BACKGROUND: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

This paper proposes a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system who are given tasks to perform within six task domains. These dialogs are given rich linguistic annotations by expert linguists for several types of reference mentions and named entity mentions, either of which can span multiple words, as well as for coreference links between mentions. The dialogs sometimes cross and blend domains, and the users exhibit complex task switching behavior such as re-initiating a previous task in the dialog by referencing the entities within it. The dataset contains a total of 8,429 dialogs with an average of 5.36 turns per dialog. We are releasing this dataset to encourage research in the field of coreference resolution, referring expression generation and identification within realistic, deep dialogs involving multiple domains. To demonstrate its utility, we also propose two baseline models for the downstream tasks: coreference resolution and referring expression generation.

The semiconductor market continues to evolve from consumer-based markets to automotive and industrial markets requiring higher quality and higher reliability; passing qualification and continuous improvement (CIP) is the traditional quality / reliability strategy in today's consumer-driven markets. However, to ensure acceptance in designing high value buildings, factories, and automobiles with increased semiconductor content, passing qualification and CIP is insufficient to guarantee a device meets adequate quality and reliability (QR) goals. These applications are driving the new DFT (design for test) technologies within the designs for achieving higher coverage with reduced test cost. With increased focus, suppliers must actively integrate QR into devices. Unfortunately, there aren't clear strategies or guidance. To this end, we propose quantified market-driven DPPM (defective parts per million) and product-level FIT (Failure in time) targets and show how DFT and design for reliability (DFR) can be used as part of an integrated strategy to enable product-level QR.