Effects of cisplatin on olfactory function in cancer patients.

A prospective analysis of olfaction was performed in 21 patients receiving cisplatin. A reduction in olfactory function was noted in only one patient. Hearing impairment was documented in nine patients, none of whom had impaired sense of smell. We conclude that cisplatin has no major deleterious effect on olfactory function at doses which cause hearing impairment.

Manifold stochastic dynamics for Bayesian learning.

We propose a new Markov Chain Monte Carlo algorithm, which is a generalization of the stochastic dynamics method. The algorithm performs exploration of the state-space using its intrinsic geometric structure, which facilitates efficient sampling of complex distributions. Applied to Bayesian learning in neural networks, our algorithm was found to produce results comparable to the best state-of-the-art method while consuming considerably less time.

Random embedding machines for pattern recognition.

Real classification problems involve structured data that can be essentially grouped into a relatively small number of clusters. It is shown that, under a local clustering condition, a set of points of a given class, embedded in binary space by a set of randomly parameterized surfaces, is linearly separable from other classes, with arbitrarily high probability. We call such a data set a local relative cluster. The size of the embedding set is shown to be inversely proportional to the squared local clustering degree. A simple parameterization by embedding hyperplanes, implementing a voting system, results in a random reduction of the nearest-neighbor method and leads to the separation of multicluster data by a network with two internal layers. This represents a considerable reduction of the learning problem with respect to known techniques, resolving a long-standing question on the complexity of random embedding. Numerical tests show that the proposed method performs as well as state-of the-art methods and in a small fraction of the time.

Multidimensional density shaping by sigmoids.

An estimate of the probability density function of a random vector is obtained by maximizing the output entropy of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's optimization method, applied to the estimated density, yields a recursive estimator for a random variable or a random sequence. A constrained connectivity structure yields a linear estimator, which is particularly suitable for "real time" prediction. A Gaussian nonlinearity yields a closed-form solution for the network's parameters, which may also be used for initializing the optimization algorithm when other nonlinearities are employed. A triangular connectivity between the neurons and the input, which is naturally suggested by the statistical setting, reduces the number of parameters. Applications to classification and forecasting problems are demonstrated.

High-capacity Hebbian storage by sparse sampling.

The capacity of networks of ternary neurons, storing, by the so-called Hebbian rule, sparse vectors over {-1, 0, 1}(N), is shown to be at least of the order of N(2)/K log N, where K=Omega(log N) is the number of nonzero elements in each vector. The error correction capability of such networks is also analyzed. These results generalize previously known capacity bounds for binary networks storing vectors of equally probable {+/-1} bits and yield considerably higher capacities for small values of K.

Second-order bounds on the domain of attraction and the rate of convergence of nonlinear dynamical systems and neural networks.

We present a method for analyzing the convergence properties of nonlinear dynamical systems yielding second-order bounds on the domain of attraction of an asymptotically stable equilibrium point and on the time of convergence in the estimated domain. We show that under certain conditions on the system, there exists an analytic solution to the corresponding optimization problem. The method is applied in analyzing the dynamics of a neural network model.

Memorizing binary vector sequences by a sparsely encoded network.

We present a neural network employing Hebbian storage and sparse internal coding, which is capable of memorizing and correcting sequences of binary vectors by association. A ternary version of the Kanerva memory, folded into a feedback configuration, is shown to perform the basic sequence memorization and regeneration function. The inclusion of lateral connections between the internal cells increases the network capacity considerably and facilitates the correction of individual input patterns and the detection of large errors. The introduction of higher delays in the transmission lines between the external input-output layer and the internal memory layer is shown to further improve the network's error correction capability.

V delta 2+ gamma delta T lymphocytes are cytotoxic to the MCF 7 breast carcinoma cell line and can be detected among the T cells that infiltrate breast tumors.

In order to study the potential role of gamma delta T lymphocytes in cytotoxicity against breast carcinoma cells, normal peripheral blood gamma delta cells were isolated and triggered with an alloantigenic lymphoblastoid B cell line and recombinant interleukin-2 and cloned. The clones expressed a CD3+V gamma 9+V delta 2+CD4-CD8- (or CD8+) phenotype. Five clones were cytotoxic to the Molt 4 T cell leukemia, but not to the alloantigen, whereas one clone lysed the alloantigen, but not the Molt 4 line. Clones that were cytotoxic to Molt 4 were either spontaneously cytotoxic against MCF 7 breast carcinoma cells or could be induced to kill MCF 7 cells by monoclonal antibodies specific for the gamma delta T cell receptor. In situ staining demonstrated that V delta 2+ as well as other subsets of gamma delta T cells can be detected in the lymphocytic infiltrate within breast carcinomas. These data showing that V delta 2+ T cells can recognize and kill cells of breast carcinoma lineage in vitro, and that cells expressing V delta 2 genes in their T cell receptor structure can be detected in the tumor, suggest that further studies of the nature of the interactions between V delta 2 T cells and breast carcinoma cells are warranted.