960 resultados para iterative multitier ensembles


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Digital interactive artwork concerned with drawing, mark-making and emergence as a mechanism to facilitate creative experiences. The artwork comes from a concept developed during a collaboration with community artists EJ Saunders and Robert Oakman at Cerebral Palsy League.

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In this paper we present a novel scheme for improving speaker diarization by making use of repeating speakers across multiple recordings within a large corpus. We call this technique speaker re-diarization and demonstrate that it is possible to reuse the initial speaker-linked diarization outputs to boost diarization accuracy within individual recordings. We first propose and evaluate two novel re-diarization techniques. We demonstrate their complementary characteristics and fuse the two techniques to successfully conduct speaker re-diarization across the SAIVT-BNEWS corpus of Australian broadcast data. This corpus contains recurring speakers in various independent recordings that need to be linked across the dataset. We show that our speaker re-diarization approach can provide a relative improvement of 23% in diarization error rate (DER), over the original diarization results, as well as improve the estimated number of speakers and the cluster purity and coverage metrics.

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Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance.

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We propose an iterative estimating equations procedure for analysis of longitudinal data. We show that, under very mild conditions, the probability that the procedure converges at an exponential rate tends to one as the sample size increases to infinity. Furthermore, we show that the limiting estimator is consistent and asymptotically efficient, as expected. The method applies to semiparametric regression models with unspecified covariances among the observations. In the special case of linear models, the procedure reduces to iterative reweighted least squares. Finite sample performance of the procedure is studied by simulations, and compared with other methods. A numerical example from a medical study is considered to illustrate the application of the method.

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A fast iterative scheme based on the Newton method is described for finding the reciprocal of a finite segment p-adic numbers (Hensel code). The rate of generation of the reciprocal digits per step can be made quadratic or higher order by a proper choice of the starting value and the iterating function. The extension of this method to find the inverse transform of the Hensel code of a rational polynomial over a finite field is also indicated.

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The importance of interlaminar stresses has prompted a fresh look at the theory of laminated plates. An important feature in modelling such laminates is the need to provide for continuity of some strains and stresses, while at the same time allowing for the discontinuities in the others. A new modelling possibility is examined in this paper. The procedure allows for discontinuities in the in-plane stresses and transverse strains and continuity in the in-plane strains and transverse stresses. This theory is in the form of a heirarchy of formulations each representing an iterative step. Application of the theory is illustrated by considering the example of an infinite laminated strip subjected to sinusoidal loading.

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Alueellisten ilmastomallien vaakasuuntainen erottelukyky on globaaleja malleja huomattavasti tarkempi, minkä vuoksi niillä on useita käyttökohteita ilmastonmuutoksen vaikutusten arvioinnissa. Tässä Pro Gradu – työssä tutkittiin alueellisten ilmastomallien tuottamia sademääräsimulaatioita sekä sadehavaintoaineistoja Euroopassa. Aineistona käytettiin ENSEMBLES-hankkeen tarjoamia 10 alueellista ilmastosimulaatiota, kahta hilamuotoista havaintoaineistoa sekä Ilmatieteen laitoksen sadeasemahavaintoja. Aineisto oli päiväkohtaista. Vuositasolla ilmastomallit ovat pääsääntöisesti sademäärää yliennustavia, mutta harha vaihtelee alueiden ja vuodenaikojen kesken. Osa tästä harhasta selittyy kuitenkin sillä, että havaintoaineistoihin sisältyy tyypillisesti sademäärän mittaustapahtumasta aiheutuva virhe. Alueellisten simulaatioiden harha pyritään minimoimaan kun halutaan kvantifioida tulevaisuuden sademääriä ilmastomallitulosten avulla. Tutkimuksessa sovellettiin tähän tarkoitettua empiiristä korjausmenetelmää tapauskohtaisella testialueella Suomessa. Korjausmenetelmä huomioi sadetapahtumien harhan niiden intensiteetin mukaan, jolloin se periaatteessa soveltuu paremmin myös rankkasateiden korjaamiseen. Korjausmenetelmässä harhan riippuvuus sadetapahtuman intensiteetistä oletetaan skenaariojaksolla samaksi kuin vertailujaksolla. Edellytyksenä korjausmenetelmän käytölle on se, että sadetapahtumien intensiteettijakauma simulaatioaineistoissa on kohtuullisen lähellä havaittua jakaumaa. Korjausmenetelmä parantaa sademäärän vuodenaikaiskeskiarvoja tarkastelualueella vertailujaksolla, vuoden kokonaissadekertymän harhan suuruus aineiston keskiarvossa on vain 7 mm. Koska sadetapahtumien intensiteettijakauma muuttuu simulaatioissa vertailu- ja skenaariojaksojen välillä, korjausmenetelmä vaikuttaa kuitenkin sademäärän muutoksen suuruuteen. Lisäksi menetelmän vaikutus sademäärän muutokseen jakautuu epätasaisesti sadetapahtuman intensiteetistä riippuen: menetelmä pienentää rankkasateiden kertymien muutosta, mutta kasvattaa sitä tavallisten sadetapahtumien osalta. Rankkasadetapahtumien erilliskäsittely korjausmenetelmässä aiheuttaa sen, että korjatusta sadetapahtumien intensiteettijakaumasta tulee epäjatkuva riippumatta siitä, mikä tarkastelujakso on kyseessä. Tässä työssä käytetty korjausmenetelmä ei ole ainoa laatuaan, perinteisesti mallitulosten korjaamiseen on käytetty vakiokertoimiin perustuvaa menetelmää kaikille sadetapahtumille. Korjausmenetelmien testaaminen on monien sovellusten kannalta tärkeää, mutta parhaan menetelmän löytäminen ei ole yksiselitteisen helppoa. Globaaleihin malleihin verrattuna alueellisten ilmastomallien ja korjausmenetelmien käyttö aiheuttavat molemmat ylimääräisen epävarmuuslähteen ilmastosimulaatioihin.

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An iterative algorithm baaed on probabilistic estimation is described for obtaining the minimum-norm solution of a very large, consistent, linear system of equations AX = g where A is an (m times n) matrix with non-negative elements, x and g are respectively (n times 1) and (m times 1) vectors with positive components.

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Among the iterative schemes for computing the Moore — Penrose inverse of a woll-conditioned matrix, only those which have an order of convergence three or two are computationally efficient. A Fortran programme for these schemes is provided.

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The Orthogonal Frequency Division Multiplexing (OFDM) is a form of Multi-Carrier Modulation where the data stream is transmitted over a number of carriers which are orthogonal to each other i.e. the carrier spacing is selected such that each carrier is located at the zeroes of all other carriers in the spectral domain. This paper proposes a new novel iterative frequency offset estimation algorithm for an OFDM system in order to receive the OFDM data symbols error-free over the noisy channel at the receiver and to achieve frequency synchronization between the transmitter and the receiver. The performance of this algorithm has been studied in AWGN, ADSL and SUI channels successfully.

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In this paper, we propose a training-based channel estimation scheme for large non-orthogonal space-time block coded (STBC) MIMO systems.The proposed scheme employs a block transmission strategy where an N-t x N-t pilot matrix is sent (for training purposes) followed by several N-t x N-t square data STBC matrices, where Nt is the number of transmit antennas. At the receiver, we iterate between channel estimation (using an MMSE estimator) and detection (using a low-complexity likelihood ascent search (LAS) detector) till convergence or for a fixed number of iterations. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed scheme at low complexities. The fact that we could show such good results for large STBCs (e.g., 16 x 16 STBC from cyclic division algebras) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot-based channel estimation and turbo coding) establishes the effectiveness of the proposed scheme.

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Accurate, reliable and economical methods of determining stress distributions are important for fastener joints. In the past the contact stress problems in these mechanically fastened joints using interference or push or clearance fit pins were solved using both inverse and iterative techniques. Inverse techniques were found to be most efficient, but at times inadequate in the presence of asymmetries. Iterative techniques based on the finite element method of analysis have wider applications, but they have the major drawbacks of being expensive and time-consuming. In this paper an improved finite element technique for iteration is presented to overcome these drawbacks. The improved iterative technique employs a frontal solver for elimination of variables not requiring iteration, by creation of a dummy element. This automatically results in a large reduction in computer time and in the size of the problem to be handled during iteration. Numerical results are compared with those available in the literature. The method is used to study an eccentrically located pin in a quasi-isotropic laminated plate under uniform tension.

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In this article, we present a novel application of a quantum clustering (QC) technique to objectively cluster the conformations, sampled by molecular dynamics simulations performed on different ligand bound structures of the protein. We further portray each conformational population in terms of dynamically stable network parameters which beautifully capture the ligand induced variations in the ensemble in atomistic detail. The conformational populations thus identified by the QC method and verified by network parameters are evaluated for different ligand bound states of the protein pyrrolysyl-tRNA synthetase (DhPylRS) from D. hafniense. The ligand/environment induced re-distribution of protein conformational ensembles forms the basis for understanding several important biological phenomena such as allostery and enzyme catalysis. The atomistic level characterization of each population in the conformational ensemble in terms of the re-orchestrated networks of amino acids is a challenging problem, especially when the changes are minimal at the backbone level. Here we demonstrate that the QC method is sensitive to such subtle changes and is able to cluster MD snapshots which are similar at the side-chain interaction level. Although we have applied these methods on simulation trajectories of a modest time scale (20 ns each), we emphasize that our methodology provides a general approach towards an objective clustering of large-scale MD simulation data and may be applied to probe multistate equilibria at higher time scales, and to problems related to protein folding for any protein or protein-protein/RNA/DNA complex of interest with a known structure.