Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning by Chun-Hao Wang, Yue-Tian-Si Ji, Li Ruan, Joshua Luhwago, Yin-Xuan Saw, Sokhey Kim, Tao Ruan, Li-Min Xiao, Rui-Jue Zhou

“…Moreover, how to perform multisource accident data label extraction, identity, and relationship extraction are still challenging problems. …”
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Weed Control in Beets by William M. Stall

“…Clopyralid (Stinger) has just received labeling for use postermergence, over-the-top of beets. …”
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Weed Control in Beets by William M. Stall

“…Clopyralid (Stinger) has just received labeling for use postermergence, over-the-top of beets. …”
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Super H-Antimagic Total Covering for Generalized Antiprism and Toroidal Octagonal Map by Amir Taimur, Gohar Ali, Muhammad Numan, Adnan Aslam, Kraidi Anoh Yannick

“…An a,d-H antimagic total labeling f is said to be super if the vertex labels are from the set 1,2,…,|VG. …”
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The Nutri-Score for a Healthy Diet: Pros and Cons by Pauline Raoul, Emanuele Rinninella, Marco Cintoni, Vincenzina Mora, Maria Cristina Mele

“…Packaging labels must provide comprehensible nutritional information for consumers and represent a crucial educational tool to prevent non-communicable diseases such as metabolic syndrome, cardiovascular diseases, and cancers. …”
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More Efficient Manual Review of Automatically Transcribed Tabular Data by Bjørn-Richard Pedersen, Rigmor Katrine Johansen, Einar Holsbø, Hilde Sommerseth, Lars Ailo Bongo

“…We recommend that reviewers indicate any uncertainty about the label they assign to an image by adding a flag to their label. …”
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Optical Imaging of Cellular Immunotherapy against Prostate Cancer by Sidhartha Tavri, Priyanka Jha, Reinhard Meier, Tobias D. Henning, Tina Müller, Daniel Hostetter, Christiane Knopp, Magnus Johansson, Verena Reinhart, Sophie Boddington, Akhilesh Sista, Winfried S. Wels, Heike E. Daldrup-Link

“…The purpose of this study was to track fluorophore-labeled, tumor-targeted natural killer (NK) cells to human prostate cancer xenografts with optical imaging (OI). …”
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Ensemble automated approaches for producing high‐quality herbarium digital records by Robert P. Guralnick, Raphael LaFrance, Julie M. Allen, Michael W. Denslow

“…Abstract Premise One of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. …”
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Research on credit card transaction security supervision based on PU learning by Renfeng CHEN, Hongbin ZHU

“…The complex and ever-evolving nature of credit card cash out methods and the emergence of various forms of fake transactions present challenges in obtaining accurate transaction information during customer interactions.In order to develop an accurate supervision method for detecting fake credit card transactions, a PU (positive-unlabeled learning) based security identification model for single credit card transactions was established.It was based on long-term transaction label data from cashed-up accounts in commercial banks’ credit card systems.A Spy mechanism was introduced into sample data annotation by selecting million positive samples of highly reliable cash-out transactions and 1.3 million samples of transactions to be labeled, and using a learner to predict the result distribution and label negative samples of non-cash-out transactions that were difficult to identify, resulting in 1.2 million relatively reliable negative sample labels.Based on these samples, 120 candidate variables were constructed, including credit card customer attributes, quota usage, and transaction preference characteristics.After importance screening of variables, nearly 50 candidate variables were selected.The XGBoost binary classification algorithm was used for model development and prediction.The results show that the proposed model achieve an identification accuracy of 94.20%, with a group stability index (PSI) of 0.10%, indicating that the single credit card transaction security identification model based on PU learning can effectively monitor fake transactions.This study improves the model discrimination performance of machine learning binary classification algorithm in scenarios where high-precision sample label data is difficult to obtain, providing a new method for transaction security monitoring in commercial bank credit card systems.…”
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A Novel Method of Imaging Lysosomes in Living Human Mammary Epithelial Cells by Kristine Glunde, Sandra E. Guggino, Yoshitaka Ichikawa, Zaver M. Bhujwalla

“…In this study, a novel method of introducing a fluorescent label into lysosomes of human mammary epithelial cells (HMECs) was evaluated. …”
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Comprehensive Study on Zero-Shot Text Classification Using Category Mapping by Kai Zhang, Qiuxia Zhang, Chung-Che Wang, Jyh-Shing Roger Jang

“…Approaches that fine-tune smaller models using label mappings and existing datasets for zero-shot classification are simpler but suffer from weaker generalization capabilities. …”
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Real world federated learning with a knowledge distilled transformer for cardiac CT imaging by Malte Tölle, Philipp Garthe, Clemens Scherer, Jan Moritz Seliger, Andreas Leha, Nina Krüger, Stefan Simm, Simon Martin, Sebastian Eble, Halvar Kelm, Moritz Bednorz, Florian André, Peter Bannas, Gerhard Diller, Norbert Frey, Stefan Groß, Anja Hennemuth, Lars Kaderali, Alexander Meyer, Eike Nagel, Stefan Orwat, Moritz Seiffert, Tim Friede, Tim Seidler, Sandy Engelhardt

“…First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. …”
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Aspect-Based Sentiment Analysis using Adaptive Aspect on Tourist Reviews in Jakarta by Ade Rifqy Setyawan, Lya Hulliyatus Suadaa, Budi Yuniarto

“…However, the multi-label approach has limited flexibility in the aspects used. …”
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Electron Microscopic Radioautographic Study on Mitochondrial DNA Synthesis in Adrenal Cortical Cells of Developing and Aging Mice by Tetsuji Nagata

“…On EM radioautograms obtained from each animal, the number of mitochondria and the mitochondrial labeling index labeled with 3H-thymidine showing DNA synthesis in each adrenal cortical cell, in three zones, were counted and the results in respective developing groups were compared. …”
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Monitoring Steady Flow Effects on Cell Distribution in Engineered Valve Tissues by Magnetic Resonance Imaging by Catalina Martinez, Angela Henao, Jose E. Rodriguez, Kyle R. Padgett, Sharan Ramaswamy

“…SPIO-labeled cells were successfully visualized using T 2 * contrast. …”
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SensorDBSCAN: Semi-Supervised Active Learning Powered Method for Anomaly Detection and Diagnosis by Petr Ivanov, Maria Shtark, Alexander Kozhevnikov, Maksim Golyadkin, Dmitry Botov, Ilya Makarov

“…Traditional supervised FDD methods offer great performance while heavily relying on large volumes of labeled data, whereas unsupervised methods do not depend on labeled data, though are inferior in performance compared to supervised ones. …”
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Cryo-Imaging and Software Platform for Analysis of Molecular MR Imaging of Micrometastases by Mohammed Q. Qutaish, Zhuxian Zhou, David Prabhu, Yiqiao Liu, Mallory R. Busso, Donna Izadnegahdar, Madhusudhana Gargesha, Hong Lu, Zheng-Rong Lu, David L. Wilson

“…We describe technological details earlier applied to GFP-labeled metastatic tumor targeting by molecular MR (CREKA-Gd) and red fluorescent (CREKA-Cy5) imaging agents. …”
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Penerapan Text Augmentation untuk Mengatasi Data yang Tidak Seimbang pada Klasifikasi Teks Berbahasa Indonesia by Iftitah Athiyyah Rahma, Lya Hulliyyatus Suadaa

“…Meanwhile, the minority label data tends to be classified incorrectly by the model, even though, in some cases, the model's ability to predict data with minority labels is more important. …”
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A travelable area boundary dataset for visual navigation of field robots by Kai Zhang, Xia Yuan, Jiachen Xu, Kaiyang Wang, Shiwei Wu, Chunxia Zhao

“…Novel guiding semantics labels, shape labels and scene complexity labels are assigned to boundaries. …”
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Vida Saludable: Entendiendo la etiqueta de datos nutricionales by Karla P. Shelnutt

“…Shelnutt, is the Spanish language version of FCS8883/FY1127 Healthy Eating: Understanding the Nutrition Facts Label. It explains the nutrition facts label — why it is needed and how to read it. …”
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