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Free, publicly-accessible full text available June 17, 2025
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Abstract A series of new isoxazole‐substituted aryl iodides
1 a –1 d have 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 iodides1 were characterized by IR,1H NMR,13C NMR and HRMS. The structure of1 a was also confirmed by single‐crystal X‐ray crystallography. Further, catalytic activity of iodoarenes1 a –1 d was screened for the oxidation of hydroquinones and sulfides. On oxidation using aryl iodides1 withm ‐CPBA as terminal oxidant, hydroquinones afforded benzoquinones while sulfides gave corresponding sulfoxides in good to excellent yields. Iodoarene1 b showed the best catalytic activity for the oxidation of sulfides and hydroquinones. Moreover, iodoarene1 b , was also utilized for α‐oxytosylation of acetophenones. -
In the single-machine
non-clairvoyant scheduling problem, the goal is to minimize the total completion time of jobs whose processing times areunknown a priori . We revisit this well-studied problem and consider the question of how to effectively use (possibly erroneous) predictions of the processing times. We study this question from ground zero by first asking what constitutes a good prediction; we then propose a new measure to gauge prediction quality and design scheduling algorithms with strong guarantees under this measure. Our approach to derive a prediction error measure based on natural desiderata could find applications for other online problems. -
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.more » « less