Enhancing Hydrogen Generation Through Nanoconfinement of Sensitizers and Catalysts in a Homogeneous Supramolecular Organic Framework.

Enrichment of molecular photosensitizers and catalysts in a confined nanospace is conducive for photocatalytic reactions due to improved photoexcited electron transfer from photosensitizers to catalysts. Herein, the self-assembly of a highly stable 3D supramolecular organic framework from a rigid bipyridine-derived tetrahedral monomer and cucurbit[8]uril in water, and its efficient and simultaneous intake of both [Ru(bpy)3 ]2+ -based photosensitizers and various polyoxometalates, that can take place at very low loading, are reported. The enrichment substantially increases the apparent concentration of both photosensitizer and catalyst in the interior of the framework, which leads to a recyclable, homogeneous, visible light-driven photocatalytic system with 110-fold increase of the turnover number for the hydrogen evolution reaction

Probing Dark Energy with the Kunlun Dark Universe Survey Telescope

Dark energy is an important science driver of many upcoming large-scale surveys. With small, stable seeing and low thermal infrared background, Dome A, Antarctica, offers a unique opportunity for shedding light on fundamental questions about the universe. We show that a deep, high-resolution imaging survey of 10,000 square degrees in \emph{ugrizyJH} bands can provide competitive constraints on dark energy equation of state parameters using type Ia supernovae, baryon acoustic oscillations, and weak lensing techniques. Such a survey may be partially achieved with a coordinated effort of the Kunlun Dark Universe Survey Telescope (KDUST) in \emph{yJH} bands over 5000--10,000 deg$^2$ and the Large Synoptic Survey Telescope in \emph{ugrizy} bands over the same area. Moreover, the joint survey can take advantage of the high-resolution imaging at Dome A to further tighten the constraints on dark energy and to measure dark matter properties with strong lensing as well as galaxy--galaxy weak lensing.Comment: 9 pages, 6 figure

Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems

Predicting conversion rate (e.g., the probability that a user will purchase an item) is a fundamental problem in machine learning based recommender systems. However, accurate conversion labels are revealed after a long delay, which harms the timeliness of recommender systems. Previous literature concentrates on utilizing early conversions to mitigate such a delayed feedback problem. In this paper, we show that post-click user behaviors are also informative to conversion rate prediction and can be used to improve timeliness. We propose a generalized delayed feedback model (GDFM) that unifies both post-click behaviors and early conversions as stochastic post-click information, which could be utilized to train GDFM in a streaming manner efficiently. Based on GDFM, we further establish a novel perspective that the performance gap introduced by delayed feedback can be attributed to a temporal gap and a sampling gap. Inspired by our analysis, we propose to measure the quality of post-click information with a combination of temporal distance and sample complexity. The training objective is re-weighted accordingly to highlight informative and timely signals. We validate our analysis on public datasets, and experimental performance confirms the effectiveness of our method.Comment: NeurIPS'2

Continuity of weak solutions to an elliptic problem on pp-fractional Laplacian

In this paper we study an elliptic variational problem regarding the $p$-fractional Laplacian in $\mathbb{R}^N$ on the basis of recent result \cite{Ha1}, which generalizes the nice work \cite{AT,AP,XZR1}, and then give some sufficient conditions under which some weak solutions to the above elliptic variational problem are continuous in $\mathbb{R}^N$. In the final appendix we correct the proofs of both \cite[Lemma 10]{PXZ1} and \cite[Lemma A.6]{PXZ} for $1<p<2$