MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers

MaLeS is an automatic tuning framework for automated theorem provers. It provides solutions for both the strategy finding as well as the strategy scheduling problem. This paper describes the tool and the methods used in it, and evaluates its performance on three automated theorem provers: E, LEO-II and Satallax. An evaluation on a subset of the TPTP library problems shows that on average a MaLeS-tuned prover solves 8.67% more problems than the prover with its default settings

Functional heterogeneity within the CD44 high human breast cancer stem cell-like compartment reveals a gene signature predictive of distant metastasis

The CD44(hi) compartment in human breast cancer is enriched in tumor-initiating cells; however, the functional heterogeneity within this subpopulation remains poorly defined. We used a triple-negative breast cancer cell line with a known bilineage phenotype to isolate and clone CD44(hi) single cells that exhibited mesenchymal/basal B and luminal/basal A features, respectively. Herein, we demonstrate in this and other triple-negative breast cancer cell lines that, rather than CD44(hi)/CD24(−) mesenchymal-like basal B cells, the CD44(hi)/CD24(lo) epithelioid basal A cells retained classic cancer stem cell features, such as tumor-initiating capacity in vivo, mammosphere formation and resistance to standard chemotherapy. These results complement previous findings using oncogene-transformed normal mammary cells showing that only cell clones with a mesenchymal phenotype exhibit cancer stem cell features. Further, we performed comparative quantitative proteomic and gene array analyses of these cells and identified potential novel markers of breast cancer cells with tumor-initiating features, such as lipolysis-stimulated lipoprotein receptor (LSR), RAB25, S100A14 and mucin 1 (MUC1), as well as a novel 31-gene signature capable of predicting distant metastasis in cohorts of estrogen receptor–negative human breast cancers. These findings strongly favor functional heterogeneity in the breast cancer cell compartment and hold promise for further refinements of prognostic marker profiling. Our work confirms that, in addition to cancer stem cells with mesenchymal-like morphology, those tumor-initiating cells with epithelial-like morphology should also be the focus of drug development

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page

The Naproche Project. Controlled Natural Language Proof Checking of Mathematical Texts

This paper discusses the semi-formal language of mathematics and presents the Naproche CNL, a controlled natural language for mathematical authoring. Proof Representation Structures, an adaptation of Discourse Representation Structures, are used to represent the semantics of texts written in the Naproche CNL. We discuss how the Naproche CNL can be used in formal mathematics, and present our prototypical Naproche system, a computer program for parsing texts in the Naproche CNL and checking the proofs in them for logical correctness

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Biological pathways are thought to be robust against a variety of internal and external perturbations. Fail-safe mechanisms allow for compensation of perturbations to maintain the characteristic function of a pathway. Pathways can undergo changes during aging, which may lead to changes in their stability. Less stable or less robust pathways may be consequential to or increase the susceptibility of the development of diseases. Among others, NF- κ B signaling is a crucial pathway in the process of aging. The NF- κ B system is involved in the immune response and dealing with various internal and external stresses. Boolean networks as models of biological pathways allow for simulation of signaling behavior. They can help to identify which proposed mechanisms are biologically representative and which ones function but do not mirror physical processes—for instance, changes of signaling pathways during the aging process. Boolean networks can be inferred from time-series of gene expression data. This allows us to get insights into the changes of behavior of pathways such as NF- κ B signaling in aged organisms in comparison to young ones