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"Jin, Peter"
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Collectivism predicts mask use during COVID-19
Since its outbreak, COVID-19 has impacted world regions differentially. Whereas some regions still record tens of thousands of new infections daily, other regions have contained the virus. What explains these striking regional differences? We advance a cultural psychological perspective on mask usage, a precautionary measure vital for curbing the pandemic. Four large-scale studies provide evidence that collectivism (versus individualism) positively predicts mask usage—both within the United States and across the world. Analyzing a dataset of all 3,141 counties of the 50 US states (based on 248,941 individuals), Study 1a revealed that mask usage was higher in more collectivistic US states. Study 1b replicated this finding in another dataset of 16,737 individuals in the 50 US states. Analyzing a dataset of 367,109 individuals in 29 countries, Study 2 revealed that mask usage was higher in more collectivistic countries. Study 3 replicated this finding in a dataset of 277,219 Facebook users in 67 countries. The link between collectivism and mask usage was robust to a host of control variables, including cultural tightness–looseness, political affiliation, demographics, population density, socioeconomic indicators, universal health coverage, government response stringency, and time. Our research suggests that culture fundamentally shapes how people respond to crises like the COVID-19 pandemic. Understanding cultural differences not only provides insight into the current pandemic, but also helps the world prepare for future crises.
Journal Article
Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction
In this study, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are firstly converted into spherical coordinates and filled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into the new data structure to store the information of range, azimuth, and intensity. Then, the dynamic mode decomposition method is applied to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds based on intensity channel pattern recognition. The coarse-fine triangle algorithm (CFTA) automatically finds the dividing value to separate the moving targets from static background according to range information. After intensity and range background subtraction, the foreground moving objects will be detected using a density-based detector and encoded into the state-space model for tracking. The output of the proposed solution includes vehicle trajectories that can enable many mobility and safety applications. The method was validated at both path and point levels and outperformed the state of the art. In contrast to the previous methods that process directly on the scattered and discrete point clouds, the dynamic classification method can establish the less sophisticated linear relationship of the 3D measurement data, which captures the spatial-temporal structure that we often desire.
Journal Article
Quantum Algorithm for Variant Maximum Satisfiability
In this paper, we proposed a novel quantum algorithm for the maximum satisfiability problem. Satisfiability (SAT) is to find the set of assignment values of input variables for the given Boolean function that evaluates this function as TRUE or prove that such satisfying values do not exist. For a POS SAT problem, we proposed a novel quantum algorithm for the maximum satisfiability (MAX-SAT), which returns the maximum number of OR terms that are satisfied for the SAT-unsatisfiable function, providing us with information on how far the given Boolean function is from the SAT satisfaction. We used Grover’s algorithm with a new block called quantum counter in the oracle circuit. The proposed circuit can be adapted for various forms of satisfiability expressions and several satisfiability-like problems. Using the quantum counter and mirrors for SAT terms reduces the need for ancilla qubits and realizes a large Toffoli gate that is then not needed. Our circuit reduces the number of ancilla qubits for the terms T of the Boolean function from T of ancilla qubits to ≈log2T+1. We analyzed and compared the quantum cost of the traditional oracle design with our design which gives a low quantum cost.
Journal Article
Transit signal priority optimization for urban traffic network considering arterial coordinated signal control
Traffic congestion has been a challenging problem in urban areas during rush hours. Transit priority (especially transit signal priority) strategy can provide smooth flow for decreasing travel times, stops, and delay. However, on urban arterials, the performance of transit signal priority scenario along arterial corridors of urban traffic network is primarily affected by coordinated signal control strategy. This article proposes a transit signal priority green duration optimization model considering passengers traveling delay at intersections and bus stops, and then the optimized transit signal priority phase plan considering arterial coordinated signal control is addressed. Consequently, the proposed method is evaluated using a VISSIM model calibrated with field traffic volume and traffic signal data of Qingliangmen-Bridge community in Nanjing, China. Comparing with the non-transit signal priority scenario, the optimized transit signal priority–based phasing plans result in the reduced delay at intersections and bus service stops. The evaluation results illustrate the promising performance of the proposed transit signal priority optimization method considering arterial coordinated signal control in reducing the passenger delay for urban traffic network during rush hours.
Journal Article
Improving Bus Operations through Integrated Dynamic Holding Control and Schedule Optimization
Bus bunching can lead to unreliable bus services if not controlled properly. Passengers will suffer from the uncertainty of travel time and the excessive waiting time. Existing dynamic holding strategies to address bus bunching have two major limitations. First, existing models often rely on large slack time to ensure the validity of the underlying model. Such large slack time can significantly reduce the bus operation efficiency by increasing the overall route travel times. Second, the existing holding strategies rarely consider the impact on the schedule planning. Undesirable results such as bus overloading issues arise when the bus fleet size is limited. This paper explores analytically the relationship between the slack time and the effect of holding control. The optimal slack time determined based on the derived relationship is found to be ten times smaller than in previous models based on numerical simulation results. An optimization model is developed with passenger-orient objective function in terms of travel cost and constraints such as fleet size limit, layover time at terminals, and other schedule planning factors. The optimal choice of control stops, control parameters, and slack time can be achieved by solving the optimization. The proposed model is validated with a case study established based on field data collected from Chengdu, China. The numerical simulation uses the field passenger demand, bus average travel time, travel time variance of road segments, and signal timings. Results show that the proposed model significantly reduce passengers average travel time compared with existing methods.
Journal Article
Biobjective Optimization and Evaluation for Transit Signal Priority Strategies at Bus Stop-to-Stop Segment
This paper proposes a new optimization framework for the transit signal priority strategies in terms of green extension, red truncation, and phase insertion at the stop-to-stop segment of bus lines. The optimization objective is to minimize both passenger delay and the deviation from bus schedule simultaneously. The objective functions are defined with respect to the segment between bus stops, which can include the adjacent signalized intersections and downstream bus stops. The transit priority signal timing is optimized by using a biobjective optimization framework considering both the total delay at a segment and the delay deviation from the arrival schedules at bus stops. The proposed framework is evaluated using a VISSIM model calibrated with field traffic volume and traffic signal data of Caochangmen Boulevard in Nanjing, China. The optimized TSP-based phasing plans result in the reduced delay and improved reliability, compared with the non-TSP scenario under the different traffic flow conditions in the morning peak hour. The evaluation results indicate the promising performance of the proposed optimization framework in reducing the passenger delay and improving the bus schedule adherence for the urban transit system.
Journal Article
Optimizing surface-engineered ultra-small gold nanoparticles for highly efficient miRNA delivery to enhance osteogenic differentiation of bone mesenchymal stromal cells
Regulation of osteogenic differentiation of bone mesenchymal stromal cells （BMSCs） plays a critical role in bone regeneration. As small non-coding RNAs, microRNAs （miRNAs） play an important role in stem cell differentiation through regulating target-mRNA expression. Unfortunately, highly efficient and safe delivery of miRNAs to BMSCs to regulate their osteogenic differentiation remains challenging. Conventional inorganic nanocrystals have shown increased toxicity owing to their larger size precluding renal clearance. Here, we developed novel, surface-engineered, ultra-small gold nanoparticles （USAuNPs, 〈10 nm） for use as highly efficient miR-5106-delivery systems to enable regulation of BMSC differentiation. We exploited the effects of AuNPs coated layer-by-layer with polyethylenimine （PEI） and liposomes （Lipo） to enhance miR-5106-delivery activity and subsequent BMSC differentiation capacity. The PEI- and Lipo-coated AuNPs （Au@PEI@Lipo） showed negligible cytotoxicity, good miRNA-5106-binding affinity, highly efficient delivery of miRNAs to BMSCs, and long-term miRNA expression （21 days）. Additionally, compared with commercial Lipofectamine 3000 and 25 kD PEI, the optimized Au@PEI@Lipo-miR-5106 nanocomplexes significantly enhanced BMSC differentiation into osteoblast-like cells through activation of the Sox9 transcription factor. Our findings reveal a promising strategy for the rational design of ultra-small inorganic nanoparticles as highly efficient miRNA-delivery platforms for tissue regeneration and disease therapy.
Journal Article
Learning to Navigate in Visual Environments
Artificially intelligent agents with some degree of autonomy in the real world must learn to complete visual navigation tasks. In this dissertation, we consider the learning problem of visual navigation, as well as implementation issues facing agents that utilize learned visual perception systems. We begin by formulating visual navigation tasks in the setting of deep reinforcement learning under partial observation. Previous approaches to deep reinforcement learning do not adequately address partial observation while remaining sample-efficient. Our first contribution is a novel deep reinforcement learning algorithm, advantage-based regret minimization (ARM), which learns robust policies in visual navigation tasks in the presence of partial observability. Next, we are motivated by performance bottlenecks arising from large scale supervised learning for training visual perception systems. Previous distributed training approaches are affected by synchronization or communication bottlenecks which limit their scaling to multiple compute nodes. Our second contribution is a distributed training algorithm, gossiping SGD, which avoids both synchronization and centralized communication. Finally, we consider how to train deep convolutional neural networks when inputs and activation tensors have high spatial resolution and do not easily fit in GPU memory. Previous approaches to reducing memory usage of deep convnets involve trading off between computation and memory usage. Our third and final contribution is an implementation of spatially parallel convolutions, which partition activation tensors along the spatial axes between multiple GPUs, and achieve practically linear strong scaling.
Dissertation
Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction
In this paper, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are firstly converted into spherical coordinates and filled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into new data structures to store the information of range, azimuth, and intensity. Then, the Dynamic Mode Decomposition method is applied to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds based on intensity channel pattern recognition. The Coarse Fine Triangle Algorithm (CFTA) automatically finds the dividing value to separate the moving targets from static background according to range information. After intensity and range background subtraction, the foreground moving objects will be detected using a density-based detector and encoded into the state-space model for tracking. The output of the proposed solution includes vehicle trajectories that can enable many mobility and safety applications. The method was validated at both path and point levels and outperformed the state-of-the-art. In contrast to the previous methods that process directly on the scattered and discrete point clouds, the dynamic classification method can establish the less sophisticated linear relationship of the 3D measurement data, which captures the spatial-temporal structure that we often desire.
Paper