Measure synchronization in dynamical system: A critical assessment

This paper aims to review the measure synchronization observed in coupled Hamiltonian systems briefly. The word `measure' implies the Lebesgue measure, which generalizes the notion of length, area, and volume in Euclidean space. During the measure synchronized state, each system shares a phase space domain with an identical invariant measure. Measure synchronization is characterized by a Hamiltonian system that displays either quasiperiodic or chaotic dynamics. This synchronization has been observed in various physical systems, such as coupled pendula, Josephson junctions, and lasers.Comment: 9 pages, 3 figure

CapText: Large Language Model-based Caption Generation From Image Context and Description

While deep-learning models have been shown to perform well on image-to-text datasets, it is difficult to use them in practice for captioning images. This is because \textit{captions} traditionally tend to be context-dependent and offer complementary information about an image, while models tend to produce \textit{descriptions} that describe the visual features of the image. Prior research in caption generation has explored the use of models that generate captions when provided with the images alongside their respective descriptions or contexts. We propose and evaluate a new approach, which leverages existing large language models to generate captions from textual descriptions and context alone, without ever processing the image directly. We demonstrate that after fine-tuning, our approach outperforms current state-of-the-art image-text alignment models like OSCAR-VinVL on this task on the CIDEr metric

Comprehending deterministic and stochastic occasional uncoupling induced synchronizations through each other

In this paper, we numerically study the stochastic and the deterministic occasional uncoupling methods of effecting identical synchronized states in low dimensional, dissipative, diffusively coupled, chaotic flows that are otherwise not synchronized when continuously coupled at the same coupling strength parameter. In the process of our attempt to understand the mechanisms behind the success of the occasional uncoupling schemes, we devise a hybrid between the transient uncoupling and the stochastic on-off coupling, and aptly name it the transient stochastic uncoupling---yet another stochastic occasional uncoupling method. Our subsequent investigation on the transient stochastic uncoupling allows us to surpass the effectiveness of the stochastic on-off coupling with very fast on-off switching rate. Additionally, through the transient stochastic uncoupling, we establish that the indicators quantifying the local contracting dynamics in the corresponding transverse manifold are generally not useful in finding the optimal coupling region of the phase space in the case of the deterministic transient uncoupling. In fact, we highlight that the autocorrelation function---a non-local indicator of the dynamics---of the corresponding response system's chaotic time-series dictates when the deterministic uncoupling could be successful. We illustrate all our heuristic results using a few well-known examples of diffusively coupled chaotic oscillators.Comment: Accepted in Eur. Phys. J.

Governanance Mechanisms for Coordination and Information Sharing in Supply Chains: The Role of Trust

In this paper, we describe the role of trust, bargaining power and contracts in governing information sharing and material flow coordination in supply chains. We present a conceptual framework showing how these governance mechanisms affect coordination and ultimately, chain performance. Five types of trust – calculative, competence, integrity, predictability and contractual - are thought to play an important role in determining the efficacy of information sharing. We pose three research questions on the relationships among trust, bargaining power, contracts and information sharing in supply chain coordination. An example from the retail distribution industry is used to illustrate these governance issues as key factors in the supply chain business model

eDWaaS: A Scalable Educational Data Warehouse as a Service

The university management is perpetually in the process of innovating policies to improve the quality of service. Intellectual growth of the students, the popularity of university are some of the major areas that management strives to improve upon. Relevant historical data is needed in support of taking any decision. Furthermore, providing data to various university ranking frameworks is a frequent activity in recent years. The format of such requirement changes frequently which requires efficient manual effort. Maintaining a data warehouse can be a solution to this problem. However, both in-house and outsourced implementation of a dedicated data warehouse may not be a cost-effective and smart solution. This work proposes an educational data warehouse as a service (eDWaaS) model to store historical data for multiple universities. The proposed multi-tenant schema facilitates the universities to maintain their data warehouse in a cost-effective solution. It also addresses the scalability issues in implementing such data warehouse as a service model.Comment: 17th International Conference on Intelligent Systems Design and Applications (ISDA 2017). Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. 7th World Congress on Information and Communication Technologies (WICT 2017). December 14-16, 2017. \copyright 2018 Springer International Publishing AG, part of Springer Natur

Interval based fuzzy systems for identification of important genes from microarray gene expression data: Application to carcinogenic development

AbstractIn the present article, we develop two interval based fuzzy systems for identification of some possible genes mediating the carcinogenic development in various tissues. The methodology involves dimensionality reduction, classifying the genes through incorporation of the notion of linguistic fuzzy sets low, medium and high, and finally selection of some possible genes mediating a particular disease, obtained by a rule generation/grouping technique. The effectiveness of the proposed methodology, is demonstrated using five microarray gene expression datasets dealing with human lung, colon, sarcoma, breast cancer and leukemia. Moreover, the superior capability of the methodology in selecting important genes, over five other existing gene selection methods, viz., Significance Analysis of Microarrays (SAM), Signal-to-Noise Ratio (SNR), Neighborhood analysis (NA), Bayesian Regularization (BR) and Data-adaptive (DA) is demonstrated, in terms of the enrichment of each GO category of the important genes based on P-values. The results are appropriately validated by earlier investigations, gene expression profiles and t-test. The proposed methodology has been able to select genes that are more biologically significant in mediating the development of a disease than those obtained by the others