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Large and moderate deviations for record numbers in some non–nearest neighbor random walks

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arXiv 2019年

作者： Li, Yuqiang Yao, Qiang Key Laboratory of Advanced Theory and Application in Statistics and Data Science–MOE School of Statistics East China Normal University China NYU–ECNU Institute of Mathematical Sciences NYU Shanghai China

The deviation principles of record numbers in random walk models have not been completely investigated, especially for the non–nearest neighbor cases. In this paper, we derive the asymptotic probabilities of large and moderate deviations for the number of "weak records" (or "ladder points") in two kinds of one–dimensional non–nearest neighbor random walks. The proofs depend only on the direct analysis of random walks. We illustrate that the traditional method of analyzing the local time of Brownian motions, which is often adopted for the simple random walks, would lead to wrong conjectures for our *** Codes 60F10, 60G50 Copyright © 2019, The Authors. All rights reserved.

关键词： Brownian movement

Search for lepton portal dark matter in the PandaX-4T experiment

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2025年 864卷

作者： Ning, Xuyang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen Bai, Yang Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Dark Matter Physics Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology New Cornerstone Science Laboratory Shanghai Jiao Tong University Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan 615000 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Beihang University Beijing 102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park 20742 MD United States School of Physics Nankai University Tianjin 300071 China School of Physics Sun Yat-Sen University Guangzhou 510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Mod

We report a search for a lepton-portal dark matter model, where dark matter couples to a charged lepton in the standard model. This simplified model naturally leads to photon-mediated dark matter interactions with nuclei, making it suitable for direct dark matter detection experiments. Matching to the framework of non-relativistic effective field theory for dark matter, we report the first sensitive search for this model using data from the PandaX-4T commissioning run. Our results yield strong constraints on Dirac fermion dark matter but relatively weaker constraints on Majorana dark matter due to the suppression of effective photon interactions. These constraints complement those obtained from the collider and indirect detection experiments. © 2025 The Author(s)

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Spatially Clustered Varying Coefficient Model

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arXiv 2020年

作者： Lin, Fangzheng Tang, Yanlin Zhu, Huichen Zhu, Zhongyi Department of Statistics Fudan University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE School of Statistics East China Normal University China Department of Mathematics Hong Kong University of Science and Technology China

In various applications with large spatial regions, the relationship between the response variable and the covariates is expected to exhibit complex spatial patterns. We propose a spatially clustered varying coefficient model, where the regression coefficients are allowed to vary smoothly within each cluster but change abruptly across the boundaries of adjacent clusters, and we develop a unified approach for simultaneous coefficient estimation and cluster identification. The varying coefficients are approximated by penalized splines, and the clusters are identified through a fused concave penalty on differences in neighboring locations, where the spatial neighbors are specified by the minimum spanning tree (MST). The optimization is solved efficiently based on the alternating direction method of multipliers, utilizing the sparsity structure from MST. Furthermore, we establish the oracle property of the proposed method considering the structure of MST. Numerical studies show that the proposed method can efficiently incorporate spatial neighborhood information and automatically detect possible spatially clustered patterns in the regression coefficients. An empirical study in oceanography illustrates that the proposed method is promising to provide informative results. Copyright © 2020, The Authors. All rights reserved.

关键词： Constrained optimization

Causal Mediation Analysis with a Three-Dimensional Image Mediator

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arXiv 2023年

作者： Chen, Minghao Zhou, Yingchun Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE Institute of Brain and Education Innovation School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai200062 China

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies, etc. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother's delivery mode on child's IQ development. It is found that the white matter in certain regions of the frontal-parietal and brainstem-cerebellum areas has mediating effects. © 2023, CC BY.

关键词： Magnetic resonance

A hybrid deep learning method for controlled stochastic Kolmogorov systems with regime-switching

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A hybrid deep learning method for controlled stochastic Kolm...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Yu Zhang Zhuo Jin Jiaqin Wei School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China Department of Actuarial Studies and Business Analytics Macquarie University NSW Australia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this paper, we employ numerical methods based on deep learning algorithms for solving controlled stochastic Kolmogorov systems with regime-switching. Different from classical control problems, each component of the state in controlled Kolmogorov systems is nonnegative. Due to the nonlinearity and complexity of the controlled stochastic Kolmogorov systems, we develop a hybrid deep learning method to numerically solve the optimal controls under this system. Subsequently, we apply the hybrid deep learning method to solve a specific case of a controlled stochastic Kolmogorov system, specifically controlled SIS (susceptible-infected-susceptible) systems. Finally, the effectiveness of the proposed hybrid deep learning method is verified through numerical results.

关键词： Deep learning Closed-form solutions Neural networks Optimal control Aerospace electronics Control systems Complexity theory Approximation methods Information technology Method of moments

CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels

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arXiv 2022年

作者： Li, Siyuan Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China

Pre-trained vision-language models like CLIP have recently shown superior performances on various downstream tasks, including image classification and segmentation. However, in fine-grained image re-identification (ReID), the labels are indexes, lacking concrete text descriptions. Therefore, it remains to be determined how such models could be applied to these tasks. This paper first finds out that simply fine-tuning the visual model initialized by the image encoder in CLIP, has already obtained competitive performances in various ReID tasks. Then we propose a two-stage strategy to facilitate a better visual representation. The key idea is to fully exploit the cross-modal description ability in CLIP through a set of learnable text tokens for each ID and give them to the text encoder to form ambiguous descriptions. In the first training stage, image and text encoders from CLIP keep fixed, and only the text tokens are optimized from scratch by the contrastive loss computed within a batch. In the second stage, the ID-specific text tokens and their encoder become static, providing constraints for fine-tuning the image encoder. With the help of the designed loss in the downstream task, the image encoder is able to represent data as vectors in the feature embedding accurately. The effectiveness of the proposed strategy is validated on several datasets for the person or vehicle ReID tasks. Code is available at https://***/Syliz517/CLIP-ReID. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

Disentangling the spatial structure and style in conditional vae

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arXiv 2019年

作者： Zhang, Ziye Sun, Li Zheng, Zhilin Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai200241 China

This paper aims to disentangle the latent space in cVAE into the spatial structure and the style code, which are complementary to each other, with one of them zs being label relevant and the other zu irrelevant. The generator is built by a connected encoder-decoder and a label condition mapping network. Depending on whether the label is related with the spatial structure, the output zs from the condition mapping network is used either as a style code or a spatial structure code. The encoder provides the label irrelevant posterior from which zu is sampled. The decoder employs zs and zu in each layer by adaptive normalization like SPADE or AdaIN. Extensive experiments on two datasets with different types of labels show the effectiveness of our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

DYNAMIC-LLAVA: EFFICIENT MULTIMODAL LARGE LANGUAGE MODELS VIA DYNAMIC VISION-LANGUAGE CONTEXT SPARSIFICATION

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arXiv 2024年

作者： Huang, Wenxuan Zhai, Zijie Shen, Yunhang Cao, Shaosheng Zhao, Fei Xu, Xiangfeng Ye, Zheyu Hu, Yao Lin, Shaohui East China Normal University China Xiamen University China Xiaohongshu Inc China Nanjing University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE China

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision understanding, reasoning, and interaction. However, the inference computation and memory increase progressively with the generation of output tokens during decoding, directly affecting the efficacy of MLLMs. Existing methods attempt to reduce the vision context redundancy to achieve efficient MLLMs. Unfortunately, the efficiency benefits of the vision context reduction in the prefill stage gradually diminish during the decoding stage. To address this problem, we proposed a dynamic vision-language context sparsification framework Dynamic-LLaVA, which dynamically reduces the redundancy of vision context in the prefill stage and decreases the memory and computation overhead of the generated language context during decoding. Dynamic-LLaVA designs a tailored sparsification inference scheme for different inference modes, i.e., prefill, decoding with and without KV cache, to achieve efficient inference of MLLMs. In practice, Dynamic-LLaVA can reduce computation consumption by ∼75% in the prefill stage. Meanwhile, throughout the entire generation process of MLLMs, Dynamic-LLaVA reduces the ∼50% computation consumption under decoding without KV cache, while saving ∼50% GPU memory overhead when decoding with KV cache, due to the vision-language context sparsification. Extensive experiments also demonstrate that Dynamic-LLaVA achieves efficient inference for MLLMs with negligible understanding and generation ability degradation or even performance gains compared to the full-context inference baselines. Code is available at https://***/Osilly/dynamic_llava . Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation

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arXiv 2022年

作者： Hu, Xueqi Zhou, Xinyue Huang, Qiusheng Shi, Zhengyi Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China

Unpaired image-to-image (I2I) translation often requires to maximize the mutual information between the source and the translated images across different domains, which is critical for the generator to keep the source content and prevent it from unnecessary modifications. The self-supervised contrastive learning has already been successfully applied in the I2I. By constraining features from the same location to be closer than those from different ones, it implicitly ensures the result to take content from the source. However, previous work uses the features from random locations to impose the constraint, which may not be appropriate since some locations contain less information of source domain. Moreover, the feature itself does not reflect the relation with others. This paper deals with these problems by intentionally selecting significant anchor points for contrastive learning. We design a query-selected attention (QS-Attn) module, which compares feature distances in the source domain, giving an attention matrix with a probability distribution in each row. Then we select queries according to their measurement of significance, computed from the distribution. The selected ones are regarded as anchors for contrastive loss. At the same time, the reduced attention matrix is employed to route features in both domains, so that source relations maintain in the synthesis. We validate our proposed method in three different I2I datasets, showing that it increases the image quality without adding learnable parameters. Codes are available at https://***/sapphire497/query-selected-attention. Copyright © 2022, The Authors. All rights reserved.

关键词： Location