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排序方式: 共有110条查询结果,搜索用时 15 毫秒
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神经信息处理系统大会(Conference on Neural Information Processing Systems,NeurIPS)是机器学习领域的顶级会议,在中国计算机学会(China Computer Federation,CCF)推荐国际学术会议中被评为人工智能领域的A类会议,一直广受关注。NeurIPS 2020收到了创纪录的9 467篇投稿,最终录用1 898篇论文。收录的论文涵盖了人工智能的各种主题,包括深度学习及其应用、强化学习与规划、纯理论研究、概率方法、优化及机器学习与社会等。本文回顾了NeurIPS 2020的亮点及论文录用情况,详细解读了特邀报告、最佳论文、口头报告及部分海报论文,希望能帮助读者快速了解NeurIPS 2020的盛况。 相似文献
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Keshun Liu 《Journal of the American Oil Chemists' Society》2021,98(12):1115-1129
For measuring trypsin inhibitor activity (TIA), there are two major official methods: American Oil Chemists Society (AOCS) method Ba 12a-2020 and International Organization for Standardization (ISO) 14902:2001. The former was recently approved. The two methods differ in sample preparation, extraction, colorimetric assay systems and TIA calculations. In this study, the two methods were symmetrically compared using three unique sets of samples: assorted protein products of soybeans, pulses, and grains; soybeans boiled for varied durations; and soy white flakes toasted for varied durations. For given samples, significant differences existed in TIA measured by the two methods, resulting from effects related to the assay systems and TIA calculations, not from the difference in sample preparation and extraction. When the same trypsin was used, TIA (in mg trypsin inhibited/g sample) measured by the two methods were highly correlated (r = 0.9973, n = 27), giving an equation of y = 0.5464x − 0.4887, where y represents ISO values and x for AOCS values. The line connecting ratios of ISO/AOCS in TIA and AOCS values remained relatively flat around 0.53 but started to curve down when TIA approached the lowest. Furthermore, for the same samples, TIA values measured by the ISO method decreased with increasing specific activity of trypsin used, while AOCS values remained consistent, leading to decreasing ratios of ISO/AOCS. Therefore, accurate and direct comparison of the two methods was impossible. It could not be resolved by simply changing ISO method's calculations as hypothesized earlier. Regardless, for most samples, ISO values were roughly about 55% of AOCS values. 相似文献
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Kenichiro Masaoka 《Journal of the Society for Information Display》2016,24(7):419-423
To measure the relative gamut sizes of wide‐gamut displays, it is herein proposed that the CIE 1931 xy chromaticity diagram be used rather than the nominally perceptually uniform CIE 1976 u′v′ chromaticity diagram. High correlations were found between the area‐coverage ratios in the xy diagram and the volume‐coverage ratios in the CIE 1976 L*a*b* color space for major standard wide‐gamut color spaces. It is also demonstrated herein that performing planimetry in the uniform u′v′ diagram does not yield accurate relative display gamut sizes, even though the large sizes obtained using the u′v′ diagram are often reported regardless of the fact that its uniformity is valid only when the luminance factor is constant. The single display gamut size metric using the xy diagram will facilitate the unbiased development of wide‐gamut displays. 相似文献
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Kenichiro Masaoka 《Journal of the Society for Information Display》2016,24(12):741-746
It is herein proposed to measure display gamut sizes by employing the International Telecommunication Union—Radiocommunication Sector Recommendation BT.2020 (Rec. 2020) area‐coverage ratios in the xy chromaticity diagram rather than the standard gamut area metrics that use the horseshoe‐shaped spectrum chromaticity area as the target in the u′v′ chromaticity diagram. It is more reasonable to use the Rec. 2020 gamut than the spectrum gamut as the target because the Rec. 2020 area‐coverage ratios in the xy diagram are better correlated than the spectrum area‐coverage ratios with the volume‐coverage ratios of object color gamuts that are visually significant in displaying natural scenes. 相似文献
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This article evaluates the status of current RES deployment, policies and barriers in the EU-27 member states and compares it to the required to meet the 2020 targets. The evaluation relies strongly on the quantitative deployment status and policy effectiveness indicators. European RES deployment and policy has progressed strongly in recent years, but the growth here has been mainly driven by effective policies in a small or medium number of top runner countries. Across Europe, the highest average policy effectiveness over six years was reached for onshore wind (4.2%), biofuels (3.6%) and biomass electricity (2.7%), while in the heat sector, all technologies score below 2%. Comparing the recent progress to the required growth for meeting the 2020 target, it appears that some countries largely exceed the interim targets of the RES Directive 2009/28/EC. Despite this, Europe will need additional policy effort to reach the 2020 target. Critical success factors include implementing effective and efficient policies that attract sufficient investments, reducing administrative and grid related barriers, especially in currently less advanced countries, upgrading the power grid infrastructure, dismantling financial barriers in the heat sector, realising sustainability standards for biomass, and lowering energy demand through increased energy efficiency efforts. 相似文献
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《International Journal of Hydrogen Energy》2019,44(35):19067-19079
In order to maximise European, national and regional research and innovation potential the European Union is investing in these fields through different funding mechanisms such as the ESIF or H2020 programme. This investment plan is part of the European 2020 strategy, where the concept of Smart Specialisation is also included.Smart Specialisation is an innovation policy concept designed to promote the efficient and effective use of public investment in regional innovation in order to achieve economic growth. The Smart Specialisation Platform was created to support this concept by assisting regions and Member States in developing, implementing and reviewing their research and innovation Smart Specialisation strategies.The Smart Specialisation Platform comprises several thematic platforms. The thematic Smart Specialisation Platform on energy (S3PEnergy) is a joint initiative of three European Commission services: DG REGIO, DG ENER, and the Joint Research Centre (JRC). The main objective of the S3PEnergy is to support the optimal and effective uptake of the Cohesion Policy funds for energy, and to better align energy innovation activities at national, local and regional level through the identification of the technologies and innovative solutions that support in the most cost-effective way the EU energy policy priorities.In the particular case of hydrogen technologies, the activities of the platform are mainly focused on supporting the new Fuel Cells and Hydrogen Joint Undertaking (FCH JU) initiative involving regions and cities. To date, more than 80 European cities and regions have committed to participate in this initiative through the signature of a Memorandum of Understanding, and more participants are expected to join. S3PEnergy is helping in the identification of potential combination of H2020 funding (provided through FCH JU) and ESIF.To identify potential synergies among these two funding sources, a mapping of the different ESIF opportunities has been performed. In order to map these opportunities, Operational Programmes (OPs) and research and innovation strategies for Smart Specialisation (RIS3) of the different European regions and Member States were analysed. The results of this mapping and analysis are presented in this paper. 相似文献