A review of parallel computing for large-scale remote sensing image mosaicking

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed

International Consolidation Order-fulfillment Model

Traditionally, there are mainly two methods for companies to fulfill orders that come from international customers: either through its foreign distributors or ship the order through international parcel carrier. However, the disadvantage of both methods are significant. International distributors charge a noticeable commission, which reduce the item profitability, and cost of international parcel is usually higher than the parcel value itself. This research proposes new order-fulfillment model to minimize the international order-fulfillment cost while keeping the delivery lead time short. The proposed order-fulfillment model utilizes freight consolidation as well as inter-model transportation concepts and requires companies to have access to a warehouse located nearby the targeted market. This research simulates a Los Anglos based company fulfilling orders from customers in mainland China. Costco's open source data and costs data from transportation providers are acquired to assess the effect of the proposed model. Current process in the research shows that companies can lower up to fifty-six percent of the cost and reduce the delivery lead time from seven to fourteen days to six days by applying the proposed model. The preliminary findings suggest that the proposed order-fulfillment model will allow companies to reach out to their international customers with less costs and increase customer satisfaction since the customers' anxiety of waiting for packages is reduced. This research provides evidence that the proposed order-fulfillment model can be a powerful tool for companies who wants to expand their business globally, and since the total delivery cost is reduced, international customers can enjoy less expensive imported goods and shorter waiting time.A five-year embargo was granted for this item.Academic Major: Logistics ManagementAcademic Major: MarketingAcademic Major: Operations Managemen

Accelerating Large Kernel Convolutions with Nested Winograd Transformation.pdf

Recent literature has shown that convolutional neural networks (CNNs) with large kernels outperform vision transformers (ViTs) and CNNs with stacked small kernels in many computer vision tasks, such as object detection and image restoration. The Winograd transformation helps reduce the number of repetitive multiplications in convolution and is widely supported by many commercial AI processors. Researchers have proposed accelerating large kernel convolutions by linearly decomposing them into many small kernel convolutions and then sequentially accelerating each small kernel convolution with the Winograd algorithm. This work proposes a nested Winograd algorithm that iteratively decomposes a large kernel convolution into small kernel convolutions and proves it to be more effective than the linear decomposition Winograd transformation algorithm. Experiments show that compared to the linear decomposition Winograd algorithm, the proposed algorithm reduces the total number of multiplications by 1.4 to 10.5 times for computing 4x4 to 31x31 convolutions.Comment: published ref to https://ieeexplore.ieee.org/document/1032193

Does Exposure to Shared Solutions Lead to Better Outcomes? An Empirical Investigation in Online Crowdsourcing Contests

Crowdsourcing contests provide an effective way to elicit novel ideas and creative solutions from collective intelligence. A key design feature of crowdsourcing contests is the competition between contest participants to complete a specific task with financial awards to the winner(s). In recent years, some crowdsourcing contest platforms provide options to contest participants for solution sharing during the competition. This study intends to evaluate the influence of exposure to shared solutions on different stakeholders, including the team, and the requester. Our study employs a multiple-level panel data from a large online crowdsourcing platform, Kaggle.com, to examine these effects. For teams, exposure to shared solutions helps new entrant teams to jump-start and help teams to achieve better performance in the subsequent submissions, and the teams’ skill level negatively moderates these positive effects. For requesters, allowing solution sharing has both benefits and costs in terms of improving the best performance of the crowd. We highlight the theoretical implications of the study and provide practical suggestions for crowdsourcing contest platforms to help them decide whether to allow solution sharing during the competition

Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation

Pre-training and fine-tuning is a paradigm for alleviating the data scarcity problem in end-to-end speech translation (E2E ST). The commonplace "modality gap" between speech and text data often leads to inconsistent inputs between pre-training and fine-tuning. However, we observe that this gap occurs in the early stages of fine-tuning, but does not have a major impact on the final performance. On the other hand, we find that there has another gap, which we call the "capacity gap": high resource tasks (such as ASR and MT) always require a large model to fit, when the model is reused for a low resource task (E2E ST), it will get a sub-optimal performance due to the over-fitting. In a case study, we find that the regularization plays a more important role than the well-designed modality adaption method, which achieves 29.0 for en-de and 40.3 for en-fr on the MuST-C dataset. Code and models are available at https://github.com/hannlp/TAB.Comment: ACL 2023 Main Conferenc

Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-driven Approach

To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes integer interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel; 2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations; and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G