Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging

We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We show that this illumination coding scheme is highly scalable in achieving flexible resolution, and robust to experimental variations. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5X resolution enhancement across 4X FOVs using only five multiplexed measurements -- more than 10X data reduction over the state-of-the-art. Typical DL algorithms tend to provide over-confident predictions, whose errors are only discovered in hindsight. We develop an uncertainty learning framework to overcome this limitation and provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and "out-of-distribution" testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable AI-augmented large-SBP phase imaging with dependable predictions.Published versio

Evolution of a Stratified Turbulent Cloud under Rotation

Localized turbulence is common in geophysical flows, where the roles of rotation and stratification are paramount. In this study, we investigate the evolution of a stratified turbulent cloud under rotation. Recognizing that a turbulent cloud is composed of vortices of varying scales and shapes, we start our investigation with a single eddy using analytical solutions derived from a linearized system. Compared to an eddy under pure rotation, the stratified eddy shows the physical manifestation of a known potential vorticity mode, appearing as a static stable vortex. In addition, the expected shift from inertial waves to inertial-gravity waves is observed. In our numerical simulations of the turbulent cloud, carried out at a constant Rossby number over a range of Froude numbers, stratification causes columnar structures to deviate from vertical alignment. This deviation increases with increasing stratification, slowing the expansion rate of the cloud. The observed characteristics of these columnar structures are consistent with the predictions of linear theory, particularly in their tilt angles and vertical growth rates, suggesting a significant influence of inertial-gravity waves. Using Lagrangian particle tracking, we have identified regions where wave activity dominates over turbulence. In scenarios of milder stratification, these inertial-gravity waves are responsible for a significant energy transfer away from the turbulent cloud, a phenomenon that attenuates with increasing stratification

Off-hour admission and mortality risk for 28 specific diseases: A systematic review and meta-analysis of 251 cohorts

Background: A considerable amount of studies have examined the relationship between off-hours (weekends and nights) admission and mortality risk for various diseases, but the results remain equivocal. Methods and Results: Through a search of EMBASE, PUBMED, Web of Science, and Cochrane Database of Systematic Reviews, we identified cohort studies that evaluated the association between off-hour admission and mortality risk for disease. In a random effects meta-analysis of 140 identified articles (251 cohorts), off-hour admission was strongly associated with increased mortality for aortic aneurysm (odds ratio [OR], 1.52; 95% confidence interval, 1.30-1.77), breast cancer (1.50, 1.21-1.86), leukemia (1.45, 1.17-1.79), respiratory neoplasm (1.32, 1.20-1.26), pancreatic cancer (1.32, 1.12-1.56), malignant neoplasm of genitourinary organs (1.27, 1.08-1.49), colorectal cancer (1.26, 1.07-1.49), pulmonary embolism (1.20, 1.13-1.28), arrhythmia and cardiac arrest (1.19, 1.09-1.29), and lymphoma (1.19, 1.06-1.34). Weaker (OR<1.19) but statistically significant association was noted for renal failure, traumatic brain injury, heart failure, intracerebral hemorrhage, subarachnoid hemorrhage, stroke, gastrointestinal bleeding, myocardial infarction, chronic obstructive pulmonary disease, and bloodstream infections. No association was found for hip fracture, pneumonia, intestinal obstruction, aspiration pneumonia, peptic ulcer, trauma, diverticulitis, and neonatal mortality. Overall, Off-hour admission was associated with increased mortality for 28 diseases combined (OR, 1.11; 95% confidence interval, 1.10-1.13).Conclusions: Off-hour admission is associated with increased mortality risk, and the associations varied substantially for different diseases. Specialists, nurses, as well as hospital administrators and health policy makers can take these findings into consideration to improve the quality and continuity of medical services

SAR² - An augmented-reality App for exploration of principles of synthetic aperture radar

SAR² is a prototype educational simulation software for the Microsoft Hololens, developed by students as part of a geoinformatics course. The aim of this software is to provide a tool to introduce and explain the concept of synthetic aperture radar (SAR) to students, as well as the general public, by visualizing and interactively exploring the process of a SAR acquisition in a 3D virtual environment. A distinctive feature of SAR² is that the SAR acquisition procedure is simulated in real time within a Unity Engine environment, using a set of algorithms which replicate the real-life SAR processing algorithms. While this provides a challenge due to the limited computational power available on the Microsoft HoloLens 1 device, it allows maximal freedom to the user in setting whatever configuration they would like to see. This would not have been possible if an approach using a pre-selected set of scenarios was chosen. The augmented-reality (AR) app works in 3 phases: - In the first step, the user is shown a terrain model, and a satellite model inspired by the TerraSAR-X. The user can adjust selected parameters of the acquisition by manipulating the satellite and model using intuitive AR controls (e.g. by physically grabbing and rotating the objects with their hands). - After configuring the parameters, the user launches the acquisition and observes it in real time. The satellite model flies over the terrain, and the "flow" of the data into the storage is immediately visualized. - After the acquisition is finished, the user can explore the focusing procedures that need to be applied to the data - namely the range and azimuth compression. Different geometrical effects (shadowing, layover) can also be explored at this stage. The SAR² app used in concert with conventional educational approaches can reinforce the learned material, clarify misconceptions, and provide intuition for the complicated concepts of synthetic aperture rada

Scalable and reliable deep learning for computational microscopy in complex media

Emerging deep learning based computational microscopy techniques promise novel imaging capabilities beyond traditional techniques. In this talk, I will discuss two microscopy applications. First, high space-bandwidth product microscopy typically requires a large number of measurements. I will present a novel physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging [1], enabling significant reduction of the required measurements, opening up real-time applications. In this technique, we design asymmetric coded illumination patterns to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5× resolution enhancement across 4× FOVs using only five multiplexed measurements. In addition, we develop an uncertainty learning framework to provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and “out-of-distribution” testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable DL-augmented large-SBP phase imaging with reliable predictions and uncertainty quantifications. Second, I will turn to the pervasive problem of imaging in scattering media. I will discuss a new deep learning- based technique that is highly generalizable and resilient to statistical variations of the scattering media [2]. We develop a statistical ‘one-to-all’ deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media. REFERENCES [1] Xue, Y., Cheng, S., Li, Y., and Tian, L., “Illumination coding meets uncertainty learning: toward reliable ai-augmented phase imaging,” arXiv:1901.02038 (2019). [2] Li, Y., Xue, Y., and Tian, L., “Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media,” Optica 5, 1181 (2018)

BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner.Comment: Accepted by The 2024 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) (Proc. CSCW 2024