Benefiting from the inductive biases learned from large-scale datasets, open-vocabulary semantic segmentation (OVSS) leverages the power of vision-language models, such as CLIP, to achieve remarkable progress without requiring task-specific training. However, due to CLIP’s pre-training nature on image-text pairs, it tends to focus on global semantic alignment, resulting in suboptimal performance when associating fine-grained visual regions with text. This leads to noisy and inconsistent predictions, particularly in local areas. We attribute this to a dispersed bias stemming from its contrastive training paradigm, which is difficult to alleviate using CLIP features alone. To address this, we propose a structure-aware feature rectification approach that incorporates instance-specific priors derived directly from the image. Specifically, we construct a region adjacency graph (RAG) based on low-level features (e.g. colour and texture) to capture local structural relationships and use it to refine CLIP features by enhancing local discrimination. Extensive experiments show that our method effectively suppresses segmentation noise, improves region-level consistency, and achieves strong performance on multiple open-vocabulary segmentation benchmarks. Project page: https://qiming-huang.github.io/RAG-OVS/.

In chip design, skew is a pivotal factor that significantly influences the overall performance for routing. A major challenge is how to achieve an appropriate trade-off between the total wire-length cost and skew. Selecting hub nodes is an effective method to improve this cost-skew trade-off. In this paper, we propose a novel reinforcement learning-based method for hub node selection, where our key idea is leveraging an effective adaptive learning strategy. Moreover, our approach is particularly suitable for solving large-scale routing instances. The empirical results suggest that our method can achieve promising performance on both small-scale and large-scale clock nets, implying its potential practical significance in EDA.

In Open Vocabulary Semantic Segmentation (OVS), we observe a consistent drop in model performance as the query vocabulary set expands, especially when it includes semantically similar and ambiguous vocabularies, such as ‘sofa’ and ‘couch’. The previous OVS evaluation protocol, however, does not account for such ambiguity, as any mismatch between model-predicted and human-annotated pairs is simply treated as incorrect on a pixel-wise basis. This contradicts the open nature of OVS, where ambiguous categories may both be correct from an open- world perspective. To address this, in this work, we study the open nature of OVS and propose a mask-wise evaluation protocol that is based on matched and mis- matched mask pairs between prediction and annotation respectively. Extensive experimental evaluations show that the proposed mask-wise protocol provides a more effective and reliable evaluation framework for OVS models compared to the previous pixel-wise approach on the perspective of open-world. Moreover, analy- sis of mismatched mask pairs reveals that a large amount of ambiguous categories exist in commonly used OVS datasets. Interestingly, we find that reducing these ambiguities during both training and inference enhances capabilities of OVS mod- els. These findings and the new evaluation protocol encourage further exploration of the open nature of OVS, as well as broader open-world challenges. Project page: https://qiming-huang.github.io/RevisitOVS/.

A neural processing unit (NPU) is a microprocessor which is specially designed for various types of neural network applications. Because of its high acceleration efficiency and lower power consumption, the airborne embedded system has widely deployed NPU to replace GPU as the new accelerator. Unfortunately, the inherent scheduler of NPU does not consider real-time scheduling. Therefore, it cannot meet real-time requirements of airborne embedded systems. At present, there is less research on the multi-task real-time scheduling of the NPU device. In this article, we first design an NPU resource management framework based on Kubernetes. Then, we propose WAMSPRES, a workload-aware NPU performance model based soft preemptive real-time scheduling method. The proposed workload-aware NPU performance model can accurately predict the remaining execution time of the task when it runs with other tasks concurrently. The soft preemptive real-time scheduling algorithm can provide approximate preemption capability by dynamically adjusting the NPU computing resources of tasks. Finally, we implement a prototype NPU scheduler of the airborne embedded system for the fixed-wing UAV. The proposed models and algorithms are validated on both the simulated and realistic task sets. Experimental results illustrate that WAMSPRES can achieve low prediction error and high scheduling success rate.

We develop a novel method called SParse Optimal Transformations (SPOT) to simultaneously select important variables and explore relationships between the response and predictor variables in high dimensional nonparametric regression analysis. Not only are the optimal transformations identified by SPOT interpretable, they can also be used for response prediction. We further show that SPOT achieves consistency in both variable selection and parameter estimation. Numerical experiments and real data applications demonstrate that SPOT outperforms other existing methods and can serve as an effective tool in practice.

SPOC-supported Data Structures (DS) course during the Autumn 2015 semester in University of Science and Technology Beijing (USTB) was created over the USTB course center and Tsinghua Online Judge (OJ) system. Data from the course center, OJ and terminal exam is analyzed to reveal the key objective factors in affecting SPOC-supported DS course, and survey from the students in our DS course is analyzed to reveal the key subjective factors in affecting the SPOC-supported DS course. A challenge has been submitted to improve the teaching effectiveness of the SPOC-supported course.

The general goal of multivariate regression analysis is to infer about the relationship between a response variable Y and a predictor vector X. Many popularly used regression methods only focus on specific aspects of this relationship. Even though the conditional distribution P(Y|X) of Y given X fully characterizes the relationship between Y and X, estimation of the conditional density is challenging and becomes quickly infeasible when the dimension of X increases. In this article, we propose to use optimal group transformations as a general approach for exploring the relationship between Y and X. This approach can be considered an automatic procedure to identify the best characteristic of P(Y|X) under which the relationship between Y and X can be fully explored. The emphasis on using group transformations allows the approach to recover intrinsic group structures among the predictors. Furthermore, we develop kernel methods for estimating the optimal group transformations based on cross-covariance and conditional covariance operators. The statistical consistency of the estimates has been established. We refer to the proposed framework and approach as the Optimal Kernel Group Transformation (OKGT) method. Simulation study and real data applications show that the OKGT method is flexible and powerful for the general purpose of high dimensional regression analysis.

In CP-ABE systems [1], attributes describe user's credentials and policies describe who can decrypt data from the server. Many researches have been conducted in Personal Health Records (PHR) to facilitate the mediation and direct connection of patient to different users (physicians, family members and clinic agents) through network. Ciphertext Policy Attribute-based Encryption mechanism is used to determine who can access sensitive PHR documents. In this paper, CP-ABE with Constant-size Ciphertext and Constant Computation-Cost is used to describe users' credentials, and a patient associate encrypted data with credentials to determine who can decrypt sensitive PHR from the server. Due to the large number involved in the access policy scheme this work is based on Constant-size Ciphertext and Constant Computation-cost.