Bistability through Triadic Closure

In Web2.0 era, social media platforms are bearing huge customer base and excessively abundant information resources. On one hand, information consumers spend a lot of time in information search. On the other hand, information providers are seeking effective methods to recognize potential customers, push targeting advertising and provide personalized information services. Generally, mining user-generated content (UGC) to discover user preferences becomes the main channel for user modeling and customer partitioning. However, on social media platforms, user preferences were often manifested in the user-defined tags, online social behaviors as well as the UGC texts. The paper proposed a social-media user partitioning model based on heterogeneous information fusion and ensemble clustering. In the model, online social behaviors and user-defined interest tags are combined with UGC texts respectively to generate basic partitions of social media users. Then, basic partitions are fused into a consensus partition based on the voting mechanism for the final user partitioning. Experiments on real world data sets demonstrate the effectiveness of the proposed model.

There Can Be Only One: Reversible Cellular Automata and the Conservation of Genki

The actions and interactions of independently acting agents in a multi-agent system must be managed if the agents are to function effectively in their shared environment. Norms, which define the obligatory, prohibited and permitted actions for an agent to perform, have been suggested as a possible method for regulating the actions of agents. Norms are local rules designed to govern the actions of individual agents whilst also allowing the agents to achieve a coherent global behaviour. However, there appear to be very few instances of norm-governed multi-agent systems beyond theoretical examples. We describe an implementation strategy for allowing autonomous agents to take a set of norms into account when determining their actions. These norms are implemented using directives, which are local rules specifying actions for an agent to perform depending on its current state. Agents using directives are implemented in a simulation test bed, called Sinatra. Using Sinatra, we investigate the ability of directives to manage agent actions. We begin with directives to manage agent interactions. We find that when agents rely on only local rules they will encounter situations where the local rules are unable to achieve the desired global behaviour. We show how a centralised control mechanism can be used to manage agent interactions that are not successfully handled by directives. Controllers, with a global view of the interaction, instruct the individual agents how to act. We also investigate the use of an existing planning tool to implement the resolution mechanism of a controller. We investigate the ability of directives to coordinate the actions of agents in order to achieve a global objective more effectively. Finally, we present a case study of how directives can be used to determine the actions of autonomous mobile robots.

Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model, M-DHIN , to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embedding method to learn the initial embeddings of a dynamic HIN at the first time stamp. We describe the features of the initial HIN via metagraphs, which retains more structural and semantic information than traditional path-oriented static models. We also adopt a complex embedding scheme to better distinguish between symmetric and asymmetric metagraphs. Unlike traditional models that process an entire network at each time stamp, we build a so-called change dataset that only includes nodes involved in a triadic closure or opening process, as well as newly added or deleted nodes. Then, we utilize the above metagraph-based mechanism to train on the change dataset. As a result of this setup, M-DHIN is scalable to large dynamic HINs since it only needs to model the entire HIN once while only the changed parts need to be processed over time. Existing dynamic embedding models only express the existing snapshots and cannot predict the future network structure. To equip M-DHIN with this ability, we introduce an LSTM-based deep autoencoder model that processes the evolution of the graph via an LSTM encoder and outputs the predicted graph. Finally, we evaluate the proposed model, M-DHIN , on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.

A biologically plausible model of myelin plasticity demonstrates how oligodendrocytes can selectively adjust conduction delays in axon bundles to allow robust communication between brain regions through long-range synchronization.

We aim at enabling a biped robot to interact with humans through real-world music in daily-life environments, e.g., to autonomously keep its steps (stamps) in time with musical beats. To achieve this, the robot should be able to robustly predict the beat times in real time while listening to musical performance with its own ears (head-embedded microphones). However, this has not previously been addressed in most studies on music-synchronized robots due to the difficulty in predicting the beat times in real-world music. To solve this problem, we implemented a beat-tracking method developed in the field of music information processing. The predicted beat times are then used by a feedback-control method that adjusts the robot's step intervals to synchronize its steps in time with the beats. The experimental results show that the robot can adjust its steps in time with the beat times as the tempo changes. The resulting robot needed about 25 [s] to recognize the tempo change after it and then synchronize its steps.

Consider a system that is always in one of two states 1 or 2. Every time a clock ticks the system updates itself according to a 2×2 matrix of transition probabilities the entry of which gives the probability that the system moves from state to state at any clock tick. A two‐state Markov chain is a system like this in which the next state depends only on the current state and not on previ;;

The purpose of any social discipline is to understand the society through their theoretical perspectives. Human communication as a social science is also without exception, its purpose is to explain the society by means of inspecting the social information as well as the contents and extensions during the operation of social information system. Social together with its system are complex and comprehensive by nature, which determines human communication existing itself as a comprehensive science with multiple theories, aspects and perspectives. It is reasonable to study with the help of related theories of other subjects in facing concrete research issues and when the existing theories lack enough power to give an explanation. Interpersonal human communication is an important part in human life, the human communication study about is rather few, however. This paper is about to solve this problem, for current human communication cannot explain the barriers occurred in the interpersonal human communication. To deal with such issue that often happens in social life, the author is appeal to information system theories to explain that the trajectory of information changes in the course of human communication could cause barriers.

Discrete- and continuous-time approaches are frequently used to model the role of heterogeneity on dynamical interacting agents on the top of complex networks. While, on the one hand, one does not expect drastic differences between these approaches, and the choice is usually based on one's expertise or methodological convenience, on the other hand, a detailed analysis of the differences is necessary to guide the proper choice of one or another approach. We tackle this problem by investigating both discrete- and continuous-time mean-field theories for the susceptible-infected-susceptible (SIS) epidemic model on random networks with power-law degree distributions. We compare the discrete epidemic link equations(ELE) and continuous pair quenched mean-field (PQMF) theories with the corresponding stochastic simulations, both theories that reckon pairwise interactions explicitly. We show that ELE converges to the PQMF theory when the time step goes to zero. We performed an epidemic localization analysis considering the inverse participation ratio (IPR). Both theories present the same localization dependence on network degree exponent γ: for γ<5/2 the epidemics are localized on the maximum k-core of networks with a vanishing IPR in the infinite-size limit while, for γ>5/2, the localization happens on hubs that do not form a densely connected set and leads to a finite value of the IPR. However, the IPR and epidemic threshold of ELE depend on the time-step discretization such that a larger time step leads to more localized epidemics. A remarkable difference between discrete- and continuous-time approaches is revealed in the epidemic prevalence near the epidemic threshold, in which the discrete-time stochastic simulations indicate a mean-field critical exponent θ=1 instead of the value θ=1/(3-γ) obtained rigorously and verified numerically for the continuous-time SIS on the same networks.

We develop robust power control strategies for cognitive radios in the presence of sensing delay and model parameter uncertainty. We use a discrete-time Markov chain (DTMC) to characterize the primary users' (PU) dynamics as well as the fading channel. The power control problem is formulated as a Markov decision process (MDP) problem, which can be optimally solved by dynamic programming. However, due to the time-varying nature of the wireless channel and the spectrum sensing overhead, typically only the delayed sensing results are available at any time. The delay in spectrum sensing, if not properly accounted for, could significantly deteriorate the power control performance. Furthermore, the false sensing data and limited feedback cause noisy estimate of the transition probability matrix, leading to further performance degradation of the power control and channel outage. We first propose power control schemes based on a delayed MDP formulation, that account for all possible current channel state based on the delayed channel state. In addition, we propose an outage constraint to protect PU transmissions and properly manage channel outage. We then propose a robust power control framework that optimizes the worst-case system performance. Extensive simulation results are provided to demonstrate the effectiveness of the proposed power control algorithms.

A general class of dynamic models on scale-free networks is studied by analytical methods and computer simulations. Each network consists of N vertices and is characterized by its degree distribution, P(k), which represents the probability that a randomly chosen vertex is connected to k nearest neighbors. Each vertex can attain two internal states described by binary variables or Ising-like spins that evolve in time according to local majority rules. Scale-free networks, for which the degree distribution has a power law tail P(k) approximately k(-gamma), are shown to exhibit qualitatively different dynamic behavior for gamma < 5/2 and gamma > 5/2, shedding light on the empirical observation that many real-world networks are scale-free with 2 < gamma < 5/2. For 2 < gamma < 5/2, strongly disordered patterns decay within a finite decay time even in the limit of infinite networks. For gamma > 5/2, on the other hand, this decay time diverges as ln(N) with the network size N. An analogous distinction is found for a variety of more complex models including Hopfield models for associative memory networks. In the latter case, the storage capacity is found, within mean field theory, to be independent of N in the limit of large N for gamma > 5/2 but to grow as N(alpha) with alpha = (5 - 2gamma)/(gamma - 1) for 2 < gamma < 5/2.

A novel adaptive time stepping variant of the Boris–Buneman integrator for the simulation of particle accelerators with space charge

Cellular automata are discrete dynamical systems that are widely used to model natural systems. Classically they are run with perfect synchrony ; i.e., the local rule is applied to each cell at each time step. A possible modification of the updating scheme consists in applying the rule with a fixed probability, called the synchrony rate. It has been shown in a previous work that varying the synchrony rate continuously could produce a discontinuity in the behaviour of the cellular automaton. This works aims at investigating the nature of this change of behaviour using intensive numerical simulations. We apply a two-step protocol to show that the phenomenon is a phase transition whose critical exponents are in good agreement with the predicted values of directed percolation.

In Prequential analysis, an inference method is viewed as a forecasting system, and the quality of the inference method is based on the quality of its predictions. This is an alternative approach to more traditional statistical methods that focus on the inference of parameters of the data generating distribution. In this paper, we introduce adaptive combined average predictors (ACAPs) for the Prequential analysis of complex data. That is, we use convex combinations of two different model averages to form a predictor at each time step in a sequence. A novel feature of our strategy is that the models in each average are re-chosen adaptively at each time step. To assess the complexity of a given data set, we introduce measures of data complexity for continuous response data. We validate our measures in several simulated contexts prior to using them in real data examples. The performance of ACAPs is compared with the performances of predictors based on stacking or likelihood weighted averaging in several model classes and in both simulated and real data sets. Our results suggest that ACAPs achieve a better trade off between model list bias and model list variability in cases where the data is very complex. This implies that the choices of model class and averaging method should be guided by a concept of complexity matching, i.e. the analysis of a complex data set may require a more complex model class and averaging strategy than the analysis of a simpler data set. We propose that complexity matching is akin to a bias-variance tradeoff in statistical modeling.

Meta-analysis offers an analytical framework for research synthesis based on a comparative study of research findings undertaken by different researchers. In this paper, we investigate the possibilities of applying meta-analysis to transport policy impact studies in environmental-economics. The heterogeneity of studies in this field however makes it difficult to apply meta-analysis directly. We therefore propose a general equilibrium framework as a benchmark for comparison, and as a synthesis of the research findings we obtain from studies underlying a met a-analysis. A meta-analysis of studies results in decision rules that obtain the environmental impact of a transport policy as a function of the characteristics of these studies, i.e. variations in environmental impact of the transport policy can be attributed to variations in specific characteristics of these studies. The transport policy impact studies underlying a meta-analysis in environmental economics can be seen to derive their characteristics from a common general equilibrium framework. This framework leads to a classification of all possible characteristics of transport impact studies, that can be used as a basis for several types of meta-analytic methods to distinguish the most relevant ones among them. Policy makers can then improve the effectivity of their policies by concentrating on these relevant characteristics. 1 Meta-Analysis in Environmental Economics In the past decades many advances have been made in the social sciences by applying principles from met a-analysis. Meta-analysis is a research methodology that serves to bring together in a succinct and systematic way findings from previous research on a given issue or topic, undertaken by different researchers. Thus, the main aim of meta-analysis is to offer an analytical framework for research synthesis, usually based on comparative case study research. This approach has gained quite some momentum in environmental economics and related disciplines (see for a review and applications, van den Bergh et al. (1997)). The popularity of meta-analysis in environmental economics is caused by two factors: (i) meta-analysis is particularly useful in case of comparison of behavioural or policy outcomes that are not reflected in the ‘measuring rod of money’; and (ii) meta-analysis can also be applied in case of qualitative effects of decisions or actions, a situation which is often present in case of environmental phenomena. The complexity of environmental economic phenomena is often rather high. Even in cases where monetary valuation is applied (e.g., in social cost-benefit analysis) the research efforts are often so high, that an easy application in other cases is often prohibitive. This has prompted the development of value transfer methods, which aim to translate research findings to other, comparable case studies (see Bal and Nijkamp (1998)). In recent years also various applications of meta-analysis to transport economics in relation to environmental economics can be observed, for example, on the comparison of price elasticities in public transport (see e.g., Nijkamp and Pepping (1998)). But as a whole, applications in this field are still scant, although there might be quite some potential for fruitful applications. An important research question is now whether it is possible to develop a research methodology that can act as a general framework for the application of meta-analysis studies at the interface of transport economics and environmental economics. This is once more important, as meta-analysis is generally shifting its meaning from a rather narrowly defined statistical approach to a general approach for comparing findings of different studies on similar topics or issues, not only empirical, but also theoretical and methodological. In the present paper it will be argued that a general equilibrium approach, including externalities and transportation, may offer such a comprehensive methodology framework. The structure of this paper is as follows. First, the changing scene of transport will be sketched (Section 2). Then the relation between transport, land use and economic activities will be outlined (Section 3). In the next section (Section 4) the foundations of a general spatial-economic equilibrium framework including the environment will be laid down. Section 5 will then be devoted to a classification of transport impact studies, while analytical approaches for comparing such impact studies will be outlined in Section 6. The paper will be concluded with some pressing research questions in Section 7. 2 Current Trends and Issues on Transport and the