Jianxin Wang Journalist Profile

Articles

How do US presidential elections affect the economy and the stock market?

Oct 28, 2024 | msn.com | Jianxin Wang

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How do US presidential elections affect the economy and the stock market?

Oct 28, 2024 | phys.org | Jianxin Wang

With the U.S. presidential election on a knife edge and less than a week away, financial markets are watching with bated breath. There's no shortage of reasons to be nervous. Both sides have outlined radically different visions of America's economic future, with major implications for the rest of the world.
How do US presidential elections affect the economy and the stock market?

Oct 27, 2024 | tolerance.ca | Jianxin Wang

By Jianxin Wang, Associate professor of finance, University of Technology Sydney On two key measures, the US economy and stock markets have performed better under Democratic presidencies than Republican ones for much of the past century. But there are some caveats. Read complete article© The Conversation -
How do US presidential elections affect the economy and the stock market?

Oct 27, 2024 | theconversation.com | Jianxin Wang

With the US presidential election on a knife edge and less than a week away, financial markets are watching with bated breath. There’s no shortage of reasons to be nervous. Both sides have outlined radically different visions of America’s economic future, with major implications for the rest of the world. All of this stands against the backdrop of simmering tensions with China and an ongoing crisis in the Middle East.
How do US presidential elections affect the economy and the stock market?

Oct 27, 2024 | thetimes.com.au | Jianxin Wang

Written by Jianxin Wang, Associate professor of finance, University of Technology Sydney With the US presidential election on a knife edge and less than a week away, financial markets are watching with bated breath. There’s no shortage of reasons to be nervous. Both sides have outlined radically different visions of America’s economic future, with major implications for the rest of the world.
scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data | Nature Communications

Aug 31, 2024 | nature.com | Yunpei Xu |Shaokai Wang |Qilong Feng |Yaohang Li |Hong-Dong Li |Jianxin Wang | +1 more

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD’s efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios.
Parameterized Complexity of Multi-winner Determination: More Effort Towards Fixed-Parameter Tractability

May 22, 2023 | arxiv.org | Jianxin Wang

arXiv:2107.04685 (cs) [Submitted on 9 Jul 2021 (v1), last revised 22 May 2023 (this version, v4)] Download a PDF of the paper titled Parameterized Complexity of Multi-winner Determination: More Effort Towards Fixed-Parameter Tractability, by Yongjie Yang and Jianxin Wang Download PDF Submission history From: Yongjie Yang [ view email] [v1] Fri, 9 Jul 2021 21:21:18 UTC (79 KB) [v2] Thu, 5 May 2022 11:06:29 UTC (77 KB) [v3] Mon, 9 Jan 2023 15:54:45 UTC (86 KB) [v4] Mon, 22 May 2023 07:22:09 UTC...