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Zoological Research 2020年第5期41卷 569-575页

作者： Yu-Ping Cao Qing-Qing Lin Wan-Yun He Jing Wang Meng-Ying Yi Lu-Chao Lv Jun Yang Jian-Hua Liu Jian-Ying Guo College of Veterinary Medicine South China Agricultural UniversityGuangzhouGuangdong 510642China Guangdong Laboratory for Lingnan Modern Agriculture GuangzhouGuangdong 510642China

DEAR EDITOR,The rise of the plasmid-encoded colistin resistance gene mcr-1 is a major concern ***,during a routine surveillance,an unexpectedly high prevalence of Escherichia coli with reduced susceptibility to colistin (69.9%) was observed in a Chinese broiler ***-three (63.9%) *** isolates were positive for *** identified mcr-1-positive E.

关键词： globally routine explain

Using Speech Recognition in Learning Primary School Mathematics via explain, Instruct and Facilitate Techniques

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Journal of Software Engineering and Applications 2014年第4期7卷 233-255页

作者： Ab Rahman Ahmad Sami M. Halawani Samir K. Boucetta Faculty of Computing and Information Technology-Rabigh King Abdulaziz University Rabigh KSA

The application of Information and Communication Technologies has transformed traditional Teaching and Learning in the past decade to computerized-based era. This evolution has resulted from the emergence of the digital system and has greatly impacted on the global education and socio-cultural development. Multimedia has been absorbed into the education sector for producing a new learning concept and a combination of educational and entertainment approach. This research is concerned with the application of Window Speech Recognition and Microsoft Visual Basic 2008 Integrated/Interactive Development Environment in Multimedia-Assisted Courseware prototype development for Primary School Mathematics contents, namely, single digits and the addition. The Teaching and Learning techniques—explain, Instruct and Facilitate are proposed and these could be viewed as instructors’ centered strategy, instructors’—learners’ dual communication and learners' active participation. The prototype is called M-EIF and deployed only users' voices;hence the activation of Window Speech Recognition is required prior to a test run.

关键词： explain Instruct and Facilitate Techniques Multimedia-Assisted Courseware Primary School Mathematics Visual Natural Language Window Speech Recognition

How to explain the Meanings of Words in Middle School English Teaching

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湖州师范学院学报 1990年第1期15卷 35-37页

作者： WangHaisheng

New words are constantly coming into a *** and technol-ogy are exploring into some new field,rich and more *** wordsget new meanings too."Heavy"used to be an adjective,meaning,"Havingweight".It is now used also as a noun,meaning"a big shot".The student haveto struggle against learning words throughout the course from the very be-ginning to the very end of the *** has a very large *** is utterly impossible for a foreign language student to learn all the wordsin a *** in English teaching teachers have to select the most use-ful words for students to *** should take care to use the

关键词： vocabulary struggle exploring ginning constantly expensive throughout impossible conveying explain

explain Everything软件辅助初中几何教学案例研究

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教育传播与技术 2020年第1期 69-74页

作者：高磊上海市奉贤区汇贤中学

随着科学技术的快速发展,教育教学方式发生迅速变革,取得进步。初中数学的几何教学中融入了一些传统的PC软件,比如Sketchpad(几何画板)或者PowerPoint等,但是有一定的局限性。而iOS平台上的explain Everything软件可以有效地将数学的几... 详细信息

随着科学技术的快速发展,教育教学方式发生迅速变革,取得进步。初中数学的几何教学中融入了一些传统的PC软件,比如Sketchpad(几何画板)或者PowerPoint等,但是有一定的局限性。而iOS平台上的explain Everything软件可以有效地将数学的几何教学与信息技术相整合,使得教学效率得到了很大提高,且与其传统的电脑多媒体相比有很大优势。然而,由于对该软件在几何教学上的研究及使用还不够成熟,因此仍然存在许多问题。本文简要给出对该软件在教学中使用的几种误区的分析和设计案例,并辅以几种辅助模式,方便广大教育工作者使用时作为参考。

关键词： explain Everything 几何教学个别化教学辅助模式应用案例

explainable machine learning in materials science

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npj Computational Materials 2022年第1期8卷 1951-1969页

作者： Xiaoting Zhong Brian Gallagher Shusen Liu Bhavya Kailkhura Anna Hiszpanski T.Yong-Jin Han Materials Science Division Lawrence Livermore National LaboratoryLivermoreCAUSA Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCAUSA

Machine learning models are increasingly used in materials studies because of their exceptional ***,the most accurate machine learning models are usually difficult to *** to this problem lie in explainable artificial intelligence(XAI),an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks(DNNs).This article attempts to provide an entry point to XAI for materials *** are defined to clarify what explain means in the context of materials *** works are reviewed to show how XAI helps materials science *** and opportunities are also discussed.

关键词： explain learning plain

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

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npj Computational Materials 2022年第1期8卷 2053-2063页

作者： Andy S.Anker Emil T.S.Kjær Mikkel Juelsholt Troels Lindahl Christiansen Susanne Linn Skjærvø Mads Ry Vogel Jørgensen Innokenty Kantor Daniel Risskov Sørensen Simon J.L.Billinge Raghavendra Selvan Kirsten M.Ø.Jensen Department of Chemistry and Nano-Science Center University of Copenhagen2100CopenhagenDenmark Department of Materials University of OxfordParks RoadOxfordUK Department of Chemistry&iNANO Aarhus University8000AarhusDenmark MAX IV Laboratory Lund University22484LundSweden Department of Physics Technical University of Denmark2880LyngbyDenmark Department of Applied Physics and Applied Mathematics Columbia UniversityNew YorkNY10027USA Condensed Matter Physics and Materials Science Department Brookhaven National LaboratoryUptonNY11973USA Department of Computer Science University of Copenhagen2100CopenhagenDenmark Department of Neuroscience University of Copenhagen2200CopenhagenDenmark

Characterization of material structure with X-ray or neutron scattering using *** Distribution Function(PDF)analysis most often rely on refining a structure model against an experimental ***,identifying a suitable model is often a ***,automated approaches have made it possible to test thousands of models for each dataset,but these methods are computationally expensive and analysing the output,*** structural information from the resulting fits in a meaningful way,is *** Machine Learning based Motif Extractor(ML-MotEx)trains an ML algorithm on thousands of fits,and uses SHAP(SHapley Additive exPlanation)values to identify which model features are important for the fit *** use the method for 4 different chemical systems,including disordered nanomaterials and ***-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.

关键词： explain structural meaningful

Reliable and explainable machine-learning methods for accelerated material discovery

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npj Computational Materials 2019年第1期5卷 186-194页

作者： Bhavya Kailkhura Brian Gallagher Sookyung Kim Anna Hiszpanski T.Yong-Jin Han Center for Applied Scientific Computing Computing DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA

Despite ML’s impressive performance in commercial applications,several unique challenges exist when applying ML in materials science *** such a context,the contributions of this work are ***,we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material ***,we show that with imbalanced data,standard methods for assessing quality of ML models break down and lead to misleading ***,we find that the model’s own confidence score cannot be trusted and model introspection methods(using simpler models)do not help as they result in loss of predictive performance(reliability-explainability trade-off).Second,to overcome these challenges,we propose a general-purpose explainable and reliable machine-learning ***,we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material *** also propose a transfer learning technique and show that the performance loss due to models’simplicity can be overcome by exploiting correlations among different material properties.A new evaluation metric and a trust score to better quantify the confidence in the predictions are also *** improve the interpretability,we add a rationale generator component to our framework which provides both model-level and decision-level ***,we demonstrate the versatility of our technique on two applications:(1)predicting properties of crystalline compounds and(2)identifying potentially stable solar cell *** also point to some outstanding issues yet to be resolved for a successful application of ML in material science.

关键词： explain overcome simpler

High-level intrinsic disorder explains the universality of CLIP binding to diverse MHC class II variants

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Cellular & Molecular Immunology 2018年第1期15卷 76-78页

作者： Vladimir N Uversky Yaping N Tu Onyekachi Nwogu Shanitra N Butler Michael Ramsamooj George Blanck Department of Molecular Medicine Morsani College of MedicineUniversity of South Floridaand Immunology ProgramTampaFLUSA H.Lee Moffitt Cancer Center and Research Institute TampaFLUSA

MHC class I and II molecules bind peptides that are either recognized as self or as foreign to the immune system via interaction with T-cell *** T-cell receptor makes molecular contacts with the peptide and the MHC molecule in the region of the MHC peptide binding *** MHC class I and II molecules are highly polymorphic,which presumably allows for great diversity of antigen-binding sites over the population,leading to a species that is relatively fit to withstand foreign *** MHC class I molecules,this allelic variation predicts extensive variation in the sequence of peptides able to bind MHC class I molecules,and this is indeed the case.

关键词： explain class intrinsic

Interpretable and explainable Machine Learning for Materials Science and Chemistry

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Accounts of Materials Research 2022年第6期3卷 597-607页

作者： Felipe Oviedo Juan Lavista Ferres Tonio Buonassisi Keith T.Butler Massachusetts Institute of Technology CambridgeMassachusetts 02139United States Microsoft AI for Good Research Lab RedmondWashington 98052United States SciML Scientific Computing DepartmentRutherford Appleton LaboratoryDidcot OX110DU.K. Department of Chemistry University of ReadingReading RG66ADU.K.

Machine learning has become a common and powerful tool in materials *** more data become available,with the use of high-performance computing and high-throughput experimentation,machine learning has proven potential to accelerate scientific research and technology *** the uptake of data-driven approaches for materials science is at an exciting,early stage,to realize the true potential of machine learning models for successful scientific discovery,they must have qualities beyond purely predictive *** predictions and inner workings of models should provide a certain degree of explainability by human experts,permitting the identification of potential model issues or limitations,building trust in model predictions,and unveiling unexpected correlations that may lead to scientific *** this work,we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific *** start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the *** show how interpretability in scientific machine learning has additional constraints compared to general *** upon formal definitions in machine learning,we formulate the basic trade-offs among the explainability,completeness,and scientific validity of model explanations in scientific *** the context of these trade-offs,we discuss how interpretable models can be constructed,what insights they provide,and what drawbacks they *** present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies,encompassing first-principles calculations,physicochemical characterization,materials development,and integration into complex *** discuss the varied impacts and uses of interpretabiltiy in these cases

关键词： explain purely insight

Graviton Momentum:A Natural Source of Dark Energy

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Chinese Physics Letters 2023年第9期40卷 1-6页

作者：孟献明 Research School of Physics Australian National UniversityCanberraACT 2601Australia

The dark energy concept in the standard cosmological model can explain the expansion of the ***,the mysteries surrounding dark energy remain,such as its source,its unusual negative pressure,its longrange force,and its unchanged density as the universe *** propose a graviton momentum hypothesis,develop a semiclassical model of gravitons,and explain the pervasive dark matter and accelerating *** graviton momentum hypothesis is incorporated into the standard model and explains well the mysteries related to dark energy.

关键词： energy explain hypothesis