Despite their success in LIDAR object detection, modern detectors are vulnerable to uncommon instances and corner cases (e.g., a runaway tire) since they are closed-set and static. Networks under the closed-set setup only predict labels of seen classes, while static models suffer from catastrophic forgetting when gradually learning novel concepts. This motivates us to formulate the open-world 3D object detection task for autonomous driving, which aims to 1) tackle the closed-set issue by identifying unseen instances as unknown and 2) incrementally learn novel classes without forgetting previously obtained knowledge. To achieve the open-world objectives, we propose Open-World 3D Detector (OW3Det), the first framework for open-world 3D object detection. The OW3Det comprises a base detector, a self-supervised unknown identifier, and a knowledge-distillation-restricted incremental learner. Although knowledge distillation facilitates preserving memories, imposing penalties on areas containing unknown objects hinders the incremental learning process. We mitigate this hindrance by employing unknown-driven pivotal mask, which eliminates unnecessary restrictions on regions overlapping with novel instances. Abundant experiments and visualizations demonstrate that the proposed OW3Det attains state-of-the-art performance.

A trustworthy 3D detector is essential in the perception system of autonomous vehicles, ensuring accurate detection of their surroundings. However, autonomous vehicles have to operate in ever-changing real-world driving scenes, where unknown objects that do not belong to the training set are commonly encountered. Confusion about known and unknown objects could result in severe and dangerous consequences for road safety. To address this problem, we improve the reliability of autonomous driving systems by formulating open-set 3D object detection task. An Open-set 3D Detector (Open3Det) is proposed to reject unknown instances while maintaining performance on known categories. Distinct from 2D objects, clear space separation exists between each 3D instance. Motivated by this, we propose selective forgetting, a novel method capable of filtering out misleading predictions. Given a close-set teacher model, knowledge distillation is introduced to build a open-set student model. The student model preserves its predictions for known objects, whereas predictions of backgrounds and unknown instances are discarded to minimize misleading results. Extensive experiments and visualizations reveal the efficacy of the proposed method.

A trustworthy 3D detector is essential in the perception system of autonomous vehicles, ensuring accurate detection of their surroundings. However, autonomous vehicles have to operate in ever-changing real-world driving scenes, where unknown objects that do not belong to the training set are commonly encountered. Confusion about known and unknown objects could result in severe and dangerous consequences for road safety. To address this problem, we improve the reliability of autonomous driving systems by formulating open-set 3D object detection task. An Open-set 3D Detector (Open3Det) is proposed to reject unknown instances while maintaining performance on known categories. Distinct from 2D objects, clear space separation exists between each 3D instance. Motivated by this, we propose selective forgetting, a novel method capable of filtering out misleading predictions. Given a close-set teacher model, knowledge distillation is introduced to build a open-set student model. The student model preserves its predictions for known objects, whereas predictions of backgrounds and unknown instances are discarded to minimize misleading results. Extensive experiments and visualizations reveal the efficacy of the proposed method.

When deploying a robot to real-world environments, it is crucial to execute tasks amidst constantly changing surroundings. The conventional kinematics control of robot arms is primarily reliant on the inverse kinematics model. Unfortunately, due to the lack of adaptability, high-precision control models often falter when the robot utilizes tools of varying lengths or when the robot arm is worn out. This work aims to address this issue by proposing a meta-learning-based control framework. We achieve rapid and seamless online adaptation by updating control models when the robot arm’s configuration changes. The control framework comprises an Adaptive Global Inverse Model (Adaptive GIM) and an Adaptive Local Inverse Model (Adaptive LIM). The Adaptive GIM employs configuration-independent meta-learning, which allows the control model to swiftly adapt to different arm configurations. The Adaptive LIM adopts a meta-learning approach for location-independent training, enabling the robot to adapt to diverse local positions. As the Adaptive GIM suffers from the adverse effects stemming from the multiple solutions of inverse kinematics, utilizing the Adaptive LIM with relative position as input can alleviate this issue and enable more precise reaching towards the target. Extensive validation conducted on PKUHR6.0 demonstrates that the proposed approach significantly enhances online adaptation speed and precision compared to existing methods

In this paper, we propose an efficient Kriging-based constrained multi-objective evolutionary algorithm for analog circuit synthesis via self-adaptive incremental learning. The incremental learning technique is introduced to reduce time complexity of training the Kriging model from$O(n^{3})$, to$O(n^{2})$, where$n$is the number of training points. The proposed approach reduces the total optimization time in three aspects. First, by reusing the previously trained models, a self-adaptive incremental learning strategy is applied to reduce the training time of the Kriging model. Second, we use non-dominated sorting and modified crowding distance to prescreen the most promising one to be simulated, which largely reduce the number of simulations. Third, as there is no internal optimization, the prediction time of the Kriging model is saved. Experimental results on two real-world circuits demonstrate that compared with the state-of-the-art multi-objective Bayesian optimization, our method can reduce the training time of Kriging model by 95% and the prediction time by 99.7% without surrendering optimization results. Compared with NSGA-II and MOEA/D, the proposed method can achieve up to 10X speed up in terms of the total optimization time while achieving better results.

The impact of process variation to advanced integrated circuits has become increasingly significant. Traditional sampling based yield analysis and optimization always require large amount of expensive simulations. This paper proposes an All Sensitivity Adversarial Importance Sampling (ASAIS) yield optimization method, which avoids samplings in outer optimization based on sensitivity. Moreover, Fast Sensitivity Importance Sampling (FSIS) yield analysis method is adopted as inner yield analysis to eliminate the sampling using transient sensitivity analysis. Experiments on SRAM show ASAIS generates more than 90X speedup of the entire yield optimization process, while FSIS speedup 3X-I5X over existing methods.

Transient sensitivity is useful for analyzing the gradients of objective functions with respect to given parameters. This is useful in variation and circuit optimization. The computational complexity for traditional transient sensitivity analysis for objective function with N time points is O(N2), which is too expensive for large N. In this paper we propose a transient sensitivity analysis method that reduces the computational complexity to O(N), enabling the analysis of performance metrices related to thousands of time points, such as SNDR, THD, SFDR, and etc.

The following topics are dealt with: cellular radio; probability; wireless channels; radiofrequency interference; mobile radio; MIMO communication; error statistics; telecommunication traffic; Long Term Evolution; quality of service.