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Journal articles on the topic 'Weight labels'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Wang, Zhe, Hao Xu, Pan Zhou, and Gang Xiao. "An Improved Multilabel k-Nearest Neighbor Algorithm Based on Value and Weight." Computation 11, no. 2 (2023): 32. http://dx.doi.org/10.3390/computation11020032.

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Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (ML-kNN) algorithm, has poor performance on imbalanced multilabel data. To address this problem, this study proposes an improved ML-kNN algorithm based on value and weight. In this improved algorithm, labels are divided into minority and majority, and different strategies are adopted for different labels. By considering the label of latent informatio

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Haunert, Jan-Henrik, and Alexander Wolff. "BEYOND MAXIMUM INDEPENDENT SET: AN EXTENDED MODEL FOR POINT-FEATURE LABEL PLACEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 109–14. http://dx.doi.org/10.5194/isprs-archives-xli-b2-109-2016.

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Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in the map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label overlaps) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the

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Haunert, Jan-Henrik, and Alexander Wolff. "BEYOND MAXIMUM INDEPENDENT SET: AN EXTENDED MODEL FOR POINT-FEATURE LABEL PLACEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 109–14. http://dx.doi.org/10.5194/isprsarchives-xli-b2-109-2016.

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Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in the map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label overlaps) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the

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D’Angelo Campos, Aline, Anna H. Grummon, Shu Wen Ng, Rebecca M. Puhl, Shelley D. Golden, and Marissa G. Hall. "Front-of-Package Food Labels and Perceived Weight Stigmatization." JAMA Network Open 8, no. 6 (2025): e2516821. https://doi.org/10.1001/jamanetworkopen.2025.16821.

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ImportanceFront-of-package food labels (FOPLs) about nutrient content and health effects are a promising policy to improve diet quality but may also contribute to harmful weight stigma.ObjectiveTo assess whether different types of FOPLs impact perceived weight stigmatization, whether weight-neutral label content mitigates stigmatization, and possible trade-offs between perceived stigmatization and effectiveness.Design, Setting, and ParticipantsThis randomized clinical trial with a between- and within-participant design used a single-exposure online survey conducted from January 18 to 26, 2024.

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Wang, Wanzhu, and Yong Liu. "Multi-label Feature Selection based on Label-specific features and Manifold Learning." Academic Journal of Science and Technology 10, no. 1 (2024): 364–69. http://dx.doi.org/10.54097/astymd16.

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Each instance in multi-label data is associated with multiple labels, and there are irrelevant or redundant features in its feature space, which leads to the performance degradation of multi-label learning algorithms. Multi-label feature selection selects representative features from the feature space to improve the accuracy of the model. Due to the high cost of labels and the difficulty of data collection, there will be some missing labels in the data set, which affects the accuracy of feature selection. To solve this problem, a multi-label feature selection algorithm based on label-specific

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Bača, Martin, Mirka Miller, Oudone Phanalasy, Joe Ryan, Andrea Semaničová Feňovčíková, and Anita A. Sillasen. "Total Labelings of Graphs with Prescribed Weights." Journal of Combinatorial Mathematics and Combinatorial Computing 120, no. 1 (2024): 367–76. http://dx.doi.org/10.61091/jcmcc120-33.

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The total labeling of a graph G = ( V , E ) is a bijection from the union of the vertex set and the edge set of G to the set { 1 , 2 , … , | V ( G ) | + | E ( G ) | } . The edge-weight of an edge under a total labeling is the sum of the label of the edge and the labels of the end vertices of that edge. The vertex-weight of a vertex under a total labeling is the sum of the label of the vertex and the labels of all the edges incident with that vertex. A total labeling is called edge-magic or vertex-magic when all the edge-weights or all the vertex-weights are the same, respectively. When all the

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Zhang, Yaojie, Huahu Xu, Junsheng Xiao, and Minjie Bian. "JoSDW: Combating Noisy Labels by Dynamic Weight." Future Internet 14, no. 2 (2022): 50. http://dx.doi.org/10.3390/fi14020050.

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The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most advanced existing methods mainly adopt a small loss sample selection strategy, such as selecting the small loss part of the sample for network model training. However, the previous literature stopped here, neglecting the performance of the small loss sample selection strategy while training the DNNs, as well as the performance of different stages

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A. S., Saranya, and Santhosh Kumar K. R. "On the total edge irregularity strength of certain classes of cycle related graphs." Proyecciones (Antofagasta) 43, no. 1 (2024): 53–67. http://dx.doi.org/10.22199/issn.0717-6279-5728.

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For a graph G=(V,E), an edge irregular total k-labeling is a labeling of the vertices and edges of G with labels from the set {1, 2, ..., k } such that any two different edges have distinct weights. The sum of the label of edge uv and the labels of vertices u and v determines the weight of the edge uv. The smallest possible k for which the graph G has an edge irregular total k-labeling is called the total edge irregularity strength of G. We determine the exact value of the total edge irregularity strength for some cycle related graphs.

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Karaca, Adeviyye, Kamil Can Akyol, Mustafa Keşaplı, et al. "Do Clothing Labels Play a Role for Weight Estimation in Pediatric Emergencies? A Prospective, Cross-Sectional Study." Prehospital and Disaster Medicine 36, no. 3 (2021): 295–300. http://dx.doi.org/10.1017/s1049023x21000194.

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AbstractIntroduction:The aim of this study was to investigate the usability of the age value listed on the labels on children’s clothes in the age-based weight estimation method recommended by the Pediatric Advanced Life Support (PALS) guidelines.Material-Method:This prospective, cross-sectional study was organized in Antalya Training and Research Hospital Emergency Department. Children aged between 1-12 years were included in the study. The weight measurements of the children were obtained based on the age-related criteria on the labels of their clothes. The estimated values were compared wit

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Essayli, Jamal H., Jessica M. Murakami, Rebecca E. Wilson, and Janet D. Latner. "The Impact of Weight Labels on Body Image, Internalized Weight Stigma, Affect, Perceived Health, and Intended Weight Loss Behaviors in Normal-Weight and Overweight College Women." American Journal of Health Promotion 31, no. 6 (2016): 484–90. http://dx.doi.org/10.1177/0890117116661982.

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Purpose: To explore the psychological impact of weight labels. Design: A double-blind experiment that randomly informed participants that they were “normal weight” or “overweight.” Setting: Public university in Honolulu, Hawai‘i. Participants: Normal-weight and overweight female undergraduates (N = 113). Measures: The Body Image States Scale, Stunkard Rating Scale, Weight Bias Internalization Scale, Positive and Negative Affect Schedule, General Health question from the 12-item Short Form Health Survey, modified version of the Weight Loss Methods Scale, and a manipulation check. Analysis: A 2

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Liu, Jinghua, Songwei Yang, Hongbo Zhang, Zhenzhen Sun, and Jixiang Du. "Online Multi-Label Streaming Feature Selection Based on Label Group Correlation and Feature Interaction." Entropy 25, no. 7 (2023): 1071. http://dx.doi.org/10.3390/e25071071.

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Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labels are independent of each other, or, although label correlation is explored, the relationship between related labels and features is difficult to understand or specify. In real applications, both situations may occur where the labels are correlated and the features may belong specifically to some labels. Moreover, these methods treat features indi

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Crawford, Cindy, Andrea T. Lindsey, Bharathi Avula, Kumar Katragunta, Ikhlas A. Khan, and Patricia A. Deuster. "Label Accuracy and Quality of Select Weight-Loss Dietary Supplements Sold on or near US Military Bases." Nutrients 16, no. 24 (2024): 4369. https://doi.org/10.3390/nu16244369.

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Background/Objectives: Sixty-eight percent of service members are living with overweight or obesity, some who may not consult a healthcare provider when they decide to lose weight. Instead, they often turn to weight-loss dietary supplements for self-care solutions. The purpose of this case series study was to examine the label accuracy and quality of select weight-loss dietary supplements sold on or near US military bases across the country. Methods: Weight-loss dietary supplements (n = 44) were selected and purchased in GNCs, Exchanges, and Shoppettes across 12 military installations from Mar

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Indriati, Diari, Risma Listya Utami, and Putranto Hadi Utomo. "THE REFLEXIVE EDGE STRENGTH OF THE PENTAGONAL SNAKE GRAPH AND CORONA OF THE OPEN TRIANGULAR LADDER AND NULL GRAPH." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 4 (2024): 2757–66. http://dx.doi.org/10.30598/barekengvol18iss4pp2757-2766.

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Assume that be an undirected simple graph with vertex set and edge set . The edge irregular reflexive -labeling of graph is a labeling selects positive integers from 1 to as edge labels and non negative even numbers from 0 to as vertex labels, and the weights assigned to each edge are distinct, where . On graph with labeling, the weight of edge is represented by which is defined as the sum of edge label and all vertex labels incident to that edge. Reflexive edge strength of graph is the minimum of the highest label, denoted by . In this research, reflexive edge strength for pentagonal snake gr

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O’Connor, Alan. "Habitus and field: Punk record labels in Spain." Punk & Post Punk 10, no. 2 (2021): 265–89. http://dx.doi.org/10.1386/punk_00071_1.

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Following the method of Bourdieu’s Distinction (1984) and especially The Weight of the World (1999), this article presents interviews with four Spanish record labels, which provide case studies of the workings of the field. Distinction shows that uses of culture are affected by social class. The Weight of the World presents lightly edited interviews with marginalized groups in France. The interviews presented in this article attempt to relate the lifestyle or class habitus of the person interviewed to their strategies of operating a punk record label. The recorded interviews also provide a gre

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Santoso, Kiswara Agung, Febyan Gilang Cristyanto, Ikhsanul Halikin, and Ridho Alfarisi. "Advancing Graph Theory with Genetic Algorithms: AFocus on Non-Inclusive Vertex Irregular Labeling." European Journal of Pure and Applied Mathematics 17, no. 4 (2024): 3994–4002. http://dx.doi.org/10.29020/nybg.ejpam.v17i4.5538.

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Non-inclusive irregular vertex labeling is a labeling on a graph where the vertex labels are real numbers with weights. The weight is defined as the sum of the labels of the connected nodes. The main problem in labeling graphs is finding the formula to apply the required labeling rules. To find this formula, researchers must try to label various kinds of graphs to find labeling patterns. A Heuristic Algorithm is an algorithm that can always provide solutions and is approximate. One type of heuristic algorithm is a genetic algorithm, where this algorithm will generate random numbers as candidat

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Xing, Peixu, Mengxing Huang, Chenhao Wang, and Yang Cao. "High-Quality Instance Mining and Weight Re-Assigning for Weakly Supervised Object Detection in Remote Sensing Images." Electronics 13, no. 23 (2024): 4753. https://doi.org/10.3390/electronics13234753.

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Weakly supervised object detection (WSOD) in remote sensing images (RSIs) achieves high-value object classification and localization by only using image-level labels. However, two problems limit its performance. Firstly, adjacent instances are often misclassified because their pseudo-labels are determined solely based on the spatial distances between them and their corresponding seed instances. Secondly, most WSOD methods assign the highest weight to the instance that covers the discriminative part of an object, thereby urging WSOD models to focus on the discriminative part rather than the who

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Lee, Chan, Seungho Shin, Gyeong-Moon Park, and Jung Uk Kim. "Multispectral Pedestrian Detection with Sparsely Annotated Label." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4482–90. https://doi.org/10.1609/aaai.v39i4.32472.

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Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedestrians are annotated, they still have the following limitations: (i) they lack considerations for improving the quality of pseudo-labels for missing annotations, and (ii) they rely on fixed ground truth annotations, which leads to learning only a limited range of pedestrian visual appearances in the multispectral domain. To address these issues, we propose a novel framework called Sparsely Annotated Multispectral Ped

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Haidar, Amier, Felicia R. Carey, Nalini Ranjit, Natalie Archer, and Deanna Hoelscher. "Self-reported use of nutrition labels to make food choices is associated with healthier dietary behaviours in adolescents." Public Health Nutrition 20, no. 13 (2017): 2329–39. http://dx.doi.org/10.1017/s1368980017001252.

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AbstractObjectiveThe study aimed to examine nutrition label use and dietary behaviours among ethnically diverse middle- and high-school students, in Texas, USA.DesignThe School Physical Activity and Nutrition (SPAN) survey is a cross-sectional statewide study using a self-administered questionnaire to assess nutrition and physical activity behaviours. Height and weight measurements were used to determine BMI. Multivariable logistic regression was used to determine associations between nutrition label use and dietary behaviours, with gender, grade, ethnicity, BMI, parent education, socio-econom

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Bača, Martin, Muhammad Imran, Zuzana Kimáková, and Andrea Semaničová-Feňovčíková. "A new generalization of edge-irregular evaluations." AIMS Mathematics 8, no. 10 (2023): 25249–61. http://dx.doi.org/10.3934/math.20231287.

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<abstract><p>Consider a simple graph $ G = (V, E) $ of size $ m $ with the vertex set $ V $ and the edge set $ E $. A modular edge-irregular total $ k $-labeling of a graph $ G $ is a labeling scheme for the vertices and edges with the labels $ 1, 2, \dots, k $ that allows the modular weights of any two different edges to be distinct, where the modular weight of an edge is the remainder of the division of the weight (i.e., the sum of the label of the edge itself and the labels of its two end vertices) by $ m $. The maximal integer $ k $, minimized over all modular edge-irregular to

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Shah, Meena, Brooke Bouza, Beverley Adams-Huet, Manall Jaffery, Phil Esposito, and Lyn Dart. "Effect of calorie or exercise labels on menus on calories and macronutrients ordered and calories from specific foods in Hispanic participants: a randomized study." Journal of Investigative Medicine 64, no. 8 (2016): 1261–68. http://dx.doi.org/10.1136/jim-2016-000227.

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The effect of menu labels on food choices is unknown in Hispanics. This study evaluated the impact of menu labels on calories and macronutrients ordered in Hispanics. 372 Hispanics (18–65 years) were randomly assigned to menus with no labels (NL) (n=127), rank-ordered calorie labels plus a statement on energy needs per meal (CL) (n=123), or rank-ordered exercise labels showing minutes of brisk walking necessary to burn the food calories (EL) (n=122). The menus had identical food choices. Participants were instructed to select foods from the assigned menu as if having lunch in a fast food resta

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Zheng, Kecheng, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, and Zheng-Jun Zha. "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3538–46. http://dx.doi.org/10.1609/aaai.v35i4.16468.

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Many unsupervised domain adaptive (UDA) person ReID approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of domain gap, the pseudo-labels are not always reliable and there are noisy/incorrect labels. This would mislead the feature representation learning and deteriorate the performance. In this paper, we propose to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples. We build our baseline framework using the mean teacher met

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Crawford, Cindy, Bharathi Avula, Andrea T. Lindsey, Kumar Katragunta, Ikhlas A. Khan, and Patricia A. Deuster. "Label Accuracy of Weight Loss Dietary Supplements Marketed Online With Military Discounts." JAMA Network Open 7, no. 5 (2024): e249131. http://dx.doi.org/10.1001/jamanetworkopen.2024.9131.

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ImportanceDietary supplements for weight loss, among the most popular supplement products on the market, are promoted not only for losing weight and shedding fat, but also for added benefits of energy and performance, all packed into 1 capsule with multiple combinations of ingredients. Fraudulent marketing of weight loss supplements, some with exaggerated claims, some that are potentially dangerous, and some that contain illegal ingredients, is ever present, especially through online sources, where multiple manufacturers target service members by offering military discounts.ObjectivesTo examin

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Gao, Yuefang, Yiteng Cai, Xuanming Bi, Bizheng Li, Shunpeng Li, and Weiping Zheng. "Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting." Electronics 12, no. 21 (2023): 4553. http://dx.doi.org/10.3390/electronics12214553.

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Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target domains. Existing methods primarily emphasize achieving domain-invariant features through global feature adaptation, often neglecting the potential benefits of transferable local features across different domains. To address this issue, we propose a novel framework for CD-FER that combines reliabl

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Ekadipta, Isngunaenah, and Fauziah Purnama Sari. "ANALISA KESESUAIAN STANDAR LABEL PANGAN PADA KEMASAN PRODUK BISKUIT LOKAL DAN IMPOR TEREGISTRASI DI BADAN PENGAWAS OBAT DAN MAKANAN." ISTA Online Technologi Journal 2, no. 1 (2021): 1–7. http://dx.doi.org/10.62702/ion.v2i1.31.

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Abstrak Hal yang mendasari penelitian ini karena banyak ditemukan label kemasan yang beredar tidak sesuai standar Peraturan Pemerintah No. 69 Tahun 1999. Berdasarkan Peraturan Pemerintah No 69 Tahun 1999 tentang label pangan mencakup lima unsur yang harus dipenuhi dalam pencantuman informasi pada label kemasan. Tujuan penelitian ini dilakukan untuk mengetahui kesesuaian label kemasan produk biskuit lokal dan impor serta mengevaluasi pelanggaran yang terjadi pada label kemasan produk biskuit lokal dan impor yang mencakup lima unsur teknis. Metode penelitian yang digunakan metode analisis deskri

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Bača, Martin, Andrea Semaničová-Feňovčíková, and Tao-Ming Wang. "Local Antimagic Chromatic Number for Copies of Graphs." Mathematics 9, no. 11 (2021): 1230. http://dx.doi.org/10.3390/math9111230.

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An edge labeling of a graph G=(V,E) using every label from the set {1,2,⋯,|E(G)|} exactly once is a local antimagic labeling if the vertex-weights are distinct for every pair of neighboring vertices, where a vertex-weight is the sum of labels of all edges incident with that vertex. Any local antimagic labeling induces a proper vertex coloring of G where the color of a vertex is its vertex-weight. This naturally leads to the concept of a local antimagic chromatic number. The local antimagic chromatic number is defined to be the minimum number of colors taken over all colorings of G induced by l

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Suo, M., S. Li, Y. Chen, Z. Zhang, B. Zhu, and R. An. "Effectiveness evaluation of fighter using fuzzy Bayes risk weighting method." Aeronautical Journal 122, no. 1254 (2018): 1275–300. http://dx.doi.org/10.1017/aer.2018.54.

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ABSTRACTMultiple Attribute Decision Analysis (MADA), known to be simple and convenient, is one of the most commonly used methods for Effectiveness Evaluation of Fighter (EEF), in which the attribute weight assignment plays a key role. Generally, there are two parts in the index system of MADA, i.e. performance index and decision index (or label), which denote the specific performance and the category of the object, respectively. In some index systems of EEF, the labels can be easily obtained, which are presented as the generations of fighters. However, the existing methods of attribute weight

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Ding, Qianggang, Sifan Wu, Tao Dai, et al. "Knowledge Refinery: Learning from Decoupled Label." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7228–35. http://dx.doi.org/10.1609/aaai.v35i8.16888.

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Recently, a variety of regularization techniques have been widely applied in deep neural networks, which mainly focus on the regularization of weight parameters to encourage generalization effectively. Label regularization techniques are also proposed with the motivation of softening the labels while neglecting the relation of classes. Among them, the technique of knowledge distillation proposes to distill the soft label, which contains the knowledge of class relations. However, this technique needs to pre-train an extra cumbersome teacher model. In this paper, we propose a method called Knowl

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Tilukay, Meilin Imelda, and Harmanus Batkunde. "The Total Irregularity Strength of a Comb Product of Stars." InPrime: Indonesian Journal of Pure and Applied Mathematics 6, no. 2 (2024): 217–24. https://doi.org/10.15408/inprime.v6i2.42188.

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A totally irregular total k-labeling λ: V U E → {1, 2, ⋯ , k} of a graph G is a labeling where the weights of all distinct vertices and edges are unique. The weight w(x) of a vertex x is defined as the sum of its label and the labels of all edges incident to it, while the weight w(e) of an edge e is the sum of its label and the labels of its two endpoints. The minimum k for which G admits such a labeling is known as the total irregularity strength of G, denoted ts(G). This study focuses on determining ts(G) for specific classes of trees, including the comb product of stars, where the contact v

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Qian, Feng, Juan Yang, Sipeng Tang, Gao Chen, and Jingwen Yan. "Addressing Noisy Pixels in Weakly Supervised Semantic Segmentation with Weights Assigned." Mathematics 12, no. 16 (2024): 2520. http://dx.doi.org/10.3390/math12162520.

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Weakly supervised semantic segmentation (WSSS) aims to segment objects without a heavy burden of dense annotations. Pseudo-masks serve as supervisory information for training segmentation models, which is crucial to the performance of segmentation models. However, the generated pseudo-masks contain significant noisy labels, which leads to poor performance of the segmentation models trained on these pseudo-masks. Few studies address this issue, as these noisy labels remain inevitable even after the pseudo-masks are improved. In this paper, we propose an uncertainty-weight transform module to mi

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Graham, Dan J., and Robert W. Jeffery. "Predictors of nutrition label viewing during food purchase decision making: an eye tracking investigation." Public Health Nutrition 15, no. 2 (2011): 189–97. http://dx.doi.org/10.1017/s1368980011001303.

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AbstractObjectiveNutrition label use could help consumers eat healthfully. Despite consumers reporting label use, diets are not very healthful and obesity rates continue to rise. The present study investigated whether self-reported label use matches objectively measured label viewing by monitoring the gaze of individuals viewing labels.DesignThe present study monitored adults viewing sixty-four food items on a computer equipped with an eye-tracking camera as they made simulated food purchasing decisions. ANOVA and t tests were used to compare label viewing across various subgroups (e.g. normal

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Lu, Yang, Lin Chen, Yonggang Zhang, et al. "Federated Learning with Extremely Noisy Clients via Negative Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14184–92. http://dx.doi.org/10.1609/aaai.v38i13.29329.

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Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noise by a re-weighting strategy with a strong assumption, i.e., mild label noise. However, it may be violated in many real-world FL scenarios because of highly contaminated clients, resulting in extreme noise ratios, e.g., >90%. To tackle extremely noisy clients, we study the robustness of the re-weighting strategy, showing a pessimistic conclusion: minimizing the weight of clients trained over noisy data outperform

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MANDAL, BIDISHA. "Use of Food Labels as a Weight Loss Behavior." Journal of Consumer Affairs 44, no. 3 (2010): 516–27. http://dx.doi.org/10.1111/j.1745-6606.2010.01181.x.

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Liu, Zhiguang, Fengshuai Li, Guoyin Hao, Xiaoqing He, and Yuanheng Zhang. "GCN-LSTM: multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks." Computer Science and Information Systems, no. 00 (2024): 49. http://dx.doi.org/10.2298/csis240314049l.

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Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi

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Huang, Wenzhi, Junchi Zhang, and Donghong Ji. "Extracting Chinese events with a joint label space model." PLOS ONE 17, no. 9 (2022): e0272353. http://dx.doi.org/10.1371/journal.pone.0272353.

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The task of event extraction consists of three subtasks namely entity recognition, trigger identification and argument role classification. Recent work tackles these subtasks jointly with the method of multi-task learning for better extraction performance. Despite being effective, existing attempts typically treat labels of event subtasks as uninformative and independent one-hot vectors, ignoring the potential loss of useful label information, thereby making it difficult for these models to incorporate interactive features on the label level. In this paper, we propose a joint label space frame

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Pan, Junwen, Qi Bi, Yanzhan Yang, Pengfei Zhu, and Cheng Bian. "Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 2026–34. http://dx.doi.org/10.1609/aaai.v36i2.20098.

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Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annotations together with few strongly-annotated labels so as to achieve comparable segmentation performance in many unprofessional scenarios. However, these approaches only concentrate on the supervision inconsistency between strongly- and weakly-annotated instances but ignore the instance inconsistency inside the weakly-annotated instances, which inevitably leads to perfo

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Liang, Zejian, Yunxiang Zhao, Mengyuan Wang, Hong Huang, and Haiwen Xu. "Research on the Automatic Multi-Label Classification of Flight Instructor Comments Based on Transformer and Graph Neural Networks." Aerospace 12, no. 5 (2025): 407. https://doi.org/10.3390/aerospace12050407.

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With the rapid advancement of the civil aviation sector and the concurrent expansion of pilot training programs, a pressing need arises for more efficient assessment methodologies during the pilot training process. Traditional written evaluations conducted by flight instructors are often marred by subjectivity and inefficiency, rendering them inadequate to satisfy the stringent demands of Competency-Based Training and Assessment (CBTA) frameworks. To address this challenge, this study presents a novel multi-label classification model that seamlessly integrates RoBERTa, a robust language model,

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Sun, Heli, Jianbin Huang, Xiang Zhong, Ke Liu, Jianhua Zou та Qinbao Song. "Label Propagation withα-Degree Neighborhood Impact for Network Community Detection". Computational Intelligence and Neuroscience 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/130689.

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Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach withα-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of itsα-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order o

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Jacobs, Tom G., Hylke Waalewijn, and Angela Colbers. "Use of WHO paediatric weight-band dosing in drug labels." Lancet HIV 9, no. 1 (2022): e3-e4. http://dx.doi.org/10.1016/s2352-3018(21)00277-0.

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Xu, Yuanzhuo, Xiaoguang Niu, Jie Yang, et al. "Revisiting Interpolation for Noisy Label Correction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21833–41. https://doi.org/10.1609/aaai.v39i20.35489.

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Label correction methods are popular for their simple architecture in learning with noisy labels. However, they suffer severely from false label correction and achieve subpar performance compared with state-of-the-art methods. In this paper, we revisit the label correction methods through theoretical analysis of gradient scaling and demonstrate that the sample-wise dynamic and class-wise uniformity of interpolation weight prevents memorization of the mislabeled samples. We then propose DULC, a simple yet effective label correction method that uses the normalized Jensen-Shannon divergence (JSD)

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De Silva, Devarahandhi Achini Melda, Renda Kankanamge Chaturika Jeewanthi, Rajapakshage Heshani Navoda Rajapaksha, et al. "Clean vs dirty labels: Transparency and authenticity of the labels of Ceylon cinnamon." PLOS ONE 16, no. 11 (2021): e0260474. http://dx.doi.org/10.1371/journal.pone.0260474.

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Ceylon cinnamon, which was regarded as a luxury spice during ancient times, has been consumed for its medicinal properties and health benefits for thousands of years. For centuries, Arabian traders controlled the European cinnamon trade through limited supplies from a country which they did not reveal. Content marketing analysis and chemical profiling of value-added products of Ceylon cinnamon in the global marketplace are proposed to investigate the clean status of the product labels. In the present study, a mixed-method approach was employed to investigate the labels of 6 types of value-adde

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Malekzadeh, Milad, and Jed A. Long. "A network community structure similarity index for weighted networks." PLOS ONE 18, no. 11 (2023): e0292018. http://dx.doi.org/10.1371/journal.pone.0292018.

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Identification of communities in complex systems is an essential part of network analysis. Accordingly, measuring similarities between communities is a fundamental part of analysing community structure in different, yet related, networks. Commonly used methods for quantifying network community similarity fail to consider the effects of edge weights. Existing methods remain limited when the two networks being compared have different numbers of nodes. In this study, we address these issues by proposing a novel network community structure similarity index (NCSSI) based on the edit distance concep

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Lee, Yujin, David Kim, Junxiu Liu, et al. "Health and Economic Impacts of a Sugar-Sweetened Beverage Warning Label in the US: A Micro-Simulation Study." Current Developments in Nutrition 4, Supplement_2 (2020): 715. http://dx.doi.org/10.1093/cdn/nzaa051_012.

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Abstract Objectives Sugar-sweetened beverage (SSB) consumption is linked to weight gain, type 2 diabetes and cardiovascular disease (CVD). Health warning labels on SSBs have been proposed in the US to discourage consumption. Yet, the potential health and economic impacts of SSB warning labels have not been quantified. We aimed to estimate the health and economic impacts of a SSB warning label in the US. Methods We used the validated Tufts Diabetes-CVD Microsimulation Model to estimate the impact of implementing a national SSB warning label in the U.S., compared to current status quo, on incide

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Sudibyo, Nugroho Arif, and Siti Komsatun. "PELABELAN TOTAL TAK REGULER PADA BEBERAPA GRAF." Jurnal Ilmiah Matematika dan Pendidikan Matematika 10, no. 2 (2018): 9. http://dx.doi.org/10.20884/1.jmp.2018.10.2.2840.

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For a simple graph G with vertex set V (G) and edge set E(G), a labeling $\Phi:V(G)\cup U(G)\rightarrow\{1,2,...k\}$ is called a vertex irregular total k- labeling of G if for any two diferent vertices x and y, their weights wt(x) and wt(y) are distinct. The weight wt(x) of a vertex x in G is the sum of its label and the labels of all edges incident with the given vertex x. The total vertex irregularity strength of G, tvs(G), is the smallest positive integer k for which G has a vertex irregular total k-labeling. In this paper, we study the total vertex irregularity strength of some class of gr

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Gan, Langxiong, Qihao Yang, Yi Xu, Qiongyao Mao, and Chengyong Liu. "Construction of an LNG Carrier Port State Control Inspection Knowledge Graph by a Dynamic Knowledge Distillation Method." Journal of Marine Science and Engineering 13, no. 3 (2025): 426. https://doi.org/10.3390/jmse13030426.

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The Port State Control (PSC) inspection of liquefied natural gas (LNG) carriers is crucial in maritime transportation. PSC inspection requires rapid and accurate identification of defects with limited resources, necessitating professional knowledge and efficient technical methods. Knowledge distillation, as a model lightweighting approach in the field of artificial intelligence, offers the possibility of enhancing the responsiveness of LNG carrier PSC inspections. In this study, a knowledge distillation method is introduced, namely, the multilayer dynamic multi-teacher weighted knowledge disti

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Meng, Lingwen, Guobang Ban, Guanghui Xi, and Siqi Guo. "Pseudo label refining for semi-supervised temporal action localization." PLOS ONE 20, no. 2 (2025): e0318418. https://doi.org/10.1371/journal.pone.0318418.

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The training of temporal action localization models relies heavily on a large amount of manually annotated data. Video annotation is more tedious and time-consuming compared with image annotation. Therefore, the semi-supervised method that combines labeled and unlabeled data for joint training has attracted increasing attention from academics and industry. This study proposes a method called pseudo-label refining (PLR) based on the teacher-student framework, which consists of three key components. First, we propose pseudo-label self-refinement which features in a temporal region interesting po

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Czyżycki, Tomasz, Jiří Hrivnák, and Lenka Motlochová. "Generalized Dual-Root Lattice Transforms of Affine Weyl Groups." Symmetry 12, no. 6 (2020): 1018. http://dx.doi.org/10.3390/sym12061018.

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Discrete transforms of Weyl orbit functions on finite fragments of shifted dual root lattices are established. The congruence classes of the dual weight lattices intersected with the fundamental domains of the affine Weyl groups constitute the point sets of the transforms. The shifted weight lattices intersected with the fundamental domains of the extended dual affine Weyl groups form the sets of labels of Weyl orbit functions. The coinciding cardinality of the point and label sets and corresponding discrete orthogonality relations of Weyl orbit functions are demonstrated. The explicit countin

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Liu, Yujing, Zongqian Wu, Zhengyu Lu, et al. "Noisy Node Classification by Bi-level Optimization Based Multi-Teacher Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19033–40. https://doi.org/10.1609/aaai.v39i18.34095.

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Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teach

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Tarlowski, Andrzej. "Naming Patterns and Inductive Inference: The Case of Birds." Journal of Cognition and Culture 11, no. 1-2 (2011): 189–216. http://dx.doi.org/10.1163/156853711x568743.

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AbstractAlthough past research demonstrated that online presentation of labels plays a role in inductive inference few studies have shown that naming practices affect stable category representations that enter into inductive judgments. In this study we provide evidence for a relationship between naming and inductive inference by examining Polish and Spanish speakers’ inferences within the taxonomic class Aves. Birds in Polish are named with one label, ptak, while Spanish uses two labels, ave and pájaro. Size is the feature that determines whether Spanish speakers label a bird as ave or pájaro.

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An, Pei, Junxiong Liang, Xing Hong, et al. "Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System." Remote Sensing 15, no. 3 (2023): 627. http://dx.doi.org/10.3390/rs15030627.

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Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, semi-supervised 3D object detection (SSOD-3D) has emerged as a technique to alleviate the shortage of labeled samples. However, it is still a challenging problem for SSOD-3D to learn 3D object detection from noisy pseudo labels. In this paper, to dynamically filter the unreliable pseudo labels, we first introduce a self-paced

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Yan, Xuesong, Qinghua Wu, and Victor S. Sheng. "A Double Weighted Naive Bayes with Niching Cultural Algorithm for Multi-Label Classification." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (2016): 1650013. http://dx.doi.org/10.1142/s0218001416500130.

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Multi-label classification is to assign an instance to multiple classes. Naive Bayes (NB) is one of the most popular algorithms for pattern recognition and classification. It has a high performance in single label classification. It is naturally extended for multi-label classification under the assumption of label independence. As we know, NB is based on a simple but unrealistic assumption that attributes are conditionally independent given the class. Therefore, a double weighted NB (DWNB) is proposed to demonstrate the influences of predicting different labels based on different attributes. O

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