Online Learning with Ensembles

Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules, however, using an ensemble yields no improvement. This is explained by showing that for any learning rule $f$ a transform $\tilde{f}$ exists, such that a single student using $\tilde{f}$ has the same generalization behaviour as an ensemble of $f$-students.Comment: 8 pages, 1 figure. Submitted to J.Phys.

Dynamical transitions in the evolution of learning algorithms by selection

We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate a sequence of populations of algorithms which can be used by neural networks for supervised learning of a rule that generates examples. In opposition to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process and pay particular attention to the temporal order of appearance of functional structures responsible for the improvements in the learning process, as measured by the generalization capabilities of the resulting algorithms. The effect of such appearances can be described as dynamical phase transitions. The concepts of phenotypic and genotypic entropies, which serve to describe the distribution of fitness in the population and the distribution of symbols respectively, are used to monitor the dynamics. In different runs the phase transitions might be present or not, with the system finding out good solutions, or staying in poor regions of algorithm space. Whenever phase transitions occur, the sequence of appearances are the same. We identify combinations of variables and operators which are useful in measuring experience or performance in rule extraction and can thus implement useful annealing of the learning schedule.Comment: 11 pages, 11 figures, 2 table

Rare lymphoid malignancies of the breast: report of two cases illustrating potential diagnostic techniques.

Two cases of lymphoid malignancy involving the breast are herein presented. Both patients were admitted with a palpable breast mass. Ultrasound demonstrated hypoechoic, ill-defined lesions of the breast in both patients; mammogram also showed spiculated breast densities. Both patients underwent core biopsy, which revealed lymphomatous cells. Total-body evaluation was also performed by computed tomography and positron emission tomography/computed tomography revealing no other fluorodeoxyglucose-avid foci in the first case and supra and subdiaphragmatic disease in the second one

Functional Optimisation of Online Algorithms in Multilayer Neural Networks

We study the online dynamics of learning in fully connected soft committee machines in the student-teacher scenario. The locally optimal modulation function, which determines the learning algorithm, is obtained from a variational argument in such a manner as to maximise the average generalisation error decay per example. Simulations results for the resulting algorithm are presented for a few cases. The symmetric phase plateaux are found to be vastly reduced in comparison to those found when online backpropagation algorithms are used. A discussion of the implementation of these ideas as practical algorithms is given

Solitary pulmonary nodules: Morphological and metabolic characterisation by FDG-PET-MDCT [Nodulo polmonare solitario: Caratterizzazione morfologico-metabolica mediante imaging integrato TCms/FDG-PET]

Purpose. This study was done to analyse the additional morphological and functional information provided by the integration of [18F]-2-fluoro- 2-deoxy-D-glucose positron emission tomography ([18F]-FDG-PET) with contrast-enhanced multidetector computed tomography (MDCT) in the characterisation of indeterminate solitary pulmonary nodules (SPNs). Materials and methods. Fifty-six SPNs, previously classified as indeterminate, were evaluated using a Discovery ST16 PET/CT system (GE Medical Systems) with nonionic iodinated contrast material and [18F]-FDG as a positron emitter. Images were evaluated on a dedicated workstation. Semiquantitative parameters of [18F]-FDG uptake and morphological, volumetric and densitometric parameters before and after contrast administration were analysed. Results were correlated with the histological and follow-up findings. Results. Twenty-six SPNs were malignant and 30 were benign. Malignant lesions at both PET/CT and histology had a mean diameter of 1.8±1.2 cm, a volume doubling time (DT) of 222 days, a mean standardized uptake value (SUV) of 4.7 versus 1.08 in benign lesions and a mean postcontrast enhancement of 44.8 HU as opposed to 4.8 HU in benign nodules. Malignant lesions had a significantly shorter doubling time and significantly greater postcontrast enhancement compared with benign nodules. Based on the SUV and using a cut-off value of >2.5, PET/CT had a sensitivity of 76.9%, specificity of 100%, diagnostic accuracy of 89.2%, positive predictive value (PPV) of 100% and negative predictive value (NPV) of 83.3%. Based on doubling time (cut off <400 days), it had a sensitivity of 76.9%, specificity of 93.3%, accuracy of 85.7%, PPV of 90.9% and NPV of 82.3%. Based on postcontrast enhancement (cut off >15 HU), it had a sensitivity of 92.3%, specificity of 100%, accuracy of 96.4%, PPV of 100% and NPV of 93.7%. Conclusion. PET/CT allows accurate analysis of anatomical/morphological and metabolic/functional correlations of SPN, providing useful data for identifying and locating the disease, for differentiating between malignant and benign nodules and for establishing the aggressiveness and degree of vascularity of pulmonary lesions. Therefore, partly in view of the considerable reduction in time and cost of the single examinations, we believe that PET/CT will gain an increasingly dominant role in the diagnostic and therapeutic approach to lung cancer, especially in the preclinical phase. © 2007 Springer-Verlag

Cryptography based on neural networks - analytical results

Mutual learning process between two parity feed-forward networks with discrete and continuous weights is studied analytically, and we find that the number of steps required to achieve full synchronization between the two networks in the case of discrete weights is finite. The synchronization process is shown to be non-self-averaging and the analytical solution is based on random auxiliary variables. The learning time of an attacker that is trying to imitate one of the networks is examined analytically and is found to be much longer than the synchronization time. Analytical results are found to be in agreement with simulations

Mesoscopic structural organization in fluorinated pyrrolidinium based room temperature ionic liquids

In this contribution the microscopic and mesoscopic structural organization in a series of fluorinated room temperature ionic liquids, based on N-methyl-N-alkylpyrrolidinium cations and on bis(perfluoroalkylsulfonyl)imide anions, is investigated, using a synergy of experimental (X-ray and neutron scattering) and computational (Molecular Dynamics) techniques. The proposed ionic liquids are of high interest as electrolyte media for lithium battery applications. Together with information on their good ion transport properties in conjunction with low viscosity, we also describe the existence of nm-scale spatial organization induced by the segregation of fluorous moieties into domains. This study shows the strong complementarity between X-ray/neutron scattering in detecting the complex segregated morphology in these systems at mesoscopic spatial scales and MD simulations in successfully delivering a robust description of the segregated morphology at atomistic level

Role of combined DWIBS/3D-CE-T1w whole-body MRI in tumor staging: Comparison with PET-CT

Objectives: To assess the diagnostic performance of whole-body magnetic resonance imaging (WB-MRI) by diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) in malignant tumor detection and the potential diagnostic advantages in generating fused DWIBS/3D-contrast enhanced T1w (3D-CE-T1w) images. Methods: 45 cancer patients underwent 18F-FDG PET-CT and WB-MRI for staging purpose. Fused DWIBS/3D-CE T1w images were generated off-line. 3D-CE-T1w, DWIBS images alone and fused with 3D-CE T1w were compared by two readers groups for detection of primary diseases and local/distant metastases. Diagnostic performance between the three WB-MRI data sets was assessed using receiver operating characteristic (ROC) curve analysis. Imaging exams and histopathological results were used as standard of references. Results: Areas under the ROC curves of DWIBS vs. 3D-CE-T1w vs. both sequences in fused fashion were 0.97, 0.978, and 1.00, respectively. The diagnostic performance in tumor detection of fused DWIBS/3DCE- T1w images were statistically superior to DWIBS (p < 0.001) and 3D-CE-T1w (p ≤ 0.002); while the difference between DWIBS and 3D-CE-T1w did not show statistical significance difference. Detection rates of malignancy did not differ between WB-MRI with DWIBS and 18F-FDG PET-CT. Conclusion: WB-MRI with DWIBS is to be considered as alternative tool to conventional whole-body methods for tumor staging and during follow-up in cancer patients