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In the past few years, there has been much work on incorporating fairness requirements into the design of algorithmic rankers, with contributions from the data management, algorithms, information retrieval, and recommender systems communities. In this tutorial, we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. During the first part of the tutorial, we present a classification framework for fairness-enhancing interventions, along which we will then relate the technical methods. This framework allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. Next, we discuss fairness in score-based ranking and in supervised learning-to-rank. We conclude with recommendations for practitioners, to help them select a fair ranking method based on the requirements of their specific application domain. 

N-heterocycles are ubiquitous in natural products, pharmaceuticals, organic materials, and numerous functional molecules. Among the current synthetic approaches, transition metal-catalyzed C–H functionalization has gained considerable attention in recent years due to its advantages of simplicity, high atomic economy, and the ready availability of starting materials. In the field of N-heterocycle synthesis via C–H functionalization, nickel has been recognized as one of the most important catalysts. In this review, we will introduce nickel-catalyzed intramolecular and intermolecular pathways for N-heterocycle synthesis from 2008 to 2021. 

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. In the first part of this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking. In this second part of this survey, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods. 

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. In this first part of this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking. In the second part of this survey, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods. 

Atomically dispersed catalysts have been shown highly active for preferential oxidation of carbon monoxide in the presence of excess hydrogen (PROX). However, their stability has been less than ideal. We show here that the introduction of a structural component to minimize diffusion of the active metal center can greatly improve the stability without compromising the activity. Using an Ir dinuclear heterogeneous catalyst (DHC) as a study platform, we identify two types of oxygen species, interfacial and bridge, that work in concert to enable both activity and stability. The work sheds important light on the synergistic effect between the active metal center and the supporting substrate and may find broad applications for the use of atomically dispersed catalysts. 

The transient directing group (TDG) strategy allowed long awaited access to the direct β-C(sp 3 )–H functionalization of unmasked aliphatic aldehydes via palladium catalysis. However, the current techniques are restricted to terminal methyl functionalization, limiting their structural scopes and applicability. Herein, we report the development of a direct Pd-catalyzed methylene β-C–H arylation of linear unmasked aldehydes by using 3-amino-3-methylbutanoic acid as a TDG and 2-pyridone as an external ligand. Density functional theory calculations provided insights into the reaction mechanism and shed light on the roles of the external and transient directing ligands in the catalytic transformation.