A Spatio-Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks

Abstract.  Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio-temporal process with many sources of variation, both biological and technical. We present a spatio-temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.     

A model for collaboration networks giving rise to a power-law distribution with an exponential cutoff

Recently several authors have proposed stochastic evolutionary models for the growth of complex networks that give rise to power-law distributions. These models are based on the notion of preferential attachment leading to the “rich get richer” phenomenon. Despite the generality of the proposed stochastic models, there are still some unexplained phenomena, which may arise due to the limited size of networks such as protein, e-mail, actor and collaboration networks. Such networks may in fact exhibit an exponential cutoff in the power-law scaling, although this cutoff may only be observable in the tail of the distribution for extremely large networks. We propose a modification of the basic stochastic evolutionary model, so that after a node is chosen preferentially, say according to the number of its inlinks, there is a small probability that this node will become inactive. We show that as a result of this modification, by viewing the stochastic process in terms of an urn transfer model, we obtain a power-law distribution with an exponential cutoff. Unlike many other models, the current model can capture instances where the exponent of the distribution is less than or equal to two. As a proof of concept, we demonstrate the consistency of our model empirically by analysing the Mathematical Research collaboration network, the distribution of which has been shown to be compatible with a power law with an exponential cutoff.     

What are friends for? Elective communities of single people

The paper presents the results of a qualitative research on friendship networks of single people. The research has been undertaken using social network analysis tools (egonetworks) and qualitative interviews, on a non-representative sample of 23 heterosexual singles of Milan, aged between 25 and 35 years, 12 male and 11 female.     

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

Inference in hybrid Bayesian networks using dynamic discretization

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.     

概念复合理论对广告语中汉语熟语翻新的认知阐释

运用Fauconnier和Turner等人提出的概念复合理论和概念整合网络,从认知的层面对广告语中汉语熟语创新使用的意义构建进行分析。旨在说明概念的合成是一种具有普遍性的认知活动,具有极强的认知阐释力。并且通过分析证明广告语中汉语熟语创新使用的意义构建可以从认知的角度运用复合理论来阐释,为广告熟语创新使用的研究提供一种新的研究角度和方法。     

A Screening Methodology for the Identification and Ranking of Infrastructure Vulnerabilities Due to Terrorism

The extreme importance of critical infrastructures to modern society is widely recognized. These infrastructures are complex and interdependent. Protecting the critical infrastructures from terrorism presents an enormous challenge. Recognizing that society cannot afford the costs associated with absolute protection, it is necessary to identify and prioritize the vulnerabilities in these infrastructures. This article presents a methodology for the identification and prioritization of vulnerabilities in infrastructures. We model the infrastructures as interconnected digraphs and employ graph theory to identify the candidate vulnerable scenarios. These scenarios are screened for the susceptibility of their elements to a terrorist attack, and a prioritized list of vulnerabilities is produced. The prioritization methodology is based on multiattribute utility theory. The impact of losing infrastructure services is evaluated using a value tree that reflects the perceptions and values of the decisionmaker and the relevant stakeholders. These results, which are conditional on a specified threat, are provided to the decisionmaker for use in risk management. The methodology is illustrated through the presentation of a portion of the analysis conducted on the campus of the Massachusetts Institute of Technology.     

试析《中俄睦邻友好合作条约》的生命力

《中俄睦邻友好合作条约》签署13年来,中俄关系在这个新世纪两国关系发展纲领性文件的指导下顺利前行,各领域合作都达到前所未有的高水平,中俄政治互信进一步加强。两国政界、学界赞声一片。本文通过对中俄关系的历史和现实进行剖析,探寻中俄关系健康发展的源动力,试析《中俄睦邻友好合作条约》的生命力。     

Sovereignty at What Scale? An Inquiry into Multiple Dimensions of Food Sovereignty

Abstract

Food sovereignty has struggled to make inroads into changing the structures and processes underlying the corporate food regime. One reason is that scale is still underspecified in the politics, strategies, and theories of food sovereignty. We suggest that much can be learned from examining the multiple dimensions of scale inherent in ongoing food sovereignty struggles. A gap exists between these in vivo experiments and the maturing academic theory of scale. The concept of ‘sovereignty’ can be opened up to reveal that movements, peoples, and communities, for example, are creating multiple sovereignties and are exercising sovereignty in more relational ways. Relational scale can aid movements and scholars to map and evaluate how spatial and temporal processes at and among various levels work to reinforce dominant agri-food systems but could also be reconfigured to support progressive alternatives. Finally, we apply relational scale to suggest practical strategies for realizing food sovereignty, using examples from the Potato Park in the Peruvian Andes.     

Contemporary urban culture: how community structures endure in an individualised society

This paper examines the ways in which socio-cultural norms have shifted to accommodate higher levels of autonomy in urban communities. Largely critiquing traditional concepts of community as well as current dystopian perspectives on the fluid and vapid state of social organisation, it will show how individualism has been incorporated into societal norms, producing highly autonomous personalised networks that, when combined with large amounts of social interaction and imaginative cultural appropriation, create the common social and cultural practices emblematic of community structure. It also illustrates how many activities, traditionally viewed as indicative of social decay, are socially productive, in that they go towards generating the common bonds and world-views that unite individuals across urban landscapes. Focusing particularly on the spaces of community interaction, the construction of common identities and common sense of belonging, this paper sets out to explore alternative modes of community creation and enactment in a contemporary urban environment.