Two-dimensional materials for electrocatalytic water splitting

氢能是一种高热值、无污染的洁净能源.电解水制氢被认为是一种有效利用可再生能源,如风能、太阳能等,实现能量储存和转换的前沿科技.二维材料独特的结构和电子特性使其在催化电解水反应中具有广阔的应用前景.本文系统综述了二维材料在催化电解水反应中的两个半反应——阴极析氢反应（HER）和阳极析氧反应（OER）中的关键科学问题和最新进展,并展望了该领域所面临的挑战和机遇.Hydrogen is a kind of clean energy with high calorific value and non-pollution. There are many methods for hydrogen production. Fuel processing technologies transform a hydrogen containing material such as coal, petroleum, or natural gas into a hydrogen rich stream. However, these processes need an external heat source for the reactor and produce large amounts of carbon dioxide. Hydrogen production by electrolysis of water is regarded as an advanced technology to make effective use of renewable resources, such as wind power, solar power, etc., to achieve energy storage and conversion. Water electrolysis includes hydrogen evolution reaction（HER） and oxygen evolution reaction（OER）. These reactions are normally catalyzed by precious metals, such as platinum（Pt） and iridium（Ir）-based catalysts, which limits the large-scale application of electrolysis of water. Thus, it is necessary to develop alternative catalysts with low cost and high performance. Two-dimensional（2D） materials have considerable application prospect in electrocatalysis of H2 O because of their unique structural and electronic properties. In addition, 2D materials with a reduced dimension compared with the bulk material exhibits several distinctive properties, such as high specifc surface area, high thermal and electric conductivity and more catalytic active sites. In this review, the key scientific issues and the latest advances in the two half-reactions（HER and OER） of electrocatalytic water splitting with 2D materials are systematically summarized. The mechanisms of HER and OER are discussed briefly. The involved 2D materials for HER in this work include graphene, graphene encapsulated transition-metal catalysts, g-C_3N_4 and 2D transition-metal dichalcogenides, while for OER contain layered double hydroxide（LDH） and graphene encapsulated transition-metal catalysts materials. For graphene, g-C_3N_4 and 2D transition-metal dichalcogenides, there are various techniques to enhance the catalytic activity of the materials, s国家科技部重点研发计划（2016YFA0204100,2016YFA0200200）; 国家自然科学基金（21573220）; 中国科学院前沿科学重点研究计划（QYZDB-SSW-JSC020）和中国科学院战略性先导科技专项（XDA09030100）资

Plenty is Plague: Fine-Grained Learning for Visual Question Answering

纪荣嵘教授团队的论文提出了一种基于强化学习的细粒度学习策略FG-A1C，旨在通过分析视觉问答任务中的样本多样性及标签的冗余性问题来针对性地挑选训练样本以提高模型的训练效率及减少标记支出。 该论文由厦门大学媒体分析与计算实验室的周奕毅博士后助理研究员、纪荣嵘教授（通信作者）、孙晓帅副教授、苏劲松副教授，以及西安交通大学孟德宇教授、清华大学高跃副教授和澳大利亚阿德莱德大学沈春华教授等合作完成。Visual Question Answering (VQA) has attracted extensive research focus recently. Along with the ever-increasing data scale and model complexity, the enormous training cost has become an emerging challenge for VQA. In this paper, we show such a massive training cost is indeed plague. In contrast, a fine-grained design of the learning paradigm can be extremely beneficial in terms of both training efficiency and model accuracy. In particular, we argue that there exist two essential and unexplored issues in the existing VQA training paradigm that randomly samples data in each epoch, namely, the "difficulty diversity" and the "label redundancy". Concretely, "difficulty diversity" refers to the varying difficulty levels of different question types, while "label redundancy" refers to the redundant and noisy labels contained in individual question type. To tackle these two issues, in this paper we propose a fine-grained VQA learning paradigm with an actor-critic based learning agent, termed FG-A1C. Instead of using all training data from scratch, FG-A1C includes a learning agent that adaptively and intelligently schedules the most difficult question types in each training epoch. Subsequently, two curriculum learning based schemes are further designed to identify the most useful data to be learned within each inidividual question type. We conduct extensive experiments on the VQA2.0 and VQA-CP v2 datasets, which demonstrate the significant benefits of our approach. For instance, on VQA-CP v2, with less than 75% of the training data, our learning paradigms can help the model achieves better performance than using the whole dataset. Meanwhile, we also shows the effectivenesss of our method in guiding data labeling. Finally, the proposed paradigm can be seamlessly integrated with any cutting-edge VQA models, without modifying their structures.This work is supported by the National Key R&D Program (No.2017YFC0113000, and No.2016YFB1001503), Nature Sci- ence Foundation of China (No.U1705262, No.61772443, and No.61572410), Post Doctoral Innovative Talent Support Pro-gram under Grant BX201600094, China Post-Doctoral Sci- ence Foundation under Grant 2017M612134, Scientific Re-search Project of National Language Committee of China (Grant No. YB135-49), and Nature Science Foundation of Fu-jian Province, China (No. 2017J01125 and No. 2018J01106). 本项研究得到了厦门大学“人工智能分析引擎”双一流重大专项的支持、国家重点研发专项和国家自然科学基金海峡基金等项目的支持

二维催化材料在电解水中的研究进展

氢能是一种高热值、无污染的洁净能源.电解水制氢被认为是一种有效利用可再生能源,如风能、太阳能等,实现能量储存和转换的前沿科技.二维材料独特的结构和电子特性使其在催化电解水反应中具有广阔的应用前景.本文系统综述了二维材料在催化电解水反应中的两个半反应——阴极析氢反应(HER)和阳极析氧反应(OER)中的关键科学问题和最新进展,并展望了该领域所面临的挑战和机遇

Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the l(1) norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.</p

麋鹿角柄发育规律与相关内分泌及信号通路检测

鹿类角柄终生有2次发育，第1次在胚胎期，第2次在青春期，出生后角柄的激活和首次发育对于鹿角再生极为重要，角柄的青春期发育对于鹿类个性及等级序位形成、鹿角大小、争夺配偶、种间竞争和应对天敌等有重要意义。利用红外测距仪，开展麋鹿（Elaphurus davidianus）角柄生长发育的宏观测量，结合青春期骨膜组织取样、血液激素水平和信号通路表达情况检测，对麋鹿角柄发育情况进行研究。结果表明：与成年不同，幼龄雄性麋鹿在第1年秋季开始出生后的首次角柄发育，由毛旋处长出骨质突起，翌年2—3月角柄长至2.5~3.0 cm，逐渐形成初角基，5—6月首次生茸，冬至脱落，此时形成完整的角柄；随青春期发育和年龄增长，角柄逐渐变粗变短；角柄围长由2岁时的（17.17±1.00）cm增加到3岁时的（35.43±0.83）cm，差异极显著（F1，9=4.128 1，n=9，p=0.005 3）；角柄直径由2岁时的（3.66±0.59）cm增加到3岁时的（5.91±0.72）cm，差异极显著（F1，9=3.174 0，n=9，p=0.024 6）。血液激素检测表明睾酮分泌水平与角柄发育密切相关，睾酮分泌水平由1岁时的（564.27±41.16） pg/mL增加到3岁时的（737.96±66.57） pg/mL，差异极显著（F1，9=4.303 0，n=9，p=0.002 2）；分子检测结果也显示TGF-β/Smads信号通路参与了角柄发育过程。研究揭示了麋鹿角柄发育规律，探讨与之相关的内分泌因素及分子表达情况，为今后持续开展麋鹿角柄发育及茸再生机制多样化奠定了基础

麋鹿常见疾病及其防治

野生动物疾病与人类的健康息息相关。麋鹿（Elaphurus davidianus）是我国特有湿地物种，国家一级保护野生动物，世界自然保护联盟（IUCN）物种红色名录将其濒危等级定为野外灭绝（EW）。世界各地现存的麋鹿均为19世纪末保存在英国乌邦寺的18只麋鹿的后代，麋鹿经历了多次遗传瓶颈，遗传多样性较低，其疾病类型具有自身特性；虽然目前麋鹿种群逐渐扩大，但疾病仍是影响麋鹿种群健康和稳定扩大的重要因素。结合日常饲养管理，从细菌、病毒、寄生虫和普通疾病四大类型方面，综述了国内外麋鹿常见的疾病，并给出相应的防治措施，为麋鹿的健康管理和科学保护，以及为生物物种安全和公共卫生安全提供理论依据