935 resultados para Unconditional Convergence


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The acceptance-probability-controlled simulated annealing with an adaptive move generation procedure, an optimization technique derived from the simulated annealing algorithm, is presented. The adaptive move generation procedure was compared against the random move generation procedure on seven multiminima test functions, as well as on the synthetic data, resembling the optical constants of a metal. In all cases the algorithm proved to have faster convergence and superior escaping from local minima. This algorithm was then applied to fit the model dielectric function to data for platinum and aluminum.

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The popular Newmark algorithm, used for implicit direct integration of structural dynamics, is extended by means of a nodal partition to permit use of different timesteps in different regions of a structural model. The algorithm developed has as a special case an explicit-explicit subcycling algorithm previously reported by Belytschko, Yen and Mullen. That algorithm has been shown, in the absence of damping or other energy dissipation, to exhibit instability over narrow timestep ranges that become narrower as the number of degrees of freedom increases, making them unlikely to be encountered in practice. The present algorithm avoids such instabilities in the case of a one to two timestep ratio (two subcycles), achieving unconditional stability in an exponential sense for a linear problem. However, with three or more subcycles, the trapezoidal rule exhibits stability that becomes conditional, falling towards that of the central difference method as the number of subcycles increases. Instabilities over narrow timestep ranges, that become narrower as the model size increases, also appear with three or more subcycles. However by moving the partition between timesteps one row of elements into the region suitable for integration with the larger timestep these the unstable timestep ranges become extremely narrow, even in simple systems with a few degrees of freedom. As well, accuracy is improved. Use of a version of the Newmark algorithm that dissipates high frequencies minimises or eliminates these narrow bands of instability. Viscous damping is also shown to remove these instabilities, at the expense of having more effect on the low frequency response.

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Using the method of quantum trajectories we show that a known pure state can be optimally monitored through time when subject to a sequence of discrete measurements. By modifying the way that we extract information from the measurement apparatus we can minimize the average algorithmic information of the measurement record, without changing the unconditional evolution of the measured system. We define an optimal measurement scheme as one which has the lowest average algorithmic information allowed. We also show how it is possible to extract information about system operator averages from the measurement records and their probabilities. The optimal measurement scheme, in the limit of weak coupling, determines the statistics of the variance of the measured variable directly. We discuss the relevance of such measurements for recent experiments in quantum optics.

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In this paper, methods are presented for automatic detection of the nipple and the pectoral muscle edge in mammograms via image processing in the Radon domain. Radon-domain information was used for the detection of straight-line candidates with high gradient. The longest straight-line candidate was used to identify the pectoral muscle edge. The nipple was detected as the convergence point of breast tissue components, indicated by the largest response in the Radon domain. Percentages of false-positive (FP) and false-negative (FN) areas were determined by comparing the areas of the pectoral muscle regions delimited manually by a radiologist and by the proposed method applied to 540 mediolateral-oblique (MLO) mammographic images. The average FP and FN were 8.99% and 9.13%, respectively. In the detection of the nipple, an average error of 7.4 mm was obtained with reference to the nipple as identified by a radiologist on 1,080 mammographic images (540 MLO and 540 craniocaudal views).

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Background: There is only limited knowledge on how the quantification of valvular regurgitation by color Doppler is affected by changing blood viscosity. This study was designed to evaluate the effect of changing blood viscosity on the vena contracta width using an in vitro model of valvular insufficiency capable of providing ample variation in the rate and stroke volume. Methods: We constructed a pulsatile flow model filled with human blood at varying hematocrit (15%, 35%, and 55%) and corresponding blood viscosity (blood/water viscosity: 2.6, 4.8, 9.1) levels in which jets were driven through a known orifice (7 mm(2)) into a 110 mL compliant receiving chamber (compliance: 2.2 mL/mmHg) by a pulsatile pump. In addition, we used variable pump stroke volumes (5, 7.5, and 10 mL) and rates (40, 60, and 80 ppm). Vena contracta region was imaged using a 3.5 MHz transducer. Pressure and volume in the flow model were kept constant during each experimental condition, as well as ultrasound settings. Results: Blood viscosity variation in the experimental range did not induce significant changes in vena contracta dimensions. Also, vena contracta width did not change from normal to low hematocrit and viscosity levels. A very modest increase only in vena contracta dimension was observed at very high level of blood viscosity when hematocrit was set to 55% . Pump rate, in the evaluated range, did not influence vena contracta width. These results in controlled experimental settings suggest that the vena contracta is an accurate quantitative method for quantifying valvular regurgitation even when this condition is associated with anemia, a frequent finding in patients with valvular heart disease.

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Clinical and demographic presurgical variables may be associated with unfavorable postsurgical neurological outcome in patients with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS). However, few reports include preoperative psychiatric disorders as a factor predictive of long-term post-surgical MTLE-HS neurological Outcome. We used Engel`s criteria to follow 186 postsurgical patients with MTLE-HS for an average of 6 years. DSM-IV criteria and psychiatric comorbidity criteria specific to epilepsy (interictal dysphoric disorder, postictal and interictal psychosis) were used to assess presurgical psychiatric disorders. Kaplan-Meier event-free Survival and adjusted hazard ratios were estimated with unconditional logistic regression. Seventy-seven (41.4%) patients had a preoperative Axis I psychiatric diagnosis. Thirty-six patients had depression, I I interictal dysphoric disorder, 14 interictal psychosis, 6 postictal psychosis, and 10 anxiety disorders. Twenty-three (12.4%) patients had Axis 11 personality disorders. Regarding seizure outcome, preoperative anxiety disorders (P = 0.009) and personality disorders (P = 0.003) were positively Correlated with Engel class I B (remaining auras) or higher. These findings emphasize the importance of presurgical psychiatric evaluation, counseling, and Postsurgical follow-up of patients with epilepsy and psychiatric disorders. (C) 2009 Elsevier Inc. All rights reserved.

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In this paper we present the composite Euler method for the strong solution of stochastic differential equations driven by d-dimensional Wiener processes. This method is a combination of the semi-implicit Euler method and the implicit Euler method. At each step either the semi-implicit Euler method or the implicit Euler method is used in order to obtain better stability properties. We give criteria for selecting the semi-implicit Euler method or the implicit Euler method. For the linear test equation, the convergence properties of the composite Euler method depend on the criteria for selecting the methods. Numerical results suggest that the convergence properties of the composite Euler method applied to nonlinear SDEs is the same as those applied to linear equations. The stability properties of the composite Euler method are shown to be far superior to those of the Euler methods, and numerical results show that the composite Euler method is a very promising method. (C) 2001 Elsevier Science B.V. All rights reserved.

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In this paper we discuss implicit Taylor methods for stiff Ito stochastic differential equations. Based on the relationship between Ito stochastic integrals and backward stochastic integrals, we introduce three implicit Taylor methods: the implicit Euler-Taylor method with strong order 0.5, the implicit Milstein-Taylor method with strong order 1.0 and the implicit Taylor method with strong order 1.5. The mean-square stability properties of the implicit Euler-Taylor and Milstein-Taylor methods are much better than those of the corresponding semi-implicit Euler and Milstein methods and these two implicit methods can be used to solve stochastic differential equations which are stiff in both the deterministic and the stochastic components. Numerical results are reported to show the convergence properties and the stability properties of these three implicit Taylor methods. The stability analysis and numerical results show that the implicit Euler-Taylor and Milstein-Taylor methods are very promising methods for stiff stochastic differential equations.

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Evaluative learning theory states that affective learning, the acquisition of likes and dislikes, is qualitatively different from relational learning, the learning of predictive relationships among stimuli. Three experiments tested the prediction derived from evaluative learning theory that relational learning, but not affective learning, is affected by stimulus competition by comparing performance during two conditional stimuli, one trained in a superconditioning procedure and the other in a blocking procedure. Ratings of unconditional stimulus expectancy and electrodermal responses indicated stimulus competition in relational learning. Evidence for stimulus competition in affective learning was provided by verbal ratings of conditional stimulus pleasantness and by measures of blink startle modulation. Taken together, the present experiments demonstrate stimulus competition in relational and affective learning, a result inconsistent with evaluative learning theory. (C) 2001 Academic Press.

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We obtain the finite-temperature unconditional master equation of the density matrix for two coupled quantum dots (CQD's) when one dot is subjected to a measurement of its electron occupation number using a point contact (PC). To determine how the CQD system state depends on the actual current through the PC device, we use the so-called quantum trajectory method to derive the zero-temperature conditional master equation. We first treat the electron tunneling through the PC barrier as a classical stochastic point process (a quantum-jump model). Then we show explicitly that our results can be extended to the quantum-diffusive limit when the average electron tunneling rate is very large compared to the extra change of the tunneling rate due to the presence of the electron in the dot closer to the PC. We find that in both quantum-jump and quantum-diffusive cases, the conditional dynamics of the CQD system can be described by the stochastic Schrodinger equations for its conditioned state vector if and only if the information carried away from the CQD system by the PC reservoirs can be recovered by the perfect detection of the measurements.

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We describe the conditional and unconditional dynamics of two coupled quantum dots when one dot is subjected to a measurement of its occupation number by coupling it to a third readout dot via the Coulomb interaction. The readout dot is coupled to source and drain leads under weak bias, and a tunnel current flows through a single bound state when energetically allowed. The occupation of the quantum dot near the readout dot shifts the bound state of the readout dot from a low conducting state to a high conducting state. The measurement is made by continuously monitoring the tunnel current through the readout dot. We show that there is a difference between the time scale for the measurement-induced decoherence between the localized states of the dots, and the time scale on which the system becomes localized due to the measurement.

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We apply the quantum trajectory method to current noise in resonant tunneling devices. The results from dynamical simulation are compared with those from unconditional master equation approach. We show that the stochastic Schrodinger equation approach is useful in modeling the dynamical processes in mesoscopic electronic systems.

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We present the conditional quantum dynamics of an electron tunneling between two quantum dots subject to a measurement using a low transparency point contact or tunnel junction. The double dot system forms a single qubit and the measurement corresponds to a continuous in time readout of the occupancy of the quantum dot. We illustrate the difference between conditional and unconditional dynamics of the qubit. The conditional dynamics is discussed in two regimes depending on the rate of tunneling through the point contact: quantum jumps, in which individual electron tunneling current events can be distinguished, and a diffusive dynamics in which individual events are ignored, and the time-averaged current is considered as a continuous diffusive variable. We include the effect of inefficient measurement and the influence of the relative phase between the two tunneling amplitudes of the double dot/point contact system.

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GLUT4 is a mammalian facilitative glucose transporter that is highly expressed in adipose tissue and striated muscle. In response to insulin, GLUT4 moves from intracellular storage areas to the plasma membrane, thus increasing cellular glucose uptake. While the verification of this 'translocation hypothesis' (Cushman SW. Wardzala LJ. J Biol Chem 1980;255: 4758-4762 and Suzuki K, Kono T. Proc Natl Acad Sci 1980;77: 2542-2545) has increased our understanding of insulin-regulated glucose transport, a number of fundamental questions remain unanswered. Where is GLUT4 stored within the basal cell? How does GLUT4 move to the cell surface and what mechanism does insulin employ to accelerate this process) Ultimately we require a convergence of trafficking studies with research in signal transduction. However, despite more than 30 years of intensive research we have still not reached this point. The problem is complex, involving at least two separate signal transduction pathways which feed into what appears to be a very dynamic sorting process. Below we discuss some of these complexities and highlight new data that are bringing us closer to the resolution of these questions.

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The agency relationship between managers and shareholders has the potential to influence decision-making in the firm which in turn potentially impacts on firm characteristics such as value and leverage. Prior evidence has demonstrated an association between ownership structure and firm value. This paper extends the literature by examining a further link between ownership structure and capital structure. Using an agency framework, it is argued that the distribution of equity ownership among corporate managers and external blockholders may have a significant relation with leverage. The empirical results provide support for a positive relation between external blockholders and leverage, and non-linear relation between the level of managerial share ownership and leverage. The results also suggest that the relation between external block ownership and leverage varies across the level of managerial share ownership. These results are consistent with active monitoring by blockholders, and the effects of convergence-of-interests and management entrenchment.