CORTEXA
← Browse

Xin Yang

8 papers indexed

semantic_scholarIEEE transactions on power electronics2026-08-01Cited by 2

Plenty of Room at the Bottom and Top: Future Perspectives on GaN Power Devices and Applications

Zineng Yang, Xin Yang, Jingcun Liu, Hongchang Cui, Kuokchi Lei, Yujun Guo, et al.

Gallium nitride (GaN) power devices have achieved commercial success primarily in the 30–900 V range, particularly in consumer electronics. Yet, to borrow from Richard Feynman's famous remark, there remains plenty of room at both the “bottom” and the “top”— not only in voltage cl…

View free PDFSource page
arxivcs.LGcs.AI2026-07-23

TOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement Learning

Chaofan Pan, Lingfei Ren, Xiangyu Jiang, Yanhua Li, Xuemei Cao, Xiangkun Wang, et al.

Offline Reinforcement Learning (RL) agents are trained on fixed behavioral trajectories, which makes trajectory-level deletion important when selected data must be removed after training. Evaluating such deletion is difficult because a lower membership score can reflect trajector…

View free PDFSource page
arxivcs.RO2026-07-19

Multi-Resolution Voxelized Map-Based Stereo Visual-Inertial Odometry

Shuyi Pan, Hangtian Wang, Zhaoxing Zhang, Chengliang Zhang, Zikang Yuan, Xin Yang

Incorporating prior maps significantly enhances the accuracy and robustness of pose estimation in visual-inertial odometry (VIO). However, the large data volume of such maps, combined with limited transmission bandwidth, makes it impractical to continuously load local maps onto a…

View free PDFSource page
arxivcs.AIcs.RO2026-07-15

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination

Haotian Liang, Mingkang Chen, Yufei Huang, Yuchun Guo, Xiaomeng Zhu, Xiangli Shi, et al.

Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that empha…

View free PDFSource page
arxivcs.AIcs.CLcs.LG2026-07-07

EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents

Mianqiu Huang, Taofeng Xue, Chong Peng, Jinrui Ding, Sicheng Fan, Jiale Hong, et al.

Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments. Although imitation learning and offline trajectory refinement provide strong priors, static traces cannot cover the causal feedback loop of r…

View free PDFSource page
arxivcs.CVcs.AI2026-07-03

MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model

Wanshu Fan, Xiangyu Li, Cong Wang, Kin-man Lam, Xin Yang, Haiyan Zhang, et al.

Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks. Existing methods based on Convolu…

View free PDFSource page
arxivcs.CV2026-06-28

Enhancing Part-Level Point Grounding for Any Open-Source MLLMs

Jin-Cheng Jhang, Fu-En Wang, Xin Yang, Nan Qiao, Lu Xia, Min Sun, et al.

Visual grounding aims to associate free-form textual queries with specific regions in an image. While recent Multimodal Large Language Models (MLLMs) have demonstrated promising capabilities in this domain, they primarily excel at object-level grounding and often struggle with pa…

View free PDFSource page
crossrefRemote Sensing2022-01-11Cited by 92

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Rui Liu, Xin Yang, Chong Xu, Liangshuai Wei, Xiangqiang Zeng

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learn…

View free PDFSource page