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The Tools To Create Something Special In The Melodic Techno Realm

It came to our attention that our malware sample reporting form was rejecting submissions for quite some time now. The problem has been resolved and members of the public may once again use this form to submit malware samples to us. We apologize for any inconvenience caused.

Hayabusa 2 expected to return with out-of-this-world material in December

Video Close-up footage of asteroid Ryugu, taken by the Hayabusa 2 spacecraft as it touched down to retrieve a sample, reveals the near-Earth object’s surface may have been torched by the Sun as its orbit changed over time.…

musicisart magazine Osunlade Samples Dionne Warwick

Producers Osunlade sampled Dionne Warwick‘s classic R&B original Walk the Way You Talk. Osunlade turned their creation “Dionne” into a powerful message with an addictive, motivating dance beat. Released by Dionne Warwick in 1970, the phrasing sampled plays the words “Just because you say things are gonna change, saying something’s wrong isn’t good enough.” The meaning still […]

The post Osunlade Samples Dionne Warwick appeared first on musicisart magazine.

We have created sample presentations for a variety of market sectors, including education, healthcare, restaurants, estate agents, retail, hotels, training centers and theme park attractions.

George C. Linderman and Stefan Steinerberger
Math. Comp. 89 (2020), 1933-1952.
Abstract, references and article information

Nora Linscheid
Apr 14, 2020; 0:RA119.001878v1-mcp.RA119.001878
Research

Teng-Fei Yuan
Apr 1, 2020; 61:580-586
Methods

Genetic and genomic research has greatly advanced our understanding of heart disease. Yet, comprehensive, in-depth, quantitative maps of protein expression in hearts of living humans are still lacking. Using samples obtained during valve replacement surgery in patients with mitral valve prolapse (MVP), we set out to define inter-chamber differences, the intersect of proteomic data with genetic or genomic datasets, and the impact of left atrial dilation on the proteome of patients with no history of atrial fibrillation (AF).  We collected biopsies from right atria (RA), left atria (LA) and left ventricle (LV) of seven male patients with mitral valve regurgitation with dilated LA but no history of AF. Biopsy samples were analyzed by high-resolution mass spectrometry (MS), where peptides were pre-fractionated by reverse phase high-pressure liquid chromatography prior to MS measurement on a Q-Exactive-HF Orbitrap instrument. We identified 7,314 proteins based on 130,728 peptides. Results were confirmed in an independent set of biopsies collected from three additional individuals. Comparative analysis against data from post-mortem samples showed enhanced quantitative power and confidence level in samples collected from living hearts. Our analysis, combined with data from genome wide association studies suggested candidate gene associations to MVP, identified higher abundance in ventricle for proteins associated with cardiomyopathies and revealed the dilated LA proteome, demonstrating differential representation of molecules previously associated with AF, in non-AF hearts. This is the largest dataset of cardiac protein expression from human samples collected in vivo. It provides a comprehensive resource that allows insight into molecular fingerprints of MVP and facilitates novel inferences between genomic data and disease mechanisms. We propose that over-representation of proteins in ventricle is consequent not to redundancy but to functional need, and conclude that changes in abundance of proteins known to associate with AF are not sufficient for arrhythmogenesis.

Analyzing global steroid metabolism in humans can shed light on the etiologies of steroid-related diseases. However, existing methods require large amounts of serum and lack the evaluation of accuracy. Here, we developed an LC/MS/MS method for the simultaneous quantification of 12 steroid hormones: testosterone, pregnenolone, progesterone, androstenedione, corticosterone, 11-deoxycortisol, cortisol, 17-hydroxypregnenolone, 17-hydroxyprogesterone, dehydroepiandrosterone, estriol, and estradiol. Steroids and spiked internal standards in 100 μl serum were extracted by protein precipitation and liquid-liquid extraction. The organic phase was dried by evaporation, and isonicotinoyl chloride was added for steroid derivatization, followed by evaporation under nitrogen and redissolution in 50% methanol. Chromatographic separation was performed on a reverse-phase PFP column, and analytes were detected on a triple quadrupole mass spectrometer with ESI. The lower limits of quantification ranged from 0.005 ng/ml for estradiol to 1 ng/ml for cortisol. Apparent recoveries of steroids at high, medium, and low concentrations in quality control samples were between 86.4% and 115.0%. There were limited biases (–10.7% to 10.5%) between the measured values and the authentic values, indicating that the method has excellent reliability. An analysis of the steroid metabolome in pregnant women highlighted the applicability of the method in clinical serum samples. We conclude that the LC/MS/MS method reported here enables steroid metabolome analysis with high accuracy and reduced serum consumption, indicating that it may be a useful tool in both clinical and scientific laboratory research.

We conducted a two-sample Mendelian randomisation study to investigate the causal associations of type 2 diabetes mellitus (T2DM) with risk of overall cancer and 22 site-specific cancers. Summary-level data for cancer were extracted from the Breast Cancer Association Consortium and UK Biobank. Genetic predisposition to T2DM was associated with higher odds of pancreatic, kidney, uterine and cervical cancer, lower odds of oesophageal cancer and melanoma, but not associated with 16 other site-specific cancers or overall cancer. The odds ratios (95% confidence interval) were 1.13 (1.04, 1.22), 1.08 (1.00, 1.17), 1.08 (1.01, 1.15), 1.07 (1.01, 1.15), 0.89 (0.81, 0.98), and 0.93 (0.89, 0.97) for pancreatic, kidney, uterine, cervical, and oesophageal cancer and melanoma, respectively. The association between T2DM and pancreatic cancer was also observed in a meta-analysis of this and a previous Mendelian randomisation study (odds ratio 1.08; 1.02, 1.14; p=0.009). There was limited evidence supporting causal associations between fasting glucose and cancer. Genetically predicted fasting insulin levels were positively associated with cancers of the uterus, kidney, pancreas and lung. The present study found causal detrimental effects of T2DM on several cancers. We suggested to reinforce the cancers screening in T2DM patients to enable the early detection of cancer.

OBJECTIVE

Preanalytical processing of blood samples can affect plasma glucose measurement because on-going glycolysis by cells prior to centrifugation can lower its concentration. In June 2017, ACT Pathology changed the processing of oral glucose tolerance test (OGTT) blood samples for pregnant women from a delayed to an early centrifugation protocol. The effect of this change on the rate of gestational diabetes mellitus (GDM) diagnosis was determined.

The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level $alpha$. This paradigm serves naturally in applications such as severe disease diagnosis and spam detection, where people have clear priorities among the two error types. Recently, Tong, Feng, and Li (2018) proposed a nonparametric umbrella algorithm that adapts all scoring-type classification methods (e.g., logistic regression, support vector machines, random forest) to respect the given type I error (i.e., conditional probability of classifying a class $0$ observation as class $1$ under the 0-1 coding) upper bound $alpha$ with high probability, without specific distributional assumptions on the features and the responses. Universal the umbrella algorithm is, it demands an explicit minimum sample size requirement on class $0$, which is often the more scarce class, such as in rare disease diagnosis applications. In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, which does not need the minimum sample size requirements on class $0$ observations and thus is suitable for small sample applications such as rare disease diagnosis. Leveraging both the existing nonparametric and the newly proposed parametric thresholding rules, we propose four LDA-based NP classifiers, for both low- and high-dimensional settings. On the theoretical front, we prove NP oracle inequalities for one proposed classifier, where the rate for excess type II error benefits from the explicit parametric model assumption. Furthermore, as NP classifiers involve a sample splitting step of class $0$ observations, we construct a new adaptive sample splitting scheme that can be applied universally to NP classifiers, and this adaptive strategy reduces the type II error of these classifiers. The proposed NP classifiers are implemented in the R package nproc.

Consider an unknown smooth function $f: [0,1]^d ightarrow mathbb{R}$, and assume we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) + eta_i) mod 1$, for $x_i in [0,1]^d$, where $eta_i$ denotes the noise. Given the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) mod 1$. We formulate a natural approach for solving this problem, which works with angular embeddings of the noisy mod 1 samples over the unit circle, inspired by the angular synchronization framework. This amounts to solving a smoothness regularized least-squares problem -- a quadratically constrained quadratic program (QCQP) -- where the variables are constrained to lie on the unit circle. Our proposed approach is based on solving its relaxation, which is a trust-region sub-problem and hence solvable efficiently. We provide theoretical guarantees demonstrating its robustness to noise for adversarial, as well as random Gaussian and Bernoulli noise models. To the best of our knowledge, these are the first such theoretical results for this problem. We demonstrate the robustness and efficiency of our proposed approach via extensive numerical simulations on synthetic data, along with a simple least-squares based solution for the unwrapping stage, that recovers the original samples of $f$ (up to a global shift). It is shown to perform well at high levels of noise, when taking as input the denoised modulo $1$ samples. Finally, we also consider two other approaches for denoising the modulo 1 samples that leverage tools from Riemannian optimization on manifolds, including a Burer-Monteiro approach for a semidefinite programming relaxation of our formulation. For the two-dimensional version of the problem, which has applications in synthetic aperture radar interferometry (InSAR), we are able to solve instances of real-world data with a million sample points in under 10 seconds, on a personal laptop.

Marco A. R. Ferreira.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 734--744.

Abstract:
We study the limiting behavior of the one-at-a-time Gibbs sampler for the intrinsic conditional autoregressive model with centering on the fly. The intrinsic conditional autoregressive model is widely used as a prior for random effects in hierarchical models for spatial modeling. This model is defined by full conditional distributions that imply an improper joint “density” with a multivariate Gaussian kernel and a singular precision matrix. To guarantee propriety of the posterior distribution, usually at the end of each iteration of the Gibbs sampler the random effects are centered to sum to zero in what is widely known as centering on the fly. While this works well in practice, this informal computational way to recenter the random effects obscures their implied prior distribution and prevents the development of formal Bayesian procedures. Here we show that the implied prior distribution, that is, the limiting distribution of the one-at-a-time Gibbs sampler for the intrinsic conditional autoregressive model with centering on the fly is a singular Gaussian distribution with a covariance matrix that is the Moore–Penrose inverse of the precision matrix. This result has important implications for the development of formal Bayesian procedures such as reference priors and Bayes-factor-based model selection for spatial models.

Jamye Curry, Xin Dang, Hailin Sang.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 425--454.

Abstract:
We study a rank based univariate two-sample distribution-free test. The test statistic is the difference between the average of between-group rank distances and the average of within-group rank distances. This test statistic is closely related to the two-sample Cramér–von Mises criterion. They are different empirical versions of a same quantity for testing the equality of two population distributions. Although they may be different for finite samples, they share the same expected value, variance and asymptotic properties. The advantage of the new rank based test over the classical one is its ease to generalize to the multivariate case. Rather than using the empirical process approach, we provide a different easier proof, bringing in a different perspective and insight. In particular, we apply the Hájek projection and orthogonal decomposition technique in deriving the asymptotics of the proposed rank based statistic. A numerical study compares power performance of the rank formulation test with other commonly-used nonparametric tests and recommendations on those tests are provided. Lastly, we propose a multivariate extension of the test based on the spatial rank.

Si Cheng, Daniel J. Eck, Forrest W. Crawford.

Source: Statistics Surveys, Volume 14, 1--31.

Abstract:
A finite set is “hidden” if its elements are not directly enumerable or if its size cannot be ascertained via a deterministic query. In public health, epidemiology, demography, ecology and intelligence analysis, researchers have developed a wide variety of indirect statistical approaches, under different models for sampling and observation, for estimating the size of a hidden set. Some methods make use of random sampling with known or estimable sampling probabilities, and others make structural assumptions about relationships (e.g. ordering or network information) between the elements that comprise the hidden set. In this review, we describe models and methods for learning about the size of a hidden finite set, with special attention to asymptotic properties of estimators. We study the properties of these methods under two asymptotic regimes, “infill” in which the number of fixed-size samples increases, but the population size remains constant, and “outfill” in which the sample size and population size grow together. Statistical properties under these two regimes can be dramatically different.

Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.

Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which extit{adapt} themselves to obtain better estimators over a sequence of iterations. Although it is straightforward to show that they have the same $mathcal{O}(1/sqrt{N})$ convergence rate as standard importance samplers, where $N$ is the number of Monte Carlo samples, the behaviour of adaptive importance samplers over the number of iterations has been left relatively unexplored. In this work, we investigate an adaptation strategy based on convex optimisation which leads to a class of adaptive importance samplers termed extit{optimised adaptive importance samplers} (OAIS). These samplers rely on the iterative minimisation of the $chi^2$-divergence between an exponential-family proposal and the target. The analysed algorithms are closely related to the class of adaptive importance samplers which minimise the variance of the weight function. We first prove non-asymptotic error bounds for the mean squared errors (MSEs) of these algorithms, which explicitly depend on the number of iterations and the number of samples together. The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $L_2$ errors of the optimised samplers converge to the optimal rate of $mathcal{O}(1/sqrt{N})$ and the rate of convergence in the number of iterations are explicitly provided. When the target does not belong to the exponential family, the rate of convergence is the same but the asymptotic $L_2$ error increases by a factor $sqrt{ ho^star} > 1$, where $ ho^star - 1$ is the minimum $chi^2$-divergence between the target and an exponential-family proposal.

Changliang Zou, Guanghui Wang, Runze Li.

Source: The Annals of Statistics, Volume 48, Number 1, 413--439.

Abstract:
In multiple change-point analysis, one of the major challenges is to estimate the number of change-points. Most existing approaches attempt to minimize a Schwarz information criterion which balances a term quantifying model fit with a penalization term accounting for model complexity that increases with the number of change-points and limits overfitting. However, different penalization terms are required to adapt to different contexts of multiple change-point problems and the optimal penalization magnitude usually varies from the model and error distribution. We propose a data-driven selection criterion that is applicable to most kinds of popular change-point detection methods, including binary segmentation and optimal partitioning algorithms. The key idea is to select the number of change-points that minimizes the squared prediction error, which measures the fit of a specified model for a new sample. We develop a cross-validation estimation scheme based on an order-preserved sample-splitting strategy, and establish its asymptotic selection consistency under some mild conditions. Effectiveness of the proposed selection criterion is demonstrated on a variety of numerical experiments and real-data examples.

Monika Bhattacharjee, Arup Bose.

Source: The Annals of Statistics, Volume 47, Number 6, 3470--3503.

Abstract:
Consider a high-dimensional linear time series model where the dimension $p$ and the sample size $n$ grow in such a way that $p/n o 0$. Let $hat{Gamma }_{u}$ be the $u$th order sample autocovariance matrix. We first show that the LSD of any symmetric polynomial in ${hat{Gamma }_{u},hat{Gamma }_{u}^{*},ugeq 0}$ exists under independence and moment assumptions on the driving sequence together with weak assumptions on the coefficient matrices. This LSD result, with some additional effort, implies the asymptotic normality of the trace of any polynomial in ${hat{Gamma }_{u},hat{Gamma }_{u}^{*},ugeq 0}$. We also study similar results for several independent MA processes. We show applications of the above results to statistical inference problems such as in estimation of the unknown order of a high-dimensional MA process and in graphical and significance tests for hypotheses on coefficient matrices of one or several such independent processes.

Alessandro Rinaldo, Larry Wasserman, Max G’Sell.

Source: The Annals of Statistics, Volume 47, Number 6, 3438--3469.

Abstract:
Several new methods have been recently proposed for performing valid inference after model selection. An older method is sample splitting: use part of the data for model selection and the rest for inference. In this paper, we revisit sample splitting combined with the bootstrap (or the Normal approximation). We show that this leads to a simple, assumption-lean approach to inference and we establish results on the accuracy of the method. In fact, we find new bounds on the accuracy of the bootstrap and the Normal approximation for general nonlinear parameters with increasing dimension which we then use to assess the accuracy of regression inference. We define new parameters that measure variable importance and that can be inferred with greater accuracy than the usual regression coefficients. Finally, we elucidate an inference-prediction trade-off: splitting increases the accuracy and robustness of inference but can decrease the accuracy of the predictions.

Ioannis Ntzoufras, Claudia Tarantola, Monia Lupparelli.

Source: Bayesian Analysis, Volume 14, Number 3, 797--823.

Abstract:
We introduce a novel Bayesian approach for quantitative learning for graphical log-linear marginal models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. The likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and Markov Chain Monte Carlo (MCMC) methods are needed. Finally, a well-defined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for such models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm that employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformation. We exploit a conditional conjugate setup to build an efficient proposal on probability parameters. The proposed methodology is illustrated by a simulation study and a real dataset.

L. F. South, A. N. Pettitt, C. C. Drovandi.

Source: Bayesian Analysis, Volume 14, Number 3, 773--796.

Abstract:
Sequential Monte Carlo (SMC) methods for sampling from the posterior of static Bayesian models are flexible, parallelisable and capable of handling complex targets. However, it is common practice to adopt a Markov chain Monte Carlo (MCMC) kernel with a multivariate normal random walk (RW) proposal in the move step, which can be both inefficient and detrimental for exploring challenging posterior distributions. We develop new SMC methods with independent proposals which allow recycling of all candidates generated in the SMC process and are embarrassingly parallelisable. A novel evidence estimator that is easily computed from the output of our independent SMC is proposed. Our independent proposals are constructed via flexible copula-type models calibrated with the population of SMC particles. We demonstrate through several examples that more precise estimates of posterior expectations and the marginal likelihood can be obtained using fewer likelihood evaluations than the more standard RW approach.

Qingyuan Zhao, Jingshu Wang, Wes Spiller, Jack Bowden, Dylan S. Small.

Source: Statistical Science, Volume 34, Number 2, 317--333.

Abstract:
Instrumental variable analysis is a widely used method to estimate causal effects in the presence of unmeasured confounding. When the instruments, exposure and outcome are not measured in the same sample, Angrist and Krueger ( J. Amer. Statist. Assoc. 87 (1992) 328–336) suggested to use two-sample instrumental variable (TSIV) estimators that use sample moments from an instrument-exposure sample and an instrument-outcome sample. However, this method is biased if the two samples are from heterogeneous populations so that the distributions of the instruments are different. In linear structural equation models, we derive a new class of TSIV estimators that are robust to heterogeneous samples under the key assumption that the structural relations in the two samples are the same. The widely used two-sample two-stage least squares estimator belongs to this class. It is generally not asymptotically efficient, although we find that it performs similarly to the optimal TSIV estimator in most practical situations. We then attempt to relax the linearity assumption. We find that, unlike one-sample analyses, the TSIV estimator is not robust to misspecified exposure model. Additionally, to nonparametrically identify the magnitude of the causal effect, the noise in the exposure must have the same distributions in the two samples. However, this assumption is in general untestable because the exposure is not observed in one sample. Nonetheless, we may still identify the sign of the causal effect in the absence of homogeneity of the noise.

Media reports suggest that adolescents are being charged with sex crimes and even placed on sex offender registries because of participating in the "sexting" of sexual images, but the nature and outcomes of such incidents have not been described empirically.

This is the first study to examine types of sexting cases handled by police. It informs clinicians by identifying characteristics of "aggravated" versus milder incidents and finding that most youth were not arrested. Sex offender registration was rare. (Read the full article)

Although there is some older literature examining how arrest prevalence accumulates through adolescence and adulthood, there is no contemporary research examining the arrest histories of a representative sample of American youth.

Using a contemporary US sample of adolescents and young adults, we estimated the cumulative arrest prevalence through age 23. The results suggest a substantial increase in the cumulative prevalence of arrest since the 1960s. (Read the full article)

Newborn screening (NBS) programs may store bloodspot samples and use them for secondary purposes, such as research. Recent public controversies and lawsuits over storage and secondary uses underscore the need to engage the public on these issues.

This public engagement study identifies values underlying citizens’ acceptance of and discomfort with research from NBS samples. Well-designed methods of public education and civic discourse on the risks and benefits of storage and secondary uses of NBS samples are required. (Read the full article)

Research has highlighted an important relationship between motor coordination and emotional functioning in children and adolescents. Few studies have provided support for this idea; research is therefore needed to further understand the relationship between the motor and emotional domains.

The results suggest that the relationship between motor coordination and emotional functioning (anxious and depressive symptoms) in an adolescent sample may be understood in terms of a mechanism whereby motor coordination has an indirect impact on emotional functioning via self-perceptions. (Read the full article)

Known rates of nonsuicidal self-injury, hurting oneself without the intent to die, are between ~7% and 24% in samples of early adolescents and older adolescents, yet research has not reported rates for youth younger than 11 years old.

Children as young as 7 years old report engaging in nonsuicidal self-injury. There is a grade by gender interaction for nonsuicidal self-injury, such that ninth-grade girls report the greatest rates of engagement and do so by cutting themselves. (Read the full article)

Physical punishment is associated with aggression, delinquency, and internalizing conditions in childhood, as well as a range of Axis I mental disorders in adulthood. More research is needed on the possible long-term relationship between physical punishment and mental health.

To our knowledge, this is the first nationally representative examination of physical punishment and a range of Axis I and II disorders, gender interactions, and proportion of mental disorders in the general population that may be attributable to physical punishment. (Read the full article)

Lifestyle and vulnerability associated with food allergy might predispose affected children to being bullied. Our previous parent survey identified high rates of bullying in this population, but child reports and emotional impact were not assessed.

Bullying was common, often involving threats with food. Bullied food-allergic children, compared with nonbullied, report higher anxiety and lower quality of life. Parental awareness of bullying (~50% of cases) was associated with better social and emotional functioning in the child. (Read the full article)

Most studies focus on health of foster children or local samples of young children. One previous study examined a national cohort longitudinally but did not address the full age group or range of conditions at the time of initial investigation.

Using 2 approaches to assess children (aged 0–17.5 years) who have chronic health conditions, we found that regardless of placement, investigated children had much higher rates of these conditions than the general population at the time of initial assessment. (Read the full article)

The Delaware Division of Public Health (DPH) has advised the operator of Broadkiln Beach Water Company located in Sussex County to issue a boil water notice to customers after water samples collected from the system tested positive for the presence of E-coli bacteria on Friday, July 20. Because the Broadkiln Beach Water Company shares a connection with PrimeHook Water Company, anyone who uses drinking water from either system should not drink the water without boiling it first.

  In Android, it is very easy to create an app that can play video clips; it is also easy to create a game app with 3D graphic surface. But it is not straightforward to create an app that can play...

The Xiaomi Redmi Note 7 Pro is a big upgrade from its predecessor with a 48MP camera and a powerful mid-range chipset tuned for gaming, but is it best mid-range phone to buy right now? Watch our review to find out.

This community transmission test is running separately from the testing and tracking of symptomatic travellers as well as those who have come in contact with confirmed Covid-19 cases.

Hello,

Would anyone know how to setup a PSS or QPSS simulation with 25% dutycycle clock sources or if such a thing is possible with QPSS.

Fig1 (below) is a snapshot of the circuit I am trying to characterize. This has 4 clock ports each with 25%duty cycle in the ON state. Fig2 below shows two of these clocks.

Each path in the circuit consists of two switches with a low pass RC sandwiched in between. The Input is a 50Ohm port sine wave and the output is a 1K resistor. The output nets of all paths are connected together.

I am trying to determine the swept frequency response from input to output (voltage) when the input is from 500Mhz to  510MHz. The Period (T=1/Fp) of each of the pulses is such that Fp=500MHz. The first pulse source has a delay=0, second has delay=T/4, third delay=2T/4, etc...

I am currently getting it working and seeing the correct result (bandpass response) with Transient but the problem is doing a dft at 500MHz with 10KHz spacings needs at least 100us and takes up a lot of time and disk space.

Many Thanks,
Chris.

Fig1

Fig2

(MENAFN - Jordan News Agency) Zarqa, May 9 (Petra) - Director of Zarqa Health Affairs, Dr. Mohammed Tahan, said the epidemiological investigation team... ......