Gotowe bibliografie tematyczne / Limited training data

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Gotowa bibliografia na temat „Limited training data”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Limited training data"

Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright i Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting". Proceedings on Privacy Enhancing Technologies 2021, nr 2 (29.01.2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.

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Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

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McLaughlin, Niall, Ji Ming i Danny Crookes. "Robust Multimodal Person Identification With Limited Training Data". IEEE Transactions on Human-Machine Systems 43, nr 2 (marzec 2013): 214–24. http://dx.doi.org/10.1109/tsmcc.2012.2227959.

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Zhang, Mingyang, Berrak Sisman, Li Zhao i Haizhou Li. "DeepConversion: Voice conversion with limited parallel training data". Speech Communication 122 (wrzesień 2020): 31–43. http://dx.doi.org/10.1016/j.specom.2020.05.004.

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Qian, Tieyun, Bing Liu, Li Chen, Zhiyong Peng, Ming Zhong, Guoliang He, Xuhui Li i Gang Xu. "Tri-Training for authorship attribution with limited training data: a comprehensive study". Neurocomputing 171 (styczeń 2016): 798–806. http://dx.doi.org/10.1016/j.neucom.2015.07.064.

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Saunders, Sara L., Ethan Leng, Benjamin Spilseth, Neil Wasserman, Gregory J. Metzger i Patrick J. Bolan. "Training Convolutional Networks for Prostate Segmentation With Limited Data". IEEE Access 9 (2021): 109214–23. http://dx.doi.org/10.1109/access.2021.3100585.

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Zhao, Yao, Dong Joo Rhee, Carlos Cardenas, Laurence E. Court i Jinzhong Yang. "Training deep‐learning segmentation models from severely limited data". Medical Physics 48, nr 4 (19.02.2021): 1697–706. http://dx.doi.org/10.1002/mp.14728.

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Hoffbeck, J. P., i D. A. Landgrebe. "Covariance matrix estimation and classification with limited training data". IEEE Transactions on Pattern Analysis and Machine Intelligence 18, nr 7 (lipiec 1996): 763–67. http://dx.doi.org/10.1109/34.506799.

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Cui, Kaiwen, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Fangneng Zhan i Shijian Lu. "GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 1 (28.06.2022): 499–507. http://dx.doi.org/10.1609/aaai.v36i1.19928.

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Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-fitting. Several prior studies address this issue by expanding the distribution of the limited training data via massive and hand-crafted data augmentation. We handle data-limited image generation from a very different perspective. Specifically, we design GenCo, a Generative Co-training network that mitigates the discriminator over-fitting issue by introducing multiple complementary discriminators that provide diverse supervision from multiple distinctive views in training. We instantiate the idea of GenCo in two ways. The first way is Weight-Discrepancy Co-training (WeCo) which co-trains multiple distinctive discriminators by diversifying their parameters. The second way is Data-Discrepancy Co-training (DaCo) which achieves co-training by feeding discriminators with different views of the input images. Extensive experiments over multiple benchmarks show that GenCo achieves superior generation with limited training data. In addition, GenCo also complements the augmentation approach with consistent and clear performance gains when combined.

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Kim, June-Woo, i Ho-Young Jung. "End-to-end speech recognition models using limited training data*". Phonetics and Speech Sciences 12, nr 4 (grudzień 2020): 63–71. http://dx.doi.org/10.13064/ksss.2020.12.4.063.

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Tambouratzis, George, i Marina Vassiliou. "Swarm Algorithms for NLP - The Case of Limited Training Data". Journal of Artificial Intelligence and Soft Computing Research 9, nr 3 (1.07.2019): 219–34. http://dx.doi.org/10.2478/jaiscr-2019-0005.

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Abstract The present article describes a novel phrasing model which can be used for segmenting sentences of unconstrained text into syntactically-defined phrases. This model is based on the notion of attraction and repulsion forces between adjacent words. Each of these forces is weighed appropriately by system parameters, the values of which are optimised via particle swarm optimisation. This approach is designed to be language-independent and is tested here for different languages. The phrasing model’s performance is assessed per se, by calculating the segmentation accuracy against a golden segmentation. Operational testing also involves integrating the model to a phrase-based Machine Translation (MT) system and measuring the translation quality when the phrasing model is used to segment input text into phrases. Experiments show that the performance of this approach is comparable to other leading segmentation methods and that it exceeds that of baseline systems.

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Rozprawy doktorskie na temat "Limited training data"

Chang, Eric I.-Chao. "Improving wordspotting performance with limited training data". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/38056.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaves 149-155).
by Eric I-Chao Chang.
Ph.D.

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Zama, Ramirez Pierluigi <1992&gt. "Deep Scene Understanding with Limited Training Data". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9815/1/zamaramirez_pierluigi_tesi.pdf.

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Scene understanding by a machine is a challenging task due to the profound variety of nature. Nevertheless, deep learning achieves impressive results in several scene understanding tasks such as semantic segmentation, depth estimation, or optical flow. However, these kinds of approaches need a large amount of labeled data, leading to massive manual annotations, which are incredibly tedious and expensive to collect. In this thesis, we will focus on understanding a scene through deep learning with limited data availability. First of all, we will tackle the problem of the lack of data for semantic segmentation. We will show that computer graphics come in handy to our purpose, both to create a new, efficient tool for annotation as well to render synthetic annotated datasets quickly. However, a network trained only on synthetic data suffers from the so-called domain-shift problem, i.e. unable to generalize to real data. Thus, we will show that we can mitigate this problem using a novel deep image to image translation technique. In the second part of the thesis, we will focus on the relationship between scene understanding tasks. We argue that building a model aware of the connections between tasks is the first building stone to create more robust, efficient, performant models that need less annotated training data. In particular, we demonstrate that we can decrease the need for labels by exploiting the relationship between visual tasks. Finally, in the last part, we propose a novel unified framework for comprehensive scene understanding, which exploits the synergies between tasks to be more robust, efficient, and performant.

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McLaughlin, N. R. "Robust multimodal person identification given limited training data". Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.579747.

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Abstract This thesis presents a novel method of audio-visual fusion, known as multi- modal optimal feature fusion (MOFF), for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowl- edge about the corruption. Furthermore, it is assumed there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature rep- resentation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Similarity-based optimal feature selection and multi- condition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Low-level feature fusion is performed using optimal feature selection, which automatically changes the weighting given to each modality based on the level of corruption. The framework for robust person identification is also applied to noise robust speaker identification, given very limited training data. Experiments have been carried out on a bimodal data set created from the SPIDRE speaker recogni- tion database and AR face recognition database, with variable noise corruption of speech and occlusion in the face images. Combining both modalities using MOFF, leads to significantly improved identification accuracy compared to the component unimodal systems, even with simultaneous corruption of both modal- ities. A novel piecewise-constant illumination model (PCIlVI) is then introduced for illumination invariant facial recognition. This method can be used given a single training facial image for each person, and assuming no prior knowledge of the illumination conditions of both the training and testing images. Small areas of the face are represented using magnitude Fourier features, which takes advan- tage of the shift-invariance of the magnitude Fourier representation, to increase robustness to small misalignment errors and small facial expression changes. Fi- nally, cosine similarity is used as an illumination invariant similarity measure, to compare small facial areas. Experiments have been carried out on the YaleB, ex- tended YaleB and eMU-PIE facial illumination databases. Facial identification accuracy using PCIlVI is comparable to or exceeds that of the literature.

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Li, Jiawei. "Person re-identification with limited labeled training data". HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/541.

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With the growing installation of surveillance video cameras in both private and public areas, it is an immediate requirement to develop intelligent video analysis system for the large-scale camera network. As a prerequisite step of person tracking and person retrieval in intelligent video analysis, person re-identification, which targets in matching person images across camera views is an important topic in computer vision community and has been received increasing attention in the recent years. In the supervised learning methods, the person re-identification task is formulated as a classification problem to extract matched person images/videos (positives) from unmatched person images/videos (negatives). Although the state-of-the-art supervised classification models could achieve encouraging re-identification performance, the assumption that label information is available for all the cameras, is impractical in large-scale camera network. That is because collecting the label information of every training subject from every camera in the large-scale network can be extremely time-consuming and expensive. While the unsupervised learning methods are flexible, their performance is typically weaker than the supervised ones. Though sufficient labels of the training subjects are not available from all the camera views, it is still reasonable to collect sufficient labels from a pair of camera views in the camera network or a few labeled data from each camera pair. Along this direction, we address two scenarios of person re-identification in large-scale camera network in this thesis, i.e. unsupervised domain adaptation and semi-supervised learning and proposed three methods to learn discriminative model using all available label information and domain knowledge in person re-identification. In the unsupervised domain adaptation scenario, we consider data with sufficient labels as the source domain, while data from the camera pair missing label information as the target domain. A novel domain adaptive approach is proposed to estimate the target label information and incorporate the labeled data from source domain with the estimated target label information for discriminative learning. Since the discriminative constraint of Support Vector Machines (SVM) can be relaxed into a necessary condition, which only relies on the mean of positive pairs (positive mean), a suboptimal classification model learning without target positive data can be those using target positive mean. A reliable positive mean estimation is given by using both the labeled data from the source domain and potential positive data selected from the unlabeled data in the target domain. An Adaptive Ranking Support Vector Machines (AdaRSVM) method is also proposed to improve the discriminability of the suboptimal mean based SVM model using source labeled data. Experimental results demonstrate the effectiveness of the proposed method. Different from the AdaRSVM method that using source labeled data, we can also improve the above mean based method by adapting it onto target unlabeled data. In more general situation, we improve a pre-learned classifier by adapting it onto target unlabeled data, where the pre-learned classifier can be domain adaptive or learned from only source labeled data. Since it is difficult to estimate positives from the imbalanced target unlabeled data, we propose to alternatively estimate positive neighbors which refer to data close to any true target positive. An optimization problem for positive neighbor estimation from unlabeled data is derived and solved by aligning the cross-person score distributions together with optimizing for multiple graphs based label propagation. To utilize the positive neighbors to learn discriminative classification model, a reliable multiple region metric learning method is proposed to learn a target adaptive metric using regularized affine hulls of positive neighbors as positive regions. Experimental results demonstrate the effectiveness of the proposed method. In the semi-supervised learning scenario, we propose a discriminative feature learning using all available information from the surveillance videos. To enrich the labeled data from target camera pair, image sequences (videos) of the tagged persons are collected from the surveillance videos by human tracking. To extract the discriminative and adaptable video feature representation, we propose to model the intra-view variations by a video variation dictionary and a video level adaptable feature by multiple sources domain adaptation and an adaptability-discriminability fusion. First, a novel video variation dictionary learning is proposed to model the large intra-view variations and solved as a constrained sparse dictionary learning problem. Second, a frame level adaptable feature is generated by multiple sources domain adaptation using the variation modeling. By mining the discriminative information of the frames from the reconstruction error of the variation dictionary, an adaptability-discriminability (AD) fusion is proposed to generate the video level adaptable feature. Experimental results demonstrate the effectiveness of the proposed method.

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Qu, Lizhen [Verfasser], i Gerhard [Akademischer Betreuer] Weikum. "Sentiment analysis with limited training data / Lizhen Qu. Betreuer: Gerhard Weikum". Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2013. http://d-nb.info/1053680104/34.

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Guo, Zhenyu. "Data famine in big data era : machine learning algorithms for visual object recognition with limited training data". Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46412.

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Big data is an increasingly attractive concept in many fields both in academia and in industry. The increasing amount of information actually builds an illusion that we are going to have enough data to solve all the data driven problems. Unfortunately it is not true, especially for areas where machine learning methods are heavily employed, since sufficient high-quality training data doesn't necessarily come with the big data, and it is not easy or sometimes impossible to collect sufficient training samples, which most computational algorithms depend on. This thesis mainly focuses on dealing situations with limited training data in visual object recognition, by developing novel machine learning algorithms to overcome the limited training data difficulty. We investigate three issues in object recognition involving limited training data: 1. one-shot object recognition, 2. cross-domain object recognition, and 3. object recognition for images with different picture styles. For Issue 1, we propose an unsupervised feature learning algorithm by constructing a deep structure of the stacked Hierarchical Dirichlet Process (HDP) auto-encoder, in order to extract "semantic" information from unlabeled source images. For Issue 2, we propose a Domain Adaptive Input-Output Kernel Learning algorithm to reduce the domain shifts in both input and output spaces. For Issue 3, we introduce a new problem involving images with different picture styles, successfully formulate the relationship between pixel mapping functions with gradient based image descriptors, and also propose a multiple kernel based algorithm to learn an optimal combination of basis pixel mapping functions to improve the recognition accuracy. For all the proposed algorithms, experimental results on publicly available data sets demonstrate the performance improvements over previous state-of-arts.

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Säfdal, Joakim. "Data-Driven Engine Fault Classification and Severity Estimation Using Interpolated Fault Modes from Limited Training Data". Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173916.

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Today modern vehicles are expected to be safe, environmentally friendly, durable and economical. Monitoring the health of the vehicle is therefore more important than ever. As the complexity of vehicular systems increases the need for efficient monitoring methods has increased as well. Traditional methods of deriving models for the systems are today not as efficient as the complexity of the systems increases the time and skill needed to implement the models. An alternative is data driven methods where a collection of data associated with the behavior of the system is used to draw conclusions of the state of the system. Faults are however rare events and collecting sufficient data to cover all possible faults threatening a vehicle would be impossible. A method for drawing conclusions from limited historical data would therefore be desirable. In this thesis an algorithm using distiguishability as a method for fault classification and fault severity estimation is proposed. Historical data is interpolated over a fault severity vector using Gaussian process regression as a way to estimate fault modes for unknown fault sizes. The algorithm is then tested against validation data to evaluate the ability to detect and identify known fault classes and fault serveries, separate unknown fault classes from known fault classes, and estimate unknown fault sizes. The purpose of the study is to evaluate the possibility to use limited historical data to reduce the need for costly and time consuming data collection. The study shows promising results as fault class identification and fault size estimation using the proposed algorithm seem possible for fault sizes not included in the historical data.

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Lapin, Maksim [Verfasser], i Bernt [Akademischer Betreuer] Schiele. "Image classification with limited training data and class ambiguity / Maksim Lapin ; Betreuer: Bernt Schiele". Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2017. http://d-nb.info/1136607927/34.

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Trávníčková, Kateřina. "Interaktivní segmentace 3D CT dat s využitím hlubokého učení". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432864.

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This thesis deals with CT data segmentation using convolutional neural nets and describes the problem of training with limited training sets. User interaction is suggested as means of improving segmentation quality for the models trained on small training sets and the possibility of using transfer learning is also considered. All of the chosen methods help improve the segmentation quality in comparison with the baseline method, which is the use of automatic data specific segmentation model. The segmentation has improved by tens of percents in Dice score when trained with very small datasets. These methods can be used, for example, to simplify the creation of a new segmentation dataset.

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Morgan, Joseph Troy. "Adaptive hierarchical classification with limited training data". Thesis, 2002. http://wwwlib.umi.com/cr/utexas/fullcit?p3115506.

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Książki na temat "Limited training data"

Adaptive Hierarchial Classification with Limited Training Data. Storming Media, 2002.

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Malina, Robert M. The influence of physical activity and training on growth and maturation. Redaktorzy Neil Armstrong i Willem van Mechelen. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198757672.003.0032.

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Physical activity in the general youth population and systematic training for sport among young athletes seems to have no effect on size attained and rate of growth in height, or on maturity status and timing. However, activity and training may influence body weight and composition. While both favourably influence bone mineral, variable effects are noted in some sports. Activity has a minimal effect on fatness in normal weight youth, but regular training generally has a positive influence on fatness in youth athletes. Data for fat-free/lean tissue mass are suggestive, but limited. Constitutional factors play a central role in the selection and retention of young athletes in a sport.

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Raveesh, B. N., Swaran P. Singh i Soumitra Pathare. Coercion and mental health services in the Indian subcontinent and the Middle East. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198788065.003.0016.

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Mental health law in the Indian subcontinent and the Middle East has been evolving over the past few decades. There have been rapid socio-economic, cultural, and psychosocial changes in the traditional, rural and family-centred societies. People with mental disorders are amongst the most vulnerable in these societies but there is meagre literature on the issue about coercion, coercive practice, standard measures on coercion, and restraint in this region. There are problems of limited resources and training and inadequate service provision. Anecdotal evidence suggests that coercion, is common, both in mental health facilities and in the community. This chapter reviews the provision of mental health care in the region, with relevant legislative developments, and the limited research data on coercion.

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Financial management: Control weaknesses limited Customs' ability to ensure that duties were properly assessed : report to the Commissioner, U.S. Customs Service. Washington, D.C: The Office, 1994.

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Dallmeijer, Annet, i Jost Schnyder. Exercise capacity and training in cerebral palsy and other neuromuscular diseases. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199232482.003.0035.

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Chapter 35 gives an understanding of the role of exercise in the functional assessment and clinical management of children with neuromuscular diseases, especially for children with CP and PMD. Current knowledge about exercise capacity and training possibilities with respect to the different fitness components (aerobic power, anaerobic power, muscular strength) will be described as well as the level of physical activity and training recommendations. Practical advice and suggestions are given on how to build up and execute an adapted programme for physical activity, sports, and exercise. Data will be summarized to recognize the possibilities as well as the limits of exercise, and also to permit a regular evaluation and a constant adaptation of a physical activity programme.

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Williams, Craig A. Maximal intensity exercise. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199232482.003.0017.

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Despite a surge of interest over the past 10 years in young people’s maximal intensity exercise, the growth and maturation of anaerobic performance is still poorly understood. This observation is interesting for a number of reasons. First, during the prepubertal years, children’s physical activity patterns are characterized by short duration but high intensity bouts of effort.5 Second, investigators are limited by the range of available methodologies, most of which are assessing external but indirect mechanical indices of maximal intensity so as to deduce metabolic changes. Third, there are few data available from females. Finally, due to the importance of maximal intensity efforts during team sports and the increasing emphasis on organized youth sport programmes, the differentiation between growth and maturation and training adaptations of maximal intensity performance need to be addressed. As a consequence of these four observations, important reliability and validity issues need to be resolved prior to paediatric exercise scientists determining which key factors influence maximal intensity exercise during childhood and adolescence. This chapter will therefore focus on the variables that have been most commonly measured and review the explanatory factors related to maximal intensity exercise during growth and maturation.

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Wolbarst, Anthony, i Nathan Yanasak. An Introduction to MRI. Medical Physics Publishing, 2019. http://dx.doi.org/10.54947/9781930524200.

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This introduction to the science and technology of MRI has been written at the beginning graduate level primarily for professional medical physicists and engineers in training. Others, such as physicians with physical science backgrounds, may well also find it to be of interest. From Devon Godfrey in Medical Physics International… "The authors manage to successfully take the reader on a journey from the discovery and fundamentals of NMR all the way to novel k-space sampling and advanced MR imaging sequences—and their underlying molecular physics—in a manner that is quite thorough, yet should be approachable even to a reader with limited prior MRI knowledge. I believe this will be an excellent source for graduate students and professionals alike, and intend to incorporate it into my own teaching." From Andrew Maidment in Medical Physics…"As with all of Wolbarst’s books, the figures are of high quality, and I am sure they will find their way into many PowerPoint presentations in the future." This book will help readers understand not just the basics of MRI, but how recent variations on its original implementation have produced the many alternative interpretations of data that have made MRI such a powerful diagnostic tool. Several more advanced topics—like Fourier analysis, k-space, and statistical distributions—are introduced as they are needed.

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Części książek na temat "Limited training data"

Thuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen i Latifur Khan. "Data Stream Classification with Limited Labeled Training Data". W Big Data Analytics with Applications in Insider Threat Detection, 149–70. Boca Raton : Taylor & Francis, CRC Press, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315119458-14.

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Song, Jingkuan, Xu Zhao, Lianli Gao i Liangliang Cao. "Large-Scale Video Understanding with Limited Training Labels". W Big Data Analytics for Large-Scale Multimedia Search, 89–120. Chichester, UK: John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119376996.ch4.

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Zhang, Bodong, Beatrice Knudsen, Deepika Sirohi, Alessandro Ferrero i Tolga Tasdizen. "Stain Based Contrastive Co-training for Histopathological Image Analysis". W Medical Image Learning with Limited and Noisy Data, 106–16. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7_11.

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Zhang, Yiqing, Yimeng Dai, Jianzhong Qi, Xinxing Xu i Rui Zhang. "Citation Field Learning by RNN with Limited Training Data". W Lecture Notes in Computer Science, 219–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04503-6_23.

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Tseng, Shih-Lun, i Huei-Yung Lin. "Fish Detection Using Convolutional Neural Networks with Limited Training Data". W Lecture Notes in Computer Science, 735–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41404-7_52.

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Ding, Guoli, Jianhua Chen, Robert Lax i Peter Chen. "Efficient Learning of Pseudo-Boolean Functions from Limited Training Data". W Lecture Notes in Computer Science, 323–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11425274_34.

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Naga, Varun, Tejas Sudharshan Mathai, Angshuman Paul i Ronald M. Summers. "Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training". W Medical Image Learning with Limited and Noisy Data, 55–64. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7_6.

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Hu, Yangwen, Zhehao Zhong, Ruixuan Wang, Hongmei Liu, Zhijun Tan i Wei-Shi Zheng. "Data Augmentation in Logit Space for Medical Image Classification with Limited Training Data". W Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 469–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87240-3_45.

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Pace, Danielle F., Adrian V. Dalca, Tom Brosch, Tal Geva, Andrew J. Powell, Jürgen Weese, Mehdi H. Moghari i Polina Golland. "Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease". W Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 334–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00889-5_38.

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Galke, Lukas, Gunnar Gerstenkorn i Ansgar Scherp. "A Case Study of Closed-Domain Response Suggestion with Limited Training Data". W Communications in Computer and Information Science, 218–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99133-7_18.

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Streszczenia konferencji na temat "Limited training data"

Milan, A., T. Pham, K. Vijay, D. Morrison, A. W. Tow, L. Liu, J. Erskine i in. "Semantic Segmentation from Limited Training Data". W 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8461082.

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Wang, S. L., W. H. Lau i S. H. Leung. "Automatic Lipreading with Limited Training Data". W 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.301.

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Qian, Tieyun, Bing Liu, Li Chen i Zhiyong Peng. "Tri-Training for Authorship Attribution with Limited Training Data". W Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-2057.

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Nguyen, Le T., Ming Zeng, Patrick Tague i Joy Zhang. "Recognizing new activities with limited training data". W the 2015 ACM International Symposium. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2802083.2808388.

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Vaessen, Nik, i David van Leeuwen. "Training speaker recognition systems with limited data". W Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-135.

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Baertlein, Brian A., i Ajith H. Gunatilaka. "Optimizing fusion architectures for limited training data sets". W AeroSense 2000, redaktorzy Abinash C. Dubey, James F. Harvey, J. Thomas Broach i Regina E. Dugan. SPIE, 2000. http://dx.doi.org/10.1117/12.396308.

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Peche, Marius, Marelie Davel i Etienne Barnard. "Phonotactic spoken language identification with limited training data". W Interspeech 2007. ISCA: ISCA, 2007. http://dx.doi.org/10.21437/interspeech.2007-443.

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D'Cruz, Ashwin, Christopher Tegho, Sean Greaves i Lachlan Kermode. "Detecting Tear Gas Canisters With Limited Training Data". W 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2022. http://dx.doi.org/10.1109/wacv51458.2022.00135.

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Wang, Chenwei, Siyi Luo, Lin Liu, Yin Zhang, Jifang Pei, Yulin Huang i Jianyu Yang. "SAR ATR under Limited Training Data Via MobileNetV3". W 2023 IEEE Radar Conference (RadarConf23). IEEE, 2023. http://dx.doi.org/10.1109/radarconf2351548.2023.10149606.

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Lin, James, Kevin Kilgour, Dominik Roblek i Matthew Sharifi. "Training Keyword Spotters with Limited and Synthesized Speech Data". W ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053193.

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Raporty organizacyjne na temat "Limited training data"

Willi, Joseph, Keith Stakes, Jack Regan i Robin Zevotek. Evaluation of Ventilation-Controlled Fires in L-Shaped Training Props. UL's Firefighter Safety Research Institute, październik 2016. http://dx.doi.org/10.54206/102376/mijj9867.

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Investigations of recent firefighter line of duty deaths caused by rapid fire progression have highlighted a deficiency in firefighters’ understanding of how certain tactics affect the fire dynamics of ventilation-controlled fires. Many fires are in a ventilation-limited, decay state by the time firefighters arrive at the scene, meaning that introducing additional ventilation to the environment has the potential to cause rapid and intense fire growth. To more effectively teach firefighters about the potential effects of ventilation on a compartment fire, ventilation-controlled fires should be gener- ated during training. Safely creating such fires while maintaining compliance with NFPA 1403: Standard on Live-Fire Training Evolutions allows instructors to educate students on this important principle of fire dynamics in the training environment. Structures utilized for live-fire training have evolved from typical concrete burn buildings to now include smaller purpose-built props, like those constructed from steel shipping containers or wood and gypsum board. Such props have been embraced by organizations due to their cost-effectiveness and potential to improve fire behavior training. Obtaining a thorough understanding of the capa- bilities and limitations of such props is critical for instructors to convey accurate messages during training and properly prepare firefighters for scenarios they’ll encounter in the field. Experiments were conducted to quantify the fire environment in L-shaped props with different wall constructions. One prop had an interior wall lining of gypsum board over wood studs and fiberglass insulation. The two other props were constructed from metal shipping containers with corrugated steel walls; one had ceilings and walls comprised solely of the corrugated steel, while the other had ceilings and walls comprised of rolled steel sheeting over mineral wool insulation with the corrugated steel wall as its backing. Three fuel packages were compared between the props: one contained furnishings mainly composed of synthetic materials and foam plastics; another contained wooden pallets and straw; and the third contained wooden pallets, straw, and oriented strand board (OSB). A stochastic approach was used to compare data between replicate tests and quantify the repeatability of the different props and fuel packages, all of which were deemed sufficiently repeatable. Comparisons of data between the three props revealed that thermal conditions between experiments in the two metal props were indistinguishable, suggesting that the additional layer of insulation did not significantly alter the fire environment. Additionally, thermal conditions in the gypsum-lined prop were more severe than those in the metal props. The effects of ventilation changes on fire conditions were also analyzed across various prop and fuel load combinations. Lastly, the response of the thermal environment in each prop during interior suppression was evaluated, and the results implied that the thermal exposure to the firefighter was more severe in the metal props than the gypsum prop for a brief period following the start of suppression.

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Pham, Melissa V., William R. Fields, Dustin T. Brown, Dylan A. Pasley, Juan L. Davila-Parez, William D. Meyer i Matthew D. Hiett. Bridge Resource Inventory Database for Gap Emplacement Selection (BRIDGES). U.S. Army Engineer Research and Development Center, lipiec 2023. http://dx.doi.org/10.21079/11681/47359.

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Wet gap crossings are one of the most complex maneuvers undertaken by military engineers, who, along with engineer planners, require better tools to increase the capacity for efficient use of limited bridging resources across the battlespace. Planning for bridging maneuvers often involves a complicated and inefficient system of ad hoc spreadsheets combined with an overreliance on the personal experience and training of subject matter experts (SMEs). Bridge Resource Inventory Database for Gap Emplacement Selection (BRIDGES) uses interactive mapping and database technology in order to streamline the bridging planning process and provide answers to question about myriad scenarios to maximize efficiency and provide better means of data persistence across time and data sharing across operational or planning units.

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li i Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman i John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), kwiecień 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.

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Komba, Aneth, i Richard Shukia. An Analysis of the Basic Education Curriculum in Tanzania: The Integration, Scope, and Sequence of 21st Century Skills. Research on Improving Systems of Education (RISE), luty 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/129.

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This study generated evidence on whether or not the basic education curriculum is geared towards developing problem-solving, collaboration, creativity, and critical thinking skills among those who graduate from the basic education system. It was informed by a mixed methodology research approach. The data were collected using interviews and documentary review. The findings reveal that the intention to promote 21st century skills through the basic education system in Tanzania is clear, as it is stated in various policy documents, including the Education for Self-Reliance philosophy, the 2014 Education and Training Policy and the National Curriculum Framework for Basic and Teacher Education. Furthermore, these skills are clearly reflected in every curriculum and syllabus document, yet those who graduate from the basic and advanced secondary levels are claimed to lack these skills. This suggests a variation between the enacted and the intended curriculum. We conclude that certain system elements are weak, and hence threaten the effective implementation of the curriculum. These weak system elements include limited finance, a teacher shortage, and the lack of a teacher continuous professional development programme. This research suggests that due consideration should be given to provision of the resources required for the successful implementation of the curriculum. These include, allocation of sufficient funds, the employment of more teachers and the provision of regular continuous professional development for teachers as a way to strengthen the system elements that we identified.

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Backstrom, Robert, i David Dini. Firefighter Safety and Photovoltaic Systems Summary. UL Firefighter Safety Research Institute, listopad 2011. http://dx.doi.org/10.54206/102376/kylj9621.

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Under the United States Department of Homeland Security (DHS) Assistance to Firefighter Grant Fire Prevention and Safety Research Program, Underwriters Laboratories examined fire service concerns of photovoltaic (PV) systems. These concerns include firefighter vulnerability to electrical and casualty hazards when mitigating a fire involving photovoltaic (PV) modules systems. The need for this project is significant acknowledging the increasing use of photovoltaic systems, growing at a rate of 30% annually. As a result of greater utilization, traditional firefighter tactics for suppression, ventilation and overhaul have been complicated, leaving firefighters vulnerable to potentially unrecognized exposure. Though the electrical and fire hazards associated with electrical generation and distribution systems is well known, PV systems present unique safety considerations. A very limited body of knowledge and insufficient data exists to understand the risks to the extent that the fire service has been unable to develop safety solutions and respond in a safe manner. This fire research project developed the empirical data that is needed to quantify the hazards associated with PV installations. This data provides the foundation to modify current or develop new firefighting practices to reduce firefighter death and injury. A functioning PV array was constructed at Underwriters Laboratories in Northbrook, IL to serve as a test fixture. The main test array consisted of 26 PV framed modules rated 230 W each (5980 W total rated power). Multiple experiments were conducted to investigate the efficacy of power isolation techniques and the potential hazard from contact of typical firefighter tools with live electrical PV components. Existing fire test fixtures located at the Delaware County Emergency Services Training Center were modified to construct full scale representations of roof mounted PV systems. PV arrays were mounted above Class A roofs supported by wood trusses. Two series of experiments were conducted. The first series represented a room of content fire, extending into the attic space, breaching the roof and resulting in structural collapse. Three PV technologies were subjected to this fire condition – rack mounted metal framed, glass on polymer modules, building integrated PV shingles, and a flexible laminate attached to a standing metal seam roof. A second series of experiments was conducted on the metal frame technology. These experiments represented two fire scenarios, a room of content fire venting from a window and the ignition of debris accumulation under the array. The results of these experiments provide a technical basis for the fire service to examine their equipment, tactics, standard operating procedures and training content. Several tactical considerations were developed utilizing the data from the experiments to provide specific examples of potential electrical shock hazard from PV installations during and after a fire event.

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Backstrom, Robert, i David Backstrom. Firefighter Safety and Photovoltaic Installations Research Project. UL Firefighter Safety Research Institute, listopad 2011. http://dx.doi.org/10.54206/102376/viyv4379.

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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru i Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and converted from the research code to its implementational version written in the C++ programming language. Two trailer-based TScan units have been built. The design of the prototype was iterated multiple times to account for component placement, ease of maintenance, etc. The expansion of the TScan system from a one single-sensor unit to multiple units with multiple LiDAR sensors necessitated transforming all the measurements into a common spatial and temporal reference frame. Engineering applications for performing traffic counts, analyzing speeds at intersections, and visualizing pedestrian presence data were developed. The limitations of the existing SSAM for traffic conflicts analysis with computer simulation prompted the research team to develop and implement their own traffic conflicts detection and analysis technique that is applicable to real-world data. Efficient use of the development system requires proper training of its end users. An INDOT-CRS collaborative process was developed and its execution planned to gradually transfer the two TScan prototypes to INDOT’s full control. This period will be also an opportunity for collecting feedback from the end user and making limited modifications to the system and documentation as needed.

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Megersa, Kelbesa. Effectiveness and Value for Money of Technical Assistance Approaches: In-house vs Contracting. Institute of Development Studies, lipiec 2022. http://dx.doi.org/10.19088/k4d.2022.135.

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In the development field, technical assistance (TA) broadly refers to support for a specific project or country programme in the form of technical advice, research and data sharing, and skills training, among other activities. As a result, TA may be more valuable as a development tool than the amount of funding received. The primary areas of focus for TA include developing a project pipeline, de-risking investments, and assisting TA beneficiaries in their efforts to improve business standards, as well as supporting policy reforms by developing country. Because TA recipients may face a variety of issues, effective TA programmes can take many forms. TA programmes must be established to address beneficiaries’ primary concerns. The goal for both TA recipients and donors should be to determine the main objective of the TA and to select from a variety of technical adviser, taking into account the limitations and enabling conditions for each approach (Nastase et al., 2020). Some useful principles (or good practices) when designing and implementing TA (through in-house or external contracting) include: • Importance of local ownership: • Partnerships and inclusivity: • Effectiveness: • Value-for-money (VFM): TA can be delivered in-house or by contracting out TA to other firms or suppliers. However, each approach has certain merits (VFM and other factors) and shortcomings. There is a very limited evidence base regarding an explicit discussion of the merits of in-house vs commissioned TA programming. Much of the available evidence simply describes TA programme elements – rather than the VFM behind business cases for in-house or contracted TA design and delivery.

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