FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For this purpose, this paper proposes a novel time-based system, namely FraudMove, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed FraudMove system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. FraudMove employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows FraudMove to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, FraudMove discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of FraudMove in detecting outlier trajectories. The experimental results prove that FraudMove saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems.

A Favorable Path to Domain Separation in the Orange Carotenoid Protein

The Orange Carotenoid Protein (OCP) is responsible for nonphotochemical quenching (NPQ) in cyanobacteria, a defense mechanism against potentially damaging effects of excess light conditions. This soluble two-domain protein undergoes profound conformational changes upon photoactivation, involving translocation of the ketocarotenoid inside the cavity followed by domain separation. Domain separation is a critical step in the photocycle of OCP because it exposes the N-terminal domain (NTD) to perform quenching of the phycobilisomes. Many details regarding the mechanism and energetics of OCP domain separation remain unknown. In this work, we apply metadynamics to elucidate the protein rearrangements that lead to the active, domain-separated, form of OCP. We find that translocation of the ketocarotenoid canthaxanthin has a profound effect on the energetic landscape and that domain separation only becomes favorable following translocation. We further explore, characterize, and validate the free energy surface (FES) using equilibrium simulations initiated from different states on the FES. Through pathway optimization methods, we characterize the most probable path to domain separation and reveal the barriers along that pathway. We find that the free energy barriers are relatively small (<5 kcal/mol), but the overall estimated kinetic rate is consistent with experimental measurements (>1 ms). Overall, our results provide detailed information on the requirement for canthaxanthin translocation to precede domain separation and an energetically feasible pathway to dissociation.

Systemic Risk in Chinese Commodity Futures Markets: A Graph Theory Analysis

This paper sets out to explore the contagion of systemic risk in Chinese commodity futures market based on specific tools of the graph-theory. More precisely, we use minimum spanning trees as a way to identify the most probable path for the transmission of prices shocks. In the sample of 30 kinds of Chinese commodity futures, we construct the MST and obtain the most probable and the shortest path for the transmission of a prices shock. We find that metal futures play an important role in commodity futures market and copper stands at the heart of the system (The core position of the system is very important for the transmission of system risk). And our results also reveal that when the risk occurs, the MST structure becomes smaller, leading to the most effective transmission path of risk becomes shorter.

Dynamic Ensembles versus Cones of Uncertainty: Visualizations to Support Understanding of Uncertainty in Hurricane Forecasts

Visualizations attempt to convey the uncertain track of an approaching hurricane. The current experiment contrasted decision characteristics that resulted from observing hurricane paths presented using cones of uncertainty versus a new form of dynamic ensemble. Participants made judgments about whether to evacuate a town at different eccentricities to the central predicted path of a storm. Results showed that dynamic ensembles have different properties to cone displays. Presentations of dynamic ensembles encouraged greater consideration of evacuation at locations further from the most probable path, but that were still at risk. However, dynamic ensembles resulted in lower evacuation rates at the center of the distribution, consistent with a probabilistic sense of the risk but nonetheless a potentially undesirable strategy. In addition, perceptions of the evacuation need with dynamic ensemble presentations were more strongly influenced by the amount of variability than with cones. The implications for use of dynamic ensembles are discussed.

Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

Quantifying Epigenetic Stability with Minimum Action Paths

Chromatin can adopt multiple stable, heritable states with distinct histone modifications and varying levels of gene expression. Insight on the stability and maintenance of such epigenetic states can be gained by mathematical modeling of stochastic reaction networks for histone modifications. Analytical results for the kinetic networks are particularly valuable. Compared to computationally demanding numerical simulations, they often are more convenient at evaluating the robustness of conclusions with respect to model parameters. In this communication, we developed a second-quantization based approach that can be used to analyze discrete stochastic models with a fixed, finite number of particles using a representation of the SU (2) algebra. We applied the approach to a kinetic model of chromatin states that captures the feedback between nucleosomes and the enzymes conferring histone modifications. Using a path integral expression for the transition probability, we computed the epigenetic landscape that helps to identify the emergence of bistability and the most probable path connecting the two steady states. We anticipate the generalizability of the approach will make it useful for studying more complicated models that couple epigenetic modifications with transcription factors and chromatin structure.