Academic literature on the topic 'Data distribution shift'
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Journal articles on the topic "Data distribution shift"
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Cheng, Luling, Xue Yang, Luliang Tang, Qian Duan, Zihan Kan, Xia Zhang, and Xinyue Ye. "Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data." ISPRS International Journal of Geo-Information 9, no. 4 (April 24, 2020): 281. http://dx.doi.org/10.3390/ijgi9040281.
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In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel of citizens. An analysis of the distribution of taxi-driver shifts, therefore, contributes to transportation management. Compared to the previous research using the real shift records, this study focuses on the spatiotemporal analysis of taxi-driver shifts using big trace data. A two-step strategy is proposed to automatically identify taxi-driver shifts from big trace data without the information of drivers’ identities. The first step is to pick out the frequent spatiotemporal sequential patterns from all parking events based on the spatiotemporal sequence analysis. The second step is to construct a Gaussian mixture model based on prior knowledge for further identifying taxi-driver shifts from all frequent spatiotemporal sequential patterns. The spatiotemporal distribution of taxi-driver shifts is analyzed based on two indicators, namely regional taxi coverage intensity and taxi density. Taking the city of Wuhan as an example, the experimental results show that the identification precision and recall rate of taxi-driver shift events based on the proposed method can achieve about 95% and 90%, respectively, by using big taxi trace data. The occurrence time of taxi-driver shifts in Wuhan mainly has two high peak periods: 1:00 a.m. to 4:00 a.m. and 4:00 p.m. to 5:00 p.m. Although taxi-driver shift behaviors are prohibited during the evening peak hour based on the regulation issued by Wuhan traffic administration, experimental results show that there are still some drivers in violation of this regulation. By analyzing the spatial distribution of taxi-driver shifts, we find that most taxi-driver shifts distribute in central urban areas such as Wuchang and Jianghan district.
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Islind, Anna Sigridur, Tomas Lindroth, Johan Lundin, and Gunnar Steineck. "Shift in translations: Data work with patient-generated health data in clinical practice." Health Informatics Journal 25, no. 3 (March 13, 2019): 577–86. http://dx.doi.org/10.1177/1460458219833097.
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This article reports on how the introduction of patient-generated health data affects the nurses’ and patients’ data work and unpacks how new forms of data collection trigger shifts in the work with data through translation work. The article is based on a 2.5-year case study examining data work of nurses and patients at a cancer rehabilitation clinic at a Swedish Hospital in which patient-generated health data are gathered by patients and then used outside and within clinical practice for decision-making. The article reports on how data are prepared and translated, that is, made useful by the nurses and patients. Using patient-generated health data alters the data work and how the translation of data is performed. The shift in work has three components: (1) a shift in question tactics, (2) a shift in decision-making, and (3) a shift in distribution. The data become mobile, and the data work becomes distributed when using patient-generated health data as an active part of care.
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Sharet, Nir, and Ilan Shimshoni. "Analyzing Data Changes using Mean Shift Clustering." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 07 (May 25, 2016): 1650016. http://dx.doi.org/10.1142/s0218001416500166.
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A nonparametric unsupervised method for analyzing changes in complex datasets is proposed. It is based on the mean shift clustering algorithm. Mean shift is used to cluster the old and new datasets and compare the results in a nonparametric manner. Each point from the new dataset naturally belongs to a cluster of points from its dataset. The method is also able to find to which cluster the point belongs in the old dataset and use this information to report qualitative differences between that dataset and the new one. Changes in local cluster distribution are also reported. The report can then be used to try to understand the underlying reasons which caused the changes in the distributions. On the basis of this method, a transductive transfer learning method for automatically labeling data from the new dataset is also proposed. This labeled data is used, in addition to the old training set, to train a classifier better suited to the new dataset. The algorithm has been implemented and tested on simulated and real (a stereo image pair) datasets. Its performance was also compared with several state-of-the-art methods.
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Tyagi, Dushyant. "Designing an Effective Combined Shewhart-CUSUM Control Scheme with Exponentially Distributed Data." International Journal of Mathematical, Engineering and Management Sciences 4, no. 5 (October 1, 2019): 1277–86. http://dx.doi.org/10.33889/ijmems.2019.4.5-101.
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In this paper, the Combined Shewhart-CUSUM control scheme has been proposed to monitor the production process when the quality characteristic follows exponential distribution to quickly detect the shift in the process. The simulated values of ARL are determined after the transformation of the data into approximate normal distribution by Nelson transformation method and adding Shewhart control limits to existing CUSUM Control Chart. Scheme parameters (value of k and h) and out of control ARL are calculated at various shift and at various in-control ARL. Parameters are also calculated to detect δ standard deviation shifts, which may be helpful to the quality control practitioners in designing the Combined Shewhart-CUSUM scheme when data is highly skewed.
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Kuang, Kun, Hengtao Zhang, Runze Wu, Fei Wu, Yueting Zhuang, and Aijun Zhang. "Balance-Subsampled Stable Prediction Across Unknown Test Data." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (June 30, 2022): 1–21. http://dx.doi.org/10.1145/3477052.
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In data mining and machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because of the sample selection bias, which might induce the distribution shift from training data to test data. Such a model-agnostic distribution shift usually leads to prediction instability across unknown test data. This article proposes a novel balance-subsampled stable prediction (BSSP) algorithm based on the theory of fractional factorial design. It isolates the clear effect of each predictor from the confounding variables. A design-theoretic analysis shows that the proposed method can reduce the confounding effects among predictors induced by the distribution shift, improving both the accuracy of parameter estimation and the stability of prediction across unknown test data. Numerical experiments on synthetic and real-world datasets demonstrate that our BSSP algorithm can significantly outperform the baseline methods for stable prediction across unknown test data.
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Ye, Nanyang, Lin Zhu, Jia Wang, Zhaoyu Zeng, Jiayao Shao, Chensheng Peng, Bikang Pan, Kaican Li, and Jun Zhu. "Certifiable Out-of-Distribution Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10927–35. http://dx.doi.org/10.1609/aaai.v37i9.26295.
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Machine learning methods suffer from test-time performance degeneration when faced with out-of-distribution (OoD) data whose distribution is not necessarily the same as training data distribution. Although a plethora of algorithms have been proposed to mitigate this issue, it has been demonstrated that achieving better performance than ERM simultaneously on different types of distributional shift datasets is challenging for existing approaches. Besides, it is unknown how and to what extent these methods work on any OoD datum without theoretical guarantees. In this paper, we propose a certifiable out-of-distribution generalization method that provides provable OoD generalization performance guarantees via a functional optimization framework leveraging random distributions and max-margin learning for each input datum. With this approach, the proposed algorithmic scheme can provide certified accuracy for each input datum's prediction on the semantic space and achieves better performance simultaneously on OoD datasets dominated by correlation shifts or diversity shifts. Our code is available at https://github.com/ZlatanWilliams/StochasticDisturbanceLearning.
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Rezaei, Ashkan, Anqi Liu, Omid Memarrast, and Brian D. Ziebart. "Robust Fairness Under Covariate Shift." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9419–27. http://dx.doi.org/10.1609/aaai.v35i11.17135.
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Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.
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Soultan, Alaaeldin, Diego Pavón-Jordán, Ute Bradter, Brett K. Sandercock, Wesley M. Hochachka, Alison Johnston, Jon Brommer, et al. "The future distribution of wetland birds breeding in Europe validated against observed changes in distribution." Environmental Research Letters 17, no. 2 (February 1, 2022): 024025. http://dx.doi.org/10.1088/1748-9326/ac4ebe.
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Abstract Wetland bird species have been declining in population size worldwide as climate warming and land-use change affect their suitable habitats. We used species distribution models (SDMs) to predict changes in range dynamics for 64 non-passerine wetland birds breeding in Europe, including range size, position of centroid, and margins. We fitted the SDMs with data collected for the first European Breeding Bird Atlas and climate and land-use data to predict distributional changes over a century (the 1970s–2070s). The predicted annual changes were then compared to observed annual changes in range size and range centroid over a time period of 30 years using data from the second European Breeding Bird Atlas. Our models successfully predicted ca. 75% of the 64 bird species to contract their breeding range in the future, while the remaining species (mostly southerly breeding species) were predicted to expand their breeding ranges northward. The northern margins of southerly species and southern margins of northerly species, both, predicted to shift northward. Predicted changes in range size and shifts in range centroids were broadly positively associated with the observed changes, although some species deviated markedly from the predictions. The predicted average shift in core distributions was ca. 5 km yr−1 towards the north (5% northeast, 45% north, and 40% northwest), compared to a slower observed average shift of ca. 3.9 km yr−1. Predicted changes in range centroids were generally larger than observed changes, which suggests that bird distribution changes may lag behind environmental changes leading to ‘climate debt’. We suggest that predictions of SDMs should be viewed as qualitative rather than quantitative outcomes, indicating that care should be taken concerning single species. Still, our results highlight the urgent need for management actions such as wetland creation and restoration to improve wetland birds’ resilience to the expected environmental changes in the future.
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Lone, Showkat Ahmad, Zahid Rasheed, Sadia Anwar, Majid Khan, Syed Masroor Anwar, and Sana Shahab. "Enhanced fault detection models with real-life applications." AIMS Mathematics 8, no. 8 (2023): 19595–636. http://dx.doi.org/10.3934/math.20231000.
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<abstract> <p>Nonconforming events are rare in high-quality processes, and the time between events (TBE) may follow a skewed distribution, such as the gamma distribution. This study proposes one- and two-sided triple homogeneously weighted moving average charts for monitoring TBE data modeled by the gamma distribution. These charts are labeled as the THWMA TBE charts. Monte Carlo simulations are performed to approximate the run length distribution of the one- and two-sided THWMA TBE charts. The THWMA TBE charts are compared to competing charts like the DHWMA TBE, HWMA TBE, THWMA TBE, DEWMA TBE, and EWMA TBE charts at a single shift and over a range of shifts. For the single shift comparison, the average run length (ARL) and standard deviation run length (SDRL) measures are used, whereas the extra quadratic loss (EQL), relative average run length (RARL) and performance comparison index (PCI) measures are employed for a range of shifts comparison. The comparison reveals that the THWMA TBE charts outperform the competing charts at a single shift as well as at a certain range of shifts. Finally, two real-life data applications are presented to evaluate the applicability of the THWMA TBE charts in practical situations, one with boring machine failure data and the other with hospital stay time for traumatic brain injury patients.</p> </abstract>
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Walther, Gian-Reto, Silje Berger, and Martin T. Sykes. "An ecological ‘footprint’ of climate change." Proceedings of the Royal Society B: Biological Sciences 272, no. 1571 (June 28, 2005): 1427–32. http://dx.doi.org/10.1098/rspb.2005.3119.
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Recently, there has been increasing evidence of species' range shifts due to changes in climate. Whereas most of these shifts relate ground truth biogeographic data to a general warming trend in regional or global climate data, we here present a reanalysis of both biogeographic and bioclimatic data of equal spatio-temporal resolution, covering a time span of more than 50 years. Our results reveal a coherent and synchronous shift in both species' distribution and climate. They show not only a shift in the northern margin of a species, which is in concert with gradually increasing winter temperatures in the area, they also confirm the simulated species' distribution changes expected from a bioclimatic model under the recent, relatively moderate climate change.
Dissertations / Theses on the topic "Data distribution shift"
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Dadalto, Câmara Gomes Eduardo. "Improving artificial intelligence reliability through out-of-distribution and misclassification detection." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG018.
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Cette thèse explore l'intersection cruciale entre l'apprentissage automatique (IA) et la sécurité, visant à résoudre les défis liés au déploiement de systèmes intelligents dans des scénarios réels. Malgré des progrès significatifs en IA, des préoccupations liées à la confidentialité, à l'équité et à la fiabilité ont émergé, incitant à renforcer la fiabilité des systèmes d'IA. L'objectif central de la thèse est de permettre aux algorithmes d'IA d'identifier les écarts par rapport au comportement normal, contribuant ainsi à la sécurité globale des systèmes intelligents.La thèse commence par établir les concepts fondamentaux de la détection des données hors distribution (OOD) et de la détection des erreurs de classification dans le chapitre 1, fournissant une littérature essentielle et expliquant les principes clés. L'introduction souligne l'importance de traiter les problèmes liés au comportement non intentionnel et nuisible en IA, en particulier lorsque les systèmes d'IA produisent des résultats inattendus en raison de divers facteurs tels que des divergences dans les distributions de données.Dans le chapitre 2, la thèse introduit une nouvelle méthode de détection de données hors distribution basée sur la distance géodésique Fisher-Rao entre les distributions de probabilité. Cette approche unifie la formulation des scores de détection pour les logits du réseau et les espaces latents, contribuant à une robustesse et une fiabilité accrues dans l'identification des échantillons en dehors de la distribution d'entraînement.Le chapitre 3 présente une méthode de détection des données hors distribution non supervisée qui analyse les trajectoires neuronales sans nécessiter de supervision ou d'ajustement d'hyperparamètres. Cette méthode vise à identifier les trajectoires d'échantillons atypiques à travers diverses couches, améliorant l'adaptabilité des modèles d'IA à des scénarios divers.Le chapitre 4 se concentre sur la consolidation et l'amélioration de la détection hors distribution en combinant efficacement plusieurs détecteurs. La thèse propose une méthode universelle pour combiner des détecteurs existants, transformant le problème en un test d'hypothèse multivarié et tirant parti d'outils de méta-analyse. Cette approche améliore la détection des changements de données, en en faisant un outil précieux pour la surveillance en temps réel des performances des modèles dans des environnements dynamiques et évolutifs.Dans le chapitre 5, la thèse aborde la détection des erreurs de classification et l'estimation de l'incertitude par une approche axée sur les données, introduisant une solution pratique en forme fermée. La méthode quantifie l'incertitude par rapport à un observateur, distinguant entre prédictions confiantes et incertaines même face à des données difficiles. Cela contribue à une compréhension plus nuancée de la confiance du modèle et aide à signaler les prédictions nécessitant une intervention humaine.La thèse se termine en discutant des perspectives futures et des orientations pour améliorer la sécurité en IA et en apprentissage automatique, soulignant l'évolution continue des systèmes d'IA vers une plus grande transparence, robustesse et fiabilité. Le travail collectif présenté dans la thèse représente une avancée significative dans le renforcement de la sécurité en IA, contribuant au développement de modèles d'apprentissage automatique plus fiables et dignes de confiance, capables de fonctionner efficacement dans des scénarios réels divers et dynamiquesThis thesis explores the intersection of machine learning (ML) and safety, aiming to address challenges associated with the deployment of intelligent systems in real-world scenarios. Despite significant progress in ML, concerns related to privacy, fairness, and trustworthiness have emerged, prompting the need for enhancing the reliability of AI systems. The central focus of the thesis is to enable ML algorithms to detect deviations from normal behavior, thereby contributing to the overall safety of intelligent systems.The thesis begins by establishing the foundational concepts of out-of-distribution (OOD) detection and misclassification detection in Chapter 1, providing essential background literature and explaining key principles. The introduction emphasizes the importance of addressing issues related to unintended and harmful behavior in ML, particularly when AI systems produce unexpected outcomes due to various factors such as mismatches in data distributions.In Chapter 2, the thesis introduces a novel OOD detection method based on the Fisher-Rao geodesic distance between probability distributions. This approach unifies the formulation of detection scores for both network logits and feature spaces, contributing to improved robustness and reliability in identifying samples outside the training distribution.Chapter 3 presents an unsupervised OOD detection method that analyzes neural trajectories without requiring supervision or hyperparameter tuning. This method aims to identify atypical sample trajectories through various layers, enhancing the adaptability of ML models to diverse scenarios.Chapter 4 focuses on consolidating and enhancing OOD detection by combining multiple detectors effectively. It presents a universal method for ensembling existing detectors, transforming the problem into a multi-variate hypothesis test and leveraging meta-analysis tools. This approach improves data shift detection, making it a valuable tool for real-time model performance monitoring in dynamic and evolving environments.In Chapter 5, the thesis addresses misclassification detection and uncertainty estimation through a data-driven approach, introducing a practical closed-form solution. The method quantifies uncertainty relative to an observer, distinguishing between confident and uncertain predictions even in the face of challenging or unfamiliar data. This contributes to a more nuanced understanding of the model's confidence and helps flag predictions requiring human intervention.The thesis concludes by discussing future perspectives and directions for improving safety in ML and AI, emphasizing the ongoing evolution of AI systems towards greater transparency, robustness, and trustworthiness. The collective work presented in the thesis represents a significant step forward in advancing AI safety, contributing to the development of more reliable and trustworthy machine learning models that can operate effectively in diverse and dynamic real-world scenarios
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Lowry, Sonia L. "Analysis of statnamic load test data using a load shed distribution model." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001238.
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Bickel, Steffen. "Learning under differing training and test distributions." Phd thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2009/3333/.
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One of the main problems in machine learning is to train a predictive model from training data and to make predictions on test data. Most predictive models are constructed under the assumption that the training data is governed by the exact same distribution which the model will later be exposed to. In practice, control over the data collection process is often imperfect. A typical scenario is when labels are collected by questionnaires and one does not have access to the test population. For example, parts of the test population are underrepresented in the survey, out of reach, or do not return the questionnaire. In many applications training data from the test distribution are scarce because they are difficult to obtain or very expensive. Data from auxiliary sources drawn from similar distributions are often cheaply available.
This thesis centers around learning under differing training and test distributions and covers several problem settings with different assumptions on the relationship between training and test distributions-including multi-task learning and learning under covariate shift and sample selection bias. Several new models are derived that directly characterize the divergence between training and test distributions, without the intermediate step of estimating training and test distributions separately. The integral part of these models are rescaling weights that match the rescaled or resampled training distribution to the test distribution. Integrated models are studied where only one optimization problem needs to be solved for learning under differing distributions. With a two-step approximation to the integrated models almost any supervised learning algorithm can be adopted to biased training data.
In case studies on spam filtering, HIV therapy screening, targeted advertising, and other applications the performance of the new models is compared to state-of-the-art reference methods.Eines der wichtigsten Probleme im Maschinellen Lernen ist das Trainieren von Vorhersagemodellen aus Trainingsdaten und das Ableiten von Vorhersagen für Testdaten. Vorhersagemodelle basieren üblicherweise auf der Annahme, dass Trainingsdaten aus der gleichen Verteilung gezogen werden wie Testdaten. In der Praxis ist diese Annahme oft nicht erfüllt, zum Beispiel, wenn Trainingsdaten durch Fragebögen gesammelt werden. Hier steht meist nur eine verzerrte Zielpopulation zur Verfügung, denn Teile der Population können unterrepräsentiert sein, nicht erreichbar sein, oder ignorieren die Aufforderung zum Ausfüllen des Fragebogens. In vielen Anwendungen stehen nur sehr wenige Trainingsdaten aus der Testverteilung zur Verfügung, weil solche Daten teuer oder aufwändig zu sammeln sind. Daten aus alternativen Quellen, die aus ähnlichen Verteilungen gezogen werden, sind oft viel einfacher und günstiger zu beschaffen. Die vorliegende Arbeit beschäftigt sich mit dem Lernen von Vorhersagemodellen aus Trainingsdaten, deren Verteilung sich von der Testverteilung unterscheidet. Es werden verschiedene Problemstellungen behandelt, die von unterschiedlichen Annahmen über die Beziehung zwischen Trainings- und Testverteilung ausgehen. Darunter fallen auch Multi-Task-Lernen und Lernen unter Covariate Shift und Sample Selection Bias. Es werden mehrere neue Modelle hergeleitet, die direkt den Unterschied zwischen Trainings- und Testverteilung charakterisieren, ohne dass eine einzelne Schätzung der Verteilungen nötig ist. Zentrale Bestandteile der Modelle sind Gewichtungsfaktoren, mit denen die Trainingsverteilung durch Umgewichtung auf die Testverteilung abgebildet wird. Es werden kombinierte Modelle zum Lernen mit verschiedenen Trainings- und Testverteilungen untersucht, für deren Schätzung nur ein einziges Optimierungsproblem gelöst werden muss. Die kombinierten Modelle können mit zwei Optimierungsschritten approximiert werden und dadurch kann fast jedes gängige Vorhersagemodell so erweitert werden, dass verzerrte Trainingsverteilungen korrigiert werden. In Fallstudien zu Email-Spam-Filterung, HIV-Therapieempfehlung, Zielgruppenmarketing und anderen Anwendungen werden die neuen Modelle mit Referenzmethoden verglichen.
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Neubert, Karin. "Das nichtparametrische Behrens-Fisher-Problem: ein studentisierter Permutationstest und robuste Konfidenzintervalle für den Shift-Effekt." Doctoral thesis, 2006. http://hdl.handle.net/11858/00-1735-0000-000D-F21D-C.
Full textBooks on the topic "Data distribution shift"
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Berg, John C. Leave It in the Ground. ABC-CLIO, LLC, 2019. http://dx.doi.org/10.5040/9798400677960.
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Employing scientific explanations and hard data, this book shows why coal is such a problem, how the pro-coal forces got to be so powerful, and how those forces might be defeated through political activism. Coal provided the energy to build modern civilization. This energy source raised standards of living, multiplied the earth's population, and enabled people in developed countries to enjoy leisure time. Today, we know that if we burn all the coal available, climate change will continue to increase. But the use of coal isn't purely an environmental issue; there are also political and economic forces at play. This book examines the politics and environmental impact of coal production and distribution, presenting a clear point of view—that we must shift away from coal use—backed by hard data and supplying specific prescriptions for opposing and regulating the coal industry. Author John C. Berg explains how ending the burning of coal (and of oil and natural gas) is a political problem rather than a technical one; explodes the "clean coal" myth, providing scientific documentation of how burning coal emits more greenhouse gases per unit of energy than any other fuel; and describes how controlling coal use in the United States will also serve to restore the possibility of a meaningful international climate agreement. Additionally, readers will understand the critical importance of activism—from local to international—in spurring government regulation to control the coal industry, which can only be defeated politically.
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Ray, Ranjan. The Link between Preferences, Prices, Inequality, and Poverty. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198812555.003.0007.
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This paper documents the shift in the literature on prices from being exclusively a macro-topic featuring in the study of inflation, national income accounting, and cross-country income comparisons to one that is firmly rooted in micro-involving economic analysis of household behaviour, welfare, and the distributional implications of changes in relative prices. This paper brings together results from some of the recent studies on Indian National Sample Survey data that examine the effect of price changes on inequality and poverty. It also contains evidence on spatial prices in the context of a large heterogeneous country such as India.
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Ballon, Paola, and Jorge Dávalos. Inequality and the changing nature of work in Peru. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/925-9.
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This paper identifies the socioeconomic drivers of earnings inequality in Peru in the period 2004–18. Using the ENAHO household surveys and data on routine task content of occupations, we apply inequality decomposition methods to the real earnings distribution, its quantiles, and the Gini index. We find that in this period inequality has reduced, with great improvement attributed to reductions in the gender wage gap and macroeconomic factors. However, we did not find strong evidence for factors related to changes in workers’ attributes or shifts in job characteristics, except for a slight enhancing effect of the task content of occupations, which increases in importance as we move from ‘poorer’ to ‘richer’ deciles.
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Gaiha, Raghav, Raghbendra Jha, Vani S. Kulkarni, and Nidhi Kaicker. Diets, Nutrition, and Poverty. Edited by Ronald J. Herring. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780195397772.013.029.
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This chapter addresses a persistent tension in current debates over food security, with illustrative data from India. The case allows us to disaggregate concepts in food policy that are often lumped together, so as to better understand what is at stake in rapidly changing economies more generally. Despite rising incomes, there has been sustained decline in per capita nutrient intake in India in recent years. The assertion by Deaton and Dreze (2009) that poverty and undernutrition are unrelated is critically examined. A demand-based model in which food prices and expenditure played significant roles proved robust, while allowing for lower calorie “requirements” due to less strenuous activity patterns, life-style changes, and improvements in the epidemiological environment. This analysis provides reasons for not delinking nutrition and poverty; it confirms the existence of poverty-nutrition traps in which undernutrition perpetuates poverty. A new measure of child undernutrition that allows for multiple anthropometric failures (e.g., wasting, underweight, and stunting) points to much higher levels of undernutrition than conventional ones. Dietary changes over time, and their nutritional implications, have welfare implications at both ends of the income and social-status pyramids. Since poverty is multidimensional, money-metric indicators such as minimum income or expenditure are not reliable, because these cannot adequately capture all the dimensions. The emergent shift of the disease burden toward predominately food-related noncommunicable diseases (NCDs) poses an additional challenge. Finally, the complexity of normative issues in food policy is explored. Current approaches to food security have veered toward a “right-to-food” approach. There are, however, considerable problems with creating appropriate mechanisms for effectuating that right; these are explored briefly. Cash transfers touted to avoid administrative costs and corruption involved in rural employment guarantee and targeted food-distribution programs are likely to be much less effective if the objective is to enable large segments of the rural population to break out of nutrition-poverty traps. The chapter ends by exploring an alternative model, based on the same normative principle: a “right to policies,” or a “right to a right.”
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Fleury, James, Bryan Hikari Hartzheim, and Stephen Mamber, eds. The Franchise Era. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474419222.001.0001.
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As Hollywood shifts towards the digital era, the role of the media franchise has become more prominent. Over a series of essays by a range of international scholars, this edited collection argues that the franchise is now an integral element of American media culture. As such, the collection explores the production, distribution, and marketing of franchises as a historical form of media-making. In particular, the essays analyze the complex industrial practice of managing franchises across interconnected online platforms with a global scope, presenting a network of scholarly texts that critically look at the collision of new and old industrial logics against an ever more fragmented and consolidated mediascape. The authors address how traditional incumbents like film studios and television networks have responded to the rise of big data, Silicon Valley companies like Facebook, Apple, Amazon, Netflix, and Google; the ways in which legacy franchises are adapting to new media platforms and technologies; the significant historical continuities and deviations in franchise-making and how they shape the representation of on-screen texts across digital displays; and, finally, how emerging media formats are expanding the possibility for transmedia experiences. In this regard, The Franchise Era: Managing Media in the Digital Economy offers an in-depth analysis of the tectonic shifts that have disrupted entertainment companies in the twenty-first century, demonstrating that the media franchise stands front and center in this high-stakes environment.
Book chapters on the topic "Data distribution shift"
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Oza, Poojan, Hien V. Nguyen, and Vishal M. Patel. "Multiple Class Novelty Detection Under Data Distribution Shift." In Computer Vision – ECCV 2020, 432–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58571-6_26.
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Ashmore, Rob, and Matthew Hill. "“Boxing Clever”: Practical Techniques for Gaining Insights into Training Data and Monitoring Distribution Shift." In Lecture Notes in Computer Science, 393–405. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99229-7_33.
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Stade, Dawid, and Martin Manns. "Robotic Assembly Line Balancing with Multimodal Stochastic Processing Times." In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains, 78–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_8.
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AbstractIn this paper, a genetic algorithm for the robotic assembly line balancing problem (RALBP) is developed that supports multimodal stochastic processing times and multiple parallel-working robots per workstation. It has the objective to minimize the amount of workstations at a given production rate and probability limit for violating the cycle time (PL). The algorithm is evaluated on the BARTHOLD data set in a range of 1 % to 50 % for PL using an experimentally determined and a normal distribution for the task times. The increase of PL results in a shift of tasks from rear to front stations, because more tasks can be assigned to each station. The shift using normal distributed task times is stronger. This demonstrates the importance of realistic stochastic distribution assumptions. For practical applicability, more constraint types have to be included in the future.
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Dasu, Tamraparni, Shankar Krishnan, Dongyu Lin, Suresh Venkatasubramanian, and Kevin Yi. "Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams." In Advances in Intelligent Data Analysis VIII, 21–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03915-7_3.
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Xiang, Brian, and Abdelrahman Abdelmonsef. "Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift." In HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction, 617–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17615-9_44.
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Clement, Tobias, Hung Truong Thanh Nguyen, Nils Kemmerzell, Mohamed Abdelaal, and Davor Stjelja. "Coping with Data Distribution Shifts: XAI-Based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction." In Lecture Notes in Computer Science, 147–59. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8391-9_12.
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Brajesh, Saurabh. "Big Data Analytics in Retail Supply Chain." In Big Data, 1473–94. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch067.
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Retail sector is in the state of flux. On one hand they face hurdles such as new technologies, tumultuous economy, new sales & distribution channels, and on the other hand they have rapidly increasing population of demanding consumers. To overcome these challenges, and to remain relevant and competitive in the market, the retail sector needs to have paradigm shift in their approach. If we track and analyze the pattern of purchasing decisions of consumers, we would find that it involves various stages of decision lifecycle. Data generated at many of these stages can be recorded, digitized, and transformed into matrices and strategic information. These matrices & information would prove to be the vital element for retail industries in their strategic decisions. We need to focus on mechanism to extract valuable insights from retail supply chain. These insights could be further leveraged to provide competitive advantage to the retailers and at the same time a better retail experience to the customers.
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Shimano, Koji, Yui Oyake, and Tsuyoshi Kobayashi. "Methods and Practices for Analyzing Vegetation Shift Using Phytosociological Hierarchical Data." In Vegetation Index and Dynamics - Methodologies for Teaching Plant Diversity and Conservation Status [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003759.
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We introduce a procedure to predict the vegetation shift using traditional phytosociological survey (cover data). The cover value is generally obtained for each layer of the layered plant community, but usually maximum cover value over the layers used for the vegetation classification and recognition (C-max procedure). As an ameliorate procedure, we propose the procedure of every coverage of all layers used to evaluate vegetation shift (C-all procedure). The C-all procedure enables us to embrace the information on vertical gradient of species distribution in the surveyed communities. In the case of our observations and analyses, tree species with smaller (or no) cover in the upper layer but greater cover in the lower layer can be dominant in the upper layer in the future, resulting in vegetation shift (changes in dominant species of the community). Every general community analysis (cluster analysis, INSPAN, and TWINSPAN) followed by C-all procedure supports such prediction for some types of Japanese forests. In the forests, changes in species composition have been conventionally predicted by measuring the trunk diameter and height of trees. Our proposal suggests that traditional phytosociological survey is also convenient for studying forest succession and regeneration.
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Ben Loussaief, Eddardaa, and Domenec Puig. "Towards Cross-Sites Generalization for Prostate MRI Segmentation to Unseen Data." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220341.
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Learning a model from multi-source data is a challenging and topical learning problem. Thus, generalization capacity has been proposed to deal with the domain shift (i.e. various imaging vendors, modalities, and protocols) across domains. This paper tackles the out-of-distribution generalization for prostate segmentation in MRI imaging. We propose a simple approach based on the pretraining-finetuning scheme to boost the deep neural network’s generalization to unseen data in prostate MRI segmentation. This paper introduces an objective loss that seeks to minimize cross-domain distribution by adapting Kullback–Leibler (KL) divergence. To manifest the effectiveness of our approach, we perform experiments on a multi-source public dataset for prostate MRI imaging collected from six vendors. As a result, the proposed model can yield promising cross-domains generalization capacity to unseen target domain.
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Péron, Guillaume. "Spatial demography." In Demographic Methods across the Tree of Life, 259–72. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198838609.003.0015.
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Demographic methods can be used to study the spatial response of individuals and populations to current global changes. The first mechanism underlying range shifts is a change in the spatial distribution of births and deaths. The spatial regression of demographic rates with geostatistical and spatially explicit models documents the intrinsic growth rate across the range of a population. The population distribution is expected to shift towards areas with the largest intrinsic growth rate, both mechanistically and because these areas are attractive to dispersing individuals. The second mechanism is indeed movement, including emigration away from places that recently became inhospitable and immigration into newly available locations. The analysis of dispersal fluxes using movement data, or indirectly by comparing the observed and intrinsic growth rates in integrated population models, documents these fluxes. Combining these two mechanisms in integral projection models or in individual-based simulations is expected to yield major advances in predictive spatial ecology, that is, mechanistic species distribution models.
Conference papers on the topic "Data distribution shift"
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Zhu, Yichen, Jian Yuan, Bo Jiang, Tao Lin, Haiming Jin, Xinbing Wang, and Chenghu Zhou. "Prediction with Incomplete Data under Agnostic Mask Distribution Shift." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/525.
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Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed features and a mask that indicates the missing pattern. Existing methods assume that the training and testing distributions are the same, which may be violated in real-world scenarios. In this paper, we consider prediction with incomplete data in the presence of distribution shift. We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i.e., mask distribution may shift agnostically between training and testing. To achieve generalization, we leverage the observation that for each mask, there is an invariant optimal predictor. To avoid the exponential explosion when learning them separately, we approximate the optimal predictors jointly using a double parameterization technique. This has the undesirable side effect of allowing the learned predictors to rely on the intra-mask correlation and that between features and mask. We perform decorrelation to minimize this effect. Combining the techniques above, we propose a novel prediction method called StableMiss. Extensive experiments on both synthetic and real-world datasets show that StableMiss is robust and outperforms state-of-the-art methods under agnostic mask distribution shift.
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Xie, Hui, Xuanxuan Liu, and Li Guo. "Semi-supervised One-pass Learning under Distribution Shift." In ICBDT 2023: 2023 6th International Conference on Big Data Technologies. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3627377.3627446.
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Hu, Xuanming, Wei Fan, Kun Yi, Pengfei Wang, Yuanbo Xu, Yanjie Fu, and Pengyang Wang. "Boosting Urban Prediction via Addressing Spatial-Temporal Distribution Shift." In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00025.
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Gao, Yuan, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang. "Alleviating Structural Distribution Shift in Graph Anomaly Detection." In WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3539597.3570377.
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Guo, Lan-Zhe, Zhi Zhou, and Yu-Feng Li. "RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403214.
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Huang, Biwei, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, and Bernhard Scholkopf. "Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows." In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.114.
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Zhu, Yichen, and Bo Jiang. "StableMiss+: Prediction with Incomplete Data Under Agnostic Mask Distribution Shift." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446980.
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Xiao, Teng, Zhengyu Chen, and Suhang Wang. "Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599487.
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Barrows, Josh, Valentin Radu, Matthew Hill, and Fabio Ciravegna. "Active Learning with Data Distribution Shift Detection for Updating Localization Systems." In 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2021. http://dx.doi.org/10.1109/ipin51156.2021.9662543.
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Caron, Matthew. "Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020933.
Full textReports on the topic "Data distribution shift"
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Dubeck, Margaret M., Jonathan M. B. Stern, and Rehemah Nabacwa. Learning to Read in a Local Language in Uganda: Creating Learner Profiles to Track Progress and Guide Instruction Using Early Grade Reading Assessment Results. RTI Press, June 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0068.2106.
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The Early Grade Reading Assessment (EGRA) is used to evaluate studies and monitor projects that address reading skills in low- and middle-income countries. Results are often described solely in terms of a passage-reading subtask, thereby overlooking progress in related skills. Using archival data of cohort samples from Uganda at two time points in three languages (Ganda, Lango, and Runyankore-Rukiga), we explored a methodology that uses passage-reading results to create five learner profiles: Nonreader, Beginner, Instructional, Fluent, and Next-Level Ready. We compared learner profiles with results on other subtasks to identify the skills students would need to develop to progress from one profile to another. We then used regression models to determine whether students’ learner profiles were related to their results on the various subtasks. We found membership in four categories. We also found a shift in the distribution of learner profiles from Grade 1 to Grade 4, which is useful for establishing program effectiveness. The distribution of profiles within grades expanded as students progressed through the early elementary grades. We recommend that those who are discussing EGRA results describe students by profiles and by the numbers that shift from one profile to another over time. Doing so would help describe abilities and instructional needs and would show changes in a meaningful way.
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Maupin, Julie, and Dr Michael Mamoun. DTPH56-06-T-0004 Plastic Pipe Failure, Risk, and Threat Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2006. http://dx.doi.org/10.55274/r0012119.
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Reports, publications, papers, and databases were reviewed to better define risks and threats to plastic gas distribution piping. Failure modes were described for plastic PE piping with the most significant being slow crack growth (SCG). Short-term mechanical tests such as tensile, quick burst, melt index, and density tests did not show a correlation with a material's susceptibility to SCG failure. The bend-back test was able to visually identify 1971 low-ductile inner wall materials. PENT test failure times were reported for materials manufactured during the period1972-1985. The PENT test did not show correlations with the material's susceptibility to SCG failure for these materials. Life expectancy was determined to be a key measure of the susceptibility of PE gas pipe materials to SCG field failures. Long-term hydrostatic stress-rupture data combined with the Rate Process Method or with the Bi-Directional Shift Functions predicted the remaining life expectancy of several PE materials at 60�F average field temperature under varying loading conditions. Data showed rock impingement loads and pipe squeeze-offs can result in the greatest reduction in remaining life expectancy. Lower operating field temperatures and pressures significantly increased the predicted remaining life expectancy of PE materials. Fifty-five PE pipe samples that failed in field service were examined in the laboratory to identify the root cause of the failures. Eight of the samples underwent in-depth analysis, which included density and melts index tests and differential scanning calorimetry, infrared spectroscopy, and microscopic examination of the fracture surfaces. The samples were combined with another set of additional data resulting in 45 material, 36 procedural, 12 quality control, and 7 miscellaneous failures. A separate categorization method attributed a total of 321 failures to their respective pipe/component, with most occurring at joints. RCP in large diameter PE materials was investigated through laboratory testing. Critical pressure was determined for 6 pipe materials. The critical temperature was determined for 3 materials.
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Aterido, Reyes, Mary Hallward-Driemeier, and Carmen Pagés. Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations across Firms. Inter-American Development Bank, October 2007. http://dx.doi.org/10.18235/0011259.
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Using firm level data on 70,000 enterprises in 107 countries, this paper finds important effects of access to finance, business regulations, corruption, and to a lesser extent, infrastructure bottlenecks in explaining patterns of job creation at the firm level. The paper focuses on how the impact of the investment climate varies across sizes of firms. The results suggest strong composition effects: A weak business environment shifts downward the size distribution of firms. In the case of finance and business regulations this occurs by reducing the employment growth of all firms, particularly micro and small firms. On the other hand, corruption and poor access to infrastructure reduce employment growth by affecting the growth of medium size and large firms. With significant differences between firms with less than 10 employees and SMEs, these results indicate significant reforms are needed to spur micro firms to grow into the ranks of the SMEs.
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Salavisa, Isabel, Mark Soares, and Sofia Bizarro. A Critical Assessment of Organic Agriculture in Portugal: A reflection on the agro-food system transition. DINÂMIA'CET-Iscte, 2021. http://dx.doi.org/10.15847/dinamiacet-iul.wp.2021.05.
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Over the last few decades, the organic agriculture sector has experienced sustained growth. Globally, as well as in the European Union and Portugal, organic production accounts for just under 10% of total Utilised Agricultural Area (UAA) (FiBL, 2019; Eurostat, 2019; DGADR, 2019; INE, 2019; GPP, 2019). This growth has been seen in terms of production, number of producers, amount of retail sales, imports and exports. This article attempts to build on the multi-level perspective (MLP) of the socio-technical (ST) transitions theory by employing a whole systems analysis (Geels, 2018) of organic agriculture in Portugal, which defends an integrated vision of the systems, where multiple interactions occur within and among the niche, the regime and the landscape levels. This approach has been employed in order to develop a critical analysis of the current state of the Portuguese organic agriculture sector, stressing the multiplicity of elements that are contributing to the agro-food system´s transformation into a more sustainable one. In fact, the agro-food system is related with climate change but also has connections with other domains such as public health, water management, land use and biodiversity. Therefore, it is affected by shifts in these areas. This analysis considers developments in increasing domestic organic production, number of producers, amount of retail sales, imports, exports, market innovations, and the sector´s reconfiguration. The organic sector´s increase has been attributed to European regulation, institutionalization, standardization, farmer certification, external (government) subsidy support programs, incremental market improvements (visibility and product access), the emergence of new retailers, the rise of supporting consumers and a shift away from conventional agriculture (Truninger, 2010; DGADR, 2019; Pe´er et al, 2019). However, together with positive incentives, this sector also faces numerous barriers that are hindering a faster transformation. Difficulties for the sector to date have included: product placement; a disconnect between production, distribution and marketing systems; high transport costs; competition from imports; European subsidies focused on extensive crops (pastures, olive groves, and arable crops), entailing a substantial growth in the area of pasture to the detriment of other crops; the fact that the products that are in demand (fresh vegetables and fruit) are being neglected by Portuguese producers; expensive certification procedures; lack of adequate support and market expertise for national producers; the hybrid configuration of the sector; and price. Organic agriculture as a niche-innovation is still not greatly contributing to overall agricultural production. The low supply of organic products, despite its ever-increasing demand, suggests that a transition to increased organic production requires a deeper and faster food system reconfiguration, where an array of distinct policies are mobilized and a diversity of actions take place at different levels (Geels, 2018; Pe´er et al, 2019). This paper will attempt to contribute an overall critical assessment of the organic sector´s features and evolution and will identify some of the main obstacles to be overcome, in order to boost the sustainability transition of the agro-food system in Portugal.
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Shapovalova, Daria, Tavis Potts, John Bone, and Keith Bender. Measuring Just Transition : Indicators and scenarios for a Just Transition in Aberdeen and Aberdeenshire. University of Aberdeen, October 2023. http://dx.doi.org/10.57064/2164/22364.
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The North East of Scotland is at the forefront of the global energy transition. With the transformation of the UK’s energy sector over coming decades, the lives of communities and workers in the North East will be directly affected as we collectively transition to a Net Zero economy. A Just Transition refers to a fair distribution of the burdens and benefits as society and the economy shifts to a sustainable low-carbon economy. It calls for action on providing decent green jobs, building community wealth, and embedding participation. While it is a well-established concept in the academic literature and in policy there is a notable lack of approaches and data on measuring progress towards a Just Transition. In Scotland, with Just Transition planning underway, there are calls for clarity by the Scottish Parliament, Just Transition Commission, and many stakeholders on how to evaluate progress in a place-based context. The project ‘Just Transition for Workers and Communities in Aberdeen and Aberdeenshire’ brought together an interdisciplinary team from the University of Aberdeen Just Transition Lab to identify and collate the relevant evidence, and engage with a range of local stakeholders to develop regional Just Transition indicators. Previous work on this project produced a Rapid Evidence Assessment on how the oil and gas industry has shaped our region and what efforts and visions have emerged for a Just Transition. Based on the findings and a stakeholder knowledge-exchange event, we have developed a set of proposed indicators, supported by data and/or narrative, for a transition in Aberdeen and Aberdeenshire across four themes: 1) Employment and skills, 2) Equality and wellbeing, 3) Democratic participation, and 4) Community empowerment, revitalisation and Net Zero. Some of the indicators are compiled from national/local data sets, including data on jobs and skills, fuel poverty or greenhouse gas emissions. Other indicators require further data collection and elaboration, but nevertheless represent important aspects of Just Transition in the region. These include workers’ rights protection, community ownership, participation and empowerment. We propose four narrative scenarios as springboards for further dialogue, policy development, investment and participation on Just Transition in Aberdeen and Aberdeenshire. Indicators, as proxies for evaluating progress, can be used as decision support tools, a means of informing policy, and supporting stakeholder dialogue and action as we collectively progress a Just Transition in the North East. There are no shortcuts on a way to a Just Transition. Progress towards achieving it will require a clear articulation of vision and objectives, co-developed with all stakeholders around the table. It will require collaboration, trust, difficult conversations, and compromise as we develop a collective vision for the region. Finally, it will require strong political will, substantive policy and legal reform, public and private investment, and building of social licence as we collectively build a Net Zero future in the North East.
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Health Innovation & Technology in Latin America & the Caribbean. Inter-American Development Bank, April 2024. http://dx.doi.org/10.18235/0012923.
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The report offers a comprehensive analysis of health startups in Latin America and the Caribbean (LAC), delving into the challenges, trends, and investments in the region. It identifies major health challenges faced by LAC populations alongside issues of access, funding, data, workforce, technology, and regulation. Additionally, it outlines 50 health innovation and technology trends shaping the future of healthcare in LAC, with 15 strategic shifts indicating fundamental transformations. The report maps the landscape of health startups, analyzing their distribution by country, sub-sector, and cluster, while also highlighting key venture capital investors and funding activities. Furthermore, it examines IDB Lab's role in the sector and its portfolio of health innovation projects, offering insights into funding types, regions, sub-sectors, and ecosystem players in LAC, both historically and post-pandemic. The report is accompanied by a compendium of health innovation startups in the region, available at: https://publications.iadb.org/en/health-innovation-technology-latin-america-caribbean-market-landscape-and-compendium-companies.