14-08 Big Data Analytics to Aid Developing Livable Communities

In transportation, ubiquitous deployment of low-cost sensors combined with powerful computer hardware and high-speed network makes big data available. USDOT defines big data research in transportation as a number of advanced techniques applied to the capture, management and analysis of very large and diverse volumes of data. Data in transportation are usually well organized into tables and are characterized by relatively low dimensionality and yet huge numbers of records. Therefore, big data research in transportation has unique challenges on how to effectively process huge amounts of data records and data streams. The purpose of this study is to conduct research on the problems caused by large data volume and data streams and to develop applications for data analysis in transportation. To process large number of records efficiently, we have proposed to aggregate the data at multiple resolutions and to explore the data at various resolutions to balance between accuracy and speed. Techniques and algorithms in statistical analysis and data visualization have been developed for efficient data analytics using multiresolution data aggregation. Results will be helpful in setting up a primitive stage towards a rigorous framework for general analytical processing of big data in transportation

Quantification of The Performance of CMIP6 Models for Dynamic Downscaling in The North Pacific and Northwest Pacific Oceans

Selecting a reliable global climate model as the driving forcing in simulations with dynamic downscaling is critical for obtaining a reliable regional ocean climate. With respect to their accuracy in providing physical quantities and long-term trends, we quantify the performances of 17 models from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) over the North Pacific (NP) and Northwest Pacific (NWP) oceans for 1979–2014. Based on normalized evaluation measures, each model’s performance for a physical quantity is mainly quantified by the performance score (PS), which ranges from 0 to 100. Overall, the CMIP6 models reasonably reproduce the physical quantities of the driving variables and the warming ocean heat content and temperature trends. However, their performances significantly depend on the variables and region analyzed. The EC-Earth-Veg and CNRM-CM6-1 models show the best performances for the NP and NWP oceans, respectively, with the highest PS values of 85.89 and 76.97, respectively. The EC-Earth3 model series are less sensitive to the driving variables in the NP ocean, as reflected in their PS. The model performance is significantly dependent on the driving variables in the NWP ocean. Nevertheless, providing a better physical quantity does not correlate with a better performance for trend. However, MRI-ESM2-0 model shows a high performance for the physical quantity in the NWP ocean with warming trends similar to references, and it could thus be used as an appropriate driving forcing in dynamic downscaling of this ocean. This study provides objective information for studies involving dynamic downscaling of the NP and NWP oceans

Vertically aligned InGaN nanowires with engineered axial In composition for highly efficient visible light emission.

We report on the fabrication of novel InGaN nanowires (NWs) with improved crystalline quality and high radiative efficiency for applications as nanoscale visible light emitters. Pristine InGaN NWs grown under a uniform In/Ga molar flow ratio (UIF) exhibited multi-peak white-like emission and a high density of dislocation-like defects. A phase separation and broad emission with non-uniform luminescent clusters were also observed for a single UIF NW investigated by spatially resolved cathodoluminescence. Hence, we proposed a simple approach based on engineering the axial In content by increasing the In/Ga molar flow ratio at the end of NW growth. This new approach yielded samples with a high luminescence intensity, a narrow emission spectrum, and enhanced crystalline quality. Using time-resolved photoluminescence spectroscopy, the UIF NWs exhibited a long radiative recombination time (τr) and low internal quantum efficiency (IQE) due to strong exciton localization and carrier trapping in defect states. In contrast, NWs with engineered In content demonstrated three times higher IQE and a much shorter τr due to mitigated In fluctuation and improved crystal quality

Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forward and inverse problems. While promising, the expensive computational costs to obtain solutions often restrict their broader applicability. We demonstrate that the computations in automatic differentiation (AD) can be significantly reduced by leveraging forward-mode AD when training PINN. However, a naive application of forward-mode AD to conventional PINNs results in higher computation, losing its practical benefit. Therefore, we propose a network architecture, called separable PINN (SPINN), which can facilitate forward-mode AD for more efficient computation. SPINN operates on a per-axis basis instead of point-wise processing in conventional PINNs, decreasing the number of network forward passes. Besides, while the computation and memory costs of standard PINNs grow exponentially along with the grid resolution, that of our model is remarkably less susceptible, mitigating the curse of dimensionality. We demonstrate the effectiveness of our model in various PDE systems by significantly reducing the training run-time while achieving comparable accuracy. Project page: https://jwcho5576.github.io/spinn/Comment: To appear in NeurIPS 2022 Workshop on The Symbiosis of Deep Learning and Differential Equations (DLDE) - II, 12 pages, 5 figures, full paper: arXiv:2306.1596

Separable Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental limitation of training PINNs to solve multi-dimensional PDEs and approximate highly complex solution functions. The number of training points (collocation points) required on these challenging PDEs grows substantially, but it is severely limited due to the expensive computational costs and heavy memory overhead. To overcome this issue, we propose a network architecture and training algorithm for PINNs. The proposed method, separable PINN (SPINN), operates on a per-axis basis to significantly reduce the number of network propagations in multi-dimensional PDEs unlike point-wise processing in conventional PINNs. We also propose using forward-mode automatic differentiation to reduce the computational cost of computing PDE residuals, enabling a large number of collocation points (>10^7) on a single commodity GPU. The experimental results show drastically reduced computational costs (62x in wall-clock time, 1,394x in FLOPs given the same number of collocation points) in multi-dimensional PDEs while achieving better accuracy. Furthermore, we present that SPINN can solve a chaotic (2+1)-d Navier-Stokes equation significantly faster than the best-performing prior method (9 minutes vs 10 hours in a single GPU), maintaining accuracy. Finally, we showcase that SPINN can accurately obtain the solution of a highly nonlinear and multi-dimensional PDE, a (3+1)-d Navier-Stokes equation.Comment: arXiv admin note: text overlap with arXiv:2211.0876

Post-translational regulation of miRNA pathway components, AGO1 and HYL1, in plants

Post-translational modifications (PTMs) of proteins are essential to increase the functional diversity of the proteome. By adding chemical groups to proteins, or degrading entire proteins by phosphorylation, glycosylation, ubiquitination, neddylation, acetylation, lipidation, and proteolysis, the complexity of the proteome increases, and this then influences most biological processes. Although small RNAs are crucial regulatory elements for gene expression in most eukaryotes, PTMs of small RNA microprocessor and RNA silencing components have not been extensively investigated in plants. To date, several studies have shown that the proteolytic regulation of AGOs is important for host-pathogen interactions. DRB4 is regulated by the ubiquitin-proteasome system, and the degradation of HYL1 is modulated by a de-etiolation repressor, COP1, and an unknown cytoplasmic protease. Here, we discuss current findings on the PTMs of microprocessor and RNA silencing components in plants

Nontuberculous mycobacterial infection in a clinical presentation of Fitz-Hugh-Curtis syndrome: a case report with multigene diagnostic approach

BACKGROUND: Fitz-Hugh-Curtis syndrome (FHCS) is caused by inflammation of perihepatic capsules associated with pelvic inflammatory disease. In recent years, infections with nontuberculous mycobacteria (NTM) have been increasingly occurring in immunocompromised and immunocompetent patients. However, NTM has never been reported in patients with FHCS. We present the first case of a patient with extrapulmonary NTM infection in a clinical presentation of FHCS. CASE PRESENTATION: A 26-year-old Korean woman presented with right upper quadrant and suprapubic pain. She was initially suspected to have FHCS. However, she was refractory to conventional antibiotic therapy. Laparoscopy revealed multiple violin-string adhesions of the parietal peritoneum to the liver and miliary-like nodules on the peritoneal surfaces. Diagnosis of NTM was confirmed by the polymerase chain reaction analysis results of biopsy specimens that showed caseating granulomas with positive acid-fast bacilli. Treatment with anti-NTM medications was initiated, and the patient’s symptoms were considerably ameliorated. CONCLUSIONS: An awareness of NTM as potential pathogens, even in previously healthy adults, and efforts to exclude other confounding diseases are important to establish the diagnosis of NTM disease. NTM infection can cause various clinical manifestations, which in the present case, overlapped with the symptoms of perihepatic inflammation seen in FHCS