In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability to characterize unique cancer traits. In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data. In particular, we leverage novel StochasticNet radiomic sequencers for extracting custom radiomic features tailored for characterizing unique cancer tissue phenotype. Using StochasticNet radiomic sequencers discovered using a wealth of lung CT data, we perform binary classification on 42,340 lung lesions obtained from the CT scans of 93 patients in the LIDC-IDRI dataset. Preliminary results show significant improvement over previous state-of-the-art methods, indicating the potential of the proposed discovery radiomics framework for improving cancer screening and diagnosis.
[EX A]: Discovery Radiomics via StochasticNet Sequencers for Cancer Detection

[EX Q]: LTL synthesis – the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic – is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applications. In this research note we consider De Giacomo and Vardi’s variant of the synthesis problem for LTL formulas interpreted over finite rather than infinite traces. Rather surprisingly, given the existing claims on complexity, we establish that LTL synthesis is EXPTIME-complete for the finite interpretation, and not 2EXPTIME-complete as previously reported. Our result coincides nicely with the planning perspective where non-deterministic planning with full observability is EXPTIME-complete and partial observability increases the complexity to 2EXPTIME-complete; a recent related result for LTL synthesis shows that in the finite case with partial observability, the problem is 2EXPTIME-complete.
[EX A]: Finite LTL Synthesis is EXPTIME-complete

[EX Q]: The goal of semi-supervised learning methods is to effectively combine labeled and unlabeled data to arrive at a better model. Many methods rely on graph-based approaches, where labels are propagated through a graph over the input examples. In most current methods, the propagation mechanism underlying the learning objective is based on random walks. While theoretically elegant, random walks suffer from several drawbacks which can hurt predictive performance. In this work, we explore dynamic infection processes as an alternative propagation mechanism. In these, unlabeled nodes can be “infected” with the label of their already infected neighbors. We provide an efficient, scalable, and parallelizable algorithm for estimating the expected infection outcomes. We also describe an optimization view of the method, relating it to Laplacian approaches. Finally, experiments demonstrate that the method is highly competitive across multiple benchmarks and for various learning settings.
[EX A]:
Semi-Supervised Learning with Competitive Infection Models