The impact of regulation on market quality

This dissertation studies the impact of market structure changes on market efficiency and integrity. Thematically, it is concerned with the actual behaviour of market participants and their associated impact on key market variables such as the degree of liquidity, the size of trading costs, the quality of price discovery and the integrity of the market itself. The fundamental changes to the trading landscape brought about by fragmentation have significantly changed the way that many traders execute transactions. In light of the vast and complex changes that have recently occurred in markets, this thesis conducts an empirical investigation of these microstructure issues. These studies contribute to the understanding of modern markets, the health of which is integral for effective price discovery and liquidity provision. The four studies in this dissertation examine several key market microstructure issues, including: causes of the pre-bid price run-up ahead of takeover announcements; the impact high frequency trading has on market efficiency and integrity; and the effect of both the introduction and regulation of dark trading. The outcomes of these studies are comprehensively discussed and their contributions to the field are duly noted. Given the significant and rapid change occurring in current equity markets, the findings in this dissertation are relevant for market practitioners, exchange venue designers, and market regulators

The impact of regulation on market quality

This dissertation studies the impact of market structure changes on market efficiency and integrity. Thematically, it is concerned with the actual behaviour of market participants and their associated impact on key market variables such as the degree of liquidity, the size of trading costs, the quality of price discovery and the integrity of the market itself. The fundamental changes to the trading landscape brought about by fragmentation have significantly changed the way that many traders execute transactions. In light of the vast and complex changes that have recently occurred in markets, this thesis conducts an empirical investigation of these microstructure issues. These studies contribute to the understanding of modern markets, the health of which is integral for effective price discovery and liquidity provision. The four studies in this dissertation examine several key market microstructure issues, including: causes of the pre-bid price run-up ahead of takeover announcements; the impact high frequency trading has on market efficiency and integrity; and the effect of both the introduction and regulation of dark trading. The outcomes of these studies are comprehensively discussed and their contributions to the field are duly noted. Given the significant and rapid change occurring in current equity markets, the findings in this dissertation are relevant for market practitioners, exchange venue designers, and market regulators

Discovery and Follow-up Observations of the Young Type Ia Supernova 2016coj

The Type~Ia supernova (SN~Ia) 2016coj in NGC 4125 (redshift $z=0.004523$) was discovered by the Lick Observatory Supernova Search 4.9 days after the fitted first-light time (FFLT; 11.1 days before $B$-band maximum). Our first detection (pre-discovery) is merely $0.6\pm0.5$ day after the FFLT, making SN 2016coj one of the earliest known detections of a SN Ia. A spectrum was taken only 3.7 hr after discovery (5.0 days after the FFLT) and classified as a normal SN Ia. We performed high-quality photometry, low- and high-resolution spectroscopy, and spectropolarimetry, finding that SN 2016coj is a spectroscopically normal SN Ia, but with a high velocity of \ion{Si}{2} $\lambda$6355 ($\sim 12,600$\,\kms\ around peak brightness). The \ion{Si}{2} $\lambda$6355 velocity evolution can be well fit by a broken-power-law function for up to a month after the FFLT. SN 2016coj has a normal peak luminosity ($M_B \approx -18.9 \pm 0.2$ mag), and it reaches a $B$-band maximum \about16.0~d after the FFLT. We estimate there to be low host-galaxy extinction based on the absence of Na~I~D absorption lines in our low- and high-resolution spectra. The spectropolarimetric data exhibit weak polarization in the continuum, but the \ion{Si}{2} line polarization is quite strong ($\sim 0.9\% \pm 0.1\%$) at peak brightness.Comment: Submitte

HLA-DQA1*05 carriage associated with development of anti-drug antibodies to infliximab and adalimumab in patients with Crohn's Disease

Anti-tumor necrosis factor (anti-TNF) therapies are the most widely used biologic drugs for treating immune-mediated diseases, but repeated administration can induce the formation of anti-drug antibodies. The ability to identify patients at increased risk for development of anti-drug antibodies would facilitate selection of therapy and use of preventative strategies.This article is freely available via Open Access. Click on Publisher URL to access the full-text

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead