Self-supervised representation learning has achieved promising results for downstream visual tasks in natural images. However, its use the medical domain, where there is an underlying anatomical structural similarity, remains underexplored. To address this shortcoming, we propose a self-supervised multi-task framework sequential 2D images, which explicitly aims to exploit structures via multipl...

in this paper, a hybrid method for limited resource allocation and leveling in complex multi-stage, multi-product and multi-period production planning problems with aim of lot-size determination and total cost minimization has been proposed. this problem consists of multiple products with sequential production processes that are produced in different periods to meet the customers demand. by det...

of the Dissertation Optimal Sequential Resource Sharing and Exchange in Multi-Agent Systems

Sequential auctions can be used to provide solutions to the multi-robot task-allocation problem. In this paper we extend previous work on sequential auctions and propose an algorithm that clusters and auctions uninitiated task clusters repeatedly upon the completion of individual tasks. We demonstrate empirically that our algorithm results in lower overall team costs than other sequential aucti...

In this paper we develop a framework for a sequential decision making under budget constraints for multi-class classification. In many classification systems, such as medical diagnosis and homeland security, sequential decisions are often warranted. For each instance, a sensor is first chosen for acquiring measurements and then based on the available information one decides (rejects) to seek mo...

Establishing the scalability of a concurrent algorithm a priori, before implementing and evaluating it on a concrete multi-core platform, seems difficult, if not impossible. In the context of search data structures however, according to all practical work of the past decade, algorithms that scale share a common characteristic: They all resemble standard sequential implementations for their resp...

This short paper describes a simple coding technique, Sparse Sequential Dirichlet Coding, for multi-alphabet memoryless sources. It is appropriate in situations where only a small, unknown subset of the possible alphabet symbols can be expected to occur in any particular data sequence. We provide a competitive analysis which shows that the performance of Sparse Sequential Dirichlet Coding will ...