Discovery of latent subcommunities in a blog's readership

The blogosphere has grown to be a mainstream forum of social interaction as well as a commercially attractive source of information and influence. Tools are needed to better understand how communities that adhere to individual blogs are constituted in order to facilitate new personal, socially-focused browsing paradigms, and understand how blog content is consumed, which is of interest to blog authors, big media, and search. We present a novel approach to blog subcommunity characterization by modeling individual blog readers using mixtures of an extension to the LDA family that jointly models phrases and time, Ngram Topic over Time (NTOT), and cluster with a number of similarity measures using Affinity Propagation. We experiment with two datasets: a small set of blogs whose authors provide feedback, and a set of popular, highly commented blogs, which provide indicators of algorithm scalability and interpretability without prior knowledge of a given blog. The results offer useful insight to the blog authors about their commenting community, and are observed to offer an integrated perspective on the topics of discussion and members engaged in those discussions for unfamiliar blogs. Our approach also holds promise as a component of solutions to related problems, such as online entity resolution and role discovery.

An improved approach for the discovery of causal models via MML

Discovering a precise causal structure accurately reflecting the given data is one of the most essential tasks in the area of data mining and machine learning. One of the successful causal discovery approaches is the information-theoretic approach using the Minimum Message Length Principle[19]. This paper presents an improved and further experimental results of the MML discovery algorithm. We introduced a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. The experimental results of the current version of the discovery system show that: (1) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal models with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.

The discovery of generalized causal models with mixed variables using MML criterion

One major difficulty frustrating the application of linear causal models is that they are not easily adapted to cope with discrete data. This is unfortunate since most real problems involve both continuous and discrete variables. In this paper, we consider a class of graphical models which allow both continuous and discrete variables, and propose the parameter estimation method and a structure discovery algorithm based on Minimum Message Length and parameter estimation. Experimental results are given to demonstrate the potential for the application of this method.

Discovery of late Changhsingian (latest Permian) brachiopod Attenuatella species from South China

Attenuatella mengi sp. nov. and ?Attenuatella sp. from the Talung Formation, southern Guangxi Zhuang Autonomous Region, South China, are described herein. This discovery represents the first report of Attenuatella from the late Changhsingian (latest Permian) in South China and provides evidence that Attenuatella expanded its range from high-latitude cold-water regions to palaeoequatorial warm water areas in the Late Permian. Attenuatella
species appear to have been pseudoplanktonic, judging from their hair-like spinose ornamentation, which could have contributed to the global palaeogeographical distribution of Attenuatella.

Discovery of structural and functional features in RNA pseudoknots

An RNA pseudoknot consists of nonnested double-stranded stems connected by single-stranded loops. There is increasing recognition that RNA pseudoknots are one of the most prevalent RNA structures and fulfill a diverse set of biological roles within cells, and there is an expanding rate of studies into RNA pseudoknotted structures as well as increasing allocation of function. These not only produce valuable structural data but also facilitate an understanding of structural and functional characteristics in RNA molecules. PseudoBase is a database providing structural, functional, and sequence data related to RNA pseudoknots. To capture the features of RNA pseudoknots, we present a novel framework using quantitative association rule mining to analyze the pseudoknot data. The derived rules are classified into specified association groups regarding structure, function, and category of RNA pseudoknots. The discovered association rules assist biologists in filtering out significant knowledge of structure-function and structure-category relationships. A brief biological interpretation to the relationships is presented, and their potential correlations with each other are highlighted.

Discovery of Ordovician-Devonian strata in the south of the Qiangtang area, Tibet

Low-grade metamorphic rocks are exposed extensively in the Qiangtang area, Tibet. There is a great dispute about the age of the strata because of no reliable fossil evidence and other chronological evidence. Cephalopods, crinoids, graptolites and tentaculites fossils are discovered at Tashi Hill north of Gangtang Co. Primary identification indicates that the cephalopod is Sinoceras,Michelinoceras, Wennanoceras, Archigeisonoceras etc. and the crinoid is Monograptus. The Sinoceras-Michelinoceras assemblage is the key fossil assemblage of Mid Ordovician Baotaan age, which reflects the Mid Ordovician aspects. There are several tens of meters of fossil-free clastic rocks below the Mid Ordovician cephalopod-bearing horizon, which are probably Ordovician sediments. The discovery of Ordovician-Devonian biotas and strata provide new important evidence for the stratigraphic division and correlation and the study of the tectonic evolution of the Qiangtang area.

Discovery of early paleozoic strata in south of Qiangtang, Northern Tibet and its significance

Due to the lack of fossil evidence, the age of Amugang Group, previously referring to the large area of metamorphic formations developed along the south of Longmucuo-Shuanghu suture in southern Qiangtang area, northern Tibet, has been under heated debate. Some geologists were convinced that the age of metamorphic formations was of Pre-Devonian according to the emergence of nonmetamorphic fossil-beating Devonian in Chasang area, north of the suture. Most geologists believed that its age was of Pre-Sinian. From 2003 to 2004, we collected a large amount of Nautiloid and Graptolite fossils in upper metamorphic formations of Mayigangri area and determined the age to be middle and Late Ordovician-Silurian. It is the first time to discover the Early Paleozoic strata based on reliable fossils, which not only provides evidence to its age ascription, classification of the strata and its correlation to adjacent areas but also offers data to lithofacies, palaeogeography and tectonic pattern of studied area.

Discovery of Early Devonian strata in northern Qiangtang, Tibet, and the establishment of the Pingshagou Formation

Although it is important to determine whether there exist sedimentary strata older than the Upper Permian in the northern Qiantang area in Tibet, there has been no report of such old strata in this area. During the geological mapping of the Mayigangri area, we discovered strata that contain the bivalves Eoschizodus roemeri (Beushausen), E. minor (Beushausen) , E. infiatus (Roemer), Actinodesma (Actinodesma) cf . maneiforme Sandberger, A . (Actindesma) cf . vespertilio Maurer, and the brachiopod Huananochonetes subquadratus Sun & Chen. These fossils indicate a late Early Devonian age (Emsian) , thus the strata represent the first discovered Lower Devonian rocks in northen Qiangtang, confirming the existence of strata older than the Upper Permian. A new stratigraphic term, the Pingshagou Formation , is introduced. The new data provide constraints on the tectonic, palaeogeographic, and palaeobiogeographic history of the north Qiangtang area in the Early Devonian.

Discovery of the Early Ordovician strata in Xianza County, Tibet and its significance

There is no any sediments contain fossils which formed earlier than Middle Ordovician in northern Tibet yet. It is very important whether have developed the Cambrian to Early Ordovician sedimentary strata. During geological mapping of Xainza County in 2000, Tetragraptus approximatus, the typical one of graptolite belts which cosmepolitically distributed in the Arenigian of Early Ordovician, was collected from the so-call Pre-Sinian Nianqingtanggula Group. So there is developped the Early Ordovician sediments and we have established Zakang Formation and Taduo Formation which perfected the Plaeozoic sequences in northern Tibet. According to these, we can realize tectonic control, palaeogeographrc pattern as well as palaeobiogeographic distribution of the Qinghai-Tibet Plateau in the early Paleozoic and also provided the materials to the cosmepolitical biostratigraphic contrast.

Towards discovery of influence and personality traits through social link prediction

Estimation of a person’s influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user’s textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms.

Discovery of activity structures using the hierarchical hidden Markov model

In building a surveillance system for monitoring people behaviours, it is important to understand the typical patterns of people's movement in the environment. This task is difficult when dealing with high-level behaviours. The flat model such as the hidden Markov model (HMM) is inefficient in differentiating between signatures of such behaviours. This paper examines structure learning for high-level behaviours using the hierarchical hidden Markov model (HHMM).We propose a two-phase learning algorithm in which the parameters of the behaviours at low levels are estimated first and then the structures and parameters of the behaviours at high levels are learned from multi-camera training data. Our algorithm is then evaluated using data from a real environment, demonstrating the robustness of the learned structure in recognising people's behaviour.