Recent advances in multistep solution nanosynthesis of nanostructured three-dimensional complexes of semiconductive materials

AbstractConstructing simply nanostructured zero-, one-, and two-dimensional crystallites into three-dimensional multifunctional assemblies and systems at low-cost is essential and highly challenging in materials science and engineering. Compared to the simply nanostructured components, a three-dimensional (3D) complex made with a precisely controlled spatial organization of all structural nanocomponents can enable us to concert functionalities from all the nanocomponents. Methodologically, so doing in nm-scales via a solution chemistry route may be much easier and less expensive than via other mechanisms. Hence, we discuss herein some recent advances in multistep solution syntheses of nanostructured 3D complexes of semiconductors with a focus mainly on their synthetic strategies and detailed mechanisms

Knowledge-based Transfer Learning Explanation

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.Comment: Accepted by International Conference on Principles of Knowledge Representation and Reasoning, 201

Direct Determination of Electron-Phonon Coupling Matrix Element in a Correlated System

High-resolution electron energy loss spectroscopy measurements have been carried out on an optimally doped cuprate Bi2Sr2CaCu2O8+{\delta}. The momentum-dependent linewidth and the dispersion of an A1 optical phonon are obtained. Based on these data as well as the detailed knowledge of the electronic structure from angle-resolved photoemission spectroscopy, we develop a scheme to determine the full structure of electron-phonon coupling for a specific phonon mode, thus providing a general method for directly resolving the EPC matrix element in systems with anisotropic electronic structures

Relational Message Passing for Fully Inductive Knowledge Graph Completion

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the ontological schema of KG. Extensive evaluation on multiple benchmarks has shown the effectiveness of techniques involved in RMPI and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes and data are available at https://github.com/zjukg/RMPI.Comment: under revie

Simultaneous extraction and purification of alkaloids from Sophora flavescens Ait. by microwave-assisted aqueous two-phase extraction with ethanol/ammonia sulfate system

A rapid and effective method of integrating extraction and purification for alkaloids from Sophora flavescens Ait. was developed by microwave-assisted aqueous two-phase extraction (MAATPE) based on the high efficiency of microwave-assisted extraction (MAE) and the demixing effect of aqueous two-phase extraction (ATPE). The aqueous two-phase system (ATPS), ethanol/ammonia sulfate was chosen from seven combinations of ethanol/salt systems, and its extraction properties were investigated in detail. Key factors, namely, the compositions of ATPS, solvent-to-materials ratio, and the extraction temperature were selected for optimization of the experimental conditions using response surface methodology (RSM) on the basis of the results of the single-factor experiment. The final optimized conditions were, the compositions of ATPS: ethanol 28% (w/w) and (NH4)2SO4 18% (w/w), solvent-to-material ratio 60:1, temperature 90 C, extraction time 5 min, and microwave power 780 W. MAATPE was superior to MAE, the latter using a single solvent, not only in extraction yield but also in impurity content. Moreover, compared with the combination of MAE and ATPE in the two-step mode, MAATP demonstrated fewer impurities, a better yield (63.78 ± 0.45 mg/g) and a higher recovery (92.09 ± 0.14%) in the extraction and purification of alkaloids. A continuous multiphase-extraction model of MAATPE was proposed to explicate the extraction mechanism. MAATPE revealed that the interaction between microwave and ATPS cannot only cause plant cell rupture but also accelerate demixing, improving mass-transfer from solid–liquid extraction to liquid– liquid purification. MAATPE simplified procedures also contributed to the lower loss occurrence, better extraction efficiency, and reduced impurity to target constituents.The Science and Technology Project of Guangzhou (No. 2008Z1-E301) and Faculty Development fund Project of Guangdong Pharmaceutical University (No. 52104109

Benchmarking knowledge-driven zero-shot learning

External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone are limited with incomplete semantics. Some very recent studies thus propose to use Knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still in short of standard benchmarks for studying and comparing different external knowledge settings and different KG-based ZSL methods. In this paper, we proposed six resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC), zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC). Each resource has a normal ZSL benchmark and a KG containing semantics ranging from text to attribute, from relational knowledge to logical expressions. We have clearly presented these resources including their construction, statistics, data formats and usage cases w.r.t. different ZSL methods. More importantly, we have conducted a comprehensive benchmarking study, with two general and state-of-the-art methods, two setting-specific methods and one interpretable method. We discussed and compared different ZSL paradigms w.r.t. different external knowledge settings, and found that our resources have great potential for developing more advanced ZSL methods and more solutions for applying KGs for augmenting machine learning. All the resources are available at https://github.com/China-UK-ZSL/Resources_for_KZSL.Comment: Published in Journal of Web Semantics, 2022. Final version please refer to our Github repository