Motion planning and control of connected and autonomous vehicles (CAVs) for improving traffic efficiency and safety in intersections still meets many challenges due to its dynamic and complex nature. In this article, an innovative collision-free and time-optimal multivehicle motion planning method for the CAVs at unsignalized intersection scenarios is proposed. We systematically analyze the regularity of intersection crossing mode and summarize the overall conflict scenario. To eliminate the vehicles potential collision, a learning-based iterative optimization (LBIO) algorithm is designed to solve the collision-free trajectories generating problem iteratively and offline. The terminal constraint set, terminal cost, and global safe constraints of the LBIO are constructed and updated from the historical data in previous iterations. The algorithm can finally converge to time-optimal trajectories for multivehicle only after several iterations. To apply the trained trajectories into the continuous intersection traffic flow, an online cluster-based motion planning (CBMP) algorithm is developed to coordinate the vehicle velocities and movements in the cooperative control area surrounding the intersection. With an LTV-MPC algorithm for the low-level control, the proposed approach is validated on the SUMO in typical intersection scenarios. The results show that the proposed method allows the potentially conflicting vehicles passing the intersection simultaneously and quickly without waiting, and significantly improves the overall traffic efficiency.

This paper proposes an iterative learning-based cooperative motion planning and decision-making approach to achieve time-optimal coordination control of connected and autonomous vehicles (CAVs) at on-ramps. In this work, a decentralized learning-based iterative optimization method (DLIO) is first developed to offline train the vehicle merging trajectories in space-time. To guarantee safety and convergence properties at each iteration, the collision-free terminal constraint set and approximated merging time cost are designed using the historical vehicle states as a dataset. For adapting the trained trajectories into dynamic traffic flow at on-ramps online, we systematically analyze the arrival time of inter-vehicle potential actions and model a decision tree to express all possible vehicle cluster passing sequences. Then, a heuristic Monte-Carlo tree search (HMCTS) algorithm with a modified searching principle is presented to derive a minimum-time passing sequence. Also, a customized two-stage velocity planning method is used to regulate the vehicle flow following the optimal sequence and trained condition. The proposed approach is verified on the SUMO and compared with three baselines under different on-ramps traffic demands. Results show that our approach enables the conflicting vehicles to merge into the mainline without queuing, rendering both robust and high efficiency multi-vehicle coordination.

Existing graph level anomaly detection methods are predominantly unsupervised due to high costs for obtaining labels, yielding sub-optimal detection accuracy when compared to supervised methods. Moreover, they heavily rely on the assumption that the training data exclusively consists of normal graphs. Hence, even the presence of a few anomalous graphs can lead to substantial performance degradation. To alleviate these problems, we propose across-domain graph level anomaly detection method, aiming to identify anomalous graphs from a set of unlabeled graphs (target domain) by using easily accessible normal graphs from a different but related domain (source domain). Our method consists of four components: a feature extractor that preserves semantic and topological information of individual graphs while incorporating the distance between different graphs; an adversarial domain classifier to make graph level representations domain-invariant; a one-class classifier to exploit label information in the source domain; and a class aligner to align classes from both domains based on pseudolabels. Experiments on seven benchmark datasets show that the proposed method largely outperforms state-of-the-art methods.

This article proposes a novel cooperative motion planning and decision-making approach for connected and automated vehicles (CAVs) at unsignalized intersections, where a multivehicle collision-free trajectories generating problem is modeled as a constrained optimization problem. A learning-based iterative optimization (LBIO) algorithm is developed to solve the problem iteratively and obtain velocity-optimal trajectories using the historical vehicle states at previous iterations as data sets. To make the trained trajectories adapt to continuous and time-varying traffic flow, an online decision-making algorithm based on Monte Carlo tree search (MCTS) is presented to derive a time-optimal vehicle passing sequence, where a tree structure is built to efficiently express all possible cluster-dividing modes between vehicles. In addition, we propose a trajectory planning algorithm to regulate velocities of vehicles in the cooperative control area surrounding the intersection. The proposed approach is validated on the SUMO under typical intersection scenarios. Results show that our approach enables potentially conflicting vehicles to go through the intersection simultaneously without queuing and significantly improves the overall traffic efficiency.

SUMMARY: The number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner. It can visualize the flux optimizing process in real-time and the ultimate flux outcome. It will also function as a database to enhance the consistency and to facilitate sharing of flux studies. AVAILABILITY AND IMPLEMENTATION: CeCaFLUX is freely available at https://www.cecaflux.net, the source code can be downloaded at https://github.com/zhzhd82/CeCaFLUX. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary It is prevalent nowadays for data owners to outsource their data to the cloud. Most of the traditional searchable schemes are proposed under the honest and curious model, lacking verification of integrity and correctness of the retrieval results. Because the cloud server is not completely trustworthy and there may be improper execution of the users' retrieval requests, it is necessary to verify the retrieval results returned by the cloud server. In order to address these issues, we propose a verifiable ranked ciphertext retrieval scheme (VRCRS) based on bilinear mapping. For the purpose of ranking the keywords and improving the security of ciphertext retrieval scheme, we utilize the lucene search engine toolkit to score keywords, and improve the traditional inverted index structure for building a secure inverted index structure. By adding dummy words into the dictionary of keywords, the proposed scheme can resist the keywords statistics attack of malicious server. To ensure the correctness and integrity of returned ciphertext, we apply bilinear mapping to generate validation tags for keywords. The theoretical analysis and experimental results show that the proposed scheme is secure and efficient. VRCRS can actually identify the illegal behavior of cloud server such as tampering and forgery.

Graph convolution neural network is one of the hot demanding research areas in the last few years. Due to the issues of data irregularity and computation complexity driven by typical GNN networks, we propose a spatial temporal graph convolution network for action recognition system on FPGA(STARS). STARS has redesigned several computing kernels based on the original layers of ST-GCN and adopted specific algorithm with optimization strategies for different kernels. To optimize the performance of accelerator, the ping-pong buffers for data transmission and dynamic quantification for model inference are implemented. The effectiveness of STARS driven accelerator is verified on Xilinx Pynq-Z1 prototyping board.

Pretrained language models (PLMs) learn stereotypes held by humans and reflected in text from their training corpora, including gender bias.When PLMs are used for downstream tasks such as picking candidates for a job, people's lives can be negatively affected by these learned stereotypes.Prior work usually identifies a linear gender subspace and removes gender information by eliminating the subspace.Following this line of work, we propose to use DensRay, an analytical method for obtaining interpretable dense subspaces.We show that DensRay performs on-par with prior approaches, but provide arguments that it is more robust and provide indications that it preserves language model performance better.By applying DensRay to attention heads and layers of BERT we show that gender information is spread across all attention heads and most of the layers.Also we show that DensRay can obtain gender bias scores on both token and sentence levels.Finally, we demonstrate that we can remove bias multilingually, e.g.,

In the detection applications of synthetic aperture radar (SAR) data, a crucial problem is developing precise models for clutter statistics. Generalized gamma distribution (GΓD) has been widely applied in many fields of signal processing, and it has been demonstrated to be an appropriate model for describing the statistical behaviors of SAR sea clutter, wherein parameter estimation is a key issue for determining the practical application of GΓD. Work that contains three major aspects is performed in this paper. First, an approximate estimator for GΓD parameters based on the well-known “method-of-log-cumulants” is derived; a theoretical comparison between the approximate estimator and other known estimators is also presented. Second, based on this estimator, a scheme of parameter estimation is further given by comprehensively considering estimation precision, speed, and applicable conditions. The simulation results show that the presented scheme is fast and effective. Third, we assess the fitting performance of GΓD and the proposed scheme using real SAR sea clutter data, and compare the model with generalized-K distribution. The experiments on single-look complex and multilook processing L-band ALOS-PALSAR and C-band RADARSAT-2 SAR data verify the effectiveness of the proposed scheme of GΓD parameter estimation. Moreover, several examples of ship detection in real SAR images testify to the usefulness of the proposed scheme in practical applications.

Undoubtedly, visualization of the trajectory clustering outputs is very important and some researches have been done on visualization of the clustering results. Still importantly, the research on visualizing the procedure of clustering, which is also of great value, is little touched. In this paper, we propose a novel 3D visualization tool, which comprehensively illustrates the Agglomerative Information Bottleneck (AIB) based clustering scheme, to help users understand the clustering approach vividly and clearly. The point of the proposed metaphor makes use of the visualization, together with rich interactions, to demonstrate the iterative clustering procedure, the corresponding results and the clustering results. The experiment demonstrates the effectiveness of our 3D visualization tool for trajectory analysis.

This paper proposes a novel three-dimensional (3D) visualization tool for the trajectory analysis, helping users understand the trajectory data from different perspectives. The details of a single and a set trajectories are well covered in multiscale views by the four main linked windows, namely Traj View, Color Bar, Multi Property and Track Map. We take advantage of and further improve the color bar and the parallel coordinates to effectively present the important attributes of trajectories and also the relationship between different attributes. In addition, the interactive actions, such as keyboard and mouse operations, provide a rich and wonderful user experience.

In this paper, an innovative 3D visualization tool is proposed to facilitate the quickly browsing and understanding of the video sequence for users. Taking advantage of the major windows, our tool presents the multiscale key frames and the video content clearly and effectively. Namely, KF View provides a wonderful navigation of the video key frames with different levels of details. Frame View presents an interesting view of the whole video content. SIM View allows an expressive exploration of the similarities between key frames and also between key frames and the video frames. Importantly, together with many convenient and attractive interactions, this tool is quite efficient to help users grasp the video information soundly.

Class loaders are a powerful mechanism for dynamically loading software components on the Java platform. They are unusual in supporting all of the following features: laziness, type-safe linkage, user-defined extensibility, and multiple communicating namespaces.We present the notion of class loaders and demonstrate some of their interesting uses. In addition, we discuss how to maintain type safety in the presence of user-defined dynamic class loading.

We show how a set of building blocks can be used to construct programming language interpreters, and present implementations of such building blocks capable of supporting many commonly known features, including simple expressions, three different function call mechanisms (call-by-name, call-by-value and lazy evaluation), references and assignment, nondeterminism, first-class continuations, and program tracing.