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The slogan that "information is power" has undergone a slight change. Today, "information updating" is in the focus of interest. The largest source of information today is the World Wide Web. Fast search methods are needed to utilize this enormous source of information. In this paper our novel crawler using support vector classification and on-line reinforcement learning is described. We launched crawler searches from different sites, including sites that offer, at best, very limited information about the search subject. This case may correspond to typical searches of non-experts. Results indicate that the considerable performance improvement of our crawler over other known crawlers is due to its on-line adaptation property. We used our crawler to characterize basic topic-specific properties of WWW environments. It was found that topic-specific regions have a broad distribution of valuable documents. Expert sites are excellent starting points, whereas mailing lists can form trape for the crawler. These properties of the WWW and the emergence of intelligent "high-performance" crawlers that monitor and search for novel information together predict a significant increase of communication load on the WWW in the near future.

A novel depth-from-motion vision model based on leaky integrate-and-fire (I&F) neurons incorporates the implications of recent neurophysiological findings into an algorithm for object discovery and depth analysis. Pulse-coupled I&F neurons capture the edges in an optical flow field and the associated time of travel of those edges is encoded as the neuron parameters, mainly the time constant of the membrane potential and synaptic weight. Correlations between spikes and their timing thus code depth in the visual field. Neurons have multiple output synapses connecting to neighbouring neurons with an initial Gaussian weight distribution. A temporally asymmetric learning rule is used to adapt the synaptic weights online, during which competitive behaviour emerges between the different input synapses of a neuron. It is shown that the competition mechanism can further improve the model performance. After training, the weights of synapses sourced from a neuron do not display a Gaussian distribution, having adapted to encode features of the scenes to which they have been exposed.

The paper investigates the role of selection on evolutionary rescue of population. The statistical mechanics technique is used to model dynamics of a population experiencing a natural selection and an abrupt change in the environment. The paper assesses the selective pressure produced by two different mechanisms: by strength of resistance and by strength of selection (by intraspecific competition). It is shown that both mechanisms are capable of providing an evolutionary rescue of population in particular conditions. However, for a small level of an extinction rate, the population cannot be rescued without intraspecific competition.

In this paper, we consider the spread of an epidemic on a changing network, specifically focusing on two phenomena. The first part of the paper investigates a possible mechanism of disease outbreaks on college campuses. We present a toy model, dividing students into extroverts (high-degree nodes with a large number of contacts) and introverts (low-degree nodes with a small number of contacts). In our model, the average degree of extroverts is evolving with time, and its dynamics is coupled with the current epidemic situation: extroverts tend to increase their number of contacts for low level of epidemic, but as more and more students get infected, they start decreasing their average degree. Another phenomenon analyzed in the paper is vaccination: how should the vaccine be allocated to best benefit the population? We consider two possible vaccination strategies: (1) vaccinating people starting from high risk groups (older people with a higher risk of mortality) or (2) prioritizing vaccination of people with a higher number of contacts (such as college students) to decrease the epidemic outbreak. Both phenomena show the importance of diversity in the number of contacts.

Traffic simulations are made more realistic by giving individual drivers intentions, i.e., an idea of where they want to go. One possible implementation of this idea is to give each driver an exact pre-computed path, that is, a sequence of roads this driver wants to follow. This paper shows, in a realistic road network, how repeated simulations can be used so that drivers can explore different paths, and how macroscopic quantities such as locations of jams or network throughput change as a result of this.

Recently, South Asian countries have committed their mitigation targets to the United Nations Framework Convention on Climate Change. This study examines the effectiveness of these efforts by developing a dynamic computable general equilibrium-water-energy (CGE-WE) model. Using the GTAP database version 9, it examines how different sectors respond to these policies in South Asia. Besides, it argues that an improved irrigation system can reduce the output losses caused by the mitigation policies. In a nutshell, the cost of improving irrigation system is USD 159.7 million in Bangladesh, 224 million in India, 9.1 million in Nepal, 38.5 million in Pakistan and 10.4 million in Sri Lanka. The proposed adaptation strategy can save more than USD 76.43 billion in the region after fulfilling the region’s commitment toward the global mitigation efforts.

This paper presents a script-independent methodology for optical character recognition (OCR) based on the use of hidden Markov models (HMM). The feature extraction, training and recognition components of the system are all designed to be script independent. The training and recognition components were taken without modification from a continuous speech recognition system; the only component that is specific to OCR is the feature extraction component. To port the system to a new language, all that is needed is text image training data from the new language, along with ground truth which gives the identity of the sequences of characters along each line of each text image, without specifying the location of the characters on the image. The parameters of the character HMMs are estimated automatically from the training data, without the need for laborious handwritten rules. The system does not require presegmentation of the data, neither at the word level nor at the character level. Thus, the system is able to handle languages with connected characters in a straightforward manner. The script independence of the system is demonstrated in three languages with different types of script: Arabic, English, and Chinese. The robustness of the system is further demonstrated by testing the system on fax data. An unsupervised adaptation method is then described to improve performance under degraded conditions.

In order to accelerate the convergence and improve the calculation accuracy for parameter optimization of the Muskingum model, we propose a novel, adaptive hybrid particle swarm optimization (AHPSO) algorithm. With the decreasing of inertial weight factor proposed, this method can gradually converge to a global optimal with elite individuals obtained by hybrid PSO. In the paper, we analyzed the feasibility and the advantages of the AHPSO algorithm. Then, we verified its efficiency and superiority by application of the Muskingum model. We intensively evaluated the error fitting degree based on the comparison with four known formulas: the test method (TM), the least residual square method (LRSM), the nonlinear programming method (NPM), and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The results show that the AHPSO has a higher precision. In addition, we compared the AHPSO algorithm with the binary-encoded genetic algorithm (BGA), the Gray genetic algorithm (GGA), the Gray-encoded accelerating genetic algorithm (GAGA) and the particle swarm optimization (PSO), and results show that AHPSO has faster convergent speed. Moreover, AHPSO has a competitive advantage compared with the above eight methods in terms of robustness. With the efficiency of this approach it can be extended to estimate parameters of other dynamic models.

With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorithms.

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.

Not long ago it was thought that the most important characteristics of the mechanics of soft tissues were their complex mechanical properties: they often exhibit nonlinear, anisotropic, nearly incompressible, viscoelastic behavior over finite strains. Indeed, these properties endow soft tissues with unique structural capabilities that continue to be extremely challenging to quantify via constitutive relations. More recently, however, we have come to appreciate an even more important characteristic of soft tissues, their homeostatic tendency to adapt in response to changes in their mechanical environment. Thus, to understand well the biomechanical properties of a soft tissue, we must not only quantify their structure and function at a given time, we must also quantify how their structure and function change in response to altered stimuli. In this paper, we introduce a new constrained mixture theory model for studying growth and remodeling of soft tissues. The model melds ideas from classical mixture and homogenization theories so as to exploit advantages of each while avoiding particular difficulties. Salient features include the kinetics of the production and removal of individual constituents and recognition that the neighborhood of a material point of each constituent can have a different, evolving natural (i.e. stress-free) configuration.

This article describes a methodology for building integtated planning-reacting systems. The work is based on a formal approach to building the reactive component (the reactor); this allows us to formalize the concept of a planner improving a reactive system. Our novel planner design emphasizes how the planner can use the reactor to focus its reasoning, as well as how the reactor is guided by the planner to improve its behavior.

The reactive component (the reactor) uses a process-based model of robot computation, the RS model. This gives us a powerful representation for actions with precise formal semantics. The duty of the planning component (the planner) is to adapt the reactor to suit a set of objectives and the possibilities afforded by the environment. Planner and reactor both operate continually, separately, and in a complementary fashion. The approach is illustrated with a kitting robot domain problem.

On the basis of a conceptual analysis of the Informational Macrodynamics' equations, this paper introduces a unified information systemic model of evolution including the information functional mechanisms of self-organization, mutation, adaptation, control, the double spiral's genetics with coding language, the system's generation, decaying, and heredity, considered as the information regularities of developing macrosystems.

The complexity of the Nautilus pompilius shell is analyzed in terms of its fractal dimension and its equiangular spiral form. Our findings assert that the shell is fractal from its birth and that its growth is dictated by a self-similar criterion (we obtain the fractal dimension of the shell as a function of time). The variables that have been used for the analysis show an exponential dependence on the number of chambers/age of the cephalopod, a property inherited from its form.

This paper presents a new adaptive fuzzy control scheme that is formulated and constructed directly in the control objective space. The idea of the objective-centered for-mulism on the basis of decomposition of closed-loop response profile is clarified first followed by a detailed description of the scheme. Unlike the existing adaptive fuzzy control methods, the rules and the membership functions of the fuzzy controller in the new scheme are fixed and the adaptation is done on the input and output weighting factors of the fuzzy controller. A simulation analysis is conducted to evaluate the controller performance in regulating a structure-varying process, and to illustrate the advantage of the scheme in controlling plants that can not be easily handled by other control approaches.

Collaborative work is characterized by frequently changing situations and corresponding demands for tool support and interaction behavior provided by the collaboration environment. Current approaches to address these changing demands include manual tailoring by end-users and automatic adaptation of single user tools or for individual users. Few systems use context as a basis for adapting collaborative work environments, mostly focusing on document recommendation and awareness provision. In this paper, we present, firstly, a generic four layer framework for modeling and exploiting context. Secondly, a generic adaptation process translating user activity into state, deriving context for a given focus, and executing adaptation rules on this context. Thirdly, a collaboration domain model for describing collaboration environments and collaborative situations. Fourthly, examples of exploiting our approach to support context-based adaptation in four typical collaboration situations: co-location, co-access, co-recommendation, and co-dependency.

We investigate the Minority Game, a toy model for agents buying and selling a commodity. The dynamics of the model are decomposed into two processes, firstly, an agent's choice of active strategy, secondly, the interaction between agents while they play. The latter is suitably described by the Hamming distance between strategies. Here, we argue that the first process is described by the history distribution.

Yak genome provides new insights into high altitude adaptation.

Gentris and Shanghai Institutes of Preventative Medicine expand collaboration.

Chinese researchers identify rice gene enhancing quality, productivity.

Quintiles opens new Center of Excellence in Dalian to support innovative drug development.

BGI demonstrated genomic data transfer at nearly 10 gigabits per second between US and China.

Quintiles deepens investment in China - New Quintiles China Headquarters and local lab testing solution announced.

Beike earns AABB Accreditation for cord blood and cord tissue banking.

Epigenomic differences between newborns and centenarians provide insight to the understanding of aging.

Motivated by emerging applications, we consider sensor networks where the sensors themselves (not just the sinks) are mobile. Furthermore, we focus on mobility scenarios characterized by heterogeneous, highly changing mobility roles in the network. To capture these high dynamics of diverse sensory motion we propose a novel network parameter, the mobility level, which, although simple and local, quite accurately takes into account both the spatial and speed characteristics of motion. We then propose adaptive data dissemination protocols that use the mobility level estimation to optimize performance, by basically exploiting high mobility (redundant message ferrying) as a cost-effective replacement of flooding, e.g. the sensors tend to dynamically propagate less data in the presence of high mobility, while nodes of high mobility are favored for moving data around. These dissemination schemes are enhanced by a distance-sensitive probabilistic message flooding inhibition mechanism that further reduces communication cost, especially for fast nodes of high mobility level, and as distance to data destination decreases. Our simulation findings demonstrate significant performance gains of our protocols compared to non-adaptive protocols, i.e. adaptation increases the success rate and reduces latency (even by 15%) while at the same time significantly reducing energy dissipation (in most cases by even 40%). Also, our adaptive schemes achieve significantly higher message delivery ratio and satisfactory energy-latency trade-offs when compared to flooding when sensor nodes have limited message queues.

Arteries exhibit a remarkable ability to adapt in response to sustained alterations in hemodynamic loading as well as to disease, injury, and clinical treatment. A better understanding of such adaptations will be aided greatly by formulating, testing, and refining appropriate theoretical frameworks for modeling the biomechanics and associated mechanobiology. The goal of this brief review is to highlight some recent developments in the use of a constrained mixture theory of arterial growth and remodeling, with particular attention to the requisite constitutive relations, and to highlight future directions of needed research.