Opsins are a large group of proteins with seven transmembrane segments (TMSs) that are found in all domains of life. There are two types of opsins that are sometimes considered nonhomologous: type I is known from prokaryotes and some eukaryotes, while type II is known only from Eumetazoan animals. Type II opsins are members of the family of G-protein coupled receptors (GPCRs), which facilitate signal transduction across cell membranes. While previous studies have concluded that multiple transmembrane-containing protein families-including type I opsins-originated by internal domain duplication, the origin of type II opsins has been speculated on but never tested. Here we show that type II opsins do not appear to have originated through a similar internal domain duplication event. This provides further evidence that the two types of opsins are nonhomologous, indicating a convergent evolutionary origin, in which both groups of opsins evolved a seven-TM structure and light sensitivity independently. This convergence may indicate an important role for seven-TM protein structure for retinal-based light sensitivity.

Abstract-We propose a framework for discovery of collaborative community structure in Wiki-based knowledge repositories based on raw-content generation analysis. We leverage topic modelling in order to capture agreement and opposition of contributors and analyze these multi-modal relations to map communities in the contributor base. The key steps of our approach include (i) modeling of pairwise variable-strength contributor interactions that can be both positive and negative, (ii) synthesis of a global network incorporating all pairwise interactions, and (iii) detection and analysis of community structure encoded in such networks.The global community discovery algorithm we propose outperforms existing alternatives in identifying coherent clusters according to objective optimality criteria. Analysis of the discovered community structure reveals coalitions of commoninterest editors who back each other in promoting some topics and collectively oppose other coalitions or single authors. We couple contributor interactions with content evolution and reveal the global picture of opposing themes within the self-regulated community base for both controversial and featured articles in Wikipedia.

Spatial networks, in which nodes and edges are embedded in space, play a vital role in the study of complex systems. For example, many social networks attach geo-location information to each user, allowing the study of not only topological interactions between users, but spatial interactions as well. The defining property of spatial networks is that edge distances are associated with a cost, which may subtly influence the topology of the network. However, the cost function over distance is rarely known, thus developing a model of connections in spatial networks is a difficult task. In this paper, we introduce a novel model for capturing the interaction between spatial effects and network structure. Our approach represents a unique combination of ideas from latent variable statistical models and spatial network modeling. In contrast to previous work, we view the ability to form long/short-distance connections to be dependent on the individual nodes involved. For example, a node's specific surroundings (e.g. network structure and node density) may make it more likely to form a long distance link than other nodes with the same degree. To capture this information, we attach a latent variable to each node which represents a node's spatial reach. These variables are inferred from the network structure using a Markov Chain Monte Carlo algorithm. We experimentally evaluate our proposed model on 4 different types of real-world spatial networks (e.g. transportation, biological, infrastructure, and social). We apply our model to the task of link prediction and achieve up to a 35% improvement over previous approaches in terms of the area under the ROC curve. Additionally, we show that our model is particularly helpful for predicting links between nodes with low degrees. In these cases, we see much larger improvements over previous models.

Many real world applications produce data with uncertainties drawn from measurements over a continuous domain space. Recent research in the area of probabilistic databases has mainly focused on managing and querying discrete data in which the domain is limited to a small number of values (i.e. on the order of 10). When the size of the domain increases, current methods fail due to their nature of explicitly storing each value/probability pair. Such methods are not capable of extending their use to continuous-valued attributes. In this paper, we provide a scalable, accurate, space efficient probabilistic data synopsis for uncertain attributes defined over a continuous domain. Our synopsis construction methods are all error-aware to ensure that our synopsis provides an accurate representation of the underlying data given a limited space budget. Additionally, we are able to provide approximate query results over the synopsis with error bounds.We provide an extensive experimental evaluation to show that our proposed methods improve upon the current state of the art in terms of construction time and query accuracy. In particular, our synopsis can be constructed in O(N 2 ) time (where N is the number of tuples in the database). We also demonstrate the ability of our synopsis to answer a variety of interesting queries on a real data set and show that our query error is reduced by up to an order of magnitude over the previous state-of-the-art method.

Abstract-Accurately estimating the current positions of moving objects is a challenging task due to the various forms of data uncertainty (e.g. limited sensor precision, periodic updates from continuously moving objects). However, in many cases, groups of objects tend to exhibit similarities in their movement behavior. For example, vehicles in a convoy or animals in a herd both exhibit tightly coupled movement behavior within the group. While such statistical dependencies often increase the computational complexity necessary for capturing this additional structure, they also provide useful information which can be utilized to provide more accurate location estimates.In this paper, we propose a novel model for accurately tracking coordinated groups of mobile uncertain objects. We introduce an exact and more efficient approximate inference algorithm for updating the current location of each object upon the arrival of new (uncertain) location observations. Additionally, we derive probability bounds over the groups in order to process probabilistic threshold range queries more efficiently. Our experimental evaluation shows that our proposed model can provide 4X improvements in tracking accuracy over competing models which do not consider group behavior. We also show that our bounds enable us to prune up to 50% of the database, resulting in more efficient processing over a linear scan.