The shape and mechanics of curved fold origami structures

We develop recursion equations to describe the three-dimensional shape of a sheet upon which a series of concentric curved folds have been inscribed. In the case of no stretching outside the fold, the three-dimensional shape of a single fold prescribes the shape of the entire origami structure. To better explore these structures, we derive continuum equations, valid in the limit of vanishing spacing between folds, to describe the smooth surface intersecting all the mountain folds. We find that this surface has negative Gaussian curvature with magnitude equal to the square of the fold's torsion. A series of open folds with constant fold angle generate a helicoid

Improving Missing Data Imputation with Deep Generative Models

Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models. Recent studies introduced solutions to the problem of imputing missing values based on deep generative models. Previous experiments with Generative Adversarial Networks and Variational Autoencoders showed interesting results in this domain, but it is not clear which method is preferable for different use cases. The goal of this work is twofold: we present a comparison between missing data imputation solutions based on deep generative models, and we propose improvements over those methodologies. We run our experiments using known real life datasets with different characteristics, removing values at random and reconstructing them with several imputation techniques. Our results show that the presence or absence of categorical variables can alter the selection of the best model, and that some models are more stable than others after similar runs with different random number generator seeds

A New Approach to Axial Vector Model Calculations II

We further develop the new approach, proposed in part I (hep-th/9807072), to computing the heat kernel associated with a Fermion coupled to vector and axial vector fields. We first use the path integral representation obtained for the heat kernel trace in a vector-axialvector background to derive a Bern-Kosower type master formula for the one-loop amplitude with $M$ vectors and $N$ axialvectors, valid in any even spacetime dimension. For the massless case we then generalize this approach to the full off-diagonal heat kernel. In the D=4 case the SO(4) structure of the theory can be broken down to $SU(2) \times SU(2)$ by use of the 't Hooft symbols. Various techniques for explicitly evaluating the spin part of the path integral are developed and compared. We also extend the method to external fermions, and to the inclusion of isospin. On the field theory side, we obtain an extension of the second order formalism for fermion QED to an abelian vector-axialvector theory.Comment: Sequel to hep-th/9807072, references added, some clarifications and corrections, 29 pages, RevTex, 8 diagrams using epsfig.st

Are cost models useful for telecoms regulators in developing countries?

Worldwide privatization of the telecommunications industry, and the introduction of competition in the sector, together with the ever-increasing rate of technological advance in telecommunications, raise new and critical challenges for regulation. Fo matters of pricing, universal service obligations, and the like, one question to be answered is this: What is the efficient cost of providing the service to a certain area or type of customer? As developing countries build up their capacity to regulate their privatized infrastructure monopolies, cost models are likely to prove increasingly important in answering this question. Cost models deliver a number of benefits to a regulator willing to apply them, but they also ask for something in advance: information. Without information, the question cannot be answered. The authors introduce cost models and establish their applicability when different degrees of information are available to the regulator. They do no by running a cost model with different sets of actual data form Argentina's second largest city, and comparing results. Reliable, detailed information is generally scarce in developing countries. The authors establish the minimum information requirements for a regulator implementing a cost proxy model approach, showing that this data constraint need not be that binding.ICT Policy and Strategies,Decentralization,Environmental Economics&Policies,Economic Theory&Research,Business Environment,ICT Policy and Strategies,Environmental Economics&Policies,Geographical Information Systems,Economic Theory&Research,Educational Technology and Distance Education

Social Preferences and Voting: An Exploration Using a Novel Preference Revealing Mechanism

Public referenda are frequently used to determine the provision of public goods. As public programs have distributional consequences, a compelling question is what role if any social preferences have on voting behavior. This paper explores this issue using laboratory experiments wherein voting outcomes lead to a known distribution of net benefits across participants. Preferences are elicited using a novel Random Price Voting Mechanism (RPVM), which is a more parsimonious mechanism than dichotomous choice referenda, but gives consistent results. Results suggest that social preferences, in particular a social efficiency motive, lead to economically meaningful deviations from self-interested voting choices and increase the likelihood that welfare-enhancing programs are implemented.Institutional and Behavioral Economics, Research Methods/ Statistical Methods, C91, C92, D64, D72, H41,

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep