Efficient coordination of heterogeneous AI chips (GPU/NPU/DCU) in data centers is crucial for Large Language Model (LLM) training, but this process is hindered by architectural mismatches, protocol fragmentation, and network partitioning. Existing solutions fail to achieve unified resource management across different chips, resulting in severe resource fragmentation and reduced allreduce efficiency. To overcome these limitations, this paper proposes the unified coordination framework UniOrch, which not only integrates three core functionalities, including hardware abstraction, software standardization, and communication coordination, but also enables the training and inference of large models across heterogeneous AI chips. UniOrch's hardware-agnostic bare-metal cloud eliminates virtualization overhead through Border Gateway Protocol Ethernet Virtual Private Network (BGP EVPN) overlay networks and gateway-based chip integration; its PyTorch-based adaptation layer masks hardware differences and reduces migration costs; the Transformer Collective Communication Library (TCCL) unifies NCCL, HCCL, and OpenMPIprotocols to support seamless hybrid parallel training. Furthermore, the framework ' score scheduling mechanism is the Heterogeneous Hybrid Estimation Model (HHEM), which employs a two stage cost model combining static analysis with dynamic runtime feedback to dynamically allocate computing power based on Transformer task loads, ensuring cross-chip task synchronization, resource pooling, and dynamic allocation. Deployment verification in real-world production environments (e.g., China Construction Bank) shows that UniOrch achieves significant improvements: resource utilization of heterogeneous AI infrastructure is increased by 35%, cross-chip latency is reduced by 42%, accuracy loss in heterogeneous environments is <0.8%.

Autonomous driving systems (ADSs) have attracted wide attention in the machine learning communities. With the help of deep neural networks (DNNs), ADSs have shown both satisfactory performance under significant uncertainties in the environment and the ability to compensate for system failures without external intervention. However, the vulnerability of ADSs has raised concerns since DNNs have been proven vulnerable to adversarial attacks. In this paper, we present a comprehensive survey of current physical adversarial vulnerabilities in ADSs. We first divide the physical adversarial attack methods and defense methods by their restrictions of deployment into three scenarios: the real-world, simulator-based, and digital-world scenarios. Then, we consider the adversarial vulnerabilities that focus on various sensors in ADSs and separate them as camera-based, light detection and ranging (LiDAR) based, and multifusion-based attacks. Subsequently, we divide the attack tasks by traffic elements. For the physical defenses, we establish the taxonomy with reference to input image preprocessing, adversarial example detection, and model enhancement for the DNN models to achieve full coverage of the adversarial defenses. Based on the above survey, we finally discuss the challenges in this research field and provide further outlook on future directions.

The adversarial examples have demonstrated the vulnerability of machine learning models. While the data augmentation strategy has been a cornerstone in circumventing overfitting within the realm of standard training paradigms, the prior research has proved the limited efficiency of data augmentation in ameliorating overfitting concerns, specifically within the scope of adversarial training. In this work, we have shown that a data augmentation strategy could enhance the generalization of adversarial training. Our framework, Adversarial Denoising-based Training(ADT), first utilizes the adversarial denoising strategy to improve the generalization of adversarial training. Specifically, we use patch-wise adversarial denoising as a data augmentation strategy to boost the robustness of adversarial training. We have evaluated our method on the MNIST, CIFAR-10, Tiny-ImageNet, and GTSRB datasets. The result shows that our framework could successfully improve the adversarial robustness of models against both digital and physical adversarial examples.

Adversarial attacks deceive deep neural networks by adding subtle perturbations to benign examples, threatening various applications. However, traditional methods lack effective transferability to unknown black-box networks. Addressing this, we introduce a new method, Patch-based Transfer Attack (PTA), comprising Sensitive Area Localization (SAL) and Robust Perturbations Generation (RPG). SAL identifies highly activated regions, while RPG employs adversarial fine-tuning (AFT) and adversarial feature mixing (AFM) to target robust features, enhancing transferability. AFT adapts the surrogate model to current adversarial examples, and AFM combines adversarial and clean example features, compelling the focus on robust features. Our comprehensive tests demonstrate PTA’s superior transferability, outperforming existing methods, especially when integrated with other techniques.

Impulsive control is widely applied to achieve the consensus of multiagent networks (MANs). It is noticed that malicious agents may have adverse effects on the global behaviors, which, however, are not taken into account in the literature. In this study, a novel delayed impulsive control strategy based on sampled data is proposed to achieve the resilient consensus of MANs subject to malicious agents. It is worth pointing out that the proposed control strategy does not require any information on the number of malicious agents, which is usually required in the existing works on resilient consensus. Under appropriate control gains and sampling period, a necessary and sufficient graphic condition is derived to achieve the resilient consensus of the considered MAN. Finally, the effectiveness of the resilient delayed impulsive control is well demonstrated via simulation studies.