943 research outputs found

    Satisfiability Modulo Theory based Methodology for Floorplanning in VLSI Circuits

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    This paper proposes a Satisfiability Modulo Theory based formulation for floorplanning in VLSI circuits. The proposed approach allows a number of fixed blocks to be placed within a layout region without overlapping and at the same time minimizing the area of the layout region. The proposed approach is extended to allow a number of fixed blocks with ability to rotate and flexible blocks (with variable width and height) to be placed within a layout without overlap. Our target in all cases is reduction in area occupied on a chip which is of vital importance in obtaining a good circuit design. Satisfiability Modulo Theory combines the problem of Boolean satisfiability with domains such as convex optimization. Satisfiability Modulo Theory provides a richer modeling language than is possible with pure Boolean SAT formulas. We have conducted our experiments on MCNC and GSRC benchmark circuits to calculate the total area occupied, amount of deadspace and the total CPU time consumed while placing the blocks without overlapping. The results obtained shows clearly that the amount of dead space or wasted space is reduced if rotation is applied to the blocks.Comment: 8 pages,5 figure

    A study of the personal income distribution in Australia

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    We analyze the data on personal income distribution from the Australian Bureau of Statistics. We compare fits of the data to the exponential, log-normal, and gamma distributions. The exponential function gives a good (albeit not perfect) description of 98% of the population in the lower part of the distribution. The log-normal and gamma functions do not improve the fit significantly, despite having more parameters, and mimic the exponential function. We find that the probability density at zero income is not zero, which contradicts the log-normal and gamma distributions, but is consistent with the exponential one. The high-resolution histogram of the probability density shows a very sharp and narrow peak at low incomes, which we interpret as the result of a government policy on income redistribution.Comment: 7 pages, 4 figures, Proceedings of the Econophysics Colloquium, Canberra, 14-18 November 200

    Studies of complex systems in condensed matter physics and economics

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    This dissertation reports the study of complex systems from two very different fields. The dissertation is divided into two parts. The first part involves study of angular magnetoresistance in quasi-one-dimensional organic conductors and graphene bilayers (chapter 2 and 3). The second part is devoted to the modeling and empirical study of personal income distribution (chapter 4 and 5). First, we study the effect of crystal superstructures, produced by orientational ordering of the ReO4 and ClO4 anions in the quasi-one-dimensional organic conductors (TMTSF)2ReO4 and (TMTSF)2ClO4, on the angular magnetoresistance oscillations (AMRO) observed in these materials. Folding of the Brillouin zone due to anion ordering generates effective tunneling amplitudes between distant chains. These amplitudes cause multiple peaks in interlayer conductivity for the magnetic field orientations along the rational crystallographic directions (the Lebed magic angles). Different wave vectors of the anion ordering in (TMTSF)2ReO4 and (TMTSF)2ClO4 result in the odd and even Lebed angles, as observed experimentally. When a strong magnetic field is applied parallel to the layers and perpendicular the chains and exceeds a certain threshold, the interlayer tunneling between different branches of the folded electron spectrum becomes possible, and interlayer conductivity should increase sharply. This effect can be utilized to probe the anion ordering gaps in (TMTSF)2ClO4 and (TMTSF)4ReO4. An application of this effect to kappa-(ET)2Cu(NCS)2 is also briefly discussed. Next, we study AMRO in graphene bilayers. We calculate the interlayer conductivity and investigate the effects of a parallel magnetic field on the low energy bands of graphene bilayer. Next, we analyze the data on personal income distribution from the Australian Bureau of Statistics. We compare fits of the data to the exponential, log-normal, and gamma distributions. The exponential function gives a good (albeit not perfect) description of 98% of the population in the lower part of the distribution. The log-normal and gamma functions do not improve the fit significantly, despite having more parameters, and mimic the exponential function. We find that the probability density at zero income is not zero, which contradicts the log-normal and gamma distributions, but is consistent with the exponential one. The high-resolution histogram of the probability density shows a very sharp and narrow peak at low incomes, which we interpret as the result of a government policy on income redistribution. We also analyze data on individual income from Internal Revenue Service and University of Maryland. Finally, we discuss a model which captures the two-class structure of income distribution in the USA

    Expressing linear equality constraints in feedforward neural networks

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    We seek to impose linear, equality constraints in feedforward neural networks. As top layer predictors are usually nonlinear, this is a difficult task if we seek to deploy standard convex optimization methods and strong duality. To overcome this, we introduce a new saddle-point Lagrangian with auxiliary predictor variables on which constraints are imposed. Elimination of the auxiliary variables leads to a dual minimization problem on the Lagrange multipliers introduced to satisfy the linear constraints. This minimization problem is combined with the standard learning problem on the weight matrices. From this theoretical line of development, we obtain the surprising interpretation of Lagrange parameters as additional, penultimate layer hidden units with fixed weights stemming from the constraints. Consequently, standard minimization approaches can be used despite the inclusion of Lagrange parameters -- a very satisfying, albeit unexpected, discovery. Examples ranging from multi-label classification to constrained autoencoders are envisaged in the future

    Clinical profile and factors associated with microalbuminuria in type 1 diabetes mellitus in children and adolescents

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    Background: The aim of this study was to determine the pattern of clinical presentation and factor associated with microalbuminuria.Methods: Urinary albumin excretion of children and adolescents diagnosed with type 1 diabetes mellitus attending diabetic clinic of Katihar medical college hospital over a period of one year. Collected blood and urine samples were analysed for glycated haemoglobin, cholesterol, triglycerides, and for 12 hour urinary albumin concentration. Blood pressures were recorded and clinical data collected.Results: During the study period 215 patients were diagnosed with type1 DM. Out of 215, fourty-three patients (20%) had persistent microalbuminuria. Factor associated with microalbuminuria in diabetic patients include duration of diabetes mellitus, higher blood pressure, higher cholesterol and triglyceride levels.Conclusion: Type 1 DM is treatable and testing is acceptable and accessible to the patients. As microalbuminuria is an early microvascular complications, it is highly recommended to screen all diabetic patients for the incidence of microalbuminuria and modifiable risk factors like dyslipidemia at the onset and then yearly assessment. Efforts need to be intensified in education of health workers and population at large for quick presentation and prompt diagnosis in order to predict overt diabetic nephropathy and also to prevent its progression.
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