Distributed physics informed neural network for data-efficient solution to partial differential equations

The physics informed neural network (PINN) is evolving as a viable method to solve partial differential equations. In the recent past PINNs have been successfully tested and validated to find solutions to both linear and non-linear partial differential equations (PDEs). However, the literature lacks detailed investigation of PINNs in terms of their representation capability. In this work, we first test the original PINN method in terms of its capability to represent a complicated function. Further, to address the shortcomings of the PINN architecture, we propose a novel distributed PINN, named DPINN. We first perform a direct comparison of the proposed DPINN approach against PINN to solve a non-linear PDE (Burgers' equation). We show that DPINN not only yields a more accurate solution to the Burgers' equation, but it is found to be more data-efficient as well. At last, we employ our novel DPINN to two-dimensional steady-state Navier-Stokes equation, which is a system of non-linear PDEs. To the best of the authors' knowledge, this is the first such attempt to directly solve the Navier-Stokes equation using a physics informed neural network.Comment: 16 pages, 8 figure

PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation

Effective training of deep image segmentation models is challenging due to the need for abundant, high-quality annotations. Generating annotations is laborious and time-consuming for human experts, especially in medical image segmentation. To facilitate image annotation, we introduce Physics Informed Contour Selection (PICS) - an interpretable, physics-informed algorithm for rapid image segmentation without relying on labeled data. PICS draws inspiration from physics-informed neural networks (PINNs) and an active contour model called snake. It is fast and computationally lightweight because it employs cubic splines instead of a deep neural network as a basis function. Its training parameters are physically interpretable because they directly represent control knots of the segmentation curve. Traditional snakes involve minimization of the edge-based loss functionals by deriving the Euler-Lagrange equation followed by its numerical solution. However, PICS directly minimizes the loss functional, bypassing the Euler Lagrange equations. It is the first snake variant to minimize a region-based loss function instead of traditional edge-based loss functions. PICS uniquely models the three-dimensional (3D) segmentation process with an unsteady partial differential equation (PDE), which allows accelerated segmentation via transfer learning. To demonstrate its effectiveness, we apply PICS for 3D segmentation of the left ventricle on a publicly available cardiac dataset. While doing so, we also introduce a new convexity-preserving loss term that encodes the shape information of the left ventricle to enhance PICS's segmentation quality. Overall, PICS presents several novelties in network architecture, transfer learning, and physics-inspired losses for image segmentation, thereby showing promising outcomes and potential for further refinement

Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail

Future of sustainable fashion lies in adoption of AI for a better understanding of consumer shopping behaviour and using this understanding to further optimize product design, development and sourcing to finally reduce the probability of overproducing inventory. Explainability and interpretability are highly effective in increasing the adoption of AI based tools in creative domains like fashion. In a fashion house, stakeholders like buyers, merchandisers and financial planners have a more quantitative approach towards decision making with primary goals of high sales and reduced dead inventory. Whereas, designers have a more intuitive approach based on observing market trends, social media and runways shows. Our goal is to build an explainable new product forecasting tool with capabilities of interventional analysis such that all the stakeholders (with competing goals) can participate in collaborative decision making process of new product design, development and launch

A study to assess the perceptions of first year medical students for choosing medical school as a career

Background: There are more than 44000 seats in over 350 medical colleges in India for pursuing the MBBS course. Yet medicine is not among the top vocation in most career advisories and the best school students do not aspire to be doctors.Methods: The present study was a cross sectional study done on 150 students of first semester of GR Medical College, Gwalior, Madhya Pradesh, India on a predesigned or pre validated questionnaire. Only 104 students participated in the study.Results: A total of 104 students participated in the study. Among them, 68 (65.39%) were males and 36 (34.61%) females. The maximum percentage of students was of the age group of 20 years i.e. 26 (25%). The choice of a career in the medical field is a complex personal decision influenced by a multitude of factors. Career choices are influenced by both graduates inclination before starting medical school as well as any exposure during training in medical school.Conclusions: These data showed that the maximum percentages of the Medical Students were satisfied with the medical school as 95 (91.34%) but still some of them have regrets. In choosing medical school they wants to help poor, earn money and personal development, and influenced by some doctor relative. These were important factors for decision making in medical school.

Improvement in Germination through Seed Pre-treatment in Ghingaru (Pyracantha crenulata (D. DON) M. ROEMER): An Important Wild Edible Ethno-medicinal Plant

484-486Ghingaru (Pyracantha crenulata (D. DON) M. ROEMER) is an important thorny Himalayan wild edible shrub. Fruits of the plant are rich source of anti-oxidants and used in preparation of cardio-tonic. However, scant scientific information is available on it mass propagation through seeds. The present study reports effect of chemical seed pre-treatments with sodium chloride, potassium nitrate, thio-urea along with hydro-priming on seed germination in ghingaru. Results revealed significant (P ≤ 0.05) improvement in germination of the stored ghingaru seeds through pre-treatment with potassium nitrate (300 or 400 mM) than the control and other treatments. The findings may be useful in mass propagation of the important plant as a source of nutrient rich food, medicines and other uses

Soft side of knowledge transfer partnership between universities and small to medium enterprises: exploratory study to understand process improvement

The study explores the soft side of knowledge transfer partnerships between universities and small to medium enterprises (SMEs), a topic which is often neglected in the knowledge management literature. The aim of this paper is to uncover the issues which emerge during information of a partnership between heterogeneous organisations and universities. In addition, the study unfolds the criticalities of typical process improvement capability that supports the knowledge transfer partnerships between universities and SMEs. Using multiple cases, this study unravels the dominant elements that influence knowledge transfer process development, governance, implications and responsibilities. The major contribution of this study is the development of a framework based on empirical evidence using three Knowledge Transfer Partnerships (KTPs) which illustrates the way in which soft factors in knowledge transfer partnership phases may have an impact on success or failure of university-industry collaborations for innovation

Geographic information system for improving maternal and newborn health: recommendations for policy and programs

This correspondence argues and offers recommendations for how Geographic Information System (GIS) applied to maternal and newborn health data could potentially be used as part of the broader efforts for ending preventable maternal and newborn mortality. These recommendations were generated from a technical consultation on reporting and mapping maternal deaths that was held in Washington, DC from January 12 to 13, 2015 and hosted by the United States Agency for International Development’s (USAID) global Maternal and Child Survival Program (MCSP). Approximately 72 participants from over 25 global health organizations, government agencies, donors, universities, and other groups participated in the meeting.The meeting placed emphases on how improved use of mapping could contribute to the post-2015 United Nation’s Sustainable Development Goals (SDGs), agenda in general and to contribute to better maternal and neonatal health outcomes in particular. Researchers and policy makers have been calling for more equitable improvement in Maternal and Newborn Health (MNH), specifically addressing hard-to-reach populations at sub-national levels. Data visualization using mapping and geospatial analyses play a significant role in addressing the emerging need for improved spatial investigation at subnational scale. This correspondence identifies key challenges and recommendations so GIS may be better applied to maternal health programs in resource poor settings. The challenges and recommendations are broadly grouped into three categories: ancillary geospatial and MNH data sources, technical and human resources needs and community participation