In order to tackle complex tasks, multi-chiplet systems integrate various types of chiplets such as CPUs, GPUs, and other accelerators on the active interposer. Interposer Network-on-Chip (NoC) serves as the foundatio...
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The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine...
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The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI
This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology *** purpose of this study is to overcome the challenges faced in automated nucl...
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This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology *** purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping *** this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation ***,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain *** proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ *** findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.
Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input p...
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Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable ***,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic *** this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training *** learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation *** experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual *** reproducibility,we release the code and data publicly at:https://***/siat‐nlp/HSEMEC‐code‐data.
L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands...
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L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.
Network-on-Chip (NoC), known for its high bandwidth and scalability, is extensively utilized in chip multiprocessors. However, as technology advances to the nanometer scale, NoC is becoming increasingly vulnerable to ...
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This note shows two design examples of PID con-Trollers, whose state variables estimate the selected state vari-Ables of the controlled plant systems, for LTI Parametrically Dependent (LTIPD) systems. The authors have...
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The monitoring of oceanographic and coastal dynamics is essential for understanding the effects of climate change, predicting natural disasters, and managing coastal resources. Remote sensing technology, particularly ...
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This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one han...
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Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block ***,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).Th...
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Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block ***,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).These features change storage hierarchy and allow CPU to access persistent data using load/store ***,we can directly build a file system on ***,traditional file systems are designed based on slow block *** use a deep and complex software stack to optimize file system *** design results in software overhead being the dominant factor affecting NVMM file ***,scalability,crash consistency,data protection,and cross-media storage should be reconsidered in NVMM file *** survey existing work on optimizing NVMM file ***,we analyze the problems when directly using traditional file systems on NVMM,including heavy software overhead,limited scalability,inappropriate consistency guarantee techniques,***,we summarize the technique of 30 typical NVMM file systems and analyze their advantages and ***,we provide a few suggestions for designing a high-performance NVMM file system based on real hardware Optane DC persistent memory ***,we suggest applying various techniques to reduce software overheads,improving the scalability of virtual file system(VFS),adopting highly-concurrent data structures(e.g.,lock and index),using memory protection keys(MPK)for data protection,and carefully designing data placement/migration for cross-media file system.
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