A Formal Framework for Speedup Learning from Problems and Solutions

Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply this framework to two different representations of learned knowledge, namely control rules and macro-operators, and prove theorems that identify sufficient conditions for learning in each representation. Our proofs are constructive in that they are accompanied with learning algorithms. Our framework captures both empirical and explanation-based speedup learning in a unified fashion. We illustrate our framework with implementations in two domains: symbolic integration and Eight Puzzle. This work integrates many strands of experimental and theoretical work in machine learning, including empirical learning of control rules, macro-operator learning, Explanation-Based Learning (EBL), and Probably Approximately Correct (PAC) Learning.Comment: See http://www.jair.org/ for any accompanying file

A Comparative Study on Prediction of Endometriosis Causing Infertility Using Machine Learning Techniques: in Detail

The purpose of this study is to utilize Artificial Intelligence to analyse and predict endometriosis problem in women. All traditional methods are used before to develop or to predict the likelihood of endometriosis based on the symptoms presented. By identifying the symptoms of endometriosis, the machine learning algorithms can determine the type of endometriosis and the appropriate course of action for patients. This technology can be used to educate women globally on the signs and symptoms of endometriosis and help them take preventive measures to avoid this deadly disease. The results of this research demonstrate the potential of advanced technology to revolutionize healthcare by providing early detection and treatment options for endometriosis. In areas with limited access to medical care, this tool can aid in identifying ovarian cancer and reducing mortality rates. By detecting and diagnosing endometriosis at an early stage, this program can play a significant role in promoting women's health and wellbeing. The methodology proposed in this study produces classification results that are on par with cutting-edge deep learning techniques. In addition, the methodology provides visual explanations that offer valuable insights into the inner workings of each model and enhance the accuracy and reliability of the predictions

Managing Security in the Built Environment – A Review of Environmental Crime Prevention Strategies

The practice of urban planning and building design largely follow traditional methods while transforming the structure and quality of life in the built environment. The process of managing cities requires updates and integration of new technologies and research efforts. In this context, development of smart, sustainable, energy efficient, healthy, safe and secure built environment is a priority that is shaping modern cities all over the world. Though management of Indian cities tries to address these issues to a certain extent, it can be said that, as compared to many developed countries, aspects of safety and security have not been the top priority of our planners and designers. Research on Environmental Criminology and Environmental Psychology clearly indicates the important role and benefits of crime prevention through appropriate design of the built environment. In this context, this paper highlights the role of crime prevention strategies and the relevance of environmental criminology in current building practices for addressing security concerns in India

Water waves over a time-dependent bottom: Exact description for 2D potential flows

Two-dimensional potential flows of an ideal fluid with a free surface are considered in situations when shape of the bottom depends on time due to external reasons. Exact nonlinear equations describing surface waves in terms of the so called conformal variables are derived for an arbitrary time-evolving bottom parameterized by an analytical function. An efficient numerical method for the obtained equations is suggested.Comment: revtex4, 7 pages, 19 EPS figures; corrected version with more numerical result

What has finite element analysis taught us about diabetic foot disease and its management?:a systematic review

Over the past two decades finite element (FE) analysis has become a popular tool for researchers seeking to simulate the biomechanics of the healthy and diabetic foot. The primary aims of these simulations have been to improve our understanding of the foot's complicated mechanical loading in health and disease and to inform interventions designed to prevent plantar ulceration, a major complication of diabetes. This article provides a systematic review and summary of the findings from FE analysis-based computational simulations of the diabetic foot.A systematic literature search was carried out and 31 relevant articles were identified covering three primary themes: methodological aspects relevant to modelling the diabetic foot; investigations of the pathomechanics of the diabetic foot; and simulation-based design of interventions to reduce ulceration risk.Methodological studies illustrated appropriate use of FE analysis for simulation of foot mechanics, incorporating nonlinear tissue mechanics, contact and rigid body movements. FE studies of pathomechanics have provided estimates of internal soft tissue stresses, and suggest that such stresses may often be considerably larger than those measured at the plantar surface and are proportionally greater in the diabetic foot compared to controls. FE analysis allowed evaluation of insole performance and development of new insole designs, footwear and corrective surgery to effectively provide intervention strategies. The technique also presents the opportunity to simulate the effect of changes associated with the diabetic foot on non-mechanical factors such as blood supply to local tissues.While significant advancement in diabetic foot research has been made possible by the use of FE analysis, translational utility of this powerful tool for routine clinical care at the patient level requires adoption of cost-effective (both in terms of labour and computation) and reliable approaches with clear clinical validity for decision making

Reports of the AAAI 2019 spring symposium series

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates

The T2K ND280 Off-Axis Pi-Zero Detector

The Pi-Zero detector (P{\O}D) is one of the subdetectors that makes up the off-axis near detector for the Tokai-to-Kamioka (T2K) long baseline neutrino experiment. The primary goal for the P{\O}D is to measure the relevant cross sections for neutrino interactions that generate pi-zero's, especially the cross section for neutral current pi-zero interactions, which are one of the dominant sources of background to the electron neutrino appearance signal in T2K. The P{\O}D is composed of layers of plastic scintillator alternating with water bags and brass sheets or lead sheets and is one of the first detectors to use Multi-Pixel Photon Counters (MPPCs) on a large scale.Comment: 17 pages, submitted to NIM