Advanced end-to-end autonomous driving systems predict other vehicles' motions and plan ego vehicle's trajectory. The world model that can foresee the outcome of the trajectory has been used to evaluate the end-to-end autonomous driving system. However, existing world models predominantly emphasize the trajectory of the ego vehicle and leave other vehicles uncontrollable. This limitation hinders their ability to realistically simulate the interaction between the ego vehicle and the driving scenario. In addition, it remains a challenge to match multiple trajectories with each vehicle in the video to control the video generation. To address above issues, a driving World Model named EOT-WM is proposed in this paper, unifying Ego-Other vehicle Trajectories in videos. Specifically, we first project ego and other vehicle trajectories in the BEV space into the image coordinate to match each trajectory with its corresponding vehicle in the video. Then, trajectory videos are encoded by the Spatial-Temporal Variational Auto Encoder to align with driving video latents spatially and temporally in the unified visual space. A trajectory-injected diffusion Transformer is further designed to denoise the noisy video latents for video generation with the guidance of ego-other vehicle trajectories. In addition, we propose a metric based on control latent similarity to evaluate the controllability of trajectories. Extensive experiments are conducted on the nuScenes dataset, and the proposed model outperforms the state-of-the-art method by 30% in FID and 55% in FVD. The model can also predict unseen driving scenes with self-produced trajectories.

Magnetic resonance diffusion tensor imaging (DTI) is a unique non-invasive technique for measuring in vivo water molecule diffusion, reflecting tissue microstructure. However, acquiring high-quality DTI typically requires numerous diffusion-weighted images (DWIs) in multiple directions, resulting in long scan times that restrict its use in clinical and research settings. To address this limitation, we propose Diff-DTI, a fast DTI processing framework based on a feature-enhanced joint diffusion model, to reduce the number of DWIs needed for tensor fitting. Diff-DTI models the joint probability distribution of DWIs and DTI maps, supporting guided generation during inference. The incorporated feature enhancement fusion module further enhances image precision and details generated by the diffusion model. Experiments were performed on three public DWI datasets. Results demonstrate that Diff-DTI achieves up to 10-fold acceleration (using 6 DWIs) while maintaining relatively low normalized mean square error (NMSE) for DTI maps (2.89% for FA, 0.89% for MD, 0.95% for AD, and 0.98% for RD). Even using Diff-DTI with only 3 DWIs, the NMSEs of the generated DTI maps showed a gradual decrease, with 3.51% for FA, 0.89% for MD, 1.13% for AD, and 1.10% for RD. We conclude that Diff-DTI can significantly reduce the number of acquired DWIs and the scan time, without compromising image quality too much.

Time series data contains the amount of information to reflect the development process and state of a subject. Especially, the complexity is a valuable factor to illustrate the feature of the time series. However, it is still an open issue to measure the complexity of sophisticated time series due to its uncertainty. In this study, based on the belief Re´nyi divergence, a novel time series complexity measurement algorithm, called belief Re´nyi divergence of divergence (BRe´DOD), is proposed. Specifically, the BRe´DOD algorithm takes the boundaries of time series value into account. What is more, according to the Dempster-Shafer (D-S) evidence theory, the time series is converted to the basic probability assignments (BPAs) and it measures the divergence of a divergence sequence. Then, the secondary divergence of the time series is figured out to represent the complexity of the time series. In addition, the BRe´DOD algorithm is applied to sets of cardiac inter-beat interval time series, which shows the superiority of the proposed method over classical machine learning methods and recent well-known works.

Dempster–Shafer (D-S) evidence theory is invaluable in the domain of multisource information fusion for handing uncertainty problems. However, there may be counter-intuitive phenomenon when facing highly conflicting information. In this paper, a novel symmetric enhanced belief χ 2 ${\chi }^{2}$ divergence measure, called S E B χ 2 $SEB{\chi }^{2}$ , is proposed to measure the discrepancy between basic probability assignments (BPAs). The S E B χ 2 $SEB{\chi }^{2}$ divergence consider the features of BPAs as the influence of both single-element subsets and multielement subsets is taken into account. Furthermore, the S E B χ 2 $SEB{\chi }^{2}$ divergence is proven to be symmetric, nonnegative and nondegenerate, which are desirable properties for conflict management. Then, a new algorithm for multisource information fusion based on the S E B χ 2 $SEB{\chi }^{2}$ divergence measure is derived. Finally, an application for pattern classification is used to illustrate the superiority of the proposed S E B χ 2 $SEB{\chi }^{2}$ divergence measure-based fusion method over other existing well-known and recent related works with a better classification accuracy of 94.39%.

Heat dissipation and the related thermal-mechanical stress problems are the major obstacles in the development of the three-dimensional integrated circuit (3D IC). Reliability management techniques can be used to alleviate such problems and enhance the reliability of 3D IC. However, it is difficult to obtain the time-varying stress information at runtime, which limits the effectiveness of the reliability management. In this article, we propose a fast stress estimation method for runtime reliability management using artificial neural network (ANN). The new method builds ANN-based stress model by training offline using temperature and stress data. The ANN stress model is then used to estimate the important stress information, such as the maximum stress around each TSV, for reliability management at runtime. Since there are a variety of potential ANN structures to choose from for the ANN stress model, we analyze and test three ANN-based stress models with three major types of ANNs in this work: the normal ANN-based stress model, the ANN stress model with hand-crafted feature extraction, and the convolutional neural network–(CNN) based stress model. The structures of each ANN stress model and the functions of these structures in 3D IC stress estimation are demonstrated and explained. The new runtime stress estimation method is tested using the three ANN stress models with different layer configurations. Experiments show that the new method is able to estimate important stress information at extremely fast speed with good accuracy for runtime 3D IC reliability enhancement. Although all three ANN stress models show acceptable capabilities in runtime stress estimation, the CNN-based stress model achieves the best performance considering both stress estimation accuracy and computing overhead. Comparison with traditional method reveals that the new ANN-based stress estimation method is much more accurate with a slightly larger but still very small computing overhead.