Wei-Keng Liao

The objective of RAPIDS2 SciDAC Institute for Computer Science and Data is to assist Office of Science (SC) application teams in overcoming computer science and data challenges in the use of DOE supercomputing resources to achieve science breakthroughs.

Recent innovations have made it possible to produce millions of distinct nanoparticles on a chip. These vast volumes of data are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image preprocessing step before model training can produce significantly higher-performing models in a fraction of the time and make the model more robust to different image noise levels and microscope acquisition settings. The proposed image processing pipeline effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures, resulting in higher performance and significant cost savings. Experiments demonstrate superior performance relative to our baseline, including a 15% improvement in recall and more than a 10% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives in our application, and our best-performing model obtains a precision of 97.3% and weighted F-score of 95.9% on an unseen test set. Additionally, model training time is reduced from 15.5 hours to 32 seconds. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will offer a considerable speedup in the laboratory, allowing researchers to rapidly and accurately analyze much greater volumes of data and accelerate materials discovery.

Large-scale parallel applications can face significant I/O performance bottlenecks, making efficient I/O crucial. This work presents a comparative study of several parallel I/O implementations in the Weather Research and Forecasting model, including PnetCDF blocking and non-blocking I/O options, netCDF4, HDF5 Log VOL, and ADIOS. For I/O methods creating files in a canonical data layout, PnetCDF's non-blocking option offers up to 2x improvement over its blocking option and up to 4.5x over HDF5 via netCDF4, demonstrating the effectiveness of the write request aggregation technique. The HDF5 Log VOL outperforms ADIOS with a 4x improvement in write performance when creating files in the log layout, although both require non-negligible time to convert the file back to canonical order for post-run analysis. From these results we extract some observations that can guide I/O strategies for modern parallel codes.

Prediction of the autogenous shrinkage referred to as the reduction of apparent volume of concrete under seal and isothermal conditions is of great significance in the service life analysis and design of durable concrete structures, especially with the increasing use of concrete with low water-to-cement ratios. However, due to the highly complex mechanism of autogenous shrinkage, it is hard to design accurate mechanistic models for it. Existing state-of-the-art models for autogenous shrinkage do not perform well for several reasons such as not being able to capture faster shrinkage change at early ages (swelling), coefficients used are derived using statistical optimization methods to fit certain databases only, and mechanism to identify the most influencing factors on autogenous shrinkage is not present. Moreover, it is also challenging to deploy a machine learning framework directly to perform predictive analysis due to the sparse and noisy nature of the available experimental dataset. In this paper, we study and propose a method to combine the physics-based knowledge and the predictive ability of deep regression neural networks to mitigate the shortcomings of the existing models. We introduce a novel data augmentation technique that utilizes physics based knowledge to improve the accuracy while maintaining the characteristics of autogenous shrinkage in its predictions simultaneously. Using state-of-the-art B4 model, a genetic algorithm, and a deep neural network trained using raw data for comparison, we show that the proposed methods help improve the accuracy of the model as compared to other methods. We also observe that the proposed method is able to successfully learn and predict the swelling component of the shrinkage strain curve as well, which cannot be predicted using the existing state-of-the-art models.

The goal of “carbon peaking and carbon neutrality” accelerates the development of renewable energy, and also places higher demands on industries to improve energy efficiency. On the other hand, the high proportion of renewable energy raises the operating costs of the power system, and expands the cross-subsidy to guarantee stable tariffs for residents and agriculture. To reasonably evacuate the increasing costs, a differential electricity tariff for the high-energy-consuming industry is designed in this paper. The total tariff cross-subsidy of residential and agricultural users is first calculated based on the average feed-in tariff and transmission and distribution tariffs by voltage levels. Then, a comprehensive energy consumption index (CECI) is designed to evaluate the energy consumption efficiency of each industry. Finally, the tariff increases for different high-energy-consuming industries are calculated according to the CECI of each industry, forming a differentiated apportionment of emission reduction costs for the power system.

The immune system protects its host from infection. Dysfunction of the immune system can cause autoimmune diseases and inflammatory diseases. Monitoring the immune system provides crucial information in informing treatment strategies and assessing the effect of therapies. While measures such as complete blood count to determine the leukocyte subsets are extensively used clinically, our ability to assess leukocyte function is limited, especially for the cells of the innate immune system, such as neutrophils. Here we introduce the idea of assessing neutrophil function from simple-to-obtain phase microscopy images. We developed an experimental pipeline using measurement of reactive oxygen species generation as a label of neutrophil function. We generated a large neutrophil imaging dataset and explored different deep learning approaches to predict neutrophil activation state. Our work demonstrates the potential of using deep learning models to evaluate functional aspects of the immune system, which could provide significant insight into immune disease prognostic monitoring that can be easily adapted to clinical settings.