbitrary Style Transfer.

Although the research of arbitrary style transfer (AST) has achieved great progress in recent years, few studies pay special attention to the perceptual evaluation of AST images that are usually influenced by complicated factors, such as structure-preserving, style similarity, and overall vision (OV). Existing methods rely on elaborately designed hand-crafted features to obtain quality factors and apply a rough pooling strategy to evaluate the final quality. However, the importance weights between the factors and the final quality will lead to unsatisfactory performances by simple quality pooling. In this article, we propose a learnable network, named collaborative learning and style-adaptive pooling network (CLSAP-Net) to better address this issue. The CLSAP-Net contains three parts, i.e., content preservation estimation network (CPE-Net), style resemblance estimation network (SRE-Net), and OV target network (OVT-Net). Specifically, CPE-Net and SRE-Net use the self-attention mechanism and a joint regression strategy to generate reliable quality factors for fusion and weighting vectors for manipulating the importance weights. Then, grounded on the observation that style type can influence human judgment of the importance of different factors, our OVT-Net utilizes a novel style-adaptive pooling strategy guiding the importance weights of factors to collaboratively learn the final quality based on the trained CPE-Net and SRE-Net parameters. In our model, the quality pooling process can be conducted in a self-adaptive manner because the weights are generated after understanding the style type. The effectiveness and robustness of the proposed CLSAP-Net are well validated by extensive experiments on the existing AST image quality assessment (IQA) databases. Our code will be released at https://github.com/Hangwei-Chen/CLSAP-Net.

WaveNet: Wavelet Network With Knowledge Distillation for RGB-T Salient Object Detection.

In recent years, various neural network architectures for computer vision have been devised, such as the visual transformer and multilayer perceptron (MLP). A transformer based on an attention mechanism can outperform a traditional convolutional neural network. Compared with the convolutional neural network and transformer, the MLP introduces less inductive bias and achieves stronger generalization. In addition, a transformer shows an exponential increase in the inference, training, and debugging times. Considering a wave function representation, we propose the WaveNet architecture that adopts a novel vision task-oriented wavelet-based MLP for feature extraction to perform salient object detection in RGB (red-green-blue)-thermal infrared images. In addition, we apply knowledge distillation to a transformer as an advanced teacher network to acquire rich semantic and geometric information and guide WaveNet learning with this information. Following the shortest-path concept, we adopt the Kullback-Leibler distance as a regularization term for the RGB features to be as similar to the thermal infrared features as possible. The discrete wavelet transform allows for the examination of frequency-domain features in a local time domain and time-domain features in a local frequency domain. We apply this representation ability to perform cross-modality feature fusion. Specifically, we introduce a progressively cascaded sine-cosine module for cross-layer feature fusion and use low-level features to obtain clear boundaries of salient objects through the MLP. Results from extensive experiments indicate that the proposed WaveNet achieves impressive performance on benchmark RGB-thermal infrared datasets. The results and code are publicly available at https://github.com/nowander/WaveNet.

Risk factors for early-onset colorectal cancer: systematic review and meta-analysis.

Background: The incidence of early-onset colorectal cancer (EOCRC), which means colorectal cancer diagnosed in patients under 50 years, has been increasing around the world. However, the etiology remains unclear. This study aims to identify risk factors for EOCRC. Methods: This systematic review was conducted in PubMed, Embase, Scopus, and Cochrane Library databases from inception to November 25, 2022. We examined risk factors for EOCRC, including demographic factors, chronic conditions, and lifestyle behaviors or environmental factors. Random-effects/fixed-effects meta-analysis was adopted to combine effect estimates from published data. Study quality was evaluated with the Newcastle-Ottawa Scale (NOS). Statistical analysis was performed Revman5.3. Studies not suitable for the meta-analysis were analyzed by a systematic review. Results: A total of 36 studies were identified for this review, and 30 studies were included in the meta-analysis. Significant risk factors for EOCRC included male (OR=1.20; 95% CI, 1.08-1.33), Caucasian (OR=1.44; 95% CI, 1.15-1.80), a family history of CRC (OR=5.90; 95% CI, 3.67-9.48), inflammatory bowel disease (OR=4.43; 95% CI, 4.05-4.84), obesity (OR=1.52; 95%CI, 1.20-1.91), overweight (OR=1.18; 95% CI, 1.12-1.25), triglycerides (OR=1.12; 95% CI, 1, 08-1.18), hypertension (OR=1.16; 95% CI, 1.12-1.21), metabolic syndrome (OR=1.29; 95% CI, 1.15-1.45), smoking (OR=1.44; 95% CI, 1.10-1.88), alcohol consumption (OR=1.41; 95% CI, 1.22-1.62), a sedentary lifestyle (OR=1.24; 95% CI, 1.05-1.46), red meat (OR=1.10; 95% CI, 1.04-1.16), processed meat (OR=1.53; 95% CI, 1.13-2.06), Western dietary patterns (OR=1.43; 95% CI, 1.18-1.73) and sugar-sweetened beverages (OR=1.55; 95% CI, 1.23-1.95). However, no statistical differences were found for hyperlipidemia and hyperglycemia. Vitamin D may be a protective factor (OR=0.72; 95% CI, 0.56-0.92). There was considerable heterogeneity among studies (I2>60%). Conclusions: The study provides an overview of the etiology and risk factors of EOCRC. Current evidence can provide baseline data for risk prediction models specific to EOCRC and risk-tailored screening strategies.

Self-Supervised Attentive Generative Adversarial Networks for Video Anomaly Detection.

Video anomaly detection (VAD) refers to the discrimination of unexpected events in videos. The deep generative model (DGM)-based method learns the regular patterns on normal videos and expects the learned model to yield larger generative errors for abnormal frames. However, DGM cannot always do so, since it usually captures the shared patterns between normal and abnormal events, which results in similar generative errors for them. In this article, we propose a novel self-supervised framework for unsupervised VAD to tackle the above-mentioned problem. To this end, we design a novel self-supervised attentive generative adversarial network (SSAGAN), which is composed of the self-attentive predictor, the vanilla discriminator, and the self-supervised discriminator. On the one hand, the self-attentive predictor can capture the long-term dependences for improving the prediction qualities of normal frames. On the other hand, the predicted frames are fed to the vanilla discriminator and self-supervised discriminator for performing true-false discrimination and self-supervised rotation detection, respectively. Essentially, the role of the self-supervised task is to enable the predictor to encode semantic information into the predicted normal frames via adversarial training, in order for the angles of rotated normal frames can be detected. As a result, our self-supervised framework lessens the generalization ability of the model to abnormal frames, resulting in larger detection errors for abnormal frames. Extensive experimental results indicate that SSAGAN outperforms other state-of-the-art methods, which demonstrates the validity and advancement of SSAGAN.

Toward Multicenter Skin Lesion Classification Using Deep Neural Network With Adaptively Weighted Balance Loss.

Recently, deep neural network-based methods have shown promising advantages in accurately recognizing skin lesions from dermoscopic images. However, most existing works focus more on improving the network framework for better feature representation but ignore the data imbalance issue, limiting their flexibility and accuracy across multiple scenarios in multi-center clinics. Generally, different clinical centers have different data distributions, which presents challenging requirements for the network's flexibility and accuracy. In this paper, we divert the attention from framework improvement to the data imbalance issue and propose a new solution for multi-center skin lesion classification by introducing a novel adaptively weighted balance (AWB) loss to the conventional classification network. Benefiting from AWB, the proposed solution has the following advantages: 1) it is easy to satisfy different practical requirements by only changing the backbone; 2) it is user-friendly with no tuning on hyperparameters; and 3) it adaptively enables small intraclass compactness and pays more attention to the minority class. Extensive experiments demonstrate that, compared with solutions equipped with state-of-the-art loss functions, the proposed solution is more flexible and more competent for tackling the multi-center imbalanced skin lesion classification task with considerable performance on two benchmark datasets. In addition, the proposed solution is proved to be effective in handling the imbalanced gastrointestinal disease classification task and the imbalanced DR grading task. Code is available at https://github.com/Weipeishan2021.

Use of Symptom Checklist 90 for exploring psychological factors among the parents of children hospitalized for burn injuries in Shanghai: a cross-sectional study.

To better improve the conditions for the recovery of children with burn injuries, timely understanding of the psychological status of parents is important. A cross-sectional survey on it using convenience sampling was conducted at two hospitals. Besides basic information, the Symptom Checklist 90, Eysenck Personality Questionnaire, Social Support Rate Scale, and Simplified Coping Style Questionnaire were used, and the key factors were identified via multivariate linear regression analysis and path analysis. A total of 196 guardians were recruited, 180 valid and completed questionnaires were obtained, including 58 men (32.2%) and 122 women (67.8%), and their average age was 30.3 years (standard deviation = 7.6). Of these, 151 participants (83.9%) were parents. Multivariate analysis revealed that children's age, parent gender, P score, negative coping style, and religion were the main factors that affected parents' psychology. Moreover, path analysis showed that P score, children's age, and negative coping style had the greatest impact on the total average score. These results suggest that during hospitalization, the following three factors should be focused on: older children, higher parental psychoticism, and increased negative coping style.

Optical coherence tomography of the pulmonary arteries in children with congenital heart diseases: A systematic review.

Importance: Optical coherence tomography (OCT) is a high-resolution intravascular imaging tool and has shown promise for providing real-time quantitative and qualitative descriptions of pulmonary vascular structures in vivo in adult pulmonary hypertension (PH), while not popular in pediatric patients with congenital heart diseases (CHD). Objective: The aim of this review is to summarize all the available evidence on the use of OCT for imaging pulmonary vascular remodeling in pediatric patients. Methods: We conducted the systematic literature resources (Cochran Library database, Medline via PubMed, EMBASE, and Web of Knowledge) from January 2010 to December 2021 and the search terms were "PH", "child", "children", "pediatric", "OCT", "CHD", "pulmonary vessels", "pulmonary artery wall". Studies in which OCT was used to image the pulmonary vessels in pediatric patients with CHD were considered for inclusion. Results: Five studies met the inclusion criteria. These five papers discussed the study of OCT in the pulmonary vasculature of different types of CHD, including common simple CHD, complex cyanotic CHD, and Williams-Beuren syndrome. In biventricular anatomy, pulmonary vascular remodeling was primarily reflected by pulmonary intima thickening from two-dimensional OCT. In single-ventricle anatomy, due to the state of hypoxia, the morphology of pulmonary vessels was indirectly reflected by the number and shape of nourishing vessels from three-dimensional OCT. Interpretation: OCT may be an adequate imaging procedure for the demonstration of pulmonary vascular structures and provide additional information in pediatric patients.

Dynamically attentive viewport sequence for no-reference quality assessment of omnidirectional images.

Omnidirectional images (ODIs) have drawn great attention in virtual reality (VR) due to the capability of providing an immersive experience to users. However, ODIs are usually subject to various quality degradations during different processing stages. Thus, the quality assessment of ODIs is of critical importance to the community of VR. The quality assessment of ODIs is quite different from that of traditional 2D images. Existing IQA methods focus on extracting features from spherical scenes while ignoring the characteristics of actual viewing behavior of humans in continuously browsing an ODI through HMD and failing to characterize the temporal dynamics of the browsing process in terms of the temporal order of viewports. In this article, we resort to the law of gravity to detect the dynamically attentive regions of humans when viewing ODIs. In this article, we propose a novel no-reference (NR) ODI quality evaluation method by making efforts on two aspects including the construction of Dynamically Attentive Viewport Sequence (DAVS) from ODIs and the extraction of Quality-Aware Features (QAFs) from DAVS. The construction of DAVS aims to build a sequence of viewports that are likely to be explored by viewers based on the prediction of visual scanpath when viewers are freely exploring the ODI within the exploration time via HMD. A DAVS that contains only global motion can then be obtained by sampling a series of viewports from the ODI along the predicted visual scanpath. The subsequent quality evaluation of ODIs is performed merely based on the DAVS. The extraction of QAFs aims to obtain effective feature representations that are highly discriminative in terms of perceived distortion and visual quality. Finally, we can adopt a regression model to map the extracted QAFs to a single predicted quality score. Experimental results on two datasets demonstrate that the proposed method is able to deliver state-of-the-art performance.

Toward Top-Down Just Noticeable Difference Estimation of Natural Images.

Just noticeable difference (JND) of natural images refers to the maximum pixel intensity change magnitude that typical human visual system (HVS) cannot perceive. Existing efforts on JND estimation mainly dedicate to modeling the diverse masking effects in either/both spatial or/and frequency domains, and then fusing them into an overall JND estimate. In this work, we turn to a dramatically different way to address this problem with a top-down design philosophy. Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map. We conduct subjective experiments to determine the critical points of 500 images and find that the distribution of cumulative normalized KLT coefficient energy values over all 500 images at these critical points can be well characterized by a Weibull distribution. Given a testing image, its corresponding critical point is determined by a simple weighted average scheme where the weights are determined by a fitted Weibull distribution function. The performance of the proposed JND model is evaluated explicitly with direct JND prediction and implicitly with two applications including JND-guided noise injection and JND-guided image compression. Experimental results have demonstrated that our proposed JND model can achieve better performance than several latest JND models. In addition, we also compare the proposed JND model with existing visual difference predicator (VDP) metrics in terms of the capability in distortion detection and discrimination. The results indicate that our JND model also has a good performance in this task. The code of this work are available at https://github.com/Zhentao-Liu/KLT-JND.

The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus.

BACKGROUND: The Peucedanum genus is the backbone member of Apiaceae, with many economically and medically important plants. Although the previous studies on Peucedanum provide us with a good research basis, there are still unclear phylogenetic relationships and many taxonomic problems in Peucedanum, and a robust phylogenetic framework of this genus still has not been obtained, which severely hampers the improvement and revision of taxonomic system for this genus. The plastid genomes possessing more variable characters have potential for reconstructing a robust phylogeny in plants. RESULTS: In the current study, we newly sequenced and assembled seven Peucedanum plastid genomes. Together with five previously published plastid genomes of Peucedanum, we performed a comprehensively comparative analyses for this genus. Twelve Peucedanum plastomes were similar in terms of genome structure, codon bias, RNA editing sites, and SSRs, but varied in genome size, gene content and arrangement, and border of SC/IR. Fifteen mutation hotspot regions were identified among plastid genomes that can serve as candidate DNA barcodes for species identification in Peucedanum. Our phylogenetic analyses based on plastid genomes generated a phylogeny with high supports and resolutions for Peucedanum that robustly supported the non-monophyly of genus Peucedanum. CONCLUSION: The plastid genomes of Peucedanum showed both conservation and diversity. The plastid genome data were efficient and powerful for improving the supports and resolutions of phylogeny for the complex Peucedanum genus. In summary, our study provides new sights into the plastid genome evolution, taxonomy, and phylogeny for Peucedanum species.

Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and an Objective Metric.

Numerous single image super-resolution (SISR) algorithms have been proposed during the past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) observation. However, how to fairly compare the performance of different SISR algorithms/results remains a challenging problem. So far, the lack of comprehensive human subjective study on large-scale real-world SISR datasets and accurate objective SISR quality assessment metrics makes it unreliable to truly understand the performance of different SISR algorithms. We in this paper make efforts to tackle these two issues. Firstly, we construct a real-world SISR quality dataset (i.e., RealSRQ) and conduct human subjective studies to compare the performance of the representative SISR algorithms. Secondly, we propose a new objective metric, i.e., KLTSRQA, based on the Karhunen-Loéve Transform (KLT) to evaluate the quality of SISR images in a no-reference (NR) manner. Experiments on our constructed RealSRQ and the latest synthetic SISR quality dataset (i.e., QADS) have demonstrated the superiority of our proposed KLTSRQA metric, achieving higher consistency with human subjective scores than relevant existing NR image quality assessment (NR-IQA) metrics. The dataset and the code will be made available at https://github.com/Zhentao-Liu/RealSRQ-KLTSRQA.

Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection.

Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criterion for detecting anomalies. However, this hypothesis cannot be always tenable since the deep AE usually captures the low-level shared features between normal and abnormal data, which leads to similar reconstruction errors for them. To tackle this problem, we propose a self-supervised representation-augmented deep AE for unsupervised VAD, which can enlarge the gap of anomaly scores between normal and abnormal samples by introducing autoencoding transformation (AT). Essentially, AT is introduced to facilitate AE to learn the high-level visual semantic features of normal images by introducing a self-supervision task (transformation reconstruction). In particular, our model inputs the original and transformed images into the encoder for obtaining latent representations; afterward, they are fed to the decoder for reconstructing both the original image and applied transformation. In this way, our model can utilize both image and transformation reconstruction errors to detect anomaly. Extensive experiments indicate that the proposed method outperforms other state-of-the-art methods, which demonstrates the validity and advancement of our model.

Weakly Supervised Video Anomaly Detection via Self-Guided Temporal Discriminative Transformer.

Weakly supervised video anomaly detection is generally formulated as a multiple instance learning (MIL) problem, where an anomaly detector learns to generate frame-level anomaly scores under the supervision of MIL-based video-level classification. However, most previous works suffer from two drawbacks: 1) they lack ability to model temporal relationships between video segments and 2) they cannot extract sufficient discriminative features to separate normal and anomalous snippets. In this article, we develop a weakly supervised temporal discriminative (WSTD) paradigm, that aims to leverage both temporal relation and feature discrimination to mitigate the above drawbacks. To this end, we propose a transformer-styled temporal feature aggregator (TTFA) and a self-guided discriminative feature encoder (SDFE). Specifically, TTFA captures multiple types of temporal relationships between video snippets from different feature subspaces, while SDFE enhances the discriminative powers of features by clustering normal snippets and maximizing the separability between anomalous snippets and normal centers in embedding space. Experimental results on three public benchmarks indicate that WSTD outperforms state-of-the-art unsupervised and weakly supervised methods, which verifies the superiority of the proposed method.

Hanseniatrifoliolata, a new species (Apiaceae) from Shaanxi, China.

Hanseniatrifoliolata Q.P.Jiang & X.J.He (Apiaceae), is described as new from Shaanxi Province, northwest China. The mericarp features of H.trifoliolata resemble H.himalayensis and H.phaea and molecular phylogenetic analyses (combining ITS and plastid genomes data) suggest that H.trifoliolata is closely related to the group formed by H.oviformis and H.forbesii. The new species H.trifoliolata has unique 3-foliolate leaves and differ from other Hansenia species in its leaves, umbel numbers and size. A comprehensive description of H.trifoliolata is provided, including habitat environment and detailed morphological traits.