varunr14/newsummary huggingface dataset:
Common to much work on land cover classification in multispectral imagery is the use of single satellite images for training the classifiers for the different land types . Unfortunately more often than not decision boundaries derived in this manner do not extrapolate well from one image to another . This happens for several reasons most having to do with the fact that different satellite images correspond to different view angles on the earth s surface different sun angles different seasons and so on . In this paper we get around these limitations of the current state of the art by first proposing a new integrated representation for all of the images overlapping and non overlapping that cover a large geographic ROI . In addition to helping understand the data variability in the images this representation also makes it possible to create the ground truth that can be used for ROI based wide area learning of the classifiers . We use this integrated representation in a new Bayesian framework for data classification that is characterized by learning of the decision boundaries from a sampling of all the satellite data available for an entire geographic ROI probabilistic modeling of within class and between class variations as opposed to the more traditional probabilistic modeling of the feature vectors extracted from the measurement data and using variance based ML and MAP classifiers whose decision boundary calculations incorporate all of the multi view data for a geographic point if that point is selected for learning and testing . We show results with the new classification framework for an ROI in Chile whose size is roughly 10 000 square kilometers . This ROI is covered by 189 satellite images with varying degrees of overlap . We compare the classification performance of the proposed ROI based framework with the results obtained by extrapolating the decision boundaries learned from a single image to the entire ROI . Using a 10 fold cross validation test we demonstrate significant increases in the classification accuracy for five of the six land cover classes . In addition we show that our variance based Bayesian classifier outperforms a traditional Support Vector Machine based approach to classification for four out of six classes . 


Summary: A wide area learner. Efficient sampling for training. Classify with variances.
In this paper we cast multi target tracking as a dense subgraph discovering problem on the undirected relation graph of all given target hypotheses . We aim to extract multiple clusters in which each cluster contains a set of hypotheses of one particular target . In the presence of occlusion or similar moving targets or when there is no reliable evidence for the target s presence each target trajectory is expected to be fragmented into multiple tracklets . The proposed tracking framework can efficiently link such fragmented target trajectories to build a longer trajectory specifying the true states of the target . In particular a discriminative scheme is devised via learning the targets appearance models . Moreover the smoothness characteristic of the target trajectory is utilised by suggesting a smoothness tracklet affinity model to increase the power of the proposed tracker to produce persistent target trajectories revealing different targets moving paths . The performance of the proposed approach has been extensively evaluated on challenging public datasets and also in the context of team sports where team players tend to exhibit quick and unpredictable movements . Systematic experimental results conducted on a large set of sequences show that the proposed approach performs better than the state of the art trackers in particular when dealing with occlusion and fragmented target trajectory . 


Summary: A multi target tracking is formulated as a dense subgraph discovering problem. Both local and global cues are exploited to represent the tracklet affinity model. The distinguishable appearance based models are learned for the targets.
The performance of physical assets has become a major determinant success factor for urban flood control . However managing these assets is always challenging as there are a huge number of diverse assets involved which are distributed throughout the city and owned by different agencies . Aiming at improving the management efficiency of these assets and ensuring their performance this paper proposes the concept of cloud asset based on cloud computing mobile agent and various smart devices . Through hardware integration and software encapsulation cloud asset could sense its real time status adapt to varied working scenarios be controlled remotely and shared among agencies . It enables accurate real time control of every asset and thus improves the management efficiency and effectiveness . This paper first presents the concept of cloud asset with its technical architecture and then analyses the software agent model for cloud asset which is the key enabler to realize UPnP management of assets and provides mobility and intelligence for them . After that the framework of cloud asset enabled workflow management is built in which cloud asset could be easily found and dynamically invoked by different workflows . Finally a demonstrative case is provided to verify the effectiveness of cloud asset . 


Summary: The concept of cloud asset is first proposed for urban flood control. The framework of cloud asset is worked out from both hardware and software aspects. Cloud asset enabled workflow management is presented. A case is given to verify the effectiveness of cloud asset.
A growing conceptual and empirical literature is advancing the idea that language extends our cognitive skills . One of the most influential positions holds that language qua material symbols facilitates individual thought processes by virtue of its material properties . Extending upon this model we argue that language enhances our cognitive capabilities in a much more radical way the skilful engagement of public material symbols facilitates evolutionarily unprecedented modes of collective perception action and reasoning creating dialogically extended minds . We relate our approach to other ideas about collective minds and review a number of empirical studies to identify the mechanisms enabling the constitution of interpersonal cognitive systems . 


Summary: Language is conceived as an intersubjective engagement enabling shared cognition. Experimental studies highlight ways in which language enables intersubjective informational and behavioural synergies. We therefore argue for language as skilful joint activity leading to dialogically extended minds.
Monocular plenoptic cameras are slightly modified off the shelf cameras that have novel capabilities as they allow for truly passive high resolution range sensing through a single camera lens . Commercial plenoptic cameras however are presently delivering range data in non metric units which is a barrier to novel applications e.g . in the realm of robotics . In this work we revisit the calibration of focused plenoptic cameras and bring forward a novel approach that leverages traditional methods for camera calibration in order to deskill the calibration procedure and to increase accuracy . First we detach the estimation of parameters related to either brightness images or depth data . Second we present novel initialization methods for the parameters of the thin lens camera model the only information required for calibration is now the size of the pixel element and the geometry of the calibration plate . The accuracy of the calibration results corroborates our belief that monocular plenoptic imaging is a disruptive technology that is capable of conquering new markets as well as traditional imaging domains . 


Summary: A novel method for the calibration of focused plenoptic monocular cameras is proposed. In this way the camera will deliver metric depth information instead of disparities. We detach the intrinsic camera parameters related to either brightness or depth data. We present novel initialization methods for all parameters. The accuracy is demonstrated on independent ground truth validation data.
Sensors are becoming ubiquitous in everyday life generating data at an unprecedented rate and scale . However models that assess impacts of human activities on environmental and human health have typically been developed in contexts where data scarcity is the norm . Models are essential tools to understand processes identify relationships associations and causality formalize stakeholder mental models and to quantify the effects of prevention and interventions . They can help to explain data as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results . We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing Big Data and more importantly make the vital step from Big Data to Big Information . In this paper we illustrate current developments and identify key research needs using human and environmental health challenges as an example . 


Summary: Sensors and models play vital roles in harnessing Big Data to extract information. Data analytics can help to diminish monitoring burden and support locating sensors. Exploring Big Data is essential to detect universal associations across space and time. Ethical challenges and issues of standards and harmonisation need to be addressed. Citizen science needs robust sensors and models to crowd source and interpret data.
This paper proposes a new extended Process to Product Modeling method for integrated and seamless information delivery manual and model view definition development . Current IDM development typically uses Business Process Modeling Notation to represent a process map . Exchange requirements and functional parts specify the information required when information is exchanged between different activities . A set of information requirements specifically defined as a subset of Industry Foundation Classes is called an MVD . Currently however PMs ERs FPs and MVDs are developed as separate documents through independent development steps . Moreover even though ERs and FPs are designed to be reused tracking and reusing the ERs and FPs developed by others is practically impossible . The xPPM method is proposed to provide a tight connection between PMs ERs FPs and MVDs and to improve the reusability of predefined ERs and FPs . The theoretical framework is based on the approach of the Georgia Tech Process to Product Modeling to suit the IDM development process . An xPPM tool is developed and the validity of xPPM is analyzed through the reproduction of existing IDMs and MVDs . The benefits and limitations of xPPM and lessons from the applicability tests are discussed . 


Summary: An extended Process to Product Modeling xPPM method has been proposed. xPPM is for efficient integrated and seamless IDM and MVD development. The applicability of xPPM is analyzed through reproduction of existing IDMs. The problems of the current IDM MVD development are discussed. The benefits and limitations of xPPM and lessons are discussed.
Modeling the energy performance of existing buildings enables quick identification and reporting of potential areas for building retrofit . However current modeling practices of using energy simulation tools do not model the energy performance of buildings at their element level . As a result potential retrofit candidates caused by construction defects and degradations are not represented . Furthermore due to manual modeling and calibration processes their application is often time consuming . Current application of 2D thermography for building diagnostics is also facing several challenges due to a large number of unordered and non geo tagged images . To address these limitations this paper presents a new computer vision based method for automated 3D energy performance modeling of existing buildings using thermal and digital imagery captured by a single thermal camera . First using a new image based 3D reconstruction pipeline which consists of Graphic Processing Unit based Structure from Motion and Multi View Stereo algorithms the geometrical conditions of an existing building is reconstructed in 3D . Next a 3D thermal point cloud model of the building is generated by using a new 3D thermal modeling algorithm . This algorithm involves a one time thermal camera calibration deriving the relative transformation by forming the Epipolar geometry between thermal and digital images and the MVS algorithm for dense reconstruction . By automatically superimposing the 3D building and thermal point cloud models 3D spatio thermal models are formed which enable the users to visualize query and analyze temperatures at the level of 3D points . The underlying algorithms for generating and visualizing the 3D spatio thermal models and the 3D registered digital and thermal images are presented in detail . The proposed method is validated for several interior and exterior locations of a typical residential building and an instructional facility . The experimental results show that inexpensive digital and thermal imagery can be converted into ubiquitous reporters of the actual energy performance of existing buildings . The proposed method expedites the modeling process and has the potential to be used as a rapid and robust building diagnostic tool . 


Summary: Image based 3D modeling method of actual building energy performance is presented. Building environments and their energy performances are jointly visualized in 3D. Automatically 3D registered thermal images assist with localizing energy problems. 3D spatio thermal models can facilitate energy building diagnostics and retrofit analysis.
We propose a framework for the robust and fully automatic segmentation of magnetic resonance brain images called Multi Atlas Label Propagation with Expectation Maximisation based refinement . The presented approach is based on a robust registration approach highly performant label fusion and intensity based label refinement using EM . We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy . We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity refined posterior probabilities . The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge . In this context we show that MALP EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state of the art automatic labelling techniques . To demonstrate the versatility of the proposed approach we employed MALP EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury . We employ a protocol to assess segmentation quality if no manual reference labels are available . Based on this protocol three independent blinded raters confirmed on 13 MR brain scans with pathology that MALP EM is superior to established label fusion techniques . We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification or Glasgow Outcome Score . Specifically we show that we are able to stratify TBI patients with favourable outcomes from non favourable outcomes with 64.7 accuracy using acute phase MR images and 66.8 accuracy using follow up MR images . Furthermore we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0 accuracy . The thalamus putamen pallidum and hippocampus are particularly affected . Their involvement predicts TBI disease progression . 


Summary: A robust and fully automatic segmentation framework for MR brain scans is proposed. A heterogeneous cohort of 125 scans of patients who had sustained TBI is segmented. The approach compares favourably to the state of the art on benchmark and TBI data. MRI based biomarkers correlate with outcome relevant clinical variables in TBI. Evidence that subcortical structures are particularly affected in TBI is presented.
Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way . In this paper a Human Robot Interaction system able to recognize gestures usually employed in human non verbal communication is introduced and an in depth study of its usability is performed . The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps . A static gesture consisting in pointing at an object is also recognized . The pointed location is then estimated in order to detect candidate objects the user may refer to . When the pointed object is unclear for the robot a disambiguation procedure by means of either a verbal or gestural dialogue is performed . This skill would lead to the robot picking an object in behalf of the user which could present difficulties to do it by itself . The overall system which is composed by a NAO and Wifibot robots a KinectTM v2 sensor and two laptops is firstly evaluated in a structured lab setup . Then a broad set of user tests has been completed which allows to assess correct performance in terms of recognition rates easiness of use and response times . 


Summary: We present a multi robot human interaction system with two robots and a depth sensor. It includes a static and dynamic gestures recognition module. The set of gestures is described using arm body and facial head features. Interactive disambiguation for floor and object detection based on pointed location. Tested with several real users as well as with an offline test setting.
Background estimation in video consists in extracting a foreground free image from a set of training frames . Moving and stationary objects may affect the background visibility thus invalidating the assumption of many related literature where background is the temporal dominant data . In this paper we present a temporal spatial block level approach for background estimation in video to cope with moving and stationary objects . First a Temporal Analysis module obtains a compact representation of the training data by motion filtering and dimensionality reduction . Then a threshold free hierarchical clustering determines a set of candidates to represent the background for each spatial location . Second a Spatial Analysis module iteratively reconstructs the background using these candidates . For each spatial location multiple reconstruction hypotheses are explored to obtain its neighboring locations by enforcing inter block similarities and intra block homogeneity constraints in terms of color discontinuity color dissimilarity and variability . The experimental results show that the proposed approach outperforms the related state of the art over challenging video sequences in presence of moving and stationary objects . 


Summary: We propose a new temporal spatial block based Background estimation approach to compute a foreground free image for video sequences. Threshold free clustering is proposed to discover similar blocks over time which contain the background data. An iterative spatial reconstruction selects blocks to obtain the final background. The performance improvement is validated in two datasets 36 sequences using 13 state of the art algorithms.
Graphical abstract MODSIM MODFLOW MODSIM MODFLOW is the integration of two widely used and freely available codes used within the field of water resource planning and management . The nature of the coupling in MODSIM MODFLOW sets it apart from other river operations hydrologic model couplings in that the codes iterate at the time step level and share information via computer memory . Users are required to construct working MODSIM and MODFLOW models prior to using the integrated code and must adhere to each code s input file formatting requirements . Using the custom code interface available with MODSIM the models are integrated by mapping surface water features represented in both models to one another . Eric Morway with MODFLOW contributions from Rich Niswonger and MODSIM contributions from Enrique Triana Research supported by grant from the Water Sustainability Climate Program jointly funded by the National Science Foundation and U.S. Department of Agriculture National Institute of Food Agriculture and by the U.S. Geological Survey s Groundwater Resources Program . C C Fortran Windows with.NET Framework 3.5 installed Download MODSIM from http modsim.engr.colostate.edu . A version of MODFLOW compiled as dynamic link library and required by the integrated code is available upon request from the corresponding author . Finally users will need to download and install the C redistributable package for Microsoft Visual Studio 2012 available from Microsoft . Inevitably as each generation must learn the land and the waters will instruct us in the ways of community . Justice Gregory J. Hobbs Jr . 


Summary: The river reservoir operations model MODSIM is coupled to MODFLOW. Models simulate physics based hydrology and priority rule based water allocation. Coupling uses a Picard iteration and an implicit mass conservative solution. Model simulates conjunctive use of surface water and groundwater. Helps evaluate groundwater sustainability resulting from alternative management.
Humic substances are ubiquitous in the environment and have manifold functions . While their composition is well known information on the chemical structure and three dimensional conformation is scarce . Here we describe the Vienna Soil Organic Matter Modeler which is an online tool to generate condensed phase computer models of humic substances . Many different models can be created that reflect the diversity in composition and conformations of the constituting molecules . To exemplify the modeler 18 different models are generated based on two experimentally determined compositions to explicitly study the effect of varying e.g . the amount of water molecules in the models or the pH . Molecular dynamics simulations were performed on the models which were subsequently analyzed in terms of structure interactions and dynamics linking macroscopic observables to the microscopic composition of the systems . We are convinced that this new tool opens the way for a wide range of in silico studies on soil organic matter . 


Summary: We developed an online tool to generate models of Soil Organic Matter SOM . Molecular dynamics simulations of SOM models are now possible. Influence of composition on structure interactions and dynamics is studied. Our work opens the way to new and exciting research in soil sciences.
Simulation modelling in ecology is a field that is becoming increasingly compartmentalized . Here we propose a Database Approach To Modelling to create unity in dynamical ecosystem modelling with differential equations . In this approach the storage of ecological knowledge is independent of the language and platform in which the model will be run . To create an instance of the model the information in the database is translated and augmented with the language and platform specifics . This process is automated so that a new instance can be created each time the database is updated . We describe the approach using the simple Lotka Volterra model and the complex ecosystem model for shallow lakes PCLake which we automatically implement in the frameworks OSIRIS GRIND for MATLAB ACSL R DUFLOW and DELWAQ . A clear advantage of working in a database is the overview it provides . The simplicity of the approach only adds to its elegance . 


Summary: Scientific and educational experience with the proposed Database Approach To Modelling DATM shows the following It facilitated overview of and insight in the model by developers and users. Allowed for a much more dynamic scientific development of the model. Allowed for a direct implementation of these developments in multiple platforms.