This paper presents the design and implementation of DOT, a flexible architecture for data transfer. This architecture separates content negotiation from the data transfer itself. Applications determine what data they need to send and then use a new transfer service to send it. This transfer service acts as a common interface between applications and the lower-level network layers, facilitating...

We provide a case study of current inefficiencies in how traffic to well-known cloud-storage providers (e.g., Dropbox, Google Drive, Microsoft OneDrive) can vary significantly in throughput (e.g., a factor of 5 or more) depending on the location of the source and sink of the data. Our case study supplements previous work on resilient overlay networks (RON) and other related ideas. These ineffic...

When training and testing data are drawn from different distributions, most statistical models need to be retrained using the newly collected data. Transfer learning is a family of algorithms that improves the classifier learning in a target domain of interest by transferring the knowledge from one or multiple source domains, where the data falls in a different distribution. In this paper, we c...

AMS02 science activities collaborated by Southeast university and CERN led by Samuel C.C. Ting are generating the unprecedented volume of data. The existing FTP has caused user to discomfort by delay and data loss according to network status. Therefore, it is necessary to efficiently raise transfer performance and minimize data loss. In this paper, we will introduce a new large file transfer to...

The problem of data augmentation in feature space is considered. A new architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for the modeling of feature trajectories induced by variations of object pose. This architecture exploits a parametrization of the pose manifold in terms of pose and appearance. This leads to a deep encoder/decoder network architecture, where the encode...

This paper explains a new technique that takes advantage of advancements in digital imaging technology to visually transfer large amounts of data quickly between computing machines. Rather than seeking to improve upon well developed systems already in use to move information between computers, such as data compression, broadband cable, or high-speed wireless, this paper explores the potential u...

Emerging Grids will play a significant role in the computational, data, storage, and network requirements of High Energy Physics experiments coming online in the next few years. One such requirement, the bulk transfer of data over advanced high speed optical networks is necessary as such experiments are highly distributed with resources and participants from research laboratories and institutio...

Due to catastrophic forgetting, deep learning remains highly inappropriate when facing incremental learning of new classes and examples over time. In this contribution, we introduce Transfer Incremental Learning using Data Augmentation (TILDA). TILDA combines transfer learning from a pre-trained Deep Neural Network (DNN) as feature extractor, a Nearest Class Mean (NCM) inspired classifier and m...

System-level exploration of memory architectures is one of the key issues in successful implementation of datatransfer dominated applications. Usually, one of the main design bottlenecks is the memory access bandwidth. Transformations, rearranging the layout of the data records stored in memory, are very effective to improve the locality of the data transfers but usually lead to a large memory ...