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.

Example Input: There are already quite a few tools for solving the Satisfiability Modulo Theories (SMT) problems. In this paper, we present VolCE, a tool for counting the solutions of SMT constraints, or in other words, for computing the volume of the solution space. Its input is essentially a set of Boolean combinations of linear constraints, where the numeric variables are either all integers or all reals, and each variable is bounded. The tool extends SMT solving with integer solution counting and volume computation/estimation for convex polytopes. Effective heuristics are adopted, which enable the tool to deal with high-dimensional problem instances efficiently and accurately.
Example Output: A Tool for Computing and Estimating the Volume of the Solution Space of SMT(LA) Constraints

Example Input: Automated learning of patients’ demographics can be seen as multilabel problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where they can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multilabel learning algorithms. The best performing multi label ensembles include BR/Hoeffding Tree, CC/Hoeffding Tree, BCC/Hoeffding Tree, BR/JRIP, CC/JRIP, BCC/ JRIP respectively. In the empirical part of this study, we used on the UCI Diabetics dataset of over 100,000 records, collected from 130 US hospitals. The dataset consisted of attributes that included personal demographics, diagnoses code, lab results, etc. Experiments conducted on datasets of 1000, 10000, 20000 samples, show that BR/JRip model achieves a high overall accuracy of 0.533 (1000 samples), 0.702 (10000 samples), 0.569 (20000 samples), improving over the baseline model ZeroR with accuracy of 0.526, 0.586, .562 respectively. Loss functions such as Rank Loss, One Error, Hamming Loss, and Zero One Loss are also low for BCC/JRIP model for all samples of dataset, making it the best candidate for better performance given the label dependencies.
Example Output: Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients

Example Input: There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn­ ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern­ ing how the presence of one cause of an effect makes an­ other cause less probable) support the hypothesis that humans reach the same judgments as algorithms for do­ ing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and de­ pendencies as those used by certain algorithms for learn­ ing the structure of a Bayesian network. Based on this indication, a subjective definition of causality is for­ warded. Finally, methods for further testing the accu­ racy of these claims are discussed.
Example Output:
THE CoGNITIVE PROCESSING OF CAUSAL KNOWLEDGE