Factors Influencing Auction Outcomes: Bidder Turnout, Auction Houses and Market Conditions

This study provides new evidence on the outcomes of auctions of residential real estate, focusing on the effects of bidder turnout, pricing, market conditions and auction houses. Our analysis of properties offered for sale by auction in Singapore from 1995 to 2000 shows that several variables are significant in explaining why an auction results in a sale or not. These variables included the state of the market, the timing of the auction (year), the number of bidders at the auction and the auction house. We also find that the probability of a sale is higher for distress sales, for more homogenous properties and for those located in the central region. Finally, we provide a sensitivity analysis of how market conditions and the choice of auction house influence the probability of a sale.

X-Ray Diffuse Scattering

Contains research summary and reports on two research projects.Joint Services Electronics Program (Contract DAAL03-86-K-0002)Joint Services Electronics Program (Contract DAAL03-89-C-0001

Virtual Tours as Emerging Technologies to Engage Children and Youth with their Country’s Historical Conflicts

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X-Ray Diffuse Scattering

Contains introduction and reports on three research projects.Joint Services Electronics Program DAAL03-89-C-000

Design and implementation of mlearning for calculus in tertiary education

Using IT in education has become prevalent worldwide. It is believed that IT not only motivates students to learn in class, but also encourages students to take the initiative to explore and inquire subject matters outside classroom by themselves (i.e. independent learning). The Hong Kong Education Bureau offered a series of professional development programmes to principals, vice principals, middle managers as well as teachers who have joined the "Support Scheme for e-Learning in Schools" since April 2014 to 2016/17 school year to further equip schools to implement e-learning. In primary and secondary education, for example, the HKSAR Government (2004) has been giving support to schools for using e-textbooks and e-learning resources during lessons. At tertiary level, other e-learning platforms such as MOODLE and BLACKBOARD have been widely used in different institutions. In recent years, the rapid development of mobile apps has been provided learners with ample opportunities to learn independently. There are various learning apps such as Math Tricks, Complete Mathematics, Mathematics Dictionary, IXL Maths Practice, etc on the market for primary and secondary education; nonetheless, the use of mobile apps in students’ learning of disciplinary subject seems to be new in tertiary level, and its effectiveness in students’ learning is worth investigation. Through this study, the project team hopes to a) explore whether a disciplinary-specific mobile app can help learners to acquire and apply disciplinary knowledge in and beyond classroom, b) identify elements to be included in a mobile app to helps students to learn outside classroom

Synchrotron X-Ray Studies of Surface Disordering

Contains an introduction and reports on three research projects.Joint Services Electronics Program Contract DAAL03-92-C-000

A Riemann solver at a junction compatible with a homogenization limit

We consider a junction regulated by a traffic lights, with n incoming roads and only one outgoing road. On each road the Phase Transition traffic model, proposed in [6], describes the evolution of car traffic. Such model is an extension of the classic Lighthill-Whitham-Richards one, obtained by assuming that different drivers may have different maximal speed. By sending to infinity the number of cycles of the traffic lights, we obtain a justification of the Riemann solver introduced in [9] and in particular of the rule for determining the maximal speed in the outgoing road.Comment: 19 page

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Structural characterization of natural nickel and copper binding ligands along the US GEOTRACES Eastern Pacific Zonal Transect

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Marine Science 3 (2016): 243, doi:10.3389/fmars.2016.00243.Organic ligands form strong complexes with many trace elements in seawater. Various metals can compete for the same ligand chelation sites, and the final speciation of bound metals is determined by relative binding affinities, concentrations of binding sites, uncomplexed metal concentrations, and association/dissociation kinetics. Different ligands have a wide range of metal affinities and specificities. However, the chemical composition of these ligands in the marine environment remains poorly constrained, which has hindered progress in modeling marine metal speciation. In this study, we detected and characterized natural ligands that bind copper (Cu) and nickel (Ni) in the eastern South Pacific Ocean with liquid chromatography tandem inductively coupled plasma mass spectrometry (LC-ICPMS), and high-resolution electrospray ionization mass spectrometry (ESIMS). Dissolved Cu, Ni, and ligand concentrations were highest near the coast. Chromatographically unresolved polar compounds dominated ligands isolated near the coast by solid phase extraction. Offshore, metal and ligand concentrations decreased, but several new ligands appeared. One major ligand was detected that bound both Cu2+ and Ni2+. Based on accurate mass and fragmentation measurements, this compound has a molecular formula of [C20H21N4O8S2+M]+ (M = metal isotope) and contains several azole-like metal binding groups. Additional lipophilic Ni complexes were also present only in oligotrophic waters, with masses of 649, 698, and 712 m/z (corresponding to the 58Ni metal complex). Molecular formulae of [C32H54N3O6S2Ni]+ and [C33H56N3O6S2Ni]+ were determined for two of these compounds. Addition of Cu and Ni to the samples also revealed the presence of additional compounds that can bind both Ni and Cu. Although these specific compounds represent a small fraction of the total dissolved Cu and Ni pool, they highlight the compositional diversity and spatial heterogeneity of marine Ni and Cu ligands, as well as variability in the extent to which different metals in the same environment compete for ligand binding.Support was provided by the National Science Foundation (NSF) program in Chemical Oceanography (OCE-1356747, OCE-1233261, OCE-1233733, OCE-1233502, and OCE-1237034), the NSF Science and Technology Center for Microbial Oceanography Research and Education (C-MORE; DBI-0424599), the Gordon and Betty Moore Foundation (#3298 and 3934), and the Simons Foundation (#329108, DR)