Kavli Affiliate: Ke Wang | First 5 Authors: GLM-4. 5 Team, GLM-4. 5 Team, , , | Summary: We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens […]
Kavli Affiliate: Xian Chen | First 5 Authors: Xian Chen, Xian Chen, , , | Summary: Fine-grained time series data are crucial for accurate and timely online change detection. While both collective anomalies and change points can coexist in such data, their joint online detection has received limited attention. In this research, we develop a […]
Kavli Affiliate: Zhuo Li | First 5 Authors: Lefei Shen, Lefei Shen, , , | Summary: Recent studies have revealed that vision models pre-trained on images can perform well in time series forecasting by reformulating forecasting as an image reconstruction task, suggesting their potential as universal time series foundation models. However, effective cross-modal transfer from […]
Kavli Affiliate: Jing Wang | First 5 Authors: Cong-han Wang, Cong-han Wang, , , | Summary: A nonlocal version of the massive Thirring model (MTM) and its solutions are presented. We start from a 4-component system that can be reduced to the classical MTM and nonlocal MTM. Bilinear form of the 4-component system and general […]
Kavli Affiliate: Jing Wang | First 5 Authors: Wentao Qu, Wentao Qu, , , | Summary: Denoising Diffusion Probabilistic Models (DDPMs) have shown success in robust 3D object detection tasks. Existing methods often rely on the score matching from 3D boxes or pre-trained diffusion priors. However, they typically require multi-step iterations in inference, which limits […]
Kavli Affiliate: Zhuo Li | First 5 Authors: Haonan Yang, Haonan Yang, , , | Summary: Time Series Forecasting (TSF) faces persistent challenges in modeling intricate temporal dependencies across different scales. Despite recent advances leveraging different decomposition operations and novel architectures based on CNN, MLP or Transformer, existing methods still struggle with static decomposition strategies, […]
Kavli Affiliate: Zhuo Li | First 5 Authors: Haonan Yang, Haonan Yang, , , | Summary: Time Series Forecasting (TSF) faces persistent challenges in modeling intricate temporal dependencies across different scales. Despite recent advances leveraging different decomposition operations and novel architectures based on CNN, MLP or Transformer, existing methods still struggle with static decomposition strategies, […]
Kavli Affiliate: Zhuo Li | First 5 Authors: Haonan Yang, Haonan Yang, , , | Summary: Time Series Forecasting (TSF) faces persistent challenges in modeling intricate temporal dependencies across different scales. Despite recent advances leveraging different decomposition operations and novel architectures based on CNN, MLP or Transformer, existing methods still struggle with static decomposition strategies, […]
Kavli Affiliate: Huawei Zhang | Summary:The proper motions (PMs) of M31 and M33 are key to understanding the Local Group’s dynamical evolution. However, measurement discrepancies between Gaia blue and red samples, regarding whether the transverse velocity is remarkable, introduce significant ambiguity. In this work, we remeasure the systemic PMs of M31 and M33 using massive […]
Kavli Affiliate: Jing Wang | First 5 Authors: Yucheng Xie, Yucheng Xie, , , | Summary: Diffusion models have advanced from text-to-image (T2I) to image-to-image (I2I) generation by incorporating structured inputs such as depth maps, enabling fine-grained spatial control. However, existing methods either train separate models for each condition or rely on unified architectures with […]