High Probability Regions Research Articles

BIOM-27. VOXEL-WISE SURVIVAL ANALYSIS IN GLIOBLASTOMA: TUMOR PROBABILITY MAPS REVEAL CRITICAL REGIONS NEAR WHITE MATTER TRACTS

Abstract BACKGROUND Glioblastoma is the most aggressive primary brain tumor, characterized by rapid growth and poor prognosis. Understanding spatial determinants of survival in GBM is crucial for improving prognostic accuracy and treatment strategies. Our study employs voxel-wise survival analysis (VSA) of tumor probability maps (TPMs) derived from MRI to identify high-risk brain regions. TPMs are generated through radio-pathomic mapping and can predict non-enhancing tumor regions not captured by traditional imaging. We aim to determine if high tumor probability regions are near previously identified critical white matter (WM) tracts and assess their impact on survival outcomes. METHODS We analyzed data from 374 GBM patients from the UCSF-PDGM-v2 dataset. Using a pretrained radio-pathomic model, TPMs were predicted based on T1, T1C, ADC, and FLAIR MRI sequences. All TPMs were rigidly registered to MNI space using ANTs. A Cox proportional hazards model was applied voxel-wise to assess the association between tumor probability and overall survival (OS). A p-value map was generated, thresholded, and cluster corrected (p < 0.05) for further analysis. RESULTS The VSA revealed that high tumor probability regions were significantly associated with poorer OS in GBM. Gray matter (GM) regions near WM tracts with high tumor probability showed markedly reduced survival. GM regions adjacent to the left IFOF (p < 0.05, HR = 1.19, 845 vs. 961 days for high/low TPM), Corpus Callosum Forceps Minor (p = 0.0001, HR = 1, 740 vs. 902 days), Body of the Corpus Callosum (p = 0.005, HR = 0.97, 748 vs. 798 days for high/low TPM) and other WM tracts, such as the left thalamic radiation superior and left reticulospinal tracts had similar trends. CONCLUSIONS Our study demonstrates that regions with high tumor probability, particularly those in the GM near critical WM tracts, are strongly associated with worse overall survival in GBM patients.

Normalizing flow sampling with Langevin dynamics in the latent space

AbstractNormalizing flows (NF) use a continuous generator to map a simple latent (e.g. Gaussian) distribution, towards an empirical target distribution associated with a training data set. Once trained by minimizing a variational objective, the learnt map provides an approximate generative model of the target distribution. Since standard NF implement differentiable maps, they may suffer from pathological behaviors when targeting complex distributions. For instance, such problems may appear for distributions on multi-component topologies or characterized by multiple modes with high probability regions separated by very unlikely areas. A typical symptom is the explosion of the Jacobian norm of the transformation in very low probability areas. This paper proposes to overcome this issue thanks to a new Markov chain Monte Carlo algorithm to sample from the target distribution in the latent domain before transporting it back to the target domain. The approach relies on a Metropolis adjusted Langevin algorithm whose dynamics explicitly exploits the Jacobian of the transformation. Contrary to alternative approaches, the proposed strategy preserves the tractability of the likelihood and it does not require a specific training. Notably, it can be straightforwardly used with any pre-trained NF network, regardless of the architecture. Experiments conducted on synthetic and high-dimensional real data sets illustrate the efficiency of the method.

Sample selection of adversarial attacks against traffic signs

In the real world, the correct recognition of traffic signs plays a crucial role in vehicle autonomous driving and traffic monitoring. The research on its adversarial attack can test the security of vehicle autonomous driving system and provide enlightenment for improving the recognition algorithm. However, with the development of transportation infrastructure, new traffic signs may be introduced. The adversarial attack model for traffic signs needs to adapt to the addition of new types. Based on this, class incremental learning for traffic sign adversarial attacks has become an interesting research field. We propose a class incremental learning method for adversarial attacks on traffic signs. First, this method uses Pinpoint Region Probability Estimation Network (PRPEN) to predict the probability of each pixel being attacked in old samples. It helps to identify the high attack probability regions of the samples. Subsequently, based on the size of high probability pixel concentration area, the replay sample set is constructed. Old samples with smaller concentration areas receive higher priority and are prioritized for incremental learning. The experimental results show that compared with other sample selection methods, our method selects more representative samples and can train PRPEN more effectively to generate probability maps, thereby better generating adversarial attacks on traffic signs.

Joint Bayesian estimation of process and measurement noise statistics in nonlinear Kalman filtering

Bayesian filtering aims at sequentially estimating the states and parameters in nonlinear dynamical systems, and finds applications in a variety of engineering fields. Extended and unscented Kalman filter are popular choices for this purpose as they typically achieve a good trade-off between computational complexity and accuracy, while still quantifying posterior uncertainty. However, the accuracy of the identified states and parameters, both in terms of their posterior mean and variance, is heavily influenced by the assumed statistics of both the process and measurement noise terms. Therefore, learning the noise characteristics becomes an essential aspect of Bayesian filtering, but is non-trivial especially in cases where process and measurement noise statistics are jointly non-identifiable. This article proposes a methodology that decomposes measurement and process noise learning in a fully Bayesian setting. For measurement noise estimation, we improve upon a Bayesian method that leverages conjugacy of the inverse-Wishart distribution with respect to the Gaussian likelihood model. For process noise estimation, Bayesian model averaging is coupled with a mutation scheme to compare the performance of competing models while exploring high-probability regions in the space of the unknown process noise variance. The algorithm is first tested on simple numerical models for both state estimation and parameter learning, highlighting the superior accuracy of the proposed measurement noise correction and the benefits of decoupling process and measurement noise learning in non-identifiable settings. Then we demonstrate the usefulness of this algorithm in more challenging problems, namely identification of a multi-degree-of-freedom system under unmeasured excitation, and modeling of the highly nonlinear response of a full-scale bridge pier using experimental data.

USVs Path Planning for Maritime Search and Rescue Based on POS-DQN: Probability of Success-Deep Q-Network

Efficient maritime search and rescue (SAR) is crucial for responding to maritime emergencies. In traditional SAR, fixed search path planning is inefficient and cannot prioritize high-probability regions, which has significant limitations. To solve the above problems, this paper proposes unmanned surface vehicles (USVs) path planning for maritime SAR based on POS-DQN so that USVs can perform SAR tasks reasonably and efficiently. Firstly, the search region is allocated as a whole using an improved task allocation algorithm so that the task region of each USV has priority and no duplication. Secondly, this paper considers the probability of success (POS) of the search environment and proposes a POS-DQN algorithm based on deep reinforcement learning. This algorithm can adapt to the complex and changing environment of SAR. It designs a probability weight reward function and trains USV agents to obtain the optimal search path. Finally, based on the simulation results, by considering the complete coverage of obstacle avoidance and collision avoidance, the search path using this algorithm can prioritize high-probability regions and improve the efficiency of SAR.

Exploring Channel-Aware Typical Features for Out-of-Distribution Detection

Detecting out-of-distribution (OOD) data is essential to ensure the reliability of machine learning models when deployed in real-world scenarios. Different from most previous test-time OOD detection methods that focus on designing OOD scores, we delve into the challenges in OOD detection from the perspective of typicality and regard the feature’s high-probability region as the feature’s typical set. However, the existing typical-feature-based OOD detection method implies an assumption: the proportion of typical feature sets for each channel is fixed. According to our experimental analysis, each channel contributes differently to OOD detection. Adopting a fixed proportion for all channels results in several channels losing too many typical features or incorporating too many abnormal features, resulting in low performance. Therefore, exploring the channel-aware typical features is crucial to better-separating ID and OOD data. Driven by this insight, we propose expLoring channel-Aware tyPical featureS (LAPS). Firstly, LAPS obtains the channel-aware typical set by calibrating the channel-level typical set with the global typical set from the mean and standard deviation. Then, LAPS rectifies the features into channel-aware typical sets to obtain channel-aware typical features. Finally, LAPS leverages the channel-aware typical features to calculate the energy score for OOD detection. Theoretical and visual analyses verify that LAPS achieves a better bias-variance trade-off. Experiments verify the effectiveness and generalization of LAPS under different architectures and OOD scores.

A GPR-based emulator for semi-numerical reionization code SCRIPT: parameter inference from 21 cm data

Semi-numerical models of reionization typically involve a large number of unknown parameters whose values are constrained by comparing with observations. Increasingly often, exploring this parameter space using semi-numerical simulations can become computationally intensive, thus necessitating the use of emulators. In this work, we present a likelihood emulator based on Gaussian Process Regression (GPR) for our semi-numerical reionization code, SCRIPT, and use it for parameter inference using mock 21 cm power spectrum data and Bayesian MCMC analysis. A unique aspect of our methodology is the utilization of coarse resolution simulations to identify high-probability regions within the parameter space, employing only a moderate amount of computational time. Samples drawn from these high-probability regions are used to construct the training set for the emulator. The subsequent MCMC using this GPR-trained emulator is found to provide parameter posteriors that agree reasonably well with those obtained using conventional MCMC. The computing time for the analysis, which includes both generation of training sets and training the emulator, is reduced by approximately an order of magnitude. This methodology is particularly advantageous in scenarios where one wants to use different parametrizations of reionization models and/or needs to start with broad prior distributions on the parameters, offering an efficient and effective means of parameter inference.

Ignition Probability of Turbulent Non-Premixed Swirling Flames in a Double-Swirl Burner

ABSTRACT In this paper, the effect of swirl number on mixing structure and ignition probability in turbulent non-premixed natural gas/air flame has been investigated. The flames are visualized by a digital camera and inverted by Abel’s transformation. Maps of ignition probability are generated by changing the spark position axially and radially while the flow field and mixing topology are established using numerical simulation. Three zones were defined based on the probability of successful ignition. The first one was the ineffective zone where the probability of a successful ignition was less than 20%. The second one was called the transition zone where the probability of a successful ignition was between 20% and 80%, and a successful ignition was highly dependent on the spark location. The third one was called the high-probability zone where the probability of a successful ignition was more than 80%. It was found that although the increment in swirl number improves the rate of mixing, it does not result in a better distribution of successful ignition probability. In high swirl numbers, the high-probability region is very small but at lower swirl numbers, the high-probability region is distributed at the burner exit, resulting IN better ignition characteristics.

Bayesian calibration and fitting of nuclear thermal–hydraulic models by Markov chain Monte Carlo methods using the Gibbs sampler

This paper applies Bayesian methods to obtain the joint probability distribution function (PDF) of the unknown parameters of some of the correlation types used in thermal hydraulics, which are conditioned to a set of results of separate effect experiments. The problem is solved by Markov chain Monte Carlo methods using the Gibbs sampler. In this paper it is shown how to obtain the conditional PDFs for each one of the parameters, which define two typical correlations that appear in thermal–hydraulic problems, i) the heat transfer in the walls of a pipe and ii) the penetration length of steam discharged in a subcooled pool. The equations for the conditional probabilities of each one of the parameters are deduced and then used to build the Gibbs sampler which is then programmed in R. The PDFs are obtained for each one of the parameters and from these the maximum a posteriori of each one of the parameters is obtained. The new correlations obtained in this way are compared with the experimental data and show satisfactory performance for the case of the penetration of steam in a subcooled pool. Also, we study two cases one with a small number of experimental data and another one with a large number, it is observed that if the number of parameters is small the Markov chain converges fast to the region of high probability of the parameters for both cases with a small number of data and with large number of data, but if the number of parameters increases then the convergence for the case with small number of data becomes poor, however the convergence continuous being very fast for the case with large number of experimental data.

An autonomous coverage path planning algorithm for maritime search and rescue of persons-in-water based on deep reinforcement learning

The prevalence of maritime transportation and operations is increasing, leading to a gradual increase in drowning accidents at sea. In the context of maritime search and rescue (SAR), it is essential to develop effective search plans to improve the survival probability of persons-in-water (PIWs). However, conventional SAR search plans typically use predetermined patterns to ensure complete coverage of the search area, disregarding the varying probabilities associated with the PIW distribution. To address this issue, this study has proposed a maritime SAR vessel coverage path planning framework (SARCPPF) suitable for multiple PIWs. This framework comprises three modules, namely, drift trajectory prediction, the establishment of a multilevel search area environment model, and coverage search. First, sea area-scale drift trajectory prediction models were employed using the random particle simulation method to forecast drift trajectories. A hierarchical probability environment map model was established to guide the SAR of multiple SAR units. Subsequently, we integrated deep reinforcement learning with a reward function that encompasses multiple variables to guide the navigation behavior of ship agents. We developed a coverage path planning algorithm aimed at maximizing the success rates within a limited timeframe. The experimental results have demonstrated that our model enables vessel agents to prioritize high-probability regions while avoiding repeated coverage.

Gaussian and Gaussian-pulsed-like Fermi velocity graphene structures

Gaussian and Gaussian-related structures are quite attractive due to its versatility to modulate the electronic transport, including its possibility as electron filters. Here, we show that these non-conventional profiles are not the exception when dealing with Fermi velocity barriers in monolayer graphene. In particular, we show that Gaussian Fermi velocity graphene barriers (G-FVGBs) and Gaussian-pulsed-like Fermi velocity graphene superlattices (GPL-FVGSLs) can serve as electron band-pass filters and oscillating conductance structures. We reach this conclusion by theoretically studying the transmission and transport properties of the mentioned structures. The study is based on the continuum model, the transfer matrix method and the Landauer–Büttiker formalism. We find nearly flat transmission bands or pass bands for G-FVGBs modulable through the system parameters. The pass bands improve as the maximum ratio of Fermi velocities () increases, however its omnidirectional range is reduced. These characteristics result in a decaying conductance (integrated transmission) with . The integrated transmission remains practically unaltered with the size of the system due to the saturation of the electron pass band filtering. In the case of GPL-FVGSLs the GPL profile results in regions of high transmission probability that can merge as flat transmission minibands if the pulse fraction and the superlattice parameters are appropriately tuned. The GPL profile also results in conductance (integrated transmission) oscillations that can be multiplied or reduced in number by adjusting the pulse fraction as well as the superlattice parameters.

Ecological Niche Modeling of Aedes and Culex Mosquitoes: A Risk Map for Chikungunya and West Nile Viruses in Zambia.

The circulation of both West Nile Virus (WNV) and Chikungunya Virus (CHIKV) in humans and animals, coupled with a favorable tropical climate for mosquito proliferation in Zambia, call for the need for a better understanding of the ecological and epidemiological factors that govern their transmission dynamics in this region. This study aimed to examine the contribution of climatic variables to the distribution of Culex and Aedes mosquito species, which are potential vectors of CHIKV, WNV, and other arboviruses of public-health concern. Mosquitoes collected from Lusaka as well as from the Central and Southern provinces of Zambia were sorted by species within the Culex and Aedes genera, both of which have the potential to transmit viruses. The MaxEnt software was utilized to predict areas at risk of WNV and CHIKV based on the occurrence data on mosquitoes and environmental covariates. The model predictions show three distinct spatial hotspots, ranging from the high-probability regions to the medium- and low-probability regions. Regions along Lake Kariba, the Kafue River, and the Luangwa Rivers, as well as along the Mumbwa, Chibombo, Kapiri Mposhi, and Mpika districts were predicted to be suitable habitats for both species. The rainfall and temperature extremes were the most contributing variables in the predictive models.

Stability of individual differences in implicitly guided attention.

Daily activities often occur in familiar environments, affording us an opportunity to learn. Laboratory studies have shown that people readily acquire an implicit spatial preference for locations that frequently contained a search target in the past. These studies, however, have focused on group characteristics, downplaying the significance of individual differences. In a pre-registered study, we examined the stability of individual differences in two variants of an implicit location probability learning (LPL) task. We tested the possibility that individual differences were stable in variants that shared the same search process, but not in variants involving different search processes. In Experiment 1, participants performed alternating blocks of T-among-Ls and 5-among-2s search tasks. Unbeknownst to them, the search target appeared disproportionately often in one region of space; the high-probability regions differed between the two tasks. LPL transferred between the two tasks. In addition, individuals who showed greater LPL in the T-task also did so in the 5-task and vice versa. In Experiment 2, participants searched for either a camouflaged-T against background noise or a well-segmented T among well-segmented Ls. These two tasks produced task-specific learning that did not transfer between tasks. Moreover, individual differences in learning did not correlate between tasks. Thus, LPL is associated with stable individual differences across variants, but only when the variants share common search processes.

A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images.

Segmentation of the carotid section encompassing the common carotid artery (CCA), the bifurcation and the internal carotid artery (ICA) from three-dimensional ultrasound (3DUS) is required to measure the vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT), shown to be sensitive to treatment effect. We proposed an approach to combine a centerline extraction network (CHG-Net) and a dual-stream centerline-guided network (DSCG-Net) to segment the lumen-intima (LIB) and media-adventitia boundaries (MAB) from 3DUS images. Correct arterial location is essential for successful segmentation of the carotid section encompassing the bifurcation. We addressed this challenge by using the arterial centerline to enhance the localization accuracy of the segmentation network. The CHG-Net was developed to generate a heatmap indicating high probability regions for the centerline location, which was then integrated with the 3DUS image by the DSCG-Net to generate the MAB and LIB. The DSCG-Net includes a scale-based and a spatial attention mechanism to fuse multi-level features extracted by the encoder, and a centerline heatmap reconstruction side-branch connected to the end of the encoder to increase the generalization ability of the network. Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.8±1.9% and 92.3±5.4% for CCA MAB and LIB, respectively, and 93.2±4.4% and 89.0±10.0% for ICA MAB and LIB, respectively. Our approach outperformed four state-of-the-art 3D CNN models, even after their performances were boosted by centerline guidance. The efficiency afforded by the framework would allow it to be incorporated into the clinical workflow for improved quantification of plaque change.

Species, Habitat Characteristics, and Screening Suggestions of Cadmium Hyperaccumulators in China

Phytoremediation, as a green and effective in-situ remediation technology for heavy metal-contaminated soil, has attracted the attention of Chinese scholars and has resulted in a series of achievements over the past 20 years. In this study, the species characteristics, distribution of field discovery sites in various vegetation zones, habitat characteristics, habitat geological characteristics, and geochemistry of cadmium (Cd) of the Cd hyperaccumulators in China reported in the relevant literature from the past 20 years (from 2002 to 2021) were summarized by searching for related keywords. Finally, suggestions were proposed for the screening of new Cd hyperaccumulators. The results showed that a total of 45 species of Cd hyperaccumulators in China have been reported so far. In terms of plant species, they belonged to 22 families and 36 genera, among which Compositae with 14 species was the most abundant. There were 25 species discovered through the field investigation, which were mainly distributed in the subtropical broadleaf evergreen forest region of southern China. Additionally, the Cd hyperaccumulators discovered by field surveys were mainly found in high Cd-concentrated soils surrounding lead-zinc mines. In conclusion, abundant plant resources, high concentrations of heavy metal soils, and long-term domestication jointly promoted the formation of hyperaccumulators. Therefore, the region with these three points could be considered a high probability region for the presence of hyperaccumulators, and the screening of hyperaccumulators could be carried out around this. We proposed that the screening of new hyperaccumulators can be carried out through the following six steps:the identification and investigation of high probability areas, the enrichment capability test, the enrichment capability test in low concentration levels, the enrichment capability test between different ecotypes, the succession of enrichment capability, and the test of remediation proficiency.

Alchemical Metadynamics: Adding Alchemical Variables to Metadynamics to Enhance Sampling in Free Energy Calculations.

Performing alchemical transformations, in which one molecular system is nonphysically changed to another system, is a popular approach adopted in performing free energy calculations associated with various biophysical processes, such as protein-ligand binding or the transfer of a molecule between environments. While the sampling of alchemical intermediate states in either parallel (e.g., Hamiltonian replica exchange) or serial manner (e.g., expanded ensemble) can bridge the high-probability regions in the configurational space between two end states of interest, alchemical methods can fail in scenarios where the most important slow degrees of freedom in the configurational space are, in large part, orthogonal to the alchemical variable, or if the system gets trapped in a deep basin extending in both the configurational and alchemical space. To alleviate these issues, we propose to use alchemical variables as an additional dimension in metadynamics, making it possible to both sample collective variables and to enhance sampling in free energy calculations simultaneously. In this study, we validate our implementation of "alchemical metadynamics" in PLUMED with test systems and alchemical processes with varying complexities and dimensionalities of collective variable space, including the interconversion between the torsional metastable states of a toy system and the methylation of a nucleoside both in the isolated form and in a duplex. We show that multidimensional alchemical metadynamics can address the challenges mentioned above and further accelerate sampling by introducing configurational collective variables. The method can trivially be combined with other metadynamics-based algorithms implemented in PLUMED. The necessary PLUMED code changes have already been released for general use in PLUMED 2.8.

SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction.

Predicting the future trajectories of pedestrians is of increasing importance for many applications such as autonomous driving and social robots. Nevertheless, current trajectory prediction models suffer from limitations such as lack of diversity in candidate trajectories, poor accuracy, and instability. In this paper, we propose a novel Sequence Entropy Energy-based Model named SEEM, which consists of a generator network and an energy network. Within SEEM we optimize the sequence entropy by taking advantage of the local variational inference of f-divergence estimation to maximize the mutual information across the generator in order to cover all modes of the trajectory distribution, thereby ensuring SEEM achieves full diversity in candidate trajectory generation. Then, we introduce a probability distribution clipping mechanism to draw samples towards regions of high probability in the trajectory latent space, while our energy network determines which trajectory is most representative of the ground truth. This dual approach is our so-called all-then-one strategy. Finally, a zero-centered potential energy regularization is proposed to ensure stability and convergence of the training process. Through experiments on both synthetic and public benchmark datasets, SEEM is shown to substantially outperform the current state-of-the-art approaches in terms of diversity, accuracy and stability of pedestrian trajectory prediction.

Hot Subdwarf Stars Identified in LAMOST DR8 with Single-lined and Composite Spectra

A total of 222 hot subdwarf stars were identified with LAMOST DR8 spectra, among which 131 stars show composite spectra and have been decomposed, while 91 stars present single-lined spectra. Atmospheric parameters of all sample stars were obtained by fitting hydrogen (H) and helium (He) line profiles with synthetic spectra. Two long-period composite sdB binaries were newly discovered by combining our sample with the non-single-star data from Gaia DR3. One of the new systems presents the highest eccentricity (i.e., 0.5 ± 0.09) among known wide sdB binaries, which is beyond model predictions. A total of 15 composite sdB stars fall in the high-probability binary region of the RUWE−AEN plane and deserve priority follow-up observations to further study their binary nature. A distinct gap is clearly presented among temperatures of cool companions for our composite-spectrum sample, but we could not come to a conclusion as to whether this feature is connected to the formation history of hot subdwarf stars before their binary natures are confirmed.

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Aiming to address the problem of unknown dynamic target trajectory prediction and search path optimization in unmanned aerial vehicle (UAV) swarm path planning, this paper proposes a target search algorithm based on a modified target probability map (TPM). First, using the TPM, the proposed algorithm generates a high-probability distribution region of a target with directionality to fit the target trajectory and realizes the trajectory prediction of an unknown dynamic target. Then, the distributed ant colony (ACO) algorithm and the artificial potential field (APF) algorithm are combined to generate and optimize the UAV swarm search result and return path with the goal of maximizing task execution efficiency. Finally, the Monte Carlo simulation method is used to analyze the effectiveness of the proposed algorithm, and the results are evaluated from five aspects, including the number of targets captured. The simulation results show that under the condition of an unknown dynamic target trajectory, the average target captured rate and average unknown region search rate of the MTPM method were higher than that of the traditional TPM method, and the performance was improved by 14.6% and 10.7%, respectively.

Accelerating cosmological inference with Gaussian processes and neural networks – an application to LSST Y1 weak lensing and galaxy clustering

ABSTRACT Studying the impact of systematic effects, optimizing survey strategies, assessing tensions between different probes and exploring synergies of different data sets require a large number of simulated likelihood analyses, each of which cost thousands of CPU hours. In this paper, we present a method to accelerate cosmological inference using emulators based on Gaussian process regression and neural networks. We iteratively acquire training samples in regions of high posterior probability which enables accurate emulation of data vectors even in high dimensional parameter spaces. We showcase the performance of our emulator with a simulated 3×2 point analysis of LSST-Y1 with realistic theoretical and systematics modelling. We show that our emulator leads to high-fidelity posterior contours, with an order of magnitude speed-up. Most importantly, the trained emulator can be re-used for extremely fast impact and optimization studies. We demonstrate this feature by studying baryonic physics effects in LSST-Y1 3×2 point analyses where each one of our MCMC runs takes approximately 5 min. This technique enables future cosmological analyses to map out the science return as a function of analysis choices and survey strategy.