Mixture density networks

Minimization of a sum-of-squares or cross-entropy error function leads to network outputs which approximate the conditional averages of the target data, conditioned on the input vector. For classifications problems, with a suitably chosen target coding scheme, these averages represent the posterior probabilities of class membership, and so can be regarded as optimal. For problems involving the prediction of continuous variables, however, the conditional averages provide only a very limited description of the properties of the target variables. This is particularly true for problems in which the mapping to be learned is multi-valued, as often arises in the solution of inverse problems, since the average of several correct target values is not necessarily itself a correct value. In order to obtain a complete description of the data, for the purposes of predicting the outputs corresponding to new input vectors, we must model the conditional probability distribution of the target data, again conditioned on the input vector. In this paper we introduce a new class of network models obtained by combining a conventional neural network with a mixture density model. The complete system is called a Mixture Density Network, and can in principle represent arbitrary conditional probability distributions in the same way that a conventional neural network can represent arbitrary functions. We demonstrate the effectiveness of Mixture Density Networks using both a toy problem and a problem involving robot inverse kinematics.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

The design and validation of an optical coherence tomography-based classification system for focal vitreomacular traction

PurposeTo develop and validate a classification system for focal vitreomacular traction (VMT) with and without macular hole based on spectral domain optical coherence tomography (SD-OCT), intended to aid in decision-making and prognostication.MethodsA panel of retinal specialists convened to develop this system. A literature review followed by discussion on a wide range of cases formed the basis for the proposed classification. Key features on OCT were identified and analysed for their utility in clinical practice. A final classification was devised based on two sequential, independent validation exercises to improve interobserver variability.ResultsThis classification tool pertains to idiopathic focal VMT assessed by a horizontal line scan using SD-OCT. The system uses width (W), interface features (I), foveal shape (S), retinal pigment epithelial changes (P), elevation of vitreous attachment (E), and inner and outer retinal changes (R) to give the acronym WISPERR. Each category is scored hierarchically. Results from the second independent validation exercise indicated a high level of agreement between graders: intraclass correlation ranged from 0.84 to 0.99 for continuous variables and Fleiss' kappa values ranged from 0.76 to 0.95 for categorical variables.ConclusionsWe present an OCT-based classification system for focal VMT that allows anatomical detail to be scrutinised and scored qualitatively and quantitatively using a simple, pragmatic algorithm, which may be of value in clinical practice as well as in future research studies.

The effects of metallurgical variables on the mechanical properties of high-chromium cast irons

The.use of high-chromium cast irons for abrasive wear resistance is restricted due to their poor fracture toughness properties. An.attempt was made to improve the fracture characteristics by altering the distribution, size and.shape of the eutectic carbide phase without sacrificing their excellent wear resistance. This was achieved by additions of molybdenum or tungsten followed by high temperature heat treatments. The absence of these alloying elements or replacement of them with vanadium or manganese did not show any significant effect and the continuous eutectic carbide morphology remained the same after application of high temperature heat treatments. The fracture characteristics of the alloys with these metallurgical variables were evaluated for both sharp-cracks and blunt notches. The results were used in conjunction with metallographic and fractographic observations to establish possible failure mechanisms. The fracture mechanism of the austenitic alloys was found to be controlled not only by the volume percent but was also greatly influenced by the size and distribution of the eutectic carbides. On the other hand, the fracture mechanism of martensitic alloys was independent of the eutectic carbide morphology. The uniformity of the secondary carbide precipitation during hardening heat treatments was shown to be a reason for consistant fracture toughness results being obtained with this series of alloys although their eutectic carbide morphologies were different. The collected data were applied to a model which incorporated the microstructural parameters and correlated them with the experimentally obtained valid stress intensity factors. The stress intensity coefficients of different short-bar fracture toughness test specimens were evaluated from analytical and experimental compliance studies. The.validity and applicability of this non-standard testing technique for determination of the fracture toughness of high-chromium cast irons were investigated. The results obtained correlated well with the valid results obtained from standard fracture toughness tests.