Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile indust...Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.展开更多
Image captioning refers to automatic generation of descriptive texts according to the visual content of images.It is a technique integrating multiple disciplines including the computer vision(CV),natural language proc...Image captioning refers to automatic generation of descriptive texts according to the visual content of images.It is a technique integrating multiple disciplines including the computer vision(CV),natural language processing(NLP)and artificial intelligence.In recent years,substantial research efforts have been devoted to generate image caption with impressive progress.To summarize the recent advances in image captioning,we present a comprehensive review on image captioning,covering both traditional methods and recent deep learning-based techniques.Specifically,we first briefly review the early traditional works based on the retrieval and template.Then deep learning-based image captioning researches are focused,which is categorized into the encoder-decoder framework,attention mechanism and training strategies on the basis of model structures and training manners for a detailed introduction.After that,we summarize the publicly available datasets,evaluation metrics and those proposed for specific requirements,and then compare the state of the art methods on the MS COCO dataset.Finally,we provide some discussions on open challenges and future research directions.展开更多
Relation contexts have been proved to be useful for many challenging vision tasks.In the field of3D object detection,previous methods have been taking the advantage of context encoding,graph embedding,or explicit rela...Relation contexts have been proved to be useful for many challenging vision tasks.In the field of3D object detection,previous methods have been taking the advantage of context encoding,graph embedding,or explicit relation reasoning to extract relation contexts.However,there exist inevitably redundant relation contexts due to noisy or low-quality proposals.In fact,invalid relation contexts usually indicate underlying scene misunderstanding and ambiguity,which may,on the contrary,reduce the performance in complex scenes.Inspired by recent attention mechanism like Transformer,we propose a novel 3D attention-based relation module(ARM3D).It encompasses objectaware relation reasoning to extract pair-wise relation contexts among qualified proposals and an attention module to distribute attention weights towards different relation contexts.In this way,ARM3D can take full advantage of the useful relation contexts and filter those less relevant or even confusing contexts,which mitigates the ambiguity in detection.We have evaluated the effectiveness of ARM3D by plugging it into several state-of-the-art 3D object detectors and showing more accurate and robust detection results.Extensive experiments show the capability and generalization of ARM3D on 3D object detection.Our source code is available at https://github.com/lanlan96/ARM3D.展开更多
Electrochemical carbon dioxide reduction(ECR)is an attractive pathway to synthesize useful fuels and chemical feedstocks,especially when paired with renewable electricity as the energy source.In this overview,we exami...Electrochemical carbon dioxide reduction(ECR)is an attractive pathway to synthesize useful fuels and chemical feedstocks,especially when paired with renewable electricity as the energy source.In this overview,we examine the recently witnessed advances and on-going pursuits of ECR in terms of the key fundamental mechanisms,basic experimentation principles,electrocatalysts and the electrochemical setup for ECR,aiming at offering timely key insights into solving the unsettled bottleneck issues.The reaction pathways are discussed in relation to the generation of single-,double-and multi-carbon products by the ECR,as well as the underlying principles in catalyst design to form them both efficiently and selectively.For the rational design of electrocatalysis,we look into the critically important roles played by various in situ and operando experimental techniques and computational simulations,where the key priorities are to engineer the highly active and selec-tive ECR catalysts for the specifically targeted products.Indeed,with the purposely designed high activity and selectivity,one would be able to“magically”transform a bottle of CO_(2)-riched“coke drink”to a glass of“beer”with the desired alcohol product in a layman term,instead of a bottle of formic acid.Nonetheless,there are still considerable complications and challenges ahead.As a dynamically rapid-advancing research frontier for both energy and the environment,there are great opportunities and obstacles in the ECR scale up.展开更多
All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and va...All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and value information of objects could play fundamental roles in the process of information understanding and decisionmaking in human thinking.Therefore,the questions of where and how the content information and the value information be produced from the formal information become critical in the theory of information understanding and decision-making.A conjectural theory that may reasonably answer the question is presented here in the paper.展开更多
基金financially supported via Australian Research Council(FT180100705)the support by the National Natural Science Foundation of China(22209103)+3 种基金the support from UTS Chancellor's Research Fellowshipsthe support from Open Project of State Key Laboratory of Advanced Special Steel,the Shanghai Key Laboratory of Advanced Ferrometallurgy,Shanghai University(SKLASS 2021-**)Joint International Laboratory on Environmental and Energy Frontier MaterialsInnovation Research Team of High-Level Local Universities in Shanghai。
文摘Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.
基金supported by Beijing Natural Science Foundation of China(L201023)the Natural Science Foundation of China(62076030)。
文摘Image captioning refers to automatic generation of descriptive texts according to the visual content of images.It is a technique integrating multiple disciplines including the computer vision(CV),natural language processing(NLP)and artificial intelligence.In recent years,substantial research efforts have been devoted to generate image caption with impressive progress.To summarize the recent advances in image captioning,we present a comprehensive review on image captioning,covering both traditional methods and recent deep learning-based techniques.Specifically,we first briefly review the early traditional works based on the retrieval and template.Then deep learning-based image captioning researches are focused,which is categorized into the encoder-decoder framework,attention mechanism and training strategies on the basis of model structures and training manners for a detailed introduction.After that,we summarize the publicly available datasets,evaluation metrics and those proposed for specific requirements,and then compare the state of the art methods on the MS COCO dataset.Finally,we provide some discussions on open challenges and future research directions.
基金National Nature Science Foundation of China(62132021,62102435,62002375,62002376)National Key R&D Program of China(2018AAA0102200)NUDT Research Grants(ZK19-30)。
文摘Relation contexts have been proved to be useful for many challenging vision tasks.In the field of3D object detection,previous methods have been taking the advantage of context encoding,graph embedding,or explicit relation reasoning to extract relation contexts.However,there exist inevitably redundant relation contexts due to noisy or low-quality proposals.In fact,invalid relation contexts usually indicate underlying scene misunderstanding and ambiguity,which may,on the contrary,reduce the performance in complex scenes.Inspired by recent attention mechanism like Transformer,we propose a novel 3D attention-based relation module(ARM3D).It encompasses objectaware relation reasoning to extract pair-wise relation contexts among qualified proposals and an attention module to distribute attention weights towards different relation contexts.In this way,ARM3D can take full advantage of the useful relation contexts and filter those less relevant or even confusing contexts,which mitigates the ambiguity in detection.We have evaluated the effectiveness of ARM3D by plugging it into several state-of-the-art 3D object detectors and showing more accurate and robust detection results.Extensive experiments show the capability and generalization of ARM3D on 3D object detection.Our source code is available at https://github.com/lanlan96/ARM3D.
基金support of the Green Energy Programme(R284-000-185-731)supported by the National University of Singapore,and the Tier 1 Grant(R284-000-193-114)supported by MOE for research conducted at the National University of Singapore.
文摘Electrochemical carbon dioxide reduction(ECR)is an attractive pathway to synthesize useful fuels and chemical feedstocks,especially when paired with renewable electricity as the energy source.In this overview,we examine the recently witnessed advances and on-going pursuits of ECR in terms of the key fundamental mechanisms,basic experimentation principles,electrocatalysts and the electrochemical setup for ECR,aiming at offering timely key insights into solving the unsettled bottleneck issues.The reaction pathways are discussed in relation to the generation of single-,double-and multi-carbon products by the ECR,as well as the underlying principles in catalyst design to form them both efficiently and selectively.For the rational design of electrocatalysis,we look into the critically important roles played by various in situ and operando experimental techniques and computational simulations,where the key priorities are to engineer the highly active and selec-tive ECR catalysts for the specifically targeted products.Indeed,with the purposely designed high activity and selectivity,one would be able to“magically”transform a bottle of CO_(2)-riched“coke drink”to a glass of“beer”with the desired alcohol product in a layman term,instead of a bottle of formic acid.Nonetheless,there are still considerable complications and challenges ahead.As a dynamically rapid-advancing research frontier for both energy and the environment,there are great opportunities and obstacles in the ECR scale up.
基金The work was supported in part by the National Natural Science Foundation of China(Grant Nos.60575034 and 60873001)。
文摘All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and value information of objects could play fundamental roles in the process of information understanding and decisionmaking in human thinking.Therefore,the questions of where and how the content information and the value information be produced from the formal information become critical in the theory of information understanding and decision-making.A conjectural theory that may reasonably answer the question is presented here in the paper.
