Parallel clustering of high-dimensional social media data streams

We introduce Cloud DIKW as an analysis environment supporting scientific discovery through integrated parallel batch and streaming processing, and apply it to one representative domain application: social media data stream clustering. Recent work demonstrated that high-quality clusters can be generated by representing the data points using high-dimensional vectors that reflect textual content and social network information. Due to the high cost of similarity computation, sequential implementations of even single-pass algorithms cannot keep up with the speed of real-world streams. This paper presents our efforts to meet the constraints of real-time social stream clustering through parallelization. We focus on two system-level issues. Most stream processing engines like Apache Storm organize distributed workers in the form of a directed acyclic graph, making it difficult to dynamically synchronize the state of parallel workers. We tackle this challenge by creating a separate synchronization channel using a pub-sub messaging system. Due to the sparsity of the high-dimensional vectors, the size of centroids grows quickly as new data points are assigned to the clusters. Traditional synchronization that directly broadcasts cluster centroids becomes too expensive and limits the scalability of the parallel algorithm. We address this problem by communicating only dynamic changes of the clusters rather than the whole centroid vectors. Our algorithm under Cloud DIKW can process the Twitter 10% data stream in real-time with 96-way parallelism. By natural improvements to Cloud DIKW, including advanced collective communication techniques developed in our Harp project, we will be able to process the full Twitter stream in real-time with 1000-way parallelism. Our use of powerful general software subsystems will enable many other applications that need integration of streaming and batch data analytics.Comment: IEEE/ACM CCGrid 2015: 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 201

A Multigrid Multilevel Monte Carlo Method for Stokes–Darcy Model with Random Hydraulic Conductivity and Beavers–Joseph Condition

A multigrid multilevel Monte Carlo (MGMLMC) method is developed for the stochastic Stokes–Darcy interface model with random hydraulic conductivity both in the porous media domain and on the interface. Three interface conditions with randomness are considered on the interface between Stokes and Darcy equations, especially the Beavers–Joesph interface condition with random hydraulic conductivity. Because the randomness through the interface affects the flow in the Stokes domain, we investigate the coupled stochastic Stokes–Darcy model to improve the fidelity. Under suitable assumptions on the random coefficient, we prove the existence and uniqueness of the weak solution of the variational form. To construct the numerical method, we first adopt the Monte Carlo (MC) method and finite element method, for the discretization in the probability space and physical space, respectively. In order to improve the efficiency of the classical single-level Monte Carlo (SLMC) method, we adopt the multilevel Monte Carlo (MLMC) method to dramatically reduce the computational cost in the probability space. A strategy is developed to calculate the number of samples needed in MLMC method for the stochastic Stokes–Darcy model. In order to accomplish the strategy for MLMC method, we also present a practical method to determine the variance convergence rate for the stochastic Stokes–Darcy model with Beavers–Joseph interface condition. Furthermore, MLMC method naturally provides the hierarchical grids and sufficient information on these grids for multigrid (MG) method, which can in turn improve the efficiency of MLMC method. In order to fully make use of the dynamical interaction between these two methods, we propose a multigrid multilevel Monte Carlo (MGMLMC) method with finite element discretization for more efficiently solving the stochastic model, while additional attention is paid to the interface and the random Beavers–Joesph interface condition. The computational cost of the proposed MGMLMC method is rigorously analyzed and compared with the SLMC method. Numerical examples are provided to verify and illustrate the proposed method and the theoretical conclusions

Exploring techniques for vision based human activity recognition: Methods, systems, and evaluation

With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognitio

A Dual-Porosity-Stokes Model and Finite Element Method for Coupling Dual-Porosity Flow and Free Flow

In this paper, we propose and numerically solve a new model considering confined flow in dual-porosity media coupled with free flow in embedded macrofractures and conduits. Such situation arises, for example, for fluid flows in hydraulic fractured tight/shale oil/gas reservoirs. The flow in dual-porosity media, which consists of both matrix and microfractures, is described by a dual-porosity model. And the flow in the macrofractures and conduits is governed by the Stokes equation. Then the two models are coupled through four physically valid interface conditions on the interface between dual-porosity media and macrofractures/conduits, which play a key role in a physically faithful simulation with high accuracy. All the four interface conditions are constructed based on fundamental properties of the traditional dual-porosity model and the well-known Stokes-Darcy model. The weak formulation is derived for the proposed model, and the well-posedness of the model is analyzed. A finite element semidiscretization in space is presented based on the weak formulation, and four different schemes are then utilized for the full discretization. The convergence of the full discretization with the backward Euler scheme is analyzed. Four numerical experiments are presented to validate the proposed model and demonstrate the features of both the model and the numerical method, such as the optimal convergence rate of the numerical solution, the detail flow characteristics around macrofractures and conduits, and the applicability to the real world problems

Effect of Polyphenols from Enteromorpha prolifera on Reducing Blood Glucose in Type 2 Diabetic Mice

Objective: The aim of this work was to explore the hypoglycemic effect of Enteromorpha prolifera polyphenols (EPP) on type 2 diabetic mice, and to provide theoretical basis for the development of EPP products. Methods: EPP crude was extracted from Enteromorpha prolifera by ethanol solution extraction, and purified by silica gel column chromatography. The model of type 2 diabetes in mice was established by alloxan. The experimental mice were divided into blank control group, model control group, EPP low dose group (EPP-L), EPP midium dose group (EPP-M), EPP high dose group (EPP-H), and positive control group. The basic indicators of mice, as well as lipid metabolism and serum antioxidant indicators, were measured by continuous gastric perfusion for 4 weeks. Results: At the 4th week of the experiment, the averaged weight of mice in EPP-H group reached 32.16±1.97 g, which was not significantly different from that of the positive control group (P>0.05). The fasting blood glucose concentration of EPP-H group was 11.78±1.62 mmol/L, and there was no significant difference between EPP-H group and the positive control group (P>0.05). EPP-H clearly had significantly improved both the glucose tolerance (P<0.05) and the abnormal glucose tolerance of mice. The concentrations of superoxide dismutase, catalase, glutathione peroxidase, high-density lipoprotein cholesterol, and serum insulin levels in EPP-H mice were 157.36±6.71 U/mg, 168.07±1.77 U/mg, 378.14±9.74 U/mg, 1.31±0.04 mmol/L, and 19.03±2.01 mU/L, respectively, compared with the model control group, it was significantly increased (P<0.01), and the contents of malondialdehyde, triglyceride, total cholesterol and low-density lipoprotein cholesterol were significantly decreased (P<0.05). Conclusion: Polyphenols from Enteromorpha prolifera can effectively improve the basal metabolism of type 2 diabetic mice and repair the peroxide damage of diabetic mice, and may play a role in lowering blood sugar

Paeoniflorin Ameliorates Chronic Stress-Induced Depression-Like Behaviors and Neuronal Damages in Rats via Activation of the ERK-CREB Pathway

Neuronal damage is related to the onset and treatment of depressive disorders. Antidepressant-like effects have been elicited by paeoniflorin on animal models. The aim of this study is to demonstrate whether the neuroprotective effect of paeoniflorin on rats suffered from chronic unpredictable mild stress (CUMS) was regulated by the ERK-CREB signaling pathway. Results showed that paeoniflorin not only ameliorated depressive-like behavior with low locomotor activity and prolonged immobility duration in our forced swimming test but also reduced sucrose consumption. Paeoniflorin treatment decreased the degree of neuronal damage in the hippocampus of the model rats. Conversely, it markedly increased the mRNA levels of ERK1, ERK2, and CREB and the levels of ERK, p-ERK, CREB, and p-CREB protein expression in the hippocampus. Blockade of the ERK-CREB axis with the ERK-specific inhibitor U0126 repressed the neuroprotective and antidepressant-like effects of paeoniflorin on rats in the setting of chronic-mild-stress and abolished the recoveries of p-ERK mediated by paeoniflorin treatment. Thus, paeoniflorin possibly exerted a neuroprotective effect modulated by the ERK-CREB signaling pathway on CUMS-induced hippocampal damage in rats

GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content

The mobile cloud gaming industry has been rapidly growing over the last decade. When streaming gaming videos are transmitted to customers' client devices from cloud servers, algorithms that can monitor distorted video quality without having any reference video available are desirable tools. However, creating No-Reference Video Quality Assessment (NR VQA) models that can accurately predict the quality of streaming gaming videos rendered by computer graphics engines is a challenging problem, since gaming content generally differs statistically from naturalistic videos, often lacks detail, and contains many smooth regions. Until recently, the problem has been further complicated by the lack of adequate subjective quality databases of mobile gaming content. We have created a new gaming-specific NR VQA model called the Gaming Video Quality Evaluator (GAMIVAL), which combines and leverages the advantages of spatial and temporal gaming distorted scene statistics models, a neural noise model, and deep semantic features. Using a support vector regression (SVR) as a regressor, GAMIVAL achieves superior performance on the new LIVE-Meta Mobile Cloud Gaming (LIVE-Meta MCG) video quality database.Comment: Accepted to IEEE SPL 2023. The implementation of GAMIVAL has been made available online: https://github.com/lskdream/GAMIVA

Hydrodynamic and Geostress Controls on CBM Enrichment in the Anze Block, Southern Qinshui Basin, North China

Acknowledgments This research was funded by the National Natural Science Foundation of China (grant nos. 42130806, 41830427, 41922016, 41772160, and 42102227).Peer reviewedPublisher PD