Business Plan-CHOI Weight Loss Consultancy

This is a business plan of CHOI Weight Loss Consultancy which documents the overall information concerning creation of this startup. The new status of China that it has the largest population in the world has catch Chinese government attention as well as Chinese people. China now is home to 43.2 million obese men and 46.4 million obese women. Obesity has been a growing program in the country due to modern convenient life. The central government has brandished a plan called Healthy China 2030, with the aim of making China healthy again in the next 13 years. However, the programmes of current weight loss market have less focuses on providing sustainability, education and psychological help. With the purposes to solve those problems, this business plan aims to provide service of sustainable and effective weight loss approach for Chinese female in today’s modern busy life. One of the founders Luyi, as a weight management consultant in the industry for years, she is passionate in helping overweight people. Siyi, with relevant working experience in marketing and most importantly, used to be struggling to lose weight which makes her full of passion to engage in this industry. The service of CHOI is a 28-day online service programs based on WeChat that consist of personal health information analysis, diet and meal plan, workout plan, peer company, meal and exercise supervision, courses on health and nutrition knowledge, psychological help, plan adjustment and maintenance. The service focus on providing courses of nutrition courses and providing encouragement to help people form healthy habits. In order to achieve this, hiring experienced consultant teams which includes fitness trainer, dietician, consultant psychologist, weight management consultants and doctor is at the heart of business concerns that competencies should include solid academic background and rich practical experienced. Moreover, hiring high-qualified programme leaders is also another key strategy of CHOI. In addition to this, implementing effective digital marketing is a main marketing strategy to increase the brand awareness with less cost. The service will be designed within 3 months and will be launched in July 2018. It is estimated that the company will start to earn positive profits in fourth quarter. After then, the profits will continue to growth and reach RMB 180,410 (GBP 20,045) in year 3 quarter 4. We believe that CHOI will become the most reliable online weight management consultant in China and lead Chinese female to wellbeing

HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty

Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve near real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, we propose Hybrid Parallel DESPOT (HyP-DESPOT), a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds up online planning by up to several hundred times, compared with the original DESPOT algorithm, in several challenging robotic tasks in simulation

What Truly Matters in Trajectory Prediction for Autonomous Driving?

In the autonomous driving system, trajectory prediction plays a vital role in ensuring safety and facilitating smooth navigation. However, we observe a substantial discrepancy between the accuracy of predictors on fixed datasets and their driving performance when used in downstream tasks. This discrepancy arises from two overlooked factors in the current evaluation protocols of trajectory prediction: 1) the dynamics gap between the dataset and real driving scenario; and 2) the computational efficiency of predictors. In real-world scenarios, prediction algorithms influence the behavior of autonomous vehicles, which, in turn, alter the behaviors of other agents on the road. This interaction results in predictor-specific dynamics that directly impact prediction results. As other agents' responses are predetermined on datasets, a significant dynamics gap arises between evaluations conducted on fixed datasets and actual driving scenarios. Furthermore, focusing solely on accuracy fails to address the demand for computational efficiency, which is critical for the real-time response required by the autonomous driving system. Therefore, in this paper, we demonstrate that an interactive, task-driven evaluation approach for trajectory prediction is crucial to reflect its efficacy for autonomous driving