Tensors are effective representations for complex and time-varying networks. The factorization of a tensor provides a high-quality low-rank compact basis for each dimension of the tensor, which facilitates the interpretation of important structures of the represented data. Many existing tensor factorization (TF) methods assume there is one tensor that needs to be decomposed to low-rank factors....

Sudden loading injuries to the low back are a concern. Current models are limited in their ability to quantify the time-varying nature of the sudden loading event. The method of approach used six males who were subjected to sudden loads. Response data (EMG and kinematics) were input into a system identification model to yield time-varying torso stiffness estimates. The results show estimates of...

To describe detailed behaviour of cell spreading under the influence of substrate stiffness, A549 cells cultured on the surfaces of polydimethylsiloxane (PDMS) and polyacrylamide (PAAm) with bulk rigidities ranging from 0.1 kPa to 40 kPa were in situ observed. The spreading behaviour of cells on PAAm presented a positive correlation between spreading speed and substrate stiffness. After computi...

We consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random ...

Real-time information dissemination is essential for the success of key applications such as transportation management and battlefield monitoring. In these applications, relevant information should be disseminated to interested users in a timely fashion. However, it is challenging to support timely information dissemination due to the limited and even time-varying network bandwidth. Thus, a nai...

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In this paper, we present a new method for modeling timeevolving correlation networks, using a Mean Reversion Autoregressive Model, and apply this to stock market data. The work is motivated by the assumption that the price and return of a stock eventually regresses back towards their mean or average. This allows us to model the stock correlation time-series as an autoregressive process with a ...

Path following control problem of autonomous vehicles is investigated, concerning both unmeasurable sliding effects and lateral disturbances which lead to some difficulties in designing autonomous control under complex environment. To deal with the sliding effects, sideslip angles are modeled and reconstructed by estimating the tire cornering stiffness, which plays important role in analyzing t...