The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model

A case of porphyria cutanea tarda in the setting of hepatitis C infection and tobacco usage

Porphyria cutanea tarda (PCT) is the most common type of porphyria, presenting in middle-aged patients with a photodistributed vesiculobullous eruption, milia, and scars. Porphyria cutanea tarda occurs in relation to inhibition of uroporphyrinogen decarboxylase, a key enzyme in the heme biosynthesis pathway. A number of genetic and acquired factors increase susceptibility to PCT by reducing uroporphyrinogen decarboxylase activity. A handful of other vesiculobullous conditions may mimic PCT both clinically and histologically; therefore, both skin biopsy and laboratory evaluation are helpful in confirming the diagnosis. We report a case of PCT in the setting of cigarette usage and untreated hepatitis C infection

Algorithm for heart rate extraction in a novel wearable acoustic sensor.

Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds - S1 and S2 - that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S1 and S2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long-term wearable vital signs monitoring

Extreme ultraviolet mask surface cleaning effects on lithography process performance

Extreme UV (EUV) masks are expected to undergo cleaning processes in order to maintain the lifetimes necessary for high volume manufacturing. For this study, the impact of repetitive cleaning of EUV masks on imaging performance is evaluated. Two high quality industry standard EUV masks are used, with one of the masks undergoing repeated cleaning and the other one kept as a reference. Lithographic performance, in terms of process window analysis and line edge roughness, was monitored after every two cleans and was compared to the reference mask performance. Surface analysis by atomic force microscopy did not show changes in the midspatial frequency roughness measured after each clean. After a total of eight cleans, minimal degradation is observed in the lithographic performance of the mask. From these observations, the authors conclude that the cleaning cycles completed thus far did not damage the mask multilayer or the absorber structures. The cleaning cycles will be continued until significant loss in imaging fidelity is found. © 2010 American Vacuum Society

Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding

Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required. After that, we designed a model which still keeps an RNN as the encoder, while using a non-autoregressive convolutional decoder. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabelled corpora, and in both cases the transferability is evaluated on a set of downstream NLP tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks

Rethinking Skip-thought: A Neighborhood based Approach

We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model

A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented using a multivariate non-linear function, is initially decomposed into a number of functional components via the wellknown analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-theparameters problem, which can be solved using least-squares type methods. An efficient model structure determination approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to represent high-order and high dimensional non-linear systems

Antioxidant modified amphiphilic polymer improves intracellular cryoprotectant delivery and alleviates oxidative stress in HeLa cells

The design and synthesis of a dual-function, cell permeating polymer with an antioxidative property is described and its use for the intracellular delivery of the cryoprotectant trehalose into HeLa cells is demonstrated. The polymer, PVitE-25, was created by grafting the water insoluble hydrophobic antioxidant (±)-α-Tocopherol (vitamin E) onto pendant carboxylate groups of a biocompatible cell permeating polymer, poly (L-lysine iso-phthalamide) (PLP). The modification increased the intracellular delivery efficiency of the polymer and also introduced an antioxidative effect that was able to reduce 85% of reactive oxygen species (ROS) in HeLa cells incubated with 1 mM hydrogen peroxide (H2O2), as determined by 2′,7′-Dichlorofluorescin diacetate (DCFH-DA) probe. PVitE-25 was also used to load the cropreservative trehalose into HeLa cells prior to freezing such that the level of cell viability measured 48 hours after cell revival was comparable to that observed with a standard Me2SO-based cryopreservation protocol. This is the first report of a synthetic intracellular delivery system that facilitates the intracellular delivery of the cryoprotectant, trehalose, and mitigates oxidative stress during the freeze thaw cycle of cryopreservation