In this thesis we consider two classes of binary matroids, even cycle matroids and even cut matroids. They are a generalization of graphic and cographic matroids respectively. We focus on two main problems for these classes of matroids. We first consider the Isomorphism Problem, that is the relation between two representations of the same matroid. A representation of an even cycle matroid is a pair formed by a graph together with a special set of edges of the graph. Such a pair is called a signed graph. A representation for an even cut matroid is a pair formed by a graph together with a special set of vertices of the graph. Such a pair is called a graft. We show that two signed graphs representing the same even cycle matroid relate to two grafts representing the same even cut matroid. We then present two classes of signed graphs and we solve the Isomorphism Problem for these two classes. We conjecture that any two representations of the same even cycle matroid are either in one of these two classes, or are related by a local modification of a known operation, or form a sporadic example. The second problem we consider is finding the excluded minors for these classes of matroids. A difficulty when looking for excluded minors for these classes arises from the fact that in general the matroids may have an arbitrarily large number of representations. We define degenerate even cycle and even cut matroids. We show that a 3-connected even cycle matroid containing a 3-connected non-degenerate minor has, up to a simple equivalence relation, at most twice as many representations as the minor. We strengthen this result for a particular class of non-degenerate even cycle matroids. We also prove analogous results for even cut matroids.

Probability matching is the tendency to predict outcomes in accordance with their actual contingencies in a binary choice task. It is, however, a suboptimal response if the aim is to maximize correct predictions. I review two theories that attempt to explain why probability matching occurs: the pattern-search hypothesis and dual-systems theory. These theories are tested in two studies which suggest that dual-systems theory provides a better account of probability matching behavior. Studies 3, 4, and 5 then provide evidence for an extension of the dual-systems theory, called expectation matching, which is intended to explain why probability matching is the intuitive response to a binary choice problem.

In the 2010, targets of National Science and Technology Program - Energy¡¥s project plan had mentioned about the development of power line communication (PLC). This shows the importance of PLC. The data transmission occur burst errors easily by the noise interference from the environment. In order to reduce the error rate, we design a modulation/demodulation chip with error correctable and high error detected ability for power line communication in this thesis. The proposed design consists of Cyclic Redundancy Check (CRC), Pulse Width Modulation (PWM), Frequency Shift Keying (FSK), Forward Error Correction (i.e. binary BCH code), and interleaving techniques. The CRC can detect the errors occurred in the digital communication. The probability of finding error is 99.997%. The BCH code is capable of correcting any combination of 3 or fewer errors in block. The function of PWM is to generate the digital pulses that exhibit the changeable pulse width according to the swing of the input voltage. In the telecommunication, FSK is a frequency modulation scheme such that the digital information can be transmitted through the discrete frequency changes of the carrier. Interleaving can make burst errors look like random errors. The design is implemented TSMC 0.18£gm process. The fabricated chip area is 1.16 millimeter square with 3.3V/1.8V supply voltages. The measured data shows that the proposed design is fully functional and consumes 55.5 £gW.

A nonogram is a logic puzzle where one shades certain cells of a 2D grid to reveal a hidden image. One uses the sequences of numbers on the left and the top of the grid to figure out how many and which cells to shade. We propose a new technique to solve a nonogram using compressive sensing. Our method avoids (1) partial fill-ins, (2) heuristics, and (3) over-complication, and only requires that we solve a binary integer programming problem. / text

No description available.

In longitudinal studies, observational units (commonly referred to as individuals) drawn from some population of interest are followed prospectively over time, and measurements from each individual are taken repeatedly at different points in time with the ultimate goal of characterizing the important features of the population. Longitudinal data naturally arise in many areas of study, where the characterization of the population may be achieved by investigating the effects of covariates on a response. Two or more correlated responses from each individual are also common in longitudinal studies, giving rise to multivariate longitudinal data. For example, the National Longitudinal Survey of Children and Youth (NLSCY) is a long-term study to observe the development of Canadian children. In this survey, measurements about factors influencing a child's social, emotional and behavioral development are collected over time; anxiety and aggression reported for each child in this study may be considered as two response variables to characterize the emotional and behavioral development of children. Since in longitudinal studies, information is collected repeatedly from each individual over time, the occurrence of an event at a particular time point may increase/decrease the likelihood of the occurrence of another event in future. Failure to take into account this phenomenon in analyzing longitudinal data may lead to erroneous conclusion. Moreover, repeated responses (e.g., anxiety and aggression) from an individual may exhibit correlation over time. Separate analyses of such multivariate longitudinal responses ignore this correlation, and as a result, cannot reveal the potential association among the responses which could be of paramount importance in many applications. Therefore, analysis of multivariate longitudinal data requires substantial extension of the standard longitudinal methods. In this thesis, we describe a methodology based on the transition models for multivariate longitudinal binary data to address the transitional behavior between two states characterized by binary responses for two different responses (i.e., two processes). Transitional analysis of multivariate longitudinal binary data can address the longitudinal association within processes and enable marginal interpretation of covariate effects. In addition, estimation and inference of the association between the processes can also be achieved via such models. We illustrate this approach with an application to the NLSCY data, where anxiety and aggression (two correlated responses) are modeled as a function of covariates (gender, depression of person most knowledgeable, number of siblings and family status) to identify their effects on behavioral development of Canadian children. In addition, the extent and direction of the association between two responses are estimated. Gender of the child is found statistically significant for both directions of transition, i.e., from low to high and high to low, of aggression. On contrary, gender of the child is found statistically not significant for both transitions of anxiety. Meanwhile, depression of person most knowledgeable is found marginally significant in the high to low direction for aggression. For association parameters, all four directions of associations between anxiety and aggression are found statistically significant.

In the standard theory of pulsar spin-up, a neutron star (NS) in a binary system accretes matter from its companion star; this serves to transfer angular momentum to the NS, increasing the spin frequency of the pulsar. Measurement of the orbital parameters and system geometry, and in particular the final system masses, thus provide important constraints for theories regarding binary evolution. We present results from an investigation of three binary pulsar systems. PSR J1802-2124 is in an intermediate-mass pulsar binary system with a massive white dwarf companion in a compact orbit with a period of 16.8 hours. We have per-formed timing analysis on almost five years of data in order to determine the amount of Shapiro delay experienced by the incoming pulsar signal as it traverses the potential well of the companion star on its way to Earth. We find the pulsar in this system to have a relatively low mass at 1.24 ± 0.11 M®, and the companion mass to be 0.79 ± 0.04111.).We argue that the full set of system properties indicates that the system underwent a common-envelope phase in its evolutionary history. The double pulsar system PSR 0737-3039A/B is a highly relativistic double neutron star (DNS) binary, with a 2.4-hour orbital period. The low mass of the second-formed NS, as well the low system eccentricity and proper motion, have suggested a different evolutionary scenario compared to other known DNS systems. We describe analysis of the pulse profile shape over six years of observations, and present the constraints this provides on the system geometry. We find the recycled pulsar in this system, PSR 0737-3039A,to have a low misalignment angle between its spin and orbital angular momentum axes, with a 95.4% upper limit of 14 °, assuming emission from both magnetic poles. This tight constraint lends credence to the idea that the supernova that formed the second pulsar was relatively symmetric, possibly involving electron captures onto an 0-Ne-Mg core. We have also conducted timing analysis of PSR J1756-2251 using four years of data, and have obtained tight constraints on the component masses and orbital parameters in this DNS system. We have measured four post-Keplerian timing parameters for this pulsar; the Shapiro delay s parameter, with a 5% measured uncertainty, is consistent at just above the la level with the predictions of general relativity. The pulsar in this system has a fairly typical NS mass of 1.312 ± O.017M®, and the companion NS to be relatively light, with a mass of 1.2581017 Mo. This, together with the somewhat low orbital eccentricity of this system (e 0.18), suggests a similar evolution to that of the double pulsar. We investigate this further, through a similar pulse profile analysis to that performed with PSR J0737-3039A, with the goal of constraining the geometry of this system.

The resistivity and thermopower of two series of amorphous alloys have been measured between 4 and 300K. The alloys studied are MgZn and CuZr, both in the largest concentration range available as amorphous materials. The alloys were measured in both the 'as made' and 'relaxed' states, as well as some partial or totally crystallized samples. The simple Ziman theory was found at variance with the experimental results in both cases (even for MgZn, proven to be free-electron like as required by the theory). A simple two component model is proposed as an extreme simplification of the Faber-Ziman theory of liquid metallic alloys. The excellent agreement obtained indicates that metallic glasses should be considered as the alloys they really are. A full Faber-Ziman calculation is performed for CuZr and from this follows the conclusion that the term containing the energy dependence of the pseudo-potential (r), usually assumed to be small, is probably of comparable magnitude to that of the disorder scattering (q). The suggested correlations between the electron-phonon mass-enhancement parameter (lamda) (determined from superconductivity experiments) and the thermopowers are studied and both found not to be valid for CuZr amorphous alloys.

This thesis studies the one-factor latent trait model for binary data. In examines the sensitivity of the model when the assumptions about the model are violated, it investigates the information about the prior distribution when the model is estimated semi-parametrically and it also examines the goodness-of-fit of the model using Monte-Carlo simulations. Latent trait models are applied to data arising from psychometric tests, ability tests or attitude surveys. The data are often contaminated by guessing, cheating, unwillingness to give the true answer or gross errors. To study the sensitivity of the model when the data are contaminated we derive the Influence Function of the parameters and the posterior means, a tool developed in the frame of robust statistics theory. We study the behaviour of the Influence Function for changes in the data and also the behaviour of the parameters and the posterior means when the data are artificially contaminated. We further derive the Influence Function of the parameters and the posterior means for changes in the prior distribution and study empirically the behaviour of the model when the prior is a mixture of distributions. Semiparametric estimation involves estimation of the prior together with the item parameters. A new algorithm for fully semiparametric estimation of the model is given. The bootstrap is then used to study the information on the latent distribution than can be extracted from the data when the model is estimated semiparametrically. The use of the usual goodness-of-fit statistics has been hampered for latent trait models because of the sparseness of the tables. We propose the use of Monte-Carlo simulations to derive the empirical distribution of the goodness-of-fit statistics and also the examination of the residuals as they may pinpoint to the sources of bad fit.

No description available.