The Failure of Home Depot in China - A Case Study

In modern business management, organizational behavior is a crucial factor in influencing the success. Especially, when considering the market performance of a business, the decision-making behavior may be an important factor. In different local conditions, organizational behavior might be a determining factor in success. The paper, by studying the case of the US Home Depot in China, attempts to analyze the behavioral factors impacting the success in the local place of its targeted market. Based on the two factors including incompetent local adaptation and the wrong entry time and entry mode leading to failure, some recommendations are made. Firstly, the company could conduct a more detailed and systematic research of the local conditions, especially the local cultural and economic features. Secondly, it is crucially important to know the exact time and the suitable entry mode. Thirdly, that the Home Depot could have study the communicative mode with the locals heavily influenced by the local cultural traditions and conventions in order to avoid blind entry strategy. To conclude, for the success of cross-border businesses, being adaptable to the local consuming habits is an important premise of the sustainable development for the Home Depot in the international market

A Portable Random Key Predistribution Scheme for Distributed Sensor Network

A distributed sensor network (DSN) can be deployed to collect information for military or civilian applications. However, due to the characteristics of DSNs such as limited power, key distribution for a distributed sensor network is complex. In this paper, a neighbor-based path key establishing method and a seed-based algorithm are put forward to improve the original random key pre-distribution scheme. The new scheme is portable because it is independent of the routing protocol. Moreover, the connectivity of the entire network also approaches 1. In particular, the new scheme can keep high connectivity by setting a small amount of redundancy in parameter values when the number of neighbors drops because of the node dormancy or death. The resilience against node capture in our scheme is not lower than that in the l-path scheme and the basic schemes when the number of hops in a path is larger than 5, and the simulation result shows that the efficiency of our scheme is also slightly higher

TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes

Video data and algorithms have been driving advances in multi-object tracking (MOT). While existing MOT datasets focus on occlusion and appearance similarity, complex motion patterns are widespread yet overlooked. To address this issue, we introduce a new dataset called BEE23 to highlight complex motions. Identity association algorithms have long been the focus of MOT research. Existing trackers can be categorized into two association paradigms: single-feature paradigm (based on either motion or appearance feature) and serial paradigm (one feature serves as secondary while the other is primary). However, these paradigms are incapable of fully utilizing different features. In this paper, we propose a parallel paradigm and present the Two rOund Parallel matchIng meChanism (TOPIC) to implement it. The TOPIC leverages both motion and appearance features and can adaptively select the preferable one as the assignment metric based on motion level. Moreover, we provide an Attention-based Appearance Reconstruct Module (AARM) to reconstruct appearance feature embeddings, thus enhancing the representation of appearance features. Comprehensive experiments show that our approach achieves state-of-the-art performance on four public datasets and BEE23. Notably, our proposed parallel paradigm surpasses the performance of existing association paradigms by a large margin, e.g., reducing false negatives by 12% to 51% compared to the single-feature association paradigm. The introduced dataset and association paradigm in this work offers a fresh perspective for advancing the MOT field. The source code and dataset are available at https://github.com/holmescao/TOPICTrack

CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence

In this paper, we propose a novel probabilistic variant of iterative closest point (ICP) dubbed as CoBigICP. The method leverages both local geometrical information and global noise characteristics. Locally, the 3D structure of both target and source clouds are incorporated into the objective function through bidirectional correspondence. Globally, error metric of correntropy is introduced as noise model to resist outliers. Importantly, the close resemblance between normal-distributions transform (NDT) and correntropy is revealed. To ease the minimization step, an on-manifold parameterization of the special Euclidean group is proposed. Extensive experiments validate that CoBigICP outperforms several well-known and state-of-the-art methods.Comment: 6 pages, 4 figures. Accepted to IROS202

DLPFA: Deep Learning based Persistent Fault Analysis against Block Ciphers

Deep learning techniques have been widely applied to side-channel analysis (SCA) in recent years and shown better performance compared with traditional methods. However, there has been little research dealing with deep learning techniques in fault analysis to date. This article undertakes the first study to introduce deep learning techniques into fault analysis to perform key recovery. We investigate the application of multi-layer perceptron (MLP) and convolutional neural network (CNN) in persistent fault analysis (PFA) and propose deep learning-based persistent fault analysis (DLPFA). DLPFA is first applied to advanced encryption standard (AES) to verify its availability. Then, to push the study further, we extend DLPFA to PRESENT, which is a lightweight substitution–permutation network (SPN)-based block cipher. The experimental results show that DLPFA can handle random faults and provide outstanding performance with a suitable selection of hyper-parameters

Generating axial magnetic fields via two plasmon decay driven by a twisted laser

We propose a new way of axial magnetic fields generation in a non-relativistic laser intensity regime by using a twisted light carrying orbital angular momentum (OAM) to stimulate two-plasmon decay (TPD) in a plasma. The growth of TPD driven by an OAM light in a Laguerre-Gauss (LG) mode is investigated through three dimensional fluid simulations and theory. A theory based on the assumption that the electron plasma waves (EPWs) are locally driven by a number of local plane-wave lasers predicts the maximum growth rate proportional to the peak amplitude of the pump laser field and is verified by the simulations. The OAM conservation during its transportation from the laser to the TPD daughter EWPs is shown by both the theory and the simulations. The theory predicts generation of ~40T axial magnetic fields through the OAM absorption via TPD, which has perspective applications in the field of high energy density physics.Comment: 6 pages, 3 figures