Contractive De-noising Auto-encoder

Auto-encoder is a special kind of neural network based on reconstruction. De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to the input by corrupting the original data first and then reconstructing the original input by minimizing the reconstruction error function. And contractive auto-encoder (CAE) is another kind of improved auto-encoder to learn robust feature by introducing the Frobenius norm of the Jacobean matrix of the learned feature with respect to the original input. In this paper, we combine de-noising auto-encoder and contractive auto- encoder, and propose another improved auto-encoder, contractive de-noising auto- encoder (CDAE), which is robust to both the original input and the learned feature. We stack CDAE to extract more abstract features and apply SVM for classification. The experiment result on benchmark dataset MNIST shows that our proposed CDAE performed better than both DAE and CAE, proving the effective of our method.Comment: Figures edite

Therapie bei Progression und Rezidiv des Ovarialkarzinoms

Secondary surgery after failure of primary treatment is a promising and reasonable option only for patients with a relapse-free interval of at least 6-12 months who should have ideally achieved a tumor-free status after primary therapy. As after primary surgery, size of residual tumor is the most significant predictor of survival after secondary surgery. Even in the case of multiple tumor sites, complete removal of the tumor can be achieved in nearly 30% of the patients. Treatment results are much better in specialized oncology centers with optimal interdisciplinary cooperation compared with smaller institutions. Chemotherapy can be used both for consolidation after successful secondary surgery and for palliation in patients with inoperable recurrent disease. Since paclitaxel has been integrated into first-line chemotherapy, there is no defined standard for second-line chemotherapy. Several cytotoxic agents have shown moderate activity in this setting, including treosulfan, epirubicin, and newer agents such as topotecan, gemcitabine, vinorelbine, and PEG(polyethylene glycol)-liposomal doxorubicin. Thus, the German Arbeitsgemeinschaft Gynakologische Onkologie (AGO) has initiated several randomized studies in patients with recurrent ovarian cancer in order to define new standards for second-line chemotherapy

Anomalous Neutrino Reactions at HERA

We study the sensitivity of HERA to new physics using the helicity suppressed reaction $e_R p \rightarrow \nu X$, where the final neutrino can be a standard model one or a heavy neutrino. The approach is model independent and is based on an effective lagrangian parametrization. It is shown that HERA will put significant bounds on the scale of new physics, though, in general, these are more modest than previously thought. If deviations from the standard model are observed in the above processes, future colliders such as the SSC and LHC will be able to directly probe the physics responsible for these discrepancies}Comment: 11 Pages + 2 figures is TOPDRAWER (included at the end or available by mail). Report UCRHEP-T113 (requires the macropackage PHYZZX). A line in the TeX file requesting an input file has been removed, it caused problem

Conformal Field Theory Correlators from Classical Field Theory on Anti-de Sitter Space II. Vector and Spinor Fields

We use the AdS/CFT correspondence to calculate CFT correlation functions of vector and spinor fields. The connection between the AdS and boundary fields is properly treated via a Dirichlet boundary value problem.Comment: 14 pages, LaTeX2e with amsmath,amsfonts packages; v2:interactions section corrected, reference adde

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks

Hegemonia consensual: por uma teorização sobre a Política Externa Brasileira no pós-Guerra Fria

As abordagens convencionais sobre hegemonia enfatizam elementos de coerção e exclusão, características que não explicam adequadamente o mecanismo de crescimento de vários projetos regionais ou as características das políticas externas dos poderes emergentes. Este artigo desenvolve o conceito de hegemonia consensual, explicando como uma estrutura pode ser articulada, disseminada e mantida sem recorrer à força para recrutar a participação de outros atores. A ideia central é a construção de uma visão estrutural, ou hegemonia, que inclui específica e nominalmente subordinação, que engajam em um processo de diálogo e interações, causando a subordinação das partes para absorverem apropriadamente a substância e os requisitos da hegemonia como seus próprios. A utilidade da hegemonia consensual como instrumento analítico, especificamente para o estudo do regionalismo e das políticas externas dos mercados e poderes emergentes, é demonstrada pela política externa brasileira no pós-Guerra Fria, indicando para ambos como a hegemonia consensual pode ser perseguida e onde fundam-se os limites naturais de suas ideias-base

Model of the Quark Mixing Matrix

The structure of the Cabibbo-Kobayashi-Maskawa (CKM) matrix is analyzed from the standpoint of a composite model. A model is constructed with three families of quarks, by taking tensor products of sufficient numbers of spin-1/2 representations and imagining the dominant terms in the mass matrix to arise from spin-spin interactions. Generic results then obtained include the familiar relation $|V_{us}| = (m_d/m_s)^{1/2} - (m_u/m_c)^{1/2}$, and a less frequently seen relation $|V_{cb}| = \sqrt{2} [(m_s/m_b) - (m_c/m_t)]$. The magnitudes of $V_{ub}$ and $V_{td}$ come out naturally to be of the right order. The phase in the CKM matrix can be put in by hand, but its origin remains obscure.Comment: Presented by Mihir P. Worah at DPF 92 Meeting, Fermilab, November, 1992. 3 pages, LaTeX fil

Statistical Mechanics of Soft Margin Classifiers

We study the typical learning properties of the recently introduced Soft Margin Classifiers (SMCs), learning realizable and unrealizable tasks, with the tools of Statistical Mechanics. We derive analytically the behaviour of the learning curves in the regime of very large training sets. We obtain exponential and power laws for the decay of the generalization error towards the asymptotic value, depending on the task and on general characteristics of the distribution of stabilities of the patterns to be learned. The optimal learning curves of the SMCs, which give the minimal generalization error, are obtained by tuning the coefficient controlling the trade-off between the error and the regularization terms in the cost function. If the task is realizable by the SMC, the optimal performance is better than that of a hard margin Support Vector Machine and is very close to that of a Bayesian classifier.Comment: 26 pages, 12 figures, submitted to Physical Review

Speaker recognition with hybrid features from a deep belief network

Learning representation from audio data has shown advantages over the handcrafted features such as mel-frequency cepstral coefficients (MFCCs) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different lengths. In particular, we study the use of features from different levels of deep belief network for quantizing the audio data into vectors of audio word counts. These vectors represent the audio scripts of different lengths that make them easier to train a classifier. We show in the experiment that the audio word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio word count vector and the MFCC features

Asymptotic Structure of Symmetry Reduced General Relativity

Gravitational waves with a space-translation Killing field are considered. In this case, the 4-dimensional Einstein vacuum equations are equivalent to the 3-dimensional Einstein equations with certain matter sources. This interplay between 4- and 3- dimensional general relativity can be exploited effectively to analyze issues pertaining to 4 dimensions in terms of the 3-dimensional structures. An example is provided by the asymptotic structure at null infinity: While these space-times fail to be asymptotically flat in 4 dimensions, they can admit a regular completion at null infinity in 3 dimensions. This completion is used to analyze the asymptotic symmetries, introduce the analog of the 4-dimensional Bondi energy-momentum and write down a flux formula. The analysis is also of interest from a purely 3-dimensional perspective because it pertains to a diffeomorphism invariant 3-dimensional field theory with {\it local} degrees of freedom, i.e., to a midi-superspace. Furthermore, due to certain peculiarities of 3 dimensions, the description of null infinity does have a number of features that are quite surprising because they do not arise in the Bondi-Penrose description in 4 dimensions.Comment: 39 Pages, REVTEX, CGPG-96/5-