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 Statistics , 2008, DOI: 10.1088/1742-5468/2009/08/L08004 Abstract: Given a network and a partition in n communities, we address the issues how communities influence each other'' and when two given communities do communicate''. We prove that, for a small-world network, among communities, a simple superposition principle applies and each community plays the role of a microscopic spin governed by a sort of effective TAP (Thouless, Anderson and Palmer) equations. The relative susceptibilities derived from these equations calculated at finite or zero temperature (where the method provides an effective percolation theory) give us the answers to the above issues. As for the already studied case n=1, these equations are exact in the paramagnetic regions (at T=0 this means below the percolation threshold) and provide effective approximations in the other regions. However, unlike the case n=1, asymmetries among the communities may lead, via the TAP-like structure of the equations, to many metastable states whose number, in the case of negative short-cuts among the communities, may grow exponentially fast with n and glassy scenarios with a remarkable presence of abrupt jumps take place. Furthermore, as a byproduct, from the relative susceptibilities a natural and efficient method to detect the community structure of a generic network emerges.
 生态学报 , 2004, Abstract: Tianjin, lying on the west shore of the Bohai Sea, is especially important as a stop-over site for waterbirds during south-north migration in China. It is important therefore to study the temporal structure of migratory waterbird communities in this area in order to understand the mechanism of waterbird's migration in the Asian Pacific Region. In addition, analyzing correlations among waterbird migration patterns, based on data from sampling key migration areas in Tianjin, will help determine interspecific ...
 Gábor Szécsi KOME : International Journal of Pure Communication Inquiry , 2012, Abstract: The electronically mediated communication has transformed our notionof the relation between place and community. With a greater proportionof our communicative acts taking place via electronic media, physical co-presence, the co-located interpersonal relations are diminishing as determinants of the nature of human interactions. This paper argues that the electronically mediated communication contributes to the construction of new, mediated forms of communities which are based on the interaction or operational synthesis of virtual and physical communities. The appearance of these new forms of communitiesleads to a new conceptualization of the relation between self and community. The aim of this paper is to show that the medium of the mediatization and new conceptualization of community is a specific pictorial language of electronically mediated communication, the semantic structure of which offers new opportunities to grasp and understand the complex notion of new mediated communities and to adopt the idea of a new global, community building language in local and national communities.
 Computer Science , 2014, Abstract: Massive Open Online Courses (MOOCs) bring together thousands of people from different geographies and demographic backgrounds -- but to date, little is known about how they learn or communicate. We introduce a new content-analysed MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to extract communities of learners based on the nature of their online forum posts. We see that BNMF yields a superior probabilistic generative model for online discussions when compared to other models, and that the communities it learns are differentiated by their composite students' demographic and course performance indicators. These findings suggest that computationally efficient probabilistic generative modelling of MOOCs can reveal important insights for educational researchers and practitioners and help to develop more intelligent and responsive online learning environments.
 PLOS ONE , 2011, DOI: 10.1371/journal.pone.0028438 Abstract: The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM), which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063). After two rounds of enrichment by a selected helper (Prevotella oris OT311) we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of manipulating microbial communities in a targeted fashion as well as associating certain bacterial modules to clinical traits (e.g.: obesity, Crohn's disease, periodontal disease, etc).
 Computer Science , 2014, Abstract: The concepts of quantum correlation complexity and quantum communication complexity were recently proposed to quantify the minimum amount of resources needed in generating bipartite classical or quantum states in the single-shot setting. The former is the minimum size of the initially shared state $\sigma$ on which local operations by the two parties (without communication) can generate the target state $\rho$, and the latter is the minimum amount of communication needed when initially sharing nothing. In this paper, we generalize these two concepts to multipartite cases, for both exact and approximate state generation. Our results are summarized as follows. (1) For multipartite pure states, the correlation complexity can be completely characterized by local ranks of sybsystems. (2) We extend the notion of PSD-rank of matrices to that of tensors, and use it to bound the quantum correlation complexity for generating multipartite classical distributions. (3) For generating multipartite mixed quantum states, communication complexity is not always equal to correlation complexity (as opposed to bipartite case). But they differ by at most a factor of 2. Generating a multipartite mixed quantum state has the same communication complexity as generating its optimal purification. But for correlation complexity of these two tasks can be different (though still related by less than a factor of 2). (4) To generate a bipartite classical distribution $P(x,y)$ approximately, the quantum communication complexity is completely characterized by the approximate PSD-rank of $P$. The quantum correlation complexity of approximately generating multipartite pure states is bounded by approximate local ranks.
 Computer Science , 2015, Abstract: We study the effect that the amount of correlation in a bipartite distribution has on the communication complexity of a problem under that distribution. We introduce a new family of complexity measures that interpolates between the two previously studied extreme cases: the (standard) randomised communication complexity and the case of distributional complexity under product distributions. We give a tight characterisation of the randomised complexity of Disjointness under distributions with mutual information $k$, showing that it is $\Theta(\sqrt{n(k+1)})$ for all $0\leq k\leq n$. This smoothly interpolates between the lower bounds of Babai, Frankl and Simon for the product distribution case ($k=0$), and the bound of Razborov for the randomised case. The upper bounds improve and generalise what was known for product distributions, and imply that any tight bound for Disjointness needs $\Omega(n)$ bits of mutual information in the corresponding distribution. We study the same question in the distributional quantum setting, and show a lower bound of $\Omega((n(k+1))^{1/4})$, and an upper bound, matching up to a logarithmic factor. We show that there are total Boolean functions $f_d$ on $2n$ inputs that have distributional communication complexity $O(\log n)$ under all distributions of information up to $o(n)$, while the (interactive) distributional complexity maximised over all distributions is $\Theta(\log d)$ for $6n\leq d\leq 2^{n/100}$. We show that in the setting of one-way communication under product distributions, the dependence of communication cost on the allowed error $\epsilon$ is multiplicative in $\log(1/\epsilon)$ -- the previous upper bounds had the dependence of more than $1/\epsilon$.