TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation

One-shot image generation (OSG) with generative adversarial networks that learn from the internal patches of a given image has attracted world wide attention. In recent studies, scholars have primarily focused on extracting features of images from probabilistically distributed inputs with pure convolutional neural networks (CNNs). However, it is quite difficult for CNNs with limited receptive domain to extract and maintain the global structural information. Therefore, in this paper, we propose a novel structure-preserved method TcGAN with individual vision transformer to overcome the shortcomings of the existing one-shot image generation methods. Specifically, TcGAN preserves global structure of an image during training to be compatible with local details while maintaining the integrity of semantic-aware information by exploiting the powerful long-range dependencies modeling capability of the transformer. We also propose a new scaling formula having scale-invariance during the calculation period, which effectively improves the generated image quality of the OSG model on image super-resolution tasks. We present the design of the TcGAN converter framework, comprehensive experimental as well as ablation studies demonstrating the ability of TcGAN to achieve arbitrary image generation with the fastest running time. Lastly, TcGAN achieves the most excellent performance in terms of applying it to other image processing tasks, e.g., super-resolution as well as image harmonization, the results further prove its superiority

Landscape Pattern Analysis and Quality Evaluation in Beijing Hanshiqiao Wetland Nature Reserve

AbstractTaking the Landsat TM and ASTER images of Hanshiqiao wetland nature reserve in 1988, 1996 and 2004 as data source, based on the landscape types from imagery classification, the reserve landscape pattern and its changes were analyzed, meanwhile, the landscape quality and its changes were evaluated and discussed. Several landscape pattern indices were analyzed, the results indicated that from 1988 to 2004, as the result of natural factors and human disturbances, the landscape structure has been changed, landscape fragmentation has become more and more serious, patches have been tended to regular shape, and connectivity of the natural wetland has been weakened. In addition, the landscape quality was evaluated based on the indicators of pressure, state and response. The results showed that during 1996-2004 periods, the landscape quality for Hanshiqiao wetland nature reserve has degraded obviously, which was mainly influenced by human activities breaking into wetland landscape. Effective wetland management and control is therefore needed to solve the issues of the wetland loss and degradation in Hanshiqiao wetland nature reserve

Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK

High-order Takagi-Sugeno-Kang (TSK) fuzzy classifiers possess powerful classification performance yet have fewer fuzzy rules, but always be impaired by its exponential growth training time and poorer interpretability owing to High-order polynomial used in consequent part of fuzzy rule, while Low-order TSK fuzzy classifiers run quickly with high interpretability, however they usually require more fuzzy rules and perform relatively not very well. Address this issue, a novel TSK fuzzy classifier embeded with knowledge distillation in deep learning called HTSK-LLM-DKD is proposed in this study. HTSK-LLM-DKD achieves the following distinctive characteristics: 1) It takes High-order TSK classifier as teacher model and Low-order TSK fuzzy classifier as student model, and leverages the proposed LLM-DKD (Least Learning Machine based Decoupling Knowledge Distillation) to distill the fuzzy dark knowledge from High-order TSK fuzzy classifier to Low-order TSK fuzzy classifier, which resulting in Low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to High-order TSK classifier, as well as high interpretability; specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it compute teacher/student soft labels by the softmax function with distillating temperature parameter; 3) By reformulating the Kullback-Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model. The advantages of HTSK-LLM-DKD are verified on the benchmarking UCI datasets and a real dataset Cleveland heart disease, in terms of classification performance and model interpretability

Visualizing traffic causality for analyzing network anomalies

ABSTRACT Monitoring network traffic and detecting anomalies are essential tasks that are carried out routinely by security analysts. The sheer volume of network requests often makes it difficult to detect attacks and pinpoint their causes. We design and develop a tool to visually represent the causal relations for network requests. The traffic causality information enables one to reason about the legitimacy and normalcy of observed network events. Our tool with a special visual locality property supports different levels of visualbased querying and reasoning required for the sensemaking process on complex network data. Leveraging the domain knowledge, security analysts can use our tool to identify abnormal network activities and patterns due to attacks or stealthy malware. We conduct a user study that confirms our tool can enhance the readability and perceptibility of the dependency for host-based network traffic

The role of indoleamine 2,3-dioxygenase 1 in early-onset post-stroke depression

BackgroundThe immune-inflammatory response has been widely considered to be involved in the pathogenesis of post-stroke depression (PSD), but there is ambiguity about the mechanism underlying such association.MethodsAccording to Diagnostic and Statistical Manual of Mental Disorders (5th edition), depressive symptoms were assessed at 2 weeks after stroke onset. 15 single nucleotide polymorphisms (SNPs) in genes of indoleamine 2,3-dioxygenase (IDO, including IDO1 and IDO2) and its inducers (including pro-inflammatory cytokines interferon [IFN]-γ, tumor necrosis factor [TNF]-α, interleukin [IL]-1β, IL-2 and IL-6) were genotyped using SNPscan™ technology, and serum IDO1 levels were detected by double-antibody sandwich enzyme-linked immune-sorbent assay.ResultsFifty-nine patients (31.72%) were diagnosed with depression at 2 weeks after stroke onset (early-onset PSD). The IDO1 rs9657182 T/T genotype was independently associated with early-onset PSD (adjusted odds ratio [OR] = 3.008, 95% confidence interval [CI] 1.157-7.822, p = 0.024) and the frequency of rs9657182 T allele was significantly higher in patients with PSD than that in patients with non-PSD (χ2 = 4.355, p = 0.037), but these results did not reach the Bonferroni significance threshold (p > 0.003). Serum IDO1 levels were also independently linked to early-onset PSD (adjusted OR = 1.071, 95% CI 1.002-1.145, p = 0.044) and patients with PSD had higher serum IDO1 levels than patients with non-PSD in the presence of the rs9657182 T allele but not homozygous C allele (t = -2.046, p = 0.043). Stroke patients with the TNF-α rs361525 G/G genotype had higher serum IDO1 levels compared to those with the G/A genotype (Z = -2.451, p = 0.014).ConclusionsOur findings provided evidence that IDO1 gene polymorphisms and protein levels were involved in the development of early-onset PSD and TNF-α polymorphism was associated with IDO1 levels, supporting that IDO1 which underlie strongly regulation by cytokines may be a specific pathway for the involvement of immune-inflammatory mechanism in the pathophysiology of PSD

Associations of vitamin D-related single nucleotide polymorphisms with post-stroke depression among ischemic stroke population

ObjectiveTo investigate the relationship between single nucleotide polymorphisms (SNPs) related to vitamin D (VitD) metabolism and post-stroke depression (PSD) in patients with ischemic stroke.MethodsA total of 210 patients with ischemic stroke were enrolled at the Department of Neurology in Xiangya Hospital, Central South University, from July 2019 to August 2021. SNPs in the VitD metabolic pathway (VDR, CYP2R1, CYP24A1, and CYP27B1) were genotyped using the SNPscan™ multiplex SNP typing kit. Demographic and clinical data were collected using a standardized questionnaire. Multiple genetic models including dominant, recessive, and over-dominant models were utilized to analyze the associations between SNPs and PSD.ResultsIn the dominant, recessive, and over-dominant models, no significant association was observed between the selected SNPs in the CYP24A1 and CYP2R1 genes and PSD. However, univariate and multivariate logistic regression analysis revealed that the CYP27B1 rs10877012 G/G genotype was associated with a decreased risk of PSD (OR: 0.41, 95% CI: 0.18–0.92, p = 0.030 and OR: 0.42, 95% CI: 0.18–0.98, p = 0.040, respectively). Furthermore, haplotype association analysis indicated that rs11568820-rs1544410-rs2228570-rs7975232-rs731236 CCGAA haplotype in the VDR gene was associated with a reduced risk of PSD (OR: 0.14, 95% CI: 0.03–0.65, p = 0.010), whereas no significant association was observed between haplotypes in the CYP2R1 and CYP24A1 genes and PSD.ConclusionOur findings suggest that the polymorphisms of VitD metabolic pathway genes VDR and CYP27B1 may be associated with PSD in patients with ischemic stroke