346,234 research outputs found
Незалежність аксіоматики функціональних залежностей армстронга
У роботі показано, що аксіоматика Армстронга щодо функціональних залежностей, яка складається з аксіоми рефлексивності та двох правил виведення, є незалежною в тому розумінні, що без втрати повноти не можна опустити ні єдину аксіому, ні жодне з правил виведення.It is shown that Armstrong’s axiomatic system (as for the functional dependences of relational databases), which consist of the axiom of reflexivity and two inference rules is independent, i.e. completeness of Armstrong’s axiomatic system is violated if removed one of its components
ICA as a preprocessing technique for classification
In this paper we propose the use of the independent component
analysis (ICA) [1] technique for improving the classification rate of decision
trees and multilayer perceptrons [2], [3]. The use of an ICA for the preprocessing
stage, makes the structure of both classifiers simpler, and therefore
improves the generalization properties. The hypothesis behind the proposed
preprocessing is that an ICA analysis will transform the feature space into a
space where the components are independent, and aligned to the axes and
therefore will be more adapted to the way that a decision tree is constructed.
Also the inference of the weights of a multilayer perceptron will be much easier
because the gradient search in the weight space will follow independent
trajectories. The result is that classifiers are less complex and on some databases
the error rate is lower. This idea is also applicable to regressio
Genome-Wide Survey of MicroRNA - Transcription Factor Feed-Forward Regulatory Circuits in Human
In this work, we describe a computational framework for the genome-wide
identification and characterization of mixed
transcriptional/post-transcriptional regulatory circuits in humans. We
concentrated in particular on feed-forward loops (FFL), in which a master
transcription factor regulates a microRNA, and together with it, a set of joint
target protein coding genes. The circuits were assembled with a two step
procedure. We first constructed separately the transcriptional and
post-transcriptional components of the human regulatory network by looking for
conserved over-represented motifs in human and mouse promoters, and 3'-UTRs.
Then, we combined the two subnetworks looking for mixed feed-forward regulatory
interactions, finding a total of 638 putative (merged) FFLs. In order to
investigate their biological relevance, we filtered these circuits using three
selection criteria: (I) GeneOntology enrichment among the joint targets of the
FFL, (II) independent computational evidence for the regulatory interactions of
the FFL, extracted from external databases, and (III) relevance of the FFL in
cancer. Most of the selected FFLs seem to be involved in various aspects of
organism development and differentiation. We finally discuss a few of the most
interesting cases in detail.Comment: 51 pages, 5 figures, 4 tables. Supporting information included.
Accepted for publication in Molecular BioSystem
Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange
Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version
Faster Query Answering in Probabilistic Databases using Read-Once Functions
A boolean expression is in read-once form if each of its variables appears
exactly once. When the variables denote independent events in a probability
space, the probability of the event denoted by the whole expression in
read-once form can be computed in polynomial time (whereas the general problem
for arbitrary expressions is #P-complete). Known approaches to checking
read-once property seem to require putting these expressions in disjunctive
normal form. In this paper, we tell a better story for a large subclass of
boolean event expressions: those that are generated by conjunctive queries
without self-joins and on tuple-independent probabilistic databases. We first
show that given a tuple-independent representation and the provenance graph of
an SPJ query plan without self-joins, we can, without using the DNF of a result
event expression, efficiently compute its co-occurrence graph. From this, the
read-once form can already, if it exists, be computed efficiently using
existing techniques. Our second and key contribution is a complete, efficient,
and simple to implement algorithm for computing the read-once forms (whenever
they exist) directly, using a new concept, that of co-table graph, which can be
significantly smaller than the co-occurrence graph.Comment: Accepted in ICDT 201
Image metadata estimation using independent component analysis and regression
In this paper, we describe an approach to camera metadata estimation using regression based on Independent Component Analysis (ICA). Semantic scene classification of images using camera metadata related to capture conditions has had some success in the past. However, different makes and models of camera capture different types of metadata and this severely hampers the application of this kind of approach in real systems that consist of photos captured by many different users. We propose to address this issue by using regression to predict the missing metadata from observed data, thereby providing more complete (and hence more useful) metadata for the entire image corpus. The proposed approach uses an ICA based approach to regression
Computer science in Dutch secondary education: independent or integrated?
Nowadays, in Dutch secondary education, computer science is integrated within school subjects. About ten years ago computer science was considered an independent subject, but in the mid-1980s this idea changed. In our study we investigated whether the objectives of teaching computer science as an independent subject are met when computer science is integrated within school subjects. The main problem was that there was no formal curriculum of computer science as an independent subject. Therefore we interviewed 13 experts in the field of computer science and then compared this formal curriculum with the operational (integrated) curriculum, which is still in the development stage. It appears that most of the components of the formal curriculum are being covered by the operational curriculum, and we therefore concluded that these curricula are equivalent, although there may be differences in the level of teaching. In our opinion the best approach to computer science is to combine the independent and the integrated approaches
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