University of Texas at Dallas

About: University of Texas at Dallas is a education organization based out in Richardson, Texas, United States. It is known for research contribution in the topics: Population & Computer science. The organization has 14986 authors who have published 35589 publications receiving 1293714 citations. The organization is also known as: UT-Dallas & UT Dallas.

Hot Markets, Investor Sentiment, and IPO Pricing

Abstract: We model an IPO company's optimal response to the presence of sentiment investors and short sale constraints. Given regulatory constraints on price discrimination, the optimal mechanism involves the issuer allocating stock to 'regular' institutional investors for subsequent resale to sentiment investors, at prices the regulars maintain by restricting supply. Because the hot market can end prematurely, carrying IPO stock in inventory is risky, so to break even in expectation regulars require the stock to be underpriced - even in the absence of asymmetric information. However, the offer price still exceeds fundamental value, as it capitalizes the regulars' expected gain from trading with the sentiment investors. This resolves the apparent paradox that issuers, while shrewdly timing their IPOs to take advantage of optimistic valuations, appear not to price their stock very aggressively. The model generates a number of new and refutable empirical predictions regarding the extent of long-run underperformance, offer size, flipping, and lock-ups.

Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

Abstract: Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In order to determine whether an instance belongs to a novel class, the classification model sometimes needs to wait for more test instances to discover similarities among those instances. A maximum allowable wait time Tc is imposed as a time constraint to classify a test instance. Furthermore, most existing stream classification approaches assume that the true label of a data point can be accessed immediately after the data point is classified. In reality, a time delay Tl is involved in obtaining the true label of a data point since manual labeling is time consuming. We show how to make fast and correct classification decisions under these constraints and apply them to real benchmark data. Comparison with state-of-the-art stream classification techniques prove the superiority of our approach.

Performance of $b$-Jet Identification in the ATLAS Experiment

Abstract: The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.

Exome sequencing links mutations in PARN and RTEL1 with familial pulmonary fibrosis and telomere shortening

Abstract: Idiopathic pulmonary fibrosis (IPF) is an age-related disease featuring progressive lung scarring. To elucidate the molecular basis of IPF, we performed exome sequencing of familial kindreds with pulmonary fibrosis. Gene burden analysis comparing 78 European cases and 2,816 controls implicated PARN, an exoribonuclease with no previous connection to telomere biology or disease, with five new heterozygous damaging mutations in unrelated cases and none in controls (P = 1.3 × 10(-8)); mutations were shared by all affected relatives (odds in favor of linkage = 4,096:1). RTEL1, an established locus for dyskeratosis congenita, harbored significantly more new damaging and missense variants at conserved residues in cases than in controls (P = 1.6 × 10(-6)). PARN and RTEL1 mutation carriers had shortened leukocyte telomere lengths, and we observed epigenetic inheritance of short telomeres in family members. Together, these genes explain ~7% of familial pulmonary fibrosis and strengthen the link between lung fibrosis and telomere dysfunction.