基于同步EEG-fMRI采集的情绪认知重评数据特征融合分析研究

脑电(Electroencephalography, EEG)与功能磁共振成像(Functional magnetic resonance imaging, fMRI)为脑科学研究提供了互补的时空信息. 为研究大脑在对情绪图片采取认知重评策略时的神经活动, 基于同步采集的EEG-fMRI数据, 应用典型相关分析、经验模态分解及k-均值聚类等算法对融合情绪数据进行交叉关联和盲源分离, 得到空间上的fMRI图像和与之对应的EEG时间演变信号. 结果表明: 时域上, CCA分离出的脑电成分在认知重评状态下有明显的晚期正电位(Late positive potential, LPP) (潜伏期200ms～900ms)出现, 而且认知重评策略诱发下的LPP 波幅明显小于观看负性诱发的LPP波幅(F(1, 224)= 28.72, P<0.01), 而大于观看中性诱发的LPP波幅(F(1, 224)= 63.32, P<0.01); 与之对应的空域上, 可以明显地看出和情绪调节相关的扣带回, 额叶、颞叶等区域有明显激活区, 采用情绪认知重评策略时的脑区激活强度明显小于观看负性状态, 而大于观看中性, 且观看中性状态下被激活的与情绪相关的区域相对较少. 研究表明, 这种融合数据分析技术通过计算两种模态数据之间潜在的线性相关性, 可以有效地分离出大脑在时空上神经活动情况, 达到了同时描绘出大脑神经活动的时间信息与空间信息的效果.     

Verifying Timing Constraints of Real-Time Systems by Means of Evolutionary Testing

Many industrial products are based on the use of embedded computer systems. Usually, these systems have to fulfil real-time requirements, and correct system functionality depends on their logical correctness as well as on their temporal correctness. In order to verify the temporal behavior of real-time systems, previous scientific work has, to a large extent, concentrated on static analysis techniques. Although these techniques offer the possibilty of providing safe estimates of temporal behavior for certain cases, there are a number of cases in practice for which static analysis can not be easily applied. Furthermore, no commercial tools for timing analysis of real-world programs are available. Therefore, the developed systems have to be thoroughly tested in order to detect existing deficiencies in temporal behavior, as well as to strengthen the confidence in temporal correctness. An investigation of existing test methods shows that they mostly concentrate on testing for logical correctness. They are not specialised in the examination of temporal correctness which is also essential to real-time systems. For this reason, existing test procedures must be supplemented by new methods which concentrate on determining whether the system violates its specified timing constraints. Normally, a violation means that outputs are produced too early, or their computation takes too long. The task of the tester therefore is to find the input situations with the longest or shortest execution times, in order to check whether they produce a temporal error. If the search for such inputs is interpreted as a problem of optimization, evolutionary computation can be used to automatically find the inputs with the longest or shortest execution times. This automatic search for accurate test data by means of evolutionary computation is called evolutionary testing. Experiments using evolutionary testing on a number of programs with up to 1511 LOC and 5000 input parameters have successfully identified new longer and shorter execution times than had been found using other testing techniques. Evolutionary testing, therefore, seems to be a promising approach for the verification of timing constraints. A combination of evolutionary testing and systematic testing offers further opportunities to improve the test quality, and could lead to an effective test strategy for real-time systems.     

Power spectral based detection of brain activation from fMR images

An efficient method for detecting activation on single and multiple epoch functional MRI (fMRI) data based on power spectral density of time-series and hidden Markov model is presented. Conventional methods of analysis of fMRI data are generally based on time-domain correlation analysis concentrating mainly on the multiple epoch data and generally do not provide good results for single epoch data. The main focus of this study is the analysis of single epoch data, constrained by certain experiments such as pain response, sleep, administration of pharmacological agents, which can only have a single or very few stimulus cycles. Further, our method obviates the need to exclusively model the hemodynamic response function and correctly identifies the voxels with delayed activation. We demonstrate the efficacy of our method in detecting brain activation by using both synthetic and real fMRI data.     

Checking Finite Traces Using Alternating Automata

Alternating automata have been commonly used as a basis for static verification of reactive systems. In this paper we show how alternating automata can be used in runtime verification. We present three algorithms to check at runtime whether a reactive program satisfies a temporal specification, expressed by a linear-time temporal logic formula. The three methods start from the same alternating automaton but traverse the automaton in different ways: depth-first, breadth-first, and backwards, respectively. We then show how an extension of these algorithms, that collects statistical data while verifying the execution trace, can be used for a more detailed analysis of the runtime behavior. All three methods have been implemented and experimental results are presented.     

Dynamics and topographic organization of recursive self-organizing maps

Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographic maps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizing map (SOM) for processing sequential data, recursive SOM(RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter beta (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data.     

High resolution spatio‐temporal water vapour mapping using GPS and MERIS observations

Improved knowledge of atmospheric water vapour and its temporal and spatial variability is of great scientific interest for climate research and weather prediction. Moreover, the availability of fine resolution water vapour maps is expected to reduce significant errors in applications using the Global Positioning System, GPS, or radar interferometry. Several methods exist to estimate water vapour using satellite systems. Combining radiances as measured in two spectral bands of the Medium Resolution Imaging Spectrometer (MERIS) results in an Integrated Water Vapor (IWV) product with high spatial resolution, up to 300 m, but a limited temporal resolution of about three days, in case of cloud free conditions. On the other hand, IWV estimates can be derived from the zenith total delays as observed by continuous GPS networks. The GPS IWV estimates have a higher temporal resolution of typically 1 hour, but, even in Western Europe, inter‐station distances are at least tenths of kilometres. Here we describe how to obtain IWV products with high spatio‐temporal resolution by combining GPS and MERIS IWV estimates. For this purpose an analysis is made of MERIS and GPS based IWV data, retrieved at the same day over Western Europe. A variance–covariance analysis is performed and is subsequently applied to produce time series of combined high‐resolution water vapour maps using Kriging. The research presented here is a first step towards near real‐time fine resolution water vapour products.     

Comparison of pixel- and object-based classification in land cover change mapping

Land use/land cover (LULC) change occurs when humans alter the landscape, and this leads to increasing loss, fragmentation and spatial simplification of habitat. Many fields of study require monitoring of LULC change at a variety of scales. LULC change assessment is dependent upon high-quality input data, most often from remote sensing-derived products such as thematic maps. This research compares pixel- and object-based classifications of Landsat Thematic Mapper (TM) data for mapping and analysis of LULC change in the mixed land use region of eastern Ontario for the period 1995–2005. For single date thematic maps of 10 LULC classes, quantitative and visual analyses showed no significant accuracy difference between the two methods. The object-based method produced thematic maps with more uniform and meaningful LULC objects, but it suffered from absorption of small rare classes into larger objects and the incapability of spatial parameters (e.g. object shape) to contribute to class discrimination. Despite the similar map accuracies produced by the two methods, temporal change maps produced using post-classification comparison (PCC) and analysed using intensive visual analysis of errors of omission and commission revealed that the object-based maps depicted change more accurately than maximum likelihood classification (MLC)-derived change maps.     

一种加强SSD小目标检测能力的Atrous滤波器设计

针对实时目标检测SSD（single shot multiBox detector）算法对小目标检测能力偏差的问题,提出了一种提高特征图分辨率的Atrous滤波器设计策略。改进算法在SSD网络结构的基础上,把第三、四层卷积层产生的特征图经过规范化后连接在一起,然后通过Atrous卷积运算提高这些特征图分辨率。这些特征图共同提供小目标的所需的特征。另外该SSD改进算法还加入SeLU（scaled exponential linear units）激活函数,并在数据预处理阶段设计了一套数据增广方法。实验表明,该改进算法框架相对于原SSD算法框架具有更高的检测精度、更优良的鲁棒性,以及在小目标检测上效果明显。     

Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta

Because of the importance of rice for the global food security and because of the role of inundated paddy fields in greenhouse gases emissions, monitoring the rice production world-wide has become a challenging issue for the coming years. Local rice mapping methods have been developed previously in many studies by using the temporal change of the backscatter from C-band synthetic aperture radar (SAR) co-polarized data. The studies indicated in particular the need of a high observation frequency. In the past, the operational use of these methods has been limited by the small coverage and the poor acquisition frequency of the available data (ERS-1/2, Radarsat-1). In this paper, the method is adapted for the first time to map rice at large scale, by using wide-swath images of the Advanced SAR (ASAR) instrument onboard ENVISAT. To increase the observation frequency, data from different satellite tracks are combined. The detection of rice fields is achieved by exploiting the high backscatter increase at the beginning of the growing cycle, which allows the production of rice maps early in the season (in the first 50 days). The method is tested in the Mekong delta in Vietnam. The mapping results are compared to existing rice maps in the An Giang province, with a good agreement (higher than 81%). The rice planted areas are retrieved from the maps and successfully validated with the official statistics available at each province (R² = 0.92). These results show that the method is useful for large scale early mapping of rice areas, using current and future C band wide-swath SAR data.     

C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multiattribute (multidimensional) and temporal in nature. Data. mining and business intelligence, techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique for representing trends in multiattribute temporal data using a clustering- based approach. We introduce Cluster-based Temporal Representation of EveNt Data (C-TREND), a system that implements the temporal cluster graph construct, which maps multiattribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time. In this paper, we present our temporal clustering-based technique, discuss its algorithmic implementation and performance, demonstrate applications of the technique by analyzing data on wireless networking technologies and baseball batting statistics, and introduce a set of metrics for further analysis of discovered trends.     

A generalization of spatial and temporal fusion methods for remotely sensed surface parameters

Remotely sensed surface parameters, such as vegetation index, leaf area index, surface temperature, and evapotranspiration, show diverse spatial scales and temporal dynamics. Generally the spatial and temporal resolutions of remote-sensing data should match the characteristics of surface parameters under observation. These requirements sometimes cannot be provided by a single sensor due to the trade-off between spatial and temporal resolutions. Many spatial and temporal fusion (STF) methods have been proposed to derive the required data. However, the methodology suffers from disorderly development. To better inform future research, this study generalizes the existing methods from around 100 studies as spatial or temporal categories based on their physical assumptions related to spatial scales and temporal dynamics. To be specific, the assumptions are related to the scale invariance of the temporal information and temporal constancy of the spatial information. The spatial information can be contexture or spatial details. Experiments are conducted using Landsat data acquired on 13 dates in two study areas and simulated Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results are presented to demonstrate the typical methods from each category. This study concludes the following. (1) Contexture methods depend heavily on how components maps (contexture) are defined. They are not recommended except when components maps can be estimated properly from observed images. (2) The spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) methods belong to the temporal and spatial categories, respectively. Thus, STARFM and ESTARFM should be better applied to temporal variance – dominated and spatial variance – -dominated areas, respectively. (3) Non-linear methods, such as the sparse representation-based spatio-temporal reflectance fusion model, can successfully address land-cover changes in addition to phonological changes, thereby providing a promising option for STF problems in the future.     

基于栈式循环神经网络的血液动力学状态估计方法

利用fMRI数据准确地估计血液动力学状态, 能得到一种更接近神经元层面的大脑活动的客观表示, 这将促进人们对大脑运行机理的深刻理解, 推动脑认知的进一步发展.迄今为止, 人们已经提出了许多血液动力学状态估计方法.然而, 这些方法大都只考虑了相邻时刻血液动力学状态之间的关系, 忽视了更深层次的时序特征.而对模型参数先验信息的需求也使一些方法在实际应用中受到了限制.为此, 本文提出了一种基于循环神经网络的血液动力学状态估计新方法.首先, 利用血液动力学模型中非线性函数的反函数建立BOLD信号与血液动力学状态之间的映射关系, 并构建模型的反演过程.然后, 采用一种堆叠三个RNN模块的栈式神经网络结构来拟合这种映射关系, 使其能够以BOLD信号作为输入, 得到血液动力学状态的估计值.最后, 在仿真数据上验证新方法的性能.实验结果表明:与一些代表算法相比, 新方法能够更合理地提取fMRI数据中的时间特性, 有效地拟合BOLD信号与血液动力学状态之间的动态非线性关系.     

Salient objects detection in dynamic scenes using color and texture features

Visual saliency is an important research topic in the field of computer vision due to its numerous possible applications. It helps to focus on regions of interest instead of processing the whole image or video data. Detecting visual saliency in still images has been widely addressed in literature with several formulations. However, visual saliency detection in videos has attracted little attention, and is a more challenging task due to additional temporal information. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In our work, we model the dynamic textures in a dynamic scene with local binary patterns to compute the dynamic saliency map, and we use color features to compute the static saliency map. Both saliency maps are computed using a bio-inspired mechanism of human visual system with a discriminant formulation known as center surround saliency, and are fused in a proper way. The proposed model has been extensively evaluated with diverse publicly available datasets which contain several videos of dynamic scenes, and comparison with state-of-the art methods shows that it achieves competitive results.     

Regional scale hierarchical classification of temporal series of AVHRR vegetation index

In this paper a hierarchical approach is taken to classify temporal sequences of images of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), using Iberia as an example. Iberia is a convenient area of study because it has a high environmental diversity and very strong environmental gradients, and yet a reduced size at the spatial resolution of current global data-sets. An Iberian subset of a global temporal series of AVHRR-NDVI images facilitates test and validation of different approaches while producing results that are likely to be valid over much larger areas. Our hierarchical clustering approach yields maps with nested legends. We compare these maps to a digitized map of potential natural vegetation, which reveals a clear bioclimatic control. The highest level of the hierarchical classification separates vegetation with a Summer peak of NDVI from vegetation with a Spring peak of NDVI. Such a discontinuity corresponds to the discontinuity between Atlantic and Submediterranean vegetation in the vegetation map. Lower levels in the hierarchical classification produce maps of increasing complexity but that keep a high degree of spatial continuity. A correspondence analysis between a 16-classes NDVI map and the digitized map of potential vegetation produces an ordination that is bioclimatically coherent. According to the known characteristics of the potential vegetation units, the two first correspondence axes can be interpreted, respectively, as water availability and temperature. These results are a consequence of the temporal NDVI series being an accurate signal of vegetative phenology, which in turn is a fundamental vegetation property. A comparison of our results with several global land cover digital maps by means of the Wilk's ratio indicates that the global maps do not produce an appropriate partition of the region in terms of the NDVI temporal course. We conclude that the analysis of temporal series of NDVI yield relevant ecological information at finer scales and with more detailed legends that had not been attempted until now, and, therefore, are suitable for regional scale applications. Our results also indicate the interest of a bioclimatic analysis and modeling of the NDVI signatures for their correct ecological understanding. Maps at a global scale can be produced based on such an understanding.     

Multi‐sensor data fusion and comparison of total column ozone

With many remote‐sensing instruments onboard satellites exploring the Earth's atmosphere, most data are processed to gridded daily maps. However, differences in the original spatial, temporal, and spectral resolution—as well as format, structure, and temporal and spatial coverage—make the data merging, or fusion, difficult. NASA Goddard Earth Sciences Data and Information Services Center (GES‐DISC) has archived several data products for various sensors in different formats, structures, and multi‐temporal and spatial scales for ocean, land, and atmosphere. In this investigation using Earth science data sets from multiple sources, an attempt was made to develop an optimal technique to merge the atmospheric products and provide interactive, online analysis tools for the user community. The merged/fused measurements provide a more comprehensive view of the atmosphere and improve coverage and accuracy, compared with a single instrument dataset. This paper describes ways of merging/fusing several NASA Earth Observing Systems (EOS) remote‐sensing datasets available at GES‐DISC. The applicability of various methods was investigated for merging total column ozone to implement these methods into Giovanni, the online interactive analysis tool developed by GES‐DISC. Ozone data fusion of MODerate resolution Imaging Spectrometer (MODIS) Terra and Aqua Level‐3 daily data sets was conducted, and the results were found to provide better coverage. Weighted averaging of Terra and Aqua data sets, with the consequent interpolation through the remaining gaps using Optimal Interpolation (OI), also was conducted and found to produce better results. Ozone Monitoring Instrument (OMI) total column ozone is reliable and provides better results than Atmospheric Infrared Sounder (AIRS) and MODIS. However, the agreement among these instruments is reasonable. The correlation is high (0.88) between OMI and AIRS total column ozone, while the correlation between OMI and MODIS Terra/Aqua fused total column ozone is 0.79.     

受限波兹曼机联合稀疏近似的脑功能检测模型

人脑功能连通性检测是神经科学研究的重要技术．使用受限制波兹曼机（RestrictedBoltzmannMachine,RBM）对大量多被试功能磁共振（functionalMagneticResonanceImaging,fMRI）数据进行建模可以检测人脑功能连接,但是不能有效检测单被试数据的功能连接．本文研究一种新颖的融合了稀疏近似与RBM技术的脑功能连通性检测模型,该模型充分利用fMRI数据的稀疏性,采用稀疏近似理论对fMRI数据进行空间域稀疏近似压缩,然后使用RBM建立模型,以检测脑功能连通性．实验结果表明,该融合模型可以有效地提取单被试数据的脑功能时间域混合模型及其相应的脑功能图谱,解决了RBM在单被试数据分析上的瓶颈．     

Temporally Consistent Depth Map Prediction Using Deep Convolutional Neural Network and Spatial-Temporal Conditional Random Field

Deep convolutional neural networks (DCNNs) based methods recently keep setting new records on the tasks of predicting depth maps from monocular images. When dealing with video-based applications such as 2D (2-dimensional) to 3D (3-dimensional) video conversion, however, these approaches tend to produce temporally inconsistent depth maps, since their CNN models are optimized over single frames. In this paper, we address this problem by introducing a novel spatial-temporal conditional random fields (CRF) model into the DCNN architecture, which is able to enforce temporal consistency between depth map estimations over consecutive video frames. In our approach, temporally consistent superpixel (TSP) is first applied to an image sequence to establish the correspondence of targets in consecutive frames. A DCNN is then used to regress the depth value of each temporal superpixel, followed by a spatial-temporal CRF layer to model the relationship of the estimated depths in both spatial and temporal domains. The parameters in both DCNN and CRF models are jointly optimized with back propagation. Experimental results show that our approach not only is able to significantly enhance the temporal consistency of estimated depth maps over existing single-frame-based approaches, but also improves the depth estimation accuracy in terms of various evaluation metrics.     

脑激活任务区分度的分析及应用研究

大脑在执行不同类型任务时激活模式各不相同,变化很大,各个脑区的变化程度也不同。据此,提出任务区分度计算这一全新的方法。用相似性度量对任务态功能磁共振成像（functional Magnetic Resonance Imaging,fMRI）分析,衡量大脑在执行不同条件时各个脑区激活模式的区分程度,揭示大脑各个区域对任务的表征能力。实验对正常人和狂躁症患者记忆提取任务的fMRI数据进行分析,使用皮尔逊相关分析、余弦相似度分析和欧几里德距离计算3种常用的相似性度量方法,并计算各个脑区的任务区分度。结果表明区分度较高的脑区参与记忆、注意和视觉信息等功能,表明了该方法的准确性和科学性。狂躁症患者在负责记忆和注意等脑区的任务区分度较正常人低,表明患者脑功能受损。此外,研究还发现基于皮尔逊相关分析的区分度计算表现较好。通过与SVM方法的对比证明了该方法在区分不同任务的激活模式时的优越性。综上,基于相似性度量的脑激活任务区分度的方法能够适用于任务态fMRI分析及其相应的脑功能分析。     

Advances in remote sensing of hydraulic roughness

Riparian vegetation plays a crucial role in affecting the floodplain hydraulic roughness, which in turn significantly influences the dynamics of flood waves. Systematic detection, identification and assessment of flow resistance factors using conventional field sampling is often unfeasible as these techniques are time-consuming and expensive. As in many other environmental monitoring problems, remote sensing may provide unprecedented mapping capabilities. In this article we present an overview focusing on the different methods that can be used to remotely derive floodplain hydraulic roughness. The overview is based on an extensive literature review on recent estimation techniques of riparian roughness using remote sensing data from different platforms. The outlined methods of floodplain roughness parameterization include: (1) classification-derived hydraulic roughness maps and (2) estimation of vegetation hydrodynamic properties. Possible directions for a multiscale analysis of riparian flow resistance are also described in a short section by focusing on the potential of data assimilation methods for the estimation of floodplain roughness. The literature reveals that many valuable remote-sensing techniques have been developed for riparian corridor parameterization. Methodologies based on the fusion of multispectral/temporal imagery with data of different origin, such as light detection and ranging (LiDAR) and radar/microwave, appear to be powerful tools for characterizing riparian ecosystems for hydraulic purposes.     

细化Transformer网络的弱监督图像语义分割

图像级标签的弱监督图像语义分割方法是目前比较热门的研究方向,类激活图生成方式是最为常用的解决该类问题的主要工作方法。由于类激活图的稀疏性,导致判别区域的准确性降低。针对上述问题,提出了一种改进的Transformer网络弱监督图像学习方法。首先,引入空间注意力交换层来扩大类激活图的覆盖范围;其次,进一步设计了一个注意力自适应模块,来指导模型增强弱区域的类响应;特别地,在类生成过程中,构建了一个自适应跨域来提高模型分类性能。该方法在Pascal VOC 2012 验证集和测试集上分别达到了73.5%和73.0%。实验结果表明,细化Transformer网络学习方法有助于提高弱监督图像的语义分割性能。