Lewiscorneliussen0638

With the rapid development of swarm intelligence, the consensus of multiagent systems (MASs) has attracted substantial attention due to its broad range of applications in the practical world. Inspired by the considerable gap between control theory and engineering practices, this article is aimed at addressing the mean square consensus problems for stochastic dynamical nonlinear MASs in directed networks by designing proportional-integral (PI) protocols. In light of the general algebraic connectivity, consensus underlying PI protocols for a directed strongly connected network is investigated, and due to the M-matrix approaches, consensus with PI protocols for a directed network containing a spanning tree is studied. By constructing appropriate Lyapunov functions, combining with the stochastic analysis technique and LaSalle's invariant principles, some sufficient conditions are derived under which the stochastic dynamical MASs realize consensus in mean square. Numerical simulations are finally presented to illustrate the validity of the main results.The adaptive hinging hyperplane (AHH) model is a popular piecewise linear representation with a generalized tree structure and has been successfully applied in dynamic system identification. In this article, we aim to construct the deep AHH (DAHH) model to extend and generalize the networking of AHH model for high-dimensional problems. The network structure of DAHH is determined through a forward growth, in which the activity ratio is introduced to select effective neurons and no connecting weights are involved between the layers. Then, all neurons in the DAHH network can be flexibly connected to the output in a skip-layer format, and only the corresponding weights are the parameters to optimize. With such a network framework, the backpropagation algorithm can be implemented in DAHH to efficiently tackle large-scale problems and the gradient vanishing problem is not encountered in the training of DAHH. In fact, the optimization problem of DAHH can maintain convexity with convex loss in the output layer, which brings natural advantages in optimization. Different from the existing neural networks, DAHH is easier to interpret, where neurons are connected sparsely and analysis of variance (ANOVA) decomposition can be applied, facilitating to revealing the interactions between variables. A theoretical analysis toward universal approximation ability and explicit domain partitions are also derived. Numerical experiments verify the effectiveness of the proposed DAHH.Aging is traditionally thought to be caused by complex and interacting factors such as DNA methylation. The traditional formula of DNA methylation aging is based on linear models and little work has explored the effectiveness of neural networks, which can learn non-linear relationships. DNA methylation data typically consists of hundreds of thousands of feature space and a much less number of biological samples. This leads to overfitting and a poor generalization of neural networks. We propose Correlation Pre-Filtered Neural Network (CPFNN) that uses Spearman Correlation to pre-filter the input features before feeding them into neural networks. We compare CPFNN with the statistical regressions (i.e. Horvaths and Hannums formulas), the neural networks with LASSO regularization and elastic net regularization, and the Dropout Neural Networks. CPFNN outperforms these models by at least 1 year in term of Mean Absolute Error (MAE), with a MAE of 2.7 years. We also test for association between the epigenetic age with Schizophrenia and Down Syndrome (p=0.024 and p less then 0.001, respectively). We discover that for a large number of candidate features, such as genome-wide DNA methylation data, a key factor in improving prediction accuracy is to appropriately weight features that are highly correlated with the outcome of interest.Semi-supervised learning (SSL) provides a way to improve the performance of prediction models (e.g., classifier) via the usage of unlabeled samples. An effective and widely used method is to construct a graph that describes the relationship between labeled and unlabeled samples. Practical experience indicates that graph quality significantly affects the model performance. In this paper, we present a visual analysis method that interactively constructs a high-quality graph for better model performance. In particular, we propose an interactive graph construction method based on the large margin principle. We have developed a river visualization and a hybrid visualization that combines a scatterplot, a node-link diagram, and a bar chart to convey the label propagation of graph-based SSL. Based on the understanding of the propagation, a user can select regions of interest to inspect and modify the graph. We conducted two case studies to showcase how our method facilitates the exploitation of labeled and unlabeled samples for improving model performance.The balance between high accuracy and high speed has always been a challenging task in semantic image segmentation. Compact segmentation networks are more widely used in the case of limited resources, while their performances are constrained. In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively. learn more Specifically, we propose a pixel-wise similarity distillation (PSD) module that utilizes residual attention maps to capture more detailed spatial dependencies across multiple layers. Compared with exiting methods, the PSD module greatly reduces the amount of calculation and is easy to expand. Furthermore, considering the differences in characteristics between semantic segmentation task and other computer vision tasks, we propose a category-wise similarity distillation (CSD) module, which can help the compact segmentation network strengthen the global category correlation by constructing the correlation matrix. Combining these two modules, DSD framework has no extra parameters and only a minimal increase in FLOPs. Extensive experiments on four challenging datasets, including Cityscapes, CamVid, ADE20K, and Pascal VOC 2012, show that DSD outperforms current state-of-the-art methods, proving its effectiveness and generality. The code and models will be publicly available.Geometric nanoconfinement, in one and two dimensions, has a fundamental influence on the segmental dynamics of polymer glass-formers and can be markedly different from that observed in the bulk state. In this work, with the use of dielectric spectroscopy, we have investigated the glass transition behavior of poly(2-vinylpyridine) (P2VP) confined within alumina nanopores and prepared as a thin film supported on a silicon substrate. P2VP is known to exhibit strong, attractive interactions with confining surfaces due to the ability to form hydrogen bonds. Obtained results show no changes in the temperature evolution of the α-relaxation time in nanopores down to 20 nm size and 24 nm thin film. There is also no evidence of an out-of-equilibrium behavior observed for other glass-forming systems confined at the nanoscale. Nevertheless, in both cases, the confinement effect is seen as a substantial broadening of the α-relaxation time distribution. We discussed the results in terms of the importance of the interfacial energy between the polymer and various substrates, the sensitivity of the glass-transition temperature to density fluctuations, and the density scaling concept.Activation of the toll-like receptors 7 and 8 has emerged as a promising strategy for cancer immunotherapy. Herein, we report the design and synthesis of a series of pyrido[3,2-d]pyrimidine-based toll-like receptor 7/8 dual agonists that exhibited potent and near-equivalent agonistic activities toward TLR7 and TLR8. In vitro, compounds 24e and 25a significantly induced the secretion of IFN-α, IFN-γ, TNF-α, IL-1β, IL-12p40, and IP-10 in human peripheral blood mononuclear cell assays. In vivo, compounds 24e, 24m, and 25a significantly suppressed tumor growth in CT26 tumor-bearing mice by remodeling the tumor microenvironment. Additionally, compounds 24e, 24m, and 25a markedly improved the antitumor activity of PD-1/PD-L1 blockade. In particular, compound 24e combined with the anti-PD-L1 antibody led to complete tumor regression. These results demonstrated that TLR7/8 agonists (24e, 24m, and 25a) held great potential as single agents or in combination with PD-1/PD-L1 blockade for cancer immunotherapy.The biodistribution of molecular imaging probes or tracers mainly depends on the chemical nature of the probe and the preferred metabolization and excretion routes. Small molecules have rather short half-lives while antibodies reside inside the organism for a longer period of time. An excretion via kidneys and bladder is faster than a mainly hepatobiliary elimination. To manipulate the biodistribution behavior of probes, different strategies have been pursued, including utilizing serum albumin as an inherent transport mechanism for small molecules. Here, we modified an existing small molecular fluorescent probe targeted to the endothelin-A receptor (ETAR) with three different albumin-binding moieties to search for an optimal modification strategy. A diphenylcyclohexyl (DPCH) group, a p-iodophenyl butyric acid (IPBA), and a fatty acid (FA) group were attached via amino acid linkers. All three modifications result in transient albumin binding of the developed compounds, as concluded from gel electrophoresis inv probes substantially and enables near infrared optical imaging of subcutaneous tumors for several days in vivo. Because the unmodified probe already exhibits reasonable results, the attachment of albumin-binding moieties does not lead to a substantially improved imaging outcome in terms of target-to-background ratios. On the other hand, because the implemented transient albumin binding results in an overall higher amount of probe inside tumor lesions, this strategy might be adaptable for theranostic or therapeutic approaches in a future clinical routine.The human fecal metabolome is increasingly studied to explore the impact of diet and lifestyle on health and the gut microbiome. However, systematic differences and confounding factors related to age, sex, and diet remain largely unknown. In this study, absolute concentrations of fecal metabolites from 205 healthy Danes (105 males and 100 females, 49 ± 31 years old) were quantified using 1H NMR spectroscopy and the newly developed SigMa software. The largest systemic variation was found to be highly related to age. Fecal concentrations of short-chain fatty acids (SCFA) were higher in the 18 years old group, while amino acids (AA) were higher in the elderly. Sex-related metabolic differences were weak but significant and mainly related to changes in SCFA. The concentrations of butyric, valeric, propionic, and isovaleric acids were found to be higher in males compared to females. Sex differences were associated with a stronger, possibly masking, effect from differential intake of macronutrients. Dietary fat intake decreased levels of SCFA and AA of both sexes, while carbohydrate intake showed weak correlations with valeric and isovaleric acids in females.