End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraint

Epidermal Growth Factor Receptor-PI3K Signaling Controls Cofilin Activity To Facilitate Herpes Simplex Virus 1 Entry into Neuronal Cells

Herpes simplex virus type 1 (HSV-1) establishes latency in neurons and can cause severe disseminated infection with neurological impairment and high mortality. This neurodegeneration is thought to be tightly associated with virus-induced cytoskeleton disruption. Currently, the regulation pattern of the actin cytoskeleton and the involved molecular mechanisms during HSV-1 entry into neurons remain unclear. Here, we demonstrate that the entry of HSV-1 into neuronal cells induces biphasic remodeling of the actin cytoskeleton and an initial inactivation followed by the subsequent activation of cofilin, a member of the actin depolymerizing factor family that is critical for actin reorganization. The disruption of F-actin dynamics or the modulation of cofilin activity by mutation, knockdown, or overexpression affects HSV-1 entry efficacy and virus-mediated cell ruffle formation. Binding of the HSV-1 envelope initiates the epidermal growth factor receptor (EGFR)-phosphatidylinositide 3-kinase (PI3K) signaling pathway, which leads to virus-induced early cofilin phosphorylation and F-actin polymerization. Moreover, the extracellular signal-regulated kinase (ERK) kinase and Rho-associated, coiled-coil-containing protein kinase 1 (ROCK) are recruited as downstream mediators of the HSV-1-induced cofilin inactivation pathway. Inhibitors specific for those kinases significantly reduce the virus infectivity without affecting virus binding to the target cells. Additionally, lipid rafts are clustered to promote EGFR-associated signaling cascade transduction. We propose that HSV-1 hijacks cofilin to initiate infection. These results could promote a better understanding of the pathogenesis of HSV-1-induced neurological diseases

Cofilin-1 is involved in regulation of actin reorganization during influenza A virus assembly and budding

Influenza A virus (IAV) assembly and budding on host cell surface plasma membrane requires actin cytoskeleton reorganization. The underlying molecular mechanism involving actin reorganization remains unclarified. In this study, we found that the natural antiviral compound petagalloyl glucose (PGG) inhibits F-actin reorganization in the host cell membrane during the late stage of IAV infection, which are associated with the suppression of total cofilin-1 level and its phosphorylation. Knock-down of cofilin-1 reduces viral yields. These findings provide the first evidence that cofilin-1 plays an important role in regulating actin reorganization during IAV assembly and budding

Heat-Shock Protein 90 Promotes Nuclear Transport of Herpes Simplex Virus 1 Capsid Protein by Interacting with Acetylated Tubulin

Although it is known that inhibitors of heat shock protein 90 (Hsp90) can inhibit herpes simplex virus type 1 (HSV-1) infection, the role of Hsp90 in HSV-1 entry and the antiviral mechanisms of Hsp90 inhibitors remain unclear. In this study, we found that Hsp90 inhibitors have potent antiviral activity against standard or drug-resistant HSV-1 strains and viral gene and protein synthesis are inhibited in an early phase. More detailed studies demonstrated that Hsp90 is upregulated by virus entry and it interacts with virus. Hsp90 knockdown by siRNA or treatment with Hsp90 inhibitors significantly inhibited the nuclear transport of viral capsid protein (ICP5) at the early stage of HSV-1 infection. In contrast, overexpression of Hsp90 restored the nuclear transport that was prevented by the Hsp90 inhibitors, suggesting that Hsp90 is required for nuclear transport of viral capsid protein. Furthermore, HSV-1 infection enhanced acetylation of α-tubulin and Hsp90 interacted with the acetylated α-tubulin, which is suppressed by Hsp90 inhibition. These results demonstrate that Hsp90, by interacting with acetylated α-tubulin, plays a crucial role in viral capsid protein nuclear transport and may provide novel insight into the role of Hsp90 in HSV-1 infection and offer a promising strategy to overcome drug-resistance

House Age, Price and Rent: Implications from Land-Structure Decomposition

Big cities often witness land price outgrowing structure price. For such cities this paper derives two predictions regarding the dynamics between house prices, rent and structure age. First, older houses have a higher price growth rate than younger ones, even after controlling for location and other attributes; second, the age depreciation of house price, defined as the decline of house price with respect to house age, is slower than the similarly-defined age depreciation of rent. These hypotheses are supported by the micro-data on housing market in Beijing. These two inferences have implications for both real estate valuation and house price index construction. Keywords: Land price, Structure price, House prices, Rent, DepreciationNational Natural Science Foundation (China) (71625004)National Natural Science Foundation (China) (71273154)National Natural Science Foundation (China) (71322307)National Natural Science Foundation (China) (71533004)China. Ministry of Science and Technology. National Key Technologies R&D Program (2016YFC0502804

Based on event-triggered anti-disturbance tracking control for hypersonic flight vehicles with T-S disturbance modeling

In this paper, a novel based on event-triggered anti-disturbance dynamical tracking control is discussed for HFVs with unknown exogenous disturbances. Firstly, the event-triggered mechanism is introduced into the control system. Secondly, the external disturbance is described by T-S model and estimated by disturbance observer. Next, by combining state feedback with disturbance estimation, a PI-type feedback controller is proposed to ensure the HFV models stability and the output tracking error convergence zero. Finally, the simulation result shows the algorithm is effective. Meanwhile, it can obtain satisfactory tracking performance and anti-disturbance tracking performance

Fuzzy modeling-based fault diagnosis and tolerant control for complex non-Gaussian stochastic systems

This paper discusses a novel fault diagnosis and tolerant control problem for a class of non-Gaussian stochastic distribution systems with unknown fault based on two-step fuzzy modeling. Following square fuzzy logic approximation for the output probability density functions (PDFs) of non-Gaussian processes, the T-S weight models are employed to describe the nonlinear relations between the fuzzy weight dynamics and the control input. By utilizing the typical projection algorithm, the adaptive fuzzy filter is designed to successfully estimate the size of system fault. Meanwhile, the error system stability in present of fault can also be guaranteed. Moreover, the feedback control input can be constructed by using convex optimization algorithm. The satisfactory control performance and stability can be achieved by the designed optimization algorithm

An integrated metabolomic approach to elucidate the mechanism of Chrysanthemi Flos processed products in ameliorating metabolic syndrome

As an important medicinal and dietary flower tea, Chrysanthemi Flos has hypotensive, hypolipidemic and hypoglycaemic benefits. This study investigated the effects of two types of processed Chrysanthemi Flos products—oven-dried Chrysanthemi Flos (DCF) and shade-dried Chrysanthemi Flos (SCF)—on rats with metabolic syndrome (MetS) and their potential mechanisms of action. The pharmacodynamic analysis showed that DCF and SCF improved hypertension, regulated lipid profiles, and reduced liver and kidney damage. Metabolomics analysis revealed eight metabolites, including L-serine, oxaloacetate and succinate as potential biomarkers for mitigating metabolic disorders in MetS rats. Our findings provide scientific evidence for the integration of Chrysanthemi Flos-based products into clinical dietary interventions, offering insights into the mechanisms through which these products can ameliorate MetS. These results have important implications for the development of functional foods that can improve metabolic health

SLOTH: Structured Learning and Task-Based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraints