Counting Human Flow with Deep Neural Network

Human flow counting has many applications in space management. This study applied channel state information (CSI) available in IEEE 802.11n networks to characterize the flow count. Raw inputs including mean, standard deviation and five-number summary were extracted from windowed CSI data. Due to the large number of raw inputs, stacked denoising autoencoders were used to extract hierarchical features from raw inputs and a final layer of softmax regression was used to model the flow counting problem. It is found that this deep neural network structure beats other popular classification algorithms including random forest, logistic regression, support vector machine and multilayer perceptron in predicting the flow count with attractive speed performance

Exploring Time Series Spectral Features in Viral Hashtags Prediction

Viral hashtags spread across a large population of Internet users very quickly. Previous studies use features mostly in an aggregate sense to predict the popularity of hashtags, for example, the total number of hyperlinks in early tweets adopting a tag. Since each tweet is time stamped, many aggregate features can be decomposed into fine-grained time series such as a series of numbers of hyperlinks in early adopting tweets. This research utilizes frequency domain tools to analyze these time series. In particular, we apply scalogram analysis to study the series of adoption time lapses and the series of mentions and hyperlinks in early adopting tweets. Besides continuous wavelet transforms (CWTs), we also use fast wavelet transforms (FWTs) to analyze the time series. Through experiments with two sets of tweets collected in different seasons, out-of-sample cross validations show that wavelet spectral features can generally improve the prediction performance, and discrete FWT yields results as good as the more complicated CWT-based methods with scalogram analysis

Virtual Machines Performance Modeling with Support Vector Regressions

Virtualization is a key technology in cloudcomputing to render on-demand provisioning of virtual services.Xen, an open source paravirtualized virtual machine monitor(hypervisor), has been adopted by many leading data centersof the world today. A scheduler in Xen handles CPU resourcessharing among virtual machines hosted on the same physicalsystem. This study is focused on a scheduler in the currentXen release - the Credit scheduler. Credit uses two parameters(weight and cap) to fine tune CPU resources sharing. Previousstudies have shown that these two parameters can impact variousperformance measures of virtual machines hosted on Xen. In thisstudy, we present a holistic procedure to establish performancemodels of virtual machines. Empirical data of two commonly usedmeasures, namely calculation power and network throughput,were collected by simulations under various settings of weightand cap. We then employed a powerful machine learning tool(multi-kernel support vector regression) to learn performancemodels from the empirical data. These models were evaluatedsatisfactorily by using established procedures in machinelearning

Gene Regulatory Network Modeling with Multiple Linear Regressions

[[abstract]]Gene regulatory network modeling is a difficult inverse problem. Given limited amount of experimental data about gene expressions, a dynamic model is sought to fit the data to infer interesting biological processes. In this study, a well-known ecological system, the Lotka-Volterra system of differential equations, is used to model the dynamics of genes regulations. After replacing derivatives by estimated slopes, this system is decoupled into several independent systems of linear equations. Coefficients of the original Lotka-Volterra system are inferred from these linear systems by using multiple linear regressions. Two function approximation techniques, namely the cubic spline and the artificial neural network, are used to help estimate the stated slopes. It is found that the cubic spline interpolation and multiple linear regressions have provided useful solutions to the gene regulatory network problem

因果解釋性研究的啟發式貝氏迴歸方法－以資訊系統影響研究為例

[[abstract]]Causal explanatory study is a very important research method in empirical research whereof research models are frequently validated by multiple linear regressions (MLR) with significant factors sought. An alternative to MLR is Bayesian regressions where statistical inferences are made with samples drawn from posterior distributions. Efficient simulation algorithms of the Markov chain Monte Carlo type have made Bayesian regressions practical. We propose a heuristic method based on the outputs of MLR to construct informative priors for Bayesian regressions. Data collected from two empirical studies of information systems (IS) impact on performance is used to demonstrate the proposed method. Deviance information criterion shows that this heuristic procedure significantly improves a Bayesian modeling with uninformative priors. When credible intervals are used to locate significant factors, it is found that the heuristic Bayesian approach, capable of finding delicate drivers, can help design better diagnostics for IS problems.[[abstract]]因果解釋性研究是實證研究中很重要的一種研究方法，在實證研究中學者常使用複迴歸方法來驗證研究模式並找到顯著因子。貝氏迴歸是一種不同於複迴歸的分析工具，它使用事後機率抽取樣本來做統計推論，由於馬可夫鏈蒙地卡羅演算法可以有效率依機率分佈來抽取樣本，貝氏迴歸分析已變得越來越可行。本研究提出一個基於複迴歸分析結果的啟發式方法來建構貝氏迴歸分析的資訊事前機率，來自於兩個不同的實證研究資料將被用來測試此方法，這兩個實證研究皆是在探討資訊系統對績效的影響。偏離資訊法則顯示出此一啟發式方法能顯著的改善使用非資訊事前機率塑模的貝氏迴歸分析，當信任區間被用來尋找顯著因子時，我們發現此一新方法能找到更細膩的因子且可以設計出更好的方法來診斷資訊系統問題

Inhibition of gastric emptying and intestinal transit by amphetamine through a mechanism involving an increased secretion of CCK in male rats

1. The effect of amphetamine on gastrointestinal (GI) transit and the plasma levels of cholecystokinin (CCK) were studied in male rats. 2. Gastric emptying was inhibited both acutely and chronically by the administration of amphetamine. GI transit was decreased by the acute administration of amphetamine but not affected by the chronic administration of amphetamine. 3. Plasma CCK levels were increased dose-dependently by amphetamine. 4. Proglumide, a CCK receptor antagonist, prevented amphetamine-induced inhibition of gastric emptying and the decrease in GI transit in male rats. 5. The selective CCK(A) receptor antagonist, lorglumide, dose-dependently attenuated the amphetamine-induced inhibition of gastric emptying in male rats. In contrast, the selective CCK(B) receptor antagonist, PD 135,158, did not reverse the effect of amphetamine on gastric emptying. 6. Both lorglumide and PD 135,158 reversed the inhibitory effect of amphetamine on GI transit in male rats. 7. These results suggest that amphetamine-induced inhibition of gastric emptying and intestinal transit is due in part to a mechanism associated with the hypersecretion of endogenous CCK