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Journal articles on the topic 'Qml'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 May 2022

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1

Horvath, Robert G. "Influence of loading rate on the capacity of a model pile in clay." Canadian Geotechnical Journal 32, no. 2 (April 1, 1995): 364–68. http://dx.doi.org/10.1139/t95-036.

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Loading tests were carried out on a model pile embedded in clay to examine the influence of rate of loading on the capacity of the pile. The pile was loaded to failure using constant rate of penetration (CRP), quick maintained loading (QML), and quick continuous loading (QCL) methods of loading. The QCL test models the Statnamic loading test, which has been recently developed in Canada. The CRP tests were used as a reference, and the results were normalized using the CRP test results. The durations of the QML and QCL tests varied from approximately 0.1 s to 17 min, which are significantly faster than normal loading rates. Applied loads and point load were measured using load cells, and top displacement was measured using a displacement transducer. The test results showed an increase in pile capacity with increased rate of loading. Damping was found to be significant for the QCL tests (duration = 0.1 s) and negligible for the QML tests (duration ≥ 10 s). Correcting the results of the QCL tests for damping, using the equilibrium point method developed for Statnamic testing, greatly improved the correlation of the QCL and QML test results. Key words : model piles, axial loading, loading rate, clay, laboratory study, test methods.

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2

Karandashev, Konstantin, and O. Anatole von Lilienfeld. "An orbital-based representation for accurate quantum machine learning." Journal of Chemical Physics 156, no. 11 (March 21, 2022): 114101. http://dx.doi.org/10.1063/5.0083301.

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We introduce an electronic structure based representation for quantum machine learning (QML) of electronic properties throughout chemical compound space. The representation is constructed using computationally inexpensive ab initio calculations and explicitly accounts for changes in the electronic structure. We demonstrate the accuracy and flexibility of resulting QML models when applied to property labels, such as total potential energy, HOMO and LUMO energies, ionization potential, and electron affinity, using as datasets for training and testing entries from the QM7b, QM7b-T, QM9, and LIBE libraries. For the latter, we also demonstrate the ability of this approach to account for molecular species of different charge and spin multiplicity, resulting in QML models that infer total potential energies based on geometry, charge, and spin as input.

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3

Pang, Wei, and George M. Coghill. "Learning Qualitative Differential Equation models: a survey of algorithms and applications." Knowledge Engineering Review 25, no. 1 (March 2010): 69–107. http://dx.doi.org/10.1017/s0269888909990348.

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AbstractOver the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives.

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Murakami, Sawako, Yukari Nagahiro, Masakazu Shimada, Nobuo Kanno, Shuji Suzuki, Takuya Yogo, Yasuji Harada, and Yasushi Hara. "Effect of Limb Position on Measurements of the Quadriceps Muscle Length/Femoral Length Ratio in Normal Beagle Dogs." Veterinary and Comparative Orthopaedics and Traumatology 33, no. 04 (May 14, 2020): 279–86. http://dx.doi.org/10.1055/s-0040-1702235.

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Abstract Objectives Dogs with patella alta reportedly have a shorter extensor mechanism than normal dogs. The present study aimed to measure the length of the extensor mechanism and to assess the effect of limb position on the quadriceps muscle length (QML)/femoral bone length (FL) ratio. Study Design Three-dimensional computed tomography images were taken of 12 Beagle dogs. Each dog underwent computed tomographic imaging 24 times with different limb positions. The QML/FL was measured on each image, along with the hip flexion–extension, hip abduction–adduction and stifle flexion–extension angles. Multiple regression analysis was used to determine the effect of these angles on the QML/FL. Results The QML/FL was increased with the hip extended (standardized partial regression coefficient 0.855 with linear plotting, 0.829 with log plotting) and with the stifle flexed (standardized partial regression coefficient 0.814 with linear plotting, 0.800 with log plotting). The partial regression coefficient of the hip abduction–adduction angle was small, indicating a small impact on the QML/FL. The 95% confidence range of the QML/FL with the hip extended and stifle flexed was 0.87 to 1.00 (mean ± standard deviation: 0.93 ± 0.03). Conclusion The QML/FL was more influenced by joint angles when the hip was flexed or the stifle was extended. Hence, these positions should be avoided when evaluating the QML/FL so that the QML is less altered by slight positioning disparities.

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Fernandes, Juliêta Lopes, Maria Luci Esteves Santiago, Andrea Conceição Gomes Lima, Edirlane Soares do Nascimento, and Thais Norberta Bezerra de Moura. "DESEMPENHO MOTOR DE ESCOLARES QUILOMBOLAS ENTRE 8 E 10 ANOS." Revista Baiana de Saúde Pública 42, no. 4 (August 12, 2020): 611–23. http://dx.doi.org/10.22278/2318-2660.2018.v42.n4.a2752.

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O desenvolvimento motor é uma sequência de etapas evolutivas que ocorre de acordo com a faixa etária, determinado por meio dos requisitos de interação da criança com a tarefa, individualidade biológica e condições do ambiente. O objetivo deste estudo é analisar o desempenho motor de escolares quilombolas entre 8 anos a 10 anos de idade. Trata-se de uma pesquisa quantitativa, do tipo transversal, com amostra constituída por 52 escolares. O instrumento de coleta de dados utilizado foi o protocolo de testes da Escala de Desenvolvimento Motor (EDM) de Rosa Neto. A seleção da amostra foi do tipo intencional. Apresentaram classificação normal médio as variáveis coordenação motora fina, com Quociente Motor (QM) (QM1 = 100,47), motricidade global (QM2 = 100,42) e equilíbrio (QM3 = 98,07). Já a variável esquema corporal teve a classificação normal baixo (QM4 = 82,06). As menores classificações foram nas áreas de organização espacial (QM5 = 73,3) com classificação inferior, e a organização temporal, muito inferior (QM6 = 69,0). Na classificação geral dos escolares, 94% (n = 49) obtiveram níveis de desenvolvimento motor normal (médio e baixo). Um percentual de 4% (n = 2) foi classificado como inferior e 2% (n = 1), muito inferior. Conclui-se que o desenvolvimento motor da maioria dos escolares quilombolas encontra-se dentro dos parâmetros da normalidade. Palavras-chave: Desenvolvimento infantil. Desempenho psicomotor. Estudantes.

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6

Wang, Ji, Wenwu Zhang, and Tianrun Zhang. "Passively Q-Switched and Mode-Locked Er3+-Doped Ring Fiber Laser with Pulse Width of Hundreds of Picoseconds." Photonics 8, no. 12 (December 8, 2021): 560. http://dx.doi.org/10.3390/photonics8120560.

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Greatly improving the energy of a single mode-locked pulse while ensuring the acquisition of the width of short pulses will contribute to the application of mode-locked pulse in basic research, such as precision machining. This report has investigated a Q-switched and mode-locked (QML) erbium doped ring fiber laser based on the nonlinear polarization rotation (NPR) technology and a mechanical Q-switched device. Without the working of the mechanical Q-switched device, the fiber laser exported the continuous-wave mode-locked (CWML) pulse, with a width of 212.5 ps, and a repetition frequency of 81.97 MHz. For the CWML operation, the maximum output average power is 25.7 mW, and the energy is only 0.31 nJ. For the QML operation, 18.03 mW average power is achieved at the Q-switching frequency of 100 Hz. The energy of the QML pulse is increased by over 1100 times to 360.6 nJ. The width of the QML pulse is 203.1 ps measured by an autocorrelation curve, with the time-band product (TBP) being 0.598. The power instability is 0.5% (RMS) and 0.7% (RMS), respectively, for CWML and QML operation within 120 min. Furthermore, the spectral signal-to-noise ratio is about 60 dB. For the QML operation, the power instability is 0.48% (RMS) within 60 s and 0.37% (RMS) within 10 s. After frequency stabilization, the frequency fluctuation is ±100 Hz in the long-term of 1200 s, with the frequency stability (FS) calculated to be 2.44 × 10−6. It indicates that the QML fiber laser has good power stability and frequency stability.

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7

Melstrom, Richard T. "Estimating a model of sportfishing trip expenditures using a quasi-maximum likelihood approach." Tourism Economics 23, no. 2 (September 20, 2016): 448–59. http://dx.doi.org/10.5367/te.2015.0511.

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This article presents an exponential model of tourist expenditures estimated by a quasi-maximum likelihood (QML) technique. The advantage of this approach is that, unlike conventional OLS and Tobit estimators, it produces consistent parameter estimates under conditions of a corner solution at zero and heteroscedasticity. An application to sportfishing evaluates the role of socioeconomic demographics and species preferences on trip spending. The bias from an inappropriate estimator is illustrated by comparing the results from QML and OLS estimation, which shows that OLS significantly overstates the impact of trip duration on trip expenditures compared with the QML estimator. Both sets of estimates imply that trout and bass anglers spend significantly more on their fishing trips compared with other anglers.

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8

Hakim, Edward B. "DoD microcircuit qualification innovation–QML." Quality and Reliability Engineering International 6, no. 1 (January 1990): 47–50. http://dx.doi.org/10.1002/qre.4680060109.

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9

Cavaliere, Giuseppe, and Iliyan Georgiev. "ROBUST INFERENCE IN AUTOREGRESSIONS WITH MULTIPLE OUTLIERS." Econometric Theory 25, no. 6 (December 2009): 1625–61. http://dx.doi.org/10.1017/s0266466609990272.

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We consider robust methods for estimation and unit root (UR) testing in autoregressions with infrequent outliers whose number, size, and location can be random and unknown. We show that in this setting standard inference based on ordinary least squares estimation of an augumented Dickey–Fuller (ADF) regression may not be reliable, because (a) clusters of outliers may lead to inconsistent estimation of the autoregressive parameters and (b) large outliers induce a jump component in the asymptotic distribution of UR test statistics. In the benchmark case of known outlier location, we discuss why the augmentation of the ADF regression with appropriate dummy variables not only ensures consistent parameter estimation but also gives rise to UR tests with significant power gains, growing with the number and the size of the outliers. In the case of unknown outlier location, the dummy-based approach is compared with a robust, mixed Gaussian, quasi maximum likelihood (QML) approach, novel in this context. It is proved that, when the ordinary innovations are Gaussian, the QML and the dummy-based approach are asymptotically equivalent, yielding UR tests with the same asymptotic size and power. Moreover, as a by-product of QML the outlier dates can be consistently estimated. When the innovations display tails fatter than Gaussian, the QML approach ensures further power gains over the dummy-based method. Simulations show that the QML ADF-typet-test, in conjunction with standard Dickey–Fuller critical values, yields the best combination of finite-sample size and power.

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10

Kim, Min, Jun Yin, In Hyeok Hwang, Dong Hui Park, Eun Kyeong Lee, Min Ji Kim, and Min Won Lee. "Anti-Acne Vulgaris Effects of Pedunculagin from the Leaves of Quercus mongolica by Anti-Inflammatory Activity and 5α-Reductase Inhibition." Molecules 25, no. 9 (May 5, 2020): 2154. http://dx.doi.org/10.3390/molecules25092154.

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Quercus mongolica (QM)—a member of the Fagaceae family—has been used as traditional medicine in Korea, China and Mongolia as a treatment for inflammation of oral, genital or anal mucosa and for external inflammation of skin. To treat acne vulgaris (AV), we evaluated the inhibition of inflammatory cytokines (IL-6 and IL-8) of QM leaf extract (QML) and its main compound, pedunculagin (PD) in vitro and 5α-reductase inhibitory activity by western blotting. As results, QML and PD showed potent NO production inhibitory activity compared with the positive control (PC), NG-monomethyl-L-arginine (L-NMMA). QML and PD was also showed the decreases of IL-6 and IL-8 compared with the PC, EGCG and exhibited potent 5α-reductase type 1 inhibitory activities compared with the PC, dutasteride.

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Srikumar, Maiyuren, Charles D. Hill, and Lloyd C. L. Hollenberg. "Clustering and enhanced classification using a hybrid quantum autoencoder." Quantum Science and Technology 7, no. 1 (December 21, 2021): 015020. http://dx.doi.org/10.1088/2058-9565/ac3c53.

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Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically represent—their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states—which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

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12

Hu, Hongchang. "QML Estimators in Linear Regression Models with Functional Coefficient Autoregressive Processes." Mathematical Problems in Engineering 2010 (2010): 1–30. http://dx.doi.org/10.1155/2010/956907.

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This paper studies a linear regression model, whose errors are functional coefficient autoregressive processes. Firstly, the quasi-maximum likelihood (QML) estimators of some unknown parameters are given. Secondly, under general conditions, the asymptotic properties (existence, consistency, and asymptotic distributions) of the QML estimators are investigated. These results extend those of Maller (2003), White (1959), Brockwell and Davis (1987), and so on. Lastly, the validity and feasibility of the method are illuminated by a simulation example and a real example.

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13

Winokur, P. S., F. W. Sexton, D. M. Fleetwood, M. D. Terry, M. R. Shaneyfelt, P. V. Dressendorfer, and J. R. Schwank. "Implementing QML for radiation hardness assurance." IEEE Transactions on Nuclear Science 37, no. 6 (1990): 1794–805. http://dx.doi.org/10.1109/23.101193.

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14

Samy Elkhouly, Mona. "QML Estimation of GARCH(1,1) Process." مجلة البحوث المالیة والتجاریة 18, no. 1 (January 1, 2017): 417–35. http://dx.doi.org/10.21608/jsst.2017.59251.

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15

Messenger, C. G. "The QML system for monolithic microcircuits." IEEE Circuits and Devices Magazine 6, no. 1 (January 1990): 34–37. http://dx.doi.org/10.1109/101.47584.

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16

Plata-Cesar, Nely, Jose Raymundo Marcial-Romero, and Jose Antonio Hernandez-Servin. "Reversibility for Quantum Programming Language QML." IEEE Latin America Transactions 18, no. 10 (October 2020): 1692–98. http://dx.doi.org/10.1109/tla.2020.9387639.

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17

Choi, Jiyeon, Ardie Septian, and Won Sik Shin. "Influence of Salinity on the Removal of Ni and Zn by Phosphate-Intercalated Nano Montmorillonite (PINM)." Minerals 10, no. 11 (November 2, 2020): 980. http://dx.doi.org/10.3390/min10110980.

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The salinity influence on the adsorptions of Ni and Zn onto phosphate-intercalated nano montmorillonite (PINM) were investigated. Single adsorption isotherm models fitted the single adsorption data well. The adsorption capacity of Ni was higher than that of Zn onto PINM at different salinities. The single adsorption parameters from Langmuir model (QmL and bL) were compared with the binary adsorption (QmL* and bL*). The QmL* of Zn was lower than that of Ni. The simultaneous presence of Ni and Zn decreased the adsorption capacities. The single and binary adsorptions onto PINM were affected by the salinity. The competitive Langmuir model (CLM), P-factor, Murali and Aylmore (M−A) models, and ideal adsorbed solution theory (IAST) were satisfactory in predicting the binary adsorption data; the CLM showed the best fitting results. Our results showed that the PINM can be used as an active Ni and Zn adsorbent for a permeable reactive barrier (PRB) in the remediation of saline groundwater.

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18

Djurović, Igor, Marko Simeunović, and Vladimir V. Lukin. "Estimating DOA and PPS parameters of signal received by ULA in heavy noise environment." Journal of Electrical Engineering 71, no. 3 (June 1, 2020): 175–84. http://dx.doi.org/10.2478/jee-2020-0024.

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AbstractEstimation of the direction-of-arrival (DOA) and parameters of polynomial phase signal (PPS) impinging on the uniform linear array (ULA) of sensors in heavy-tailed noise environments is considered in this paper. To estimate signal parameters, a recently proposed quasi maximum-likelihood (QML) estimator is adopted. The proposed algorithm consists of two successive steps: (1) noise influence mitigation by using the proposed normalization strategy and (2) signal parameters estimation using the DOA-QML approach. The algorithm performance is evaluated for both monocomponent and multicomponent signals.

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19

Christensen, Anders S., and O. Anatole von Lilienfeld. "Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties." CHIMIA International Journal for Chemistry 73, no. 12 (December 18, 2019): 1028–31. http://dx.doi.org/10.2533/chimia.2019.1028.

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The identification and use of structure–property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties.

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20

Djurović, Igor. "On parameters of the QML PPS estimator." Signal Processing 116 (November 2015): 1–6. http://dx.doi.org/10.1016/j.sigpro.2015.04.006.

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21

Francq, Christian, and Le Quyen Thieu. "QML INFERENCE FOR VOLATILITY MODELS WITH COVARIATES." Econometric Theory 35, no. 1 (February 1, 2018): 37–72. http://dx.doi.org/10.1017/s0266466617000512.

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The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained for a wide class of asymmetric GARCH models with exogenous covariates. The true value of the parameter is not restricted to belong to the interior of the parameter space, which allows us to derive tests for the significance of the parameters. In particular, the relevance of the exogenous variables can be assessed. The results are obtained without assuming that the innovations are independent, which allows conditioning on different information sets. Monte Carlo experiments and applications to financial series illustrate the asymptotic results. In particular, an empirical study demonstrates that the realized volatility can be a helpful covariate for predicting squared returns.

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22

Olejnik, Jakub, and Alicja Olejnik. "QML estimation with non-summable weight matrices." Journal of Geographical Systems 22, no. 4 (July 4, 2020): 469–95. http://dx.doi.org/10.1007/s10109-020-00326-2.

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Abstract This paper revisits the theory of asymptotic behaviour of the well-known Gaussian Quasi-Maximum Likelihood estimator of parameters in mixed regressive, high-order autoregressive spatial models. We generalise the approach previously published in the econometric literature by weakening the assumptions imposed on the spatial weight matrix. This allows consideration of interaction patterns with a potentially larger degree of spatial dependence. Moreover, we broaden the class of admissible distributions of model residuals. As an example application of our new asymptotic analysis we also consider the large sample behaviour of a general group effects design.

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Meitz, Mika, and Pentti Saikkonen. "PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS." Econometric Theory 27, no. 6 (May 31, 2011): 1236–78. http://dx.doi.org/10.1017/s0266466611000041.

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This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi-maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.

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24

Quoreshi, A. M. M. Shahiduzzaman, Reaz Uddin, and Naushad Mamode Khan. "Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework." Journal of Risk and Financial Management 12, no. 2 (April 27, 2019): 74. http://dx.doi.org/10.3390/jrfm12020074.

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This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and FGLS in terms of eliminating serial correlations, but the estimator can be sensitive to start value. Hence, two-stage QML has been suggested. In empirical estimation on two stock transaction data for Ericsson and AstraZeneca, the 2SQML turns out relatively more efficient than CLS and FGLS. The empirical results suggest that both of the series have long memory properties that imply that the impact of macroeconomic news or rumors in one point of time has a persistence impact on future transactions.

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25

Settar, A., N. I. Fatmi, and M. Badaoui. "On the computational estimation of high order GARCH model." Mathematical Modeling and Computing 8, no. 4 (2021): 797–806. http://dx.doi.org/10.23939/mmc2021.04.797.

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To guarantee the non-negativity of the conditional variance of the GARCH process, it is sufficient to assume the non-negativity of its parameters. This condition was empirically violated besides rendering the GARCH model more restrictive. It was subsequently relaxed for some GARCH orders by necessary and sufficient constraints. In this paper, we generalized an approach for the QML estimation of the GARCH(p,q) parameters for all orders $p\geq 1$ and $q\geq1$ using a constrained Kalman filter. Such an approach allows a relaxed QML estimation of the GARCH without the need to identify and/or apply the relaxed constraints to the parameters. The performance of our method is demonstrated through Monte Carlo simulations and empirical applications to real data.

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Plata César, Nely, and José-Raymundo Marcial-Romero. "Historial y reversibilidad en el sublenguaje clásico QML." Research in Computing Science 147, no. 12 (December 31, 2018): 299–310. http://dx.doi.org/10.13053/rcs-147-12-28.

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Pecht, M. G., J. Fink, E. Hakim, and J. Wyler. "An assessment of the Qualified Manufacturer List (QML)." IEEE Aerospace and Electronic Systems Magazine 12, no. 7 (July 1997): 39–42. http://dx.doi.org/10.1109/62.596656.

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28

Firus, Viktoria, Steffen Becker, and Jens Happe. "Parametric Performance Contracts for QML-specified Software Components." Electronic Notes in Theoretical Computer Science 141, no. 3 (December 2005): 73–90. http://dx.doi.org/10.1016/j.entcs.2005.04.036.

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29

Wieder, Marcus, Josh Fass, and John D. Chodera. "Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution." Chemical Science 12, no. 34 (2021): 11364–81. http://dx.doi.org/10.1039/d1sc01185e.

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We show how alchemical free energies can be calculated with QML potentials to identify deficiencies in RRHO approximations for computing tautomeric free energies, and how these potentials can be learned from experiment to improve prediction accuracy.

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Nguyen, Quoc Chuong, Le Bin Ho, Lan Nguyen Tran, and Hung Q. Nguyen. "Qsun: an open-source platform towards practical quantum machine learning applications." Machine Learning: Science and Technology 3, no. 1 (March 1, 2022): 015034. http://dx.doi.org/10.1088/2632-2153/ac5997.

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Abstract Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms (VQAs) have been proposed and tested on QVMs to surpass the limitations of quantum hardware. Our goal is to exploit further the VQAs towards practical applications of quantum machine learning (QML) using state-of-the-art quantum computers. In this paper, we first introduce a QVM named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting VQAs. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of QML: quantum linear regression and quantum neural network.

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Francq, Christian, Guillaume Lepage, and Jean-Michel Zakoïan. "Two-stage non Gaussian QML estimation of GARCH models and testing the efficiency of the Gaussian QMLE." Journal of Econometrics 165, no. 2 (December 2011): 246–57. http://dx.doi.org/10.1016/j.jeconom.2011.08.001.

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32

Todros, Koby, and Joseph Tabrikian. "QML-Based Joint Diagonalization of Positive-Definite Hermitian Matrices." IEEE Transactions on Signal Processing 58, no. 9 (September 2010): 4656–73. http://dx.doi.org/10.1109/tsp.2010.2051428.

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33

Djurovic, Igor, and Marko Simeunovic. "Resolving aliasing effect in the QML estimation of PPSs." IEEE Transactions on Aerospace and Electronic Systems 52, no. 3 (June 2016): 1494–99. http://dx.doi.org/10.1109/taes.2016.150383.

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34

Belis, Vasilis, Samuel González-Castillo, Christina Reissel, Sofia Vallecorsa, Elías F. Combarro, Günther Dissertori, and Florentin Reiter. "Higgs analysis with quantum classifiers." EPJ Web of Conferences 251 (2021): 03070. http://dx.doi.org/10.1051/epjconf/202125103070.

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We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.

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35

Song, Lei, Hongchang Hu, and Xiaosheng Cheng. "Hypothesis Testing in Generalized Linear Models with Functional Coefficient Autoregressive Processes." Mathematical Problems in Engineering 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/862398.

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The paper studies the hypothesis testing in generalized linear models with functional coefficient autoregressive (FCA) processes. The quasi-maximum likelihood (QML) estimators are given, which extend those estimators of Hu (2010) and Maller (2003). Asymptotic chi-squares distributions of pseudo likelihood ratio (LR) statistics are investigated.

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Heathcote, Andrew, and Scott Brown. "Reply to Speckman and Rouder: A theoretical basis for QML." Psychonomic Bulletin & Review 11, no. 3 (June 2004): 577–78. http://dx.doi.org/10.3758/bf03196614.

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Francq, Christian, and Jean-Michel Zakoïan. "QML ESTIMATION OF A CLASS OF MULTIVARIATE ASYMMETRIC GARCH MODELS." Econometric Theory 28, no. 1 (August 3, 2011): 179–206. http://dx.doi.org/10.1017/s0266466611000156.

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We establish the strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) of the parameters of a class of multivariate asymmetric generalized autoregressive conditionally heteroskedastic processes, allowing for cross leverage effects. The conditions required to establish the asymptotic properties of the QMLE are mild and coincide with the minimal ones in the univariate case. In particular, no moment assumption is made on the observed process. Instead, we require strict stationarity, for which a necessary and sufficient condition is established. The asymptotic results are illustrated by Monte Carlo experiments, and an application to a bivariate exchange rates series is proposed.

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Su, Liangjun, and Zhenlin Yang. "QML estimation of dynamic panel data models with spatial errors." Journal of Econometrics 185, no. 1 (March 2015): 230–58. http://dx.doi.org/10.1016/j.jeconom.2014.11.002.

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39

Grattage, Jonathan. "An Overview of QML With a Concrete Implementation in Haskell." Electronic Notes in Theoretical Computer Science 270, no. 1 (February 2011): 165–74. http://dx.doi.org/10.1016/j.entcs.2011.01.015.

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Corradi, Valentina, and Emma M. Iglesias. "Bootstrap refinements for QML estimators of the GARCH(1,1) parameters." Journal of Econometrics 144, no. 2 (June 2008): 500–510. http://dx.doi.org/10.1016/j.jeconom.2008.03.003.

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Sottili, Samuel Bombardelli, José Augusto Ayres Florentino, and Adriana Marques Toigo. "Comparação do desenvolvimento motor de crianças de 06 a 10 anos que participam de um projeto social e de crianças que não participam, na cidade de Porto Alegre, RS." Saúde e Desenvolvimento Humano 7, no. 2 (July 12, 2019): 35. http://dx.doi.org/10.18316/sdh.v7i2.5537.

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Introdução: Os projetos sociais servem para tirar da rua crianças que nas horas vagas estariam em situações vulneráveis, propiciando as mais diversas atividades que ajudarão a desenvolver capacidades motoras, sociais e cognitivas. Objetivo: Verificar o desenvolvimento motor de alunos com idades entre 6 a 10 anos, participantes de um projeto social situado na cidade de Porto Alegre, RS, comparando-as com crianças que não faziam parte do referido projeto e também não praticavam nenhuma atividade extraescolar. Método: A amostra foi composta por 39 indivíduos, os quais foram divididos em dois grupos: o que participou do projeto há, no mínimo 4 meses e no máximo 3 anos (G1; n = 13), e o que não participou do projeto, mas cujos indivíduos estudavam em uma escola da mesma região (G2; n = 26). Foram aplicados os testes de escala de desenvolvimento motor (EDM) de Francisco Rosa Neto em ambos os grupos, com o intuito de avaliar se as crianças estão com a idade psicomotora de acordo com sua faixa etária. Resultados e conclusões: As diferenças entre os grupos foram estatisticamente significativas para o (QMG), tendo o G1 obtido escore de normal médio e o G2, de normal baixo. No QM1 (motricidade fina), houve diferenças estatisticamente significativas e o G1 obteve escore normal médio, e as do G2 apresentaram escore normal baixo. O mesmo ocorreu com as variáveis QM2 e QM3. No QM2 (motricidade global), o G1 apresentou resultado normal alto e o G2, normal baixo; no QM3 (equilíbrio), o G1 obteve escore normal médio e o G2, normal baixo. Nas demais variáveis, houve diferenças entre os grupos, mas estas não foram estatisticamente significantes.

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Bibi, A. "Asymptotic Properties of QML Estimation of Multivariate Periodic CCC − GARCH Models." Mathematical Methods of Statistics 27, no. 3 (July 2018): 184–204. http://dx.doi.org/10.3103/s106653071803002x.

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Djurović, Igor. "QML-RANSAC: PPS and FM signals estimation in heavy noise environments." Signal Processing 130 (January 2017): 142–51. http://dx.doi.org/10.1016/j.sigpro.2016.06.022.

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Wintenberger, Olivier. "Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model." Scandinavian Journal of Statistics 40, no. 4 (October 14, 2013): 846–67. http://dx.doi.org/10.1111/sjos.12038.

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Milczarek, Hubert, Czesław Leśnik, Igor Djurović, and Adam Kawalec. "Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study." Sensors 21, no. 8 (April 17, 2021): 2840. http://dx.doi.org/10.3390/s21082840.

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Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

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Sami, Muhammad, and Akram M. Zeki. "Mini-ATX Computer System in Vehicle." Advanced Materials Research 1115 (July 2015): 484–87. http://dx.doi.org/10.4028/www.scientific.net/amr.1115.484.

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The aim of this study is to create and assemble the system with customizing/building Linux kernel and environments to be compatible and efficient on mini-ITX computer. The objective of the study is to create/customizing lightweight operating system using GNU/Linux to be used on computer to be used on vehicle. The system would also optimize the size and functionalities most probably would be implemented on car computer system.Keywords: mini-ATX, CarPC, Linux, Ubuntu, Qt, QML

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Buczyński, Mateusz, and Marcin Chlebus. "Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels." e-Finanse 14, no. 2 (June 1, 2018): 67–82. http://dx.doi.org/10.2478/fiqf-2018-0013.

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AbstractIn the literature, there is no consensus as to which Value-at-Risk forecasting model is the best for measuring market risk in banks. In the study an analysis of Value-at-Risk forecasting model quality over varying economic stability periods for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and historical simulation models in periods with contrasting volatility trends (increasing, constantly high and decreasing) for countries economically developed (the USA – S&P 500, Germany - DAX and Japan – Nikkei 225) and economically developing (China – SSE COMP, Poland – WIG20 and Turkey – XU100) were compared. The data samples used in the analysis were selected from the period 01.01.1999 – 24.03.2017. To assess the VaR forecast quality: excess ratio, Basel traffic light test, coverage tests (Kupiec test, Christoffersen test), Dynamic Quantile test, cost functions and Diebold-Marino test were used. Obtained results show that the quality of Value-at-Risk forecasts for the models varies depending on a volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found to be the most robust models in the different volatility periods. The results show as well that the CAViaR model forecasts were less appropriate in the increasing volatility period. Moreover, no significant differences for the VaR forecast quality were found for the developed and developing countries.

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Christian Francq and Jean-Michel Zakoïan. "Looking for Efficient QML Estimation of Conditional VaRs at Multiple Risk Levels." Annals of Economics and Statistics, no. 123/124 (2016): 9. http://dx.doi.org/10.15609/annaeconstat2009.123-124.0009.

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Liu, Shew Fan, and Zhenlin Yang. "Modified QML estimation of spatial autoregressive models with unknown heteroskedasticity and nonnormality." Regional Science and Urban Economics 52 (May 2015): 50–70. http://dx.doi.org/10.1016/j.regsciurbeco.2015.02.003.

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Sehu, Marjoree, Helen Haswell, David Drummond, and Renu Vohra. "Assessment of the proficiency of gram stain interpretation at QML pathology, Queensland." Pathology 41 (January 2009): 57. http://dx.doi.org/10.1097/01268031-200941001-00120.

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