Non-hierarchical emergent structure: a case study in alternative management

"Most well established organizations beyond a certain size adopt a hierarchical structure and accompanying bureaucracy as a means to organize and coordinate the work of the organization. This case study describes a medium-sized family-run organization in the US, with 600 employees and Dollar 2 billion in sales turnover, which functions without formal management structures or titles. Data collected through personal observation and interviews reveal emergent structures, partly in response to the alternative management approach. The paper describes these alternative management methods, the advantages and disadvantages, as well as the conditions that make this approach possible. The paper concludes by discussing the transferability and suitability of these methods to organizations in general." (author's abstract

Restoring stability in a neglected acetabular fracture with dual mobility total hip arthroplasty: a case report

Neglected acetabular fractures present with varied degrees of soft tissue and bone loss and distorted anatomy resulting in high hip centres. The basic principles of achieving stability and restoring motion are to augment the acetabular bone loss, prevent protrusio in the long term and use a prosthesis that prevents dislocation. We report a case of a 22 year old neglected acetabular fracture managed successfully with a femoral head autograft, anti protrusio cage and dual mobility total hip arthroplasty

Probabilistic Analysis of the Median Rule: Asymptotics and Applications

The solution of integer optimization problems by relaxation methods consists of three parts. First, the discrete problem is converted into a continuous optimization problem, which is generally more tractable. Second, the relaxed problem is solved efficiently, yielding a optimal solution in the continuous space. Finally, an assignment procedure is used to map this solution to a suitable discrete solution. One heuristic - we call it the relaxation heuristic - that often guides the choice and design of assignment algorithms is: given a continuous optimal solution, the corresponding integer optimal solution is likely to be nearby (with respect to some well defined metric). Intuitively, this heuristic is reasonable for objective functions that are, say, Lipschitz functions. For such functions, an assignment algorithm might map the continuous optimal solution to the nearest feasible solution in the discrete space, in the hope that the discrete solution will be optimal as well. In this paper, we consider properties of a particular assignment algorithm known as the median rule. Define a binary vector to be balanced when the numbers of its 1 \u27s and 0\u27s differ at most by one. The median rule used to assign n-dimensional real vectors to n-dimensional balanced binary vectors, may be loosely described as follows: map the ith component of a real vector to a 0 or 1, depending on whether that component is smaller or greater than the median value of the vector components. We address two aspects of the median rule. The first result is that given a real vector, the median rule produces the closest balanced binary vector, with respect to any Schur-convex distance criteria. This includes several Minkowski norms, entropy measures, gauge functions etc. In this sense, the median rule optimally implements the relaxation heuristic. The second result addresses the issue of relaxation error. Though the median rule produces the nearest balanced integer solution to a given real vector, it is possible that this solution is sub-optimal, and the actual optimal solution is located elsewhere. The difference between the actual optimal cost and the cost of the solution obtained by the median rule is called the relaxation error. We consider the optimization of real valued, parametrized, multivariable Lipschitz functions where domains are the set of balanced binary vectors. Varying the parameters over the range of their values, we obtain an ensemble of such problems. Each problem instance in the ensemble has an optimal real cost, an integer cost, and an associated relaxation error. We establish upper bounds on the probability that the relaxation error is greater than a given threshold t. In general, these bounds depend on the random model being considered. These results have an immediate bearing on the important graph bisection width problem, which involves the minimization of a certain semidefinite quadratic cost function over balanced binary domains. This important problem arises in a variety of areas including load balancing, [11,16], storage management [22], distributed directories [15], and VLSI design [10]. The results obtained indicate that the median rule in a certain precise sense, is an optimal assignment procedure for this problem. The rest of the paper is organized as follows: In section 3, we prove the shortest distance properties of the median rule. In section 4, we introduce the concept of relaxation error and the Lipschitz bisection problem. Upper bounds on the relaxation error are obtained in Section 5. A discussion on these results is given in Section 6

Putting Humpty-Dumpty together again: Reconstructing functions from their projections.

We present a problem decomposition approach to reduce neural net training times. The basic idea is to train neural nets in parallel on marginal distributions obtained from the original distribution (via projection), and then reconstruct the original table from the marginals (via a procedure similar to the join operator in database theory). A function is said to be reconstructible, if it may be recovered without error from its projections. Most distributions are non-reconstructible. The main result of this paper is the Reconstruction theorem, which enables non-reconstructible functions to be expressed in terms of reconstructible ones, and thus facilitates the application of decomposition methods

Characterization of a Class of Sigmoid Functions with Applications to Neural Networks

Sigmoid functions, whose graphs are S-shaped curves, appear in a great variety of contexts, such as the transfer functions in many neural networks. Their ubiquity is no accident; these curves are the among the simplest non-linear curves, striking a graceful balance between linear and non-linear behavior

WHO Growth Charts for Diagnosis of Malnutrition

Child malnutrition is a major public health issue worldwide. An estimated 144 million children under age 5 are stunted, 47 million are wasted and 38.3 million have overweight or obesity. Around 45% of deaths among children under 5 years of age are linked to undernutrition.1 Measuring the growth of infants and children is an important part of child health surveillance and gives an idea about the nutritional status of the baby. Inadequate infant growth leads to under-nutrition in children in many low- and middle-income countries, which, if followed later in life by an increased intake of calories, can result in overweight or obesity and non- communicable diseases

Pulmonary Hypertension Registry of Kerala (PROKERALA) – Rationale, design and methods

AbstractBackgroundPulmonary hypertension (PH) is a disease associated with a high morbidity and mortality. There is paucity of data regarding PH from the developing countries including India.Idiopathic pulmonary arterial hypertension is the most important etiological factor in the western world, but PH secondary to rheumatic heart disease, chronic obstructive pulmonary disease and untreated congenital heart disease could well be the predominant causes in developing countries like India.The main objective of the PROKERALA study – Pulmonary hypertension Registry Of Kerala is to collect data regarding the etiology, practice patterns and one-year outcomes of patients diagnosed to have PH.MethodsThe study is a hospital-based registry in the state of Kerala supported and funded by the Cardiological Society of India, Kerala Chapter. A total of 77 hospitals have agreed to participate in the registry. PH was defined as systolic pulmonary artery pressure derived by echocardiography of more than 50mmHg (by tricuspid regurgitation jet) or mean PA pressure more than 25mmHg obtained at cardiac catheterization.A detailed questionnaire is administered which includes the demographic characteristics, risk factors, family history, ECG data, 6 minute walk test distance, chest X ray findings and echocardiographic data. Details of PH specific therapy and one-year follow-up data are collected.From a preliminary survey in the region, we estimated that we will be able to collect 2000 cases over a period of one year

On Inverse Sigmoid Functions

Networks with sigmoid node functions have been shown to be universal approximators, and can use straightforward implementations of learning algorithms. Mathematically, what is common to different sigmoid functions used by different researchers? We establish a common representation of inverse sigmoid functions in terms of the Guass Hypergeometric function, generalizing different node function formulations. We also show that the continuous Hopfield network equation can be transformed into a Legendre differential equation, without assuming the specific form of the node function; this establishes a link between Hopfield nets and the method of function approximation using Legendre polynomial