Global Context-Aware Progressive Aggregation Network for Salient Object Detection

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively

NMR Spectra Denoising with Vandermonde Constraints

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high.Comment: 10 pages, 9 figure

Modeling reaction time within a traffic simulation model

Human reaction time has a substantial effect on modeling of human behavior at a microscopic level. Drivers and pedestrian do not react to an event instantaneously; rather, they take time to perceive the event, process the information, decide on a response and finally enact their decision. All these processes introduce delay. As human movement is simulated at increasingly fine-grained resolutions, it becomes critical to consider the delay due to reaction time if one is to achieve accurate results. Most existing simulators over-simplify the reaction time implementation to reduce computational overhead and memory requirements. In this paper, we detail the framework which we are developing within the SimMobility Short Term Simulator (a microscopic traffic simulator), which is capable of explicitly modeling reaction time for each person in a detailed, flexible manner. This framework will enable modelers to set realistic reaction time values, relying on the simulator to handle implementation and optimization considerations. Following this, we report our findings demonstrating the impact of reaction time on traffic dynamics within several simulation scenarios. The findings indicate that in the incorporation of reaction time within microscopic simulations improves the traffic dynamics that produces more realistic traffic condition.Singapore-MIT Alliance for Research and Technolog

Machine learning for real-time demand forecasting

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 89-92).For a taxi company, the capability to forecast taxi demand distribution in advance provides valuable decision supports. This thesis studies real-time forecasting system of spatiotemporal taxi demand based on machine learning approaches. Traditional researches usually examine a couple of candidate models by setting up an evaluation metric and testing the overall forecasting performance of each model, finally the best model is selected. However, the best model might be changing from time to time, since the taxi demand patterns are sensitive to the dynamic factors such as date, time, weather, events and so on. In this thesis, we first study range searching techniques and their applications to taxi data modeling as a foundation for further research. Then we discuss machine learning approaches to forecast taxi demand, in which the pros and cons of each proposed candidate model are analyzed. Beyond single models, we build a five-phase ensemble estimator that makes several single models work together in order to improve the forecasting accuracy. Finally, all the forecasting approaches are evaluated in a case study over rich taxi records of New York City. Experiments are conducted to simulate the operation of real-time forecasting system. Results prove that multi-model ensemble estimators do produce better forecasting performances than single models.by Runmin Xu.S.M.S.M. in Transportatio

A Novel On-Ramp Merging Strategy for Connected and Automated Vehicles Based on Game Theory

Connected and automated vehicles (CAVs) have attracted much attention of researchers because of its potential to improve both transportation network efficiency and safety through control algorithms and reduce fuel consumption. However, vehicle merging at intersection is one of the main factors that lead to congestion and extra fuel consumption. In this paper, we focused on the scenario of on-ramp merging of CAVs, proposed a centralized approach based on game theory to control the process of on-ramp merging for all agents without any collisions, and optimized the overall fuel consumption and total travel time. For the framework of the game, benefit, loss, and rules are three basic components, and in our model, benefit is the priority of passing the merging point, represented via the merging sequence (MS), loss is the cost of fuel consumption and the total travel time, and the game rules are designed in accordance with traffic density, fairness, and wholeness. Each rule has a different degree of importance, and to get the optimal weight of each rule, we formulate the problem as a double-objective optimization problem and obtain the results by searching the feasible Pareto solutions. As to the assignment of merging sequence, we evaluate each competitor from three aspects by giving scores and multiplying the corresponding weight and the agent with the higher score gets comparatively smaller MS, i.e., the priority of passing the intersection. The simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. Moreover, the proposed method improved the fuel economy and saved the travel time

Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments

It has been well-recognized that driving behaviors significantly impact the fuel consumption of vehicles. To explore how well deep learning methods can predict fuel consumption precisely and efficiently and then guide drivers to go in an energy-saving way, we propose a fuel consumption prediction model, namely FuelNet, based on long short-term memory (LSTM) neural networks in this study. First, we develop the proposed FuelNet model with numerous vehicle kinematics data and corresponding fuel consumption data collected in the test field and real-world scenarios. And we analyze the relationship between the prediction accuracy and different combinations of input variables, training set size, and the sampling interval of the raw data. Second, we conduct intensive field tests to demonstrate the applicability of our model to fuel consumption prediction for different speed conditions and vehicle types. Furthermore, the superior prediction performance of FuelNet is shown by comparing it with five other types of models, such as the physical model, statistical and regression model, conventional neural networks model, and other deep learning models. Finally, we apply it to three real case studies, which verify that FuelNet can precisely predict fuel consumption for different driving trajectories in many scenarios such as signalized intersection (average value of RE is 0.049), campus environments (RE is 0.030), urban roads (RE is 0.077), and highways (RE is 0.097), as well as can contribute to detecting abnormal fuel consumption

Exogenous Hydrogen Sulfide Protects against Doxorubicin-Induced Inflammation and Cytotoxicity by Inhibiting p38MAPK/NFκB Pathway in H9c2 Cardiac Cells

Background/Aim:We have demonstrated that exogenous hydrogen sulfide (H2S) protects H9c2 cardiac cells against the doxorubicin (DOX)-induced injuries by inhibiting p38 mitogen-activated protein kinase (MAPK) pathway and that the p38 MAPK/nuclear factor-κB (NF-κB) pathway is involved in the DOX-induced inflammatory response and cytotoxicity. The present study attempts to test the hypothesis that exogenous H2S might protect cardiomyocytes against the DOX-induced inflammation and cytotoxicity through inhibiting p38 MAPK/NF-κB pathway. Methods: H9c2 cardiac cells were exposed to 5µM DOX for 24 h to establish a model of DOX cardiotoxicity. The cells were pretreated with NaHS( a donor of H2S) or other drugs before exposure to DOX. Cell viability was analyzed by cell counter kit 8 ( CCK-8), The expression of NF-κB p65 and inducible nitric oxide synthase (iNOS) was detected by Western blot assay. The levels of interleukin-1ß (IL-1ß), IL-6 and tumor necrosis factor-a (TNF-a) were tested by enzyme-linked immunosorbent assay (ELISA). Results: Our findings demonstrated that pretreatment of H9c2 cardiac cells with NaHS for 30 min before exposure to DOX markedly ameliorated the DOX-induced phosphorylation and nuclear translocation of NF-κB p65 subunit. Importantly, the pretreatment with NaHS significantly attenuated the p38 MAPK/NF-κB pathway-mediated inflammatory responses induced by DOX, as evidenced by decreases in the levels of IL-1ß, IL-6 and TNF-a. In addition, application of NaHS or IL-1ß receptor antagonist (IL-1Ra) or PDTC (an inhibitor of NF-κB) attenuated the DOX-induced expression of iNOS and production of nitric oxide (NO), respectively. Furthermore, IL-1Ra also dramatically reduced the DOX-induced cytotoxicity and phosphorylation of NF-κB p65. The pretreatment of H9c2 cells with N-acetyl-L-cysteine (NAC), a scavenger of reactive oxygen species (ROS) prior to exposure to DOX depressed the phosphorylation of NF-κB p65 induced by DOX. Conclusion: The present study has demonstrated the new mechanistic evidence that exogenous H2S attenuates the DOX-induced inflammation and cytotoxicity by inhibiting p38 MAPK/NF-κB pathway in H9c2 cardiac cells. We also provide novel data that the interaction between NF-κB pathway and IL-1ß is important in the induction of DOX-induced inflammation and cytotoxicity in H9c2 cardiac cells