Firms Growth Dynamics, Competition and Power Law Scaling

We study the growth dynamics of the size of manufacturing firms considering competition and normal distribution of competency. We start with the fact that all components of the system struggle with each other for growth as happened in real competitive bussiness world. The detailed quantitative agreement of the theory with empirical results of firms growth based on a large economic database spanning over 20 years is good .Further we find that this basic law of competition leads approximately a power law scaling over a wide range of parameters. The empirical datas are in accordance with present theory rather than a simple power law.Comment: 10 pages, 8 figure

A Case Study in Matching Service Descriptions to Implementations in an Existing System

A number of companies are trying to migrate large monolithic software systems to Service Oriented Architectures. A common approach to do this is to first identify and describe desired services (i.e., create a model), and then to locate portions of code within the existing system that implement the described services. In this paper we describe a detailed case study we undertook to match a model to an open-source business application. We describe the systematic methodology we used, the results of the exercise, as well as several observations that throw light on the nature of this problem. We also suggest and validate heuristics that are likely to be useful in partially automating the process of matching service descriptions to implementations.Comment: 20 pages, 19 pdf figure

EMPLOYEES PERCEPTION OF VIOLATION OF NORMS OF JUSTICE AND ITS RELATION WITH INTENTION TO LEAVE AND ORGANIZATIONAL CITIZENSHIP BEHAVIOUR

Purpose: The present research work was done to study the employees’ perception of Violation of Norms of Justice in Public and Private Organizations and also its relation to the outcome variables like Organizational Citizenship Behaviour (OCB) and Intention to Leave.Design/Methodology/Approach: The study was done on 250 employees of Public and Private Organizations (125 from each). By an interview done on some of the employees from both the sectors i.e. Public and Private, a scale for measuring perceived violation of norms of justice was developed. For measuring other variables, standard scales were used. Data were collected through questionnaire method. For analysis, correlational method and t-test were used.Findings: Results showed that Public and Private sector employees differed significantly in their perception of violation of norms of justice. Results also showed that the employees’ perception of violation of norms of justice is negatively related with the outcome variables like Organizational Citizenship Behaviour (OCB) and Intention to LeavePractical Implications: The study has implications for scholars of organizational behavior, to look into the organizational problems from the perspective of employees i.e. what do the employees think of existing social norms.Social Implications: This study shows that employees/humans are vital factors in the running/success of any organizations/society.Originality: The research is original in the sense that it tries to compare employees’ perception of Justice Norms in both Public and Private Organizations

Probabilistic game approaches for network cost allocation

In a restructured power market, the network cost is to be allocated between multiple players utilizing the system in varying capacities. Cooperative game approaches based on Shapley value and Nucleolus provide stable models for embedded cost allocation of power networks. Varying network usage necessitates the introduction of probabilistic approaches to cooperative games. This paper proposes a variety of probabilistic cooperative game approaches. These have variably been modeled based upon the probability of existence of players, the probability of existence of coalitions, and the probability of players joining a particular coalition along with their joining in a particular sequence. Application of these approaches to power networks reflects the system usage in a more justified way. Consistent and stable results qualify the application of probabilistic cooperative game approaches for cost allocation of power networks.Cooperative games, embedded cost allocation, probabilistic games, transmission pricing

Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation

Automated feature extraction capability and significant performance of Deep Neural Networks (DNN) make them suitable for Internet of Things (IoT) applications. However, deploying DNN on edge devices becomes prohibitive due to the colossal computation, energy, and storage requirements. This paper presents a novel approach for designing and training lightweight DNN using large-size DNN. The approach considers the available storage, processing speed, and maximum allowable processing time to execute the task on edge devices. We present a knowledge distillation based training procedure to train the lightweight DNN to achieve adequate accuracy. During the training of lightweight DNN, we introduce a novel early halting technique, which preserves network resources; thus, speedups the training procedure. Finally, we present the empirically and real-world evaluations to verify the effectiveness of the proposed approach under different constraints using various edge devices.Comment: 13 pages, 7 figures, 11 table

FedAR+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments

With the enhancement of people\u27s living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels that may appear when an appliance gets connected to a non-designated smart plug. While addressing these issues jointly, we propose a novel federated learning approach to appliance recognition, called FedAR+, enabling decentralized model training across clients in a privacy-preserving way even with mislabeled training data. FedAR+ introduces an adaptive noise handling method, essentially a joint loss function incorporating weights and label distribution, to empower the appliance recognition model against noisy labels. By deploying smart plugs in an apartment complex, we collect a labeled dataset that, along with two existing datasets, are utilized to evaluate the performance of FedAR+. Experimental results show that our approach can effectively handle up to 30% concentration of noisy labels while outperforming the prior solutions by a large margin on accuracy

A bicharacteristic formulation of the ideal MHD equations

On a characteristic surface Ω of a hyperbolic system of first-order equations in multi-dimensions (x, t), there exits a compatibility condition which is in the form of a transport equation along a bicharacteristic on Ω . This result can be interpreted also as a transport equation along rays of the wavefront Ωt in x-space associated with Ω. For a system of quasi-linear equations, the ray equations (which has two distinct parts) and the transport equation form a coupled system of underdetermined equations. As an example of this bicharacteristic formulation, we consider two-dimensional unsteady flow of an ideal magnetohydrodynamics gas with a plane aligned magnetic field. For any mode of propagation in this two-dimensional flow, there are three ray equations: two for the spatial coordinates x and y and one for the ray diffraction. In spite of little longer calculations, the final four equations (three ray equations and one transport equation) for the fast magneto-acoustic wave are simple and elegant and cannot be derived in these simple forms by use of a computer program like REDUCE