Timing-driven global placement plays a decisive role in the final performance of very large-scale integration (VLSI) circuits, but is consistently challenged by the trade-off between design accuracy and efficiency. Most existing methods rely on coarse-grained net-weighting strategies. While these approaches are straightforward to implement, they cannot precisely identify and optimize complex timing paths, such as paths with sharing effects or large slack deviations. To overcome this bottleneck, we propose a high-precision slack-aware global placement engine called HPPlacer, which includes the following three key techniques: 1) a local clock buffer-to-flip-flop connection optimization method, 2) a path-level differentiable timing optimization model, and 3) a dynamic adjustment mechanism-based pin-pair weighting strategy. With the proposed method, efficient chip placement with excellent timing behaviors can be generated automatically within a short period of time. The experimental results on multiple benchmark circuits confirm that HPPlacer leads to significant improvements in both timing performance and wirelength compared to state-of-the-art placement tools.

Sleep quality plays a crucial role in human health, with sleep stages serving as key indicators of overall well-being. Despite the advancements in sleep stage classification, existing methods encounter several limitations: 1) the use of fixed-size convolution kernels restricts the adaptive extraction of multi-scale features, and 2) redundancy in multi-modal signals results in inadequate attention to highly correlated features. In this paper, we introduce DeformSleepNet, a novel architecture that combines standard and deformable convolutions to enable adaptive multi-scale feature extraction across various frequency bands. The Multi-level Feature Encoder (MFE) update different features in different manner, achieving a trade-off between accuracy and efficiency, effectively balancing accuracy and computational efficiency. Additionally, the Content-Aware Fusion Module (CAFM) dynamically adjusts the importance of different features during fusion, thereby enhancing the overall classification performance. Experimental evaluations conducted on three publicly available datasets demonstrate that DeformSleepNet outperforms main-stream methods. Ablation studies further validate the contribution of each individual module, highlighting the robustness and effectiveness of the proposed approach.

Gaze estimation is a crucial component of human-robot Interaction (HRI). While previous gaze estimation methods have been widely applied in advertising and gaming with head-mounted devices or complicated camera systems, little research has been conducted in everyday HRI scenarios. During interactions, robots have the potential to infer human intention through static gaze directions and dynamic eye movements, enabling them to behave more intelligently and friendly. This paper combines appearance-based gaze estimation methods with eye movement analysis methods to infer human intentions, particularly in human-robot interaction scenarios. Real interactions were conducted to test the accuracy and robustness of the methods developed. The experiments demonstrate that our methods deliver practical, robust, and accurate results.

Fine-grained image retrieval has been extensively explored in a zero-shot manner. A deep model is trained on the seen part and then evaluated the generalization performance on the unseen part. However, this setting is infeasible for many real-world applications since (1) the retrieval dataset can be non-fixed so that new data are added constantly, and (2) data samples of the seen categories are also common in practice and are important for evaluation. In this paper, we explore lifelong fine-grained image retrieval (LFGIR), which learns continuously on a sequence of new tasks with data from different datasets. We first use knowledge distillation to minimize catastrophic forgetting on old tasks. Training continuously on different datasets causes large domain shifts between the old and new tasks while image retrieval is sensitive to even small shifts in the features. This tends to weaken the effectiveness of knowledge distillation by the frozen teacher. To mitigate the impact of domain shifts, we use the network inversion method to generate images of the old tasks. In addition, we design an on-the-fly teacher which transfers knowledge captured on a new task to the student to improve better generalization performance, thereby achieving a better balance between old and new tasks in the end. We name the whole framework as Dual Knowledge Distillation (DKD), whose efficacy is demonstrated by extensive experimental results on sequential tasks including seven datasets.

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. Theoretically, we prove that a differentially private learning algorithm used for training the GAN does not overfit to a certain degree, i.e., the generalization gap can be bounded. Moreover, some recent works, such as the Bayesian GAN, can be re-interpreted based on our theoretical insight from privacy protection. Quantitatively, to evaluate the information leakage of well-trained GAN models, we perform various membership attacks on these models. The results show that previous Lipschitz regularization techniques are effective in not only reducing the generalization gap but also alleviating the information leakage of the training dataset.