During recent years there has been an increased interest to acquire or extend, on a large-scale, highquality semantic lexicons. The methodology is usually corpus-driven. It is based on the (re-)use of machine readable resources of various types, and the application of cost effective ways to eliminate the acquistion bottleneck, i.e. derivational morphology, customization of off-the-shelf resourc...

In this paper, we present the Stork data scheduler as a solution for mitigating the data bottleneck in e-Science and data-intensive scientific discovery. Stork focuses on planning, scheduling, monitoring and management of data placement tasks and application-level end-to-end optimization of networked inputs/outputs for petascale distributed e-Science applications. Unlike existing approaches, St...

Dynamic Performance Stubs support performance improvement. They can be used to identify “hidden” bottlenecks and also to provide better estimations of the gain from performance improvement. The idea behind these stubbing mechanism will be described, the core concept will be explained and a closer look on the possibility of creating stubs will be given. Furthermore an introduction on “How to use...

Lijie Heng 9032366867 Contributions of the Paper This paper investigates potential non-access bottlenecks which may arise after fixing the bottlenecks at the edges of the network by updating the access links in the future. The first contribution of this paper is to propose a good method for measuring the non-access Internet bottleneck links by choosing representative sources and destinations. B...

Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize nonlinear classifiers and original optimization objective could fail when pseudo-invariant features geometric skews exist. Inspired by IRM, in this paper we propose novel formulation generalization, dubbed invariant information bot...