RightsACM allow an authors' version of their own ACMcopyrighted work on their personal server or on servers belonging to their employers. As a collective and highly dynamic social group, human crowd is a fascinating phenomenon which has been constantly concerned by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This paper assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the timescale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user. We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in these work. We have also discussed some open problems in the area. This paper will provide the researchers with useful information and insights on the state-of-the-art of the technologies in crowd modeling and simulation as well as future research directions.

It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learning model is developed. The model yields a patient-level sensitivity 93.1% and specificity 93.9% [with areas under the receiver operating characteristic curve of 0.956 (95% confidence interval: 0.928-0.977)] on the multi-center external validation dataset, superior to that of human experts. With the help of the model, the performances of human experts with various levels are improved. Moreover, the diagnosis based on smartphone photos of sonographic gallbladder images through a smartphone app and based on video sequences by the model still yields expert-level performances. The ensembled deep learning model in this study provides a solution to help radiologists improve the diagnosis of BA in various clinical application scenarios, particularly in rural and undeveloped regions with limited expertise.

Simulation study on evacuation scenarios has gained tremendous attention in recent years. Two major research challenges remain along this direction: (1) how to portray the effect of individuals' adaptive behaviors under various situations in the evacuation procedures and (2) how to simulate complex evacuation scenarios involving huge crowds at the individual level due to the ultrahigh complexity of these scenarios. In this study, a simulation framework for general evacuation scenarios has been developed. Each individual in the scenario is modeled as an adaptable and autonomous agent driven by a weight-based decision-making mechanism. The simulation is intended to characterize the individuals' adaptable behaviors, the interactions among individuals, among small groups of individuals, and between the individuals and the environment. To handle the second challenge, this study adopts GPGPU to sustain massively parallel modeling and simulation of an evacuation scenario. An efficient scheme has been proposed to minimize the overhead to access the global system state of the simulation process maintained by the GPU platform. The simulation results indicate that the "adaptability" in individual behaviors has a significant influence on the evacuation procedure. The experimental results also exhibit the proposed approach's capability to sustain complex scenarios involving a huge crowd consisting of tens of thousands of individuals. Site. He is the Founder of the IEEE International Symposium on High Performance Distributed Computing (HPDC) and the cofounder of the IEEE International Conference on Autonomic Computing. His current research focuses on autonomic computing, high performance distributed computing, design and analysis of high speed networks, benchmarking and evaluating parallel and distributed systems, developing software design tools for high performance computing and communication systems, and network-centric applications. He is co-author/editor of four books on parallel and distributed computing: Autonomic

Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been developed using general-purpose computing on the graphics processing unit (GPGPU), namely, G-EEMD. A spectral entropy facilitated by G-EEMD was, therefore, proposed to analyze the EEG data for estimating the depth of anesthesia (DoA) in a real-time manner. In terms of EEG data analysis, G-EEMD has dramatically improved the run-time performance by more than 140 times compared to the original serial EEMD implementation. G-EEMD also performs far better than another parallelized implementation of EEMD bases on conventional CPU-based distributed computing technology despite the latter utilizes 16 high-end computing nodes for the same computing task. Furthermore, the results obtained from a pharmacokinetics/pharmacodynamic (PK/PD) model analysis indicate that the EEMD method is slightly more effective than its precedent alternative method (EMD) in estimating DoA, the coefficient of determination R(2) by EEMD is significantly higher than that by EMD (p < 0.05, paired t-test) and the prediction probability P(k) by EEMD is also slighter higher than that by EMD (p < 0.2, paired t-test).