Does social information affect the settlement decisions of resident birds in their second breeding attempt?A case study of the Japanese Tit(Parus minor)

Individuals may gather information about environmental conditions when deciding where to breed in order to maximize their lifetime fitness.They can obtain social information by observing conspecifics and heterospecifics with similar ecological needs.Many studies have shown that birds can rely on social information to select their nest sites.The location of active nests and the reproductive success of conspecifics and heterospecifics can provide accurate predictions about the quality of the breeding habitat.Some short-lived species can facultatively reproduce two and/or more times within a breeding season.However,few studies have focused on how multiplebrooding individuals select nest sites for their second breeding attempts.In this study,we use long-term data to test whether the Japanese Tit(Parus minor)can use social information from conspecifics and/or heterospecifics(the Eurasian Nuthatch Sitta europaea,the Daurian Redstart Phoenicurus auroreus and the Yellow-rumped Flycatcher Ficedula zanthopygia)to select a nest site for the second breeding attempt.Our results showed that the nest boxes occupied by tits on their second breeding attempt tended to be surrounded by more breeding conspecific nests,successful first nests of conspecifics,and fewer failed first nests of conspecifics than the nest boxes that remained unoccupied(the control group).However,the numbers of breeding heterospecific nests,successful heterospecific nests,and failed heterospecific nests did not differ between the nest boxes occupied by tits on their second breeding attempt and the unoccupied nest boxes.Furthermore,the tits with local successful breeding experience tended to choose areas with more successful first nests of conspecifics than those without successful breeding experience.Thus,we suggest that conspecifics'but not heterospecifics'social information within the same breeding season is the major factor influencing the nest site selection of Japanese Tits during second breeding attempts.

Modern Electronic Information Industry to Promote Economic and Social Information—An Interview with Electronics Industry Minister Hu Qili

Spurred by the world information tide, China has organized a series of information projects, called the "Three Gold" projects. Recently I had an interview with Mr. Hu Qili, Minister of Electronics Industry, about the establishment of China’s modern Electronic Information Industry. Mr. Hu told me that information is the mark of development of a modern society and electronics is the major means of carrying information. Establishing

The Profound Revelation of Human Social Relations and the Profound Realization of Information Right of Citizens——The social significance of network technique advancement in the perspective of “Entity Cube”

The R＆D group of relationship searching in Microsoft Asia Research Institute explains ＂Entity Cube＂ launched in 2008 as follows：

Recommender Systems Based on Tensor Decomposition

Recommender system is an effective tool to solve the problems of information overload.The traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and items.While social networks contain abundant social information,such as tags,places and times.Researches show that the social information has a great impact on recommendation results.Tags not only describe the characteristics of items,but also reflect the interests and characteristics of users.Since the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender systems.The original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between tags.Experiments on real dataset show that the proposed algorithm achieves superior performance to existing algorithms.

Epidemic-Like Proximity-Based Traffic Offloading

Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.

Adjusting the information service strategies according to users' requirements—A survey of the library users' information requirements in the four library systems of Zhejiang province

Based on a survey of the circumstances of the information requirements in social science from more than 3,800 library users in 48 libraries of the four major library systems in Zhejiang Province,this paper analyzes the present main characteristics of the users' requirements to the social science document information and puts forward some proposals for adjusting the document information service strategies for the library and information institutions.

Understanding taxi drivers＇ routing choices from spatial and social traces

Most of our learning comes from other people or from our own experience. For instance, when a taxi driver is seeking passengers on an unknown road in a large city, what should the driver do？ Alternatives include cruising around the road or waiting for a time period at the roadside in the hopes of finding a passenger or just leaving for another road enroute to a destination he knows （e.g., hotel taxi rank）？ This is an interesting problem that arises everyday in cities all over the world. There could be different answers to the question poised above, but one fundamental problem is how the driver learns about the likelihood of finding passengers on a road that is new to him （as he has not picked up or dropped off passengers there before）. Our observation from large scale taxi driver trace data is that a driver not only learns from his own experience but through interactions with other drivers. In this paper, we first formally define this problem as socialized information learning （SIL）, second we propose a framework including a series of models to study how a taxi driver gathers and learns information in an uncertain environment through the use of his social network. Finally, the large scale real life data and empirical experiments confirm that our models are much more effective, efficient and scalable that prior work on this problem.

Enhancing recommender systems by incorporating social information

Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality.Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings.How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge.In this paper,based on a factor graph model,we formalize the problem in a semi-supervised probabilistic model,which can incorporate different user information,user relationships,and user-item ratings for learning to predict the unknown ratings.We evaluate the method in two different genres of datasets,Douban and Last.fm.Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms.Furthermore,a distributed learning algorithm is developed to scale up the approach to real large datasets.

Path planning of unmanned aerial vehicle based on improved gravitational search algorithm

Path planning of Uninhabited Aerial Vehicle(UAV) is a complicated global optimum problem.In the paper,an improved Gravitational Search Algorithm(GSA) was proposed to solve the path planning problem.Gravitational Search Algorithm(GSA) is a newly presented under the inspiration of the Newtonian gravity,and it is easy to fall local best.On the basis of introducing the idea of memory and social information of Particle Swarm Optimization(PSO),a novel moving strategy in the searching space was designed,which can improve the quality of the optimal solution.Subsequently,a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the search.Particle position was updated according to the selection rules of survival of the fittest.In this way,the population is always moving in the direction of the optimal solution.The feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PSO,basic GSA and two other GSA models.

An exploration of the relationship between recruitment communication and foraging in stingless bees

Social information is widely used in the animal kingdom and can be highly adaptive.In social insects,foragers can use social information to find food,avoid danger,or choose a new nest site.Copying others allows individuals to obtain information without having to sample the environment.When foragers communicate information they will often only advertise high-quality food sources,thereby filtering out less adaptive information.Stingless bees,a large pantropical group of highly eusocial bees,face intense inter-and intra-specific competition for limited resources,yet display disparate foraging strategies.Within the same environment there are species that communicate the location of food resources to nest-mates and species that do not.Our current understanding of why some species communicate foraging sites while others do not is limited.Studying freely foraging colonies of several co-existing stingless bee species in Brazil,we investigated if recruitment to specific food locations is linked to 1)the sugar content of forage,2)the duration of foraging trips,and 3)the variation in activity of a colony from 1 day to another and the variation in activity in a species over a day.We found that,contrary to our expectations,species with recruitment communication did not return with higher quality forage than species that do not recruit nestmates.Furthermore,foragers from recruiting species did not have shorter foraging trip durations than those from weakly recruiting species.Given the intense inter-and intraspecific competition for resources in these environments,it may be that recruiting species favor food resources that can be monopolized by the colony rather than food sources that offer high-quality rewards.

Identifying the skeptics and the undecided through visual cluster analysis of local network geometry

By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or at their boundaries(the skeptics).Identifying these nodes is relevant in marketing applications like voter targeting,because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters.So far this identification task is not as well studied as other network analysis tasks like clustering,identifying central nodes,and detecting motifs.We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.

A Comparative Analysis on Weibo and Twitter

Weibo is the Twitter counterpart in China that has attracted hundreds of millions of users. We crawled an almost complete Weibo user network that contains 222 million users and 27 billion links in 2013. This paper analyzes the structural properties of this network, and compares it with a Twitter user network. The topological properties we studied include the degree distributions, connected components, distance distributions, reciprocity,clustering coefficient, Page Rank centrality, and degree assortativity. We find that Weibo users have a higher diversity index, higher Gini index, but a lower reciprocity and clustering coefficient for most of the nodes. A surprising observation is that the reciprocity of Weibo is only about a quarter of the reciprocity of the Twitter user network. We also show that Weibo adoption rate correlates with economic development positively, and Weibo network can be used to quantify the connections between provinces and regions in China. In particular, point-wise mutual information is shown to be accurate in quantifying the strength of connections. We developed an interactive analyzing software framework for this study, and released the data and code online.

BUSINESS ENTERPRISES AND THE ENVIRONMENTAL INFORMATION ACT IN NORWAY

Making market operators responsible for having and giving access to information about their impact on the environment is an important part of changing the way the economy operates in relation to environmental sustainability. To this end, the Norwegian access to environmental information act has established a right for the public to access to environmental information from private entities such as business enterprises. In this point, the Norwegian Act goes further than international rules, such as the Aarhus Convention, and also further than any other national legislation. This article presents the rules on access to information from business enterprises, and presents and discusses examples from the implementation of these rules.

Joint model of user check-in activities for point-of-interest recommendation

With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.