Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

Coronary artery disease (CAD), a leading cause of mortality and morbidity, manifests as atherosclerotic plaques formed by the deposition of cholesterol and lipids within coronary walls. A plenty of machine learning methods have been developed to identify different plaques or the degree of stenosis. Few studies, however, focus on plaques vulnerability, which is crucial because vulnerable plaques are at a high risk of rupture or erosion even with less severe stenosis. To this end, we propose a weakly-supervised multi-contrastive learning network named WheelNet to differentiate vulnerable plaques from stable ones in coronary CT angiography (CCTA). Specifically, we first extract cross-sectional images along the coronary centerline and took consecutive cross-sections as one image sequence. Second, we construct a WheelNet with multiple branches to perform contrastive learning between different image sequences, dependent or independent of vulnerability labels of coronary plaques. Third, we perform patient-level feature aggregation via local-to-global feature encoding given the feature embeddings of image sequences. Finally, we differentiae patients with vulnerable coronary plaques from those with stable ones using an XGBoost classifier. Extensive experiments on the CCTA dataset of 108 patients show the superiority of our WheelNet over other state of the arts, with the diagnostic area-under-the-curve (AUC) of 0.74/0.75 with/without using vulnerability labels, respectively.

Orbital lymphoproliferative disorders (OLPDs) are complex orbital mass-like lesions ranging from benign to malignant. Precise preoperative diagnosis of OLPDs holds profound importance in facilitating timely and effective patient management. Recent studies have shown that exploiting multimodal images can boost the performance in identifying different orbital lesions. However, one or several imaging modalities are sometimes missing in practical applications, which has not yet been properly addressed in existing studies. To this end, we propose a novel OLPD diagnostic method with incomplete multimodal images based on self-/cross-representation and hypergraph ensemble. Specifically, in the first stage, we develop a self-representation network to extract unimodal features and a cross-representation network to impute missing features. In the second stage, by using unimodal features as input, we construct a hypergraph for each modality to make unimodal diagnosis; while for multimodal diagnosis we conduct a multi-view grouping fusion method to reduce the semantic gap between multimodal features and fuse multiple unimodal hypergraphs as multimodal hypergraph to perform multimodal diagnosis. In the third stage, we propose an ensemble strategy that incorporates unimodal diagnosis and multimodal diagnosis to accomplish the final decision. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art approaches.

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. We evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. Compared with state-of-the-art benchmark mobility models, the experimental results show that VeMo outperforms them by 10 percent on average.

Many essential services for autonomous vehicles, e.g., navigation on high-quality maps, are designed based on the understanding of traffic conditions, e.g., travel time/speed on road segments, traffic flow, etc. However, most existing traffic condition models lack the consideration of the differentiation for vehicles with different types (e.g., personal vehicles or trucks) and thus they cannot satisfy some type-specific services, e.g., traffic-condition-based routing for autonomous vehicles with different types. To address this challenge, we design a novel vehicular mobility based sensing model called mDrive to predict the travel speed on the road segments, which is targeted for different types of vehicles by utilizing the camera data obtained from the traffic cameras equipped in the road intersections only, without any in-vehicle GPS devices. mDrive addresses the type-aware traffic speed prediction problem with sparse sensors based on three correlations: (1) the spatial correlation of travel speed on the connected road segments; (2) the temporal correlation of travel speed on the consecutive time slots; (3) the type correlation of different vehicular types’ speed on the same road segment. We implement mDrive on traffic camera data from the Chinese city Suzhou and evaluate it by using the detailed GPS data from personal vehicles, taxis, and trucks, with road contextual data as ground truth. The experiment show mDrive outperforms state-of-the-art methods by reducing 6.2% mean relative error on average for all types of vehicles.

Given widely adopted vehicle tracking technologies, usage-based insurance has been a rising market over the past few years. With potential discounts from insurance companies, customers voluntarily install sensing devices in their vehicles for insurance companies, which are utilized to analyze their historical driving patterns to derive the risks of future driving. However, it is challenging to characterize and predict driving patterns, especially for new users with limited data. To address this issue, we propose and evaluate a system called MoCha to accurately characterize driving patterns for usage-based insurance. The key question we aim to explore with MoCha is whether we can fully explore long-term driving patterns of new users with only limited historical data of themselves by leveraging abundant data of other users and contextual information. To answer this question, we design (i) a multi-level driving pattern modeling component to capture the spatial-temporal dependency on both individual and group level, and (ii) a multi-task learning method to utilize underlying relations of driving metrics and predict multiple driving metrics simultaneously. We implement and evaluate MoCha with real-world on-board diagnostics data from a large insurance company with more than 340,000 vehicles. Further, we validate the usefulness of MoCha by predicting driving risks based on real-world claim data in a Chinese city, Shenzhen.

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging , a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging , we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process.

Instant delivery is an important urban service in recent years because of the increasing demand. An important issue for delivery platforms is to keep updating the status of couriers especially the real-time locations, which is challenging when they are in an indoor environment. We argue the previous indoor localization techniques cannot be applied in the instant delivery scenario because they require extra deployed infrastructures and extensive labor work. In this work, we perform the couriers' indoor localization transparently in a predictive manner without extra actions of couriers by existing data from the platform including order progress reports and couriers' trajectories. Specifically, we present TransLoc to predict couriers' indoor locations by addressing two challenges including uncertain reporting behaviors and uncertain indoor mobility behaviors. Our key idea lies in two insights (i) couriers' behaviors are consistent in indoor/outdoor environments; (ii) localization, as a spatial inference problem, could be converted to a temporal inference problem. We evaluate TransLoc on 565 couriers from an instant delivery company, which improves baselines by at most 72%, and achieves a competitive result compared to a label-extensive approach. As a case study, we apply TransLoc to optimize the order dispatching strategy, which reduces the delivery time by 24%.

In this paper, basis pursuit denoising (BPDN) is applied to extract three-dimensional scattering centers of complex radar targets. Since the distributions of scattering centers are usually sparse in the high-frequency optics region, the valid backscattering coefficients of radar targets can be obtained with the undersampled measurements in azimuth and elevation. It significantly reduces the time for measuring echo signals, which promotes the efficiency of radar systems. However, due to the great computation load and memory cost caused by the matrix-vector products, most of the sparse reconstruction algorithms are not suitable for the process of scattering center extraction. To solve this problem, we derive accelerated operators on the basis of support set and filtered backprojection. By combining the sparse solver SPGL1 with the proposed operators, the new algorithm not only reduces the consumption of system resources, but also estimates the backscattering coefficients accurately. Additionally, the experimental results and analysis demonstrate that the proposed technique possesses high data compression ratio and small radar cross section (RCS) reconstruction error.

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. More importantly, we evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. We compared VeMo with state-of-the-art benchmark mobility models, and the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.

With the development of connected vehicles and autonomous vehicles, various innovative sensing services are proposed to improve the driving experience based on the vehicle to vehicle (V2V) communication. However, due to the heterogeneous mobility pattern of different vehicular fleets and the dynamic nature of the vehicles, it is hard to systematically measure the feasibility of those services in large scale before implementation. In this work, we design a measurement framework called mChat to measure the interactions among heterogeneous vehicle fleets, which is quantified by the topology of the urban vehicular fleets. mChat utilizes two key metrics, i.e., static connectivity feature and dynamic coexistence feature, to explore the feasibility of real-time vehicle to vehicle service on inner-fleet and inter-fleets. We apply mChat on one month of real-world data of a Chinese city, Shenzhen, from (i) a regular vehicle fleet with 3 thousand vehicles; (ii) a taxi fleet with 14 thousand vehicles; (iii) a bus fleet with 11 thousand vehicles; (iv) a truck fleet with 3 thousand vehicles. Our measurement results reveal interesting results related to the feasibility of V2V-based sensing service, leading to utility implications for the upcoming applications for connected vehicles and autonomous vehicles.

We are witnessing a rapid growth of electrified vehicles because of the ever-increasing concerns over urban air quality and energy security. Compared with other electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to the high owning and operating costs, long charging time, and the uneven distribution of charging facilities. Moreover, the highly dynamic environment factors such as the unpredictable traffic congestions, different passenger demands, and even changing weather, can significantly affect electric bus charging efficiency and potentially hinder further development of large-scale electric bus fleets. To deal with these issues, in this paper, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines and 5,562 bus stops, which is obtained from the Chinese city Shenzhen, who has the first and the largest full electric bus network for public transit. Then we investigate the electric bus network to understand its operating and charging patterns, and further verify the feasibility and necessity of a real-time charging scheduling. With such understanding, we design bCharge, a real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of bCharge, we implement it with the real-world streaming dataset from Shenzhen, which includes GPS data of the electric bus fleet, the bus lines and stops data, coupled with the 376 electric bus charging stations data. The evaluation results show that bCharge can dramatically reduce the charging cost by 23.7% and 12.8% electricity usage simultaneously.