4,965 research outputs found
Cancer subtype identification pipeline: a classifusion approach
Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers
A single molecule switch based on two Pd nanocrystals linked by a conjugated dithiol
Tunneling spectroscopy measurements have been carried out on a single
molecule device formed by two Pd nanocrystals (dia, 5 nm) electronically
coupled by a conducting molecule, dimercaptodiphenylacetylene. The I-V data,
obtained by positioning the tip over a nanocrystal electrode, exhibit negative
differential resistance (NDR) on a background M-I-M characteristics. The NDR
feature occurs at 0.67 V at 300 K and shifts to a higher bias of 1.93 V
at 90 K. When the tip is held in the middle region of the device, a coulomb
blockade region is observed (0.3 V).Comment: Accepted in Praman
Soliton turbulences in the complex Ginzburg-Landau equation
We study spatio-temporal chaos in the complex Ginzburg-Landau equation in
parameter regions of weak amplification and viscosity. Turbulent states
involving many soliton-like pulses appear in the parameter range, because the
complex Ginzburg-Landau equation is close to the nonlinear Schr\"odinger
equation. We find that the distributions of amplitude and wavenumber of pulses
depend only on the ratio of the two parameters of the amplification and the
viscosity. This implies that a one-parameter family of soliton turbulence
states characterized by different distributions of the soliton parameters
exists continuously around the completely integrable system.Comment: 5 figure
Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures
Aggregation operators are mathematical functions that enable the fusion of information from multiple sources. Fuzzy Integrals (FIs) are widely used aggregation operators, which combine information in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. However, FIs suffer from the potential drawback of not fusing information according to the intuitively interpretable FM, leading to non-intuitive results. The latter is particularly relevant when a FM has been defined using external information (e.g. experts). In order to address this and provide an alternative to the FI, the Recursive Average (RAV) aggregation operator was recently proposed which enables intuitive data fusion in respect to a given FM. With an alternative fusion operator in place, in this paper, we define the concept of ‘A Priori’ FMs which are generated based on external information (e.g. classification accuracy) and thus provide an alternative to the traditional approaches of learning or manually specifying FMs. We proceed to develop one specific instance of such an a priori FM to support the decision level fusion step in ensemble classification. We evaluate the resulting approach by contrasting the performance of the ensemble classifiers for different FMs, including the recently introduced Uriz and the Sugeno lambda-measure; as well as by employing both the Choquet FI and the RAV as possible fusion operators. Results are presented for 20 datasets from machine learning repositories and contextualised to the wider literature by comparing them to state-of-the-art ensemble classifiers such as Adaboost, Bagging, Random Forest and Majority Voting
Monte Carlo simulations of pulse propagation in massive multichannel optical fiber communication systems
We study the combined effect of delayed Raman response and bit pattern
randomness on pulse propagation in massive multichannel optical fiber
communication systems. The propagation is described by a perturbed stochastic
nonlinear Schr\"odinger equation, which takes into account changes in pulse
amplitude and frequency as well as emission of continuous radiation. We perform
extensive numerical simulations with the model, and analyze the dynamics of the
frequency moments, the bit-error-rate, and the mutual distribution of amplitude
and position. The results of our numerical simulations are in good agreement
with theoretical predictions based on the adiabatic perturbation approach.Comment: Submitted to Physical Review E. 8 pages, 5 figure
The Triple-Alpha Process and the Anthropically Allowed Values of the Weak Scale
In multiple-universe models, the constants of nature may have different
values in different universes. Agrawal, Barr, Donoghue and Seckel have pointed
out that the Higgs mass parameter, as the only dimensionful parameter of the
standard model, is of particular interest. By considering a range of values of
this parameter, they showed that the Higgs vacuum expectation value must have a
magnitude less than 5.0 times its observed value, in order for complex
elements, and thus life, to form. In this report, we look at the effects of the
Higgs mass parameter on the triple-alpha process in stars. This process, which
is greatly enhanced by a resonance in Carbon-12, is responsible for virtually
all of the carbon production in the universe. We find that the Higgs vacuum
expectation value must have a magnitude greater than 0.90 times its observed
value in order for an appreciable amount of carbon to form, thus significantly
narrowing the allowed region of Agrawal et al.Comment: 9 pages, 1 figur
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Simulation Model and Analysis of a Data Warehouse
Data warehouses are large complex systems with many interacting nonlinear components. It is an amalgamation of many different systems and integration of such diverse elements is its primary concern. It is often difficult for a data warehouse manager to predict the performance of the system especially when demand pattern for the required data keeps changing. We have developed a simulation model using ARENA simulation package that will simulate the behavior and performance of a data warehouse system environment based on its overall design. Given such a model, a data warehouse manager can walk through various what-if scenarios and can pinpoint the areas of weaknesses in the system. This visibility could result in improved operational performance in a data warehouse
Automatic eduction and statistical analysis of coherent structures in the wall region of a confine plane
This paper describes a vortex detection algorithm used to expose and statistically characterize the
coherent flow patterns observable in the velocity vector fields measured by Particle Image
Velocimetry (PIV) in the impingement region of air curtains. The philosophy and the architecture of
this algorithm are presented. Its strengths and weaknesses are discussed. The results of a
parametrical analysis performed to assess the variability of the response of our algorithm to the 3
user-specified parameters in our eduction scheme are reviewed. The technique is illustrated in the
case of a plane turbulent impinging twin-jet with an opening ratio of 10. The corresponding jet
Reynolds number, based on the initial mean flow velocity U0 and the jet width e, is 14000. The
results of a statistical analysis of the size, shape, spatial distribution and energetic content of the
coherent eddy structures detected in the impingement region of this test flow are provided.
Although many questions remain open, new insights into the way these structures might form,
organize and evolve are given. Relevant results provide an original picture of the plane turbulent
impinging jet
Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms
The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study
Approximating Multilinear Monomial Coefficients and Maximum Multilinear Monomials in Multivariate Polynomials
This paper is our third step towards developing a theory of testing monomials
in multivariate polynomials and concentrates on two problems: (1) How to
compute the coefficients of multilinear monomials; and (2) how to find a
maximum multilinear monomial when the input is a polynomial. We
first prove that the first problem is \#P-hard and then devise a
upper bound for this problem for any polynomial represented by an arithmetic
circuit of size . Later, this upper bound is improved to for
polynomials. We then design fully polynomial-time randomized
approximation schemes for this problem for polynomials. On the
negative side, we prove that, even for polynomials with terms of
degree , the first problem cannot be approximated at all for any
approximation factor , nor {\em "weakly approximated"} in a much relaxed
setting, unless P=NP. For the second problem, we first give a polynomial time
-approximation algorithm for polynomials with terms of
degrees no more a constant . On the inapproximability side, we
give a lower bound, for any on the
approximation factor for polynomials. When terms in these
polynomials are constrained to degrees , we prove a lower
bound, assuming ; and a higher lower bound, assuming the
Unique Games Conjecture
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