Lee Mason (7247909), Blánaid Hicks (21433577), Jonas S. Almeida (14931560) · Figshare · 2025
<div> Spatial cluster analysis is crucial for understanding localized patterns in geospatial data, with wide-ranging applications for scientific discovery and decision-making. However, the dynamic nature of spatial clusters and the diverse range of clustering methods available can make analysis and interpretation challenging. We introduce ClusterRadar, a web-based tool designed to streamline this process by uniquely prioritizing longitudinal analysis and multi-method comparison of spatial clusters. It empowers users to easily perform clustering with multiple methods, directly compare results, and visualize spatiotemporal patterns through a novel design of linked interactive visualizations. ClusterRadar aims to maximize utility to a broad user base by supporting various geospatial formats and executing entirely within the browser to ensure data privacy. ClusterRadar is available at <a href="https://episphere.github.io/ClusterRadar" target="_blank">https://episphere.github.io/ClusterRadar</a>. </div>
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Deryck Brown, Power Efficiency · 2010
Cited by 0
Shahnwaz Afzal (22184546), Mohammad Ubaidullah Bokhari (22184549), Mahfooz Alam (14096469) et al. · Figshare · 2025
<div> The Internet of Things (IoT) includes vehicles, homes, and integrated sensors and many interconnected physical devices that gather and share data to interact with their environment. Data moving across multiple levels is vulnerable to various security threats, including leaks and unauthorized access. IoT faces significant challenges in balancing strict security with optimal performance metrics such as energy efficiency, throughput, and memory. We present a novel lightweight stream cipher designed to secure IoT communication and address these challenges. The proposed architecture features four main components: a logistic round module that produces 32-bit chaotic outputs; two 80-bit shift registers, LFSR and NLFSR, for key expansion; and multiplexer units to enhance confusion and diffusion. This model improves the randomness and robustness of the keystream, strengthening the cipher against cryptanalytic attacks. An ablation research is performed by methodically eliminating the chaotic map, NLFSR, and multiplexer components to assess their individual effects on encryption/decryption duration, throughput, entropy, and avalanche analysis. Experimental results demonstrate that each component significantly improves the cipher’s overall performance and security, hence confirming the architecture’s design and also demonstrate that the proposed cipher exceeds the performance of current algorithms, including Grain-128 and RSA-1024, in terms of encryption/decryption time, throughput, and energy efficiency, while maintaining comparable statistical randomness to AES and Trivium. This method achieves an average Shannon entropy of 7.9996, and successfully passing all 15 NIST statistical randomness tests. A subsequent study analyzing the avalanche effect and correlation coefficients reinforces the strength of the encryption. The proposed encryption method, designed for resource-constrained environments, provides efficient and robust cryptographic security to protect IoT data effectively. </div>
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Wright Jeremiah, Wagner Andreas · Figshare · 2011
running time for the SBRT (grey) and COBRA Toolbox (black) for 10 executions each of analysis .<b>Copyright information:</b>Taken from "The Systems Biology Research Tool: evolvable open-source software"http://www.biomedcentral.com/1752-0509/2/55BMC Systems Biology 2008;2():55-55.Published online 29 Jun 2008PMCID:PMC2446383.
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Wright, Jeremiah, Wagner, Andreas · Figshare · 2011
running time for the SBRT (grey) and COBRA Toolbox (black) for 10 executions each of analysis .<b>Copyright information:</b>Taken from "The Systems Biology Research Tool: evolvable open-source software"http://www.biomedcentral.com/1752-0509/2/55BMC Systems Biology 2008;2():55-55.Published online 29 Jun 2008PMCID:PMC2446383.
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Xikeng Liang (20571399), Shutong Xiao (13671075), Lu Ba (22453819) et al. · Figshare · 2025
<div> Spatial transcriptomics is a rapidly developing field of single-cell genomics that quantitatively measures gene expression while providing spatial information within tissues. A key challenge in spatial transcriptomics is identifying spatially structured domains, which involves analyzing transcriptomic data to find clusters of cells with similar expression patterns and their spatial distribution. To address these challenges, we propose a novel deep-learning method called DMGCN for domain identification. The process begins with preprocessing that constructs two types of graphs: a spatial graph based on Euclidean distance and a feature graph based on Cosine distance. These graphs represent spatial positions and gene expressions, respectively. The embeddings of both graphs are generated using a multi-view graph convolutional encoder with an attention mechanism, enabling separate and co-convolution of the graphs, as well as corrupted feature convolution for contrastive learning. Finally, a fully connected network (FCN) decoder is employed to generate domain labels and reconstruct gene expressions for downstream analysis. Experimental results demonstrate that DMGCN consistently outperforms state-of-the-art methods in various tasks, including spatial clustering, trajectory inference, and gene expression broadcasting. </div>
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Le Wang (165105), Shui Wang (295917) · Figshare · 2021
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Misbah Mehboob Awan (22783925), Muhammad Waseem Anwar (9316557), Wasi Haider Butt (2852081) et al. · Figshare · 2025
<div> Embedded systems often require multiple representations for design, verification, and implementation, ranging from low-level programming languages to high-level formal models and domain-specific abstractions. Generally, synchronization among different representations or notations is achieved manually, a process that is labor-intensive and prone to mistakes, adversely impacting productivity and time-to-market objectives. Despite existing tool support, there remains a lack of unified, automated mechanisms that ensure semantic consistency across heterogeneous modeling and programming notations. This article presents a scalable blended modeling framework that automates the synchronizations across an extensible set of notations using bidirectional transformations. This facilitates the system development, comprising design and verification aspects of safety-critical embedded systems, using various notations interchangeably. The applicability of the proposed framework is demonstrated using four distinct representations: C, SystemVerilog, Timed Automata, and a domain-specific modeling language. The framework supports a notation-agnostic design flow, allowing development to begin from any of the supported languages. This enables seamless transitions across notations based on design or verification needs. Validated through two industrial case studies, a ventilator system and a cruise control system, the framework achieved high round-trip transformation accuracy with minimal information losses in edge cases such as language-specific keywords. Performance evaluations revealed low transformation latency and modest memory consumption, supported by efficient Abstract Syntax Tree (AST) traversal. This research lays the groundwork for the standardization of model-to-code, code-to-model, and code-to-code transformations, significantly reducing manual engineering effort and improving the reliability and agility of embedded systems design and verification processes. </div>
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David Cachera, Thomas Wiben Jensen, David Pichardie et al. · 2005
Cited by 13
Martin Hofmann · Lecture notes in computer science · 2003
Cited by 1
David Cachera, Thomas Wiben Jensen, David Pichardie et al. · Lecture notes in computer science · 2005
Cited by 24
B. Purnachandra Rao, N. Naga Malleswara Rao · 2017 International Conference on Inventive Computing and Informatics (ICICI) · 2017
Due to high use of online activities, there will be lot of data which is getting generated. To handle this data there should be an efficient system which can process the data effectively. One such system is Hadoop Distributed File System. HDFS consists of number of nodes, and one is the master among them while all others are slave nodes. Master node is called as NameNode and slave nodes are called as DataNodes. System will record the activities in the log file. Whatever the transactions are happening at the front end everything will get recorded at backend. We can capture this data and analyze to find out the system behavior in the failure situation. Since we are using the system for large transactions we need to have an idea on usage of the resources in the HDFS such as memory usage. Analyzers can get the stats of the operating system and the other devices separately i.e, for each service we need to have one analyzer to collect the stats. This paper presents the analyzer or shipper for collecting the metrics from operating system, memory and the other installed services.
Cited by 1
Yasser EL-Manzalawy (270330), Mostafa Abbas (3158754), Qutaibah Malluhi (3158757) et al. · Figshare · 2016
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S. Arash Ostadzadeh, Marco Corina, Carlo Galuzzi et al. · 2010
One of the main issues in heterogeneous reconfigurable computing is the well-known processor/memory bottleneck. Due to the memory bandwidth limitations, the performance of execution of an application can dramatically increase via the efficient usage of the memory. In this paper, we present tQUAD, a new tool for the memory bandwidth usage analysis. This tool is capable of delivering detailed temporal memory bandwidth usage information for the functions in an application throughout a comprehensive analysis of the memory access patterns of individual functions. This tool, first in its kind, provides an accurate analysis of the task execution and memory bandwidth usage which in the end leads to a sophisticated partitioning of the tasks into different phases during the execution span of an application. Together with an accurate description of the tool, the paper presents a real case study from the multimedia domain to detail all features of the proposed tool.
Cited by 6
Martinez Peck, M. (Mariano), Bouraqadi, N. (Noury), Denker, M. (Marcus) et al. · LillOA (Université de Lille (University Of Lille)) · 2010
International audience
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Peña, Antonio J. · Zenodo (CERN European Organization for Nuclear Research) · 2022
<strong>Objectives: </strong> The objective of this course is to learn how to use systems with more than one memory subsystem. We will see the different options on using systems equipped with Intel’s Optane Persistent Memory technology. <strong>Learning Outcomes:</strong> The students who finish this course will able to leverage applications using multiple memory subsystems
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Yuxuan Ji (13991895), Wenshu Li (762627), Nan Yu (757123) · Figshare · 2025
<div> Image classification, as the core task of computer vision, has broad application value in fields such as medical diagnosis and intelligent transportation.However, the ability of differentiable neural architecture to search (NAS) for local information is weak, which limits the accuracy and long-distance information capture capability of the algorithm. Therefore, based on this, the study introduces visual attention mechanism and proposes an improved model that replaces the original convolution operator and adds residual structure in the macro structure to enhance the model’s information acquisition ability and classification accuracy. The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. The above results indicate that the research method can effectively solve the shortcomings of traditional differentiable neural architecture search in local and remote information acquisition capabilities, providing important technical support for improving the accuracy and efficiency of image classification technology. </div>
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David Berling (22170661), Jan Střeleček (22170664), Tomáš Iser (22170667) et al. · Figshare · 2025
<b>(a)</b> Computing time depending on simulated cortical volume, which for symmetry reasons, is represented by the plane spanned by radial and depth directions. Spatial resolution was . Computing time increases with increasing volume along radial (solid blue) and depth direction (dashed orange) with different slope. Original model computation time is marked with a red cross and was simulated in the same spatial volume as the first data point of the replicated model. Evaluation of the original model for different spatial volumes was not possible due to hard coding of these parameters in the published application. <b>(b)</b> Physical memory consumption depending on simulated plane in radial and depth direction. Color coding same as in (a). Memory consumption was recorded as the maximum resident size during simulation monitored with the Python built-in module resource. Parametrization of simulations shown in this figure: Light predictions with the original MATLAB software were simulated at the application’s default parametrization as described for the previous figure (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0330681#pone.0330681.g002" target="_blank">Fig 2</a>). Light predictions with the replicated model were simulated using the default model parametrization (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0330681#pone.0330681.t001" target="_blank">Table 1</a>) but for varying volume along radial direction changing the parameter xymax to with the maximum depth zmax. Volume was varied along depth direction by changing the parameter zmax to , while setting the maximum radial distance to xymax.
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Michael Privat, Robert A. Warner · Apress eBooks · 2011
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Yuxuan Ji (13991895), Wenshu Li (762627), Nan Yu (757123) · Figshare · 2025
<div> Image classification, as the core task of computer vision, has broad application value in fields such as medical diagnosis and intelligent transportation.However, the ability of differentiable neural architecture to search (NAS) for local information is weak, which limits the accuracy and long-distance information capture capability of the algorithm. Therefore, based on this, the study introduces visual attention mechanism and proposes an improved model that replaces the original convolution operator and adds residual structure in the macro structure to enhance the model’s information acquisition ability and classification accuracy. The research results show that after 600 rounds of training on the CIFAR-10 dataset, the final accuracy of the improved model reached 97.2%. The runtime memory usage on the CIFAR-100 dataset is only 44.52%, a decrease of 44.56% compared to the baseline model. In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. The above results indicate that the research method can effectively solve the shortcomings of traditional differentiable neural architecture search in local and remote information acquisition capabilities, providing important technical support for improving the accuracy and efficiency of image classification technology. </div>
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