arXiv subjects

Wendy Wang

Publications and source records attributed to Wendy Wang.

3 recordsLinked to original sources

arXiv2024

Interconnected Markets: Exploring the Dynamic Relationship Between BRICS Stock Markets and Cryptocurrency

This study aims to examine the intricate dynamics between BRICS traditional stock assets and the evolving landscape of cryptocurrencies. Using a time-varying parameter vector autoregression model (TVP-VAR), we have analyzed data from the BRICS stock market index, cryptocurrencies, and indicators from January 6, 2015, to June 29, 2023. The results show that three out of the five BRICS stock markets serve as primary sources of shocks that subsequently affect the financial network. The transcontinental (TCI) value derived from the dynamic conditional connectedness using the TVP-VAR model demonstrates a higher explanatory power than the static connectedness observed using the standard VAR model. The discoveries from this study offer valuable insights for corporations, investors, and regulators concerning systematic risk and investment strategies.

econ.GN↗

arXiv2024

Rethinking Histology Slide Digitization Workflows for Low-Resource Settings

Histology slide digitization is becoming essential for telepathology (remote consultation), knowledge sharing (education), and using the state-of-the-art artificial intelligence algorithms (augmented/automated end-to-end clinical workflows). However, the cumulative costs of digital multi-slide high-speed brightfield scanners, cloud/on-premises storage, and personnel (IT and technicians) make the current slide digitization workflows out-of-reach for limited-resource settings, further widening the health equity gap; even single-slide manual scanning commercial solutions are costly due to hardware requirements (high-resolution cameras, high-spec PC/workstation, and support for only high-end microscopes). In this work, we present a new cloud slide digitization workflow for creating scanner-quality whole-slide images (WSIs) from uploaded low-quality videos, acquired from cheap and inexpensive microscopes with built-in cameras. Specifically, we present a pipeline to create stitched WSIs while automatically deblurring out-of-focus regions, upsampling input 10X images to 40X resolution, and reducing brightness/contrast and light-source illumination variations. We demonstrate the WSI creation efficacy from our workflow on World Health Organization-declared neglected tropical disease, Cutaneous Leishmaniasis (prevalent only in the poorest regions of the world and only diagnosed by sub-specialist dermatopathologists, rare in poor countries), as well as other common pathologies on core biopsies of breast, liver, duodenum, stomach and lymph node. The code and pretrained models will be accessible via our GitHub (https://github.com/nadeemlab/DeepLIIF), and the cloud platform will be available at https://deepliif.org for uploading microscope videos and downloading/viewing WSIs with shareable links (no sign-in required) for telepathology and knowledge sharing.

eess.IV↗

arXiv2016

Leveraging Crowd for Game-based Learning: A Case Study of Privacy Education Game Design and Evaluation by Crowdsourcing

As the Internet grows in importance, it is vital to develop methods and techniques for educating end-users to improve their awareness of online privacy. Web-based education tools have been proven effective in many domains and have been increasingly adopted by many online professional and educational services. However, the design and development of Web-based education tools for online privacy is still in the early stage. The traditional solutions always involve privacy experts who have sophisticated expertise. Such involvement can make the tool development costly. Furthermore, it is not clear how inspiring and effective these education tools are to general users of varying backgrounds, specially to novice users who have rarely dealt with online privacy issues before. In this paper, we design, develop, and evaluate a game-based privacy learning system by leveraging the wisdom of a crowd of non-experts on Amazon Mechanic Turk. Empirical study demonstrates that the crowd can provide high-quality ideas of designing and developing a practical, educational privacy learning game.

cs.CY↗