Food Phone Application

This project is about implementing a food menu application for users to search and upload food information by using a mobile phone. People sometimes may just know what food they wish to eat instead of the restaurants\u27 name. Without knowing any restaurants\u27 names, our food application\u27s search only requires the name of the dish (e.g., hamburger, spaghetti, etc) in order to get the list of restaurants that serve these items and their corresponding information (e.g., location, hours, phone number, item\u27s price, etc.). An advantage of using my food application is the system not only includes Google Map, but any information other users have inputted. When a user wants to input a food item, one can either upload the item\u27s picture or a template picture to the server and input the rating and comments about the specific food item. With the rating option, my project calculates a cumulative rating result based around the original and other user\u27s input. There is also the option of having the users input a zip code to better identify where to find the food. Based on the phone\u27s capability, the system also needs to figure out the physical phone location. This requires the phone to receive the GPS signal. As a result, users can search/upload the local restaurants\u27 food without inputting the current location

Rigidity of bordered polyhedral surfaces

This paper investigates the rigidity of bordered polyhedral surfaces. Using the variational principle, we show that bordered polyhedral surfaces are determined by boundary value and discrete curvatures on the interior. As a corollary, we reprove the classical result that two Euclidean cyclic polygons (or hyperbolic cyclic polygons) are congruent if the lengths of their sides are equal

Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it has been meeting challenges in vehicular scenarios due to limited dynamics and lack of stable features. In this paper, we propose Ground-VIO, which utilizes ground features and the specific camera-ground geometry to enhance monocular VIO performance in realistic road environments. In the method, the camera-ground geometry is modeled with vehicle-centered parameters and integrated into an optimization-based VIO framework. These parameters could be calibrated online and simultaneously improve the odometry accuracy by providing stable scale-awareness. Besides, a specially designed visual front-end is developed to stably extract and track ground features via the inverse perspective mapping (IPM) technique. Both simulation tests and real-world experiments are conducted to verify the effectiveness of the proposed method. The results show that our implementation could dramatically improve monocular VIO accuracy in vehicular scenarios, achieving comparable or even better performance than state-of-art stereo VIO solutions. The system could also be used for the auto-calibration of IPM which is widely used in vehicle perception. A toolkit for ground feature processing, together with the experimental datasets, would be made open-source (https://github.com/GREAT-WHU/gv_tools)

Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening

Recently, machine learning methods have been used to propose molecules with desired properties, which is especially useful for exploring large chemical spaces efficiently. However, these methods rely on fully labelled training data, and are not practical in situations where molecules with multiple property constraints are required. There is often insufficient training data for all those properties from publicly available databases, especially when ab-initio simulation or experimental property data is also desired for training the conditional molecular generative model. In this work, we show how to modify a semi-supervised variational auto-encoder (SSVAE) model which only works with fully labelled and fully unlabelled molecular property training data into the ConGen model, which also works on training data that have sparsely populated labels. We evaluate ConGen's performance in generating molecules with multiple constraints when trained on a dataset combined from multiple publicly available molecule property databases, and demonstrate an example application of building the virtual chemical space for potential Lithium-ion battery localized high-concentration electrolyte (LHCE) diluents

Equilibria in Second Price Auctions with Information Acquisition

This paper studies equilibria in second price auctions with information acquisition in an independent private value setting. We focus on the existence and uniqueness of equilibrium in the information acquisition stage for both homogenous and heterogenous bidders. It is shown that, when the relative probability gain of information acquisition is increasing, there always exists an equilibrium and further it is symmetric and unique when bidders are homogenous. Moreover, we show that different type of bidders must choose different information levels, and further the advantaged groups with lower marginal information cost have stronger incentive to acquire information. An illustrative example with two bidders and Gaussian specification is presented to provide intuition and implications on equilibrium behavior of bidders