Text analytics directly on compression (TADOC) is a promising technology designed for handling big data analytics. However, a substantial amount of DRAM is required for high performance, which limits its usage in many...
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ISBN:
(数字)9798350317152
ISBN:
(纸本)9798350317169
Text analytics directly on compression (TADOC) is a promising technology designed for handling big data analytics. However, a substantial amount of DRAM is required for high performance, which limits its usage in many important scenarios where the capacity of DRAM is limited, such as memory-constrained systems. Non-volatile memory (NVM) is a novel storage technology that combines the advantage of reading per-formance and byte addressability of DRAM with the durability of traditional storage devices like SSD and HDD. Unfortunately, no research demonstrates how to use NVM to reduce DRAM utilization in compressed data analytics. In this paper, we propose N-TADOC, which substitutes DRAM with NVM while maintaining TADOC's analytics performance and space savings. Utilizing an NVM block device to reduce DRAM utilization presents two challenges, including poor data locality in traversing datasets and auxiliary data structure reconstruction on NVM. We develop novel designs to solve these challenges, including a pruning method with NVM pool management, bottom-up upper bound estimation, correspondent data structures, and persistence strategy at different levels of cost. Experimental results show that on four real-world datasets, N-TADOC achieves 2.04× performance speedup compared to the processing directly on the uncompressed data and 70.7% DRAM space saving compared to the original TADOC.
Purpose: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra a...
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We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly ge...
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DL techniques have increased the efficiency of decision making in different areas. However, in the case of the presence of uncertainties in the data or in the environment, decision-making requires the explainability o...
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ISBN:
(数字)9798350350180
ISBN:
(纸本)9798350350197
DL techniques have increased the efficiency of decision making in different areas. However, in the case of the presence of uncertainties in the data or in the environment, decision-making requires the explainability of the model, especially for high-stakes decision making such as medical image analysis area. This paper focuses on medical image classification CNN based deep learning models and aims to apply and compare three popular explainable AI approaches LIME, SHAP and *** results on a Pneumonia and Alzheimer’s datasets for disease detection show that the Grad-CAM method seems to outperform LIME and SHAP and able to enhance the interpretability of DL models, identify automatically the most important features that contribute to the model’s decision.
We consider a communication system where a group of users, interconnected in a bidirectional gossip network, wishes to follow a time-varying source, e.g., updates on an event, in real-time. The users wish to maintain ...
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ISBN:
(数字)9798350393187
ISBN:
(纸本)9798350393194
We consider a communication system where a group of users, interconnected in a bidirectional gossip network, wishes to follow a time-varying source, e.g., updates on an event, in real-time. The users wish to maintain their expected version ages below a threshold, and can either rely on gossip from their neighbors or directly subscribe to a server publishing about the event, if the former option does not meet the timeliness requirements. The server wishes to maximize its profit by increasing subscriptions from users and minimizing event sampling frequency to reduce costs. This leads to a Stackelberg game between the server and the users where the sender is the leader deciding its sampling frequency and the users are the followers deciding their subscription strategies. We investigate equilibrium strategies for low-connectivity and high-connectivity topologies.
In this paper, we introduce the DFC dataflow language and its runtime environment. DFC runtime library is in charge of constructing the DAG of the dataflow graph, firing the DFC tasks and the synchronizations between ...
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We reconstruct the transformations of quantum theory using a physically motivated postulate. This postulate states that transformations should be locally applicable, and recovers the linear unitary maps from pure quan...
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Cloud segmentation is a critical challenge in remote sensing image interpretation, as its accuracy directly impacts the effectiveness of subsequent data processing and analysis. Recently, vision foundation models (VFM...
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As scientific codes are ported between GPU platforms, continuous testing is required to ensure numerical robustness and identify potential numerical differences between platforms. Compiler-induced numerical difference...
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ISBN:
(数字)9798350355543
ISBN:
(纸本)9798350355550
As scientific codes are ported between GPU platforms, continuous testing is required to ensure numerical robustness and identify potential numerical differences between platforms. Compiler-induced numerical differences can occur when a program is compiled and run on different GPUs and compilers, and the numerical outcomes are different for the same input. We present a study of compiler-induced numerical differences between NVIDIA and AMD GPUs, two widely used GPUs in HPC clusters. Our approach uses a random program generator (Varity) to generate thousands of short numerical tests in CUDA and HIP, and their inputs; then, we use differential testing to check if the program produced a numerical inconsistency when run on NVIDIA and AMD GPUs, using the same compiler optimization level. We also use the AMD’s HIPIFY tool to convert CUDA tests into HIP tests and test if there are numerical inconsistencies induced by HIPIFY. In our study, we generated more than 600,000 tests and found subtle numerical differences occurring between the two classes of GPUs. We found that some of the differences come from (1) math library calls, (2) differences in floating-point precision (FP64 versus FP32), and (3) converting code to HIP with HIPIFY.
With the rapid advancement of speech recognition and semantic understanding, speech interaction systems have gained widespread usage. This paper investigates the use of intelligent error handling approaches in speech ...
With the rapid advancement of speech recognition and semantic understanding, speech interaction systems have gained widespread usage. This paper investigates the use of intelligent error handling approaches in speech interaction systems when faced with recognition or comprehension challenges. A case study focusing on the common task of “electronic spreadsheet editing” is conducted, wherein an experimental environment is developed by combining an open-source neural network code repository with a third-party speech recognition interface. Drawing from historical experience, a “triplet” paradigm is constructed to parse user command texts, and an improved approach for intelligent selection of error recovery strategies is proposed. This approach automatically determines the appropriate strategy for different speech interaction errors based on user logs. Experiments on 20 users demonstrate that our proposed intelligent error handling techniques for speech interaction, exhibit performance similar to empirically designed systems in practical tasks. Moreover, they significantly enhance user satisfaction and task completion efficiency when utilizing the system.
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