In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.

Multi-label text categorization is a type of text categorization, where each document is assigned to one or more categories.Recently, a series of methods have been developed, which train a classifier for each label, organize the classifiers in a partially ordered structure and take predictions produced by the former classifiers as the latter classifiers' features.These predictions-asfeatures style methods model high order label dependencies and obtain high performance.Nevertheless, the predictionsas-features methods suffer a drawback.When training a classifier for one label, the predictions-as-features methods can model dependencies between former labels and the current label, but they can't model dependencies between the current label and the latter labels.To address this problem, we propose a novel joint learning algorithm that allows the feedbacks to be propagated from the classifiers for latter labels to the classifier for the current label.We conduct experiments using real-world textual data sets, and these experiments illustrate the predictions-as-features models trained by our algorithm outperform the original models.

Knowledge graphs are recently used for enriching query representations in an entity-aware way for the rich facts organized around entities in it.However, few of the methods pay attention to non-entity words and clicked websites in queries, which also help conveying user intent.In this paper, we tackle the problem of intent understanding with innovatively representing entity words, refiners and clicked urls as intent topics in a unified knowledge graph based framework, in a way to exploit and expand knowledge graph which we call 'tailor'.We collaboratively exploit global knowledge in knowledge graphs and local contexts in query log to initialize intent representation, then propagate the enriched features in a graph consisting of intent topics using an unsupervised algorithm.The experiments prove intent topics with knowledge graph enriched features significantly enhance intent understanding.

The delivery of a relatively constant quality grade of iron ore is crucial to Australia mining. As a buffer, blending and target grading system, stockpile plays a key role in the iron ore quality control. However, the shape of the stockpile, the quantity housed within the stockpile and the quality grade inside the stockpile are currently unavailable to operators. Therefore, this paper describes a method to generate 3D model of a stockpile with multiple layers using laser to keep tracking the shape changes caused by the stacking and reclaiming operations. Using this 3D model, the volume can be easily calculated anytime. Experiments conducted in laboratory environment indicate good results and proved that the 3D modelling method is accurate and efficient.