Academic literature on the topic 'Weight labels'
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Journal articles on the topic "Weight labels":
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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 (February 13, 2023): 32. http://dx.doi.org/10.3390/computation11020032.
Abstract:
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 information carried by the nearest neighbors, a value calculation method is proposed and used to directly classify majority labels. Additionally, to address the misclassification problem caused by a lack of nearest neighbor information for minority labels, weight calculation is proposed. The proposed weight calculation converts distance information with and without label sets in the nearest neighbors into weights. The experimental results on multilabel datasets from different benchmarks demonstrate the performance of the algorithm, especially for datasets with high imbalance. Different evaluation metrics show that the results are improved by approximately 2–10%. The verified algorithm could be applied to a multilabel classification of various fields involving label imbalance, such as drug molecule identification, building identification, and text categorization.
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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.
Abstract:
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 weight of the labeling, interdependences between labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate for randomly generated instances.
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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.
Abstract:
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 weight of the labeling, interdependences between labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate for randomly generated instances.
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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 (March 27, 2024): 364–69. http://dx.doi.org/10.54097/astymd16.
Abstract:
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 features and manifold learning is proposed. The algorithm uses the linear relationship between the features and labels in known label samples to build a linear regression model for learning label-specific features. By using the nonlinear relation between instances and the nonlinear relation between features, we can precisely learn the label-specific features. We use the Laplacian feature mapping method to construct the instance manifold model and the feature manifold model, which are also used as the regular term constraint weight matrix. The final model can not only complete the missing labels, but also select sparse and representative features. The feature selection is carried out by analyzing the weight of the feature given by the final model. Experiments were conducted to verify the effectiveness of the proposed algorithm under different label deletion rates on four evaluation indexes.
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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 (February 2, 2022): 50. http://dx.doi.org/10.3390/fi14020050.
Abstract:
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, and the performance of the collaborative learning of the two networks from disagreement to an agreement, and making a second classification based on this. We train the network using a comparative learning method. Specifically, a small loss sample selection strategy with dynamic weight is designed. This strategy increases the proportion of agreement based on network predictions, gradually reduces the weight of the complex sample, and increases the weight of the pure sample at the same time. A large number of experiments verify the superiority of our method.
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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 (March 20, 2024): 53–67. http://dx.doi.org/10.22199/issn.0717-6279-5728.
Abstract:
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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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 (August 23, 2016): 484–90. http://dx.doi.org/10.1177/0890117116661982.
Abstract:
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 × 2 between-subjects analysis of variance explored the main effects of the assigned weight label and actual weight and interactions between assigned weight label and actual weight. Results: Significant main effects of the assigned weight label emerged on measures of body dissatisfaction, F(1, 109) = 12.40, p = .001, [Formula: see text] = 0.10, internalized weight stigma, F(1, 108) = 4.35, p = .039, [Formula: see text] = .04, and negative affect, F(1, 108) = 9.22, p = .003, [Formula: see text] = .08. Significant assigned weight label × actual weight interactions were found on measures of perceived body image, F(1, 109) = 6.29, p = .014, [Formula: see text] = .06, and perceived health, F(1, 109) = 4.18, p = .043, [Formula: see text] = .04. Conclusion: A weight label of “overweight” may have negative psychological consequences, particularly for overweight women.
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Karaca, Adeviyye, Kamil Can Akyol, Mustafa Keşaplı, Faruk Güngör, Umut Cengiz Çakır, Angelika Janitzky, and Ramazan Güven. "Do Clothing Labels Play a Role for Weight Estimation in Pediatric Emergencies? A Prospective, Cross-Sectional Study." Prehospital and Disaster Medicine 36, no. 3 (February 26, 2021): 295–300. http://dx.doi.org/10.1017/s1049023x21000194.
Abstract:
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 with the real values of the cases measured on the scale.Results:One-thousand ninety-four cases were included, the mean age of cases in age-based measurements was 6.25 years, which was 6.5 years in label-based measurements. Average weights measured 25.75kg according to age-based measurements, 26.5kg according to label-based measurements, and 26.0kg on the scales, and showed no statistical difference (P <.0001). It was estimated that 741 (67.7%) of age-based measurements and 775 (70.8%) of label-based measurements were within (±)10% values within the normal measurement limits and no significant difference was measured.Conclusion:In the emergency department and prehospital setting, children with an unknown age and that need resuscitation and interventional procedures for stabilization, and have no time for weight estimation, checking the age on clothing label (ACL) instead of the actual age (AA) can be safely used for the age-dependent weight calculation formula recommended by the PALS guide.
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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 (July 17, 2023): 1071. http://dx.doi.org/10.3390/e25071071.
Abstract:
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 individually without considering the interaction between features. Based on this, we present a novel online streaming feature selection method based on label group correlation and feature interaction (OSLGC). In our design, we first divide labels into multiple groups with the help of graph theory. Then, we integrate label weight and mutual information to accurately quantify the relationships between features under different label groups. Subsequently, a novel feature selection framework using sliding windows is designed, including online feature relevance analysis and online feature interaction analysis. Experiments on ten datasets show that the proposed method outperforms some mature MFS algorithms in terms of predictive performance, statistical analysis, stability analysis, and ablation experiments.
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O’Connor, Alan. "Habitus and field: Punk record labels in Spain." Punk & Post Punk 10, no. 2 (June 1, 2021): 265–89. http://dx.doi.org/10.1386/punk_00071_1.
Abstract:
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 great deal of concrete information on independent punk labels in Spain.
Dissertations / Theses on the topic "Weight labels":
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Chazelle, Thomas. "Influence sociale sur la représentation corporelle : Approche expérimentale de l'effet des médias et des labels de poids sur des jugements de corpulence." Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALS063.
Abstract:
La représentation corporelle est l’ensemble des fonctions cognitives permettant le suivi de l’état du corps. Elle est impliquée dans des situations diverses, comme la perception des dimensions physiques du corps, l’action, ou encore la génération d’attitudes à propos du corps. Pour réaliser ces fonctions, elle se base de manière flexible sur un ensemble d’informations sensorimotrices, ainsi que sur les croyances, attentes et émotions de l’individu. Parmi les sources d’informations disponibles à propos du corps, l’influence sociale peut être un facteur de risque, de maintien, et de sévérité des distorsions de l’image du corps. Pourtant, si l’influence sociale sur les aspects attitudinaux de la représentation corporelle est bien établie, il existe peu d’évidence expérimentale de telles influences sur ses aspects perceptifs. Cette thèse a ainsi pour objectif d’étudier l’intégration d’informations sociales à la dimension perceptive de la représentation de la corpulence. Pour cela, nous avons réalisé une série d’expériences auprès de jeunes femmes, une catégorie de la population particulièrement sujette aux distorsions de la représentation corporelle. Un premier axe se focalise sur l'influence interpersonnelle en testant l'effet de labels de poids sur des jugements perceptifs. Pour étudier leur influence informationnelle, nous avons fait varier la fiabilité de plusieurs signaux pour étudier la manière dont elles étaient combinées. Nos résultats indiquent que les labels de poids ont une influence réduite sur les jugements de corpulence. Un second axe porte sur l'influence médiatique. La surexposition visuelle à certains types de corps est associée à l’insatisfaction corporelle, et pourrait contribuer à expliquer certaines distorsions perceptives et attitudinales de la représentation corporelle. Dans ce contexte, l’adaptation visuelle à des corps pourrait expliquer comment l’exposition prolongée à des corps minces peut mener à une surestimation de la corpulence propre. Nous avons testé certaines hypothèses de cette théorie adaptative des distorsions de l'image du corps. Ces expériences soulignent certaines limites de la théorie adaptative ; en particulier, il est incertain que les effets d’adaptation puissent influencer la représentation que les individus ont de leurs propres corps. En conclusion, nos résultats indiquent que la dimension perceptive de la représentation de la corpulence pourrait résister à certains types d’influences sociales interpersonnelles et médiatiquesBody representation is the set of cognitive functions that track the state of the body. It is involved in a variety of situations, such as the perception of the physical dimensions of the body, action, and the generation of attitudes towards the body. To perform these functions, it relies on the flexible use of a range of sensorimotor information, as well as on the individual's beliefs, expectations and emotions. Among the sources of information available about the body, social influence can be a risk, maintenance, and severity factor in body image distortions. However, while social influence on the attitudinal aspects of body representation is well established, there is little experimental evidence of such influence on its perceptual aspects. The aim of this thesis is to study the integration of social information into the perceptual dimension of the representation of body size. To this end, we conducted a series of experiments with young women, a demographic that is particularly prone to distortions of body representation. A first axis focuses on interpersonal influence by testing the effect of weight labels on perceptual judgments. To investigate their informational influence, we manipulated the reliability of multiple cues to study how they were combined. Our results indicate that weight labels have a limited influence on judgments of body size. A second axis focuses on another type of social influence, media influence. Visual overexposure to specific body types is associated with body dissatisfaction, and could help explain the perceptual and attitudinal distortions of body representation. In this context, visual adaptation to bodies could explain how prolonged exposure to thin bodies can lead to an overestimation of one's own body size. We tested some of the hypotheses of this adaptation theory of body image distortion. These experiments highlight some limitations of the adaptation account; in particular, it is uncertain whether adaptation effects can influence the representation that individuals have of their own bodies. In conclusion, our results suggest that the perceptual dimension of the representation of body size may be resistant to some types of interpersonal and media social influence
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Araújo, Olegário da Cruz de. "In-store attractiveness of national brands and private labels in an emerging market." reponame:Repositório Institucional do FGV, 2018. http://hdl.handle.net/10438/20705.
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Emerging markets are considered relevant for international manufacturers and retailers to grow their turnovers. In order to achieve their goals, manufacturers and retailers are executing different initiatives to attract new customers such as in-store promotions. However, both in the US and here in Brazil, the results of these actions are questioned. Retailers are also investing in their Private Labels (PLs), which can alter the competitive dynamics within the categories. In the United States and Europe, studies were conducted to assess in-store promotions, impulses and responses in short-term and long-term sales for National Brands (NBs) and also Private Labels (PLs). The research question of this study was to evaluate if in Brazil, an emerging market, the attractiveness of Weighted Distribution, Price and Promotions of National Brands and Private Labels provide similar responses to the impulses. In order to evaluate if the impulses provide long-term residual effects for National Brands (NBs) and Private Labels (PLs), Vectors of Auto Regression (VAR) model was used in a continuous panel of self-service food stores in Greater São Paulo, which is the main metropolitan region of Brazil. The databases by categories (powdered coffee, biscuit, and ready-to-serve fruit juice) contained information of 25 months (November 2013 to November 2015) for each variable (Weighted Distribution, Price and Promotions), by NBs and PLs. The result of this study points out that there is a difference in responses to the impulses (distribution, price, and promotions) between NBs and PLs. National Brands (NBs) showed a greater number of situations with positive residual effects on long-term sales. However, the long-term response on sales occurred only for less than the half of the total potential situations. In other words, more than half of the total potential situations give an absence of statistical significance. The study indicates that there are retailers developing differentiated actions with Private Labels and obtaining, in their sales, positive long-term residual effects. Although modestly, this study contributes to the retail literature by using an econometric model (VAR) to analyze the impulse in some in-store attractiveness variables their long-term sales response to NBs and PLs in an emerging market. In short, the main contribution from the observations of the analyzed categories is that it is possible to Private Label compete without price sensibility and also positioning PL above the average price of the category/segment. The results also suggest that there is an opportunity to review the modus operandi of in-store promotion to get better results.
Mercados emergentes são importantes para as receitas totais de fabricantes e varejistas internacionais. Estudos de companhias globais de pesquisa, que atuam no Brasil, apontam que os investimentos em ações promocionais no ponto-de-venda, pelas Marcas de Fabricantes, aumentaram, mas há questionamentos quanto ao retorno destas iniciativas. Os varejistas também têm investido em Marcas Próprias. Nos Estados Unidos e Europa há vários estudos sobre o estímulos dentro do ponto-de-venda para as Marcas dos Fabricantes e Marcas Próprias e o impacto nas vendas no curto e longo prazo. O objetivo central deste estudo é avaliar se, em um mercado emergente, o nivel de atratividade das ações realizadas pelas Marcas de Fabricantes e Marcas Próprias dentro das lojas proporcionam respostas similares de curto e longo prazo aos impulsos realizados. Para analisar os efeitos destes impulsos foi utilizado o modelo de Vetores de Auto Regressão (VAR) em um painel continuo de lojas de autosserviço alimentar, na principal região metropolitana do Brasil, a Grande São Paulo. As bases de dados por categoria (Café em Pó, Biscoito e Suco Pronto para Consumo), continham informações de 25 meses (novembro de 2013 à novembro de 2015), com dados de distribuição ponderada, preço e promoções, O resultado deste estudo aponta que há diferenças entre Marcas de Fabricantes e Marcas Próprias nas respostas de longo prazo aos estímulos promocionais. Embora as Marcas de Fabricantes tenham apresentado um maior número de situações com efeitos residuais positivos nas vendas de longo prazo do que as Marcas Próprias, apenas menos da metade das situações apresentaram resultados de longo prazo. O estudo também sinaliza que há varejistas desenvolvendo ações diferenciadas com Marcas Próprias e obtendo, em suas vendas, efeitos residuais positivos de longo prazo, na mesma intensidade das Marcas de Fabricantes. Embora de forma modesta, esta pesquisa contribui para a literatura ao utilizar um modelo econométrico (VAR) para analisar os impulsos aplicados em distribuição, preço e promoção das Marcas dos Fabricantes e das Marcas Próprias em um mercado emergente. A principal contribuição deste estudo, a partir das categorias analisadas, é que a Marca Própria, não necessariamente, precisa atuar apenas com um posicionamento de preço baixo e/ou reduzir preços para competir dentro da categoria ou segmento no qual está inserida. Além disto, o estudo também sugere que as há espaço para rever as práticas promocionais ou até operacionais, considerando o baixo retorno proporcionado para Marcas de Fabricantes e Marcas Próprias.
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Simmons, Mark R. "Comparison of Weight Loss Outcome Measures in Adolescent Bariatric Surgery Patients using Growth Curve Modeling." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447689830.
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Baier, Moritz C. [Verfasser]. "Living Polymerization to Ultra-High Molecular Weight and Dye-Labeled Polyethylene for Single-Molecule Fluorescence Microscopy and Reactor Blends / Moritz C. Baier." Konstanz : Bibliothek der Universität Konstanz, 2016. http://d-nb.info/1173616454/34.
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Sarinnapakorn, Kanoksri. "Induction of Classifiers from Multi-labeled Examples: an Information-retrieval Point of View." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/16.
Abstract:
An important task of information retrieval is to induce classifiers capable of categorizing text documents. The fact that the same document can simultaneously belong to two or more categories is referred by the term multi-label classification (or categorization). Domains of this kind have been encountered in diverse fields even outside information retrieval. This dissertation discusses one challenging aspect of text categorization: the documents (i.e., training examples) are characterized by an extremely large number of features. As a result, many existing machine learning techniques are in such domains prohibitively expensive. This dissertation seeks to reduce these costs significantly. The proposed scheme consists of two steps. The first runs a so-called baseline induction algorithm (BIA) separately on different versions of the data, each time inducing a different subclassifier---more specifically, BIA is run always on the same training documents that are each time described by a different subset of the features. The second step then combines the subclassifiers by a fusion algorithm: when a document is to be classified, each subclassifier outputs a set of class labels accompanied by its confidence in these labels; these outputs are then combined into a single multi-label recommendation. The dissertation investigates a few alternative fusion techniques, including an original one, inspired by the Dempster-Shafer Theory. The main contribution is a mechanism for assigning the mass function to individual labels from subclassifiers. The system's behavior is illustrated on two real-world data sets. As indicated, in each of them the examples are described by thousands of features, and each example is labeled with a subset of classes. Experimental evidence indicates that the method can scale up well and achieves impressive computational savings in exchange for only a modest loss in the classification performance. The fusion method proposed is also shown to be more accurate than other more traditional fusion mechanisms. For a very large multi-label data set, the proposed mechanism not only speeds up the total induction time, but also facilitates the execution of the task on a small computer. The fact that subclassifiers can be constructed independently and more conveniently from small subsets of features provides an avenue for parallel processing that might offer further increase in computational efficiency.
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Mohammed, Kader Hamno. "Development of a label-free biosensor method for the identification of sticky compounds which disturb GPCR-assays." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-220645.
Abstract:
It is widely known that early estimates about the binding properties of drug candidates are important in the drug discovery process. Surface plasmon resonance (SPR) biosensors have become a standard tool for characterizing interactions between a great variety of biomolecules and it offers a unique opportunity to study binding activity. The aim of this project was to develop a SPR based assay for pre-screening of low molecular weight (LMW) drug compounds, to enable filtering away disturbing compounds when interacting with drugs. The interaction between 47 LMW compounds and biological ligands were investigated using the instrument BiacoreTM, which is based on SPR-technology.
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Mata, Jutta. "Healthy food choice." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15723.
Abstract:
Die vorliegende Dissertation setzt sich damit auseinander, wie das Zusammenspiel von essensbezogener Umwelt und Kognition Ernährungsentscheidungen beeinflusst. Im ersten Manuskript, “When Diets Last: Lower Cognitive Complexity Increases Diet Adherence” wird die Bedeutung der kognitiven Komplexität von Ernährungsregeln für das Einhalten einer Diät untersucht. Können Diäten scheitern, weil sie aus kognitiver Perspektive zu komplex sind, z.B. weil sich Diäthaltende nicht alle wichtigen Informationen merken oder verarbeiten können? 1136 Diäthaltende nahmen an einer längsschnittlichen Onlinestudie teil. Vorangegangenes Diätverhalten, Selbstwirksamkeit, Planung und wahrgenommene Regelschwierigkeit erhöhten das Risiko, die Diät vorzeitig aufzugeben, wobei Selbstwirksamkeit und wahrgenommene Regelschwierigkeit die einflussreichsten Faktoren waren. Im zweiten Manuskript „Meat Label Design: Effects on Stage Progression, Risk Perception, and Product Evaluation” wird der Einfluss gesundheitsrelevanter Information auf Labeln für Produktbewertung und Intention, Tierhaltung und Inhaltsstoffe von Lebensmitteln in die Kaufentscheidung einzubeziehen, untersucht. Es wurde betrachtet, wie Inhalt und Kontext (separate versus conjoint Darbietung) der Labelinformation die Bewertung von Fleischprodukten beeinflusst. Die Ergebnisse zeigen, dass sich bei einer conjoint im Gegensatz zur separaten Darbietung die Bewertung der Produkte umkehrt. Darüber hinaus hatten Personen, die zuvor nicht motiviert waren gesundheitsrelevante Aspekte in ihr Einkaufsverhalten einzubeziehen, nach Betrachten der Label eine höhere Intention diese zu berücksichtigen. Im dritten Manuskript, „Predicting Children’s Meal Preferences: How Much Do Parents Know?“, wurden Präferenzvorhersagen bezüglich der Essensentscheidungen Anderer erforscht. Es wurde untersucht, wie gut und mit Hilfe welcher Information Eltern die Mittagessenpräferenzen ihrer Kinder vorhersagen. Die Vorhersagegenauigkeit der Eltern entsprach der Stabilität der Essenspräferenzen ihrer Kinder, d.h. dass die Eltern so genau waren, wie möglich. Die Ergebnisse suggerieren, dass Eltern vor allem spezifisches Wissen über die Präferenzen ihrer Kinder und Projektion ihrer eigenen Vorlieben für die Vorhersagen nutzten.This dissertation focuses on food-related decision making, in particular, how food related environments and cognition interact to determine people’s food choices. The first manuscript, “When Diets Last: Lower Cognitive Complexity Increases Diet Adherence,” investigates the role of the cognitive complexity in diet adherence. Can weight loss diets fail because they are too complicated from a cognitive point of view, meaning that dieters are not able to recall or process the diet rules? The impact of excessive cognitive demands on diet adherence were investigated with 1,136 dieters in a longitudinal online-questionnaire. We measured perceived rule complexity controlling for other factors known to influence adherence. Previous diet behavior, self-efficacy, planning and perceived rule complexity predicted an increased risk to quit the diet prematurely, with self-efficacy and diet complexity being the strongest factors. The second manuscript, “Meat Label Design: Effects on Stage Progression, Risk Perception, and Product Evaluation,” presents two studies which tested the impact of health-related meat labels on product evaluation and intention. Specifically, the studies examined how informational content and the context (separate vs. conjoint evaluation) in which labels are assessed influence the evaluation of meat products. The results showed that conjoint assessment of labels can lead to contrary product rankings compared to separate evaluations. Moreover, the results suggest that being exposed to food labels containing specific health-relevant information can increase motivation to consider health aspects in those consumers without previous intention to do so. The third manuscript, “Predicting Children’s Meal Preferences: How Much Do Parents Know?” investigated prediction behavior concerning other people’s food choices. In particular, it asked how accurately and what cues parents use to predict their children’s meal choices. Overall, parents’ prediction accuracy matched the stability of children’s meal choices, implying that accuracy was as high as can be expected. The results suggest parents were able to obtain high predictive accuracy by using specific knowledge about their child’s likes and projecting their own preferences.
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McKinnon, Loretta Carmen. "The contribution of psychosocial factors to socioeconomic differences in food purchasing." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/60893/1/Loretta_McKinnon_Thesis.pdf.
Abstract:
In developed countries the relationship between socioeconomic position (SEP) and health is unequivocal. Those who are socioeconomically disadvantaged are known to experience higher morbidity and mortality from a range of chronic diet-related conditions compared to those of higher SEP. Socioeconomic inequalities in diet are well established. Compared to their more advantaged counterparts, those of low SEP are consistently found to consume diets less consistent with dietary guidelines (i.e. higher in fat, salt and sugar and lower in fibre, fruit and vegetables). Although the reasons for dietary inequalities remain unclear, understanding how such differences arise is important for the development of strategies to reduce health inequalities.
Both environmental (e.g. proximity of supermarkets, price, and availability of foods) and psychosocial (e.g. taste preference, nutrition knowledge) influences are proposed to account for inequalities in food choices. Although in the United States (US), United Kingdom (UK), and parts of Australia, environmental factors are associated with socioeconomic differences in food choices, these factors do not completely account for the observed inequalities. Internationally, this context has prompted calls for further exploration of the role of psychological and social factors in relation to inequalities in food choices. It is this task that forms the primary goal of this PhD research.
In the small body of research examining the contribution of psychosocial factors to inequalities in food choices, studies have focussed on food cost concerns, nutrition knowledge or health concerns. These factors are generally found to be influential. However, since a range of psychosocial factors are known determinants of food choices in the general population, it is likely that a range of factors also contribute to inequalities in food choices. Identification of additional psychosocial factors of relevance to inequalities in food choices would provide new opportunities for health promotion, including the adaption of existing strategies.
The methodological features of previous research have also hindered the advancement of knowledge in this area and a lack of qualitative studies has resulted in a dearth of descriptive information on this topic.
This PhD investigation extends previous research by assessing a range of psychosocial factors in relation to inequalities in food choices using both quantitative and qualitative techniques. Secondary data analyses were undertaken using data obtained from two Brisbane-based studies, the Brisbane Food Study (N=1003, conducted in 2000), and the Sixty Families Study (N=60, conducted in 1998). Both studies involved main household food purchasers completing an interviewer-administered survey within their own home. Data pertaining to food-purchasing, and psychosocial, socioeconomic and demographic characteristics were collected in each study.
The mutual goals of both the qualitative and quantitative phases of this investigation were to assess socioeconomic differences in food purchasing and to identify psychosocial factors relevant to any observed differences. The quantitative methods then additionally considered whether the associations examined differed according to the socioeconomic indicator used (i.e. income or education). The qualitative analyses made a unique contribution to this project by generating detailed descriptions of socioeconomic differences in psychosocial factors.
Those with lower levels of income and education were found to make food purchasing choices less consistent with dietary guidelines compared to those of high SEP. The psychosocial factors identified as relevant to food-purchasing inequalities were: taste preferences, health concerns, health beliefs, nutrition knowledge, nutrition concerns, weight concerns, nutrition label use, and several other values and beliefs unique to particular socioeconomic groups. Factors more tenuously or inconsistently related to socioeconomic differences in food purchasing were cost concerns, and perceived adequacy of the family diet. Evidence was displayed in both the quantitative and qualitative analyses to suggest that psychosocial factors contribute to inequalities in food purchasing in a collective manner. The quantitative analyses revealed that considerable overlap in the socioeconomic variation in food purchasing was accounted for by key psychosocial factors of importance, including taste preference, nutrition concerns, nutrition knowledge, and health concerns. Consistent with these findings, the qualitative transcripts demonstrated the interplay between such influential psychosocial factors in determining food-purchasing choices.
The qualitative analyses found socioeconomic differences in the prioritisation of psychosocial factors in relation to food choices. This is suggestive of complex cultural factors that distinguish advantaged and disadvantaged groups and result in socioeconomically distinct schemas related to health and food choices. Compared to those of high SEP, those of lower SEP were less likely to indicate that health concerns, nutrition concerns, or food labels influenced food choices, and exhibited lower levels of nutrition knowledge. In the absence of health or nutrition-related concerns, taste preferences tended to dominate the food purchasing choices of those of low SEP. Overall, while cost concerns did not appear to be a main determinant of socioeconomic differences in food purchasing, this factor had a dominant influence on the food choices of some of the most disadvantaged respondents included in this research.
The findings of this study have several implications for health promotion. The integrated operation of psychosocial factors on food purchasing inequalities indicates that multiple psychosocial factors may be appropriate to target in health promotion. It also seems possible that the inter-relatedness of psychosocial factors would allow health promotion targeting a single psychosocial factor to have a flow-on affect in terms of altering other influential psychosocial factors. This research also suggests that current mass marketing approaches to health promotion may not be effective across all socioeconomic groups due to differences in the priorities and main factors of influence in food purchasing decisions across groups.
In addition to the practical recommendations for health promotion, this investigation, through the critique of previous research, and through the substantive study findings, has highlighted important methodological considerations for future research. Of particular note are the recommendations pertaining to the selection of socioeconomic indicators, measurement of relevant constructs, consideration of confounders, and development of an analytical approach.
Addressing inequalities in health has been noted as a main objective by many health authorities and governments internationally. It is envisaged that the substantive and methodological findings of this thesis will make a useful contribution towards this important goal.
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Villars, Clément. "Mesure objective de l’activité physique en conditions de vie libre et relations avec l’adiponectine." Thesis, Lyon 1, 2011. http://www.theses.fr/2011LYO10301/document.
Abstract:
Une mesure précise de l’activité physique en conditions de vie libre est nécessaire pour une meilleure compréhension de ses relations avec la santé. Le premier objectif de ce travail thèse a été de valider l’Actiheart (qui combine la mesure de la fréquence cardiaque et du mouvement par accélérométrie) par rapport à l’eau doublement marquée (EDM). Nous montrons un bon niveau de concordance entre la dépense énergétique liée à l’activité physique (DEAP) estimée par l’Actiheart et l’EDM. Une individualisation de la relation entre la fréquence cardiaque et de la DEAP par un test d’effort est nécessaire pour une estimation fiable de la DEAP au niveau individuel et pour évaluer des changements de DEAP tels qu’induits par une intervention. En laboratoire, nous montrons que la précision de l’Actiheart est activité dépendante. Ceci nécessite la mise en place de leur reconnaissance par de nouveaux capteurs et modèles mathématiques. L’adiponectine est une hormone du tissu adipeux qui a un rôle dans le métabolisme énergétique et dont la sécrétion diminue avec l’obésité. Les effets de l’activité physique sont en revanche contradictoires dans la littérature. Le second objectif de ce travail a été d’évaluer l’effet de l’activité physique et d’une intervention avec contrôle du poids sur les taux plasmatiques d’adiponectine. Nous montrons que l’adiponectine totale et à haut poids moléculaire sont associées négativement à la variation du niveau d’activité physique. D’autres travaux sont cependant nécessaires pour comprendre les mécanismes qui sous-tendent cette modulation de l’adiponectine plasmatique qui ne semble pas liée à des variations de synthèse dans le tissu adipeux ou musculaireAccurate measurements of physical activity in free living are needed to establish what dose of physical activity is necessary for obtaining a specific health benefits. The first aim of this work was to validate the Actiheart (which combines heart rate and accelerometry sensors) with doubly labeled water (DLW). We show a good level of concordance between physical activity energy expenditure (PAEE) estimated by Actiheart and DLW. Individualization of the relationship between heart rate and PAEE by an incremental test is needed for an accurate estimate of the PAEE at the individual level and to evaluate changes induced by an intervention. In laboratory, we show that the accuracy of Actiheart is activitydependent. This requires the establishment of their recognition from new sensors and mathematical models. Adiponectin, hormone secreted by adipose tissue, has a role in energy metabolism and its secretion decreases with obesity. However the effects of physical activity remain in contradiction in published studies. The second objective of this work was to evaluate the effect of physical activity and intervention with weight control on plasma adiponectin. We show that the total and high molecular weight adiponectin were negatively associated with modifications of the physical activity level. Further work is however necessary to understand the mechanisms underlying this modulation of plasma adiponectin which does not seem related to changes in synthesis in adipose tissue or muscle
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Chen, Kuan-Tzu, and 陳冠慈. "The Impact of Reading Food Nutrition Labels for Weight Control on Working Adults’Body Mass Index." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/70805620037973064829.
Abstract:
碩士國立臺灣大學
健康政策與管理研究所
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Overweight and obesity is the world''s fifth largest cause of death risk, WHO also estimated that there are at least 2.8 million adults died of overweight or obese. According to the Department of Health, Executive Yuan, R.O.C (Taiwan), "2005-2008 National Nutrition and Health Survey" results of adult overweight and obesity rate of 44.1%, 50.8% were male. Women accounted for 36.9%. 2008 survey by the Ministry of Education student height and weight data show that one in four children are overweight or obese. Overweight and obese has become a health problem. Most of the overweight or obese are cause from diet and physical activity in global, so we focus on the effect of nutrition label using on Body Mass Index BMI. In order to provide obesity prevention and treatment plan at the future. Using 2005 National Health Interview Survey (NHIS), 18 to 64 year-old samples, 4585 female, 6219 male, include basic personal information, self-report height and weight, health behavior, the economic situation, and the health behavior in the food nutrition label reading. We use Propensity Score Matching (PSM) to control other variables. Results showed that the factor of reading food nutrition labels in gender is slightly different. The using of nutrition labels is relative to higher social-economic status, healthy eating behavior, less high-calorie and high sugar eating patterns. The food nutrition using is a part of health behavior, but we found that the user have higher BMI than the other. Government agencies should pay attention to people recommended nutrient intake for cognitive education and related policy implementation, as a health promote information.
Books on the topic "Weight labels":
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Brazil. Serviço Nacional de Aprendizagem Industrial. Departamento Nacional. Coletânea de portarias de produtos pré-medidos. Brasília: CNI SENAI, 2000.
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Day, Gloria. Weight of Labels: A Poetic Display of an Internal Shift. Lulu Press, Inc., 2018.
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Radman-Livaja, Ivan. Prices and Costs in the Textile Industry in the Light of the Lead Tags from Siscia. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198790662.003.0013.
Abstract:
This chapter considers a collection of nearly 1,200 inscribed lead tags from the ancient city of Siscia (south-west Pannonia). Most of the tags were reused several times, showing traces of older inscriptions. On one side, one can read personal names, while the other side of the tag usually carries an inscription mentioning the merchandise, usually in an abbreviated form, as well as a price and often an indication of quantity or weight. Most if not all the tags are linked to the wool trade and the textile industry, the prices indicating the value of the goods or the cost of a given service such as cleaning, fulling, or dyeing. They appear to be labels used by fullers and dyers to ensure that they could easily return their property to the clients and charge them the right fee.
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Young, Deborah E. Swatch Reference Guide for Fashion Fabrics: 5th Edition. 5th ed. Bloomsbury Publishing Inc, 2023. http://dx.doi.org/10.5040/9781501373206.
Abstract:
This all-in-one text and swatch book offers current and high-quality fashion fabric samples alongside vital information like fabric name, fiber content, yarn construction, count, coloration, weight, uses, characteristics, and comparisons to other fabrics. The textbook with activities, 210 exclusive fabric samples, Quick Reference Guide tables, pre-printed mounting boards for quick access to all swatches, and an 8x metal pick glass are all contained in a sturdy three-ring binder. Step-by-step instructions and hands-on assignments help students to apply their knowledge about fabric identification and use for specific garments, encouraging deeper learning through this workbook approach. New to this Edition -New fabric and yarn samples and more technical and performance fabrics, for a total of 210 swatches that reflect current trends in the industry -Expanded coverage of knits, socially responsible market practices, and sustainable alternatives -Discussions on the circular economy and recycling in every chapter -Stain Removal Guide with instructions for fabric care STUDIO Features Include -Watch video tutorials that bring fabric swatch exercises to life -Study smarter with self-assessment quizzes featuring scored results and personalized study tips -Review concepts with flashcards of terms and definitions -Apply knowledge through additional assignments and labs Instructor Resources -Comprehensive Fabric Chart lists swatch number, fiber content, yarn construction, fabric structure, fabric name, count, stage of dye or print, weight, and visible finishes for each of the swatches from the text -PowerPoint® slides include front, back, and 10x magnified views of all swatches from the text labeled by fabric and fiber type -Instructor's Guide provides sample course outlines, answer keys to quizzes and exams, additional assignments, and tips for overcoming common obstacles -Test Bank contains chapter quizzes with multiple exams covering both the chapter text and the swatch board information
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Zoumbaris, Sharon K., and Marjolijn Bijlefeld. Food and You. Greenwood, 2001. http://dx.doi.org/10.5040/9798400652301.
Abstract:
What is the BMI? How much should I be eating? Do I really need to exercise? Find the answers to these questions and other food, body, and health questions in this guide to understanding the fundamentals of good nutrition and its partner for optimum health--physical activity. Healthy eating can be a habit and good habits started earlier in life are easier to maintain. Good nutrition and physical activity complement each other in weight loss, cardiovascular health, and other benefits. This book provides the advice you need on how to get the most out of what you eat and how to develop healthier habits that will help keep you fit for a lifetime. Most teens don't need to resort to special diets or programs; they just need to know how to make sensible choices. Definitions of good nutrition, discussions of how the food you eat affects the way you feel and perform, and information on how to use readily available tools, such as the Nutrition Facts label and USDA Food Guide Pyramid are just some of the factors provided to help readers develop healthy habits. Bijlefeld and Zoumbaris provide information on a number of other health matters, from vegetarian diets to eating disorders to the affects of alcohol and drugs on the body. You'll also learn how to make healthy choices in grocery stores and restaurants, how to set up a kitchen of your own, and how to keep your food safe, all factors involved in helping you to stay healthy for peak performance in whatever you choose to do.
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Kirchman, David L. Degradation of organic matter. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789406.003.0007.
Abstract:
The aerobic oxidation of organic material by microbes is the focus of this chapter. Microbes account for about 50% of primary production in the biosphere, but they probably account for more than 50% of organic material oxidization and respiration (oxygen use). The traditional role of microbes is to degrade organic material and to release plant nutrients such as phosphate and ammonium as well as carbon dioxide. Microbes are responsible for more than half of soil respiration, while size fractionation experiments show that bacteria are also responsible for about half of respiration in aquatic habitats. In soils, both fungi and bacteria are important, with relative abundances and activity varying with soil type. In contrast, fungi are not common in the oceans and lakes, where they are out-competed by bacteria with their small cell size. Dead organic material, detritus, used by microbes, comes from dead plants and waste products from herbivores. It and associated microbes can be eaten by many eukaryotic organisms, forming a detritus food web. These large organisms also break up detritus into small pieces, creating more surface area on which microbes can act. Microbes in turn need to use extracellular enzymes to hydrolyze large molecular weight compounds, which releases small compounds that can be transported into cells. Fungi and bacteria use a different mechanism, “oxidative decomposition,” to degrade lignin. Organic compounds that are otherwise easily degraded (“labile”) may resist decomposition if absorbed to surfaces or surrounded by refractory organic material. Addition of labile compounds can stimulate or “prime” the degradation of other organic material. Microbes also produce organic compounds, some eventually resisting degradation for thousands of years, and contributing substantially to soil organic material in terrestrial environments and dissolved organic material in aquatic ones. The relationship between community diversity and a biochemical process depends on the metabolic redundancy among members of the microbial community. This redundancy may provide “ecological insurance” and ensure the continuation of key biogeochemical processes when environmental conditions change.
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Millan, John. Coins Weights & Measures, Ancient & Modern, of All Nations. Reduced Into English on Above 100 Tables, Collected & Methodiz'd from Newton, Folkes, ... of Specific Gravities, by Newton, Labelye. Gale Ecco, Print Editions, 2018.
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Sarkar, Ajoy K., Ingrid Johnson, and Allen C. Cohen. J.J. Pizzuto’s Fabric Science Swatch Kit: 12th Edition. 12th ed. Bloomsbury Publishing Inc, 2023. http://dx.doi.org/10.5040/9781501367892.
Abstract:
J.J. Pizzuto’s Fabric Science Swatch Kit, 12th Edition reinforces the study of textiles for students in courses such as Fashion Design, Fashion Business, Merchandising, Fashion Retailing, Interior Design, Textile Surface Design, Textile and Apparel Product Development, and Textile Production Management. The kit contains 114 (2"x 3") fabric samples, a fabric key, 32 special assignments, heavy-weight sample sheets to mount and analyze fabrics, and a pick glass--all contained in a three-ring binder. The swatches are organized in the order in which they are covered in the text: fibers, yarns, weaves, knits, dyeing, printing, and finishes. Swatches represent the types of fabrics currently available to fashion and interior designers in the field, making this resource an excellent addition to any professional library. Key Features Include -The text contains instructions and video tutorials that take students through the process of assembling their swatch kit and using a pick glass. -A Fabric Key identifies the swatches by number and fabric name, description, and fiber content. -Assignments are designed to reinforce the text and classroom lectures and have been developed by FIT instructors to broaden students’ understanding of key concepts in textiles through hands-on labs and problem solving activities. -Students will successfully develop keen observation, analysis, and report writing skills. -J.J. Pizzuto’sFabric Science Swatch Kit is an ideal companion to J.J. Pizzuto’sFabric Science, 12th Edition (ISBN 9781501367878, available as a separate purchase) or can also be used alone or in conjunction with any textiles textbook. Fabric Science Swatch Kit STUDIO Resources Include -Watch videos with step-by-step demonstrations on how to compile the swatch kit Instructor’s Resources -Instructor’s Swatch Set including 7" x 12" samples of each fabric (ISBN 9781501313677) -Instructor’s Guide provides specific information about each of the swatches in the Swatch Kit, special assignments, questions (with answers), and instructional comments -PowerPoint® presentations include full-color images from the book and provide a framework for lecture and discussion
Book chapters on the topic "Weight labels":
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Shield, Andrew DJ. "“White is a color, Middle Eastern is not a color”: Drop-Down Menus, Racial Identification, and the Weight of Labels." In Immigrants on Grindr, 185–225. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30394-5_6.
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Xu, Jianhua. "Multi-Label Weighted k-Nearest Neighbor Classifier with Adaptive Weight Estimation." In Neural Information Processing, 79–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_10.
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Pirouz, Matin. "Balanced Weighted Label Propagation." In Lecture Notes in Networks and Systems, 1–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80126-7_1.
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Wang, Hongzhi, Jung Wook Suh, John Pluta, Murat Altinay, and Paul Yushkevich. "Optimal Weights for Multi-atlas Label Fusion." In Lecture Notes in Computer Science, 73–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22092-0_7.
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Yang, Lei, Zhan Shi, Dan Feng, Wenxin Yang, Jiaofeng Fang, Shuo Chen, and Fang Wang. "MLND: A Weight-Adapting Method for Multi-label Classification Based on Neighbor Label Distribution." In Web and Big Data, 639–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_47.
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Verma, Gurudatta, and Tirath Prasad Sahu. "Exploring Label-Specific Feature Weights for Multi-label Feature Selection Using FWMABAC-MFS." In Proceedings of 4th International Conference on Frontiers in Computing and Systems, 321–35. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2611-0_22.
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Sun, Chong, Weiyu Zhou, Zhongshan Song, Fan Yin, Lei Zhang, and Jianquan Bi. "Weighted Multi-label Learning with Rank Preservation." In Big Data, 312–24. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7_22.
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Mohan, Anshuman, Wei Xiang Leow, and Aquinas Hobor. "Functional Correctness of C Implementations of Dijkstra’s, Kruskal’s, and Prim’s Algorithms." In Computer Aided Verification, 801–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_37.
Abstract:
AbstractWe develop machine-checked verifications of the full functional correctness of C implementations of the eponymous graph algorithms of Dijkstra, Kruskal, and Prim. We extend Wang et al.’s CertiGraph platform to reason about labels on edges, undirected graphs, and common spatial representations of edge-labeled graphs such as adjacency matrices and edge lists. We certify binary heaps, including Floyd’s bottom-up heap construction, heapsort, and increase/decrease priority.Our verifications uncover subtle overflows implicit in standard textbook code, including a nontrivial bound on edge weights necessary to execute Dijkstra’s algorithm; we show that the intuitive guess fails and provide a workable refinement. We observe that the common notion that Prim’s algorithm requires a connected graph is wrong: we verify that a standard textbook implementation of Prim’s algorithm can compute minimum spanning forests without finding components first. Our verification of Kruskal’s algorithm reasons about two graphs simultaneously: the undirected graph undergoing MSF construction, and the directed graph representing the forest inside union-find. Our binary heap verification exposes precise bounds for the heap to operate correctly, avoids a subtle overflow error, and shows how to recycle keys to avoid overflow.
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Wang, Xin, Songlei Jian, Kai Lu, and Xiaoping Wang. "Unified Weighted Label Propagation Algorithm Using Connection Factor." In Advanced Data Mining and Applications, 434–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49586-6_29.
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Wang, Lulu, Hong Shen, and Hui Tian. "Weighted Ensemble Classification of Multi-label Data Streams." In Advances in Knowledge Discovery and Data Mining, 551–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57529-2_43.
Conference papers on the topic "Weight labels":
1
Feng, Lei, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, and Bo An. "Can Cross Entropy Loss Be Robust to Label Noise?" In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/305.
Abstract:
Trained with the standard cross entropy loss, deep neural networks can achieve great performance on correctly labeled data. However, if the training data is corrupted with label noise, deep models tend to overfit the noisy labels, thereby achieving poor generation performance. To remedy this issue, several loss functions have been proposed and demonstrated to be robust to label noise. Although most of the robust loss functions stem from Categorical Cross Entropy (CCE) loss, they fail to embody the intrinsic relationships between CCE and other loss functions. In this paper, we propose a general framework dubbed Taylor cross entropy loss to train deep models in the presence of label noise. Specifically, our framework enables to weight the extent of fitting the training labels by controlling the order of Taylor Series for CCE, hence it can be robust to label noise. In addition, our framework clearly reveals the intrinsic relationships between CCE and other loss functions, such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). Moreover, we present a detailed theoretical analysis to certify the robustness of this framework. Extensive experimental results on benchmark datasets demonstrate that our proposed approach significantly outperforms the state-of-the-art counterparts.
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Yang, Xu, Yanan Gu, Kun Wei, and Cheng Deng. "Exploring Safety Supervision for Continual Test-time Domain Adaptation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/183.
Abstract:
Continual test-time domain adaptation aims to adapt a source pre-trained model to a continually changing target domain without using any source data. Unfortunately, existing methods based on pseudo-label learning suffer from the changing target domain environment, and the quality of generated pseudo-labels is attenuated due to the domain shift, leading to instantaneous negative learning and long-term knowledge forgetting. To solve these problems, in this paper, we propose a simple yet effective framework for exploring safety supervision with three elaborate strategies: Label Safety, Sample Safety, and Parameter Safety. Firstly, to select reliable pseudo-labels, we define and adjust the confidence threshold in a self-adaptive manner according to the test-time learning status. Secondly, a soft-weighted contrastive learning module is presented to explore the highly-correlated samples and discriminate uncorrelated ones, improving the instantaneous efficiency of the model. Finally, we frame a Soft Weight Alignment strategy to normalize the distance between the parameters of the adapted model and the source pre-trained model, which alleviates the long-term problem of knowledge forgetting and significantly improves the accuracy of the adapted model in the late adaptation stage. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on several benchmark datasets.
3
Wu, Junshuang, Richong Zhang, Yongyi Mao, Hongyu Guo, and Jinpeng Huai. "Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/731.
Abstract:
Fine-grained entity typing (FET), which annotates the entities in a sentence with a set of finely specified type labels, often serves as the first and critical step towards many natural language processing tasks. Despite great processes have been made, current FET methods have difficulty to cope with the noisy labels which naturally come with the data acquisition processes. Existing FET approaches either pre-process to clean the noise or simply focus on one of the noisy labels, sidestepping the fact that those noises are related and content dependent. In this paper, we directly model the structured, noisy labels with a novel content-sensitive weighting schema. Coupled with a newly devised cost function and a hierarchical type embedding strategy, our method leverages a random walk process to effectively weight out noisy labels during training. Experiments on several benchmark datasets validate the effectiveness of the proposed framework and establish it as a new state of the art strategy for noisy entity typing problem.
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Xiong, Feng, Jiayi Tian, Zhihui Hao, Yulin He, and Xiaofeng Ren. "SCMT: Self-Correction Mean Teacher for Semi-supervised Object Detection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/207.
Abstract:
Semi-Supervised Object Detection (SSOD) aims to improve performance by leveraging a large amount of unlabeled data. Existing works usually adopt the teacher-student framework to enforce student to learn consistent predictions over the pseudo-labels generated by teacher. However, the performance of the student model is limited since the noise inherently exists in pseudo-labels. In this paper, we investigate the causes and effects of noisy pseudo-labels and propose a simple yet effective approach denoted as Self-Correction Mean Teacher(SCMT) to reduce the adverse effects. Specifically, we propose to dynamically re-weight the unsupervised loss of each student's proposal with additional supervision information from the teacher model, and assign smaller loss weights to possible noisy proposals. Extensive experiments on MS-COCO benchmark have shown the superiority of our proposed SCMT, which can significantly improve the supervised baseline by more than 11% mAP under all 1%, 5% and 10% COCO-standard settings, and surpasses state-of-the-art methods by about 1.5% mAP. Even under the challenging COCO-additional setting, SCMT still improves the supervised baseline by 4.9% mAP, and significantly outperforms previous methods by 1.2% mAP, achieving a new state-of-the-art performance.
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Khangura, Jasan, Melanie Flores, and Jane Ishmael. "Product text labels indicate the presence of other pharmacologically active ingredients in many OTC hemp- and CBD-containing preparations." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.32.
Abstract:
Introduction: The 2018 Farm Bill changed the legal status of cannabis plants that meet the definition of industrial hemp and allowed for the rapid expansion of hemp-based products into commercial spaces. With an emphasis on industrial hemp as the source of naturally-occurring cannabinoid compounds, a niche market for cannabidiol (CBD)-containing products was quickly established in pharmacies and grocery stores. Although the U.S. Food and Drug Administration (FDA) has retained oversight of all cannabis-based products, labelling of hemp-derived products for retail markets remains largely unregulated. Under federal law, CBD cannot be added to foods, beverages, sold as a dietary supplement or marketed for a therapeutic benefit, however the perceived health benefits of CBD as an acceptable and safe ingredient contribute to the growing market for these health products. Objective: The objective of this study was to evaluate the range of over-the-counter (OTC) hemp- and CBD-based products available to consumers and determine the prevalence of other pharmacologically active agents identified as ingredients in these products. Labels were scored for the presence of: active and inactive ingredients, percent CBD, full-spectrum hemp, full-spectrum CBD or CBD isolate. Methods: Two large pharmacy chains and one medium-sized grocery store located in the Pacific Northwest were surveyed between May 2020 and February 2021 and OTC hemp-derived products on display were recorded. Identification of pharmacologically active ingredients on the product label was validated using the National Medicines Comprehensive Database. Products that were noted to have CBD or hemp ingredients were included in the study, while any products that did not accurately report the amount of CBD in the product were excluded. Products that did not list the total weight of the product were excluded from the analysis. Results: Thirty-three unique products were recorded from 19 different manufacturers. 39% of product labels indicated the presence of Full-Spectrum Hemp as part of the base product, while 66% of products listed Hemp Extract as the base product. Text labels on CBD-containing products, on average, indicated more than 3 additional pharmacologically active ingredients were contained in each product. Topical CBD products were more likely to have other ingredients such as arnica montana, menthol and camphor, whereas products for oral ingestion were more likely to have only CBD as the primary active ingredient. Text labels on 52% of topical CBD products listed the presence of 10% menthol. Average concentrations of CBD in OTC products was found to be 1.12% ± 1.48 %, based on dry weight. Conclusion: Product text labels on OTC hemp-and CBD-containing preparations is varied and presented in a non-standardized format. Topical CBD products were more likely to contain other pharmacologically active natural products that can be used for the treatment of pain symptoms. Concentrations of arnica montana, menthol and camphor were as much as 10-fold higher than the proportion of CBD contained in these products. The absence of a standard format for labelling of OTC hemp- and CBD-derived products and the frequent presence of other active ingredients has the potential to create confusion and risk for the consumer.
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Zhang, Qian-Wen, Ximing Zhang, Zhao Yan, Ruifang Liu, Yunbo Cao, and Min-Ling Zhang. "Correlation-Guided Representation for Multi-Label Text Classification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/463.
Abstract:
Multi-label text classification is an essential task in natural language processing. Existing multi-label classification models generally consider labels as categorical variables and ignore the exploitation of label semantics. In this paper, we view the task as a correlation-guided text representation problem: an attention-based two-step framework is proposed to integrate text information and label semantics by jointly learning words and labels in the same space. In this way, we aim to capture high-order label-label correlations as well as context-label correlations. Specifically, the proposed approach works by learning token-level representations of words and labels globally through a multi-layer Transformer and constructing an attention vector through word-label correlation matrix to generate the text representation. It ensures that relevant words receive higher weights than irrelevant words and thus directly optimizes the classification performance. Extensive experiments over benchmark multi-label datasets clearly validate the effectiveness of the proposed approach, and further analysis demonstrates that it is competitive in both predicting low-frequency labels and convergence speed.
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Wu, Yanan, He Liu, Songhe Feng, Yi Jin, Gengyu Lyu, and Zizhang Wu. "GM-MLIC: Graph Matching based Multi-Label Image Classification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/163.
Abstract:
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and reformulate the task of MLIC as a instance-label matching selection problem. To model such problem, we propose a novel deep learning framework named Graph Matching based Multi-Label Image Classification (GM-MLIC), where Graph Matching (GM) scheme is introduced owing to its excellent capability of excavating the instance and label relationship. Specifically, we first construct an instance spatial graph and a label semantic graph respectively, and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.
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Xia, Shi-Yu, Jiaqi Lv, Ning Xu, and Xin Geng. "Ambiguity-Induced Contrastive Learning for Instance-Dependent Partial Label Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/502.
Abstract:
Partial label learning (PLL) learns from a typical weak supervision, where each training instance is labeled with a set of ambiguous candidate labels (CLs) instead of its exact ground-truth label. Most existing PLL works directly eliminate, rather than exploiting the label ambiguity, since they explicitly or implicitly assume that incorrect CLs are noise independent of the instance. While a more practical setting in the wild should be instance-dependent, namely, the CLs depend on both the true label and the instance itself, such that each CL may describe the instance from some sensory channel, thereby providing some noisy but really valid information about the instance. In this paper, we leverage such additional information acquired from the ambiguity and propose AmBiguity-induced contrastive LEarning (ABLE) under the framework of contrastive learning. Specifically, for each CL of an anchor, we select a group of samples currently predicted as that class as ambiguity-induced positives, based on which we synchronously learn a representor (RP) that minimizes the weighted sum of contrastive losses of all groups and a classifier (CS) that minimizes a classification loss. Although they are circularly dependent: RP requires the ambiguity-induced positives on-the-fly induced by CS, and CS needs the first half of RP as the representation extractor, ABLE still enables RP and CS to be trained simultaneously within a coherent framework. Experiments on benchmark datasets demonstrate its substantial improvements over state-of-the-art methods for learning from the instance-dependent partially labeled data.
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Goyal, Palash, Divya Choudhary, and Shalini Ghosh. "Hierarchical Class-Based Curriculum Loss." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/337.
Abstract:
Classification algorithms in machine learning often assume a flat label space. However, most real world data have dependencies between the labels, which can often be captured by using a hierarchy. Utilizing this relation can help develop a model capable of satisfying the dependencies and improving model accuracy and interpretability. Further, as different levels in the hierarchy correspond to different granularities, penalizing each label equally can be detrimental to model learning. In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels based on their levels in the hierarchy, learned implicitly by the training paradigm. We theoretically show that the proposed hierarchical class-based curriculum loss is a tight bound of 0-1 loss among all losses satisfying the hierarchical constraints. We test our loss function on real world image data sets, and show that it significantly outperforms state-of-the-art baselines.
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Yin, Li'ang, Jianhua Han, Weinan Zhang, and Yong Yu. "Aggregating Crowd Wisdoms with Label-aware Autoencoders." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/184.
Abstract:
Aggregating crowd wisdoms takes multiple labels from various sources and infers true labels for objects. Recent research work makes progress by learning source credibility from data and roughly form three kinds of modeling frameworks: weighted majority voting, trust propagation, and generative models. In this paper, we propose a novel framework named Label-Aware Autoencoders (LAA) to aggregate crowd wisdoms. LAA integrates a classifier and a reconstructor into a unified model to infer labels in an unsupervised manner. Analogizing classical autoencoders, we can regard the classifier as an encoder, the reconstructor as a decoder, and inferred labels as latent features. To the best of our knowledge, it is the first trial to combine label aggregation with autoencoders. We adopt networks to implement the classifier and the reconstructor which have the potential to automatically learn underlying patterns of source credibility. To further improve inference accuracy, we introduce object ambiguity and latent aspects into LAA. Experiments on three real-world datasets show that proposed models achieve impressive inference accuracy improvement over state-of-the-art models.
Reports on the topic "Weight labels":
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Cao, Shoufeng, Uwe Dulleck, Warwick Powell, Charles Turner-Morris, Valeri Natanelov, and Marcus Foth. BeefLedger blockchain-credentialed beef exports to China: Early consumer insights. Queensland University of Technology, May 2020. http://dx.doi.org/10.5204/rep.eprints.200267.
Abstract:
The BeefLedger Export Smart Contracts project is a collaborative research study between BeefLedger Ltd and QUT co-funded by the Food Agility CRC. This project exists to deliver economic value to those involved in the production, export and consumption of Australian beef to China through: (1) reduced information asymmetry; (2) streamlined compliance processes, and; (3) developing and accessing new data-driven value drivers, through the deployment of decentralised ledger technologies and associated governance systems. This report presents early insights from a survey deployed to Chinese consumers in Nov/Dec 2019 exploring attitudes and preferences about blockchain-credentialed beef exports to China. Our results show that most local and foreign consumers were willing to pay more than the reference price for a BeefLedger branded Australian cut and packed Sirloin steak at the same weight. Although considered superior over Chinese processed Australian beef products, the Chinese market were sceptical that the beef they buy was really from Australia, expressing low trust in Australian label and traceability information. Despite lower trust, most survey respondents were willing to pay more for traceability supported Australian beef, potentially because including this information provided an additional sense of safety. Therefore, traceability information should be provided to consumers, as it can add a competitive advantage over products without traceability.
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Alonso, Pablo, Basil Kavalsky, Jose Ignacio Sembler, Hector Conroy, Salvatore Schiavo-Campo, Juan Manuel Puerta, Monika Huppi, et al. How is the IDB Serving Higher-Middle-Income Countries?: Borrowers' Perspective. Inter-American Development Bank, February 2013. http://dx.doi.org/10.18235/0010547.
Abstract:
This evaluation assesses how well IDB's current mode of engagement meets the needs and expectations of countries that have relatively higher income levels and increasing access to diverse funding sources.For the purpose of this evaluation OVE selected 10 countries as case study countries using three objective indicators: (a) income level, (b) level of access to international financial markets, and (c) the weight of the country in IDB's lending portfolio. The countries, labeled higher middle income countries (HMICs) in this evaluation, are Argentina, Brazil, Chile, Colombia, Mexico, Panama, Peru, Trinidad and Tobago, Uruguay, and Venezuela. The evaluation finds that IDB remains a valued and trusted development partner in the vast majority of the HMIC case study countries. It is still financially competitive, even in countries with investment-grade sovereign risk ratings, and is particularly so during times of international turbulence. IDB's counterparts particularly value the following aspects of their cooperation with the Bank: the discipline IDB helps impart to project design and execution, along with capacity building related to project management; the opportunity that IDB provides to learn from other experiences in the Region; the seal of approval that IDB's project financing can help provide through its social, environmental, and fiduciary safeguards; and IDB's ability to facilitate medium-to long-term project continuity across political cycles. However, the evaluation also finds that several aspects require further consideration as IDB moves forward, regarding the usefulness of country strategies; engagement with subnational governments; streamlining of IDB's different private sector windows; fine-tuning of some lending instruments; speed of project preparation and implementation; knowledge creation and sharing; and the introduction of fee-based services. The evaluation provides five recommendations to help IDB strengthen its engagement with High-Middle Income Countries.