As the adoption of LiDAR expands across various fields such as autonomous driving, robotics, and smart cities, the demand for adaptive scanning capabilities to better capture dynamic and complex scenes becomes paramount. Current LiDAR technologies, limited by fixed uniform scan patterns, struggle to prioritize critical areas, resulting in reduced perception accuracy and performance inefficiencies. This paper introduces SmartLiDAR, an advanced LiDAR system that enhances scanning efficiency and performance by adaptively optimizing scan focus through an intelligent, software-defined micro-mirror controller. Unlike traditional systems, SmartLiDAR dynamically adjusts its scan pattern based on environmental characteristics and application-specific requirements, concentrating sample points on key objects without increasing power consumption or scan time. SmartLiDAR achieves this by integrating a novel quadratic micro-mirror controller, an adaptive algorithm for generating fine-grained attention map with prioritized scan focus, and a carefully designed optimization algorithm that maps attention maps to practical scanning patterns. We prototype SmartLiDAR by building a software-defined LiDAR using commercially available optical components and FPGA. Our experimental results demonstrate that SmartLiDAR significantly enhances resolution in regions of interest by 3x and increases average object detection precision by up to 16.11%. Additionally, SmartLiDAR maintains negligible extra energy consumption and processing latency, making it suitable for real-time applications, such as autonomous vehicles.

With the development of 6G wireless networks, non-fixed monitoring methods utilizing drone networks as monitoring carriers have been receiving increasing attention. However, due to significant environmental interference affecting the hovering of drones in the air, the video signals often contain substantial random interference, which impacts the performance of current multi-object and multi-camera tracking (MOMCT) algorithms. To address this issue, this paper proposes a digital-twin-driven multi-vehicle multi-drone monitoring system, in which a simulation scenario consistent with the actual environment is created. Furthermore, a large amount of vehicle monitoring video data from multiple drone perspectives with random interference is generated, thus compensating for the shortage of multi-camera vehicle monitoring data, especially data affected by random interference. Additionally, an online tracking framework for MOMCT based on the digital twin system is set up, forming a pipeline that fully utilizes the simulated data generated by the digital-twin system for training and testing. Using this pipeline, we find that a feature detection algorithm combining AttentionNet-CBAM and ResNext101-IBN-A can effectively enhance the ability to identify target features, thereby achieving better multi-vehicle tracking results. Experimental results on the CityFlow dataset verify that the proposed method has improved the IDF1 by 1.52% and IDR by 3.58% in comparison with the state-of-the-art online MOMCT methods.

Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.

Backdoor attack to deep neural networks (DNNs) is among the predominant approaches to bring great threats into artificial intelligence. The existing methods to detect backdoor attacks focus on the perspective of distributions in DNNs, however, limited by its ability of generalization across DNN models. In this article, a critical-path-based backdoor detector (CPBD) is proposed, which approaches to detect backdoor attacks via DNN's interpretability. CPBD is designed to efficiently discover the characteristics of backdoors, which distinguish the critical paths in the attacked DNNs. To deal with the intractably large number of neurons, we propose to simplify the neurons, and the preserved key nodes are integrated into a set of critical paths. Thus, a DNN model can be formulated as a combination of several critical paths. Afterward, the detection of backdoors is performed based on the analysis of critical paths corresponding to different classes. Then, combining all the above steps, the CPBD algorithm is integrated to present the results in a standard and systematic manner. In addition, CPBD is able to locate neurons associated with malicious triggers, the combination of which is named as trigger propagation path. Extensive experiments are conducted, which testify the efficiency of the proposed method on multiple DNNs and different trigger sizes.

HD map is a key enabling technology towards fully autonomous driving. We propose VI-Map, the first system that leverages roadside infrastructure to enhance real-time HD mapping for autonomous driving. The core concept of VI-Map is to exploit the unique cumulative observations made by roadside infrastructure to build and maintain an accurate and current HD map. This HD map is then fused with on-vehicle HD maps in real time, resulting in a more comprehensive and up-to-date HD map. By extracting concise bird-eye-view features from infrastructure observations and utilizing vectorized map representations, VI-Map incurs low compute and communication overhead. We conducted end-to-end evaluations of VI-Map on a real-world testbed and a simulator. Experiment results show that VI-Map can construct decentimeter-level (up to 0.3 m) HD maps and achieve real-time (up to a delay of 42 ms) map fusion between driving vehicles and roadside infrastructure. This represents a significant improvement of 2.8× and 3× in map accuracy and coverage compared to the state-of-the-art online HD mapping approaches. A video demo of VI-Map on our real-world testbed is available at https://youtu.be/p2RO65R5Ezg.

With the development of wireless network technology, the WiFi-based indoor localization methods incorporating machine learning have attracted wide attention due to its easy deployment and low cost characteristics. However, the existing learning methods are limited to locating homogeneous and tagged target data. Such strict conditions do not exist in the actual indoor positioning environment, and therefore cannot meet people's locational needs. In this article, we design an Online Heterogeneous Transfer method in Indoor Localization(OHTLoc), a novel transfer learning approach that can realize online location prediction based on the RSS(Received Signal Strength) fingerprint and CSI(Channel State Information) data using WLANs. In particular, OHTLoc does not require any tags on the target data. This is the first time this type of algorithm has been proposed in the field of indoor localization. The prediction results of the target demonstrate showed in the experiment part demonstrate the effectiveness of the proposed technique.

3D deep learning has been applied in safety-critical scenarios, e.g., autonomous driving. Several works have raised the security problems of 3D deep learnings mainly from the perspective of adversarial attacks. In this paper, we propose a novel backdoor attack method to threaten 3D deep learning without the original training data. Several neurons are selected and made sensitive to backdoor triggers. The backdoor triggers are generated by reversing neural network, and the shape of which is constrained to map the objects in the physical world. Sufficient training data can be also generated by reverse engineering. Finally, retraining with the generated 3D trigger and training data is applied to inject backdoors, which is in no need of accessing the original training process and data.

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Summary With the rapid increase in e‐commerce data, recommender systems (RSs) have become the most prevalent methods for providing recommended services in various commercial platforms. Deep learning–based recommender methods improve recommendation results by learning latent representations; however, most cannot capture the correlations between items and ignore additional information such as time information, which leads to suboptimal suggestions. To improve recommendation accuracy, we propose a novel recommender algorithm based on graph embedding and diffusion sampling (graph2vec). Our improved model constructs a graph based on users' behavior histories and embeds the graph to a low‐dimensional vector space with a deep learning approach. To obtain more accurate embedding results, we use a revised sampling method based on information diffusion theory to capture both the depth and breadth information of a graph. Then, we recommend the top‐N items to the target user depending on the final representation vectors. Experiments are carried out with real‐world datasets to demonstrate the superior performance of graph2vec. The results show that browse‐based graph construction and diffuse‐based graph embedding help improve the recommender accuracy of the new model compared with that of the selected state‐of‐the‐art models.

BACKGROUND: It's a very urgent task to identify cancer genes that enables us to understand the mechanisms of biochemical processes at a biomolecular level and facilitates the development of bioinformatics. Although a large number of methods have been proposed to identify cancer genes at recent times, the biological data utilized by most of these methods is still quite less, which reflects an insufficient consideration of the relationship between genes and diseases from a variety of factors. RESULTS: In this paper, we propose a two-rounds random walk algorithm to identify cancer genes based on multiple biological data (TRWR-MB), including protein-protein interaction (PPI) network, pathway network, microRNA similarity network, lncRNA similarity network, cancer similarity network and protein complexes. In the first-round random walk, all cancer nodes, cancer-related genes, cancer-related microRNAs and cancer-related lncRNAs, being associated with all the cancer, are used as seed nodes, and then a random walker walks on a quadruple layer heterogeneous network constructed by multiple biological data. The first-round random walk aims to select the top score k of potential cancer genes. Then in the second-round random walk, genes, microRNAs and lncRNAs, being associated with a certain special cancer in corresponding cancer class, are regarded as seed nodes, and then the walker walks on a new quadruple layer heterogeneous network constructed by lncRNAs, microRNAs, cancer and selected potential cancer genes. After the above walks finish, we combine the results of two-rounds RWR as ranking score for experimental analysis. As a result, a higher value of area under the receiver operating characteristic curve (AUC) is obtained. Besides, cases studies for identifying new cancer genes are performed in corresponding section. CONCLUSION: In summary, TRWR-MB integrates multiple biological data to identify cancer genes by analyzing the relationship between genes and cancer from a variety of biological molecular perspective.