Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users.
Coupling Atmospheric and Biological Remote Sensing to Investigate Boundary-Layer Evolution and Animal Flight Behavior as Affected by the 2017 North American Solar Eclipse

The daytime atmospheric boundary layer is characterized by vertical convective motions that are driven by solar radiation. Lift provided by thermal updrafts is sufficiently ubiquitous that some diurnal birds and arthropods have evolved specialized flight behaviors to soar or embed in these atmospheric currents. While the diel periodicity of boundary-layer dynamics and animal flight has been characterized, rare disruptions to this cycle provide a chance to investigate animal behavioral responses to boundary layer motion and photoperiod that are disjointed from their expected circadian rhythm. To analyze these interactions, we couple radar-derived animal observations with co-located lidar measurements of the convective boundary layer over north-central Oklahoma, USA during the solar eclipse of 21 August 2017. Analysis of animal flight behavior confirmed that ascending and descending flight effort did change in the time period encompassing the solar eclipse, however, the response in behavior was coincident with proximate changes in boundary-layer turbulence. Both the animal behavioral response and decrease in atmospheric turbulence lagged changes in solar irradiance by approximately 30 min, suggesting that changes in flight activity were not cued by the eclipse directly, but rather by the modification of vertical air motions caused by the eclipse.
Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition

In recent times, deep neural networks have drawn much attention in ground-based cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for ground-based clouds. In this paper, we propose a novel method named multi-evidence and multi-modal fusion network (MMFN) for ground-based cloud recognition, which could learn extended cloud information by fusing heterogeneous features in a unified framework. Namely, MMFN exploits multiple pieces of evidence, i.e., global and local visual features, from ground-based cloud images using the main network and the attentive network. In the attentive network, local visual features are extracted from attentive maps which are obtained by refining salient patterns from convolutional activation maps. Meanwhile, the multi-modal network in MMFN learns multi-modal features for ground-based cloud. To fully fuse the multi-modal and multi-evidence visual features, we design two fusion layers in MMFN to incorporate multi-modal features with global and local visual features, respectively. Furthermore, we release the first multi-modal ground-based cloud dataset named MGCD which not only contains the ground-based cloud images but also contains the multi-modal information corresponding to each cloud image. The MMFN is evaluated on MGCD and achieves a classification accuracy of 88.63% comparative to the state-of-the-art methods, which validates its effectiveness for ground-based cloud recognition.
Advanced Features for Model Visualization in the UML and OCL Tool USE

: The tool USE (UML-based Specification Environment) offers functionalities for UML model validation, verification and exploration. The tool supports to describe system structure and behavior with class models, protocol state machines and imperative operation implementations on the modeling level, utilizing OCL constraints (a) for class and state invariants as well as (b) for operation and transition contracts. Validation and verification relies on the construction of object model snapshots. This contribution focuses on advanced visualization features that have recently been developed for object models. Using these new features, it is now feasible (a) to take advantage of various automatic layout options for object models and (b) to express advanced views on object models concentrating not only on classes but utilizing also associations.
Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data

Modern elevation-determining remote sensing technologies such as light-detection and ranging (LiDAR) produce a wealth of topographic information that is increasingly being used in a wide range of disciplines, including archaeology and geomorphology. However, automated methods for mapping topographic features have remained a significant challenge. Deep learning (DL) mask regional-convolutional neural networks (Mask R-CNN), which provides context-based instance mapping, offers the potential to overcome many of the difficulties of previous approaches to topographic mapping. We therefore explore the application of Mask R-CNN to extract valley fill faces (VFFs), which are a product of mountaintop removal (MTR) coal mining in the Appalachian region of the eastern United States. LiDAR-derived slopeshades are provided as the only predictor variable in the model. Model generalization is evaluated by mapping multiple study sites outside the training data region. A range of assessment methods, including precision, recall, and F1 score, all based on VFF counts, as well as area- and a fuzzy area-based user’s and producer’s accuracy, indicate that the model was successful in mapping VFFs in new geographic regions, using elevation data derived from different LiDAR sensors. Precision, recall, and F1-score values were above 0.85 using VFF counts while user’s and producer’s accuracy were above 0.75 and 0.85 when using the area- and fuzzy area-based methods, respectively, when averaged across all study areas characterized with LiDAR data. Due to the limited availability of LiDAR data until relatively recently, we also assessed how well the model generalizes to terrain data created using photogrammetric methods that characterize past terrain conditions. Unfortunately, the model was not sufficiently general to allow successful mapping of VFFs using photogrammetrically-derived slopeshades, as all assessment metrics were lower than 0.60; however, this may partially be attributed to the quality of the photogrammetric data. The overall results suggest that the combination of Mask R-CNN and LiDAR has great potential for mapping anthropogenic and natural landscape features. To realize this vision, however, research on the mapping of other topographic features is needed, as well as the development of large topographic training datasets including a variety of features for calibrating and testing new methods.
Deep Learning-Based Drivers Emotion Classification System in Time Series Data for Remote Applications

Aggressive driving emotions is indeed one of the major causes for traffic accidents throughout the world. Real-time classification in time series data of abnormal and normal driving is a keystone to avoiding road accidents. Existing work on driving behaviors in time series data have some limitations and discomforts for the users that need to be addressed. We proposed a multimodal based method to remotely detect driver aggressiveness in order to deal these issues. The proposed method is based on change in gaze and facial emotions of drivers while driving using near-infrared (NIR) camera sensors and an illuminator installed in vehicle. Driver’s aggressive and normal time series data are collected while playing car racing and truck driving computer games, respectively, while using driving game simulator. Dlib program is used to obtain driver’s image data to extract face, left and right eye images for finding change in gaze based on convolutional neural network (CNN). Similarly, facial emotions that are based on CNN are also obtained through lips, left and right eye images extracted from Dlib program. Finally, the score level fusion is applied to scores that were obtained from change in gaze and facial emotions to classify aggressive and normal driving. The proposed method accuracy is measured through experiments while using a self-constructed large-scale testing database that shows the classification accuracy of the driver’s change in gaze and facial emotions for aggressive and normal driving is high, and the performance is superior to that of previous methods.
Worldwide Evaluation of Ozone Radiative Forcing in the UV-B Range between 1979 and 2014

Ultraviolet (UV) radiation plays a key role in different planetary mechanisms, thus necessitating a worldwide analysis of this solar spectrum interval. This study offers a worldwide and long-term analysis of ozone radiative forcing (ORF) in the UV-B range between 1979 and 2014. The method uses monthly total ozone column (TOC) values obtained from the ERA-Interim reanalysis data collection and radiative transfer simulations. A global mean ORF of 0.011 Wm−2 is obtained, with marked differences between mid-latitude and tropical areas. The mid-latitude belts in the Northern and Southern Hemispheres exhibit the following statistically significant ORF trends between 1982 and 2014 with respect to pre-1980 values: 0.007 Wm−2 per decade in the 60–45°S belt and around 0.004 Wm−2 per decade in the 45–30°S and 45–60°N belts. The increase observed in the net UV-B radiation levels at the troposphere might have relevant photochemical effects that impact climate change.
Robust Kalman Filtering Based on Chi-square Increment and Its Application

In Global Navigation Satellite System (GNSS) positioning, gross errors seriously limit the validity of Kalman filtering and make the final positioning solutions untrustworthy. Thus, the detection and correction of gross errors have become indispensable parts of Kalman filtering. Starting by defining an incremental Chi-square method of recursive least squares, this paper extends this definition to Kalman filtering to detect gross errors, explains its nature and its relation with the currently adopted Chi-square variables of Kalman filtering in model and data spaces, and compares them with the predictive residual statistics. Two robust Kalman filtering models based on an incremental Chi-square method (CI-RKF) were established, and the one with a better incremental Chi-square component was selected based on a static accuracy evaluation experiment. We applied the selected robust model to the GNSS positioning and the GNSS/inertial measurement unit (IMU) / visual odometry (VO) integrated navigation experiment in an occluded urban area at the East China Normal University. We compared the results for conventional Kalman filtering (CKF) with a robust Kalman filtering constructed using predictive residual statistics and an Institute of Geodesy and Geophysics (IGGШ) weight factor, abbreviated as “PRS-IGG-RKF”. The results show that the overall accuracy of CI-RKF in GNSS positioning was improved by 22.68%, 54.33%, and 72.45% in the static experiment, and 12.30%, 7.50%, and 16.15% in the kinematic experiment. The integrated navigation results indicate that the CI-RKF fusion method increased the system positioning accuracy by 66.73%, 59.59%, and 59.62% in one of the severe occlusion areas, and 42.04%, 59.04%, and 52.41% in the other.
Ad-Hoc stream query processing

Many modern applications require processing large amounts of data in a real-time fashion. As a result, distributed stream processing engines (SPEs) have gained significant attention as an important new class of big data processing systems. The central design principle of these SPEs is to handle queries that potentially run forever on data streams with a query-at-a-time model, i.e., each query is optimized and executed separately. However, in many real applications, not only long-running queries but also many short-running queries are processed on data streams. In these applications, multiple stream queries are created and deleted concurrently, in an ad-hoc manner. The best practice to handle ad-hoc stream queries is to fork input stream and add additional resources for each query. However, this approach leads to redundant computation and data copy. This thesis lays the foundation for e cient ad-hoc stream query processing. To bridge the gap between stream data processing and ad-hoc query processing, we follow a top-down approach. First, we propose a benchmarking framework to analyze state-of-the-art SPEs. We provide a definition of latency and throughput for stateful operators. Moreover, we carefully separate the system under test and the driver, to correctly represent the open-world model of typical stream processing deployments. This separation enables us to measure the system performance under realistic conditions. Our solution is the first benchmarking framework to define and test the sustainable performance of SPEs. Throughout our analysis, we realize that the state-of-the-art SPEs are unable to execute stream queries in an ad-hoc manner. Second, we propose the first ad-hoc stream query processing engine for distributed data processing environments. We develop our solution based on three main requirements: (1) Integration: Ad-hoc query processing should be a composable layer that can extend stream operators, such as join, aggregation, and window operators; (2) Consistency: Ad-hoc query creation and deletion must be performed consistently and ensure exactly-once semantics and correctness; (3) Performance: In contrast to modern SPEs, ad-hoc SPEs should not only maximize data throughput but also query throughout via incremental computation and resource sharing. Third, we propose an ad-hoc stream join processing framework that integrates dynamic query processing and query re-optimization techniques with ad-hoc stream query processing. Our solution comprises an optimization layer and a stream data processing layer. The optimization layer periodically re-optimizes the query execution plan, performing join reordering and vertical and horizontal scaling at runtime without stopping the execution. The data processing layer enables incremental and consistent query processing, supporting all the actions triggered by the optimizer. The result of the second and the third contributions forms a complete ad-hoc SPE. We utilize the first contribution not only for benchmarking modern SPEs but also for evaluating the ad-hoc SPE.
Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.
A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis

Abstract Bayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on estimating parameters in a proposed fully Bayesian analysis setting for the penalized spline joint models. To achieve this aim, the joint posterior distribution of parameters in survival and longitudinal submodels is presented. The Markov chain Monte Carlo (MCMC) algorithm is then proposed, which consists of the Gibbs sampler (GS) and Metropolis Hastings (MH) algorithms to sample for the target conditional posterior distributions. The prior sensitivity analysis for the baseline hazard rate and association parameters is performed through simulation studies and a case study.
Survey on memory management techniques in heterogeneous computing systems

A major issue faced by data scientists today is how to scale up their processing infrastructure to meet the challenge of big data and high-performance computing (HPC) workloads. With today's HPC domain, it is required to connect multiple graphics processing units (GPUs) to accomplish large-scale parallel computing along with CPUs. Data movement between the processor and on-chip or off-chip memory creates a major bottleneck in overall system performance. The CPU/GPU processes all the data on a computer's memory and hence the speed of the data movement to/from memory and the size of the memory affect computer speed. During memory access by any processing element, the memory management unit (MMU) controls the data flow of the computer's main memory and impacts the system performance and power. Change in dynamic random access memory (DRAM) architecture, integration of memory-centric hardware accelerator in the heterogeneous system and Processing-in-Memory (PIM) are the techniques adopted from all the available shared resource management techniques to maximise the system throughput. This survey study presents an analysis of various DRAM designs and their performances. The authors also focus on the architecture, functionality, and performance of different hardware accelerators and PIM systems to reduce memory access time. Some insights and potential directions toward enhancements to existing techniques are also discussed. The requirement of fast, reconfigurable, self-adaptive memory management schemes in the high-speed processing scenario motivates us to track the trend. An effective MMU handles memory protection, cache control and bus arbitration associated with the processors.
Scalable Consistency in the Multi-core Era

The advent of heterogeneous many-core systems has increased the spectrum of achievable performance from multi-threaded programming. As the processor components become more distributed, the cost of synchronization and communication needed to access the shared resources increases. Concurrent linearizable access to shared objects can be prohibitively expensive in a high contention workload. Though there are various mechanisms (e.g., lock-free data structures) to circumvent the synchronization overhead in linearizable objects, it still incurs performance overhead for many concurrent data types. Moreover, many applications do not require linearizable objects and apply ad-hoc techniques to eliminate synchronous atomic updates. In this thesis, we propose the Global-Local View Model. This programming model exploits the heterogeneous access latencies in many-core systems. In this model, each thread maintains different views on the shared object: a thread-local view and a global view. As the thread-local view is not shared, it can be updated without incurring synchronization costs. The local updates become visible to other threads only after the thread-local view is merged with the global view. This scheme improves the performance at the expense of linearizability. Besides the weak operations on the local view, the model also allows strong operations on the global view. Combining operations on the global and the local views, we can build data types with customizable consistency semantics on the spectrum between sequential and purely mergeable data types. Thus the model provides a framework that captures the semantics of Multi-View Data Types. We discuss a formal operational semantics of the model. We also introduce a verification method to verify the correctness of the implementation of several multi-view data types. Frequently, applications require updating shared objects in an “all-or-nothing” manner. Therefore, the mechanisms to synchronize access to individual objects are not sufficient. Software Transactional Memory (STM) is a mechanism that helps the programmer to correctly synchronize access to multiple mutable shared data by serializing the transactional reads and writes. But under high contention, serializable transactions incur frequent aborts and limit parallelism, which can lead to severe performance degradation. Mergeable Transactional Memory (MTM), proposed in this thesis, allows accessing multi-view data types within a transaction. Instead of aborting and re-executing the transaction, MTM merges its changes using the data-type specific merge semantics. Thus it provides a consistency semantics that allows for more scalability even under contention. The evaluation of our prototype implementation in Haskell shows that mergeable transactions outperform serializable transactions even under low contention while providing a structured and type-safe interface.