Network information criterion-determining the number of hiddenunits for an artificial neural network model

The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set.     

Addendum to: generic rules to evaluate system-failure frequency

In the paper on Generic rules to evaluate system-failure frequency, (see ibid., vol.49, p.85-7, 2000) the authora did not consider the case of shorter mission times while presenting the rules to evaluate system-failure frequency. Time-specific failure-frequency calculations are required for the systems with shorter mission times. One of the practical uses of time-specific failure-frequency is in finding reasonably accurate estimates of failure-rate and reliability of large systems consisting of repairable components, by using combinatorial methods (without using Markov models). This paper shows that, with some minor modifications, the rules in the original paper for evaluating steady-state failure-frequency can be used to find the time-specific failure-frequency     

On the sensitivity of coupled resonator filters without some direct couplings

This paper presents an analysis of the sensitivity of coupled resonator filters in which some direct couplings are missing. The effect of changes in the coupling coefficients and resonant frequencies of the resonators is investigated by directly computing the gradient of the scattering parameters. It is shown that structures that are modular in the input-to-output direction are much less sensitive than those with modularity in the orthogonal direction for the same frequency response.     

A Novel Band-Reject Element for Pseudoelliptic Bandstop Filters

A novel band-reject element for the design of inline waveguide pseudoelliptic band-reject filters is introduced. The element consists of an offset partial-height post in a rectangular waveguide in which the dominant TE₁₀ mode is propagating. The location of the attenuation pole is primarily determined by the height of the post that generates it. The element allows the implementation of weak, as well as strong coupling coefficients that are encountered in asymmetric band-reject responses with broad stopbands. The coupling strength is controlled by the offset of the post with respect to the center of the main waveguide. The posts are separated by uniform sections of the main waveguide. An equivalent low-pass circuit based on the extracted pole technique is first used in a preliminary design. An improved equivalent low-pass circuit that includes a more accurate equivalent circuit of the band-reject element is then introduced. A synthesis method of the enhanced network is also presented. Filters based on the introduced element are designed, fabricated, and tested. Good agreement between measured and simulated results is achieved     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

Supramolecular π–π assembly of a neutral [Cu(salen)] complex via the templating effect of an ionic inorganic complex Na2[Cu(mnt)2] forming a framework type material having well-defined channels

A classical ionic inorganic complex Na₂[Cu(mnt)₂] (mnt²⁻ = maleonitriledithiolate = 1,2-dicyanoethylenedithiolate), that acts as a template in assembling neutral [Cu(salen)] (salen = bis(salicylidene)ethylenediamine) complexes forming a framework type arrangement, is accommodated in the channel formed in the crystal structure of a new type of host–guest compound [Cu(salen)]₄ · Na₂[Cu(mnt)₂] (1). The non-covalent supramolecular interactions among [Cu(salen)] complexes and between [Cu(salen)] and [Cu(mnt)₂]²⁻ complexes in the crystal lattice of 1 result in weak antiferromagnetic coupling.     

Dynamics of learning near singularities in layered networks

We explicitly analyze the trajectories of learning near singularities in hierarchical networks, such as multilayer perceptrons and radial basis function networks, which include permutation symmetry of hidden nodes, and show their general properties. Such symmetry induces singularities in their parameter space, where the Fisher information matrix degenerates and odd learning behaviors, especially the existence of plateaus in gradient descent learning, arise due to the geometric structure of singularity. We plot dynamic vector fields to demonstrate the universal trajectories of learning near singularities. The singularity induces two types of plateaus, the on-singularity plateau and the near-singularity plateau, depending on the stability of the singularity and the initial parameters of learning. The results presented in this letter are universally applicable to a wide class of hierarchical models. Detailed stability analysis of the dynamics of learning in radial basis function networks and multilayer perceptrons will be presented in separate work.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

A modified design approach for compact ultra‐wideband microstrip filters

A modified design approach for compact ultra‐wideband microstrip filters with cascaded/folded stepped‐impedance resonators is described. The key feature of the proposed method is to facilitate stronger coupling between stepped‐impedance resonators and, at the same time, eliminate the requirement of extremely small gaps in coupled‐line sections, as found in traditional designs. Simulations and measurements demonstrate that the filters designed with this technique exhibit good reflection, insertion‐loss, and group‐delay performance within the 3.1–10.6 GHz band. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE 2010.     

Modeling basal ganglia for understanding Parkinsonian reaching movements

We present a computational model that highlights the role of basal ganglia (BG) in generating simple reaching movements. The model is cast within the reinforcement learning (RL) framework with correspondence between RL components and neuroanatomy as follows: dopamine signal of substantia nigra pars compacta as the temporal difference error, striatum as the substrate for the critic, and the motor cortex as the actor. A key feature of this neurobiological interpretation is our hypothesis that the indirect pathway is the explorer. Chaotic activity, originating from the indirect pathway part of the model, drives the wandering, exploratory movements of the arm. Thus, the direct pathway subserves exploitation, while the indirect pathway subserves exploration. The motor cortex becomes more and more independent of the corrective influence of BG as training progresses. Reaching trajectories show diminishing variability with training. Reaching movements associated with Parkinson's disease (PD) are simulated by reducing dopamine and degrading the complexity of indirect pathway dynamics by switching it from chaotic to periodic behavior. Under the simulated PD conditions, the arm exhibits PD motor symptoms like tremor, bradykinesia and undershooting. The model echoes the notion that PD is a dynamical disease.