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Search Results: 1 - 10 of 7665 matches for " Shin-ichi Maeda "
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A Bayesian encourages dropout
Shin-ichi Maeda
Computer Science , 2014,
Abstract: Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.
Re-Evaluation of Attractor Neural Network Model to Explain Double Dissociation in Semantic Memory Disorder  [PDF]
Shin-ichi Asakawa
Psychology (PSYCH) , 2013, DOI: 10.4236/psych.2013.43A053
Abstract:

Structure of semantic memory was investigated in the way of neural network simulations in detail. In the literature, it is well-known that brain damaged patients often showed category specific disorder in various cognitive neuropsychological tasks like picture naming, categorisation, identification tasks and so on. In order to describe semantic memory disorder of brain damaged patients, the attractor neural network model originally proposed Hinton and Shallice (1991) was employed and was tried to re-evaluate the model performance. Especially, in order to answer the question about organization of semantic memory, how our semantic memories are organized, computer simulations were conducted. After the model learned data set (Tyler, Moss, Durrant-Peatfield, & Levy, 2000), units in hidden and cleanup layers were removed and observed its performances. The results showed category specificity. This model could also explain the double dissociation phenomena. In spite of the simplicity of its architecture, the attractor neural network might be considered to mimic human behavior in the meaning of semantic memory organization and its disorder. Although this model could explain various phenomenon in cognitive neuropsychology, it might become obvious that this model had one limitation to explain human behavior. As far as investigation in this study, asymmetry in category specificity between animate and inanimate objects might not be explained on this model without any additional assumptions. Therefore, further studies must be required to improve our understanding for semantic memory organisation.

Mosaic expression of pluripotency-related proteins oct-3/4 and alkaline phosphatase in human pancreatic carcinoma cell PANC-1
Masahiro Sato,Shin-ichi Maeda,Emi Inada,Issei Saitoh
Advanced Studies in Biology , 2013,
Abstract: Most current research on cancer stem cells (CSCs) associated with human tumorshas focused on the molecular and cellular analysis of hematopoietic lineagemarkers (e.g., CD44, CD138, and CD 133), which can also serve as important CSC markers in a variety of cancers. However, these markers are generallyexpressed at late stages in embryonic development. Oct-3/4, a member of thefamily of POU-domain transcriptional factors, and alkaline phosphatase (ALP) areknown to be expressed in the inner cell mass of blastocysts, germ cells, andpluripotent embryonic stem cells. We thus consider Oct-3/4 and ALP to bepromising markers for CSC. Herein, we examined expression of Oct-3/4 and ALPusing 6 established human pancreatic carcinoma cell lines. RT-PCR analysisrevealed the presence of Oct-3/4 and ALP mRNA in those cells.Immunocytochemical and cytochemical staining revealed that both Oct-3/4 andALP proteins are present as mosaics in PANC-1 cell line, one of those 6 cell lines(23% and 19%, respectively). However, Oct-3/4-positive PANC-1 cells did notexhibit overt ATP-binding cassette transporter G2 (ABCG2) activity, as revealedby Hoechst 33342 dye exclusion assay. Transfection of PANC-1 cells with anOct-3/4 promoter-directed, enhanced green fluorescent protein (EGFP) constructconfirmed the presence of Oct-3/4-positive cells. These findings indicate that inPANC-1 cells there are at least 2 subset populations, namely Oct-3/4-positive andALP-positive cells. However, it remains unknown whether expression of these 2markers overlaps. Enrichment of Oct-3/4- or ALP-positive cells by gene transferand subsequent drug selection will be helpful for further characterization of thesecells as possible CSCs.
Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias
Yohei Kondo,Kohei Hayashi,Shin-ichi Maeda
Computer Science , 2015,
Abstract: A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue, we propose Bayesian masking (BM), a sparse estimation method which imposes no regularization on the weights. The key concept of BM is to introduce binary latent variables that randomly mask features. Estimating the masking rates determines the relevance of the features automatically. We derive a variational Bayesian inference algorithm that maximizes the lower bound of the factorized information criterion (FIC), which is a recently developed asymptotic criterion for evaluating the marginal log-likelihood. In addition, we propose reparametrization to accelerate the convergence of the derived algorithm. Finally, we show that BM outperforms Lasso and automatic relevance determination (ARD) in terms of the sparsity-shrinkage trade-off.
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Kohei Hayashi,Shin-ichi Maeda,Ryohei Fujimaki
Computer Science , 2015,
Abstract: Factorized information criterion (FIC) is a recently developed approximation technique for the marginal log-likelihood, which provides an automatic model selection framework for a few latent variable models (LVMs) with tractable inference algorithms. This paper reconsiders FIC and fills theoretical gaps of previous FIC studies. First, we reveal the core idea of FIC that allows generalization for a broader class of LVMs, including continuous LVMs, in contrast to previous FICs, which are applicable only to binary LVMs. Second, we investigate the model selection mechanism of the generalized FIC. Our analysis provides a formal justification of FIC as a model selection criterion for LVMs and also a systematic procedure for pruning redundant latent variables that have been removed heuristically in previous studies. Third, we provide an interpretation of FIC as a variational free energy and uncover a few previously-unknown their relationships. A demonstrative study on Bayesian principal component analysis is provided and numerical experiments support our theoretical results.
Distributional Smoothing with Virtual Adversarial Training
Takeru Miyato,Shin-ichi Maeda,Masanori Koyama,Ken Nakae,Shin Ishii
Computer Science , 2015,
Abstract: The generalization performance of model-based prediction depends largely on the family from which we infer the model, and the majority of successful families of models possess some property of smoothness because most real world phenomena share similar smoothness properties. We would like to propose Local Distributional Smoothness (LDS), a new notion of smoothness for statistical model which respects our intuitive notion of "smooth distribution". The LDS of a model at a point is defined as the KL-divergence based robustness of the model distribution against local perturbation. Following the work of Adversarial training, we named the LDS based regularization as Virtual Adversarial training (VAT). VAT resembles adversarial training, but distinguishes itself in that it determines the adversarial direction from the model distribution alone without using the label information. The technique is therefore applicable even to semi-supervised learning. When we applied our technique to the classification task of the permutation invariant MNIST dataset, it eclipsed all the training methods that are free of generative models and pre-training. VAT also performed well even in comparison to state of the art method that uses a highly advanced generative model.
On the Concavity of the Consumption Function with a Quadratic Utility under Liquidity Constraints  [PDF]
Shin-Ichi Nishiyama, Ryo Kato
Theoretical Economics Letters (TEL) , 2012, DOI: 10.4236/tel.2012.25104
Abstract: This paper demonstrates the concavity of the consumption function of infinitely living households under liquidity constraints who are not prudent—i.e. with a quadratic utility. The concavity of the consumption function is closely related to the 3-convexity of the value function.


Changes in Attitudes toward Lumbar Spinal Stenosis Treatment  [PDF]
Shin-ichi Konno, Miho Sekiguchi
Open Journal of Orthopedics (OJO) , 2014, DOI: 10.4236/ojo.2014.46027
Abstract: As the environment surrounding healthcare continues to evolve, there is a need to revise outcome assessment criteria. A shift is being demanded in diagnosis and treatment outcome assessment practices from objective to subjective assessment and from evaluation by doctors to assessment that is based on the patient’s own perspective. Therefore, lumbar diseases must now be assessed from multiple perspectives. Some major indices for evaluation are pain and numbness, functional status, general health status, disability, and patient satisfaction. An effective assessment method for lumbar spinal stenos is that examines symptoms, quality of life, and healthcare economics as key assessment factor is reviewed.
Synthesis and Properties of Polyurethane Elastomers Containing Sucrose as a Cross-Linker  [PDF]
Kazunori Kizuka, Shin-Ichi Inoue
Open Journal of Organic Polymer Materials (OJOPM) , 2015, DOI: 10.4236/ojopm.2015.54011
Abstract: Polyaddition using isocyanate and polyol forms polyurethane elastomer (PUE). However, this method has rarely been applied to the construction of PUEs containing sucrose. Hence, the introduction of sucrose (disaccharide) as a cross-linker via polyaddition remains a challenging subject in polymer chemistry. Here, we report the synthesis of PUEs using an aromatic isocyanate (4,4’-diphenylmethane diisocyanate), polyols including a polyether polyol (polytetramethylene glycol) and two polyester polyols (polycaprolactone and polycarbonate diols), and sucrose as a crosslinker by a one-shot method. The PUEs containing sucrose were successfully produced. The use of sucrose was essential to obtain the desired PUEs containing sucrose units in the main chain.
Synthesis and Properties of Chiral Polyurethane Elastomers Using Tartaric Acids  [PDF]
Kazunori Kizuka, Shin-Ichi Inoue
Open Journal of Organic Polymer Materials (OJOPM) , 2016, DOI: 10.4236/ojopm.2016.61005
Abstract:

The polyaddition of isocyanate and polyol to form polyurethane elastomers has rarely been applied to the construction of chiral polyurethane elastomers. Hence, the introduction of chiral units via polyaddition remains a challenging subject in polymer chemistry. In this study, the synthesis of chiral polyurethane elastomers using an aromatic isocyanate, polyols (polyether and polyester polyols), and L(+)-, D(−)-, or meso-tartaric acid by a one-shot method is investigated. The polymers are characterized using FTIR and NMR spectroscopy, and their thermal properties are investigated by TGA, DMA, and DSC analyses. The optical activities of the polymers are confirmed by rotation. The use of chiral tartaric acids is essential to obtain the desired chiral polyurethane elastomers.

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