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Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system’s ease of use, and gain users’ trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus, how to effectively fuse IDs into such models becomes a critical issue. Inspired by recent advancement in prompt learning, we come up with two solutions: find alternative words to represent IDs (called discrete prompt learning) and directly input ID vectors to a pre-trained model (termed continuous prompt learning). In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages. To bridge the gap, we further propose two training strategies: sequential tuning and recommendation as regularization. Extensive experiments show that our continuous prompt learning approach equipped with the training strategies consistently outperforms strong baselines on three datasets of explainable recommendation. 

For millimeter-wave power applications, GaN high-electron mobility transistors (HEMTs) are often grown epitaxially on a high-purity semi-insulating c-axis 4H-SiC substrate. For these anisotropic hexagonal materials, the design and modeling of microstrip and coplanar interconnects require detailed knowledge of both the ordinary permittivity ε⊥ and the extraordinary permittivity εǁ perpendicular and parallel, respectively, to the c-axis. However, conventional dielectric characterization techniques make it difficult to measure εǁ alone or to separate εǁ from ε⊥. As a result, there is little data for εǁ, especially at millimeter-wave frequencies. This work demonstrates techniques for characterizing εǁ of 4H SiC using substrate-integrated waveguides (SIWs) or SIW resonators. The measured εǁ on seven SIWs and eleven resonators from 110 to 170 GHz is within ±1% of 10.2. Because the SIWs and resonators can be fabricated on the same SiC substrate together with HEMTs and other devices, they can be conveniently measured on-wafer for precise material-device correlation. Such permittivity characterization techniques can be extended to other frequencies, materials, and orientations. 

Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanation as a side output of the recommendation model, which has two problems: (1) It is difficult to evaluate the produced explanations, because they are usually model-dependent, and (2) as a result, how the explanations impact the recommendation performance is less investigated. In this article, explaining recommendations is formulated as a ranking task and learned from data, similarly to item ranking for recommendation. This makes it possible for standard evaluation of explanations via ranking metrics (e.g., Normalized Discounted Cumulative Gain). Furthermore, this article extends traditional item ranking to an item–explanation joint-ranking formalization to study if purposely selecting explanations could reach certain learning goals, e.g., improving recommendation performance. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be inevitably severer than that in traditional user–item interaction data, since not every user–item pair can be associated with all explanations. To mitigate this issue, this article proposes to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. Experiments on three large datasets verify the solution’s effectiveness on both explanation ranking and item recommendation. 

Vicinal diamines are privileged scaffolds in medicine, agrochemicals, catalysis, and other fields. While significant advancements have been made in diamination of olefins, diamination of allenes is only sporadically explored. Furthermore, direct incorporation of acyclic and cyclic alkyl amines onto unsaturated π systems is highly desirable and important, but problematic for many previously reported amination reactions including the diamination of olefins. Herein, we report a modular and practical diamination of allenes, which offers efficient syntheses of β,γ-diamino carboxylates and sulfones. This reaction features broad substrate scope, excellent functional group tolerability, and scalability. Experimental and computational studies support an ionic reaction pathway initiated with a nucleophilic addition of the in situ formed iodoamine to the electron deficient allene substrate. An iodoamine activation mode via a halogen bond with a chloride ion was revealed to substantially increase the nucleophilicity of the iodoamine and lower the activation energy barrier for the nucleophilic addition step.

 

Currently, lacking suitable test structures, little data exist for the permittivity of hexagonal materials such as GaN and SiC at millimeter-wave frequencies, especially for the extraordinary permittivity ε || as opposed to the ordinary permittivity ε ⊥ . This paper demonstrates for the first time that it is possible to characterize ε || of c-axis 4H SiC using on-wafer measurements of substrate-integrated-waveguide resonators. In fact, measurements on eleven resonators yield a relative ε || of 10.27 ± 0.03 and a loss tangent tanδ<0.02 over the D band (110-170 GHz). The on-wafer measurements of resonators and other devices fabricated on the same SiC substrate can allow material property to be closely correlated with device performance. The present approach can be extended to materials of other types and orientations.