A review of interactive narrative systems and technologies: a training perspective

As an emerging form of digital entertainment, interactive narrative has attracted great attention of researchers over the past decade. Recently, there is an emerging trend to apply interactive narrative for training and simulation. An interactive narrative system allows players to proactively interact with simulated entities in a virtual world and have the ability to alter the progression of a storyline. In simulation-based training, the use of an interactive narrative system enables the possibility to offer engaging, diverse and personalized narratives or scenarios for different training purposes. This paper provides a review of interactive narrative systems and technologies from a training perspective. Specifically, we first propose a set of key requirements in developing interactive narrative systems for simulation-based training. Then we review nine representative existing systems with respect to their system architectures, features and related mechanisms. To examine their applicability to training, we investigate and compare the reviewed systems based on the functionalities and modules that support the proposed requirements. Furthermore, we discuss some open research issues on future development of interactive narrative technologies for training applications

New early oligocene zircon U-Pb dates for the ‘Miocene’ Wenshan Basin, Yunnan, China: Biodiversity and paleoenvironment

The sedimentary basins of Yunnan, Southwest China, record detailed histories of Cenozoic paleoenvironmental change. They track regional tectonic and palaeobiological evolution, both of which are critically important for the development of modern floral diversity in southwestern China and throughout Asia more generally. However, to be useful, the sedimentary archives within the basins have to be placed within a well-constrained timeframe independent of biostratigraphy. Using high resolution U-Pb dating, we redefine the age of fossil-bearing strata in the Wenshan Basin. Regarded as Miocene for the last half century, these basin sediments encompass 30±2 and 32±1 Ma early Oligocene tuffaceous horizons, thus indicating a significantly greater antiquity than previously recognized. Together with other regional age revisions our result points to widespread Yunnan basin and orographic development as largely having taken place by the end Paleogene. This age revision provides an important new perspective on the preserved biotas and their evolution in Yunnan, and especially our understanding of the origin of Asian biodiversity which, regionally, had a near-modern composition by the early Oligocene. Crucially, this revised age evidences late Eocene-early Oligocene regional tectonism, pointing to the rise of eastern Tibet and the Hengduan Mountains before the growth of the Himalaya, and that Asia's high plant diversity has a Paleogene origin

New early oligocene zircon U-Pb dates for the ‘Miocene’ Wenshan Basin, Yunnan, China: Biodiversity and paleoenvironment

The sedimentary basins of Yunnan, Southwest China, record detailed histories of Cenozoic paleoenvironmental change. They track regional tectonic and palaeobiological evolution, both of which are critically important for the development of modern floral diversity in southwestern China and throughout Asia more generally. However, to be useful, the sedimentary archives within the basins have to be placed within a well-constrained timeframe independent of biostratigraphy. Using high resolution U-Pb dating, we redefine the age of fossil-bearing strata in the Wenshan Basin. Regarded as Miocene for the last half century, these basin sediments encompass 30±2 and 32±1 Ma early Oligocene tuffaceous horizons, thus indicating a significantly greater antiquity than previously recognized. Together with other regional age revisions our result points to widespread Yunnan basin and orographic development as largely having taken place by the end Paleogene. This age revision provides an important new perspective on the preserved biotas and their evolution in Yunnan, and especially our understanding of the origin of Asian biodiversity which, regionally, had a near-modern composition by the early Oligocene. Crucially, this revised age evidences late Eocene-early Oligocene regional tectonism, pointing to the rise of eastern Tibet and the Hengduan Mountains before the growth of the Himalaya, and that Asia's high plant diversity has a Paleogene origin

Efficacy and safety of combined immunotherapy and stereotactic radiosurgery in NSCLCBM patients and a novel prognostic nomogram: A real-world study

ObjectiveTo explore the effectiveness of combined immunotherapy (IT) and stereotactic radiosurgery (SRS) and address the gap between evidence-based clinical practice and academic knowledge of optimal timing of IT relative to SRS. In addition, to meet the unmet need for an up-to-date prognostic assessment model in the era of IT.MethodsThe data of 86 non-small cell lung cancer brain metastasis (NSCLCBM) patients treated with SRS to 268 brain metastases (BMs) were retrospectively extracted from our hospital database. The Kaplan–Meier analysis was employed for overall survival (OS) and a log-rank test for comparison between groups. Cox proportional hazards regression models were used to identify the significant prognostic factors. The prognostic nomogram was established utilizing the rms package of R software.ResultsIT was found to be associated with improved OS (from BM diagnosis: HR 0.363, 95% CI 0.199 - 0.661, P < 0.001; from SRS: HR 0.472, 95% CI 0.260 - 0.857, P = 0.014). Individuals who received IT in combination with SRS had better OS than those who didn’t (from the day of BM diagnosis: 16.8 vs. 8.4 months, P = 0.006; from the day of SRS: 12 vs. 7 months, P = 0.037). Peri-SRS timing of IT administration was a significant prognostic factor for OS (from BM diagnosis: HR 0.132, 95% CI 0.034 - 0.517, P = 0.004; from SRS: HR 0.14, 95% CI 0.044 - 0.450, P = 0.001). Initiating IT after SRS led to superior OS than concurrent or before (from BM diagnosis: 26.5 vs. 14.1 vs. 7.1 months; from SRS: 21.4 vs. 9.9 vs. 4.1 months, respectively). Additionally, we build a nomogram incorporating IT, cumulative intracranial tumor volume (CITV), and recursive partitioning analysis (RPA), demonstrating a remarkable prognosis prediction performance for SRS-treated NSCLCBM patients.ConclusionPeri-SRS IT is a promising approach in treating NSCLCBM, as improved OS was observed without significantly increasing adverse events. Receipt of IT post-SRS was associated with superior OS than those who received IT concurrently or before. Incorporating IT and CITV into the RPA index could augment its prognosis assessment value for SRS-treated NSCLCBM patients, predominantly in the wild-type

Crowd behavior modeling and simulation

191 p.As a collective and highly dynamic social group, human crowd is a fascinating phenomenon in nature. While well-organized crowd activities improve the public's enjoyment of events, uncontrolled crowd may cause accidents and event disasters in many cases. Numerous incidents with large crowd have been recorded in human history, and many of these incidents have led to severe casualties and injuries. How to predict and control the behavior of a crowd upon various events has become an intriguing and challenging issue faced by many psychologists, sociologists, and computer scientists. However, due to the inherent nature of the crowd events, it is difficult to study the crowd behaviors based on conventional empirical analysis. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds. This research follows this trend and aims to develop a generic crowd behavior modelling framework, which models human-like cognitive processes involved in decision making and action selection. The designed framework aims to serve as a general framework for model developers to construct their behavior models for different scenarios.Doctor of Philosophy (SCE

Learning behavior patterns from video for agent-based crowd modeling and simulation

This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians

Incremental route inference from low-sampling GPS data : an opportunistic approach to online map matching

With the surging of smart device sensing and mobile networking, GPS data has been widely available for identifying vehicle position and route on the road map. For many real-time applications, such as traffic sensing and route recommendation, it is critical to immediately infer travelling route with incoming GPS data. In this paper, an opportunistic approach to online map matching is proposed to incrementally infer routes from low-sampling GPS data with low output latency. Unlike the hidden Markov model (HMM)-based approach, which often experiences certain delay between the GPS observation and inference, our algorithm can produce immediate inference when a new GPS point becomes available. Furthermore, a rollback mechanism is provided to correct the already inferred route when some abnormal situations are detected during the opportunistic inference process. We evaluate the proposed algorithm using real dataset of GPS trajectories over 100 cities around the world. Experimental results show that our algorithm is better than, or at least comparable to the state-of-the-art algorithms in terms of inference accuracy. More importantly, our algorithm can yield much shorter output latency and require less execution time, which is critical for many real-time navigation applications and location-based services.Accepted versionThis work is supported by National Natural Science Foundation of China (Grant No. 61872282), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2019JM-031) and the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2019C04)