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

In Materia Neuron Spiking Plasticity for Sequential Event Processing Based on Dual-Mode Memristor

Artificial neurons are the fundamental elements in neuromorphic computing systems. Studies have revealed neuronal spike‐rate adaptation owing to intrinsic plasticity that neurons will adapt to the spiking patterns and store the events in the background spiking through clustered neuronal spiking. The event can be reactivated by specific retrieval clues instead of solely relying on synaptic plasticity. However, the neural adaptation, as well as the interactive adaptations of neuronal activity for information processing, have not been implemented. Herein, an artificial adaptive neuron via in materia modulation of the VO2/HfO2 based dual‐mode memristor is demonstrated. By changing the conductance of the HfO2 layer, the firing threshold can be modulated, thus the excitability and inhibition can be adjusted according to the previous stimuli without any complex peripherals, showing an adaptive firing rate even under the same stimuli. The artificial neuron clusters can emulate the concept of neuronal memory and neural adaptation, demonstrating spatiotemporal encoding capabilities via the correlated neural firing patterns. This conceptual work provides an alternative way to expand the computation power of spiking neural networks by exploiting the neural adaptation and could be enlightenment to maximize the synergy across both synapse and neuron in neuromorphic computing systems

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

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)

Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems

This paper proposes a novel sampling-based adaptive bounding evolutionary algorithm termed SABEA that is capable of dynamically updating the search space during the evolution process for continuous optimization problems. The proposed SABEA adopts two bounding strategies, namely fitness-based bounding and probabilistic sampling-based bounding, to select a set of individuals over multiple generations and leverage the value information from these individuals to update the search space of a given problem for improving the solution accuracy and search efficiency. To evaluate the performance of this method, SABEA is applied on top of the classic differential evolution (DE) algorithm and a DE variant, and SABEA is compared to a state-of-the-art Distribution-based Adaptive Bounding Genetic Algorithm (DABGA) on a set of 27 selected benchmark functions. The results show that SABEA can be used as a complementary strategy for further enhancing the performance of existing evolutionary algorithms and it also outperforms DABGA. Finally, a practical problem, namely the model calibration for an agent-based simulation, is used to further evaluate SABEA. The results show SABEA’s applicability to diverse problems and its advantages over the traditional genetic algorithm-based calibration method and DABGA.Accepted versio

Crowd-Level Abnormal Behavior Detection via Multi-Scale Motion Consistency Learning

Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) techniques have achieved remarkable success in detecting individual-level abnormal behaviors (e.g., sudden running, fighting and stealing), but research on VAD for CABs is rather limited. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the normal behaviors when observed locally, and the scale of CABs could vary from one scenario to another. In this paper, we present a systematic study to tackle the important problem of VAD for CABs with a novel crowd motion learning framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first captures the spatial and temporal crowd motion consistency information in a graph representation. Then, it simultaneously trains multiple feature graphs constructed at different scales to capture rich crowd patterns. An attention network is used to adaptively fuse the multi-scale features for better CAB detection. For the empirical study, we consider three large-scale crowd event datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net could substantially improve the state-of-the-art performance on all the datasets