Relevant bibliographies by topics / Intrinsic labels

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intrinsic labels'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Intrinsic labels"

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Yu, Guangya, Qi Ye, and Tong Ruan. "Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label." Bioengineering 11, no. 3 (2024): 225. http://dx.doi.org/10.3390/bioengineering11030225.

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The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledg
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Thomas, Dean E., Ilyssa E. Ramos, Kyle S. Kinskie, Kyle M. Brown, James M. Cherry, and Alexandra R. Hyler. "Abstract 4585: Exploiting intrinsic bioelectrical signatures for label-free immune cell sorting." Cancer Research 85, no. 8_Supplement_1 (2025): 4585. https://doi.org/10.1158/1538-7445.am2025-4585.

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Abstract Immune cell separation from heterogeneous samples is crucial for various downstream research applications. Standard methods for cell purification are laborious and require additional fluorescent or magnetic labels to identify cells of interest. Approaches contingent upon the use of labels may interfere with downstream testing and analysis, like single cell sequencing or downstream oncology research. Label-free cell sorting by electrokinetic dielectrophoresis (DEP) has recently been used to characterize and separate target subpopulations from complex samples. Available literature descr
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Zhang, Pengfei, and Rui Wang. "Label-Free Biosensor." Biosensors 13, no. 5 (2023): 556. http://dx.doi.org/10.3390/bios13050556.

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Label-free biosensors have become an indispensable tool for analyzing intrinsic molecular properties, such as mass, and quantifying molecular interactions without interference from labels, which is critical for the screening of drugs, detecting disease biomarkers, and understanding biological processes at the molecular level [...]
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Chen, Boli, Xin Huang, Lin Xiao, Zixin Cai, and Liping Jing. "Hyperbolic Interaction Model for Hierarchical Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7496–503. http://dx.doi.org/10.1609/aaai.v34i05.6247.

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Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main re
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Zhao, Tianna, Yuanjian Zhang, and Witold Pedrycz. "Robust Multi-Label Classification with Enhanced Global and Local Label Correlation." Mathematics 10, no. 11 (2022): 1871. http://dx.doi.org/10.3390/math10111871.

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Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness is preliminary. Low-quality data is one of the main reasons that model robustness degrades. Aiming at the cases with noisy features and missing labels, we develop a novel method called robust global and local label correlation (RGLC). In this model, subspace learning reconstructs intrinsic latent features immune from feature noise. The manifold learning ensures that outputs obtained by matrix factoriza
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Fairweather-Tait, Susan J., Thomas E. Fox, S. Gabrielle Wharf, John Eagles, Helen M. Crews, and Robert Massey. "Apparent zinc absorption by rats from foods labelled intrinsically and extrinsically with 67Zn." British Journal of Nutrition 66, no. 1 (1991): 65–71. http://dx.doi.org/10.1079/bjn19910010.

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A variety of foods (peas (Pisum sativum), chicken meat, eggs, goat's milk, human milk) enriched with the stable isotope 67Zn were prepared by means of intrinsic- and extrinsic-labelling procedures. They were fed to rats and apparent absorption of 67Zn determined from faecal excretion measurements using thermal ionization mass spectrometry. There were significant differences in the absorption of the extrinsic and intrinsic label which differed in magnitude between the foods tested. The extrinsic 67Zn was less well absorbed in peas, chicken meat, eggs, and human milk than intrinsic 67Zn, but in
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Xing, Yuying, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, and Maozu Guo. "Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5508–15. http://dx.doi.org/10.1609/aaai.v33i01.33015508.

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Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance.\
 In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instanc
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Huang, Shaoli, Xinchao Wang, and Dacheng Tao. "SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1628–36. http://dx.doi.org/10.1609/aaai.v35i2.16255.

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Data mixing augmentation has proved effective in training deep models. Recent methods mix labels mainly according to the mixture proportion of image pixels. Due to the major discriminative information of a fine-grained image usually resides in subtle regions, these methods tend to introduce heavy label noise in fine-grained recognition. We propose Semantically Proportional Mixing (SnapMix) that exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data. SnapMix generates the target label for a mixed image by estimating its intrinsic semantic composition. This
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Jia, Jinyuan, Xiaoyu Cao, and Neil Zhenqiang Gong. "Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7961–69. http://dx.doi.org/10.1609/aaai.v35i9.16971.

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In a data poisoning attack, an attacker modifies, deletes, and/or inserts some training examples to corrupt the learnt machine learning model. Bootstrap Aggregating (bagging) is a well known ensemble learning method, which trains multiple base models on random subsamples of a training dataset using a base learning algorithm and uses majority vote to predict labels of testing examples. We prove the intrinsic certified robustness of bagging against data poisoning attacks. Specifically, we show that bagging with an arbitrary base learning algorithm provably predicts the same label for a testing e
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Xiao, Qianpeng, Changbin Shao, Sen Xu, Xibei Yang, and Hualong Yu. "CC<i>k</i>EL: Compensation-based correlated <i>k</i>-labelsets for classifying imbalanced multi-label data." Electronic Research Archive 32, no. 5 (2024): 3038–58. http://dx.doi.org/10.3934/era.2024139.

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&lt;abstract&gt; &lt;p&gt;Imbalanced data distribution and label correlation are two intrinsic characteristics of multi-label data. This occurs because in this type of data, instances associated with certain labels may be sparse, and some labels may be associated with others, posing a challenge for traditional machine learning techniques. To simultaneously adapt imbalanced data distribution and label correlation, this study proposed a novel algorithm called compensation-based correlated &lt;italic&gt;k&lt;/italic&gt;-labelsets (CC&lt;italic&gt;k&lt;/italic&gt;EL). First, for each label, the CC
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Dissertations / Theses on the topic "Intrinsic labels"

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Guedes, Isabel Beja Nobre. "A relação entre os atributos intrísecos e extrínsecos do produto, qualidade percebida e qualidade experienciada : o caso dos iogurtes da marca de distribuição Pingo Doce." Master's thesis, Instituto Superior de Economia e Gestão, 2011. http://hdl.handle.net/10400.5/10278.

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Mestrado em Marketing<br>O presente estudo visa avaliar de que forma os atributos intrínsecos e extrínsecos se relacionam com a qualidade percebida e qualidade experienciada. O foco deste estudo são os iogurtes da marca Pingo Doce. Os principais objectivos deste estudo serão: (1)avaliar quais destes atributos tem mais influência na percepção de qualidade; (2)observar se a percepção de qualidade varia entre produtos de marca de fornecedor e marca da distribuição; (3) se factores sócio culturais influenciam a avaliação do produto. Adopta-se assim o modelo de Wierenga (1982), para aferir a perce
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Keighley, Simon David. "Label-free detection of nucleic acids by their intrinsic molecular charge." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612387.

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Herrera, Gonzalez Daniel Antonio. "Intrinsic and extrinsic factors that influence consumers’ intention and purchase behavior: The case of eco-labeled dairy products." Doctoral thesis, Pontificia Universidad Católica del Perú, 2020. http://hdl.handle.net/20.500.12404/16740.

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This research aims to undersatnd how intrinsic and extrinsic factors can influence consumers’ buying decision on shopping eco-labeled dairy products in order to close the gap between intention and purchase. This research is a quantitative study and uses the theory of reasoned action (TRA) of Ajzen & Fishbein (1980). Data were collected through surveys applied to consumers buying dairy products from a recognised dairy supermarket in Bogotá-Colombia. This work uses logistic regression as a statistical method to subsequently complement the analysis and enhance the results. It also uses exp
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Jin, Xiaoxia. "Investigation of Intrinsic Cell Magnetophoresis for Label-Less Cell Separation and Analysis and the Optimization of the CTV Instrumentation for Such Studies." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1268002273.

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Barulin, Aleksandr. "Label-free single protein fluorescence detection in the UV enhanced by aluminum plasmonic nanostructures." Thesis, Aix-Marseille, 2020. http://theses.univ-amu.fr.lama.univ-amu.fr/201204_BARULIN_360oitqab739occoku598wcb932u_TH.pdf.

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Les techniques de fluorescence de molécule individuelle permettent de suivre la dynamique moléculaire et les interactions dans les processus biologiques. Maintenant, la dynamique moléculaire des protéines est principalement accompagnée du marquage fluorescent externe. Cependant, une molécule attachée peut perturber la dynamique de protéines. Heureusement, la majorité des protéines contiennent le tryptophane ou la tyrosine qui absorbent et émettent la lumière dans le domaine spectral d'UV entre 260 nm et 400 nm. Ces acides aminés ont de basses efficacités quantiques, photostabilisées dans l'UV
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Books on the topic "Intrinsic labels"

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Timmel, Christiane R., and Jeffrey R. Harmer, eds. Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39125-5.

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Timmel, Christiane R., and Jeffrey R. Harmer. Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer, 2016.

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Structural information from spin-labels and intrinsic paramagnetic centres in the biosciences. Springer, 2014.

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Qin, Peter Z., and Kurt Warncke. Electron Paramagnetic Resonance Investigations of Biological Systems by Using Spin Labels, Spin Probes, and Intrinsic Metal Ions Part B. Elsevier Science & Technology Books, 2015.

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Electron Paramagnetic Resonance Investigations of Biological Systems by Using Spin Labels, Spin Probes, and Intrinsic Metal Ions, Part A. Elsevier, 2015. http://dx.doi.org/10.1016/s0076-6879(15)x0015-2.

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Electron Paramagnetic Resonance Investigations of Biological Systems by Using Spin Labels, Spin Probes, and Intrinsic Metal Ions, Part B. Elsevier, 2015. http://dx.doi.org/10.1016/s0076-6879(15)x0016-4.

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Qin, Peter Z., and Kurt Warncke. Electron Paramagnetic Resonance Investigations of Biological Systems by Using Spin Labels, Spin Probes, and Intrinsic Metal Ions Part A. Elsevier Science & Technology Books, 2015.

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Qin, Peter Z., and Kurt Warncke. Electron Paramagnetic Resonance Investigations of Biological Systems by Using Spin Labels, Spin Probes, and Intrinsic Metal Ions Part B. Elsevier Science & Technology Books, 2015.

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Qin, Peter Z., and Kurt Warncke. Electron Paramagnetic Resonance Investigations of Biological Systems by Using Spin Labels, Spin Probes, and Intrinsic Metal Ions Part A. Elsevier Science & Technology Books, 2015.

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Book chapters on the topic "Intrinsic labels"

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Schott, Moritz, Adina Zell, Sven Lautenbach, et al. "Analyzing and Improving the Quality and Fitness for Purpose of OpenStreetMap as Labels in Remote Sensing Applications." In Volunteered Geographic Information. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_2.

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AbstractOpenStreetMap (OSM) is a well-known example of volunteered geographic information. It has evolved to one of the most used geographic databases. As data quality of OSM is heterogeneous both in space and across different thematic domains, data quality assessment is of high importance for potential users of OSM data. As use cases differ with respect to their requirements, it is not data quality per se that is of interest for the user but fitness for purpose. We investigate the fitness for purpose of OSM to derive land-use and land-cover labels for remote sensing-based classification model
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Bowen, Alice M., Claudia E. Tait, Christiane R. Timmel, and Jeffrey R. Harmer. "Orientation-Selective DEER Using Rigid Spin Labels, Cofactors, Metals, and Clusters." In Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/430_2013_115.

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Jeschke, Gunnar. "Interpretation of Dipolar EPR Data in Terms of Protein Structure." In Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/430_2011_61.

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Shelke, Sandip A., and Snorri Th Sigurdsson. "Site-Directed Nitroxide Spin Labeling of Biopolymers." In Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/430_2011_62.

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Goldfarb, Daniella. "Metal-Based Spin Labeling for Distance Determination." In Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/430_2011_63.

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Ward, Richard, and Olav Schiemann. "Structural Information from Oligonucleotides." In Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/430_2012_76.

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Borbat, Peter P., and Jack H. Freed. "Pulse Dipolar Electron Spin Resonance: Distance Measurements." In Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/430_2012_82.

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Klare, Johann P., and Heinz-Jürgen Steinhoff. "Structural Information from Spin-Labelled Membrane-Bound Proteins." In Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/430_2012_88.

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Kim, Doyeon, Sangyun Lee, Moosung Lee, Juntaek Oh, Su-A. Yang, and YongKeun Park. "Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging." In Advances in Experimental Medicine and Biology. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6064-8_10.

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Smith, Julia M. H. "Portable Christianity: Relics in the Medieval West (c.700–1200)." In Proceedings of the British Academy Volume 181, 2010-2011 Lectures. British Academy, 2012. http://dx.doi.org/10.5871/bacad/9780197265277.003.0006.

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This paper uses the proxy evidence of relic inventories and labels to explore the role of relics in medieval Christianity. By means of an examination of their material nature, it argues that their primary characteristics were their fragmentary and often amorphous nature; their lack of intrinsic identification; and their easy portability. By emphasising that relic collecting was a habit that contributed to establishing religious identities and affiliations, the paper clarifies relics' role in relocating knowledge of Christian history into the homes and churches of medieval Europe. Finally, havi
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Conference papers on the topic "Intrinsic labels"

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Wang, Geng, Ruyi Gong, Yuanzhe Su, et al. "Label-free DNA spectroscopic photon-localization nanoscopy." In Novel Techniques in Microscopy. Optica Publishing Group, 2025. https://doi.org/10.1364/ntm.2025.nm1c.5.

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We present Label-free DNA Spectroscopic Photon-Localization Nanoscopy, an intrinsic-contrast 3D genomic imaging technology with nanometer resolution, leveraging DNA autofluorescence and spectral algorithms to resolve chromatin types, map genomic domains, and reconstruct 3D genome structures.
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Datta, Rupsa, Veronika Miskolci, Gina M. Gallego-López, et al. "Metabolic Characterization of Neutrophil Activation Using Label-Free Fluorescence Lifetime Imaging Microscopy." In Optical Molecular Probes, Imaging and Drug Delivery. Optica Publishing Group, 2025. https://doi.org/10.1364/omp.2025.om1e.4.

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In this work we employ fluorescence lifetime imaging microscopy of intrinsic metabolic co-factor NAD(P)H to quantify metabolic changes in neutrophils upon activation and metabolic pathway perturbations across biological systems.
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Park, YongKeun. "Holotomography and artificial intelligence: label-free 3D imaging, classification, and inference of live cells, tissues, and organoids." In Novel Optical Materials and Applications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/noma.2024.nom3h.1.

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Holotomography (HT) is a powerful label-free imaging technique that enables high-resolution, three-dimensional quantitative phase imaging (QPI) of live cells and organoids through the use of refractive index (RI) distributions as intrinsic imaging contrast1-3. Similar to X-ray computed tomography, HT acquires multiple two-dimensional holograms of a sample at various illumination angles, from which a 3D RI distribution of the sample is reconstructed by inversely solving the wave equation.
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Hou, Yingjie, Peng Guo, and Jing Zhao. "The Eco-Label Strategy of Green Manufacture Under the Influence of Consumers' Intrinsic Preferences." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831988.

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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}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/305.

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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
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Wang, Lichen, Zhengming Ding, and Yun Fu. "Adaptive Graph Guided Embedding for Multi-label Annotation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/388.

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Multi-label annotation is challenging since a large amount of well-labeled training data are required to achieve promising performance. However, providing such data is expensive while unlabeled data are widely available. To this end, we propose a novel Adaptive Graph Guided Embedding (AG2E) approach for multi-label annotation in a semi-supervised fashion, which utilizes limited labeled data associating with large-scale unlabeled data to facilitate learning performance. Specifically, a multi-label propagation scheme and an effective embedding are jointly learned to seek a latent space where unl
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Ferrero, Vincenzo J., Arvind Shankar Raman, Bryony DuPont, and Karl R. Haapala. "Understanding the Sustainability of Eco-Labeled Products When Compared to Conventional Alternatives." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68339.

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Sustainability considerations are becoming an intrinsic part of product design and manufacturing. Today’s consumers rely on package labeling to relay useful information about the environmental impact of a given product. As such, eco-labeling has become an important influence on how consumers interpret the sustainability of products. Three categories of eco-labels are theorized: Type I focuses on the use of labels that are certified by a reputable third party. Type II are eco-labels that are self-declared, potentially lacking scientific merit. Type III eco-labeling indicates the public availabi
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Liang, De-Ming, and Yu-Feng Li. "Lightweight Label Propagation for Large-Scale Network Data." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/475.

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Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named \algo to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving pr
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Li, Ziwei, Gengyu Lyu, and Songhe Feng. "Partial Multi-Label Learning via Multi-Subspace Representation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/362.

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Partial Multi-Label Learning (PML) aims to learn from the training data where each instance is associated with a set of candidate labels, among which only a part of them are relevant. Existing PML methods mainly focus on label disambiguation, while they lack the consideration of noise in the feature space. To tackle the problem, we propose a novel framework named partial multi-label learning via MUlti-SubspacE Representation (MUSER), where the redundant labels together with noisy features are jointly taken into consideration during the training process. Specifically, we first decompose the ori
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Keretchashvili, Gurami, Ting Lei, Pontus Loviken, et al. "Machine-Learning-Enabled Joint Interpretation of Dipole Sonic and Borehole Image Data." In 2023 SPWLA 64th Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2023. http://dx.doi.org/10.30632/spwla-2023-0089.

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Borehole sonic and formation image logs represent two widely used measurements for formation evaluation. Many important geomechanical and geological features, such as thin layers, breakouts, natural fractures, drilling fractures, stress effects, and intrinsic anisotropic effects, can be detected by either or both measurements. These features have noticeable impacts on geomechanical and petrophysical applications. Therefore, it is important to label and classify them as accurately as possible. On the one hand, the borehole sonic measurement, which is usually used to provide valuable inputs for
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Reports on the topic "Intrinsic labels"

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Lunn, Pete, Marek Bohacek, Jason Somerville, Áine Ní Choisdealbha, and Féidhlim McGowan. PRICE Lab: An Investigation of Consumers’ Capabilities with Complex Products. ESRI, 2016. https://doi.org/10.26504/bkmnext306.

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Executive Summary This report describes a series of experiments carried out by PRICE Lab, a research programme at the Economic and Social Research Institute (ESRI) jointly funded by the Central Bank of Ireland, the Commission for Energy Regulation, the Competition and Consumer Protection Commission and the Commission for Communications Regulation. The experiments were conducted with samples of Irish consumers aged 18-70 years and were designed to answer the following general research question: At what point do products become too complex for consumers to choose accurately between the good ones
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