Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new perspective to the offline reinforcement learning (RL) challenge. More concretely, we transform it into a supervised learning task by integrating multimodal and pre-trained language models. Our approach incorporates state information derived from images and action-related data obtained from text, thereby bolstering RL training performance and promoting long-term strategic thinking. We emphasize the contextual understanding of language and demonstrate how decision-making in RL can benefit from aligning states’ and actions’ representation with languages’ representation. Our method significantly outperforms current baselines as evidenced by evaluations conducted on Atari and OpenAI Gym environments. This contributes to advancing offline RL performance and efficiency while providing a novel perspective on offline RL.

For the diagnosis of liver cancer, conventional brightness mode (B-mode) can only provide morphological information. Multi-modal ultrasound, including shear-wave elastography (SWE) and contrast enhanced ultrasound (CEUS), can provide comprehensive diagnostic information on tumor microenvironment and tissue perfusion . The challenge is to effectively explore the multi-modal features of ultrasound. Besides, there are many fusion strategies currently available, but there is a lack of systematic comparative research on the various fusion strategies. In this study, we designed 'Lesions Pairing' to construct the dataset, addressing the challenge of small sample sizes in multi-modal learning. We then compared the effectiveness of different strategies and proposed hybrid-fusion strategies based on the combination of conventional layer-level fusion (i.e. early-fusion, mid-fusion and late-fusion), which can efficiently extract intra-/inter- modal information. Specifically, we first systematically compared different deep-learning-based fusion strategies for multi-modal ultrasound in the diagnosis of liver cancer. Secondly, based on the comparison results of a multimodal framework that integrates B-mode, SWE, CEUS ultrasound data, and clinical data simultaneously, we propose a hybrid-fusion strategies for the diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma . The experimental results showed that the area under the curve of the early-late fusion strategy combined with clinical data was 0.9854, which was superior to other single mode and other fusion strategies, increasing by 13.8–25.88 % and 2.22 %-9.79 %, respectively.

Lesion localization and tracking are critical for accurate, automated medical imaging analysis. Contrast-enhanced ultrasound (CEUS) significantly enriches traditional B-mode ultrasound with contrast agents to provide high-resolution, real-time images of blood flow in tissues and organs. However, many trackers, designed primarily for natural RGB or B-mode ultrasound images, underutilize the extensive data from dual-screen enhanced images and fail to account for respiratory motion, thus facing challenges in achieving accurate target tracking. To address the existing challenges, we propose an adaptive-weighted dual mapping (ADMNet), an online tracking framework tailored for CEUS. Firstly, we introduced a novel Multimodal Atrous Attention Fusion (MAAF) module, innovatively designed to adapt the weightage between B-mode and enhanced images in dual-screen CEUS, reflecting the clinician's dynamic focus shifts between screens. Secondly, we proposed a Respiratory Motion Compensation (RMC) module to correct motion trajectory interferences due to respiratory motion, effectively leveraging temporal information. We utilized two newly established CEUS datasets, totaling 35,082 frames, to benchmark the ADMNet against various advanced B-mode ultrasound trackers. Our extensive experiments revealed that ADMNet achieves new state-of-the-art performance in CEUS tracking. Ablation studies and visualizations further underline the effectiveness of MAAF and RMC modules, demonstrating the promising potential of ADMNet in clinical CEUS tracing, thus providing novel research avenues in this field.

Network virtualization enables cloud service providers sharing various substrate resources and serving end users with heterogeneous virtual network (VN) applications. As more and more Internet applications migrate to the cloud, the survivability of VNs, particularly for disaster survivability, has become a crucial issue. In this article, we study the VN survivability problem and propose an early-warning-time-based VN live evacuation (EVLE) scheme to combat against disaster threats. Aiming at an upcoming disaster destruction, EVLE tries to evacuate as many VNs as possible from the disaster risk zone (DRZ) before the given deadline, while sustaining their online services. The evacuation mainly includes two processes, viz., VN reconfiguration and virtual machine (VM) live migration. For a threatened VN, EVLE first remaps it at the outside of the DRZ with minimal resource cost, then exploits the postcopy technique to migrate the impacted VM to the corresponding reconfigured virtual node. During the VM migration process, the operations of basic bandwidth deployment and bandwidth upgradation are implemented, so as to encourage parallel migrations and maximize the resource utilization within the period of early-warning time. In our simulations, the EVLE and a baseline scheme, as well as a counterpart named as best-effort VN live evacuation (BVLE), are tested with different disaster scenarios. Numerical results illustrate that the EVLE, even if with a longer average evacuation time, can outperform the baseline scheme and BVLE in terms of the evacuation completion ratio under different early-warning times, and particularly can achieve distinct dominance under small early-warning times.

Speed profile prediction on ramps is a challenging problem because speed changes on ramps involve complicated lane maneuvering and frequent acceleration or deceleration depending on geometry of the ramp and traffic volumes. Ramps can be categorized into three groups based on their interconnection of freeway: freeway entering ramps, freeway exit ramps, and inter freeway ramps. However, different geographical shapes of ramps within the same category cause different speed profile distributions. To predict speed profile on any ramp types, we proposed an Intelligent Trip Modeling on Ramp (ITMR) System that consists of a ramp classification method based on the decision tree and speed profile prediction neural networks. The proposed ITMR takes inputs from geographical data on the route and also the personal driving pattern extracted from the knowledge base built with the individual historical driving data. Experimental results show that the proposed system learned dynamic ramp speed changes very well to provide accurate prediction results on multiple freeway entering ramps, exit ramps and inter freeway ramps.

Accurate prediction of the traffic information in real time such as flow, density, speed, and travel time has important applications in many areas, including intelligent traffic control systems, optimizing vehicle operations, and the routing selection for individual drivers on the road. This is also a challenging problem due to dynamic changes of traffic states by many uncertain factors along a traveling route. In this paper, we present an Intelligent Trip Modeling System (ITMS) that was developed using machine learning to predict the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The ITMS contains neural networks to predict short-term traffic speed based on the traveling day of the week, the traffic congestion levels at the sensor locations along the route, and the traveling time and distances to reach individual sensor locations. The ITMS was trained and evaluated by using ten months of traffic data provided by the California Freeway Performance Measurement System along a California Interstate I-405 route that is 26 mi long and contains 52 traffic sensors. The ITMS was also evaluated by the traffic data acquired from a 32-mi-long freeway section in the state of Michigan. Experimental results show that the proposed system, i.e., ITMS, has the capability of providing accurate predictions of dynamic traffic changes and traveling speed at the beginning of a trip and can generalize well to prediction of speed profiles on the freeway routes other than the routes the system was trained on.

Accurate prediction of traffic information such as flow, density, speed, and travel time is an important component for traffic control systems and optimizing vehicle operation. Prediction of an individual speed profile on an urban network is a challenging problem because traffic flow on urban routes is frequently interrupted and delayed by traffic lights, stop signs, and intersections. In this paper, we present an Intelligent Speed Profile Prediction on Urban Traffic Network (ISPP_UTN) that can predict a speed profile of a selected urban route with available traffic information at the trip starting time. ISPP_UTN consists of four speed prediction Neural Networks (NNs) that can predict speed in different traffic areas. ISPP_UTN takes inputs from three different categories of traffic information such as the historical individual driving data, geographical information, and traffic pattern data. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profiles are close to the real recorded speed profiles.

Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information prediction are investigated. We present a speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profile shows that NNTM-SP correctly predicts the dynamic traffic changes.

Vehicle power management has been an active research area in the past decade, and has intensified recently by the emergence of hybrid electric vehicle technologies. Research has shown that driving style and environment have strong influence over fuel consumption and emissions. In order to incorporate this type of knowledge into vehicle power management, an intelligent system has to be developed to predict the current traffic conditions. This paper presents our research in neural learning for predicting the driving environment such as road types and traffic congestions. We developed a prediction model, an effective set of features to characterize different types of roadways, and a neural network trained for online prediction of roadway types and traffic congestion levels. This prediction model was then used in conjunction with a power management strategy in a conventional (non-hybrid) vehicle. The benefits of having the predicted drive cycle available are demonstrated through simulation.

This paper presents a machine learning approach to the efficient vehicle power management and an intelligent power controller (IPC) that applies the learnt knowledge about the optimal power control parameters specific to specific road types and traffic congestion levels to online vehicle power control. The IPC uses a neural network for online prediction of roadway types and traffic congestion levels. The IPC and the prediction model have been implemented in a conventional (non-hybrid) vehicle model for online vehicle power control in a simulation program. The benefits of the IPC combined with the predicted drive cycle are demonstrated through simulation. Experiment results show that the IPC gives close to optimal performances.