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As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic. Most existing work studies the spread of epidemic at two levels including population and agent. However, there is no comprehensive statistical analysis of community lockdown measures and corresponding control effects. This paper performs a statistical analysis of the effectiveness of community lockdown based on the Agent-Level Pandemic Simulation (ALPS) model. We propose a statistical model to analyze multiple variables affecting the COVID-19 pandemic, which include the timings of implementing and lifting lockdown, the crowd mobility, and other factors. Specifically, a motion model followed by ALPS and related basic assumptions is discussed first. Then the model has been evaluated using the real data of COVID-19. The simulation study and comparison with real data have validated the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Linear regression technique is an efficient method to solve face recognition problem. It's based on the theory that images in the same class will also belong to same linear subspace and they can be represented through a linear equation. However, this method suffers from some misclassification problems for the infinite ductility of regression equation, moreover, it also doesn't make a proper and full use of the information in each sample. For overcoming these problems, a novel algorithm named the Distance Weighted Regression Classifier (DWLRC) is proposed here. It can be used for face recognition under different expression and illumination conditions through a distance weighted method, and it can also be used for optimizing the error in the final distance calculating stage. Experiments on three benchmarks show the better performance of our DWLRC compared with the traditional LRC and some state-of-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel classifier named Kernel Nearest-Farthest Subspace (KNFS) classifier is proposed for face recognition. Inspired by the kernel-based classifier and the Nearest-Farthest Subspace (NFS) classifier, KNFS can make the sample points to be linear separable by utilizing the kernel function to map linear inseparable sample points in low-dimensional space to high-dimensional kernel space. And it can improve the recognition accuracy of crossed sample points between classes. The algorithm provides the highest reported recognition accuracy on AR and AT&T database. The results are comparable with many other state-of-art face recognition algorithms. [ABSTRACT FROM AUTHOR]

Statistical analysis of dynamic systems, such as videos and dynamic functional connectivity, is often translated into a problem of analyzing trajectories of relevant features, particularly covariance matrices. As an example, in video-based action recognition, a natural mathematical representation of activity videos is as parameterized trajectories on the set of symmetric, positive-definite matrices (SPDMs). The execution rates of actions, implying arbitrary parameterizations of trajectories, complicate their analysis. To handle this challenge, we represent covariance trajectories using transported square-root vector fields, constructed by parallel translating scaled-velocity vectors of trajectories to their starting points. The space of such representations forms a vector bundle on the SPDM manifold. Using a natural Riemannian metric on this vector bundle, we approximate geodesic paths and geodesic distances between trajectories in the space of this vector bundle. This metric is invariant to the action of the re-parameterization group, and leads to a rate-invariant analysis of trajectories. In the process, we remove the parameterization variability and temporally register trajectories. We demonstrate this framework in multiple contexts, using both generative statistical models and discriminative data analysis. The latter is illustrated using several applications involving video-based action recognition and dynamic functional connectivity analysis. [ABSTRACT FROM AUTHOR]

Activity interpretation in videos results not only in recognition or labeling of dominant activities, but also in semantic descriptions of scenes. Towards this broader goal, we present a combinatorial approach that assumes availability of algorithms for detecting and labeling objects and basic actions in videos, albeit with some errors. Given these uncertain labels and detected objects, we link them into interpretable structures using the domain knowledge, under the framework of Grenander's general pattern theory. Here a semantic description is built using basic units, termed generators, that represent either objects or actions. These generators have multiple out-bonds, each associated with different types of domain semantics, spatial constraints, and image evidence. The generators combine, according to a set of pre-defined combination rules that capture domain semantics, to form larger configurations that represent video interpretations. This framework derives its representational power from flexibility in size and structure of configurations. We impose a probability distribution on the configuration space, with inferences generated using a Markov chain Monte Carlo-based simulated annealing process. The primary advantage of the approach is that it handles known challenges-appearance variabilities, errors in object labels, object clutter, simultaneous events, etc-without the need for exponentially-large (labeled) training data. Experimental results demonstrate its ability to successfully provide interpretations under clutter and the simultaneity of events. They show: (1) a performance increase of more than 30 % over other state-of-the-art approaches using more than 5000 video units from the Breakfast Actions dataset, and (2) an overall recall and precision improvement of more than 50 and 100 %, respectively, on the YouCook data set. [ABSTRACT FROM AUTHOR]