Extreme weather events, such as hurricanes, tornadoes, and floods, have caused significant damage to power grid systems, affecting critical infrastructures like substations, transmission lines, and generation plants. This damage often results in widespread power outages in disaster-affected areas, disrupting essential services such as healthcare, transportation, and national security. Annually, these power outage events due to extreme weather lead to economic losses ranging from 25 to 70 billion in the United States [1] ). Analyzing and understanding grid resiliency—particularly the dynamics of power outages under various weather events— is crucial for effective resource planning and maintaining reliable grid operations during such events. In this study, we conducted a preliminary analysis to model grid system resiliency under different extreme weather events across various states. Leveraging state-of-the-art deep learning time-series forecasting models, we aim to address the following research questions: • (R1) Can machine learning models predict grid resiliency in advance for a given weather event at a specific location? • (R2) Is it feasible to develop a generalized model to understand resiliency during extreme weather events? • (R3) Is historical data sufficient to model and predict resiliency for future extreme weather events?

HPC systems typically rely on the fixed-lifetime (FLT) data retention strategy, which only considers temporal locality of data accesses to parallel file systems. However, our extensive analysis based on the leadership-class HPC system traces suggests that the FLT approach often fails to capture the dynamics in users' behavior and leads to undesired data purge. In this study, we propose an activeness-based data retention (ActiveDR) solution, which advocates considering the data retention approach from a holistic activeness-based perspective. By evaluating the frequency and impact of users' activities, ActiveDR prioritizes the file purge process for inactive users and rewards active users with extended file lifetime on parallel storage. Our extensive evaluations based on the traces of the prior Titan supercomputer show that, when reaching the same purge target, ActiveDR achieves up to 37% file miss reduction as compared to the current FLT retention methodology.

Outdoor images are subject to degradation regarding contrast and color because atmospheric particles scatter incoming light to a camera. Existing haze models that employ model-based dehazing methods cannot avoid the dehazing artifacts. These artifacts include color distortion and overenhancement around object boundaries because of the incorrect transmission estimation from a depth error in the skyline and the wrong haze information, especially in bright objects. To overcome this problem, we present a novel optimization-based dehazing algorithm that combines radiance and reflectance components with an additional refinement using a structure-guided ℓ0-norm filter. More specifically, we first estimate a weak reflectance map and optimize the transmission map based on the estimated reflectance map. Next, we estimate the structure-guided ℓ0transmission map to remove the dehazing artifacts. The experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of qualitative and quantitative measures compared with simulated image pairs. In addition, the real-world enhancement results demonstrate that the proposed method can provide a high-quality image without undesired artifacts. Furthermore, the guided ℓ0-norm filter can remove textures while preserving edges for general image enhancement algorithms.

In the real world, the two challenges of stereo vision system include a robust system under various radiometric changes and real-time process. To extract depth information from stereoscopic images, this paper proposes Patchmatch-based robust and fast stereo matching under radiometric changes. For this, a cost function was designed and minimized for estimating an accurate disparity map. Specifically, we used a prior probability to minimize the occlusion region and a smoothness term that considers convexity of objects to extract a fine disparity map. For evaluating the performance of the proposed scheme, we used Middlebury stereo data sets with radiometric changes. The experimental result showed that the proposed method outperforms state-of-the-art methods by up to 3.35 percent better and a range of 4.71 - 27.24 times faster result in terms of bad pixel error and processing time, respectively. Therefore, we believe that the proposed scheme can be a useful tool for computer vision-based applications.

The topological derivative (TD) for shape analysis has been employed in image segmentation, and machine learning schames based on convolutional neural network (CNN) provide the high performance in the image processing. The supervised and unsupervised approaches have different roles and advantages according to their concepts. To maximize the benefits of two approaches, we propose CNN-TD based segmentation approach. A CNN-based segmentation scheme is employed to faithfully consider the characteristics of an object to be segmented in a given image, and we refine the CNN results using a TD-based scheme. Experimental results show that the proposed scheme produces better performance for the prostate segmentation than the refined results by level set-based schemes. Therefore, we believe that the proposed scheme can be a useful tool for effective medical image segmentation.

Even though numerous visual tracking methods have been proposed to deal with image streams, it is a still challenging problem to facilitate a tracking method to accurately distinguish the target from the background without drifting under the severe appearance variation of target caused by distortion of local structures. For preserving local structures of target template datasets, we present a novel structure-preserving sparse learning algorithm by obtaining sparse coefficients under maximum margin projection-based subspace representation and by updating the sparse codes under multiple task feature selection framework. To reinforce local structures of targets, we adopted a novel optimization process using an accelerated proximal gradient shrinkage operation and an efficient stopping criterion. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods.

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this paper, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulk data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. With our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.

Graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, a number of emerging graph analysis systems assume different graph data models, and support different query interface and serialization formats. Such diversity, combined with a lack of comparisons, makes it complicated to understand the trade-offs between different systems and the graph operations for which they are designed. This study presents an evaluation of graph pattern matching capabilities of six graph analysis systems, by extending the Lehigh University Benchmark to investigate the degree of effectiveness to perform the same operation over the same graph in various graph analysis systems. Through the evaluation, this study reveals both quantitative and qualitative findings.

Image smoothing has been used for image segmentation, image reconstruction, object classification, and 3D content generation. Several smoothing approaches have been used at the pre-processing step to retain the critical edge, while removing noise and small details. However, they have limited performance, especially in removing small details and smoothing discrete regions. Therefore, to provide fast and accurate smoothing, we propose an effective scheme that uses a weighted combination of the gradient, Laplacian, and diagonal derivatives of a smoothed image. In addition, to reduce computational complexity, we designed and implemented a parallel processing structure for the proposed scheme on a graphics processing unit (GPU). For an objective evaluation of the smoothing performance, the images were linearly quantized into several layers to generate experimental images, and the quantized images were smoothed using several methods for reconstructing the smoothly changed shape and intensity of the original image. Experimental results showed that the proposed scheme has higher objective scores and better successful smoothing performance than similar schemes, while preserving and removing critical and trivial details, respectively. For computational complexity, the proposed smoothing scheme running on a GPU provided 18 and 16 times lower complexity than the proposed smoothing scheme running on a CPU and the L0-based smoothing scheme, respectively. In addition, a simple noise reduction test was conducted to show the characteristics of the proposed approach; it reported that the presented algorithm outperforms the state-of-the art algorithms by more than 5.4 dB. Therefore, we believe that the proposed scheme can be a useful tool for efficient image smoothing.

In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers that have developed use de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multithreaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray's Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing de Bruin graphs as RDF graphs and propose an iterative querying approach for searching cycles to find Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

Graph analysis has revealed patterns and relationships hidden in data from a variety of domains such as transportation networks, social networks, clinical pathways, and collaboration networks. As these networks grow in size, variety and complexity, it is a challenge to find the right combination of tools and implementation of algorithms to discover new insights from the data. Addressing this challenge, our study presents an extensive empirical evaluation of three representative graph processing platforms: Pegasus, GraphX, and Urika. Each system represents a combination of options in data model, processing paradigm, and infrastructure. We benchmark each platform using three popular graph mining operations, degree distribution, connected components, and PageRank over real-world graphs. Our experiments show that each graph processing platform owns a particular strength for different types of graph operations. While Urika performs the best in non-iterative graph operations like degree distribution, GraphX outperforms iterative operations like connected components and PageRank. We conclude this paper by discussing options to optimize the performance of a graph-theoretic operation on each platform for large-scale real world graphs.

This study presents a novel image‐naturalness restoration for the purpose of achieving better image quality with respect to the human visual system. Specifically, the authors suppress the artefacts around edges using image gradient components. They also appropriately enhance the image brightness using an adaptive gamma correction. In particular, they simply create a mapping curve to adjust the contrast, and then perform contrast enhancement. Experimental results demonstrate that the proposed algorithm outperforms other state‐of‐the‐art algorithms in terms of subjective and objective quality measures, and even computational speed. Therefore they believe that the proposed approach can be a useful tool to improve the visual quality of images under non‐uniform illumination.

Topological derivative has been employed for image segmentation and restoration. The topological derivative-based segmentation uses two sparse matrices, and the computational complexity of the segmentation grows up dramatically as the image size increases due to the size of the sparse matrix. Therefore, to provide a fast and accurate segmentation with low complexity, an effective scheme is proposed with keeping the same segmentation performance. To further reduce the computational complexity, the parallel processing structure for the proposed scheme is designed and implemented on graphics processing unit (GPU). In particular, to reduce the computational cost of generating and multiplying sparse matrices that are squared symmetric, the 2D filters consisting of the coefficients at nonborder regions of sparse matrices are defined, and the multiplication is converted into a convolution filtering. In addition, to design a parallel processing for the segmentation with the proposed scheme on a GPU, an image is divided into several blocks and they are processed in parallel. Experimental results show that the proposed scheme for topological derivative-based segmentation reduces the computational complexity ~ 908 times, and the complexity of the proposed scheme is reduced ~ 17 times more from the parallel structure. In particular, the higher efficiency can be obtained from large sized images because the complexity of the proposed scheme does not depend on the image size. Moreover, the proposed scheme can provide almost identical segmentation result with the original sparse matrix-based approach. Therefore, we believe that the proposed scheme can be a useful tool for efficient topological derivative-based segmentation.

Heterogeneous graph-based recommendation frameworks have flexibility in that they can incorporate various recommendation algorithms and various kinds of information to produce better results. In this demonstration, we present a heterogeneous graph-based recommendation simulator which enables participants to experience the flexibility of a heterogeneous graph-based recommendation method. With our system, participants can simulate various recommendation semantics by expressing the semantics via meaningful paths like User → Movie → User → Movie. The simulator then returns the recommendation results on the fly based on the user-customized semantics using a fast Monte Carlo algorithm.

In this paper, we present a novel random-walk based node ranking measure, PathRank, which is defined on a heterogeneous graph by extending the Personalized PageRank algorithm. Not only can our proposed measure exploit the semantics behind the different types of nodes and edges in a heterogeneous graph, but also it can emulate various recommendation semantics such as collaborative filtering, content-based filtering, and their combinations. The experimental results show that PathRank can produce more various and effective recommendation results compared to existing approaches.

In many applications, flexibility of recommendation, which is the capability of handling multiple dimensions and various recommendation types, is very important. In this paper, we focus on the flexibility of recommendation and propose a graph-based multidimensional recommendation method. We consider the problem as an entity ranking problem on the graph which is constructed using an implicit feedback dataset (e.g. music listening log), and we adapt Personalized PageRank algorithm to rank entities according to a given query that is represented as a set of entities in the graph. Our model has advantages in that not only can it support the flexibility, but also it can take advantage of exploiting indirect relationships in the graph so that it can perform competitively with the other existing recommendation methods without suffering from the sparsity problem.

A set of lifelogs is a dataset that describes a person's life. A high quality and large set of lifelogs is expected to be useful for many applications. Only by integrating the logs that are already available from various devices and creating semantic relationships among them, we can build a useful lifelog ontology that can support various applications. In this paper, we propose Entity-Event Lifelog Ontology Model (EELOM) for designing lifelog ontology that can be used for integrating various available data sources.

This paper introduces a new fast motion estimation based on a maximum matching error constraint that can eliminate an impossible candidate block much earlier than a conventional partial distortion elimination (PDE) scheme. The main contributions of the proposed scheme are that 1) it can reduce the computational cost considerably for the matching error calculation; and 2) the proposed matching ideas can be applied to the conventional PDE algorithms without significant changes. In order to evaluate the proposed scheme, we compare the results of the proposed algorithm with several baseline approaches. The experimental results show that the proposed algorithm can reduce the computations more than 62% for motion estimation at the cost of 0.005 dB quality degradation versus the general PDE algorithm.

This paper presents a new robust digital image stabilization system, which involves a practical motion model based on a feature-histogram. The feature-histogram is used for adaptive selection of feasible motion estimation regions. By estimating the representative motion vector in the optimally selected region, the proposed algorithm can robustly remove undesired camera vibration regardless of object's movement. When compared with the traditional methods, experimental results show that the proposed algorithm can improve the performance by 7% with four times faster computation compared with the existing sum-absolute-difference (SAD) based method.

A compressed image reproduction scheme is proposed by properly decomposing and manipulating the coefficients of discrete cosine transform (DCT) directly in the compressed domain. The basic idea of the proposed approach is to decompose each DCT block into several sub-blocks and to adjust the brightness and detail components of a given image for compressing dynamic range and enhancing contrast. Image reproduction based on the subblock decomposition can be done more precisely than any approach based on the normal block-sized approach. First, DCT coefficients of each block are decomposed into several sub-blocks. Next each sub-block's coefficients are separated into brightness and detail components, and treated differently according to content analysis. Then, the enhanced coefficients are projected on the constraint sets to avoid some artefacts, and are composed back to the original order. The main advantages of the proposed algorithm are that (i) it can enhance the dynamic range and details without affecting the compressibility of the given image since it operates directly in the compressed domain, and (ii) it does not boost blocking artefacts around big edges without any further processing. In order to evaluate the proposed scheme, several base-line approaches are described and compared using enhancement quality measures.

The object of this paper is to present a simple and efficient algorithm for dynamic range compression and contrast enhancement of digital images under the noisy environment in the compressed domain. First, an image is separated into illumination and reflectance components. Next, the illumination component is manipulated adaptively for image dynamics by using a new content measure. Then, the reflectance component based on the measure of the spectral contents of the image is manipulated for image contrast. The spectral content measure is computed from the energy distribution across different spectral bands in a discrete cosine transform (DCT) block. The proposed approach also introduces a simple scheme for estimating and reducing noise information directly in the DCT domain. The main advantage of the proposed algorithm enhances the details in the dark and the bright areas with low computations without boosting noise information and affecting the compressibility of the original image since it performs on the images in the compressed domain. In order to evaluate the proposed scheme, several base-line approaches are described and compared using enhancement quality measures

We propose a method to extract depth information from a two-dimensional (2D) image. We project a sinusoidal pattern on to the object and compute the pattern's frequency as it varies with the shape of the object. The extracted frequency enables us to measure depth information. The main advantage of the proposed method is the use of only one active projected image. We expect that the proposed algorithm will be applied to vision systems, virtual environments etc. In an experiment, the algorithm showed a mean error of about 2.44 percent in measuring depth compared with ground truth.