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587 results on '"Zhang, Jingnan"'

1. Change point detection in dynamic heterogeneous networks via subspace tracking

Author

Zhang, Yuzhao, Zhang, Jingnan, Sun, Yifan, and Wang, Junhui

Subjects
Statistics - Methodology
Abstract

Dynamic networks consist of a sequence of time-varying networks, and it is of great importance to detect the network change points. Most existing methods focus on detecting abrupt change points, necessitating the assumption that the underlying network probability matrix remains constant between adjacent change points. This paper introduces a new model that allows the network probability matrix to undergo continuous shifting, while the latent network structure, represented via the embedding subspace, only changes at certain time points. Two novel statistics are proposed to jointly detect these network subspace change points, followed by a carefully refined detection procedure. Theoretically, we show that the proposed method is asymptotically consistent in terms of change point detection, and also establish the impossibility region for detecting these network subspace change points. The advantage of the proposed method is also supported by extensive numerical experiments on both synthetic networks and a UK politician social network.

Published
2023

2. Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift

Author

Feng, Xingdong, He, Xin, Wang, Caixing, Wang, Chao, and Zhang, Jingnan

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory
Abstract

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method., Comment: Poster to appear in Thirty-seventh Conference on Neural Information Processing Systems

Published
2023

12. The NiuTrans Machine Translation Systems for WMT21

Author

Zhou, Shuhan, Zhou, Tao, Wei, Binghao, Luo, Yingfeng, Mu, Yongyu, Zhou, Zefan, Wang, Chenglong, Zhou, Xuanjun, Lv, Chuanhao, Jing, Yi, Wang, Laohu, Zhang, Jingnan, Huang, Canan, Yan, Zhongxiang, Hu, Chi, Li, Bei, Xiao, Tong, and Zhu, Jingbo

Subjects
Computer Science - Computation and Language
Abstract

This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English$\leftrightarrow$$\{$Chinese, Japanese, Russian, Icelandic$\}$ and English$\rightarrow$Hausa tasks. Our primary systems are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.

Published
2021

46. Change Point Detection in Dynamic Networks via Regularized Tensor Decomposition.

Author

Zhang, Yuzhao, Zhang, Jingnan, Sun, Yifan, and Wang, Junhui

Subjects
*CHANGE-point problems, *TIME-varying networks, *MATRICES (Mathematics)
Abstract

Dynamic network captures time-varying interactions among multiple entities at different time points, and detecting its structural change points is of central interest. This article proposes a novel method for detecting change points in dynamic networks by fully exploiting the latent network structure. The proposed method builds upon a tensor-based embedding model, which models the time-varying network heterogeneity through an embedding matrix. A fused lasso penalty is equipped with the tensor decomposition formulation to estimate the embedding matrix and a power update algorithm is developed to tackle the resultant optimization task. The error bound of the obtained estimated embedding matrices is established without incurring the computational-statistical gap. The proposed method also produces a set of estimated change points, which, coupled with a simple screening procedure, assures asymptotic consistency in change point detection under much milder assumptions. Various numerical experiments on both synthetic and real datasets also support its advantage. for this article are available online. [ABSTRACT FROM AUTHOR]

Published
2024
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47. PIEZO1‐mediated mechanosensing governs NK‐cell killing efficiency and infiltration in three‐dimensional matrices.

Author

Yanamandra, Archana K., Zhang, Jingnan, Montalvo, Galia, Zhou, Xiangda, Biedenweg, Doreen, Zhao, Renping, Sharma, Shulagna, Hoth, Markus, Lautenschläger, Franziska, Otto, Oliver, del Campo, Aránzazu, and Qu, Bin

Subjects
KILLER cells, ION channels
Abstract

Natural killer (NK) cells play a vital role in eliminating tumorigenic cells. Efficient locating and killing of target cells in complex three‐dimensional (3D) environments are critical for their functions under physiological conditions. However, the role of mechanosensing in regulating NK‐cell killing efficiency in physiologically relevant scenarios is poorly understood. Here, we report that the responsiveness of NK cells is regulated by tumor cell stiffness. NK‐cell killing efficiency in 3D is impaired against softened tumor cells, whereas it is enhanced against stiffened tumor cells. Notably, the durations required for NK‐cell killing and detachment are significantly shortened for stiffened tumor cells. Furthermore, we have identified PIEZO1 as the predominantly expressed mechanosensitive ion channel among the examined candidates in NK cells. Perturbation of PIEZO1 abolishes stiffness‐dependent NK‐cell responsiveness, significantly impairs the killing efficiency of NK cells in 3D, and substantially reduces NK‐cell infiltration into 3D collagen matrices. Conversely, PIEZO1 activation enhances NK killing efficiency as well as infiltration. In conclusion, our findings demonstrate that PIEZO1‐mediated mechanosensing is crucial for NK killing functions, highlighting the role of mechanosensing in NK‐cell killing efficiency under 3D physiological conditions and the influence of environmental physical cues on NK‐cell functions. [ABSTRACT FROM AUTHOR]

Published
2024
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