Ruthenium-Based Heterocyclic Carbene-Coordinated Olefin Metathesis Catalysts

The fascinating story of olefin (or alkene) metathesis (eq 1) began almost five decades ago, when Anderson and Merckling reported the first carbon-carbon double-bond rearrangement reaction in the titanium-catalyzed polymerization of norbornene. Nine years later, Banks and Bailey reported “a new disproportionation reaction . . . in which olefins are converted to homologues of shorter and longer carbon chains...”. In 1967, Calderon and co-workers named this metal-catalyzed redistribution of carbon-carbon double bonds olefin metathesis, from the Greek word “μετάθεση”, which means change of position. These contributions have since served as the foundation for an amazing research field, and olefin metathesis currently represents a powerful transformation in chemical synthesis, attracting a vast amount of interest both in industry and academia

Pareto-Path Multi-Task Multiple Kernel Learning

A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO) problem, which considers the concurrent optimization of all task objectives involved in the Multi-Task Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel Support Vector Machine (SVM) MT-MKL framework, that considers an implicitly-defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving better classification performance, when compared to other similar MTL approaches.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System