908 research outputs found
On the relationship between matched filter theory as applied to gust loads and phased design loads analysis
A theoretical basis and example calculations are given that demonstrate the relationship between the Matched Filter Theory approach to the calculation of time-correlated gust loads and Phased Design Load Analysis in common use in the aerospace industry. The relationship depends upon the duality between Matched Filter Theory and Random Process Theory and upon the fact that Random Process Theory is used in Phased Design Loads Analysis in determining an equiprobable loads design ellipse. Extensive background information describing the relevant points of Phased Design Loads Analysis, calculating time-correlated gust loads with Matched Filter Theory, and the duality between Matched Filter Theory and Random Process Theory is given. It is then shown that the time histories of two time-correlated gust load responses, determined using the Matched Filter Theory approach, can be plotted as parametric functions of time and that the resulting plot, when superposed upon the design ellipse corresponding to the two loads, is tangent to the ellipse. The question is raised of whether or not it is possible for a parametric load plot to extend outside the associated design ellipse. If it is possible, then the use of the equiprobable loads design ellipse will not be a conservative design practice in some circumstances
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
Microscopic histology image analysis is a cornerstone in early detection of
breast cancer. However these images are very large and manual analysis is error
prone and very time consuming. Thus automating this process is in high demand.
We proposed a hierarchical system of convolutional neural networks (CNN) that
classifies automatically patches of these images into four pathologies: normal,
benign, in situ carcinoma and invasive carcinoma. We evaluated our system on
the BACH challenge dataset of image-wise classification and a small dataset
that we used to extend it. Using a train/test split of 75%/25%, we achieved an
accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that
of the extension. On the test of the BACH challenge, we've reached an accuracy
of 0.81 which rank us to the 8th out of 51 teams
DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
In recent years numerous advanced malware, aka advanced persistent threats
(APT) are allegedly developed by nation-states. The task of attributing an APT
to a specific nation-state is extremely challenging for several reasons. Each
nation-state has usually more than a single cyber unit that develops such
advanced malware, rendering traditional authorship attribution algorithms
useless. Furthermore, those APTs use state-of-the-art evasion techniques,
making feature extraction challenging. Finally, the dataset of such available
APTs is extremely small.
In this paper we describe how deep neural networks (DNN) could be
successfully employed for nation-state APT attribution. We use sandbox reports
(recording the behavior of the APT when run dynamically) as raw input for the
neural network, allowing the DNN to learn high level feature abstractions of
the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs,
we achieved an accuracy rate of 94.6%
Apollo experience report: Development of guidance targeting techniques for the command module and launch vehicle
The development of the guidance targeting techniques for the Apollo command module and launch vehicle is discussed for four types of maneuvers: (1) translunar injection, (2) translunar midcourse, (3) lunar orbit insertion, and (4) return to earth. The development of real-time targeting programs for these maneuvers and the targeting procedures represented are discussed. The material is intended to convey historically the development of the targeting techniques required to meet the defined target objectives and to illustrate the solutions to problems encountered during that development
Single muscle fiber proteomics reveals unexpected mitochondrial specialization
Mammalian skeletal muscles are composed of multinucleated cells termed slow or fast fibers according to their contractile and metabolic properties. Here, we developed a high-sensitivity workflow to characterize the proteome of single fibers. Analysis of segments of the same fiber by traditional and unbiased proteomics methods yielded the same subtype assignment. We discovered novel subtype-specific features, most prominently mitochondrial specialization of fiber types in substrate utilization. The fiber type-resolved proteomes can be applied to a variety of physiological and pathological conditions and illustrate the utility of single cell type analysis for dissecting proteomic heterogeneity
Food Recognition using Fusion of Classifiers based on CNNs
With the arrival of convolutional neural networks, the complex problem of
food recognition has experienced an important improvement in recent years. The
best results have been obtained using methods based on very deep convolutional
neural networks, which show that the deeper the model,the better the
classification accuracy will be obtain. However, very deep neural networks may
suffer from the overfitting problem. In this paper, we propose a combination of
multiple classifiers based on different convolutional models that complement
each other and thus, achieve an improvement in performance. The evaluation of
our approach is done on two public datasets: Food-101 as a dataset with a wide
variety of fine-grained dishes, and Food-11 as a dataset of high-level food
categories, where our approach outperforms the independent CNN models
Robust audiovisual speech recognition using noise-adaptive linear discriminant analysis
© 2016 IEEE.Automatic speech recognition (ASR) has become a widespread and convenient mode of human-machine interaction, but it is still not sufficiently reliable when used under highly noisy or reverberant conditions. One option for achieving far greater robustness is to include another modality that is unaffected by acoustic noise, such as video information. Currently the most successful approaches for such audiovisual ASR systems, coupled hidden Markov models (HMMs) and turbo decoding, both allow for slight asynchrony between audio and video features, and significantly improve recognition rates in this way. However, both typically still neglect residual errors in the estimation of audio features, so-called observation uncertainties. This paper compares two strategies for adding these observation uncertainties into the decoder, and shows that significant recognition rate improvements are achievable for both coupled HMMs and turbo decoding
Role of dynamic Jahn-Teller distortions in Na2C60 and Na2CsC60 studied by NMR
Through 13C NMR spin lattice relaxation (T1) measurements in cubic Na2C60, we
detect a gap in its electronic excitations, similar to that observed in
tetragonal A4C60. This establishes that Jahn-Teller distortions (JTD) and
strong electronic correlations must be considered to understand the behaviour
of even electron systems, regardless of the structure. Furthermore, in metallic
Na2CsC60, a similar contribution to T1 is also detected for 13C and 133Cs NMR,
implying the occurence of excitations typical of JT distorted C60^{2-} (or
equivalently C60^{4-}). This supports the idea that dynamic JTD can induce
attractive electronic interactions in odd electron systems.Comment: 3 figure
Deep Over-sampling Framework for Classifying Imbalanced Data
Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings
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