601,340 research outputs found
PhyloPars: estimation of missing parameter values using phylogeny
A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible. The PhyloPars web server provides a statistically consistent method that combines an incomplete set of empirical observations with the species phylogeny to produce a complete set of parameter estimates for all species. It builds upon a state-of-the-art evolutionary model, extended with the ability to handle missing data. The resulting approach makes optimal use of all available information to produce estimates that can be an order of magnitude more accurate than ad-hoc alternatives. Uploading a phylogeny and incomplete feature matrix suffices to obtain estimates of all missing values, along with a measure of certainty. Real-time cross-validation provides further insight in the accuracy and bias expected for estimated values. The server allows for easy, efficient estimation of metabolic parameters, which can benefit a wide range of fields including systems biology and ecology. PhyloPars is available at: http://www.ibi.vu.nl/programs/phylopars/
PERANCANGAN SISTEM INFORMASI AKUNTANSI (Studi kasus pada CV. MITRA TANINDO)
This study examined the Accounting Information System Design in CV.
Mitra Tanindo. This research was conducted to determine the good and the
weakness of Accounting Information System in the company and provide
recommendations to the Accounting Information Systems.
This study uses a case study approach. Method of data collection is done
by direct observation in the object of research, namely CV. Mitra Tanindo to
obtain data suitable data collection techniques. The data used in this research is
the primary data is data obtained directly from the company through interviews,
observation and documentation was processed and concluded. The analysis used
in this study is a qualitative analysis; the analysis carried out by finding strengths
and weaknesses of Accounting Information Systems is in the company.
The result of this research is there are still some weaknesses were found.
At the organizational structure, the overlap in the administrative tasks.
Accounting Information System Goods Purchase of Trade, there is no document
request merchandise purchase. Sales Accounting Information System of Cash, the
transportation section does not accept Cash Sign Documents evidence that can
not be compared with cash sales invoice before the goods delivered to the buyer.
Accounting Information System of Credit Sales, Credit Sales Invoice Document
incomplete. Payroll Accounting Information Systems ie, No Statement of Wages
as payroll documents for employees.
Based on these weaknesses, researchers gave suggestions for the design
of CV. Accounting Information Systems. Mitra Tanindo. Organizational Structure
of the Company, Making purchasing functions and sales functions. Accounting
Information System Goods Purchase of Trade, Letter of Inquiry Making a
document to record the purchase of merchandise required. Accounting
Information System of Cash Sales, received the document transport section
Invoice Cash Sales-2 and receive Cash Sales Invoice-1 and Evidence from the
Treasury in order to transport compared before sending the goods to the buyer.
Accounting Information System of Credit Sales, Cash Sales Invoice five copies
made so that the credit-related sales to get this document. Payroll Accounting
Information System is, making the Declaration of Wages.
Keywords:
Accounting Information Systems, Purchase of merchandise,
merchandise supply records, merchandise sales and payroll
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
Global estimation of child mortality using a Bayesian B-spline Bias-reduction model
Estimates of the under-five mortality rate (U5MR) are used to track progress
in reducing child mortality and to evaluate countries' performance related to
Millennium Development Goal 4. However, for the great majority of developing
countries without well-functioning vital registration systems, estimating the
U5MR is challenging due to limited data availability and data quality issues.
We describe a Bayesian penalized B-spline regression model for assessing levels
and trends in the U5MR for all countries in the world, whereby biases in data
series are estimated through the inclusion of a multilevel model to improve
upon the limitations of current methods. B-spline smoothing parameters are also
estimated through a multilevel model. Improved spline extrapolations are
obtained through logarithmic pooling of the posterior predictive distribution
of country-specific changes in spline coefficients with observed changes on the
global level. The proposed model is able to flexibly capture changes in U5MR
over time, gives point estimates and credible intervals reflecting potential
biases in data series and performs reasonably well in out-of-sample validation
exercises. It has been accepted by the United Nations Inter-agency Group for
Child Mortality Estimation to generate estimates for all member countries.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS768 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
On large-scale diagonalization techniques for the Anderson model of localization
We propose efficient preconditioning algorithms for an eigenvalue problem arising in quantum physics, namely the computation of a few interior eigenvalues and their associated eigenvectors for large-scale sparse real and symmetric indefinite matrices of the Anderson model
of localization. We compare the Lanczos algorithm in the 1987 implementation by Cullum and Willoughby with the shift-and-invert techniques in the implicitly restarted Lanczos method and in the JacobiāDavidson method. Our preconditioning approaches for the shift-and-invert symmetric indefinite linear system are based on maximum weighted matchings and algebraic multilevel incomplete
LDLT factorizations. These techniques can be seen as a complement to the alternative idea of using more complete pivoting techniques for the highly ill-conditioned symmetric indefinite Anderson matrices. We demonstrate the effectiveness and the numerical accuracy of these algorithms. Our numerical examples reveal that recent algebraic multilevel preconditioning solvers can accelerate the computation of a large-scale eigenvalue problem corresponding to the Anderson model of localization
by several orders of magnitude
The Matrix Ridge Approximation: Algorithms and Applications
We are concerned with an approximation problem for a symmetric positive
semidefinite matrix due to motivation from a class of nonlinear machine
learning methods. We discuss an approximation approach that we call {matrix
ridge approximation}. In particular, we define the matrix ridge approximation
as an incomplete matrix factorization plus a ridge term. Moreover, we present
probabilistic interpretations using a normal latent variable model and a
Wishart model for this approximation approach. The idea behind the latent
variable model in turn leads us to an efficient EM iterative method for
handling the matrix ridge approximation problem. Finally, we illustrate the
applications of the approximation approach in multivariate data analysis.
Empirical studies in spectral clustering and Gaussian process regression show
that the matrix ridge approximation with the EM iteration is potentially
useful
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A comparative survey of integrated learning systems
This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework
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