With increased frequency and intensity due to climate change, wildfires have become a growing global concern. This creates severe challenges for fire and emergency services as well as communities in the wildland-urban interface (WUI). To reduce wildf
ire risk and enhance the safety of WUI communities, improving our understanding of wildfire evacuation is a pressing need. To this end, this study proposes a new methodology to analyze human behavior during wildfires by leveraging a large-scale GPS dataset. This methodology includes a home-location inference algorithm and an evacuation-behavior inference algorithm, to systematically identify different groups of wildfire evacuees (i.e., self-evacuee, shadow evacuee, evacuee under warning, and ordered evacuee). We applied the methodology to the 2019 Kincade Fire in Sonoma County, CA. We found that among all groups of evacuees, self-evacuees and shadow evacuees accounted for more than half of the evacuees during the Kincade Fire. The results also show that inside of the evacuation warning/order zones, the total evacuation compliance rate was around 46% among all the categorized people. The findings of this study can be used by emergency managers and planners to better target public outreach campaigns, training protocols, and emergency communication strategies to prepare WUI households for future wildfire events.
Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have started to utilize machine learning models to forecast travel demand, whi
ch, in many cases, can produce higher prediction accuracy than statistical models. However, most existing machine-learning studies used a global model to predict the demand and ignored the influence of spatial heterogeneity (i.e., the spatial variations in the impacts of explanatory variables). Spatial heterogeneity can drive the parameter estimations varying over space; failing to consider the spatial variations may limit the models prediction performance. To account for spatial heterogeneity, this study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone (census-tract-to-census-tract) travel demand for ridesourcing services. Specifically, we develop a clustering framework to split the origin-destination pairs into different clusters and ensemble the cluster-specific machine learning models for prediction. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago. The results show that, with a more transparent and flexible model structure, the CEM significantly improves the prediction accuracy than the benchmark models (i.e., global machine-learning and statistical models directly trained on all observations). This study offers transportation researchers and practitioners a new methodology of travel demand forecasting, especially for new travel modes like ridesourcing and micromobility.
Anomaly detection on time series is a fundamental task in monitoring the Key Performance Indicators (KPIs) of IT systems. Many of the existing approaches in the literature show good performance while requiring a lot of training resources. In this pap
er, the online matrix profile, which requires no training, is proposed to address this issue. The anomalies are detected by referring to the past subsequence that is the closest to the current one. The distance significance is introduced based on the online matrix profile, which demonstrates a prominent pattern when an anomaly occurs. Another training-free approach spectral residual is integrated into our approach to further enhance the detection accuracy. Moreover, the proposed approach is sped up by at least four times for long time series by the introduced cache strategy. In comparison to the existing approaches, the online matrix profile makes a good trade-off between accuracy and efficiency. More importantly, it is generic to various types of time series in the sense that it works without the constraint from any trained model.
During the last few decades, online controlled experiments (also known as A/B tests) have been adopted as a golden standard for measuring business improvements in industry. In our company, there are more than a billion users participating in thousand
s of experiments simultaneously, and with statistical inference and estimations conducted to thousands of online metrics in those experiments routinely, computational costs would become a large concern. In this paper we propose a novel algorithm for estimating the covariance of online metrics, which introduces more flexibility to the trade-off between computational costs and precision in covariance estimation. This covariance estimation method reduces computational cost of metric calculation in large-scale setting, which facilitates further application in both online controlled experiments and adaptive experiments scenarios like variance reduction, continuous monitoring, Bayesian optimization, etc., and it can be easily implemented in engineering practice.
Visual object localization is the key step in a series of object detection tasks. In the literature, high localization accuracy is achieved with the mainstream strongly supervised frameworks. However, such methods require object-level annotations and
are unable to detect objects of unknown categories. Weakly supervised methods face similar difficulties. In this paper, a self-paced learning framework is proposed to achieve accurate object localization on the rank list returned by instance search. The proposed framework mines the target instance gradually from the queries and their corresponding top-ranked search results. Since a common instance is shared between the query and the images in the rank list, the target visual instance can be accurately localized even without knowing what the object category is. In addition to performing localization on instance search, the issue of few-shot object detection is also addressed under the same framework. Superior performance over state-of-the-art methods is observed on both tasks.
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts. On the other hand, there ar
e more and more ocular manifestations that have been reported in the COVID-19 patients as growing clinical evidence[1]. This inspired this project. We have conducted the joint clinical research since January 2021 at the ShiJiaZhuang City, Heibei province, China, which approved by the ethics committee of The fifth hospital of ShiJiaZhuang of Hebei Medical University. We undertake several blind tests of COVID-19 patients by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Meantime as an important part of the ongoing globally COVID-19 eye test program by AIMOMICS since February 2020, we propose a new fast screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras. This could reliably make a rapid risk screening of COVID-19 with the sustainable stable high performance in different countries and races. Our model for COVID-19 rapid prescreening have the merits of the lower cost, fully self-performed, non-invasive, importantly real-time, and thus enables the continuous health surveillance. We further implement it as the open accessible APIs, and provide public service to the world. Our pilot experiments show that our model is ready to be usable to all kinds of surveillance scenarios, such as infrared temperature measurement device at airports and stations, or directly pushing to the target people groups smartphones as a packaged application.
This letter reports the findings of the late time behavior of the out-of-time-ordered correlators (OTOCs) via a quantum kicked rotor model with $cal{PT}$-symmetric driving potential. An analytical expression of the OTOCs quadratic growth with time is
yielded as $C(t)=G(K)t^2$. Interestingly, the growth rate $G$ features a quantized response to the increase of the kick strength $K$, which indicates the chaos-assisted quantization in the OTOCs dynamics. The physics behind this is the quantized absorption of energy from the non-Hermitian driving potential. This discovery and the ensuing establishment of the quantization mechanism in the dynamics of quantum chaos with non-Hermiticity will provide insights in chaotic dynamics, promising unprecedented observations in updated experiments.
Ga2O3 is a wide-band-gap semiconductor of great interest for applications in electronics and optoelectronics. Two-dimensional (2D) Ga2O3 synthesized from top-down or bottom-up processes can reveal brand new heterogeneous structures and promising appl
ications. In this paper, we study phase transitions among three low-energy stable Ga2O3 monolayer configurations using density functional theory and a newly developed machine-learning Gaussian approximation potential, together with solid-state nudged elastic band calculations. Kinetic minimum energy paths involving direct atomic jump as well as concerted layer motion are investigated. The low phase transition barriers indicate feasible tunability of the phase transition and orientation via strain engineering and external electric fields. Large-scale calculations using the newly trained machine-learning potential on the thermally activated single-atom jumps reveal the clear nucleation and growth processes of different domains. The results provide useful insights to future experimental synthesis and characterization of 2D Ga2O3 monolayers.
Concepts of Mobility-on-Demand (MOD) and Mobility as a Service (MaaS), which feature the integration of various shared-use mobility options, have gained widespread popularity in recent years. While these concepts promise great benefits to travelers,
their heavy reliance on technology raises equity concerns as socially disadvantaged population groups can be left out in an era of on-demand mobility. This paper investigates the potential uptake of MOD transit services (integrated fixed-route and on-demand services) among travelers living in low-income communities. Specially, we analyze peoples latent attitude towards three shared-use mobility services, including ride-hailing services, fixed-route transit, and MOD transit. We conduct a latent class cluster analysis of 825 survey respondents sampled from low-income neighborhoods in Detroit and Ypsilanti, Michigan. We identified three latent segments: shared-mode enthusiast, shared-mode opponent, and fixed-route transit loyalist. People from the shared-mode enthusiast segment often use ride-hailing services and live in areas with poor transit access, and they are likely to be the early adopters of MOD transit services. The shared-mode opponent segment mainly includes vehicle owners who lack interests in shared mobility options. The fixed-route transit loyalist segment includes a considerable share of low-income individuals who face technological barriers to use the MOD transit. We also find that males, college graduates, car owners, people with a mobile data plan, and people living in poor-transit-access areas have a higher level of preferences for MOD transit services. We conclude with policy recommendations for developing more accessible and equitable MOD transit services.
In this letter, we investigate the effects of non-Hermitian driving on quantum coherence in a bipartite system. The results that the dynamical localization destroyed by the Hermitian interaction revives are an evidence of the restoration of quantum c
oherence by non-Hermitian driving. Besides, the entanglement between the two subsystems also decays with the boosting of non-hermitian driving strength, which provides another evidence that non-Hermitian driving will protect quantum coherence. The physics behind this phenomenon is the domination of the quasieigenstate with maximum imaginary value of the quasieigenvalue on the dynamics of the non-Hermitian system. Our discovery establishes a restoration mechanism of quantum coherence in interacting and dissipative quantum systems, which is highly relevant to experiments in diverse fields from many-body physics to quantum information.
