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A series of new isoxazole‐substituted aryl iodides1 a–1 dhave been synthesized by DIB‐mediated [3+2] cycloaddition reaction of 2‐iodo‐1,3‐bis(prop‐2‐yn‐1‐yloxy) benzene (4) with corresponding benzaldehyde oximes5 a–5 d. Structure of the synthesized aryl iodides1were characterized by IR,¹H NMR,¹³C NMR and HRMS. The structure of1 awas also confirmed by single‐crystal X‐ray crystallography. Further, catalytic activity of iodoarenes1 a–1 dwas screened for the oxidation of hydroquinones and sulfides. On oxidation using aryl iodides1withm‐CPBA as terminal oxidant, hydroquinones afforded benzoquinones while sulfides gave corresponding sulfoxides in good to excellent yields. Iodoarene1 bshowed the best catalytic activity for the oxidation of sulfides and hydroquinones. Moreover, iodoarene1 b, was also utilized for α‐oxytosylation of acetophenones.

 

Successfully synthesized Si(Nb)OC composites through single source precursor route and as-pyrolyzed Si(Nb)OC demonstrate good rate capability due to uniformly distributed nanosized Nb₂O₅and graphitic carbon structure in the amorphous SiOC matrix.

 

We study variants of the online linear optimization (OLO) problem with bandit feedback, where the algorithm has access to external information about the unknown cost vector. Our motivation is the recent body of work on using such “hints” towards improving regret bounds for OLO problems in the full-information setting. Unlike in the full-information OLO setting, with bandit feedback, we first show that one cannot improve the standard regret bounds of O(\sqrt{T}) by using hints, even if they are always well-correlated with the cost vector. In contrast, if the algorithm is empowered to issue queries and if all the responses are correct, then we show O(\log(T)) regret is achievable. We then show how to make this result more robust — when some of the query responses can be adversarial — by using a little feedback on the quality of the responses. 

Abstract Hypervalent iodine (HVI) reagents have gained much attention as versatile oxidants because of their low toxicity, mild reactivity, easy handling, and availability. Despite their unique reactivity and other advantageous properties, stoichiometric HVI reagents are associated with the disadvantage of generating non-recyclable iodoarenes as waste/co-products. To overcome these drawbacks, the syntheses and utilization of various recyclable hypervalent iodine reagents have been established in recent years. This review summarizes the development of various recyclable non-polymeric, polymer-supported, ionic-liquid-supported, and metal–organic framework (MOF)-hybridized HVI reagents. 1 Introduction 2 Polymer-Supported Hypervalent Iodine Reagents 2.1 Polymer-Supported Hypervalent Iodine(III) Reagents 2.2 Polymer-Supported Hypervalent Iodine(V) Reagents 3 Non-Polymeric Recyclable Hypervalent Iodine Reagents 3.1 Non-Polymeric Recyclable Hypervalent Iodine(III) Reagents 3.2 Recyclable Non-Polymeric Hypervalent Iodine(V) Reagents 3.3 Fluorous Hypervalent Iodine Reagents 4 Ionic-Liquid/Ion-Supported Hypervalent Iodine Reagents 5 Metal–Organic Framework (MOF)-Hybridized Hypervalent Iodine Reagents 6 Conclusion 

We consider the online linear optimization problem, where at every step the algorithm plays a point x_t in the unit ball, and suffers loss for some cost vector c_t that is then revealed to the algorithm. Recent work showed that if an algorithm receives a "hint" h_t that has non-trivial correlation with c_t before it plays x_t, then it can achieve a logarithmic regret guarantee, improving on the classical sqrt(T) bound. In this work, we study the question of whether an algorithm really requires a hint at every time step. Somewhat surprisingly, we show that an algorithm can obtain logarithmic regret with just O(sqrt(T)) hints under a natural query model. We give two applications of our result, to the well-studied setting of optimistic regret bounds and to the problem of online learning with abstention. 

We consider the online linear optimization problem with movement costs, a variant of online learning in which the learner must not only respond to cost vectors c_t with points x_t in order to maintain low regret, but is also penalized for movement by an additional cost. Classically, simple algorithms that obtain the optimal sqrt(T) regret already are very stable and do not incur a significant movement cost. However, recent work has shown that when the learning algorithm is provided with weak "hint" vectors that have a positive correlation with the costs, the regret can be significantly improved to log(T). In this work, we study the stability of such algorithms, and provide matching upper and lower bounds showing that incorporating movement costs results in intricate tradeoffs logarithmic and sqrt(T) regret.