463 research outputs found
Dimension-adaptive bounds on compressive FLD Classification
Efficient dimensionality reduction by random projections (RP) gains popularity, hence the learning guarantees achievable in RP spaces are of great interest. In finite dimensional setting, it has been shown for the compressive Fisher Linear Discriminant (FLD) classifier that forgood generalisation the required target dimension grows only as the log of the number of classes and is not adversely affected by the number of projected data points. However these bounds depend on the dimensionality d of the original data space. In this paper we give further guarantees that remove d from the bounds under certain conditions of regularity on the data density structure. In particular, if the data density does not fill the ambient space then the error of compressive FLD is independent of the ambient dimension and depends only on a notion of âintrinsic dimension'
Robert Merle dâAubignĂ©, 1900â1989
This biographical sketch of R. Merle dâAubignĂ© corresponds to the historic text, The Classic: Functional Results of Hip Arthroplasty with Acrylic Prosthesis, available at DOI 10.1007/s11999-008-0572-1
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
Breast cancer is one of the most common types of cancer and leading
cancer-related death causes for women. In the context of ICIAR 2018 Grand
Challenge on Breast Cancer Histology Images, we compare one handcrafted feature
extractor and five transfer learning feature extractors based on deep learning.
We find out that the deep learning networks pretrained on ImageNet have better
performance than the popular handcrafted features used for breast cancer
histology images. The best feature extractor achieves an average accuracy of
79.30%. To improve the classification performance, a random forest
dissimilarity based integration method is used to combine different feature
groups together. When the five deep learning feature groups are combined, the
average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted
features are combined with the five deep learning feature groups, the average
accuracy is improved to 87.10% (best accuracy 93.00%)
Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields
We prove the asymptotic normality of the kernel density estimator (introduced
by Rosenblatt (1956) and Parzen (1962)) in the context of stationary strongly
mixing random fields. Our approach is based on the Lindeberg's method rather
than on Bernstein's small-block-large-block technique and coupling arguments
widely used in previous works on nonparametric estimation for spatial
processes. Our method allows us to consider only minimal conditions on the
bandwidth parameter and provides a simple criterion on the (non-uniform) strong
mixing coefficients which do not depend on the bandwith.Comment: 16 page
Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs
Many real world data are sampled functions. As shown by Functional Data
Analysis (FDA) methods, spectra, time series, images, gesture recognition data,
etc. can be processed more efficiently if their functional nature is taken into
account during the data analysis process. This is done by extending standard
data analysis methods so that they can apply to functional inputs. A general
way to achieve this goal is to compute projections of the functional data onto
a finite dimensional sub-space of the functional space. The coordinates of the
data on a basis of this sub-space provide standard vector representations of
the functions. The obtained vectors can be processed by any standard method. In
our previous work, this general approach has been used to define projection
based Multilayer Perceptrons (MLPs) with functional inputs. We study in this
paper important theoretical properties of the proposed model. We show in
particular that MLPs with functional inputs are universal approximators: they
can approximate to arbitrary accuracy any continuous mapping from a compact
sub-space of a functional space to R. Moreover, we provide a consistency result
that shows that any mapping from a functional space to R can be learned thanks
to examples by a projection based MLP: the generalization mean square error of
the MLP decreases to the smallest possible mean square error on the data when
the number of examples goes to infinity
Axial Skeletal Location Predicts Poor Outcome in Ewing's Sarcoma: A Single Institution Experience
Introduction. Ewing's sarcomas (EWSs) of bone and soft tissue are neuroectodermal tumors that affect both axial and appendicular locations. We hypothesized that axial location predicted poor outcome in EWS patients. Materials and Methods. Sixty-seven patients (57 with bone EWS and 10 with soft tissue EWS) were identified from our database. Thirty-four (51%) had axial EWS and 33 (49%) had appendicular EWS. Statistical analyses identified predictors of poor outcome. Results and Discussion. Axial location, large size, metastases at presentation, lack of definitive treatment, and positive surgical margins all correlated with poor outcome in univariate analysis. In multivariate analysis, axial location still predicted poor outcome when adjusted for pretreatment variables. Axial location was not statistically predictive of poor outcome when adjusted for treatment variables. Conclusions. Anatomic location has a negative effect on outcome in EWS that cannot be completely explained by pretreatment or treatment factors. Additional studies are required to determine if there is a biologic difference between axial and appendicular EWS
Nonlinear description of transversal motion in a laminar boundary layer with streaks
The nonlinear streamwise growth of a spanwise periodic array of steady streaks in a flat plate boundary layer is numerically computed using the well known Reduced Navier-Stokes formulation. It is found that the flow configuration changes substantially when the amplitude of the streaks grows and the nonlinear effects come into play. The transversal motion (in the wall normal-spanwise plane), which is normally not considered, becomes non-negligible in the nonlinear regime, and it strongly distorts the streamwise velocity profiles, which end up being quite different from those predicted by the linear theory. We analyze in detail the resulting flow patterns for the nonlinearly saturated streaks, and compare them with available experimental results
The Five Factor Model of personality and evaluation of drug consumption risk
The problem of evaluating an individual's risk of drug consumption and misuse
is highly important. An online survey methodology was employed to collect data
including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11),
sensation seeking (ImpSS), and demographic information. The data set contained
information on the consumption of 18 central nervous system psychoactive drugs.
Correlation analysis demonstrated the existence of groups of drugs with
strongly correlated consumption patterns. Three correlation pleiades were
identified, named by the central drug in the pleiade: ecstasy, heroin, and
benzodiazepines pleiades. An exhaustive search was performed to select the most
effective subset of input features and data mining methods to classify users
and non-users for each drug and pleiad. A number of classification methods were
employed (decision tree, random forest, -nearest neighbors, linear
discriminant analysis, Gaussian mixture, probability density function
estimation, logistic regression and na{\"i}ve Bayes) and the most effective
classifier was selected for each drug. The quality of classification was
surprisingly high with sensitivity and specificity (evaluated by leave-one-out
cross-validation) being greater than 70\% for almost all classification tasks.
The best results with sensitivity and specificity being greater than 75\% were
achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance
abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure
A framework for closed-loop flow control using the parabolized stability equations
We develop a reduced-order-model framework using the parabolized stability equations and identification techniques for the closed-loop control of unsteady fluctuations along fluidic systems. These models had been successfully applied to a turbulent jet as estimation techniques and to an incompressible shear-layer for the development of closed-loop control laws. Through this paper, we propose a further investigation of the PSE-based transfer functions, exploring its flexibility to educe different control schemes and to determine the most effective sensor/actuator positions. Emphasis is be given to the feedforward and feedback configurations for flow control, and differences are understood in terms of causality. A study of the robustness to uncertainties in Reynolds and mean flow velocity, along with external perturbations is also presented. These topics allow deeper insight into the active closed-loop flow control problem and therefore may lead to more effective schemes, particularly on what concerns the experimental implementation of closed-loop control
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