50 research outputs found
Efficient numerical techniques for perspective shape from shading
The shape-from-shading (SfS) problem is a classic problem in computer vision. The task in SfS is to compute on the basis of the shading variation in a given 2-D image the 3-D depth of the depicted scene. The corresponding mathematical model eventually leads to a boundary value problem for a Hamilton-Jacobi equation. In this paper we evaluate and compare suitable numerical methods. We begin with a brief discussion of four state-of-the-art-approaches in this field. Then we give an extensive numerical comparison, thus evaluating recent improvements in this area. In the course of doing this, we introduce efficient variations of existing schemes. By this systematic investigation, we complement and extend previous works on the numerical side. The paper is finished by a conclusion
Discovering Knowledge from Local Patterns with Global Constraints
It is well known that local patterns are at the core of a lot of
knowledge which may be discovered from data. Nevertheless, use of local
patterns is limited by
their huge number and computational costs. Several approaches (e.g.,
condensed representations, pattern set discovery) aim at grouping or
synthesizing local patterns to provide a global view of the data. A
global pattern is a pattern which is a set or a synthesis of local
patterns coming from the data. In this paper, we propose the idea of
global constraints to write queries addressing global patterns. A key
point is the ability to bias the designing of global patterns according
to the expectation of the user. For instance, a global pattern can be
oriented towards the search of exceptions or a clustering. It requires
to write queries taking into account such biases. Open issues are to
design a generic framework to express powerful global constraints and
solvers to mine them. We think that global constraints are a promising
way to discover relevant global patterns
Perspective shape from shading for Phong-type non-Lambertian surfaces
The shape-from-shading (SfS) problem in computer vision is to compute at hand of the shading variation in a given 2-D image the 3-D structure of depicted objects. We introduce an efficient numerical method for a new perspective SfS model for general non-Lambertian surfaces. First, the modelling process is given in detail. The model is based on the perspective model for Lambertian surfaces recently studied by Prados et al., which we extend by use of the Phong reflection model incorporating ambient, diffuse and specular components. The arising partial differential equation (PDE) is a non-linear time-independent Hamilton-Jacobi equation. In order to compute the sought viscosity supersolution of the PDE, we introduce an artificial time into the equation and solve for the steady state. Based on a multi-scale analysis of the PDE, we construct a fully explicit numerical method and elaborate on its stability. In order to achieve fast convergence of the resulting iterative scheme, a coarse-to-fine strategy combined with a sweeping technique is employed. Numerical experiments show the benefits of our approach: While computational times stay reasonable even for quite large images, a substantial qualitative gain can be achieved by use of the new model. Moreover, the computational technique is relatively easy to implement compared to other approaches in the field
Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems
Decision trees and decision rule systems play important roles as classifiers,
knowledge representation tools, and algorithms. They are easily interpretable
models for data analysis, making them widely used and studied in computer
science. Understanding the relationships between these two models is an
important task in this field. There are well-known methods for converting
decision trees into systems of decision rules. In this paper, we consider the
inverse transformation problem, which is not so simple. Instead of constructing
an entire decision tree, our study focuses on a greedy polynomial time
algorithm that simulates the operation of a decision tree on a given tuple of
attribute values.Comment: arXiv admin note: substantial text overlap with arXiv:2305.01721,
arXiv:2302.0706
Recommended from our members
Linkage between scattering rates and superconductivity in doped ferropnictides
We report an angle-resolved photoemission study of a series of hole- and electron-doped iron-based superconductors, their parent compound BaFe2As2, and their cousins BaCr2As2 and BaCo2As2. We focus on the inner hole pocket, which is the hot spot in these compounds. More specifically, we determine the energy (E)-dependent scattering rate Γ(E) as a function of the 3d count. Moreover, for the compounds K0.4Ba0.6Fe2As2 and BaCr2As2, we derive the energy dependence of the renormalization function Z(E) and the imaginary part of the self-energy function ImΣ(E). We obtain a non-Fermi liquidlike linear in energy scattering rate Γ(E≫kBT), independent of the dopant concentration. The main result is that the slope β=Γ(E≫kBT)/E reaches its maxima near optimal doping and scales with the superconducting transition temperature. This supports the spin fluctuation model for superconductivity for these materials. In the optimally hole-doped compound, the slope of the scattering rate of the inner hole pocket is about three times bigger than the Planckian limit Γ(E)/E≈1. This result, together with the energy dependence of the renormalization function Z(E), signals very incoherent charge carriers in the normal state which transform at low temperatures to a coherent unconventional superconducting state
Sequential pattern mining for discovering gene interactions and their contextual information from biomedical texts
International audienceBackgroundDiscovering gene interactions and their characterizations from biological text collections is a crucial issue in bioinformatics. Indeed, text collections are large and it is very difficult for biologists to fully take benefit from this amount of knowledge. Natural Language Processing (NLP) methods have been applied to extract background knowledge from biomedical texts. Some of existing NLP approaches are based on handcrafted rules and thus are time consuming and often devoted to a specific corpus. Machine learning based NLP methods, give good results but generate outcomes that are not really understandable by a user.ResultsWe take advantage of an hybridization of data mining and natural language processing to propose an original symbolic method to automatically produce patterns conveying gene interactions and their characterizations. Therefore, our method not only allows gene interactions but also semantics information on the extracted interactions (e.g., modalities, biological contexts, interaction types) to be detected. Only limited resource is required: the text collection that is used as a training corpus. Our approach gives results comparable to the results given by state-of-the-art methods and is even better for the gene interaction detection in AIMed.ConclusionsExperiments show how our approach enables to discover interactions and their characterizations. To the best of our knowledge, there is few methods that automatically extract the interactions and also associated semantics information. The extracted gene interactions from PubMed are available through a simple web interface at https://bingotexte.greyc.fr/ webcite. The software is available at https://bingo2.greyc.fr/?q=node/22 webcite
Evolutionary divergence in the fungal response to fluconazole revealed by soft clustering
Background: Fungal infections are an emerging health risk, especially those involving yeast that are resistant to antifungal agents. To understand the range of mechanisms by which yeasts can respond to anti-fungals, we compared gene expression patterns across three evolutionarily distant species- Saccharomyces cerevisiae, Candida glabrata and Kluyveromyces lactis- over time following fluconazole exposure. Results: Conserved and diverged expression patterns were identified using a novel soft clustering algorithm that concurrently clusters data from all species while incorporating sequence orthology. The analysis suggests complementary strategies for coping with ergosterol depletion by azoles- Saccharomyces imports exogenous ergosterol, Candida exports fluconazole, while Kluyveromyces does neither, leading to extreme sensitivity. In support of this hypothesis we find that only Saccharomyces becomes more azole resistant in ergosterol-supplemented media; that this depends on sterol importers Aus1 and Pdr11; and that transgenic expression of sterol importers in Kluyveromyces alleviates its drug sensitivity. Conclusions: We have compared the dynamic transcriptional responses of three diverse yeast species to fluconazole treatment using a novel clustering algorithm. This approach revealed significant divergence among regulatory programs associated with fluconazole sensitivity. In future, such approaches might be used to survey
Data mining languages for business intelligence
Doctoral Thesis in Information Systems and Technologies Area of Engineering and Manag
ement Information SystemsDesde que Lunh usou, pela primeira vez, em 1958, o
termo Business Intelligence (BI), grandes
transformações se operaram na área dos sistemas e t
ecnologias de informação e, em especial,
na área dos sistemas de apoio à decisão. Atualmente
, os sistemas de BI são amplamente
utilizados nas organizações e a sua importância est
ratégica é largamente reconhecida. Estes
sistemas apresentam-se como essenciais para um comp
leto conhecimento do negócio e como
uma ferramenta insubstituível no apoio à tomada de
decisão. A divulgação das ferramentas de
Data Mining (DM) tem vindo a aumentar na área do BI, assim como o reconhecimento da
relevância da sua utilização nos sistemas de BI emp
resariais.
As ferramentas de BI são ferramentas amigáveis, ite
rativas e interativas, permitindo aos
utilizadores finais um acesso fácil. Desta forma, é
possível ao utilizador final manipular
diretamente os dados, tendo assim a possibilidade d
e extrair todo o valor para o negócio neles
contido. Um dos problemas apontados na utilização d
o DM na área do BI prende-se com o facto
de os modelos de DM serem, em geral, demasiado comp
lexos para que os utilizadores de
negócio os possam manipular diretamente, contrariam
ente ao que ocorre com as outras
ferramentas de BI.
Neste contexto, foi identificado como problema de i
nvestigação a não existência de ferramentas
de BI que possibilitem ao utilizador de negócio a m
anipulação direta dos modelos de DM e,
consequentemente, não possibilitando extrair todo o
valor potencial neles contidos. Este aspeto
reveste-se de particular importância num universo e
mpresarial no qual a concorrência é cada vez
mais forte e no qual o conhecimento do negócio, das
variáveis envolvidas e dos potenciais
cenários representam um papel fundamental para as o
rganizações poderem concorrer num
mercado extremamente exigente.
Considerando que os sistemas de BI assentam, maiori
tariamente, sobre sistemas operacionais
que utilizam sobretudo o modelo relacional de bases
de dados, a investigação efetuada inspirou-
se nos conceitos ligados ao modelo relacional de ba
ses de dados e nas linguagens a ele
associadas em particular as linguagens Query-By-Exa
mple (QBE). Estas linguagens têm uma
forte componente de interactividade, são amigáveis
e permitem iteratividade e são amplamente
utilizadas em ambiente de negócio pelos utilizadore
s finais.
Têm vindo a ser desenvolvidos esforços no sentido d
o desenvolvimento de padrões e normas na
área do DM, sendo dada grande relevância ao tema da
s bases de dados indutivas. No contexto
Data mining languages for business intelligence
iv
das bases de dados indutivas é dada grande relevânc
ia às chamadas linguagens de DM. Estes
conceitos serviram, igualmente, de inspiração a est
a investigação. Apesar da importância destas
linguagens de DM, elas não estão orientadas para os
utilizadores finais em ambientes de
negócio.
Ligando os conceitos relacionados com as linguagens
QBE e com as linguagens de DM, foi
concebida e implementada uma linguagem de DM para B
I, à qual foi dado o nome QMBE. Esta
nova linguagem é por natureza amigável, iterativa e
interativa, isto é, apresenta as mesmas
características que as ferramentas de BI habituais
permitindo aos utilizadores finais a
manipulação direta dos modelos de DM e, deste modo,
aceder a todo o valor potencial desses
modelos com todos as vantagens que daí poderão advi
r. Utilizando um protótipo de um sistema
de BI, a linguagem foi implementada, testada e aval
iada conceptualmente. Verificou-se que a
linguagem possui as propriedades desejadas, a saber
, é amigável, iterativa, interativa.
Finalmente, a linguagem foi avaliada por utilizador
es finais que já tinham experiência anterior na
utilização de DM em contexto de BI. Verificou-se qu
e na ótica destes utilizadores a utilização da
linguagem apresenta vantagens em relação à utilizaç
ão tradicional de DM no âmbito do BI.Since Lunh first used the term Business Intelligenc
e (BI) in 1958, major transformations
happened in the field of information systems and te
chnologies, especially in the area of decision
support systems. Nowadays, BI systems are widely us
ed in organizations and their strategic
importance is clearly recognized. These systems pre
sent themselves as an essential part of a
complete knowledge of business and an irreplaceable
tool in the support to decision making. The
dissemination of data mining (DM) tools is increasi
ng in the BI field, as well as the
acknowledgement of the relevance of its usage in en
terprise BI systems.
BI tools are friendly, iterative and interactive, a
llowing business users an easy access. This way,
the user can directly manipulate data, thus having
the possibility to extract all the value contained
into that business data. One of the problems noted
in the use of DM in the field of BI is related to
the fact that DM models are, generally, too complex
in order to be directly manipulated by
business users, as opposite to other BI tools.
Within this context, the nonexistence of BI tools a
llowing business users the direct manipulation
of DM models was identified as the research problem
, since that, as a consequence of business
users not directly manipulating DM models, they can
be not able of extracting all the potential
value contained in DM models. This aspect has a par
ticular relevance in an entrepreneurial
universe where competition is stronger every day an
d the knowledge of the business, the
variables involved and the possible scenarios play
a fundamental role in allowing organizations to
compete in an extremely demanding market.
Considering that the majority of BI systems are bui
lt on top of operational systems, which use
mainly the relational model for databases, the rese
arch was inspired on the concepts related to
this model and associated languages in particular Q
uery-By-Example (QBE) languages. These
languages are widely used by business users in busi
ness environments, and have got a strong
interactivity component, are user-friendly, and all
ow for iterativeness.
Efforts are being developed in order to create stan
dards and rules in the field of DM with great
relevance being given to the subject of inductive d
atabases. Within the context of inductive
databases a great relevance is given to the so call
ed DM languages. These concepts were also an
inspiration for this research. Despite their import
ance, these languages are not oriented to
business users in business environments.
Data mining languages for business intelligence
vi
Linking concepts related with QBE languages and wit
h DM languages, a new DM language for BI,
named as Query-Models-By-Example (QMBE) was conceiv
ed and implemented. This new
language is, by nature, user-friendly, iterative an
d interactive; it presents the same characteristics
as the usual BI tools allowing business users the d
irect manipulation of DM models and, through
this, the access to the potential value of these mo
dels with all the advantages that may arise.
Using a BI system prototype, the language was imple
mented, tested, and conceptually evaluated.
It has been verified that the language possesses th
e desired properties, namely, being user-
friendly, iterative, and interactive. The language
was evaluated later by business users who were
already experienced in using DM within the context
of BI. It has been verified that, according to
these users, using the language presents advantages
when comparing to the traditional use of
DM within BI
Data mining languages for business intelligence
Tese de doutoramento in Information Systems and Technologies (area of Engineering and Management Information Systems)Desde que Lunh usou, pela primeira vez, em 1958, o termo Business Intelligence (BI), grandes
transformações se operaram na área dos sistemas e tecnologias de informação e, em especial,
na área dos sistemas de apoio à decisão. Atualmente, os sistemas de BI são amplamente
utilizados nas organizações e a sua importância estratégica é largamente reconhecida. Estes
sistemas apresentam-se como essenciais para um completo conhecimento do negócio e como
uma ferramenta insubstituível no apoio à tomada de decisão. A divulgação das ferramentas de
Data Mining (DM) tem vindo a aumentar na área do BI, assim como o reconhecimento da
relevância da sua utilização nos sistemas de BI empresariais.
As ferramentas de BI são ferramentas amigáveis, iterativas e interativas, permitindo aos
utilizadores finais um acesso fácil. Desta forma, é possível ao utilizador final manipular
diretamente os dados, tendo assim a possibilidade de extrair todo o valor para o negócio neles
contido. Um dos problemas apontados na utilização do DM na área do BI prende-se com o facto
de os modelos de DM serem, em geral, demasiado complexos para que os utilizadores de
negócio os possam manipular diretamente, contrariamente ao que ocorre com as outras
ferramentas de BI.
Neste contexto, foi identificado como problema de investigação a não existência de ferramentas
de BI que possibilitem ao utilizador de negócio a manipulação direta dos modelos de DM e,
consequentemente, não possibilitando extrair todo o valor potencial neles contidos. Este aspeto
reveste-se de particular importância num universo empresarial no qual a concorrência é cada vez
mais forte e no qual o conhecimento do negócio, das variáveis envolvidas e dos potenciais
cenários representam um papel fundamental para as organizações poderem concorrer num
mercado extremamente exigente.
Considerando que os sistemas de BI assentam, maioritariamente, sobre sistemas operacionais
que utilizam sobretudo o modelo relacional de bases de dados, a investigação efetuada inspirouse
nos conceitos ligados ao modelo relacional de bases de dados e nas linguagens a ele
associadas em particular as linguagens Query-By-Example (QBE). Estas linguagens têm uma
forte componente de interactividade, são amigáveis e permitem iteratividade e são amplamente
utilizadas em ambiente de negócio pelos utilizadores finais.
Têm vindo a ser desenvolvidos esforços no sentido do desenvolvimento de padrões e normas na
área do DM, sendo dada grande relevância ao tema das bases de dados indutivas. No contexto das bases de dados indutivas é dada grande relevância às chamadas linguagens de DM. Estes
conceitos serviram, igualmente, de inspiração a esta investigação. Apesar da importância destas
linguagens de DM, elas não estão orientadas para os utilizadores finais em ambientes de
negócio.
Ligando os conceitos relacionados com as linguagens QBE e com as linguagens de DM, foi
concebida e implementada uma linguagem de DM para BI, à qual foi dado o nome QMBE. Esta
nova linguagem é por natureza amigável, iterativa e interativa, isto é, apresenta as mesmas
características que as ferramentas de BI habituais permitindo aos utilizadores finais a
manipulação direta dos modelos de DM e, deste modo, aceder a todo o valor potencial desses
modelos com todos as vantagens que daí poderão advir. Utilizando um protótipo de um sistema
de BI, a linguagem foi implementada, testada e avaliada conceptualmente. Verificou-se que a
linguagem possui as propriedades desejadas, a saber, é amigável, iterativa, interativa.
Finalmente, a linguagem foi avaliada por utilizadores finais que já tinham experiência anterior na
utilização de DM em contexto de BI. Verificou-se que na ótica destes utilizadores a utilização da
linguagem apresenta vantagens em relação à utilização tradicional de DM no âmbito do BI.Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations
happened in the field of information systems and technologies, especially in the area of decision
support systems. Nowadays, BI systems are widely used in organizations and their strategic
importance is clearly recognized. These systems present themselves as an essential part of a
complete knowledge of business and an irreplaceable tool in the support to decision making. The
dissemination of data mining (DM) tools is increasing in the BI field, as well as the
acknowledgement of the relevance of its usage in enterprise BI systems.
BI tools are friendly, iterative and interactive, allowing business users an easy access. This way,
the user can directly manipulate data, thus having the possibility to extract all the value contained
into that business data. One of the problems noted in the use of DM in the field of BI is related to
the fact that DM models are, generally, too complex in order to be directly manipulated by
business users, as opposite to other BI tools.
Within this context, the nonexistence of BI tools allowing business users the direct manipulation
of DM models was identified as the research problem, since that, as a consequence of business
users not directly manipulating DM models, they can be not able of extracting all the potential
value contained in DM models. This aspect has a particular relevance in an entrepreneurial
universe where competition is stronger every day and the knowledge of the business, the
variables involved and the possible scenarios play a fundamental role in allowing organizations to
compete in an extremely demanding market.
Considering that the majority of BI systems are built on top of operational systems, which use
mainly the relational model for databases, the research was inspired on the concepts related to
this model and associated languages in particular Query-By-Example (QBE) languages. These
languages are widely used by business users in business environments, and have got a strong
interactivity component, are user-friendly, and allow for iterativeness.
Efforts are being developed in order to create standards and rules in the field of DM with great
relevance being given to the subject of inductive databases. Within the context of inductive
databases a great relevance is given to the so called DM languages. These concepts were also an
inspiration for this research. Despite their importance, these languages are not oriented to
business users in business environments. Linking concepts related with QBE languages and with DM languages, a new DM language for BI,
named as Query-Models-By-Example (QMBE) was conceived and implemented. This new
language is, by nature, user-friendly, iterative and interactive; it presents the same characteristics
as the usual BI tools allowing business users the direct manipulation of DM models and, through
this, the access to the potential value of these models with all the advantages that may arise.
Using a BI system prototype, the language was implemented, tested, and conceptually evaluated.
It has been verified that the language possesses the desired properties, namely, being userfriendly,
iterative, and interactive. The language was evaluated later by business users who were
already experienced in using DM within the context of BI. It has been verified that, according to
these users, using the language presents advantages when comparing to the traditional use of
DM within BI