Study on the localization of the electric dipole sources

The research of dipole source localization has great significance in both clinical research and applications. For example, the EEG recording from the scalp is widely used for the localization of sources of electrical activity in the brain. This paper presents a closed formula that describes the electric field of dipoles at arbitrary position, which is a linear transformer called the transfer matrix. The expression of transfer matrix and its many useful characteristics are given, which can be used for the analysis of the electrical fields of dipoles. This paper also presents the closed formula for determining the location and magnitude of single dipole or multi-dipoles according to its electrical field distribution. A calculation result for a single dipole shows that the dipole will be located at the midpoint of a line segment if there are equivalent fields at its two ends.

Application of static and dynamic enclosures for determining dimethyl sulfide and carbonyl sulfide exchange in Sphagnum peatlands: Implications for the magnitude and direction of flux

A static enclosure method was applied to determine the exchange of dimethyl sulfide (DMS) and carbonyl sulfide (OCS) between the surface of Sphagnum peatlands and the atmosphere. Measurements were performed concurrently with dynamic (flow through) enclosure measurements with sulfur-free air used as sweep gas. This latter technique has been used to acquire the majority of available data on the exchange of S gases between the atmosphere and the continental surfaces and has been criticized because it is thought to overestimate the true flux of gases by disrupting natural S gas gradients. DMS emission rates determined by both methods were not statistically different between 4 and >400 nmol m−2 h−1, indicating that previous data on emissions of at least DMS are probably valid. However, the increase in DMS in static enclosures was not linear, indicating the potential for a negative feedback of enclosure DMS concentrations on efflux. The dynamic enclosure method measured positive OCS flux rates (emission) at all sites, while data using static enclosures indicated that OCS was consumed from the atmosphere at these same sites at rates of 3.7 to 55 nmol m−2 h−1. Measurements using both enclosure techniques at a site devoid of vegetation showed that peat was a source of both DMS and OCS. However, the rate of OCS efflux from decomposing peat was more than counterbalanced by OCS consumption by vegetation, including Sphagnum mosses, and net OCS uptake occurred at all sites. We propose that all wetlands are net sinks for OCS.

THE APPLICATION OF AN ULTRAMICROELECTRODE IN HOMOGENEOUS CATALYTIC REACTION .1. GENERAL CHARACTERISTIC OF A HOMOGENEOUS CATALYTIC REACTION AT AN ULTRAMICROELECTRODE WITH ARBITRARY GEOMETRY UNDER STEADY-STATE

A general characteristic of the electrochemical process coupling with a homogeneous catalytic reaction at an ultramicroelectrode under steady state is described. It was found that the electrochemical process coupling with homogeneous catalytic reaction has a similar steady state voltammetric wave at an ultramicroelectrode with arbitrary geometry. A method of determination for the kinetic constant of homogeneous catalytic reaction at an ultramicroelectrode with arbitrary geometry is proposed.

Dielectric responses of anisotropic graded granular composites having arbitrary inclusion shapes

The transformation field method (TFM) originated from Eshelby's transformation field theory is developed to estimate the effective permittivity of an anisotropic graded granular composite having inclusions of arbitrary shape and arbitrary anisotropic grading profile. The complicated boundary-value problem of the anisotropic graded composite is solved by introducing an appropriate transformation field within the whole composite region. As an example, the effective dielectric response for an anisotropic graded composite with inclusions having arbitrary geometrical shape and arbitrary grading profile is formulated. The validity of TFM is tested by comparing our results with the exact solution of an isotropic graded composite having inclusions with a power-law dielectric grading profile and good agreement is achieved in the dilute limit. Furthermore, it is found that the inclusion shape and the parameters of the grading profile can have profound effect on the effective permittivity at high concentrations of the inclusions. It is pointed out that TFM used in this paper can be further extended to investigate the effective elastic, thermal, and electroelastic properties of anisotropic graded granular composite materials.

Transformation field method for calculating the effective properties of isotropic graded composites having arbitrary shapes

A method of transformation field is developed to estimate the effective properties of graded composites whose inclusions have arbitrary shapes and gradient profiles by means of a periodic cell model. The boundary-value problem of graded composites having arbitrary inclusion shapes is solved by introducing the transformation field into the inclusion region. As an example, the effective dielectric response of isotropic graded composites having arbitrary shapes and gradient profiles is handled by the transformation field method (TFM). Moreover, TFM results are validated by the exact solutions of isotropic graded spherical inclusions having a power-law profile and good agreement is obtained in the dilute limit. Furthermore, it is found that the inclusion shapes and the parameters of the gradient profiles can have profound effect on the effective properties of composite systems at high concentration of inclusions.

利用接收函数方位变化研究地壳各向异性

Because of its sensitivity to the velocity discontinuity of the earth, receiver function technique has become a routine procedure used to probe interior structure of the earth. Receiver functions contain anisotropic information of the earth’s interior, however, traditional receiver function techniques such as migration imaging and waveform inversion method, which are based on isotropic media assumption, can not effectively extract the anisotropy information contained in the azimuth variation pattern. Only by using the anisotropic media, e.g. a model with symmetric axis of arbitrary orientation, computing the response, can we obtain the detailed anisotropy information hidden in the radial and transversal receiver function. Focusing on the receiver function variation pattern changing wtih different back azimuths, we introduced different kinds of symmetric systems of seismic anisotropy used often, and summarized some possible causes of anisotropy formation. We show details about how to calculate the response of a stratified anisotropy model with symmetric axis of arbitrary orientation. We also simulated receiver functions among different models and analyzed how the changing of anisotropic parameters influence the azimuth variation pattern of receiver functions. The anisotropy study by receiver function analysis was applied to Taihang Mountain Range (TMR) in North China in this thesis. The maximum entropy spectrum deconvolution technique was used to extract radial and transversal receiver functions from the waveforms of 20 portable seismic stations deployed in TMR. Considering the signal-to-noise ratio and the azimuth coverage, we got the variation pattern of receiver functions for 11 stations. After carefully analyzing the pattern of the receiver functions that we got, we obtained the reliable evidence on the existence of anisotropy in the shallow crust in TMR. Our results show that, although the thickness of the upper crustal layer is only about 1 km, the layer shows a strong anisotropy with magnitude of 8~15%; in the deeper of crust, the magnitudes of anisotropy is about 3%~5%, showing a pattern with fast-symmetric-axis. The crust anisotropy beneath TMR in North China obtained in this study also shows a significant difference in both the lateral and vertical scale, which might imply a regional anisotropy characteristic in the studied region.

Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences

J. Keppens and Q. Shen. Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences. Journal of Artificial Intelligence Research, 21:499-550, 2004.

On the positivity of the Jansen-He? operator for arbitrary mass

Iantchenko, A.; Jakuba?a-Amundsen, D.H., (2003) 'On the positivity of the Jansen-He? operator for arbitrary mass', Annales of the Institute Henri Poincar? 4 pp.1083-1099 RAE2008

Systematic Verification of Safety Properties of Arbitrary Network Protocol Compositions Using CHAIN

Formal correctness of complex multi-party network protocols can be difficult to verify. While models of specific fixed compositions of agents can be checked against design constraints, protocols which lend themselves to arbitrarily many compositions of agents-such as the chaining of proxies or the peering of routers-are more difficult to verify because they represent potentially infinite state spaces and may exhibit emergent behaviors which may not materialize under particular fixed compositions. We address this challenge by developing an algebraic approach that enables us to reduce arbitrary compositions of network agents into a behaviorally-equivalent (with respect to some correctness property) compact, canonical representation, which is amenable to mechanical verification. Our approach consists of an algebra and a set of property-preserving rewrite rules for the Canonical Homomorphic Abstraction of Infinite Network protocol compositions (CHAIN). Using CHAIN, an expression over our algebra (i.e., a set of configurations of network protocol agents) can be reduced to another behaviorally-equivalent expression (i.e., a smaller set of configurations). Repeated applications of such rewrite rules produces a canonical expression which can be checked mechanically. We demonstrate our approach by characterizing deadlock-prone configurations of HTTP agents, as well as establishing useful properties of an overlay protocol for scheduling MPEG frames, and of a protocol for Web intra-cache consistency.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

The Graph Analysis Toolbox: Image Processing on Arbitrary Graphs

Office of Naval Research (N00014-01-1-0624)

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

The Construction of Arbitrary Stable Dynamics in Non-Linear Neural Networks

In this paper, two methods for constructing systems of ordinary differential equations realizing any fixed finite set of equilibria in any fixed finite dimension are introduced; no spurious equilibria are possible for either method. By using the first method, one can construct a system with the fewest number of equilibria, given a fixed set of attractors. Using a strict Lyapunov function for each of these differential equations, a large class of systems with the same set of equilibria is constructed. A method of fitting these nonlinear systems to trajectories is proposed. In addition, a general method which will produce an arbitrary number of periodic orbits of shapes of arbitrary complexity is also discussed. A more general second method is given to construct a differential equation which converges to a fixed given finite set of equilibria. This technique is much more general in that it allows this set of equilibria to have any of a large class of indices which are consistent with the Morse Inequalities. It is clear that this class is not universal, because there is a large class of additional vector fields with convergent dynamics which cannot be constructed by the above method. The easiest way to see this is to enumerate the set of Morse indices which can be obtained by the above method and compare this class with the class of Morse indices of arbitrary differential equations with convergent dynamics. The former set of indices are a proper subclass of the latter, therefore, the above construction cannot be universal. In general, it is a difficult open problem to construct a specific example of a differential equation with a given fixed set of equilibria, permissible Morse indices, and permissible connections between stable and unstable manifolds. A strict Lyapunov function is given for this second case as well. This strict Lyapunov function as above enables construction of a large class of examples consistent with these more complicated dynamics and indices. The determination of all the basins of attraction in the general case for these systems is also difficult and open.

Working Memories for Storage and Recall of Arbitrary Temporal Sequences

A working memory model is described that is capable of storing and recalling arbitrary temporal sequences of events, including repeated items. These memories encode the invariant temporal order of sequential events that may be presented at widely differing speeds, durations, and interstimulus intervals. This temporal order code is designed to enable all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system.

The Synthesis of Arbitrary Stable Dynamics in Non-linear Neural Networks II: Feedback and Universality

We wish to construct a realization theory of stable neural networks and use this theory to model the variety of stable dynamics apparent in natural data. Such a theory should have numerous applications to constructing specific artificial neural networks with desired dynamical behavior. The networks used in this theory should have well understood dynamics yet be as diverse as possible to capture natural diversity. In this article, I describe a parameterized family of higher order, gradient-like neural networks which have known arbitrary equilibria with unstable manifolds of known specified dimension. Moreover, any system with hyperbolic dynamics is conjugate to one of these systems in a neighborhood of the equilibrium points. Prior work on how to synthesize attractors using dynamical systems theory, optimization, or direct parametric. fits to known stable systems, is either non-constructive, lacks generality, or has unspecified attracting equilibria. More specifically, We construct a parameterized family of gradient-like neural networks with a simple feedback rule which will generate equilibrium points with a set of unstable manifolds of specified dimension. Strict Lyapunov functions and nested periodic orbits are obtained for these systems and used as a method of synthesis to generate a large family of systems with the same local dynamics. This work is applied to show how one can interpolate finite sets of data, on nested periodic orbits.