Academic literature on the topic 'Limited label availability'
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Journal articles on the topic "Limited label availability"
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Maiti, A., S. J. Oude Elberink, and G. Vosselman. "EFFECT OF LABEL NOISE IN SEMANTIC SEGMENTATION OF HIGH RESOLUTION AERIAL IMAGES AND HEIGHT DATA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 275–82. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-275-2022.
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Abstract. The performance of deep learning models in semantic segmentation is dependent on the availability of a large amount of labeled data. However, the influence of label noise, in the form of incorrect annotations, on the performance is significant and mostly ignored. This is a big concern in remote sensing applications, wherein acquired datasets are spatially limited, labeling is done by domain experts with possible sources of high inter-and intra-observer variability leading to erroneous predictions. In this paper, we first simulate the label noise while conducting experiments on two different datasets with very high-resolution aerial images, height data, and inaccurate labels, responsible for the training of deep learning models. We then focus on the effect of these noises on the model performance. Different classes respond differently to the label noise. The typical size of an object belonging to a class is a crucial factor regarding the class-specific performance of the model trained with erroneous labels. Errors caused by relative shifts of labels are the most influential label errors. The model is generally more tolerant of the random label noise than other label errors. It has been observed that the accuracy gets reduced by at least 3% while 5% of label pixels are erroneous. In this regard, our study provides a new perspective of evaluating and quantifying the propagation of label noise in the model performance that is indeed important for adopting reliable semantic segmentation practices.
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Hu, Han, Yang Lei, Daisy Xin, Viktor Shkolnikov, Steven Barcelo, Jan Allebach, and Edward J. Delp. "2D Label Free Microscopy Imaging Analysis Using Machine Learning." Electronic Imaging 2020, no. 14 (January 26, 2020): 341–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.14.coimg-341.
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Separation and isolation of living cells plays an important role in the fields of medicine and biology with label-free imaging often used for isolating cells. The analysis of label-free cell images has many challenges when examining the behavior of cells. This paper presents methods to analyze label-free cells. Many of the tools we describe are based on machine learning approaches. We also investigate ways of augmenting limited availability of training data. Our results demonstrate that our proposed methods are capable of successfully segmenting and classifying label-free cells.
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Id, Ibnu Daqiqil, Pardomuan Robinson Sihombing, and Supratman Zakir. "Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model." Bulletin of Electrical Engineering and Informatics 10, no. 6 (December 1, 2021): 3361–68. http://dx.doi.org/10.11591/eei.v10i6.3259.
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When predicting data streams, changes in data distribution may decrease model accuracy over time, thereby making the model obsolete. This phenomenon is known as concept drift. Detecting concept drifts and then adapting to them are critical operations to maintain model performance. However, model adaptation can only be made if labeled data is available. Labeling data is both costly and time-consuming because it has to be done by humans. Only part of the data can be labeled in the data stream because the data size is massive and appears at high speed. To solve these problems simultaneously, we apply a technique to update the model by employing both labeled and unlabeled instances to do so. The experiment results show that our proposed method can adapt to the concept drift with pseudo-labels and maintain its accuracy even though label availability is drastically reduced from 95% to 5%. The proposed method also has the highest overall accuracy and outperforms other methods in 5 of 10 datasets.
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Ding, Chen, Yu Li, Yue Wen, Mengmeng Zheng, Lei Zhang, Wei Wei, and Yanning Zhang. "Boosting Few-Shot Hyperspectral Image Classification Using Pseudo-Label Learning." Remote Sensing 13, no. 17 (September 6, 2021): 3539. http://dx.doi.org/10.3390/rs13173539.
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Deep neural networks have underpinned much of the recent progress in the field of hyperspectral image (HSI) classification owing to their powerful ability to learn discriminative features. However, training a deep neural network often requires the availability of a large number of labeled samples to mitigate over-fitting, and these labeled samples are not always available in practical applications. To adapt the deep neural network-based HSI classification approach to cases in which only a very limited number of labeled samples (i.e., few or even only one labeled sample) are provided, we propose a novel few-shot deep learning framework for HSI classification. In order to mitigate over-fitting, the framework borrows supervision from an auxiliary set of unlabeled samples with soft pseudo-labels to assist the training of the feature extractor on few labeled samples. By considering each labeled sample as a reference agent, the soft pseudo-label is assigned by computing the distances between the unlabeled sample and all agents. To demonstrate the effectiveness of the proposed method, we evaluate it on three benchmark HSI classification datasets. The results indicate that our method achieves better performance relative to existing competitors in few-shot and one-shot settings.
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Roth, Myron. "The availability and use of chemotherapeutic sea lice control products." Contributions to Zoology 69, no. 1-2 (2000): 109–18. http://dx.doi.org/10.1163/18759866-0690102012.
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An international survey revealed that eleven compounds representing five pesticide types are currently being used on commercial salmon farms for sea lice control. These include two organophosphates (dichlorvos and azamethiphos); three pyrethrin/pyrethroid compounds (pyrethrum, cypermethrin, deltamethrin); one oxidizing agent (hydrogen peroxide); three avermectins (ivermectin, emamectin and doramectin) and two benzoylphenyl ureas (teflubenzuron and diflubenzuron). The number of compounds available in any one country is highly variable, ranging from 9 (Norway) to 6 (Chile, United Kingdom) to 4 (Ireland, Faeroes, Canada) to 2 (US)). Dichlorvos, Azamethiphos and cypermethrin were the most widely used compounds (5 countries) followed by, hydrogen peroxide, ivermectin and emamectin (4 countries each), teflubenzuron (3 countries), diflubenzuron (2 countries), and deltamethrin, pyrethrum and doramectin (1 country each). Although, like trichlorfon, dichlorvos use is being discontinued in several countries notably Norway and the Faeroes. In most instances the availability of sea lice chemotherapeutants is limited, many being used under extra-label veterinary prescription or exemption, and special investigation permits. Access to a broad range of compounds with different modes of action, as well as application methods, has only recently been acquired making assessment of chemotherapy, and therefore integrated pest management, difficult.
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Fishman, Michael A., Ashley Scherer, Jacob Topfer, and Philip S. H. Kim. "Limited Access to On-Label Formulations of Buprenorphine for Chronic Pain as Compared with Conventional Opioids." Pain Medicine 21, no. 5 (November 7, 2019): 1005–9. http://dx.doi.org/10.1093/pm/pnz197.
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Abstract Importance Buprenorphine is a Schedule III analgesic that is recommended as the firstline long-acting opioid for the treatment of chronic pain due to its ceiling effect on respiratory depression, adverse effect profile, and analgesic efficacy. However, prescription drug coverage policies commonly require that patients try and fail multiple Schedule II conventional opioids before approval of on-label use of buprenorphine for chronic pain. Design A retrospective review was performed looking at coverage of buprenorphine in the forms of Butrans and Belbuca. Patient denial letters, web searches of insurance and pharmacy benefit managers (PBMs), and an online tool (formularylookup.com) were used to assess the coverage and availability of buprenorphine for chronic pain. Results Unrestricted access to Butrans was reported for 42% of commercial lives and 11% of Medicare lives in all locations. Unrestricted access to Belbuca was reported for 53% of commercial lives and 23% of Medicare lives in all locations. Oxycodone immediate-release has unrestricted access for 84% of commercial plans and 97% of Medicare plans. Morphine extended-release has unrestricted access for 62% of commercial lives and 65% of Medicare lives. Conclusions and Relevance There are >17,000 prescription opioid–involved deaths each year in the United States. By substituting buprenorphine as the firstline treatment for chronic and even acute pain, there may be fewer prescribed conventional opioids in the United States. Schedule III buprenorphine formulations for chronic pain should be given unrestricted access for appropriate patients before considering a Schedule II opioid as a public health priority.
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Rehmani, Muhammad Asif Ali, Saad Aslam, Shafiqur Rahman Tito, Snjezana Soltic, Pieter Nieuwoudt, Neel Pandey, and Mollah Daud Ahmed. "Power Profile and Thresholding Assisted Multi-Label NILM Classification." Energies 14, no. 22 (November 14, 2021): 7609. http://dx.doi.org/10.3390/en14227609.
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Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.
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Kopp, Lisa M., Kathylynn Saboda, Bhuvana Setty, Mary Frances Wedekind, Daniel Weiser, Leya Schwartz, Michael Isakoff, Douglas James Harrison, Jonathan Benjamin Gill, and Pooja Hingorani. "Off label targeted therapy use in adolescents and young adults with sarcoma." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e21504-e21504. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e21504.
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e21504 Background: Outcomes for patients with metastatic or recurrent sarcomas remains dismal with < 20% overall survival. Due to the rarity of sarcomas, the development, testing, and approval of new therapies takes years. Most early phase clinical trials are restricted to adults leaving young patients with limited options. Little is known about the prevalence and clinical characteristics of patients receiving off-label targeted therapy (OLTT). In this multi-institutional retrospective review we evaluated OLTT use in this population. Methods: Patients with recurrent sarcoma diagnosed between the years of 2008 – 2016 were identified at six institutions. Charts were reviewed for OLTT use and additional clinical characteristics. ANOVA [Analysis of Variance] and Kruskal Wallis Rank sum tests were used for normally and non-normally distributed continuous data. Categorical data was analyzed using chi-squared tests or fisher exact tests. Results: The prevalence of OLTT use was 29% for the patients included in our analysis. Of the 99 cases, the mean age for OLTT was 18 years, ranging 3 – 34 years. Nearly half had recurrent tumor in multiple sites at the time of OLTT use, and 64% did not undergo resection prior to OLTT. Lack of clinical trial availability was the most common reason for OLTT use (31% of cases). The most common OLTT drug class used were tyrosine kinase inhibitors. Progression in 81% of cases was the primary reason for stopping OLTT and toxicity limited use in 12% of cases. One case had a complete response, 5 cases had a partial response and 4 cases had stable disease per RECIST. Conclusions: OLTT use is exceedingly prevalent in patients with recurrent sarcoma. Clinical trials for children and adolescents with recurrent sarcoma will identify optimal agents to improve outcomes in this understudied population.
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San Giovanni, Christine B., Brooke Sweeney, Joseph A. Skelton, Megan M. Kelsey, and Aaron S. Kelly. "Aversion to Off-label Prescribing in Clinical Pediatric Weight Management: The Quintessential Double Standard." Journal of Clinical Endocrinology & Metabolism 106, no. 7 (April 26, 2021): 2103–13. http://dx.doi.org/10.1210/clinem/dgab276.
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Abstract Context Pediatric obesity is now recognized as a chronic disease; yet few treatment options exist besides lifestyle modification therapy and bariatric surgery. We describe the limited availability of United States Food and Drug Administration (FDA)–approved antiobesity medications for adolescents and compare this to what is available for adults. We offer a rationale for off-label prescribing to assist with lifestyle modification therapy. We also highlight the need for more pharmacotherapy options and additional research into novel treatments for pediatric obesity. Case Description We describe a patient who is struggling with managing her weight and starting to develop complications of obesity. We offer a framework in which off-label prescribing may be beneficial to patients who have been engaging in lifestyle modification therapy yet fail to see improvement. Conclusion Lifestyle modification therapy is necessary but often insufficient in stimulating clinically meaningful weight loss when used alone in children and adolescents who struggle with weight management. Until more FDA-approved antiobesity medications are available, pediatricians may be able to help more patients achieve weight reduction goals by familiarizing themselves with the responsible use of off-label medications and implementing these tools to improve clinical outcomes. There is a critical need for more pharmacotherapy options to help pediatric patients in managing their weight and preventing or improving the insidious complications resulting from untreated obesity.
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Kim, R. B. "A multi-attribute model of Japanese consumer's purchase intention for GM foods." Agricultural Economics (Zemědělská ekonomika) 56, No. 10 (November 8, 2010): 449–59. http://dx.doi.org/10.17221/113/2009-agricecon.
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This study illustrates that consumers' GM food purchase decision is determined by a set of correlated variables. The interrelationship among the GM food purchase decision determinants is examined conceptually and empirically with a multi-attribute model, describing this interrelationship. Consumers' attitudes toward subjects such as innovation, science & technology as well as their trust towards the government's regulatory system of food safety and GM food are strong indicators of the consumers' GM food purchase decision. Given the limited availability of GM foods in the market which leads to a lack of understanding and experience of GM foods, consumers' knowledge and their search for information on food label appear to be weaker determinants of the GM food purchase decision for consumers.
Book chapters on the topic "Limited label availability"
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Al-Mutairi, Abdulrahman, and Stephen D. Wolthusen. "A Security Analysis of MPLS Service Degradation Attacks Based on Restricted Adversary Models." In Information Security in Diverse Computing Environments, 127–48. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6158-5.ch008.
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Whilst the security and integrity of exterior gateway protocols such as the Border Gateway Protocol (BGP) and, to a lesser extent, interior gateway protocols, including the Multi-Protocol Label Switching (MPLS), have been investigated previously, more limited attention has been paid to the problem of availability and timeliness that is crucial for service levels needed in critical infrastructure areas such as financial services and electric power (smart grid) networks. The authors describe a method for modeling adversaries for the analysis of attacks on quality of service characteristics underpinning such real-time networks as well as a model of policies employed by MPLS routers based on simplified networks and give an analysis of attack vectors based on assumed adversaries derived from the introduced method.
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Himaja, D., T. Maruthi Padmaja, and P. Radha Krishna. "A Survey of Class Imbalance Problem on Evolving Data Stream." In Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance, 23–41. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7371-6.ch002.
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Learning from data streams with both online class imbalance and concept drift (OCI-CD) is receiving much attention in today's world. Due to this problem, the performance is affected for the current models that learn from both stationary as well as non-stationary environments. In the case of non-stationary environments, due to the imbalance, it is hard to spot the concept drift using conventional drift detection methods that aim at tracking the change detection based on the learner's performance. There is limited work on the combined problem from imbalanced evolving streams both from stationary and non-stationary environments. Here the data may be evolved with complete labels or with only limited labels. This chapter's main emphasis is to provide different methods for the purpose of resolving the issue of class imbalance in emerging streams, which involves changing and unchanging environments with supervised and availability of limited labels.
Conference papers on the topic "Limited label availability"
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Daqaq, Mohammed F., Calvin Bradley, Nader Jalili, and Khaled Alhazza. "Feedback Delays for Ultrasensitive Sensing." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35088.
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Nanomechanical Cantilever Sensors (NMCS) have recently emerged as an effective means for label-free chemical and biological species detection. Their high selectivity, low cost, and easy mass production make them an enabling technology for micro- and nanodetection techniques. Sensitivity constitutes one of the most desirable characteristics of NMCS. However, sensitivity of current NMCS is predominately limited by their size. In other words, smaller sensors are more sensitive than larger ones. The detection of ultra-small masses or frequency variations is therefore obstructed by the availability of more advanced fabrication techniques that are capable of manufacturing smaller and smaller sensors. Even in that case, other issues such as noise, damping, and measurement difficulties become more evident. Therefore, techniques for sensitivity enhancement should be studied and implemented to allow for accurate and precise detection of even smaller parameter variations. Along that line of reasoning, we propose a simple, but effective concept to enhance sensitivity of NMCS. The proposed methodology is based on utilizing feedback delays to create a limit-cycle response whose amplitude is ultra-sensitive to frequency variations. In this paper, we explain, analyze, and derive the equations that verify the proposed concept. We then experimentally implement the sensitivity enhancement technique on a macrocantilever beam and demonstrate more than two-orders-of-magnitude sensitivity enhancement over the frequency-shift method.
Reports on the topic "Limited label availability"
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Zilberman, David, Amir Heiman, and Yanhong Jin. Use of Branding and Sampling in Agricultural Fresh Produce. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7697116.bard.
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The original proposal has three main objectives: a conceptual framework on willingness to pay (WTP) for fruits and vegetables, the introduction of branding and sampling in fresh food, and empirical applications to the United States and Israel. We modified our research plan over time based on availability of data and emergence of new problems. We expanded the range of products to include poultry and the range of techniques to use real experiments as well as more traditional surveys. We expanded the range of problems to understand attitudes toward genetically modified (GM) food. There is a growing interest in introduction of marketing tools like demonstration sampling, money-back guarantees, labeling, and brands in agriculture. These marketing tools are important for enhancing demand for agricultural products and food safety. However, the methodology needed to assess the effectiveness of these tools and understand their performance in different agricultural sectors is limited. Our analysis demonstrated the importance of brands as a marketing tool in agriculture. In particular, we showed conceptually that strong brands can be substitutes for other marketing tools like sampling or demonstration. We were able to conduct real experiments for the demand for safe chicken and show that consumers are willing to pay significantly more for products branded as more safe. Yet, using experiments in Israel and the United States, we found that WTP for brands of fresh fruits and vegetables is smaller than in other product categories. Warning labels are a sort of negative branding. The GM-free labeling is particularly important since it serves as a trade barrier to U.S. crops exports. Our analysis of acceptance of GM products found that WTP for GM products in Israel and the United States depends on framing of information about the impact ofGM and the quantity of information disclosed. Finally, in analyzing the evolution of support for Proposition 37 that aimed to introduce mandatory labeling of GM in California, we found that support for mandatory labeling ofGM products is broad as long as it is not perceived to be costly. Our project demonstrates the feasibility of conducting real experiments to assess consumer demand in agriculture. When looking at interdisciplinary groups, one can design new products and assess the WTP for their characteristics. We also show that, while branding is a very strong marketing tool, its use in fresh fruit and vegetables is likely to be limited. However, brands can be important with processed food. Furthermore, we have proven that, while some consumers strongly object to GM products, most consumers in the United States and Israel would be willing to buy them for a discount, and some would pay extra if they are associated with improved characteristics. Finally, we expanded the notion of warning labels to calorie information and showed that the response to calorie information depends on gender, education, and how the information is presented.