Non-uniform Data Research Articles

Effective potential approach to bulk thermodynamic properties and surface tension of molecular fluids I. Single component n-alkane systems

The aim of this work is to develop spherically symmetric effective potentials allowing bulk thermodynamic properties and surface tension of molecular fluids to be predicted semiempirically by the use of statistical mechanical methods. Application is made to the straight chain alkane fluids from methane to decane. An effective Lennard-Jones potential is generated with temperature-dependent parameters fitted to the critical temperature and pressure and to Pitzer's acentric factor. Insertion of this potential into the generalised van der Waals (GvdW) density functional theory yields bulk properties in good agreement with experiments. The surface tension is overestimated for the longer alkane chains. In order to account for the surface tension, an independently adjustable attractive range of interaction is required and obtained through the use of square-well potentials chosen so as to leave the bulk thermodynamics unaltered while the attractive range is fitted to the surface tension at a single temperature. The GvdW theory, which includes binding energy, entropic and profile shape contributions, then generates surface tension estimates that are of good accuracy over the full range of available experimental data. It appears that, given a sufficiently flexible form, effective potentials combined with simple statistical mechanical theory can reproduce both bulk and non-uniform fluid data of great variety in an insighful and practically useful way.

Analysis of the relationship between seismic moment and fault length for large crustal strike‐slip earthquakes between 1977–92

We have used an uniform data set to determine the scaling relation between seismic moment M0 and fault length L for 34 large, crustal, strike‐slip earthquakes in the period 1977–92. The seismic moments used were obtained by the centroid‐moment tensor analysis. The aftershocks of each event were relocated in this study and the fault length estimated from their spatial extent. For large events, we find that in spite of some scatter in the data, the trend of this uniform data set indicates that M0 ∝L². Our data set does not show any obvious change of slope around M0 ≈ (.6–.8)× 1020 N m as was seen in a previous non‐uniform data set in Romanowicz [1992].

ASSESSMENT OF LICHEN SENSITIVITY TO CLIMATE CHANGE

Experimental data on net photosynthetie rate change of lichens in response to temperature and water stress were collected and standardized. The method of nonuniform hierarchical structured data interpolation was applied to assess lichen sensitivity to climatic stress for species and territories where sensitivity has not been measured in a laboratory. The alternative method of lichen sensitivity to climatic stress assessment is the analysis of species ranges, abundance, and occurrence. This approach is especially effective for areas with a manifest climatic gradient and where multiple floristic zone boundaries occur. Assessments of sensitivity of the Negev Desert lichens to long-term temperature increase were obtained using the second approach.

Propagating and stationary superfluid turbulent fronts

We have observed that the critical heat current for the transition to superfluid turbulence in weakly nonuniform circular channels depends strongly on the flow direction. This observation is particularly surprising since no other property of the turbulence appears to have such a dependence. In a nonuniform channel the critical heat current is associated with a stationary front between the laminar and turbulent flow. We propose a new model for superfluid turbulent fronts which explains the asymmetry of the critical heat currents in a simple way. The model is based on the subcritical nature of the transition, and the generic description of such a bifurcation by the Ginzburg-Landau equation. As a bonus, the model also explains a long-standing problem in super fluid physics-the nature of propagating fronts in uniform channels. The results of our analysis of both the uniform and nonuniform channel data also provide new information about the vortex line drift velocity.

Unsupervised learning for Boltzman Machines

An unsupervised learning algorithm for a stochastic recurrent neural network based on the Boltzmann Machine architecture is formulated in this paper. The maximization of the mutual information between the stochastic output neurons and the clamped inputs is used as an unsupervised criterion for training the network. The resulting learning rule contains two terms corresponding to Hebbian and anti-Hebbian learning. It is interesting that these two terms are weighted by the amount of information transmitted in the learning synapse, giving an information-theoretic interpretation of the proportionality constant of Hebb's biological rule. The anti-Hebbian term, which can be interpreted as a forgetting function, supports the optimal coding. In this way, optimal nonlinear and recurrent implementations of data compression of Boolean patterns are obtained. As an example, the encoder problem is simulated and trained in an unsupervised way in a one layer network. Compression of non-uniform distributed binary data is included. Unsupervised classification, even for continuous inputs, is shown for the cases of four overlapping Gaussian spots and for a real-world example of thyroid diagnosis. In comparison with other techniques, the present model requires an exponentially smaller number of weights for the classification problem.

Two-dimensional operation of a scanning optical microscope by vibrating knife-edge tomography

We analyze and demonstrate a scanning optical imaging system that uses a vibrating knife edge to collect sets of tomographic projection data of two-dimensional objects. Special concerns arising from nonuniform illumination and offset projection data are covered. A tomographic filtered backprojection algorithm is implemented to reconstruct an image from collected projections. Implications for near-field scanning optical microscopy and x-ray microscopy are discussed.

Unsupervised learning for Boltzman Machines

An unsupervised learning algorithm for a stochastic recurrent neural network based on the Boltzmann Machine architecture is formulated in this paper. The maximization of the mutual information between the stochastic output neurons and the clamped inputs is used as an unsupervised criterion for training the network. The resulting learning rule contains two terms corresponding to Hebbian and anti-Hebbian learning. It is interesting that these two terms are weighted by the amount of information transmitted in the learning synapse, giving an information-theoretic interpretation of the proportionality constant of Hebb's biological rule. The anti-Hebbian term, which can be interpreted as a forgetting function, supports the optimal coding. In this way, optimal nonlinear and recurrent implementations of data compression of Boolean patterns are obtained. As an example, the encoder problem is simulated and trained in an unsupervised way in a one layer network. Compression of non-uniform distributed binary data is i...

Image reconstruction/synthesis from nonuniform data and zero/threshold crossings

We address the problem of reconstructing functions from their nonuniform data and zero/threshold crossings. We introduce a deterministic process via the Gram-Schmidt orthonormalization procedure to reconstruct functions from their nonuniform data and zero/threshold crossings. This is achieved by first introducing the nonorthogonal basis functions in a chosen 2-D domain (e.g., for a band-limited signal, a possible choice is the 2-D Fourier domain of the image) that span the signal subspace of the nonuniform data. We then use the Gram-Schmidt procedure to construct a set of orthogonal basis functions that span the linear signal subspace defined by the nonorthogonal basis functions. Next, we project the <i>N</i>-dimensional measurement vector (<i>N</i> is the number of nonuniform data or threshold crossings) onto the newly constructed orthogonal basis functions. Finally, the function at any point can be reconstructed by projecting the representation with respect to the newly constructed orthonormal basis functions onto the reconstruction basis functions that span the signal subspace of the evenly spaced sampled data. The reconstructed signal gives the minimum mean square error estimate of the original signal. This procedure gives error-free reconstruction provided that the nonorthogonal basis functions that span the signal subspace of the nonuniform data form a complete set in the signal subspace of the original band-limited signal. We apply this algorithm to reconstruct functions from their unevenly spaced sampled data and zero crossings and also apply it to solve the problem of synthesis of a 2-D band-limited function with the prescribed level crossings.

Partial indexing for nonuniform data distributions in relational DBMS's

It is well known that the effectiveness of relational database systems is greatly dependent on the efficiency of the data access strategies. For this reason, much work has been devoted to the development of new access techniques, supported by adequate access structures such as the B/sup +/trees. The effectiveness of the B/sup +/tree also depends on the data distribution characteristics; in particular, poor performance results when the data show strong key value distribution unbalancing. The aim of this paper is to present the partial index: a new access structure that is useful in such cases of unbalancing, as an alternative to the B/sup +/tree unclustered indexes. The access structures are built in the physical design phase, and at execution (or compilation) time, the optimizer chooses the most efficient access path. Thus, integration of the partial indexing technique in the design and in the optimization process are also described. >

Fast Adaptive Methods for the Free-Space Heat Equation

Standard numerical methods for the heat equation in two or more space dimensions are excellent if it is necessary to follow the evolution in great detail through many small timesteps. This paper presents efficient and accurate new adaptive methods that solve the free-space heat equation with large timesteps. These methods combine the fast Gauss transform with an adaptive refinement scheme that represents the solution as a continuous piecewise polynomial, to a user-specified degree of accuracy. The same approach is extended to solve inhomogeneous problems and to solve the heat equation in moving domains with boundaries. In problems with boundaries, it allows the use of accurate boundary representations without requiring difficult product integration formulas or precluding fast evaluation schemes. Numerical experiments in two space dimensions show these methods to be accurate and efficient, especially for highly nonuniform or discontinuous initial data or when substantial accuracy is required.

Assessing partitioning/ scheduling/storage trade-offs for regular iterative algorithms

The problem of partitioning systolic algorithms (a subclass of Regular Iterative Algorithms (RIA) has been studied by a number of researchers. One approach to partitioning the algorithm is to divide the index space into equivalence classes called blocks. The blocks are scheduled and placed on a processor array of a fixed size. This approach creates the need for auxiliary memory structures to store partial results and the need for control circuitry to direct the resulting non-uniform flow of data values within the array. The implementation of these structures and the means of comparing the cost of differences in the structures resulting from various partitioning and scheduling policies has been largely omitted in the literature. In this paper we offer a detailed model of Fixed Size Array Processors architectures. Using this model we may assess the trade-offs between auxiliary memory structure size, execution time, and control circuit complexity. The control circuits generate signals which determine the data flow through the array. We emphasize methods for controlling non-uniform data flow resulting from some partitionings of the index space. We show how to derive the control signals in such arrays from the determination of the index points. We also show how the translation of index points to control signals can be refined by a process of index pipelining which reduces the complexity of the control circuitry. Several partitioning and scheduling schemes for QR decomposition are evaluated to illustrate how the model may be used to determine a good partitioning/scheduling/storage configuration.

Modular VLSI architectures for computing the arithmetic Fourier transform

Modular, area-efficient VLSI architectures for computing the arithmetic Fourier transform (AFT) are proposed. By suitable design of PEs and I/O sequencing, nonuniform data dependencies in the AFT computation which require nonequidistant inputs and assignment of Mobius function values are resolved. The proposed design employs 2N+1 PEs to compute 2N+1 Fourier coefficients. Each PE has an adder and a fixed amount of local storage, and one PE has a multiplier. I/O with the host is performed using a fixed number of channels. This results in simple PE organization, compared with those needed in known DFT/FFT architectures. The design achieves O(N) speedup. It uses significantly fewer PEs than designs in the literature and supports real-time applications by allowing continuous sequential input. It can be extended to achieve linear speedup in a fixed size array with 2p+1 PEs, 1<or=p<or=N.<<ETX>>

The method of free-positioned point masses — geoid studies on the Gulf of Bothnia

Gravity data inversion methods using spatial source distributions are frequently investigated and applied in Geodesy. Nearly all of them construct a fixed geometry for the sources. It was shown that this is not at all necessary. We give an approach to the problem of geoid determination based on free-positioned point masses. The algorithm available is extended and refined in order to make it applicable to more ill-designed and complex problems than addressed so far. We introduce concepts to handle different data accuracies, non-uniform data coverages, and different data types. The Gulf of Bothnia serves as an area of investigation. We determine a pure gravimetric geoid solution as well as a combined solution based on gravimetric and altimetric data. The problem of gross error detection in the altimetry is treated. The prediction accuracies obtained prove the method to be suitable for such a problem.

Block access estimation for clustered data

A method is proposed for dealing with nonuniform data distributions in database organizations in order to estimate the expected number of blocks containing the tuples requested by a query. When tuples with equal attribute value are not uniformly distributed over the blocks of secondary memory that store the relation, a clustering effect is observed. This can be detected by means of a single parameter, the clustering factor, which can be stored in the system catalog. The method can be applied to uniform data distributions as well, since it is shown that a uniform distribution can be viewed as a particular instance of a class of clustered distributions. In this case the proposed method allows considerable reduction of the number of computational steps needed to compute the estimated result.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fast Algorithms for Geometric Traveling Salesman Problems

This paper describes efficient algorithms for computing approximate traveling salesman tours in multidimensional point sets. We describe implementations of a dozen starting heuristics (including Nearest Neighbor and Farthest Insertion) and three local optimizations (including 2-Opt and 3-Opt). Experiments indicate that most of the algorithms run in O(N log N) time on uniform data sets, and many run almost as fast on very nonuniform data. The program that implements the algorithms is able to solve uniform planar million-city traveling salesman problems to within a few percent of optimal in several midicomputer CPU hours. The algorithms and program apply to many distance metrics and dimensions. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Application of stochastic inverse theory to ionospheric tomography

Tomographic processing of path integral electron density records is emerging as a viable tool for ionospheric research. Tomographic processors fall into at least two major classes: those applying the Radon transform and those employing linear algebraic matrix inversion. In this paper we apply one of the latter, the “weighted, damped, least squares” technique of stochastic inversion, to two simulated but realistic data sets. This method, which repeatedly has been applied successfully to ocean acoustic tomography, is particularly suited to solving inverse problems in geophysics because it provides an orderly mechanism for judicious use of a priori or external information to complement sparse or nonuniform path integral data. The limited range of angles through which the ionosphere may be viewed on satellite‐to‐ground paths represents such a nonuniformity in ionospheric tomography. The method also provides means for estimating uncertainty in the image field, uncertainty which itself is nonuniform.

The adaptive-hash join algorithm for a hypercube multicomputer

The cube adaptive-hash join algorithm, which combines the merits of nested-loop and hybrid-hash, is presented. The performance of these algorithms is compared through analytical cost modeling. The nonuniform data value distribution of the inner relation is shown to have a greater impact than that of the outer relation. The cube adaptive-hash algorithm outperforms the cube hybrid-hash algorithm when bucket overflow occurs. In the worst case, this algorithm converges to the cube nested-loop-hash algorithm. When there is no hash table overflow, the cube adaptive-hash algorithm converges to the cube hybrid-hash algorithm. Since the cube adaptive-hash algorithm adapts itself depending on the characteristics of the relations, it is relatively immune to the data distribution.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Inhomogeneous boundary conditions and the choice of the convective patterns in a porous layer

A slightly non-uniform heating significantly modifies the onset of convection in a parallelepipedic porous layer. In the purely homogeneous case, this Rayleigh-Bérnard bifurcation has been studied in detail by [1]. This author used a local method reducing to an ordinary differential equation the original system. A similar approach of this phenomenon is still convenient when the data are no longer uniform. We specify conditions under which the coupling between the small non-uniformity in the data and the Rayleigh-Bénard bifurcation is able to replace it by a smooth transition towards convection. Then, the displacement of the conduction state privileges some configurations among different patterns which would compete in the uniform case. Circumstances are put forward, where non-uniform data impose a convective steady state that is different from the one which should be forecasted in the homogeneous case from the values of the aspect ratios of the layer. Quite different results are obtained when the heterogeneity in the data doesn't disturb the action of the symmetries of the problem on the most unstable eigenmodes. Then, convection still sets in when a conductive state destabilizes. But the critical value of the filtration Rayleigh number can be displaced. And time-periodic solutions even can appear. An example is provided, where a non-uniform but time-independent temperature on the bottom of the layer leads to an oscillatory behaviour, when the Rayleigh number just over-goes its critical value.

An ad hoc approach to the implementation of polymorphism

article Free Access Share on An ad hoc approach to the implementation of polymorphism Authors: R. Morrison Univ. of St. Andrews, Fife, Scotland, UK Univ. of St. Andrews, Fife, Scotland, UKView Profile , A. Dearle Univ. of St. Andrews, Fife, Scotland, UK Univ. of St. Andrews, Fife, Scotland, UKView Profile , R. C. H. Connor Univ. of St. Andrews, Fife, Scotland, UK Univ. of St. Andrews, Fife, Scotland, UKView Profile , A. L. Brown Univ. of St. Andrews, Fife, Scotland, UK Univ. of St. Andrews, Fife, Scotland, UKView Profile Authors Info & Claims ACM Transactions on Programming Languages and SystemsVolume 13Issue 3July 1991 pp 342–371https://doi.org/10.1145/117009.117017Published:01 July 1991Publication History 45citation656DownloadsMetricsTotal Citations45Total Downloads656Last 12 Months46Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Analytical modelling of a hierarchical buffer for a data sharing environment

In a data sharing environment, where a number of loosely coupled computing nodes share a common storage subsystem, the effectiveness of a private buffer at each node is limited due to the multi-system invalidation effect, particularly under a non-uniform data access pattern. A global shared buffer can be introduced to alleviate this problem either as a disk cache or shared memory. In this paper we developed an approximate analytic model to evaluate different shared buffer management policies (SBMPs) which differ in their choice of data granules to be put into the shared buffer. The analytic model can be used to study the trade-offs of different SBMPs and the impact of different buffer allocations between shared and private buffers. The effects of various parameters, such as, the probability of update, the number of nodes, the sizes of private and shared buffer, etc., on the performance of SBMPS are captured in the analytic model. A detailed simulation model is also developed to validate the analytic model. We show that dependency between the contents of the private and shared buffers can play an important role in determining the effectiveness of the shared buffer particularly for a small number of nodes.