A new approach to software reliability estimation is presented that combines operational testing with stratified sampling in order to reduce the number of program executions that must be checked manually for conformance to requirements. Automatic cluster analysis is applied to execution profiles in order to stratify captured operational executions. Experimental results are reported that suggest this approach can significantly reduce the cost of estimating reliability.

Updates to staging models are needed to reflect a greater understanding of tumor behavior and clinical outcomes for well-differentiated thyroid carcinomas. We used a machine learning algorithm and disease-specific survival data of differentiated thyroid carcinoma from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute to integrate clinical factors to improve prognostic accuracy. The concordance statistic (C-index) was used to cut dendrograms resulting from the learning process to generate prognostic groups. We created one computational prognostic model (7 prognostic groups with C-index = 0.8583) based on tumor size (T), regional lymph nodes (N), status of distant metastasis (M), and age to mirror the contemporary American Joint Committee on Cancer (AJCC) staging system (C-index = 0.8387). We showed that adding histologic type (papillary and follicular) improved the survival prediction of the model. We also showed that 55 is the best cutoff of age in the model, consistent with the changes from the most recent 8th edition staging manual from AJCC. The demonstrated approach has the potential to create prognostic systems permitting data driven and real time analysis that can aid decision-making in patient management and prognostication.

No abstract

Integrating additional prognostic factors into the tumor, lymph node, metastasis staging system improves the relative stratification of cancer patients and enhances the accuracy in planning their treatment options and predicting clinical outcomes. We describe a novel approach to build prognostic systems for cancer patients that can admit any number of prognostic factors. In the approach, an unsupervised learning algorithm was used to create dendrograms and the C‐index was used to cut dendrograms to generate prognostic groups. Breast cancer data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute were used for demonstration. Two relative prognostic systems were created for breast cancer. One system (7 prognostic groups with C‐index = 0.7295) was based on tumor size, regional lymph nodes, and no distant metastasis. The other system (7 prognostic groups with C‐index = 0.7458) was based on tumor size, regional lymph nodes, no distant metastasis, grade, estrogen receptor, progesterone receptor, and age. The dendrograms showed a relationship between survival and prognostic factors. The proposed approach is able to create prognostic systems that have a good accuracy in survival prediction and provide a manageable number of prognostic groups. The prognostic systems have the potential to permit a thorough database analysis of all information relevant to decision‐making in patient management and prognosis.

During language acquisition, children must learn when to generalize a pattern – applying it broadly and to new words (‘add –ed’ in English) – and when to restrict generalization, storing the pattern only with specific lexical items. But what governs when children will form productive rules during language acquisition? How do they determine when a pattern is widespread enough to generalize to novel words, and when a pattern should not extend beyond the cases they have observed in their input? One effort to quantify the conditions for generalization, the Tolerance Principle (Yang, 2016), has been shown to accurately predict children’s generalization behavior in dozens of corpus-based studies. The Tolerance Principle hypothesizes that a general rule will be formed when it is computationally more efficient than storing lexical forms individually. Here we test the Tolerance Principle in two artificial language experiments with children. In both experiments, we exposed children to a language with 9 novel nouns, some of which followed a regular pattern to form the plural (-ka) and some of which were exceptions to this rule. As predicted by the Tolerance Principle, in Experiment 1 we found that children exposed to 5 regular forms and 4 exceptions generalized, applying the regular form to 100% of novel test words. Children exposed to 3 regular forms and 6 exceptions did not extend the rule, even though the regular form was still the majority token in this condition. In Experiment 2, we found that children continued to behave categorically: either forming a productive rule (applying the regular form on all test trials) or using the regular form no more than predicted by chance. We found that the Tolerance Principle can be used to predict whether children will form a productive generalization or not based on each child’s individual vocabulary size. The Tolerance Principle appears to capture something fundamental about the way in which children form productive generalizations during language acquisition.