Three-body problem — From Newton to supercomputer plus machine learning

The famous three-body problem can be traced back to Newton in 1687, but quite few families of periodic orbits were found in 300 years thereafter. In this paper, we propose an effective approach and roadmap to numerically gain planar periodic orbits of three-body systems with arbitrary masses by means of machine learning based on an artificial neural network (ANN) model. Given any a known periodic orbit as a starting point, this approach can provide more and more periodic orbits (of the same family name) with variable masses, while the mass domain having periodic orbits becomes larger and larger, and the ANN model becomes wiser and wiser. Finally we have an ANN model trained by means of all obtained periodic orbits of the same family, which provides a convenient way to give accurate enough predictions of periodic orbits with arbitrary masses for physicists and astronomers. It suggests that the high-performance computer and artificial intelligence (including machine learning) should be the key to gain periodic orbits of the famous three-body problem.     

基于特征分离机制的深度学习植被自动提取方法

随着遥感影像分辨率的提高,植被信息的高精度提取对于了解地表植被变化规律、评价生态区域具有重要意义。针对传统方法跨季节植被提取不完整问题,本文基于高分2号(GF-2)卫星数据,提出一种基于特征分离机制的深度学习语义分割网络植被提取方法。该网络在Densenet的基础增加可分离卷积和空间金字塔结合的特征分离机制来增大感受野,更有效利用植被的特征信息,提升了模型的精度。本文通过构建高精细跨季节植被样本库,使用本文所提方法,完成了遥感影像植被信息提取,并选取总体准确度、F1值和交并比作为评价指标,对不同的传统方法和深度学习方法进行精度对比与分析。实验结果表明,本文方法提取植被的效果较好,其中F1分数达到91.91%,总体准确度达到92.79%,交并比达到85.10%。对高分1号、高分6号和高景1号遥感影像进行植被提取通用性验证,结果表明本文方法具有一定的通用能力,可以从高分辨率遥感影像中准确地、自动地提取植被。本文研究成果可为城市生态环境评价和植被的应用研究提供数据参考。     

面向倾斜摄影的深度学习航空影像匹配方法

针对传统方法和深度学习匹配方法在倾斜影像上获取匹配点少、复现率低以及精度不高等问题,本文提出一种面向倾斜摄影的深度学习航空影像匹配方法。首先,利用POS信息计算影像重叠区域,并对倾斜影像进行透视变换改正,减弱几何变形对匹配过程的影响;其次,在变换后的重叠区域影像上利用训练的多尺度特征点检测网络推理其对应的高斯热力图,在高斯热力图尺度空间检测极值点作为稳定特征点,基于自监督主方向检测网络获取特征点主方向;接着,在特征点描述阶段,结合网络学习得到的特征点位置和主方向获取尺度旋转不变GeoDesc基础描述子,并考虑图像的几何、视觉上下文信息对描述子进行增强处理;最后,通过双向比值提纯法获取初始匹配点,利用RANSAC和图约束方法剔除误匹配后获得最终匹配点结果。使用ISPRS提供的2组典型区域倾斜影像进行匹配实验,结果表明,相比于SIFT、ASIFT、SuperPoint、GeoDesc及ContextDesc等算法,本文方法能够在大视角变化和纹理信息贫乏的倾斜影像对上获取更多均匀分布的匹配点,同时复现率也要优于其他方法。     

Environmentalities of urban climate governance in Maputo,Mozambique

Interest in the role that cities can play in climate change as sites of transformation has increased but research has been limited in its practical applications and there has been limited consideration of how policies and technologies play out. These challenges necessitate a re-thinking of existing notions of urban governance in order to account for the practices that emerge from governments and a plethora of other actors in the context of uncertainty. We understand these practices to constitute adaptive governance, underpinned by social learning guiding the actions of the multiplicity of actors. The aim here is to unpack how social learning for adaptive governance requires attention to competing understandings of risk and identity, and the multiplicity of mechanisms in which change occurs or is blocked in urban climate governance. We adopt a novel lens of ‘environmentalities’ which allows us to assess the historical and institutional context and power relations in the informal settlements of Maputo, Mozambique. Our findings highlight how environmental identities around urban adaptation to climate change are constituted in the social and physical divisions between the formal and informal settlements, whilst existing knowledge models prioritise dominant economic and political interests and lead to the construction of new environmental subjects. While the findings of this study are contextually distinct, the generalizable lessons are that governance of urban adaptation occurs and is solidified within a complex multiplicity of socio-ecological relations.     

Simulating alpine vegetation net primary productivity by remote sensing in Qinghai Province,China

Primary productivity of ecosystem is important indicator about ecological assessment. Remote sensing technology has been used to monitor net primary productivity （NPP） of ecological system for several years. In this paper, the remotely sensed NPP simulation model of alpine vegetation in Qinghai Province of Tibet Plateau was set up based on the theory of light use efficiency. Firstly a new approach based on mixed pixels and Support Vector Machine （SVM） algorithm were used to correct simulated NPP values derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data. Finally, spatial distribution and monthly variation characteristics of NPP in Qinghai Province detail. The result showed in 2006 were analyzed in that NPP of vegetation in Qinghai Province in 2006 ranged from o to 422 gC/m2/a and the average NPP was 151 gC/m2/a. NPP gradually increased from northwest to southeast. NPP of different vegetation types were obviously different. The average NPP of broad-leaved forest was the largest （314 gC/m2/a）, and sparse shrub was the smallest （101 gC/m2/a）. NPP in Qinghai Province significantly changed with seasonal variation. The accumulation of NPP was primarily in the period （from April to September） with better moist and heat conditions. In July, the average NPP of vegetation reached the maximum value （43 gC/m2）. In our model, the advantage of traditional LUE models was adopted, and our study fully considered typicalcharacteristics of alpine vegetation light use efficiency and environmental factors in the study area. Alpine vegetation is the most important ecological resource of Tibet Plateau, exactly monitoring its NPP value by remote sensing is an effective protection measure.     

Marine mammal co-management in Canada’s Arctic: Knowledge co-production for learning and adaptive capacity

This paper examines the challenge of knowledge co-production and the implications for learning and adapting in the context of a narwhal co-management in Nunavut, Canada. Knowledge co-production is the collaborative process of bringing a plurality of knowledge sources and types together to address a defined problem and build an integrated or systems-oriented understanding of that problem. The paper considers knowledge co-production by examining five interrelated dimensions: knowledge gathering, sharing, integration, interpretation, and application. Voices of hunters, community representatives, and managers engaged in co-management are highlighted to identify primary challenges and opportunities. The analysis reveals how compartmentalized views of knowledge continue to constrain adaptive and collaborative management. An understanding of knowledge co-production processes, however, may help to overcome the resilience of top-down management approaches.     

浅析大学生英语学习中的语用失误及其对策

大学生在与英语国家本国人交际过程中,即使语言本身不存在语法错误,但如果对谈话对象的文化背景缺乏了解,未能根据当时的语境选用恰当的语言表达形式而造成语用失误,会导致交际失败。为进一步了解大学生英语语用能力的情况,完善大学英语教学,对某校150名学生进行了语用能力问卷调查。通过分析问卷结果及出现语用失误的原因,提出在大学英语教学中避免语用失误,培养学生英语交际能力的对策。     

Liberating learning: Thinking beyond ‘the grade’ in field‐based approaches to teaching

This paper is positioned within current debates on education development and the value of fieldwork as a pathway to fostering a nuanced, sophisticated and empathetic world view among students. Here, we focus on one form of field‐based teaching within geography, that is, intensive field studies courses taught abroad. We draw on our experience as cofacilitators of a six‐week intensive field course conducted in various parts of Thailand. The course we discuss in this paper was focused on teaching students both applied research skills (critical engagement, ethnographic research methods and ethical research practice) and substantive content (the social, cultural, political and economic aspects of Thailand from a geographer's perspective). We argue that the value of field studies lies in the ability of such a course to help students enhance and deepen broad, generalisable skills such as problem solving; ethical research practice; critical engagement with complex social issues; and independent research skills.     

基于多源遥感和机器学习方法的科尔沁沙地植被覆盖度反演

植被覆盖度是监测生态系统及其功能的关键参数,如何提高大区域植被覆盖度的反演精度,对生态脆弱区环境可持续发展至关重要。本研究基于人工神经网络、支持向量回归和随机森林等机器学习方法,利用无人机、Worldview-2与Landsat 8 OLI遥感数据,对科尔沁沙地植被覆盖度进行多尺度反演。结果表明：随机森林模型比人工神经网络、支持向量回归模型表现佳,可在单元（试验区）、区域（研究区）尺度上较高精度地反演沙地的植被覆盖度,反演值与无人机实测值均在线性水平上呈显著相关（P<0.01）;在单元、区域尺度上,构建的植被覆盖度反演模型测试集R²分别为0.84、0.80,MSE分别为0.0145、0.0370,一致性指数d分别为0.9576、0.8991。利用多源遥感数据和机器学习方法,通过局部区域的高精度反演逐步实现低空间分辨率遥感影像的大区域植被覆盖度反演,不仅可有效提高沙地植被覆盖度的反演精度（R²=0.78,大于0.63）,也为区域生态环境监测与生态系统健康评价提供支持。     

复杂场景下农村道路裂缝分割方法

针对农村道路裂缝识别中存在训练样本数量少、场景单一、提取结果不准确等问题,本文首先依托辽宁省多年份实测道路图像数据,构建具有多种类、多场景的路面裂缝数据集(PCDs),以ResNet50为编码器、SegNet为解码器,构建路面裂缝图像识别模型Res-SegNet,通过增大卷积核的大小获取更丰富的裂缝信息,使用Focal Loss损失函数,令模型更专注困难样本。然后采用分块预测方法提升裂缝在图片中的占比,使图片预测更加精细。最后通过网络模型和预测方法进行对比试验。结果表明,使用Res-SegNet识别PCDs的测试集,在不同的场景中F值为0.691,使用Res-SegNet结合分块预测识别PCDs的测试集,在不同的场景中F值达0.753。