A graph is a relational data structure suitable for representing non-Euclidean structured data. In recent years, neural networks (GNN) and their subsequent variants, which utilize deep to complete analysis representation, have shown excellent performance in various application fields. However, the propagation mechanism of existing methods relies on hand-designed GNN layer connection architectur...

To realize real-time information sharing in generic platforms, it is especially important to support dynamic message structure changes. For the case of IDL, it is necessary to rewrite applications to change data sample structures. In this paper, we propose a dynamic reconfiguration scheme of data sample structures for DDS. Instead of using IDL, which is the static data sample structure model of...

This paper presents a graph-based learning approach to pairwise constraint propagation on multi-view data. Although pairwise constraint propagation has been studied extensively, pairwise constraints are usually defined over pairs of data points from a single view, i.e., only intra-view constraint propagation is considered for multi-view tasks. In fact, very little attention has been paid to int...

We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error that we call “propagation of outliers.” This source of error is unusual in the sense that it is genera...

Distributed file systems benefit greatly from optimistic replication that allows clients to update any replica at any site. However, such systems face a new dilemma: Once data is updated at a replica site, when should it be shipped to others? In conventional file system workloads, most data is read by its writer, so it needs to be shipped only for administrative reasons. Unfortunately, shipping...

We focus on the problem of data propagation in distributed wireless sensor networks. We present two characteristic methods for data propagation: the first one performs a local, greedy optimization to minimize the number of data transmissions needed, while the second creates probabilistically optimized redundant data transmissions to trade-off energy efficiency with fault-tolerance. Both approac...