On converse bounds for classical communication over quantum channels

We explore several new converse bounds for classical communication over quantum channels in both the one-shot and asymptotic regimes. First, we show that the Matthews-Wehner meta-converse bound for entanglement-assisted classical communication can be achieved by activated, no-signalling assisted codes, suitably generalizing a result for classical channels. Second, we derive a new efficiently computable meta-converse on the amount of classical information unassisted codes can transmit over a single use of a quantum channel. As applications, we provide a finite resource analysis of classical communication over quantum erasure channels, including the second-order and moderate deviation asymptotics. Third, we explore the asymptotic analogue of our new meta-converse, the $\Upsilon$-information of the channel. We show that its regularization is an upper bound on the classical capacity, which is generally tighter than the entanglement-assisted capacity and other known efficiently computable strong converse bounds. For covariant channels we show that the $\Upsilon$-information is a strong converse bound.Comment: v3: published version; v2: 18 pages, presentation and results improve

Performance of summarization for 24 diseases.

<p>Performance of summarization for 24 diseases.</p

Relationship between ROUGE-1 and the trade-off parameter ω.

<p>Relationship between ROUGE-1 and the trade-off parameter ω.</p

Comparison of summarization performance on ROUGE-1.

<p>Comparison of summarization performance on ROUGE-1.</p

An example of relation and sentence retrieval.

<p>An example of relation and sentence retrieval.</p

Relationship between ROUGE-1 and the trade-off parameter

<p>Relationship between ROUGE-1 and the trade-off parameter </p

The impact of relation expansion, noise filtering and redundant removal.

<p>The impact of relation expansion, noise filtering and redundant removal.</p

Semantic relation network for “Angina Pectoris” after relation retrieval.

<p>Semantic relation network for “Angina Pectoris” after relation retrieval.</p

Framework of the biomedical text summarization system.

<p>Framework of the biomedical text summarization system.</p

A Single Kernel-Based Approach to Extract Drug-Drug Interactions from Biomedical Literature

<div><p>When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information of the dependency graph. In particular, our approach can efficiently represent both single subgraph topological information and the relation of two subgraphs in the dependency graph. Experimental evaluations showed that our single kernel-based approach can achieve state-of-the-art performance on the publicly available DDI corpus without exploiting multiple kernels or additional domain resources.</p> </div