Walmart\u27s Sustainability Journey: Lee Scott\u27s Founding Vision

The first case—“Lee Scott’s Founding Vision”—takes the perspective from the apex of the organization as Walmart’s CEO, Lee Scott, develops and articulates his vision of what Walmart hopes to achieve by pursuing an aggressive sustainability strategy. This case explores the pressures that led Scott to announce the company’s ambitious sustainability goals: achieving zero waste, 100% renewable energy, and selling sustainable products. It also explores the choices made when defining and communicating the scope of the strategy, particularly through an in-depth analysis of his announcement of Walmart’s new goals in his October 2005, “Twenty-First Century Leadership” speech

Intrinsic Dynamic Shape Prior for Fast, Sequential and Dense Non-Rigid Structure from Motion with Detection of Temporally-Disjoint Rigidity

While dense non-rigid structure from motion (NRSfM) has been extensively studied from the perspective of the reconstructability problem over the recent years, almost no attempts have been undertaken to bring it into the practical realm. The reasons for the slow dissemination are the severe ill-posedness, high sensitivity to motion and deformation cues and the difficulty to obtain reliable point tracks in the vast majority of practical scenarios. To fill this gap, we propose a hybrid approach that extracts prior shape knowledge from an input sequence with NRSfM and uses it as a dynamic shape prior for sequential surface recovery in scenarios with recurrence. Our Dynamic Shape Prior Reconstruction (DSPR) method can be combined with existing dense NRSfM techniques while its energy functional is optimised with stochastic gradient descent at real-time rates for new incoming point tracks. The proposed versatile framework with a new core NRSfM approach outperforms several other methods in the ability to handle inaccurate and noisy point tracks, provided we have access to a representative (in terms of the deformation variety) image sequence. Comprehensive experiments highlight convergence properties and the accuracy of DSPR under different disturbing effects. We also perform a joint study of tracking and reconstruction and show applications to shape compression and heart reconstruction under occlusions. We achieve state-of-the-art metrics (accuracy and compression ratios) in different scenarios

Embedded Large-Scale Handwritten Chinese Character Recognition

As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices. To achieve acceptable accuracy, we paid particular attention to data collection conditions, representativeness of writing styles, and training regimen. We found that, with proper care, even larger inventories are within reach. Our experiments show that accuracy only degrades slowly as the inventory increases, as long as we use training data of sufficient quality and in sufficient quantity.Comment: 5 pages, 7 figure

Detection of Anomalous Vehicle Loading

Determining the mass of a vehicle from ground based passive sensor data is important for many security and traffic safety reasons. A vehicle consists of multiple dependent and independent systems that each respond differently to changes in vehicle mass. In some cases, the responses of these vehicle systems can be measured remotely. If these remotely sensed system responses are correlated to the vehicle\u27s mass, and the required vehicle parameters were known, it would be possible to calculate the mass of the vehicle as a function of these responses. The research described here investigates multiple vehicle phenomenologies and their correlation to vehicle load. Brake temperature, engine acoustics, exhaust output, tire temperature, tire deformation, vehicle induced ground vibration, suspension response, and engine torque induced frame twist were all evaluated and assessed as potential methods of remotely measuring a vehicle\u27s mass. Extensive field experiments were designed and carried out using multiple sensors of various types; including microphones, accelerometers, high-speed video cameras, high-resolution video cameras, LiDAR, and thermal imagers. These experiments were executed at multiple locations and employed passenger vehicles, and commercial trucks with loads ranging from zero to beyond the recommended load capacity of the vehicle. The results of these experiments were used to determine if the signature for each phenomenology could be accurately observed remotely, and if so, how well they correlated to vehicle mass. The suspension response and engine torque induced frame twist phenomenologies were found to have the best correlation to vehicle mass of the phenomenologies considered, with correlation values of 90.5% and 97.7%, respectively. Physics-based models were built for both the suspension response, and the engine torque induced frame twist phenomenologies. These models detailed the relationship between each phenomenology and the mass of the vehicle. Full-scale field testing was done using improved remote detection methods, and the results were used to validate the physics-based models. The results of the full-scale field testing showed that both phenomenology could accurately calculate the mass of the vehicle remotely, given that certain vehicle parameters were accurately known. The engine torque induced frame twist phenomenology was able to find the mass of the test vehicle to within 10% of the true mass. Using the suspension response phenomenology the mass was accurately predicted as a function of its location on the vehicle. For either phenomenology to be effective, certain vehicle parameters must be known accurately; specifically the spring constant and damping coefficients of the vehicle\u27s suspension, the unloaded mass, the unloaded center of gravity, and the unloaded moment of inertia of the vehicle. The models were also used to propagate measurement and parameter uncertainty through the vehicle mass calculation to arrive at the uncertainty in the mass estimation. Finally, the results of both the phenomenologies were combined into a single vehicle mass estimate with a smaller uncertainty than the individual vehicle system estimations taken alone

Shoe–Floor Interactions in Human Walking With Slips: Modeling and Experiments

Shoe–floor interactions play a crucial role in determining the possibility of potential slip and fall during human walking. Biomechanical and tribological parameters influence the friction characteristics between the shoe sole and the floor and the existing work mainly focus on experimental studies. In this paper, we present modeling, analysis, and experiments to understand slip and force distributions between the shoe sole and floor surface during human walking. We present results for both soft and hard sole material. The computational approaches for slip and friction force distributions are presented using a spring-beam networks model. The model predictions match the experimentally observed sole deformations with large soft sole deformation at the beginning and the end stages of the stance, which indicates the increased risk for slip. The experiments confirm that both the previously reported required coefficient of friction (RCOF) and the deformation measurements in this study can be used to predict slip occurrence. Moreover, the deformation and force distribution results reported in this study provide further understanding and knowledge of slip initiation and termination under various biomechanical conditions

Few-shot image classification : current status and research trends

Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are di-vided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are iden-tified. © 2022 by the authors. Licensee MDPI, Basel, Switzerland