Hefei University of Technology

About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.

Covalently Connected Carbon Nanostructures for Current Collectors in Both the Cathode and Anode of Li-S Batteries.

Abstract: A 3D current collector made of covalently connected carbon nanostructures is presented, which can significantly improve battery performance when used as the cathode and/or anode. A Li-S cell assembled using these current collectors, with the cathode loaded with elemental sulfur and the anode loaded with lithium metal, delivers a high-rate capacity of 860 mA h g-1 at 12 C.

Surface Ligand Engineering for Near-Unity Quantum Yield Inorganic Halide Perovskite QDs and High-Performance QLEDs

Abstract: Despite the great potential of all-inorganic CsPbX3 (X = Br or I) quantum dots (QDs) for light-emitting diodes (QLEDs), their emission properties have been impeded by the long insulating ligands on the QD surface. To address the problem, an efficient surface ligand engineering method has been executed by using a short conjugation molecular ligand phenethylamine (PEA) as ligands to synthesize CsPbX3 QDs and then treating the CsPbX3 QD films with phenethylammonium bromide (PEABr) or phenethylammonium iodide (PEAI). The results indicate that the short conjugation molecular ligand is successfully adsorbed on the surface of CsPbX3 QDs to instead long insulating ligands, resulting in the remarkable enhancement of the carrier injection and transport. The incorporation of phenethylamine (PEA) as synthetic ligand causes the fewer trap states in both CsPbBr3 and CsPbI3 QDs, exhibiting the near-unity photoluminescence quantum yields (PLQYs) of 93% and 95%, respectively. The luminance of CsPbBr3 and CsPbI3 QLEDs coul...

Deep Item-based Collaborative Filtering for Top-N Recommendation

Abstract: Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user’s profile with the items that the user has consumed, ICF recommends items that are similar to the user’s profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationships between items, which are insufficient to capture the complicated decision-making process of users.In this article, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationships among items. Going beyond modeling only the second-order interaction (e.g., similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. By doing this, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user’s profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.