Dynamic Inference on Graphs using Structured Transition Models

Enabling robots to perform complex dynamic tasks such as picking up an object in one sweeping motion or pushing off a wall to quickly turn a corner is a challenging problem. The dynamic interactions implicit in these tasks are critical towards the successful execution of such tasks. Graph neural networks (GNNs) provide a principled way of learning the dynamics of interactive systems but can suffer from scaling issues as the number of interactions increases. Furthermore, the problem of using learned GNN-based models for optimal control is insufficiently explored. In this work, we present a method for efficiently learning the dynamics of interacting systems by simultaneously learning a dynamic graph structure and a stable and locally linear forward model of the system. The dynamic graph structure encodes evolving contact modes along a trajectory by making probabilistic predictions over the edges of the graph. Additionally, we introduce a temporal dependence in the learned graph structure which allows us to incorporate contact measurement updates during execution thus enabling more accurate forward predictions. The learned stable and locally linear dynamics enable the use of optimal control algorithms such as iLQR for long-horizon planning and control for complex interactive tasks. Through experiments in simulation and in the real world, we evaluate the performance of our method by using the learned interaction dynamics for control and demonstrate generalization to more objects and interactions not seen during training. We introduce a control scheme that takes advantage of contact measurement updates and hence is robust to prediction inaccuracies during execution

Financial Analysis of Meta Platforms Inc

Web3.0 is the latest generation of internet, which is about decentralization and democratization, built on blockchain technology, high computer power and high-speed network. Metaverse overlaps with Web3.0 in terms of decentralization and is built on the same base technologies but talks about virtual realities and brings immersive experiences. This study explores how the web has evolved over the years and analyzes the current market trends of Web3.0. The purpose of the study is to examine the investments made by Meta Platforms Inc. which is one of the market leaders of the Internet industry and provide an in-depth financial analysis by evaluating its financial statements and different financial metrics. The study also includes estimating the value of the company based on the Discounted Cash Flow valuation method. The valuation conducted in this study forecasts Meta Platforms Inc. to be a promising investment with high positive returns. The valuation may be utilized by analysts for further analysis in their research

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

In this paper we develop a surgical system using the da Vinci research kit (dVRK) that is capable of autonomously searching for tumors and dynamically displaying the tumor location using augmented reality. Such a system has the potential to quickly reveal the location and shape of tumors and visually overlay that information to reduce the cognitive overload of the surgeon. We believe that our approach is one of the first to incorporate state-of-the-art methods in registration, force sensing and tumor localization into a unified surgical system. First, the preoperative model is registered to the intra-operative scene using a Bingham distribution-based filtering approach. An active level set estimation is then used to find the location and the shape of the tumors. We use a recently developed miniature force sensor to perform the palpation. The estimated stiffness map is then dynamically overlaid onto the registered preoperative model of the organ. We demonstrate the efficacy of our system by performing experiments on phantom prostate models with embedded stiff inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018

Technology Roadmap for Standards of Healthcare Data Cyber Security: Influence of Public Policy on Consumer Healthcare Cyber Security Protections

From the dot com boom to now the Internet of Things (IoT) and Machine Learning era, the evolving digital world that people live in has brought new challenges for protecting personal data and information. IoT devices, smart phones, numerous apps, and more, constantly collect personal health data with many positive intentions. However, the recent overturning of Roe vs. Wade by the United States Supreme Court has generated concerns in particular on who and how personal health data can be used by both governments and private companies with unintended consequences for users. Cyber security and regulations for protecting personal health data is more important than ever before. Through both a literature review and then the creation of a technology policy based road map, this paper establishes a methodology to answer the following research question: How will cyber security technology evolve with the influence of public policy in order to better protect consumer healthcare data privacy rights

Rapid appraisal of IPP-VI training of ANMs in Uttar Pradesh

This report provides findings of a rapid appraisal of the Auxiliary Nurse Midwife (ANM) training program in Uttar Pradesh, India. The appraisal was undertaken by the Population Council and State Innovations in Family Planning Agency (SIFPSA). As SIFPSA is in the process of planning a training program to strengthen the technical skills and competence of ANMs and update their contraceptive knowledge, it was felt that a rapid appraisal of the training program would help in understanding the strengths and weaknesses of previous training efforts. The ANM training initiatives of SIFPSA would then build upon the experiences gained from earlier training programs and the existing training needs of ANMs. The emphasis was on improving ANMs\u27 technical and counseling skills by providing on-the-job practical training. The program was a three-week skill-based training, during which ANMs were attached to a CHC/PPC or a district hospital. This was followed by one week of training at an ANM Training Centre in IEC and counseling. The objective was to review how the training program was organized and how it helped improve the technical skills and competence of ANMs

Quality Control and Management in Digital Learning Platforms

The COVID-19 pandemic has changed the face of education forever. Though the educational system has seamlessly transitioned from the traditional physical classroom teaching to the digital teaching setup, there are certain barriers faced by students and faculty that affect the quality of digital learning. The aim of this paper is to identify key quality issues in the digital learning platforms used in current online education system from the perspective of students. The research is based on literature review, and responses to a structured questionnaire along with semi-structured interviews. The research shows that majority of the students are satisfied with their digital learning experience on a high level, but they face some key challenges especially in the areas of platform interface and course assessment. The student responses received were analyzed and a list of viable solutions have been recommended to address these challenges. The recommendations are based on the Continuous Process Improvement methodology of Total Quality Management. In future, the study may be extended to a wider set of students from different demographic groups. There is also a scope to evaluate these digital learning platforms from the perspective of faculty and teaching staff