Relevant bibliographies by topics / DeepSORT

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'DeepSORT'

Author: Grafiati
Published: 29 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "DeepSORT"

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Zhang, Limin, Jie Jiang, Wei Fang, and Kai Liu. "Real TimeDetection and Tracking Method of Pilot’sHeadPositionBased on MTCNN-DeepSORT." Journal of Physics: Conference Series 1682 (November 2020): 012025. http://dx.doi.org/10.1088/1742-6596/1682/1/012025.

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Wang, An, Xiaohong Cao, Lei Lu, Xinjing Zhou, and Xuecheng Sun. "Design of Efficient Human Head Statistics System in the Large-Angle Overlooking Scene." Electronics 10, no. 15 (July 31, 2021): 1851. http://dx.doi.org/10.3390/electronics10151851.

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Human head statistics is widely used in the construction of smart cities and has great market value. In order to solve the problem of missing pedestrian features and poor statistics results in a large-angle overlooking scene, in this paper we propose a human head statistics system that consists of head detection, head tracking and head counting, where the proposed You-Only-Look-Once-Head (YOLOv5-H) network, improved from YOLOv5, is taken as the head detection benchmark, the DeepSORT algorithm with the Fusion-Hash algorithm for feature extraction (DeepSORT-FH) is proposed to track heads, and heads are counted by the proposed cross-boundary counting algorithm based on scene segmentation. Specifically, Complete-Intersection-over-Union (CIoU) is taken as the loss function of YOLOv5-H to make the predicted boxes more in line with the real boxes. The results demonstrate that the recall rate and mAP@.5 of the proposed YOLOv5-H can reach up to 94.3% and 93.1%, respectively, on the SCUT_HEAD dataset. The statistics system has an extremely low error rate of 3.5% on the TownCentreXVID dataset while maintaining a frame rate of 18FPS, which can meet the needs of human head statistics in monitoring scenarios and has a good application prospect.
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Liu, Chieh-Min, and Jyh-Ching Juang. "Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique." Applied Sciences 11, no. 12 (June 17, 2021): 5619. http://dx.doi.org/10.3390/app11125619.

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This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions.
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Liu, Xin, and Zhanyue Zhang. "A Vision-Based Target Detection, Tracking, and Positioning Algorithm for Unmanned Aerial Vehicle." Wireless Communications and Mobile Computing 2021 (April 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/5565589.

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Unmanned aerial vehicles (UAV) play a pivotal role in the field of security owing to their flexibility, efficiency, and low cost. The realization of vehicle target detection, tracking, and positioning from the perspective of a UAV can effectively improve the efficiency of urban intelligent traffic monitoring. In this work, by fusing the target detection network, YOLO v4, with the detection-based multitarget tracking algorithm, DeepSORT, a method based on deep learning for automatic vehicle detection and tracking in urban environments, has been designed. With the aim of addressing the problem of UAV positioning a vehicle target, the state equation and measurement equation of the system have been constructed, and a particle filter based on interactive multimodel has been employed for realizing the state estimation of the maneuvering target in the nonlinear system. Results of the simulation show that the algorithm proposed in this work can detect and track vehicles automatically in urban environments. In addition, the particle filter algorithm based on an interactive multimodel significantly improves the performance of the UAV in terms of positioning the maneuvering targets, and this has good engineering application value.
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Shin, Minchan, and Nammee Moon. "Indoor Distance Measurement System COPS (COVID-19 Prevention System)." Sustainability 13, no. 9 (April 23, 2021): 4738. http://dx.doi.org/10.3390/su13094738.

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With the rapid spread of coronavirus disease 2019 (COVID-19), measures are needed to detect social distancing and prevent further infection. In this paper, we propose a system that detects social distancing in indoor environments and identifies the movement path and contact objects according to the presence or absence of an infected person. This system detects objects through frames of video data collected from a closed-circuit television using You Only Look Once (v. 4) and assigns and tracks object IDs using DeepSORT, a multiple object tracking algorithm. Next, the coordinates of the detected object are transformed by image warping the area designated by the top angle composition in the original frame. The converted coordinates are matched with the actual map to measure the distance between objects and detect the social distance. If an infected person is present, the object that violates the movement path and social distancing of the infected person is detected using the ID assigned to each object. The proposed system can be used to prevent the rapid spread of infection by detecting social distancing and detecting and tracking objects according to the presence of infected persons.
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Huang, Wei, Xiaoshu Zhou, Mingchao Dong, and Huaiyu Xu. "Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network." Multimedia Tools and Applications 80, no. 9 (January 19, 2021): 13911–29. http://dx.doi.org/10.1007/s11042-020-10427-1.

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AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.
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Yoshimori, Atsushi, and Jürgen Bajorath. "Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures." Future Drug Discovery 2, no. 2 (April 1, 2020): FDD36. http://dx.doi.org/10.4155/fdd-2020-0005.

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Aim: Enhancing the structure–activity relationship matrix (SARM) methodology through integration of deep learning and expansion of chemical space coverage. Background: Analog design is of critical importance for medicinal chemistry. The SARM approach, which combines systematic structural organization of compound series with analog design, is put into scientific context. Methodology: The new DeepSARM concept is introduced. The architecture of SARM-integrated deep generative models is detailed and the workflow for advanced analog design and matrix expansion described. Exemplary application: The DeepSARM approach is applied to design analogs of kinase inhibitors taking kinome-wide chemical space into account. Future perspective: Practical applications of DeepSARM will be a major focal point. Different applications are discussed. New computational features will be added to prioritize virtual candidate compounds.
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Guo, Bin, Ziqi Wang, Pei Wang, Tong Xin, Daqing Zhang, and Zhiwen Yu. "DeepStore: Understanding Customer Behaviors in Unmanned Stores." IT Professional 22, no. 3 (May 1, 2020): 55–63. http://dx.doi.org/10.1109/mitp.2019.2928272.

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Chen, Jin-Bor, Huai-Shuo Yang, Sin-Hua Moi, Li-Yeh Chuang, and Cheng-Hong Yang. "Identification of mortality-risk-related missense variant for renal clear cell carcinoma using deep learning." Therapeutic Advances in Chronic Disease 12 (January 2021): 204062232199262. http://dx.doi.org/10.1177/2040622321992624.

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Introduction: Kidney renal clear cell carcinoma (KIRCC) is a highly heterogeneous and lethal cancer that can arise in patients with renal disease. DeepSurv combines a deep feed-forward neural network with a Cox proportional hazards function and could provide optimized survival results compared with convenient survival analysis. Methods: This study used an improved DeepSurv algorithm to identify the candidate genes to be targeted for treatment on the basis of the overall mortality status of KIRCC subjects. All the somatic mutation missense variants of KIRCC subjects were abstracted from TCGA-KIRC database. Results: The improved DeepSurv model (95.1%) achieved greater balanced accuracy compared with the DeepSurv model (75%), and identified 610 high-risk variants associated with overall mortality. The results of gene differential expression analysis also indicated nine KIRCC mortality-risk-related pathways, namely the tRNA charging pathway, the D-myo-inositol-5-phosphate metabolism pathway, the DNA double-strand break repair by nonhomologous end-joining pathway, the superpathway of inositol phosphate compounds, the 3-phosphoinositide degradation pathway, the production of nitric oxide and reactive oxygen species in macrophages pathway, the synaptic long-term depression pathway, the sperm motility pathway, and the role of JAK2 in hormone-like cytokine signaling pathway. The biological findings in this study indicate the KIRCC mortality-risk-related pathways were more likely to be associated with cancer cell growth, cancer cell differentiation, and immune response inhibition. Conclusion: The results proved that the improved DeepSurv model effectively classified mortality-related high-risk variants and identified the candidate genes. In the context of KIRCC overall mortality, the proposed model effectively recognized mortality-related high-risk variants for KIRCC.
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Yoshimori, Atsushi, Huabin Hu, and Jürgen Bajorath. "Adapting the DeepSARM approach for dual-target ligand design." Journal of Computer-Aided Molecular Design 35, no. 5 (March 13, 2021): 587–600. http://dx.doi.org/10.1007/s10822-021-00379-5.

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AbstractThe structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.
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Dissertations / Theses on the topic "DeepSORT"

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Ali, Hani, and Pontus Sunnergren. "Scenanalys - Övervakning och modellering." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45036.

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Självkörande fordon kan minska trafikstockningar och minska antalet trafikrelaterade olyckor. Då det i framtiden kommer att finnas miljontals autonoma fordon krävs en bättre förståelse av omgivningen. Syftet med detta projekt är att skapa ett externt automatiskt trafikledningssystem som kan upptäcka och spåra 3D-objekt i en komplex trafiksituation för att senare skicka beteendet från dessa objekt till ett större projekt som hanterar med att 3D-modellera trafiksituationen. Projektet använder sig av Tensorflow ramverket och YOLOv3 algoritmen. Projektet använder sig även av en kamera för att spela in trafiksituationer och en dator med Linux som operativsystem. Med hjälp av metoder som vanligen används för att skapa ett automatiserat trafikledningssystem utvärderades ett målföljningssystem. De slutliga resultaten visar att systemet är relativt instabilt och ibland inte kan känna igen vissa objekt. Om fler bilder används för träningsprocessen kan ett robustare och mycket mer tillförlitligt system utvecklas med liknande metodik.
Autonomous vehicles can decrease traffic congestion and reduce the amount of traffic related accidents. As there will be millions of autonomous vehicles in the future, a better understanding of the environment will be required. This project aims to create an external automated traffic system that can detect and track 3D objects within a complex traffic situation to later send these objects’ behavior for a larger-scale project that manages to 3D model the traffic situation. The project utilizes Tensorflow framework and YOLOv3 algorithm. The project also utilizes a camera to record traffic situations and a Linux operated computer. Using methods commonly used to create an automated traffic management system was evaluated. The final results show that the system is relatively unstable and can sometimes fail to recognize certain objects. If more images are used for the training process, a more robust and much more reliable system could be developed using a similar methodology.
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Ferreira, Ellen Cristina. "Fluxo de potência ótimo multiobjetivo com restrições de segurança e variáveis discretas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-06072018-112756/.

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O presente trabalho visa a investigação e o desenvolvimento de estratégias de otimização contínua e discreta para problemas de Fluxo de Potência Ótimo com Restrições de Segurança (FPORS) Multiobjetivo, incorporando variáveis de controle associadas a taps de transformadores em fase, chaveamentos de bancos de capacitores e reatores shunt. Um modelo Problema de Otimização Multiobjetivo (POM) é formulado segundo a soma ponderada, cujos objetivos são a minimização de perdas ativas nas linhas de transmissão e de um termo adicional que proporciona uma maior margem de reativos ao sistema. Investiga-se a incorporação de controles associados a taps e shunts como grandezas fixas, ou variáveis contínuas e discretas, sendo neste último caso aplicadas funções auxiliares do tipo polinomial e senoidal, para fins de discretização. O problema completo é resolvido via meta-heurísticas Evolutionary Particle Swarm Optimization (EPSO) e Differential Evolutionary Particle Swarm Optimization (DEEPSO). Os algoritmos foram desenvolvidos utilizando o software MatLab R2013a, sendo a metodologia aplicada aos sistemas IEEE de 14, 30, 57, 118 e 300 barras e validada sob os prismas diversidade e qualidade das soluções geradas e complexidade computacional. Os resultados obtidos demonstram o potencial do modelo e estratégias de resolução propostas como ferramentas auxiliares ao processo de tomada de decisão em Análise de Segurança de redes elétricas, maximizando as possibilidades de ação visando a redução de emergências pós-contingência.
The goal of the present work is to investigate and develop continuous and discrete optimization strategies for SCOPF problems, also taking into account control variables related to in-phase transformers, capacitor banks and shunt reactors. Multiobjective optimization model is formulated under a weighted sum criteria whose objectives are the minimization of active power losses and an additional term that yields a greater reactive support to the system. Controls associated with taps and shunts are modeled either as fixed quantities, or continuous and discrete variables, in which case auxiliary functions of polynomial and sinusoidal types are applied for discretization purposes. The complete model is solved via EPSO and DEEPSO metaheuristics. Routines coded in Matlab were applied to the IEEE 14,30, 57, 118 and 300-bus test systems, where the method was validated in terms of diversity and quality of solutions and computational complexity. The results demonstrate the robustness of the model and solution approaches and uphold it as an effective support tool for the decision-making process in Power Systems Security Analysis, maximizing preventive actions in order to avoid insecure operating conditions.
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Vigo, João Pedro Antunes. "NEWEPSO - New developments and testing of EPSO and DEEPSO." Dissertação, 2016. https://repositorio-aberto.up.pt/handle/10216/85203.

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O presente trabalho pretende desenvolver o algoritmo atual do EPSO, Evolutionary Particle Swarm Optimization, procurando novas variantes do mesmo, com vista a maior eficiência computacional do mesmo, sem perder a capacidade de resolução de problemas complexos de otimização. Desta feita, são duas novas variantes que serão apresentadas ao longo deste trabalho.
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Loureiro, Fábio Manuel Soares. "Development and Testing of the Meta-Heuristic Hybrid DEEPSO." Dissertação, 2014. https://repositorio-aberto.up.pt/handle/10216/73472.

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Vigo, João Pedro Antunes. "NEWEPSO - New developments and testing of EPSO and DEEPSO." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/85203.

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O presente trabalho pretende desenvolver o algoritmo atual do EPSO, Evolutionary Particle Swarm Optimization, procurando novas variantes do mesmo, com vista a maior eficiência computacional do mesmo, sem perder a capacidade de resolução de problemas complexos de otimização. Desta feita, são duas novas variantes que serão apresentadas ao longo deste trabalho.
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Loureiro, Fábio Manuel Soares. "Development and Testing of the Meta-Heuristic Hybrid DEEPSO." Master's thesis, 2014. https://repositorio-aberto.up.pt/handle/10216/73472.

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Book chapters on the topic "DeepSORT"

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Mondal, Debnath, and Sanjay Paul. "Study on Cyclic Response of Dry Uniform Soil Deposit Using Shake Table Tests and DEEPSOIL Program." In Lecture Notes in Civil Engineering, 357–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6564-3_31.

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Conference papers on the topic "DeepSORT"

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Azhar, Muhamad Izham Hadi, Fadhlan Hafizhelmi Kamaru Zaman, Nooritawati Md Tahir, and Habibah Hashim. "People Tracking System Using DeepSORT." In 2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2020. http://dx.doi.org/10.1109/iccsce50387.2020.9204956.

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Host, Kristina, Marina Ivašić-Kos, and Miran Pobar. "Tracking Handball Players with the DeepSORT Algorithm." In 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009177605930599.

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Bin Zuraimi, Muhammad Azhad, and Fadhlan Hafizhelmi Kamaru Zaman. "Vehicle Detection and Tracking using YOLO and DeepSORT." In 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2021. http://dx.doi.org/10.1109/iscaie51753.2021.9431784.

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Jindal, Rajni, Aditya Panwar, Nishant Sharma, and Aman Rai. "Object Tracking in a Zone using DeepSORT, YOLOv4 and TensorFlow." In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456443.

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Doan, Thanh-Nghi, and Minh-Tuyen Truong. "Real-time vehicle detection and counting based on YOLO and DeepSORT." In 2020 12th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2020. http://dx.doi.org/10.1109/kse50997.2020.9287483.

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Zhang, Xu, Xiangyang Hao, Songlin Liu, Junqiang Wang, Jiwei Xu, and Jun Hu. "Multi-target tracking of surveillance video with differential YOLO and DeepSort." In Eleventh International Conference on Digital Image Processing, edited by Xudong Jiang and Jenq-Neng Hwang. SPIE, 2019. http://dx.doi.org/10.1117/12.2540269.

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Kumar, Shailender, Vishal, Pranav Sharma, and Nitin Pal. "Object tracking and counting in a zone using YOLOv4, DeepSORT and TensorFlow." In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021. http://dx.doi.org/10.1109/icais50930.2021.9395971.

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Ravikiran, Manikandan, Yuichi Nonaka, and Nestor Mariyasagayam. "A Sensitivity Analysis (and Practitioners’ Guide to) of DeepSORT for Low Frame Rate Video." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378112.

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Nayak, Avik, Haiquan Chen, Xiaojun Ruan, and Jinsong Ouyang. "DeepSpot." In SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356473.3365187.

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Mailthody, Vikram Sharma, Zaid Qureshi, Weixin Liang, Ziyan Feng, Simon Garcia de Gonzalo, Youjie Li, Hubertus Franke, Jinjun Xiong, Jian Huang, and Wen-mei Hwu. "DeepStore." In MICRO '52: The 52nd Annual IEEE/ACM International Symposium on Microarchitecture. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3352460.3358320.

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