NEC

About: NEC is a company organization based out in Tokyo, Japan. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 33269 authors who have published 57670 publications receiving 835952 citations. The organization is also known as: NEC Corporation & NEC Electronics Corporation.

Experiences using lightweight formal methods for requirements modeling

Abstract: The paper describes three case studies in the lightweight application of formal methods to requirements modeling for spacecraft fault protection systems. The case studies differ from previously reported applications of formal methods in that formal methods were applied very early in the requirements engineering process to validate the evolving requirements. The results were fed back into the projects to improve the informal specifications. For each case study, we describe what methods were applied, how they were applied, how much effort was involved, and what the findings were. In all three cases, formal methods enhanced the existing verification and validation processes by testing key properties of the evolving requirements and helping to identify weaknesses. We conclude that the benefits gained from early modeling of unstable requirements more than outweigh the effort needed to maintain multiple representations.

Predicting Peptides That Bind to MHC Molecules Using Supervised Learning of Hidden Markov Models

Abstract: The binding of a major histo- compatibility complex (MHC) molecule to a peptide originating in an antigen is essential to recognizing antigens in immune systems, and it has proved to be important to use com- puters to predict the peptides that will bind to an MHC molecule. The purpose of this paper is twofold: First, we propose to apply supervised learning of hidden Markov models (HMMs) to this problem, which can surpass existing meth- ods for the problem of predicting MHC-binding peptides. Second, we generate peptides that have high probabilities to bind to a certain MHC molecule, based on our proposed method using peptides binding to MHC molecules as a set of training data. From our experiments, in a type of cross-validation test, the discrimina- tion accuracy of our supervised learning method is usually approximately 2-15% better than those of other methods, including back- propagation neural networks, which have been regarded as the most effective approach to this problem. Furthermore, using an HMM trained for HLA-A2, we present new peptide sequences that are provided with high binding probabili- ties by the HMM and that are thus expected to bind to HLA-A2 proteins. Peptide sequences not shown in this paper but with rather high binding probabilities can be obtained from the

A structural Bayes approach to speaker adaptation

Abstract: Maximum a posteriori (MAP) estimation has been successfully applied to speaker adaptation in speech recognition systems using hidden Markov models. When the amount of data is sufficiently large, MAP estimation yields recognition performance as good as that obtained using maximum-likelihood (ML) estimation. This paper describes a structural maximum a posteriori (SMAP) approach to improve the MAP estimates obtained when the amount of adaptation data is small. A hierarchical structure in the model parameter space is assumed and the probability density functions for model parameters at one level are used as priors for those of the parameters at adjacent levels. Results of supervised adaptation experiments using nonnative speakers' utterances showed that SMAP estimation reduced error rates by 61% when ten utterances were used for adaptation and that it yielded the same accuracy as MAP and ML estimation when the amount of data was sufficiently large. Furthermore, the recognition results obtained in unsupervised adaptation experiments showed that SMAP estimation was effective even when only one utterance from a new speaker was used for adaptation. An effective way to combine rapid supervised adaptation and on-line unsupervised adaptation was also investigated.

Learning Discrete Structures for Graph Neural Networks

Abstract: Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are often noisy and incomplete or might not be available at all. With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. This allows one to apply GCNs not only in scenarios where the given graph is incomplete or corrupted but also in those where a graph is not available. We conduct a series of experiments that analyze the behavior of the proposed method and demonstrate that it outperforms related methods by a significant margin.

Semiconductor laser module

Abstract: A semiconductor laser module comprises a semiconductor laser device, an optical fiber, and a lens for coupling light emitted from the semiconductor laser device to the optical fiber There is provided a metal base having a flat portion and a pipe portion in a metal case The semiconductor laser device is mounted on a chip carrier which is mounted on the flat portion of the metal base The lens is fixed in the inside of the pipe portion of the metal base The optical fiber is protected at one end by a ferrule which is fixed to the pipe portion of the metal base A device such as a monitor photodiode and a thermistor may be mounted on the flat portion of the metal base The flat portion of the metal base may be formed with a concave portion of an area smaller than an area of the bottom of the chip carrier The chip carrier is soldered to the flat portion of the metal base by a low temperature solder to provide thermal connection therebetween, and by a spot welding at a point other than the soldered portion of the low temperature solder