Eco-evolutionary routes towards animal sociality:Ecology, behaviour and communication in communal breeding of burying beetles

To understand social evolution it is necessary to address how ecological, social and genetic forces jointly determine the biological process of animal societies and the mechanism of sociality (e.g. group living and reproducing) in animals. Burying beetles utilise carcasses as breeding resources that are needed for their offspring and themselves, and provide extended care towards developing offspring on the buried carcasses. These beetles always breed as pairs and can also form groups by sharing a single carcass with other pairs (communal breeding). In this thesis, using the communal breeding system of burying beetles (Nicrophorus vespilloides) as one of the studying models, I studied the ecological processes that shaped the formation of groups and the associated benefits and costs in terms of reproduction and other aspects. Also, I addressed how group individuals are well coordinated in behaviour and reproduction and the role of intricate chemical communication systems in influencing such group organization. My studies on burying beetle societies investigated how group-living individuals adapted to changing environments and social interactions, which will expand our understanding of the eco-evolutionary routes towards animal sociality and the evolution of social behaviour. Also, the group breeding system of burying beetles potentially offers an ecological and evolutionary model that can be applied to many other social animal systems

A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy

Text classification, the task of metadata to documents, needs a person to take significant time and effort. Since online-generated contents are explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Recently, various state-or-art text mining methods have been applied to classification process based on the keywords extraction. However, when using these keywords as features in the classification task, it is common that the number of feature dimensions is large. In addition, how to select keywords from documents as features in the classification task is a big challenge. Especially, when using traditional machine learning algorithms in big data, the computation time is very long. On the other hand, about 80% of real data is unstructured and non-labeled in the real world. The conventional supervised feature selection methods cannot be directly used in selecting entities from massive data. Usually, statistical strategies are utilized to extract features from unlabeled data for classification tasks according to their importance scores. We propose a novel method to extract key features effectively before feeding them into the classification assignment. Another challenge in the text classification is the multi-label problem, the assignment of multiple non-exclusive labels to documents. This problem makes text classification more complicated compared with a single label classification. For the above issues, we develop a framework for extracting data and reducing data dimension to solve the multi-label problem on labeled and unlabeled datasets. In order to reduce data dimension, we develop a hybrid feature selection method that extracts meaningful features according to the importance of each feature. The Word2Vec is applied to represent each document by a feature vector for the document categorization for the big dataset. The unsupervised approach is used to extract features from real online-generated data for text classification. Our unsupervised feature selection method is applied to extract depression symptoms from social media such as Twitter. In the future, these depression symptoms will be used for depression self-screening and diagnosis

Exotic Fermions and Bosons in the Quartification Model

Exotic fermions of half-integral charges at the TeV energy scale are predicted by the quartification model of Babu, Ma, and Willenbrock. We add to these one copy of their scalar analogs and discuss the ensuing phenomenological implications, i.e. radiative contributions to lepton masses and flavor-changing leptonic decays.Comment: 7 pages, including 3 figure