Context. Protoplanetary disks, which are the natural consequence of the gravitational collapse of the dense molecular cloud cores, host the formation of the known planetary systems in our universe. ...Substantial efforts have been dedicated to investigating the properties of these disks in the more mature Class II stage, either via numerical simulations of disk evolution from a limited range of initial conditions or observations of their dust continuum and line emission from specific molecular tracers. The results coming from these two standpoints have been used to draw comparisons. However, few studies have investigated the main limitations at work when measuring the embedded Class 0/I disk properties from observations, especially in a statistical fashion. Aims. In this study, we provide a first attempt to compare the accuracy of some critical disk parameters in Class 0/I systems, as derived on real ALMA observational data, with the corresponding physical parameters that can be directly defined by theoreticians and modellers in numerical simulations. The approach we follow here is to provide full post-processing of the numerical simulations and apply it to the synthetic observations the same techniques used by observers to derive the physical parameters. Methods. We performed 3D Monte Carlo radiative transfer and mock interferometric observations of the disk populations formed in a magnetohydrodynamic (MHD) simulation model of disk formation through the collapse of massive clumps with the tools RADMC-3D and CASA, respectively, to obtain their synthetic observations. With these observations, we re-employed the techniques commonly used in disk modelling from their continuum emissions to infer the properties that would most likely be obtained with real interferometers. We then demonstrated how these properties may vary with respect to the gas kinematics analyses and dust continuum modelling. Results. Our modelling procedure, based on a two-component model for the disk and the envelope, shows that the disk sizes can be properly recovered from observations with sufficient angular resolutions, with an uncertainty of a factor ≈1.6–2.2, whereas their masses cannot be accurately measured. Overall, the masses are predominantly underestimated for larger, more massive disks by a median factor of ≈2.5, and even up to 10 in extreme cases, with the conversion from flux to dust mass under the optically thin assumption. We also find that the single Gaussian fittings are not a reliable modelling technique for young, embedded disks characterised by a strong presence of the envelopes. Thus, such an approach is to be used with caution. Conclusions. The radiative transfer post-processing and synthetic observations of MHD simulations offer genuine help in linking important observable properties of young planet-forming disks to their intrinsic values in simulations. Further extended investigations that tackle the caveats of this study, such as the lack of variation in the dust composition and distribution, dust-to-gas ratio, and other shortcomings in the numerical models, would be essential for setting constraints on our understanding of disk and planet formations.
Abstract
We present a simple computer simulation model for the interaction of automatic guns with unarmed drones. Each drone is represented as a rigid body that consists of several simple bodies such ...as parallelepipeds, ellipsoids, etc. The simplified spatial trajectory of the drone is constructed using third-order Bezier curves. A relatively large group of unconnected drones may be simulated in a computationally fast way using this method. Automatic guns are agents that are shooting at drones with a single of burst fire relative to the ideal aiming point. We performed a simulation of the flight of several drones against several automatic guns for different conditions. As a result of this simulation, the spatial distribution of the drone hit probabilities, the special distribution of the fraction of defeated drones depending on the number of guns, and the influence of urban obstacles on the fraction of drones defeated are presented.
This article introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage ...of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that text data can be a useful addition to more traditional numerical data. Our methodology is implemented in the R package midasml, available from CRAN.
Statistical aggregation is often used to combine multiple opinions within a group. Such aggregates outperform individuals, including experts, in various prediction and estimation tasks. This result ...is attributed to the "wisdom of crowds." We seek to improve the quality of such aggregates by eliminating poorly performing individuals from the crowd. We propose a new measure of contribution to assess the judges' performance
relative
to the group and use positive contributors to build a weighting model for aggregating forecasts. In Study 1, we analyze 1,233 judges forecasting almost 200 current events to illustrate the superiority of our model over unweighted models and models weighted by measures of
absolute
performance. In Study 2, we replicate our findings by using economic forecasts from the European Central Bank and show how the method can be used to identify smaller crowds of the top positive contributors. We show that the model derives its power from identifying experts who consistently outperform the crowd.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2014.1909
.
This paper was accepted by James Smith, decision analysis
.
Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to ...anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.
We examined the accuracy of prediction of Canada, Japan, United Kingdom, and United States from the viewpoint of forecast errors. Compared with the forecast error of each country at the around same ...time, the forecast error of Japan is about 2 times larger. In case of Japan, even immediately before release of quarterly GDP, the forecast error is over 1 %, which is the same level of forecast error as 94 days before in the United States and 135 days before in Canada.
Evaluating the characteristics of forecast errors, it can be pointed out that Japan's forecasts are as efficient as those of other countries, and the addition of major economic statistics is unlikely to improve forecast errors. The reason for Japan's large forecast errors is the fluctuations in the GDP growth rate. These results provide evidence that volatile GDP may make the outlook worse.
Large fluctuations in Japan's quarterly GDP have already been pointed out. It is necessary to examine the factors behind the large fluctuations in the rate of change in Japan's quarterly GDP.
•International Comparison of Forecast Accuracy in Quarterly GDP Forecasts.•Forecast error of Japan is about 2 times larger than other countries at around same time.•Forecast formation is efficient in Japan as in other countries.•The large fluctuations in the rate of change in Japan's quarterly GDP affect forecast errors.
The success of genomic selection depends on the potential to predict genome-assisted breeding values (GEBVs) with high accuracy over several generations without additional phenotyping after ...estimating marker effects. Results from both simulations and practical applications have to be evaluated for this potential, which requires linkage disequilibrium (LD) between markers and QTL. This study shows that markers can capture genetic relationships among genotyped animals, thereby affecting accuracies of GEBVs. Strategies to validate the accuracy of GEBVs due to LD are given. Simulations were used to show that accuracies of GEBVs obtained by fixed regression-least squares (FR-LS), random regression-best linear unbiased prediction (RR-BLUP), and Bayes-B are nonzero even without LD. When LD was present, accuracies decrease rapidly in generations after estimation due to the decay of genetic relationships. However, there is a persistent accuracy due to LD, which can be estimated by modeling the decay of genetic relationships and the decay of LD. The impact of genetic relationships was greatest for RR-BLUP. The accuracy of GEBVs can result entirely from genetic relationships captured by markers, and to validate the potential of genomic selection, several generations have to be analyzed to estimate the accuracy due to LD. The method of choice was Bayes-B; FR-LS should be investigated further, whereas RR-BLUP cannot be recommended.
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