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Articles de revues sur le sujet « Dataset shift »

Auteur : Grafiati

Publié le 25 mai 2024

Mis à jour le 31 juillet 2025

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1

Sharet, Nir, and Ilan Shimshoni. "Analyzing Data Changes using Mean Shift Clustering." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 07 (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

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Adams, Niall. "Dataset Shift in Machine Learning." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 1 (2010): 274. http://dx.doi.org/10.1111/j.1467-985x.2009.00624_10.x.

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Guo, Lin Lawrence, Stephen R. Pfohl, Jason Fries, et al. "Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine." Applied Clinical Informatics 12, no. 04 (2021): 808–15. http://dx.doi.org/10.1055/s-0041-1735184.

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Abstract Objective The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was m

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He, Zhiqiang. "ECG Heartbeat Classification Under Dataset Shift." Journal of Intelligent Medicine and Healthcare 1, no. 2 (2022): 79–89. http://dx.doi.org/10.32604/jimh.2022.036624.

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Kim, Doyoung, Inwoong Lee, Dohyung Kim, and Sanghoon Lee. "Action Recognition Using Close-Up of Maximum Activation and ETRI-Activity3D LivingLab Dataset." Sensors 21, no. 20 (2021): 6774. http://dx.doi.org/10.3390/s21206774.

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The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides a

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McGaughey, Georgia, W. Patrick Walters, and Brian Goldman. "Understanding covariate shift in model performance." F1000Research 5 (April 7, 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.1.

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Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.

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McGaughey, Georgia, W. Patrick Walters, and Brian Goldman. "Understanding covariate shift in model performance." F1000Research 5 (June 17, 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.2.

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Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.

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McGaughey, Georgia, W. Patrick Walters, and Brian Goldman. "Understanding covariate shift in model performance." F1000Research 5 (October 17, 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.3.

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Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.

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Prasad, Pulicherla Siva, and Senthilrajan Agniraj. "Cross-Domain Adaptation Techniques for Robust Plant Disease Detection: A DANN-CORAL Hybrid Approach." International Journal of Experimental Research and Review 42 (August 30, 2024): 68–84. http://dx.doi.org/10.52756/ijerr.2024.v42.007.

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Plant disease detection with deep learning models has shown promising results, but these models often struggle with generalizing across diverse agricultural environments due to domain shifts in imaging conditions. This paper presents a novel hybrid approach focusing on cross-domain adaptation techniques to address the challenge of domain shift. Our proposed method combines the Domain-Adversarial Neural Network (DANN) with Correlation Alignment (CORAL) to mitigate domain shifts between datasets. The DANN framework enforces domain-invariant feature learning through adversarial training. Using th

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Yu, Jiongchi, Xiaofei Xie, Qiang Hu, et al. "CAShift: Benchmarking Log-Based Cloud Attack Detection under Normality Shift." Proceedings of the ACM on Software Engineering 2, FSE (2025): 1687–709. https://doi.org/10.1145/3729346.

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With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution

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Becker, Aneta, and Jarosław Becker. "Dataset shift assessment measures in monitoring predictive models." Procedia Computer Science 192 (2021): 3391–402. http://dx.doi.org/10.1016/j.procs.2021.09.112.

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Finlayson, Samuel G., Adarsh Subbaswamy, Karandeep Singh, et al. "The Clinician and Dataset Shift in Artificial Intelligence." New England Journal of Medicine 385, no. 3 (2021): 283–86. http://dx.doi.org/10.1056/nejmc2104626.

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Moreno-Torres, Jose G., Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V. Chawla, and Francisco Herrera. "A unifying view on dataset shift in classification." Pattern Recognition 45, no. 1 (2012): 521–30. http://dx.doi.org/10.1016/j.patcog.2011.06.019.

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Subbaswamy, Adarsh, Bryant Chen, and Suchi Saria. "A unifying causal framework for analyzing dataset shift-stable learning algorithms." Journal of Causal Inference 10, no. 1 (2022): 64–89. http://dx.doi.org/10.1515/jci-2021-0042.

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Abstract Recent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known as dataset shift) has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments. However, these methods consider different types of shifts and have been developed under disparate frameworks, making it difficult to theoretically analyze how solutions differ with respect to stability and accuracy. Taking a causal graphical view, we use a flexible graphical represe

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Xie, Y., K. Schindler, J. Tian, and X. X. Zhu. "EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 247–54. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-247-2021.

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Abstract. Deep learning models achieve excellent semantic segmentation results for airborne laser scanning (ALS) point clouds, if sufficient training data are provided. Increasing amounts of annotated data are becoming publicly available thanks to contributors from all over the world. However, models trained on a specific dataset typically exhibit poor performance on other datasets. I.e., there are significant domain shifts, as data captured in different environments or by distinct sensors have different distributions. In this work, we study this domain shift and potential strategies to mitiga

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Tasche, Dirk. "Factorizable Joint Shift in Multinomial Classification." Machine Learning and Knowledge Extraction 4, no. 3 (2022): 779–802. http://dx.doi.org/10.3390/make4030038.

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Factorizable joint shift (FJS) was recently proposed as a type of dataset shift for which the complete characteristics can be estimated from feature data observations on the test dataset by a method called Joint Importance Aligning. For the multinomial (multiclass) classification setting, we derive a representation of factorizable joint shift in terms of the source (training) distribution, the target (test) prior class probabilities and the target marginal distribution of the features. On the basis of this result, we propose alternatives to joint importance aligning and, at the same time, poin

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Chakraborty, Saptarshi, Debolina Paul, and Swagatam Das. "Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6930–38. http://dx.doi.org/10.1609/aaai.v35i8.16854.

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Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and finding the number of clusters in a dataset in an automated fashion. However, the merits of mean shift quickly fade away as the data dimensions increase and only a handful of features contain useful information about the cluster structure of the data. We propose a simple yet elegant feature-weighted variant of mean shift to efficiently learn the featu

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ZHAO, YUZHONG, BABAK ALIPANAHI, SHUAI CHENG LI, and MING LI. "PROTEIN SECONDARY STRUCTURE PREDICTION USING NMR CHEMICAL SHIFT DATA." Journal of Bioinformatics and Computational Biology 08, no. 05 (2010): 867–84. http://dx.doi.org/10.1142/s0219720010004987.

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Accurate determination of protein secondary structure from the chemical shift information is a key step for NMR tertiary structure determination. Relatively few work has been done on this subject. There needs to be a systematic investigation of algorithms that are (a) robust for large datasets; (b) easily extendable to (the dynamic) new databases; and (c) approaching to the limit of accuracy. We introduce new approaches using k-nearest neighbor algorithm to do the basic prediction and use the BCJR algorithm to smooth the predictions and combine different predictions from chemical shifts and ba

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Stan, Serban, and Mohammad Rostami. "Preserving Fairness in AI under Domain Shift." Journal of Artificial Intelligence Research 81 (December 13, 2024): 907–34. https://doi.org/10.1613/jair.1.16694.

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Existing algorithms for ensuring fairness in AI use a single-shot training strategy, where an AI model is trained on an annotated training dataset with sensitive attributes and then fielded for utilization. This training strategy is effective in problems with stationary distributions, where both the training and testing data are drawn from the same distribution. However, it is vulnerable with respect to distributional shifts in the input space that may occur after the initial training phase. As a result, the time-dependent nature of data can introduce biases and performance degradation into th

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KIM, Geunhwan, Hwang YOUNGSANG, and Choo YOUNGMIN. "Enhancing adaptivity of active sonar classifier considering loss landscape under dataset shift." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 9 (2024): 2098–103. http://dx.doi.org/10.3397/in_2024_3137.

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We study the loss landscape of deep neural networks and use it to solve dataset shift problems in active sonar classification. The dataset shift degrades the generalization performance of supervised learning-based classifiers. When test data samples are available, fine-tuning methods can be used to mitigate the performance decrease. However, they can induce catastrophic forgetting and negative transfer because fine-tuned weights could be overfitted to the test data distribution. These problems are more severe in active sonar datasets with small amounts and less diversity. To mitigate the two p

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Xue, Zhiyun, Feng Yang, Sivaramakrishnan Rajaraman, Ghada Zamzmi, and Sameer Antani. "Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection." Diagnostics 13, no. 6 (2023): 1068. http://dx.doi.org/10.3390/diagnostics13061068.

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Domain shift is one of the key challenges affecting reliability in medical imaging-based machine learning predictions. It is of significant importance to investigate this issue to gain insights into its characteristics toward determining controllable parameters to minimize its impact. In this paper, we report our efforts on studying and analyzing domain shift in lung region detection in chest radiographs. We used five chest X-ray datasets, collected from different sources, which have manual markings of lung boundaries in order to conduct extensive experiments toward this goal. We compared the

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Sáez, José A., and José L. Romero-Béjar. "Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation." Mathematics 10, no. 14 (2022): 2538. http://dx.doi.org/10.3390/math10142538.

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Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequen

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Chen, Heng, Erkang Zhao, Yunpeng Jia, and Lei Shi. "FSN: Feature Shift Network for Load-Domain (LD)Domain Generalization." Applied Sciences 14, no. 12 (2024): 5204. http://dx.doi.org/10.3390/app14125204.

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Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As a result, it is hard for the trained model to achieve desirable performance on the testing set. In this paper, we introduce a novel domain generalization, Load-Domain (LD) domain generalization, which is based on the analysis of the Case Western Reserve University (CWRU) bearing dataset and takes advantage of the physical information of this datas

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Aryal, Jagannath, and Bipul Neupane. "Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction." Remote Sensing 15, no. 2 (2023): 488. http://dx.doi.org/10.3390/rs15020488.

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Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The evolving FCNs suffer from the inadequate use of multi-scale feature maps in their backbone of convolutional neural networks (CNNs). Furthermore, the DL methods are not robust in cross-domain settings due to domain-shift problems. Two scale-robust novel networks, namely MSA-UNET and MSA-ResUNET, are developed in this study by aggregating the multi-s

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Becker, Jarosław, and Aneta Becker. "Predictive Accuracy Index in evaluating the dataset shift (case study)." Procedia Computer Science 225 (2023): 3342–51. http://dx.doi.org/10.1016/j.procs.2023.10.328.

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Turhan, Burak. "On the dataset shift problem in software engineering prediction models." Empirical Software Engineering 17, no. 1-2 (2011): 62–74. http://dx.doi.org/10.1007/s10664-011-9182-8.

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Peng, Zhiyong, Changlin Han, Yadong Liu, and Zongtan Zhou. "Weighted Policy Constraints for Offline Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9435–43. http://dx.doi.org/10.1609/aaai.v37i8.26130.

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Offline reinforcement learning (RL) aims to learn policy from the passively collected offline dataset. Applying existing RL methods on the static dataset straightforwardly will raise distribution shift, causing these unconstrained RL methods to fail. To cope with the distribution shift problem, a common practice in offline RL is to constrain the policy explicitly or implicitly close to behavioral policy. However, the available dataset usually contains sub-optimal or inferior actions, constraining the policy near all these actions will make the policy inevitably learn inferior behaviors, limiti

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Phongsasiri, Siriwan, and Suwanna Rasmequan. "Outlier Detection in Wellness Data using Probabilistic Mapped Mean-Shift Algorithms." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, no. 2 (2021): 258–66. http://dx.doi.org/10.37936/ecti-cit.2021152.244971.

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In this paper, the Probabilistic Mapped Mean-Shift Algorithm is proposed to detect anomalous data in public datasets and local hospital children’s wellness clinic databases. The proposed framework consists of two main parts. First, the Probabilistic Mapping step consists of k-NN instance acquisition, data distribution calculation, and data point reposition. Truncated Gaussian Distribution (TGD) was used for controlling the boundary of the mapped points. Second, the Outlier Detection step consists of outlier score calculation and outlier selection. Experimental results show that the proposed al

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Tappy, Nicolas, Anna Fontcuberta i Morral, and Christian Monachon. "Image shift correction, noise analysis, and model fitting of (cathodo-)luminescence hyperspectral maps." Review of Scientific Instruments 93, no. 5 (2022): 053702. http://dx.doi.org/10.1063/5.0080486.

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Hyperspectral imaging is an important asset of modern spectroscopy. It allows us to perform optical metrology at a high spatial resolution, for example in cathodoluminescence in scanning electron microscopy. However, hyperspectral datasets present added challenges in their analysis compared to individually taken spectra due to their lower signal to noise ratio and specific aberrations. On the other hand, the large volume of information in a hyperspectral dataset allows the application of advanced statistical analysis methods derived from machine-learning. In this article, we present a methodol

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Rodriguez-Vazquez, Javier, Miguel Fernandez-Cortizas, David Perez-Saura, Martin Molina, and Pascual Campoy. "Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images." Remote Sensing 15, no. 6 (2023): 1700. http://dx.doi.org/10.3390/rs15061700.

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This paper presents a novel semi-supervised approach for accurate counting and localization of tropical plants in aerial images that can work in new visual domains in which the available data are not labeled. Our approach uses deep learning and domain adaptation, designed to handle domain shifts between the training and test data, which is a common challenge in this agricultural applications. This method uses a source dataset with annotated plants and a target dataset without annotations and adapts a model trained on the source dataset to the target dataset using unsupervised domain alignment

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He, Yue, Xinwei Shen, Renzhe Xu, et al. "Covariate-Shift Generalization via Random Sample Weighting." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11828–36. http://dx.doi.org/10.1609/aaai.v37i10.26396.

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Shifts in the marginal distribution of covariates from training to the test phase, named covariate-shifts, often lead to unstable prediction performance across agnostic testing data, especially under model misspecification. Recent literature on invariant learning attempts to learn an invariant predictor from heterogeneous environments. However, the performance of the learned predictor depends heavily on the availability and quality of provided environments. In this paper, we propose a simple and effective non-parametric method for generating heterogeneous environments via Random Sample Weighti

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Wang, Li, Dong Li, Han Liu, JinZhang Peng, Lu Tian, and Yi Shan. "Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2487–94. http://dx.doi.org/10.1609/aaai.v36i3.20149.

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Semantic segmentation is an important task for scene understanding in self-driving cars and robotics, which aims to assign dense labels for all pixels in the image. Existing work typically improves semantic segmentation performance by exploring different network architectures on a target dataset. Little attention has been paid to build a unified system by simultaneously learning from multiple datasets due to the inherent distribution shift across different datasets. In this paper, we propose a simple, flexible, and general method for semantic segmentation, termed Cross-Dataset Collaborative Le

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Hong, Zhiqing, Zelong Li, Shuxin Zhong, et al. "CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised Pretraining." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 2 (2024): 1–26. http://dx.doi.org/10.1145/3659597.

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The increasing availability of low-cost wearable devices and smartphones has significantly advanced the field of sensor-based human activity recognition (HAR), attracting considerable research interest. One of the major challenges in HAR is the domain shift problem in cross-dataset activity recognition, which occurs due to variations in users, device types, and sensor placements between the source dataset and the target dataset. Although domain adaptation methods have shown promise, they typically require access to the target dataset during the training process, which might not be practical in

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Bock, Christoph, and Jürgen Hesser. "Analysis and Prediction of Helix Shift Errors in Homology Modeling." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 6, no. 1-2 (2006): 131–45. https://doi.org/10.3233/isb-00228.

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High sequence identity between two proteins (e.g. > 60% is a strong evidence for high structural similarity. However, internal shifts in one of the two proteins can sometimes give rise to unexpectedly high structural differences. This, in turn, causes unreliable structure predictions when two such proteins are used in homology modeling. Here, we perform a computational analysis of helix shifts and we show that their occurrence can be predicted with statistical learning methods. Our results indicate that helix shifts increase the RMS error by factor 2.6 compared to those protein pairs withou

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Wei, Weiwei, Yuxuan Liao, Yufei Wang, et al. "Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures." Molecules 27, no. 12 (2022): 3653. http://dx.doi.org/10.3390/molecules27123653.

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Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sample

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Blanza, J., X. E. Cabasal, J. B. Cipriano, G. A. Guerrero, R. Y. Pescador, and E. V. Rivera. "Indoor Wireless Multipaths Outlier Detection and Clustering." Journal of Physics: Conference Series 2356, no. 1 (2022): 012037. http://dx.doi.org/10.1088/1742-6596/2356/1/012037.

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Wireless communication systems have grown and developed significantly in recent years to fulfill the growing demand for high data rates across a wireless medium. Channel models have been used to develop various sturdy wireless systems for indoor and outdoor applications, and these are simulated in the form of datasets. The presence of outliers in clusters has been a concern in datasets, as it affects the standard deviation and mean of the dataset which reduces the data accuracy. In this study, the outliers in the Cooperation in Science and Technology (COST) 2100 MIMO channel model dataset were

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Kushol, Rafsanjany, Alan H. Wilman, Sanjay Kalra, and Yee-Hong Yang. "DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets." Diagnostics 13, no. 18 (2023): 2947. http://dx.doi.org/10.3390/diagnostics13182947.

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In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in mu

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Goel, Parth, and Amit Ganatra. "Unsupervised Domain Adaptation for Image Classification and Object Detection Using Guided Transfer Learning Approach and JS Divergence." Sensors 23, no. 9 (2023): 4436. http://dx.doi.org/10.3390/s23094436.

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Unsupervised domain adaptation (UDA) is a transfer learning technique utilized in deep learning. UDA aims to reduce the distribution gap between labeled source and unlabeled target domains by adapting a model through fine-tuning. Typically, UDA approaches assume the same categories in both domains. The effectiveness of transfer learning depends on the degree of similarity between the domains, which determines an efficient fine-tuning strategy. Furthermore, domain-specific tasks generally perform well when the feature distributions of the domains are similar. However, utilizing a trained source

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Sinha, Samarth, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, and Florian Shkurti. "DIBS: Diversity Inducing Information Bottleneck in Model Ensembles." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9666–74. http://dx.doi.org/10.1609/aaai.v35i11.17163.

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Although deep learning models have achieved state-of-the art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research. Bayesian approaches including Bayesian Neural Nets (BNNs) do not scale well to modern computer vision tasks, as they are difficult to train, and have poor generalization under dataset-shift. This motivates the need for effective ensembles which can generalize and give reliable uncertainty estimates. In this paper, we target the problem of generating effectiv

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Heffington, Colton, Brandon Beomseob Park, and Laron K. Williams. "The “Most Important Problem” Dataset (MIPD): a new dataset on American issue importance." Conflict Management and Peace Science 36, no. 3 (2017): 312–35. http://dx.doi.org/10.1177/0738894217691463.

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This article introduces the Most Important Problem Dataset (MIPD). The MIPD provides individual-level responses by Americans to “most important problem” questions from 1939 to 2015 coded into 58 different problem categories. The MIPD also contains individual-level information on demographics, economic evaluations, partisan preferences, approval and party competencies. This dataset can help answer questions about how the public prioritizes all problems, domestic and foreign, and we demonstrate how these data can shed light on how circumstances influence foreign policy attentiveness. Our explora

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Guo, Fumin, Matthew Ng, Maged Goubran, et al. "Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach." Medical Image Analysis 61 (April 2020): 101636. http://dx.doi.org/10.1016/j.media.2020.101636.

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Yuan, Wei, Lei Qiao, and Liu Tang. "Forest Wildfire Detection from Images Captured by Drones Using Window Transformer without Shift." Forests 15, no. 8 (2024): 1337. http://dx.doi.org/10.3390/f15081337.

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Cameras, especially those carried by drones, are the main tools used to detect wildfires in forests because cameras have much longer detection ranges than smoke sensors. Currently, deep learning is main method used for fire detection in images, and Transformer is the best algorithm. Swin Transformer restricts the computation to a fixed-size window, which reduces the amount of computation to a certain extent, but to allow pixel communication between windows, it adopts a shift window approach. Therefore, Swin Transformer requires multiple shifts to extend the receptive field to the entire image.

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Vescovi, R. F. C., M. B. Cardoso, and E. X. Miqueles. "Radiography registration for mosaic tomography." Journal of Synchrotron Radiation 24, no. 3 (2017): 686–94. http://dx.doi.org/10.1107/s1600577517001953.

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A hybrid method of stitching X-ray computed tomography (CT) datasets is proposed and the feasibility to apply the scheme in a synchrotron tomography beamline with micrometre resolution is shown. The proposed method enables the field of view of the system to be extended while spatial resolution and experimental setup remain unchanged. The approach relies on taking full tomographic datasets at different positions in a mosaic array and registering the frames using Fourier phase correlation and a residue-based correlation. To ensure correlation correctness, the limits for the shifts are determined

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Ngu, Noel, Aditya Taparia, Gerardo I. Simari, et al. "Multiple Distribution Shift - Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 379–83. https://doi.org/10.1609/aaaiss.v5i1.35616.

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Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often lead to degradations in performance. We introduce Multiple Distribution Shift - Aerial (MDS-A) - a collection of inter-related datasets of the same aerial domain that are perturbed in different ways to better characterize the effects of out-of-distribution performance. Specifically, MDS-A is a set of simulated aerial datasets collected under different weather conditions. We include six datasets under different simulat

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HU, Xiaoyan, Yongqiang HAO, Guofeng DAI, Donghe ZHANG, and Zuo XIAO. "2020 ionospheric high frequency doppler shift dataset of Peking University Ionosphere Station." China Scientific Data 6, no. 2 (2021): 21.86101.1/csdata.2021.0021.zh. http://dx.doi.org/10.11922/csdata.2021.0021.zh.

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Traynor, Carlos, Tarjinder Sahota, Helen Tomkinson, Ignacio Gonzalez-Garcia, Neil Evans, and Michael Chappell. "Imputing Biomarker Status from RWE Datasets—A Comparative Study." Journal of Personalized Medicine 11, no. 12 (2021): 1356. http://dx.doi.org/10.3390/jpm11121356.

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Missing data is a universal problem in analysing Real-World Evidence (RWE) datasets. In RWE datasets, there is a need to understand which features best correlate with clinical outcomes. In this context, the missing status of several biomarkers may appear as gaps in the dataset that hide meaningful values for analysis. Imputation methods are general strategies that replace missing values with plausible values. Using the Flatiron NSCLC dataset, including more than 35,000 subjects, we compare the imputation performance of six such methods on missing data: predictive mean matching, expectation-max

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Burns, Dan, Kathryn Richardson, and Corine Driessens. "A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period." NIHR Open Research 4 (November 1, 2024): 67. http://dx.doi.org/10.3310/nihropenres.13651.1.

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Machine learning methodologies are becoming increasingly popular in healthcare research. This shift to integrated data science approaches necessitates professional development of the existing healthcare data analyst workforce. To enhance this smooth transition, educational resources need to be developed. Real healthcare datasets, vital for healthcare data analysis and training purposes, have many barriers, including financial, ethical, and patient confidentiality concerns. Synthetic datasets that mimic real-world complexities offer simple solutions. The presented synthetic dataset mirrors the

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Huch, Sebastian, and Markus Lienkamp. "Towards Minimizing the LiDAR Sim-to-Real Domain Shift: Object-Level Local Domain Adaptation for 3D Point Clouds of Autonomous Vehicles." Sensors 23, no. 24 (2023): 9913. http://dx.doi.org/10.3390/s23249913.

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Perception algorithms for autonomous vehicles demand large, labeled datasets. Real-world data acquisition and annotation costs are high, making synthetic data from simulation a cost-effective option. However, training on one source domain and testing on a target domain can cause a domain shift attributed to local structure differences, resulting in a decrease in the model’s performance. We propose a novel domain adaptation approach to address this challenge and to minimize the domain shift between simulated and real-world LiDAR data. Our approach adapts 3D point clouds on the object level by l

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Wang, Xiaoyang, Chen Li, Jianqiao Zhao, and Dong Yu. "NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14006–14. http://dx.doi.org/10.1609/aaai.v35i16.17649.

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In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from the topic is mentioned and the topic shift is smooth. Our corpus contains 19.9K conversations from six domains, and 400K utterances with an average turn number of 20.1. These conversations contain in-depth discussions on related topics or widely natural transition between multiple topics. We believe either way is normal for human conversation. To facilitate the research on this corpus, we provide results of several b

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Allen, Robert C., Mattia C. Bertazzini, and Leander Heldring. "The Economic Origins of Government." American Economic Review 113, no. 10 (2023): 2507–45. http://dx.doi.org/10.1257/aer.20201919.

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We test between cooperative and extractive theories of the origins of government. We use river shifts in southern Iraq as a natural experiment, in a new archeological panel dataset. A shift away creates a local demand for a government to coordinate because private river irrigation needs to be replaced with public canals. It disincentivizes local extraction as land is no longer productive without irrigation. Consistent with a cooperative theory of government, a river shift away led to state formation, canal construction, and the payment of tribute. We argue that the first governments coordinate

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