## A geometric construction of an optimal (67, 9, 30) binary code

Write a C function to return minimum and maximum in an array. You program should make minimum number of comparisons. First of all, how do we return multiple values from a C function? We can do it either using structures or pointers. And the function declaration becomes: struct pair getMinMax int arr[], int n where arr[] is the array of size n whose minimum and maximum are needed.

Starting from 3rd, compare each element with max and min, and change max and min accordingly i. In the above implementation, worst case occurs when elements are sorted in descending order and best case occurs when elements are sorted in ascending order.

METHOD 2 Tournament Method Divide the array into two parts and compare the maximums and minimums of the two parts to get the maximum and the minimum of the whole array. Implementation C. Time Complexity: O n Total number of comparisons: let number of comparisons be T n. T n can be written as follows: Algorithmic Paradigm: Divide and Conquer. If n is even then initialize min and max as minimum and maximum of the first two elements respectively.

For rest of the elements, pick them in pairs and compare their maximum and minimum with max and min respectively. Attention reader! Writing code in comment? Please use ide. Python program of above implementation. If there is only one element then return it as min and max both. If there are more than one elements, then initialize min.

This code is contributed by. If there is only one element. If there is only two element. If there are more than 2 elements. This code is contributed by DeepakChhitarka. Python3 program of above implementation.

## Two constructions of asymptotically optimal codebooks via the trace functions

If array has even number of elements then. If array has odd number of elements then. In the while loop, pick elements in pair and. Increment the index by 2 as two. This code is contributed by Kaustav. Load Comments. We use cookies to ensure you have the best browsing experience on our website.This report was prepared under contract between the U. Department of Housing and Urban Development.

Humphrey Building, Independence Avenue, S. Her e-mail address is: Emily. Rosenoff hhs. The opinions and views expressed in this report are those of the authors. They do not necessarily reflect the views of the Department of Health and Human Services, the Department of Housing and Urban Development, the contractor or any other funding organization.

For the U. Departments of Health and Human Services HHS and Housing and Urban Development HUDthe Lewin Group and its sub-contractors, Leading Age and the Moran Company, explored the potential for publicly-subsidized senior housing to serve as a platform for efficiently managing the population health of low-income older adults with various levels of physical and mental health risk. This study task explored the feasibility of matching HUD administrative data to the HHS Centers for Medicare and Medicaid Services CMS administrative data in order to determine the extent to which this resource could track health and housing outcomes, and whether this approach could reliably support future research and policy analysis.

Medicare administrative data came from the Medicare Beneficiary Summary File and includes Medicare Parts A, B, and D enrollment, payments, and utilization, as well as information about chronic conditions. The Medicaid Analytic eXtract Person Summary file, based on state submission of Medicaid administrative data, provided enrollment, payment and utilization for Medicaid-covered services. We provide a detailed description of the study results in the Summary Report and in Appendix C.

The chart below provides a brief overview of the study objectives and corresponding results. The descriptive results summarized above highlight key areas for future analysis to better understand the health and health care utilization of HUD-assisted elderly individuals enrolled in Medicare.

This includes supplementing current data sources with additional CMS data, refining matching algorithms and study samples to better determine HUD-assisted elderly individuals' eligibility and enrollment in Medicare programs, providing distributional analyses, and conducting multivariate regressions to determine if the differences observed in descriptive comparisons remain after adjusting for confounders.

The U. This project sought to: 1 identify and examine affordable housing with services models that enable low-income older adults to live in affordable, safe, and accessible housing with access to health and supportive services needed to "age in place"; and 2 propose a demonstration design to track and measure outcomes and costs associated with promising housing with services models.

This report presents the results of Task 6: Data Analysis. It explores the feasibility of matching HUD administrative data to national health administrative data in order to determine whether health and housing outcomes can be tracked through existing administrative data sources and whether this approach can reliably support future research and policy analysis.

Given the study objective, we chose geographic areas that have unique public housing with services models. For example, Burlington, Vermont has the "Supports and Services at Home" program that incorporates an interdisciplinary team of community service providers to coordinate participating residents' health and long-term care needs.

This report lays the groundwork for federal efforts to use existing administrative data maintained separately by health and housing agencies to more effectively serve individuals including elderly and non-elderly persons with disabilities and communities thatcould benefit from a coordinated housing with services program. Understanding the characteristics of individuals and their use of health care services in different housing arrangements will ideally inform policy to promote rational and optimal care.

A large and rapidly expanding pool of low-income and modest-income older adults face the dual challenges of finding affordable and safe housing that can also accommodate changing needs as they grow older.

Inan estimated 3. The current system of multiple payers -- primarily Medicare and Medicaid -- provides few incentives for primary, acute and chronic care providers to collaborate with each other, let alone cooperate with low-income housing or aging and long-term services and supports providers. To address some of these issues, hundreds of publicly assisted largely not-for-profit housing providers and several states and private sector organizations have developed programs to bring enhanced services to residents.

Innovative housing providers across the country, working with federal, state, and community partners have, largely at their own initiative, developed many prototypes of publicly assisted housing with enhanced services for older adults. Typically, these properties employ a service coordinator available through HUD grants and, in some cases, incorporated into the properties operating budgetcomplemented by a wide array of community partnerships.

The spouse of someone who meets these guidelines is also eligible for Medicare. Those under 65 can qualify for a couple of reasons. One of the major reasons is being entitled to Social Security disability benefits for at least 2 years. There are multiple parts to Medicare, including:.

Medicaid 10,11 is a public health insurance program for low-income children and adults. The federal minimum standards for eligibility are:. Beyond these federal minimums, states can set their own standards for eligibility within the allowed federal range and can opt to cover additional services.In this paper, we present two new constructions of complex codebooks with multiplicative characters, additive characters and trace functions over finite fields, and determin the maximal cross-correlation amplitude of these codebooks.

We prove that the codebooks we constructed are asymptotically optimal with respect to the Welch bound. Theory 58 4—, In the second construction, we generalize the results in Hong et al. IEEE Trans. Theory 60 6—,we can asymptotically achieve Welch bound for any odd prime pwe also derive the whole distribution of their inner products.

The parameters of these codebooks are new. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Candes, E. Google Scholar. Conway, J. Ding, C. Theory 52 9— Theory 53 11— Delsarte, P.

Introduction to Gray Code

Geometriae Dedicate 67 3— Fickus, M. Linear Algebra Appl. Theory 62 9— Helleseth, T. Theory 52 5— Hu, H. Theory 60 2— Hong, S. Theory 60 6— Heng, Z. Theory 63 10— Heng, Z: Nearly optimal codebooks based on generalized Jacobi sums. Discrete Appl. Kovacevic, J. Trends Signal Process. Li, C. Codes Cryptogr. Lu, W. Luo, G.Theory 46no. Nombres Bordeaux 12no. Algebraic Combin. Baker, J. Dover, G. Ebert, and K. Wantz, Perfect Baer subplane partitions and three-dimensional flag-transitive planesDes.

Codes Cryptogr. Baker, Jeremy M. Dover, Gary L. Ebert, and Kenneth L. Wantz, Baer subgeometry partitionsJ. Lynn M. Benson and J. Carlson, Cohomology of the double cover of the Mathieu group M 12J. Algebrano. Bonnecaze, E.

Rains, and P. Inference 86no. Walker, 2-groups with few conjugacy classesProc. Edinburgh Math. Bray, An improved method for generating the centralizer of an involutionArch. Basel 74no. Bray and Robert T.

Cambridge Philos. BrentRecent progress and prospects for integer factorisation algorithmsComputing and Combinatorics Sydney,Lecture Notes in Comput. Dedicata 83no. Camina and Federica Spiezia, Sporadic groups and automorphisms of linear spacesJ.

Campbell, I. Hughes, G. Kemper, R. Shank, and D.Mouseover the table cells to see the produced disparity map. Clicking a cell will blink the ground truth for comparison. To change the table type, click the links below. For more information, please see the description of new features.

Submit and evaluate your own results. See snapshots of previous results. See the evaluation v. Set: test dense test sparse training dense training sparse Metric: bad 0. Small Medium Large Popout Loading MP: 5. MP: 1.

### Maximum and minimum of an array using minimum number of comparisons

OpenCV 2. Reimplementation of H. OpenCV's "semi-global block matching" method; memory-intensive 2-pass version, which can only handle the quarter-size images.

The matching cost is the sum of absolute differences over small windows. Aggregation is performed by dynamic programming along paths in 8 directions. Post filter as implemented in OpenCV.

Dense results are created by hole-filling along scanlines. Reimplementation and modification of H. OpenCV's "semi-global block matching" method; memory efficient single-pass version. Aggregation is performed by dynamic programming along paths in only 5 of 8 directions. Stereo processing by semi-global matching and mutual information. The images are Census transformed and the Hamming distance is used as pixelwise matching cost. Aggregation is performed by a kind of dynamic programming along 8 paths that go from all directions through the image.

Small disparity patches are invalidated.In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm.

We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm.

At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user.

Compared to C4. With the rapid development of mobile internet, mobile users can enjoy mobile services at anytime from anywhere, such as location-based services, mobile games, location-based advertising, and mobile phone rescue.

By the end of Marchthe number of mobile communication service users in China has reached 1. Facing the huge number of users, how to provide personalized services to customers, and how to make customer classification to mobile users based on data mining technologies have become the focus of the current academic and industry attention. There are many methods which have been used to classify the customer.

Han et al. In this study, the authors only took the customer value into consideration and did not take the social attribute of the user into consideration. Xiao et al. Bayesian network was also used as a tool to the customer classification [ 3 ].

In generally, the Bayes classifier is not as sensitive as the C4. So we select decision tree as a tool to generate rules in this paper. But most of the decision tree algorithms are greedy algorithm; greedy algorithm is usually running fast, but it does not get the optimal decision tree. To get optimal decision tree problem is NP complete problem; these methods cannot solve it. This paper puts forward a new decision model for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm.

There are many classification models which have been proposed by researchers, such as decision tree algorithm, Bayesian network, genetic algorithm, and neural net algorithm.

Zhang [ 6 ] took the annual salary, education, age, occupation, marriage, and property attributes of customer as the decision attribute set and established the classification model for Chinese customers of bank based on decision tree. She has classified the customer into risk customer, bad customer, ordinary customer, and important customer classes.

## Picture of Housing and Health: Medicare and Medicaid Use Among Older Adults in HUD-Assisted Housing

She used a single data mining method and used it in bank customer classification. The accuracy of the result may not be very accurate, so it will not be suitable to the mobile user classification. Chen [ 7 ] proposed a tree classification model based on Bayesian network algorithm. This model which the researcher proposed uses a single method to classify the trees, which may be very useful in small data sets.

For big data sets, the accuracy of the model will decrease. Moreover, as we mentioned before, the Bayesian classifier is not as sensitive as decision tree classifier. Zhou et al. Moreover as we mentioned before, neural network has a bad quality to deal with the nonnumeric data and low learning rate. Shu [ 9 ] proposed a fingerprint classification system based on a modified genetic algorithm.

In this study, an improvement of the born classification is designed by adding a joined BP operator GA; it may suit the fingerprint classification, but it is not very useful to mobile user data.Each node of the kd-tree is associated with a closed rectangular region of space, called a cell.

The max operator in 4 e ectively asso-ciates each point in Mwith a point in Bunder T. For example, if we want to see if the number 4 is in the tree or not, we start with comparing 4 with the root node's value which is 4. The digests are computed with a.

A static index on a set of 1-dimensional intervals, using an R-Tree packed based on the order of the interval midpoints. Internal nodes are labeled next to their split planes and leaf nodes are la-beled inside their volume. Building the kd-tree How do we build the kd-tree?

Create a kd-tree on P 1, and make its root the left child of u 1. Clustering 1D Neighboring structure, e. SIG07] [Adams et al. Select dimension kn with largest variance 2.

The k -d tree is a binary tree in which every leaf node is a k -dimensional point. A kd-tree is used to find the closest point in 4D-space, therefore simultaneously accounting for color and depth. KernelDensity estimator. A practical implementation of KD trees Once I needed a data structure for caching of relatively large sets of 2D points.

The "Choosing K" section below describes how the number of groups can be determined. This is a curated list of items agreed on by module owners. The max cache size is hard-coded at This allows to turn the computa- tional complexity into O Nk per-pixel instead of O Nk.

For example, for 2D simula. The available kernels are shown in the second figure of this example.