which has been adopted by several authors (2–4), has some undesirable properties. For example, changing the absolute value of the NTCP in two competing treatments plans by the same proportion can sometimes change the relative ranking of the plans. Langer et al. (1) point out that this is because, if Eq. 1 is used as the score for ranking plans, for a given value of TCP, a small fractional chang...

In this paper we study a novel approach to the problem of fusion of sensory information in tracking multiple targets in CCTV surveillance video. The approach, called “Rank and Fuse” (RAF) is based on multiple feature ranking and merging as opposed to a more typical combination of all scores (similarity or probability) in a single ranking. This has the advantages of low computational complexity,...

There has been a great deal of interest in recent years on ranking query results in relational databases. This paper presents a novel method to rank objects (e.g., tuples) by exploiting the correlations among their attribute values. Given a query, each attribute value is assigned a score according to mutual occurrences with the query and its distribution status in the columns of the attribute. ...

Broncho alveolar lavage is the most commonly used diagnostic tool for confirming alveolar hemorrhage. Golde has introduced a ranking score, based on the hemosiderin content of macrophages which enables ranking cells from 0 to 4 based on the degree of Prussian blue stain. We propose a complete image analysis scheme to automatically perform both the extraction of the cellular objects and the rank...

In this work, we introduce a score fusion scheme to improve the 3D object retrieval performance. The state of the art in 3D object retrieval shows that no single descriptor is capable of providing fine grain discrimination required by prospective 3D search engines. The proposed fusion algorithm linearly combines similarity information originating from multiple shape descriptors and learns their...

We develop a supervised ranking model to rerank candidates generated from an SMT-based grammatical error correction (GEC) system. A range of novel features with respect to GEC are investigated and implemented in our reranker. We train a rank preference SVM model and demonstrate that this outperforms both Minimum Bayes-Risk and Multi-Engine Machine Translation based re-ranking for the GEC task. ...

Ranking in bibliographic information networks is a widely studied problem due to its many applications such as advertisement industry, funding, search engines, etc. Most of the existing works on ranking in bibliographic information network are based on ranking of research papers and their authors. But the bibliographic information network can be used for solving other important problems as well...

Ranking is an important task in the field of information retrieval. Ranking may be used in different modules in natural language processing such as search engines. In this paper, we introduce a competitive ranking system which combines three different modules. The system participated in SemEval 2016 question ranking task for the Arabic language. The task is a ranking task that targets reorderin...