Sampler Scheduler for Diffusion Models

Diffusion modeling (DM) has high-quality generative performance, and the sampling problem is an important part of the DM performance. Thanks to efficient differential equation solvers, the sampling speed can be reduced while higher sampling quality is guaranteed. However, currently, there is a contradiction in samplers for diffusion-based generative models: the mainstream sampler choices are diverse, each with its own characteristics in terms of performance. However, only a single sampler algorithm can be specified on all sampling steps in the generative process. This often makes one torn between sampler choices; in other words, it makes it difficult to fully utilize the advantages of each sampler. In this paper, we propose the feasibility of using different samplers (ODE/SDE) on different sampling steps of the same sampling process based on analyzing and generalizing the updating formulas of each mainstream sampler, and experimentally demonstrate that such a multi-sampler scheduling improves the sampling results to some extent. In particular, we also verify that the combination of using SDE in the early sampling steps and ODE in the later sampling steps solves the inherent problems previously caused by using both singly. We show that our design changes improve the sampling efficiency and quality in previous work. For instance, when Number of Function Evaluations (NFE) = 24, the ODE Sampler Scheduler achieves a FID score of 1.91 on the CIFAR-10 dataset, compared to 2.02 for DPM++ 2M, 1.97 for DPM2, and 11.90 for Heun for the same NFE. Meanwhile the Sampler Scheduler with the combined scheduling of SDE and ODE reaches 1.899, compared to 18.63 for Euler a, 3.14 for DPM2 a and 23.14 for DPM++ SDE

Genome-wide identification and expression analysis of TCP family genes in Catharanthus roseus

IntroductionThe anti-tumor vindoline and catharanthine alkaloids are naturally existed in Catharanthus roseus (C. roseus), an ornamental plant in many tropical countries. Plant-specific TEOSINTE BRANCHED1/CYCLOIDEA/PCF (TCP) transcription factors play important roles in various plant developmental processes. However, the roles of C. roseus TCPs (CrTCPs) in terpenoid indole alkaloid (TIA) biosynthesis are largely unknown.MethodsHere, a total of 15 CrTCP genes were identified in the newly updated C. roseus genome and were grouped into three major classes (P-type, C-type and CYC/TB1).ResultsGene structure and protein motif analyses showed that CrTCPs have diverse intron-exon patterns and protein motif distributions. A number of stress responsive cis-elements were identified in promoter regions of CrTCPs. Expression analysis showed that three CrTCP genes (CrTCP2, CrTCP4, and CrTCP7) were expressed specifically in leaves and four CrTCP genes (CrTCP13, CrTCP8, CrTCP6, and CrTCP10) were expressed specifically in flowers. HPLC analysis showed that the contents of three classic TIAs, vindoline, catharanthine and ajmalicine, were significantly increased by ultraviolet-B (UV-B) and methyl jasmonate (MeJA) in leaves. By analyzing the expression patterns under UV-B radiation and MeJA application with qRT-PCR, a number of CrTCP and TIA biosynthesis-related genes were identified to be responsive to UV-B and MeJA treatments. Interestingly, two TCP binding elements (GGNCCCAC and GTGGNCCC) were identified in several TIA biosynthesis-related genes, suggesting that they were potential target genes of CrTCPs. DiscussionThese results suggest that CrTCPs are involved in the regulation of the biosynthesis of TIAs, and provide a basis for further functional identification of CrTCPs

A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks

Advanced Persistent Threats (APTs) are the most sophisticated attacks for modern information systems. Currently, more and more researchers begin to focus on graph-based anomaly detection methods that leverage graph data to model normal behaviors and detect outliers for defending against APTs. However, previous studies of provenance graphs mainly concentrate on system calls, leading to difficulties in modeling network behaviors. Coarse-grained correlation graphs depend on handcrafted graph construction rules and, thus, cannot adequately explore log node attributes. Besides, the traditional Graph Neural Networks (GNNs) fail to consider meaningful edge features and are difficult to perform heterogeneous graphs embedding. To overcome the limitations of the existing approaches, we present a hierarchical approach for APT detection with novel attention-based GNNs. We propose a metapath aggregated GNN for provenance graph embedding and an edge enhanced GNN for host interactive graph embedding; thus, APT behaviors can be captured at both the system and network levels. A novel enhancement mechanism is also introduced to dynamically update the detection model in the hierarchical detection framework. Evaluations show that the proposed method outperforms the state-of-the-art baselines in APT detection

Effect of the gut microbiome, plasma metabolome, peripheral cells, and inflammatory cytokines on obesity: a bidirectional two-sample Mendelian randomization study and mediation analysis

BackgroundObesity is a metabolic and chronic inflammatory disease involving genetic and environmental factors. This study aimed to investigate the causal relationship among gut microbiota abundance, plasma metabolomics, peripheral cell (blood and immune cell) counts, inflammatory cytokines, and obesity.MethodsSummary statistics of 191 gut microbiota traits (N = 18,340), 1,400 plasma metabolite traits (N = 8,299), 128 peripheral cell counts (blood cells, N = 408,112; immune cells, N = 3,757), 41 inflammatory cytokine traits (N = 8,293), and 6 obesity traits were obtained from publicly available genome-wide association studies. Two-sample Mendelian randomization (MR) analysis was applied to infer the causal links using inverse variance-weighted, maximum likelihood, MR-Egger, weighted median, weighted mode, and Wald ratio methods. Several sensitivity analyses were also utilized to ensure reliable MR results. Finally, we used mediation analysis to identify the pathway from gut microbiota to obesity mediated by plasma metabolites, peripheral cells, and inflammatory cytokines.ResultsMR revealed a causal effect of 44 gut microbiota taxa, 281 plasma metabolites, 27 peripheral cells, and 8 inflammatory cytokines on obesity. Among them, five shared causal gut microbiota taxa belonged to the phylum Actinobacteria, order Bifidobacteriales, family Bifidobacteriaceae, genus Lachnospiraceae UCG008, and species Eubacterium nodatum group. Furthermore, we screened 42 shared causal metabolites, 7 shared causal peripheral cells, and 1 shared causal inflammatory cytokine. Based on known causal metabolites, we observed that the metabolic pathways of D-arginine, D-ornithine, linoleic acid, and glycerophospholipid metabolism were closely related to obesity. Finally, mediation analysis revealed 20 mediation relationships, including the causal pathway from gut microbiota to obesity, mediated by 17 metabolites, 2 peripheral cells, and 1 inflammatory cytokine. Sensitivity analysis represented no heterogeneity or pleiotropy in this study.ConclusionOur findings support a causal relationship among gut microbiota, plasma metabolites, peripheral cells, inflammatory cytokines, and obesity. These biomarkers provide new insights into the mechanisms underlying obesity and contribute to its prevention, diagnosis, and treatment

GPR41 Regulates the Proliferation of BRECs via the PIK3-AKT-mTOR Pathway

Short-chain fatty acids (SCFAs) play a pivotal role in regulating the proliferation and development of bovine rumen epithelial cells (BRECs). G protein-coupled receptor 41 (GPR41) is involved in the signal transduction in BRECs as a receptor for SCFAs. Nevertheless, the impact of GPR41 on the proliferation of BRECs has not been reported. The results of this research showed that the knockdown of GPR41 (GRP41KD) decreased BRECs proliferation compared with the wild-type BRECs (WT) (p p p p p < 0.05) compared with the WT cells. Therefore, it was proposed that GPR41 may affect the proliferation of BRECs by mediating the PIK3-AKT-mTOR signaling pathway

Predicting the APT for Cyber Situation Comprehension in 5G-Enabled IoT Scenarios Based on Differentially Private Federated Learning

Driven by the advancements in 5G-enabled Internet of Things (IoT) technologies, the IoT devices have shown an explosive growth trend with massive data generated at the edge of the network. However, IoT systems exhibit inherent vulnerability for diverse attacks, and Advanced Persistent Threat (APT) is one of the most powerful attack models that could lead to a significant privacy leakage of systems. Moreover, recent detection technologies can hardly meet the demands of effective security defense against APTs. To address the above problems, we propose an APT Prediction Method based on Differentially Private Federated Learning (APTPMFL) to predict the probability of subsequent APT attacks occurring in IoT systems. It is the first time to apply a federated learning mechanism for aggregating suspicious activities in the IoT systems, where the APT prediction phase does not need any correlation rules. Moreover, to achieve privacy-preserving property, we further adopt a differentially private data perturbation mechanism to add the Laplacian random noises to the IoT device training data features, so as to achieve the maximum protection of privacy data. We also present a 5G-enabled edge computing-based framework to train and deploy the model, which can alleviate the computing and communication overhead of the typical IoT systems. Our evaluation results show that APTPMFL can efficiently predict subsequent APT behaviors in the IoT system accurately and efficiently

Correlation between pulmonary vascular performance and hemodynamics in patients with pulmonary arterial hypertension

Objective To explore the correlation between pulmonary vascular performance and hemodynamics in patients with pulmonary arterial hypertension (PAH), using right heart catheterization (RHC) and intravascular ultrasound (IVUS). Method A total of 60 patients underwent RHC and IVUS examinations. Of these, 27 patients were diagnosed with PAH associated with connective tissue diseases (PAH-CTD group), 18 patients were diagnosed with other types of PAH (other-types-PAH group), and 15 patients were without PAH (control group). The hemodynamics and morphological parameters of pulmonary vessels in PAH patients were assessed using RHC and IVUS. Results There were statistically significant differences in right atrial pressure (RAP), pulmonary artery systolic pressure (sPAP), pulmonary artery diastolic pressure (dPAP), mean pulmonary artery pressure (mPAP) and pulmonary vascular resistance (PVR) values between the PAH-CTD group, other-types-PAH group, and the control group (P  .05). The mean wall thickness (MWT), wall thickness percentage (WTP), pulmonary vascular compliance, dilation, elasticity modulus, stiffness index β, and other indicators were significantly different between these three groups (P < .05). Pairwise comparison showed that the average levels of pulmonary vascular compliance and dilation in PAH-CTD group and other-types-PAH group were lower than those in control group, while the average levels of elastic modulus and stiffness index β were higher than those in control group. Conclusion Pulmonary vascular performance deteriorates in PAH patients, and the performance is better in PAH-CTD patients than in other types of PAH

Rotating MaxRS queries

Given a set of weighted objects in a data space, the MaxRS problem in spatial databases studied in a VLDB 2012 paper is to find a location for a rectangular region of a given size such that the weighted sum of all the objects covered by the rectangular region centered at the optimal location is maximized. This problem is useful in lots of location-based service applications, such as finding the location for a new fast food restaurant with a limited delivery range attracting the greatest number of customers. The existing MaxRS problem assumes that the rectangular region is always placed horizontally and is non-rotatable. However, under this assumption, the weighted sum of all the covered objects may not be the greatest when the rectangular region is rotatable. In this paper, we propose a generalized MaxRS problem called rotating MaxRS without this assumption. In rotating MaxRS, the rectangular region is rotatable and can be associated with an inclination angle. The goal of our problem is to find a location and an inclination angle such that the weighted sum of all the objects covered by the rectangular region of a given size centered at this location with this inclination angle is the greatest. We also present an efficient algorithm for the problem. Extensive experiments were conducted to verify the efficiency of our algorithms based on the real and synthetic datasets. The experimental results show that the weighted sum of all the objects in the rotating MaxRS queries can be increased with up to 300% on the synthetic datasets compared with existing non-rotating MaxRS queries, which shows the significance of the new rotating MaxRS queries. (C) 2015 Elsevier Inc. All rights reserved