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Resilient Error-Bounded Lossy Compressor For Data Transfer, Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello
Resilient Error-Bounded Lossy Compressor For Data Transfer, Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello
Computer Science Faculty Research & Creative Works
Todays exa-scale scientific applications or advanced instruments are producing vast volumes of data, which need to be shared/transferred through the network/devices with relatively low bandwidth (e.g., data sharing on WAN or transferring from edge devices to supercomputers). Lossy compression is one of the candidate strategies to address the big data issue. However, little work was done to make it resilient against silent errors, which may happen during the stage of compression or data transferring. In this paper, we propose a resilient error-bounded lossy compressor based on the SZ compression framework. Specifically, we design a new independentblock-wise model that decomposes the …
Machine Learning Models And Big Data Tools For Evaluating Kidney Acceptance, Lirim Ashiku, Md Al-Amin, Sanjay Kumar Madria, Cihan H. Dagli
Machine Learning Models And Big Data Tools For Evaluating Kidney Acceptance, Lirim Ashiku, Md Al-Amin, Sanjay Kumar Madria, Cihan H. Dagli
Computer Science Faculty Research & Creative Works
The rise of on-demand healthcare and the unprecedented growth of electronic health records has given rise to big data opportunities and data analysis using machine learning. The massive and disparate data management using conventional databases is incredibly challenging and expensive to manage. It often requires specialized analytical tools for developing advanced data-driven capabilities and performing data analytics. This paper explores the capability of an open-source framework 'Apache Spark' capable of processing large amounts of data on clusters of nodes to analyze Big data and integrate technologies to provide decision support systems in healthcare settings. Next, we propose machine learning models …
Accelerating Multigrid-Based Hierarchical Scientific Data Refactoring On Gpus, Jieyang Chen, Lipeng Wan, Xin Liang, Ben Whitney, For Full List Of Authors, See Publisher's Website.
Accelerating Multigrid-Based Hierarchical Scientific Data Refactoring On Gpus, Jieyang Chen, Lipeng Wan, Xin Liang, Ben Whitney, For Full List Of Authors, See Publisher's Website.
Computer Science Faculty Research & Creative Works
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and …
Revisiting Huffman Coding: Toward Extreme Performance On Modern Gpu Architectures, Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao, Franck Cappello
Revisiting Huffman Coding: Toward Extreme Performance On Modern Gpu Architectures, Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao, Franck Cappello
Computer Science Faculty Research & Creative Works
Today’s high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today’s HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, …
Ft-Cnn: Algorithm-Based Fault Tolerance For Convolutional Neural Networks, Kai Zhao, Sheng Di, Sihuan Li, Xin Liang, For Full List Of Authors, See Publisher's Website.
Ft-Cnn: Algorithm-Based Fault Tolerance For Convolutional Neural Networks, Kai Zhao, Sheng Di, Sihuan Li, Xin Liang, For Full List Of Authors, See Publisher's Website.
Computer Science Faculty Research & Creative Works
Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus …
Multimodal Learning For Hateful Memes Detection, Yi Zhou, Zhenhao Chen, Huiyuan Yang
Multimodal Learning For Hateful Memes Detection, Yi Zhou, Zhenhao Chen, Huiyuan Yang
Computer Science Faculty Research & Creative Works
Memes are used for spreading ideas through social networks. Although most memes are created for humor, some memes become hateful under the combination of pictures and text. Automatically detecting hateful memes can help reduce their harmful social impact. Compared to the conventional multimodal tasks, where the visual and textual information is semantically aligned, hateful memes detection is a more challenging task since the image and text in memes are weakly aligned or even irrelevant. Thus, it requires the model to have a deep understanding of the content and perform reasoning over multiple modalities. This paper focuses on multimodal hateful memes …
Visualization As A Service For Scientific Data, David Pugmire, James Kress, Jieyang Chen, Hank Childs, Jong Choi, Dmitry Ganyushin, Berk Geveci, Mark Kim, Scott Klasky, Xin Liang, For Full List Of Authors, See Publisher's Website.
Visualization As A Service For Scientific Data, David Pugmire, James Kress, Jieyang Chen, Hank Childs, Jong Choi, Dmitry Ganyushin, Berk Geveci, Mark Kim, Scott Klasky, Xin Liang, For Full List Of Authors, See Publisher's Website.
Computer Science Faculty Research & Creative Works
One of the primary challenges facing scientists is extracting understanding from the large amounts of data produced by simulations, experiments, and observational facilities. The use of data across the entire lifetime ranging from real-time to post-hoc analysis is complex and varied, typically requiring a collaborative effort across multiple teams of scientists. Over time, three sets of tools have emerged: One set for analysis, another for visualization, and a final set for orchestrating the tasks. This trifurcated tool set often results in the manual assembly of analysis and visualization workflows, which are one-off solutions that are often fragile and difficult to …
Exploiting Semantic Embedding And Visual Feature For Facial Action Unit Detection, Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu
Exploiting Semantic Embedding And Visual Feature For Facial Action Unit Detection, Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu
Computer Science Faculty Research & Creative Works
Recent study on detecting facial action units (AU) has utilized auxiliary information (i.e., facial landmarks, relationship among AUs and expressions, web facial images, etc.), in order to improve the AU detection performance. As of now, no semantic information of AUs has yet been explored for such a task. As a matter of fact, AU semantic descriptions provide much more information than the binary AU labels alone, thus we propose to exploit the Semantic Embedding and Visual feature (SEV-Net) for AU detection. More specifically, AU semantic embeddings are obtained through both Intra-AU and Inter-AU attention modules, where the Intra-AU attention module …
Your 'Attention' Deserves Attention: A Self-Diversified Multi-Channel Attention For Facial Action Analysis, Xiaotian Li, Zhihua Li, Huiyuan Yang, Geran Zhao, Lijun Yin
Your 'Attention' Deserves Attention: A Self-Diversified Multi-Channel Attention For Facial Action Analysis, Xiaotian Li, Zhihua Li, Huiyuan Yang, Geran Zhao, Lijun Yin
Computer Science Faculty Research & Creative Works
Visual attention has been extensively studied for learning fine-grained features in both facial expression recognition (FER) and Action Unit (AU) detection. A broad range of previous research has explored how to use attention modules to localize detailed facial parts (e, g. facial action units), learn discriminative features, and learn inter-class correlation. However, few related works pay attention to the robustness of the attention module itself. Through experiments, we found neural attention maps initialized with different feature maps yield diverse representations when learning to attend the identical Region of Interest (ROI). In other words, similar to general feature learning, the representational …