DiT-Garment: Garment Dynamics with Diffusion Transformers
本文提出DiT-Garment模型，利用2D扩散变压器架构在UV空间中学习3D变形，以解决动态3D服装建模问题，适用于任意设计和物理材质。
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CST: Collaborative Selective Transmission for Communication-Efficient Multimodal Edge Inference
本文提出CST方法，通过主设备指导的查询-响应框架选择性传输互补信息，减少多模态边缘推理中的通信开销和延迟。
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GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels
该研究提出Gramian Chebyshev Neural Operator (GCNO)，一种基于物理的可变率压缩方法，通过识别主要传播路径来解决大规模天线阵列信道反馈成本高的问题。
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LDGM-Based Quantum Codes for Fault-Tolerant Quantum Computation
This work addresses the challenge in fault-tolerant quantum computing of simultaneously achieving high error-correction performance and low stabilizer weight. By leveraging low-density generator matrix (LDGM) codes and the Calderbank–Shor–Steane (CSS) construction, the authors design a new class of quantum error-correcting codes. Through flexible row operations, the code rate is efficiently tuned, while message-passing iterative decoding on graphs—combined with discrete density evolution analysis—enables significantly reduced stabilizer generator weights without compromising error-correction capability. The resulting quantum codes exhibit outstanding performance under the depolarizing channel, offering both low decoding complexity and high practicality. This approach provides an efficient and scalable coding solution for fault-tolerant quantum computation.
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Hybrid TRP-UE Sensing for Enhanced Target Localization
This study addresses the challenge of low localization accuracy for passive mobile targets in complex propagation environments, such as indoor factories. To overcome this limitation, the authors propose a hybrid TRP–UE cooperative sensing mechanism that, for the first time, deeply integrates user equipment (UE)-assisted sensing with base station (TRP) sensing within a 3GPP-compliant integrated sensing and communication (ISAC) architecture. By synergistically combining these two sensing modalities, the proposed approach significantly enhances the robustness and accuracy of target localization in challenging scenarios. Experimental results demonstrate that, in a representative indoor factory setting, the hybrid sensing scheme achieves substantial performance gains over a conventional TRP-only configuration, confirming its effectiveness in improving localization precision under realistic and complex channel conditions.
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DiT-Garment: Garment Dynamics with Diffusion Transformers
本文提出DiT-Garment模型，利用2D扩散变压器架构在UV空间中学习3D变形，以解决动态3D服装建模问题，适用于任意设计和物理材质。
0 citationsRead paper
CST: Collaborative Selective Transmission for Communication-Efficient Multimodal Edge Inference
本文提出CST方法，通过主设备指导的查询-响应框架选择性传输互补信息，减少多模态边缘推理中的通信开销和延迟。
0 citationsRead paper
GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels
该研究提出Gramian Chebyshev Neural Operator (GCNO)，一种基于物理的可变率压缩方法，通过识别主要传播路径来解决大规模天线阵列信道反馈成本高的问题。
0 citationsRead paper
LDGM-Based Quantum Codes for Fault-Tolerant Quantum Computation
This work addresses the challenge in fault-tolerant quantum computing of simultaneously achieving high error-correction performance and low stabilizer weight. By leveraging low-density generator matrix (LDGM) codes and the Calderbank–Shor–Steane (CSS) construction, the authors design a new class of quantum error-correcting codes. Through flexible row operations, the code rate is efficiently tuned, while message-passing iterative decoding on graphs—combined with discrete density evolution analysis—enables significantly reduced stabilizer generator weights without compromising error-correction capability. The resulting quantum codes exhibit outstanding performance under the depolarizing channel, offering both low decoding complexity and high practicality. This approach provides an efficient and scalable coding solution for fault-tolerant quantum computation.
0 citationsRead paper
Hybrid TRP-UE Sensing for Enhanced Target Localization
This study addresses the challenge of low localization accuracy for passive mobile targets in complex propagation environments, such as indoor factories. To overcome this limitation, the authors propose a hybrid TRP–UE cooperative sensing mechanism that, for the first time, deeply integrates user equipment (UE)-assisted sensing with base station (TRP) sensing within a 3GPP-compliant integrated sensing and communication (ISAC) architecture. By synergistically combining these two sensing modalities, the proposed approach significantly enhances the robustness and accuracy of target localization in challenging scenarios. Experimental results demonstrate that, in a representative indoor factory setting, the hybrid sensing scheme achieves substantial performance gains over a conventional TRP-only configuration, confirming its effectiveness in improving localization precision under realistic and complex channel conditions.
0 citationsRead paper