Entropy of Some Models of Sparse Random Graphs With Vertex-Names

Consider the setting of sparse graphs on N vertices, where the vertices have distinct "names", which are strings of length O(log N) from a fixed finite alphabet. For many natural probability models, the entropy grows as cN log N for some model-dependent rate constant c. The mathematical content of this paper is the (often easy) calculation of c for a variety of models, in particular for various standard random graph models adapted to this setting. Our broader purpose is to publicize this particular setting as a natural setting for future theoretical study of data compression for graphs, and (more speculatively) for discussion of unorganized versus organized complexity.Comment: 31 page

A Fine Balance: Effectively Managing Growth and Contraction

[Excerpt] Times of economic hardship and prosperity both pose unique challenges to companies and their talent management practices. On one hand, while companies are experiencing contraction, employers may neglect motivating and developing their employees. Conversely, during periods of expansion, companies may set themselves on an unsustainable course that may lead to dramatic consequences when leaner times prevail. Indeed, it is easy for companies to fall into talent management traps both in times of growth and recession

Antibody Conjugation and Formulation

In an era where ultra-high antibody concentrations, high viscosities, low volumes, auto-injectors, and long storage requirements are already complex problems with the current unconjugated monoclonal antibodies on the market the formulation demands for antibody-drug conjugates (ADCs) are significant. Antibodies have historically been administered at relatively low concentrations through intravenous (IV) infusion due to their large size and the inability to formulate for oral delivery. Due to the high demands associated with IV infusion and the development of novel antibody targets and unique antibody conjugates more accessible routes of administration such as intramuscular (IM), and subcutaneous (SC) are being explored. This review will summarize various site-specific and non-site-specific antibody conjugation techniques in the context of antibody-drug conjugates (ADCs) and the demands of formulation for high concentration clinical implementation

The approach to measuring the returns to secondary and tertiary qualifications in New Zealand : an investigation and update using data from the 2001 census : a research thesis submitted in fulfilment of the requirements for the degree of Master of Applied Economics at Massey University, Department of Applied and International Economics, College of Business, Massey University

This study investigates the approaches to measuring the returns to secondary and tertiary qualifications in New Zealand using the latest Census of Population and Dwellings data from 2001. It calculates the returns to qualifications using income function analysis, elaborate analysis and also extends the elaborate analysis by using the quantile regression technique. It reports returns within a narrow band for both methods and at a similar or higher magnitude to previous years. However, the results reported using the net present value (NPV) criteria reveal higher social returns to qualifications than private returns. This contradicts previous literature. In the policy implications section, the study recommends policies focus more on reducing the level of forgone earnings. Also, the study finds that income function analysis is better suited to measuring income inequality and its link with education. Furthermore, the study concludes that elaborate analysis, using the NPV criteria, allows better comparison of the marginal returns to educational investments of varying scale and duration. Finally, the quantile regression estimates show that point estimates of the mean return give a poor indication of the distribution of returns

Selecting a Small Set of Optimal Gestures from an Extensive Lexicon

Finding the best set of gestures to use for a given computer recognition problem is an essential part of optimizing the recognition performance while being mindful to those who may articulate the gestures. An objective function, called the ellipsoidal distance ratio metric (EDRM), for determining the best gestures from a larger lexicon library is presented, along with a numerical method for incorporating subjective preferences. In particular, we demonstrate an efficient algorithm that chooses the best $n$ gestures from a lexicon of $m$ gestures where typically $n \ll m$ using a weighting of both subjective and objective measures.Comment: 27 pages, 7 figure