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Journal articles on the topic 'Data distribution shift'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Cheng, Luling, Xue Yang, Luliang Tang, et al. "Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data." ISPRS International Journal of Geo-Information 9, no. 4 (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 s

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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 (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

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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 (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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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 (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

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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 (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 confo

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Lee, Giwoong, Jiseung Ahn, and Jeongyeol Choe. "HYBOOD: A Hybrid Generative Model for Out-of-Distribution Detection with Corruption Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18101–9. https://doi.org/10.1609/aaai.v39i17.33991.

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We propose HYBOOD, a hybrid out-of-distribution model based on normalizing flow followed by a simple linear classification model. In real-world settings, it is known that data corruption has a strong influence on model degradation; for example image quality like noise, blur and image geometry like translation, scaling, rotation. MNIST-C, CIFAR10-C are the general synthesized datasets to measure model performance and corruption difficulty in terms of covariate and semantic shifts. HYBOOD shows that the separability between in-distribution, covariate shift, and semantic shift can be represented

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Wang, Da, Lin Li, Wei Wei, Qixian Yu, Jianye Hao, and Jiye Liang. "Improving Generalization in Offline Reinforcement Learning via Latent Distribution Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21053–61. https://doi.org/10.1609/aaai.v39i20.35402.

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Dealing with the distribution shift is a significant challenge when building offline reinforcement learning (RL) models that can generalize from a static dataset to out-of-distribution (OOD) scenarios. Previous approaches have employed pessimism or conservatism strategies. More recently, data-driven work has taken a distributional perspective, treating offline data as a domain adaptation problem. However, these methods use heuristic techniques to simulate distribution shifts, resulting in a limited diversity of artificially created distribution gaps. In this paper, we propose a novel perspecti

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Ye, Nanyang, Lin Zhu, Jia Wang, et al. "Certifiable Out-of-Distribution Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (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 certifiab

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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 (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 o

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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 TB

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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 (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 tempera

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Soultan, Alaaeldin, Diego Pavón-Jordán, Ute Bradter, et al. "The future distribution of wetland birds breeding in Europe validated against observed changes in distribution." Environmental Research Letters 17, no. 2 (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

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Kuang, Kun, Ruoxuan Xiong, Peng Cui, Susan Athey, and Bo Li. "Stable Prediction with Model Misspecification and Agnostic Distribution Shift." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4485–92. http://dx.doi.org/10.1609/aaai.v34i04.5876.

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For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real applications, however, we often have little prior knowledge on the test data and on the underlying true model. Under model misspecification, agnostic distribution shift between training and test data leads to inaccuracy of parameter estimation and instability of prediction across unknown test data. To address these problems, we propose a novel Decorre

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Anjum, Adeel, Muhammad Zafar Iqbal, Muhammad Kashif, and Madiha Ghamkhar. "Shift Detection Comparison For Sized Biased And Area Biased Exponential Distributions Based Control Charts." Migration Letters 21, no. 8 (2024): 1–13. http://dx.doi.org/10.59670/ml.v21is11.10731.

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Probability distribution models are very helpful to solving many real-life problems. The development of new or extensions to an existing model is a very attractive area of research. Among many types of probability distribution models, weighted distributions are becoming more important for modeling data. Weighted distributions can help discover defects in manufacturing and outliers by weighting significant observations. Control charts are the most important tools to monitor process efficiency. In recent decades, researchers have focused on weighted distributions, especially those with size and

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Chan, Chin-Cheng. "Modeling phase shift data of phase-detection autofocus by skew-normal distribution." Journal of Electronic Imaging 28, no. 03 (2019): 1. http://dx.doi.org/10.1117/1.jei.28.3.033001.

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Gwon, Kyungpil, and Joonhyuk Yoo. "Out-of-Distribution (OOD) Detection and Generalization Improved by Augmenting Adversarial Mixup Samples." Electronics 12, no. 6 (2023): 1421. http://dx.doi.org/10.3390/electronics12061421.

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Deep neural network (DNN) models are usually built based on the i.i.d. (independent and identically distributed), also known as in-distribution (ID), assumption on the training samples and test data. However, when models are deployed in a real-world scenario with some distributional shifts, test data can be out-of-distribution (OOD) and both OOD detection and OOD generalization should be simultaneously addressed to ensure the reliability and safety of applied AI systems. Most existing OOD detectors pursue these two goals separately, and therefore, are sensitive to covariate shift rather than s

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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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Guan, Lisha, Yong Chen, Kevin W. Staples, Jie Cao, and Bai Li. "The influence of complex structure on the spatial dynamics of Atlantic cod (Gadus morhua) in the Gulf of Maine." ICES Journal of Marine Science 74, no. 9 (2017): 2379–88. http://dx.doi.org/10.1093/icesjms/fsx064.

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Abstract Atlantic cod (Gadus morhua) in the Gulf of Maine (GOM) is an iconic marine fishery stock that has experienced a substantial distributional shift since the mid-1990s. A geostatistical delta-generalized linear mixed model was utilized to hindcast yearly season-specific distributions of GOM cod. These distributions were calculated using the spring and fall bottom trawl survey data for the stock, along with cell-based bathymetry and bottom temperature data for the study area for the years 1982–2013. The centre of stock distribution (the centre of gravity), spatial extent in latitude and l

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Monich, Yury V., and Yury D. Nechipurenko. "Correlations in Compositional Data without Log Transformations." Axioms 12, no. 12 (2023): 1084. http://dx.doi.org/10.3390/axioms12121084.

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This article proposes a method for determining the p-value of correlations in compositional data, i.e., those data that arise as a result of dividing original values by their sum. Data organized in this way are typical for many fields of knowledge, but there is still no consensus on methods for interpreting correlations in such data. In the second decade of the new millennium, almost all newly emerging methods for solving this problem have become based on the log transformation of data. In the method proposed here, there are no log transformations. We return to the early stages of attempting t

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Bai, Haoyue, Rui Sun, Lanqing Hong, et al. "DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6705–13. http://dx.doi.org/10.1609/aaai.v35i8.16829.

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While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework for a wide range of applications is challenging, mainly due to different kinds of distribution shifts in the real world, such as the shift across domains or the extrapolation of correlation. Most of the previous approaches can only solve one specific distribution shift, leading to unsatisfactor

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Cheung, William W. L., Jessica J. Meeuwig, Ming Feng, et al. "Climate-change induced tropicalisation of marine communities in Western Australia." Marine and Freshwater Research 63, no. 5 (2012): 415. http://dx.doi.org/10.1071/mf11205.

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A major observed and predicted impact of climate change on marine species is the poleward shift in their distributions and the resulting changes in community structure. Here, we used a Dynamic Bioclimate Envelope Model to project range shift of exploited marine fishes and invertebrates in Western Australia. We combined published data and expert knowledge to predict current species distributions for 30 tropical, sub-tropical and temperate species that occur along the coast of Western Australia. Using outputs from both a Regional Oceanographic Model and a Global Circulation Model, we simulated c

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Engelhard, Georg H., John K. Pinnegar, Laurence T. Kell, and Adriaan D. Rijnsdorp. "Nine decades of North Sea sole and plaice distribution." ICES Journal of Marine Science 68, no. 6 (2011): 1090–104. http://dx.doi.org/10.1093/icesjms/fsr031.

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Abstract Engelhard, G. H., Pinnegar, J. K., Kell, L. T., and Rijnsdorp, A. D. 2011. Nine decades of North Sea sole and plaice distribution. – ICES Journal of Marine Science, 68: 1090–1104. Recent studies based mainly on research survey data suggest that within the North Sea, sole Solea solea and plaice Pleuronectes platessa have exhibited distribution shifts in recent decades—on average southward for sole and northward to deeper waters for plaice. Various hypotheses may account for such shifts, including climate change effects and more intensive fishing in southern and shallower waters; but th

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Orlov, A. "Estimation of gamma distribution parameters." Industrial laboratory. Diagnostics of materials 91, no. 1 (2025): 79–88. https://doi.org/10.26896/1028-6861-2025-91-1-79-88.

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Statements of problems of statistical analysis of data with a gamma distribution are related to classical mathematical statistics. Oddly enough, not all alone were solved within the framework of parametric statistics, which was at the forefront of the development of statistical science in the first third of the 20th century. As with the beta distribution, gaps need to be filled. This is necessary because the gamma distribution is currently widely used in theoretical and applied work. An example is GOST 11.011–83 «Applied statistics. Rules for determining estimates and confidence limits for gam

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Chang, Jyun-Ping, Jun-Hong Weng, Hsun-Ching Hsu, Pei-Yuan Lee, and Pin Han. "A Data Transmission Method with Spectral Switches via Electroabsorption." Applied Sciences 12, no. 3 (2022): 979. http://dx.doi.org/10.3390/app12030979.

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In the past, the waveguide electroabsorption effect has generally been used as an intensity modulator for quasi-monochromatic light, such as lasers. Here, we study how this effect affects polychromatic light spectra. We find that for light with a Gaussian distribution spectrum, the spectral peak shift (red shift or blue shift) can be controlled by the magnitude of the applied voltage, as long as the center wavelength and the spectral band are properly selected. This result can be used as a data transmission scheme at the integrated chip level or in free space. It may offer a good option for so

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Vestfals, Cathleen D., Lorenzo Ciannelli, and Gerald R. Hoff. "Changes in habitat utilization of slope-spawning flatfish across a bathymetric gradient." ICES Journal of Marine Science 73, no. 7 (2016): 1875–89. http://dx.doi.org/10.1093/icesjms/fsw112.

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Abstract Understanding how fish distributions may change in response to environmental variability is important for effective management of fish populations, as predicted climate change will likely alter their habitat use and population dynamics. This research focused on two commercially- and ecologically-important flatfish species in the eastern Bering Sea (EBS), Greenland halibut ( Reinhardtius hippoglossoides ) and Pacific halibut ( Hippoglossus stenolepis ), which may be especially sensitive to climate-induced shifts in habitat due to strong seasonally and ontogenetically variable distribut

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Tomov, Mite, Leonard Abazovski, and Anastasija Ignjatovska. "CONTRIBUTION TO THE SPC IMPLEMENTATION BY SELECTING AN APPROPRIATE VARIABLE CONTROL CHART." Journal of Production Engineering 24, no. 1 (2021): 50–54. http://dx.doi.org/10.24867/jpe-2021-01-050.

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The research in this paper contributes to the practical implementation of the Statistical Process Control (SPC), primarily in the serial production by selecting an appropriate variable control chart. The paper proposes a procedure algorithm where the deciding criteria used to select the variable control chart include: the data distribution type (whether an approximately normal distribution or not), the number of elements in the subgroup (n) for grouped data, the shift size value and the percentage difference between the shift size value and the mean value of the shifts calculated for each subg

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Jeong, Il-Kyoung, M. J. Graf, and R. H. Heffner. "Effects of Bragg peak profiles and nanoparticle sizes on the real-space pair distribution function." Journal of Applied Crystallography 38, no. 1 (2005): 55–61. http://dx.doi.org/10.1107/s0021889804025841.

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A study of the effects of Bragg peak profiles and nanoparticle size broadening on the real-space pair distribution function (PDF) is presented, using `synthetic' powder diffraction data. Bragg peak profiles from both asymmetric time-of-flight (TOF) spallation neutron data and symmetric synchrotron X-ray data are considered. Due to their asymmetric peak profiles, the TOF data cause artificial shifts of the PDF peak positions towards higher pair distances. Coupled with this effect is a broadening of the PDF peak widths due to aQ-dependent spectrometer resolution, making reliable refinement of th

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Chen, Zining, Xingshuang Luo, Weiqiu Wang, Zhicheng Zhao, Fei Su, and Aidong Men. "Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2420–28. https://doi.org/10.1609/aaai.v39i3.32243.

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Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performance decay on traditional AD methods. From this perspective, few previous work has explored AD with distribution shift, and the distribution-invariant normality learning has been proposed based on the Reverse Distillation (RD) framework. However, we observe the misalignment issue between the teacher and the student network that causes detection failure, thereby propose FiCo, Filter or Compensate, to address the distrib

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Odedina, Joe. "Online Streaming: A Paradigm Shift for Nollywood Movie Distribution." International Journal of Current Research in the Humanities 27, no. 1 (2024): 159–73. http://dx.doi.org/10.4314/ijcrh.v27i1.10.

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The Nigerian movie distribution and exhibition business has undergone major transformation in the last few years due to technological innovation. The study is about Nollywood paradigm shift from hard copy movie distribution to online streaming services. The aim is to ascertain why producers are moving to soft screening through online pay-to-view platforms. The objectives were to find out if this new trend is financially beneficial to the producers and whether it will likely continue in the future. The study adopted qualitative methods to interrogate existing data on some of the most popular No

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Li, Bao-Chun, and Ya-Zhou Wang. "Charged-Hadron Pseudorapidity Distributions in p-p and Pb-Pb Collisions at LHC Energies." Advances in High Energy Physics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/515420.

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Pseudorapidity distributions of charged hadrons produced in p-p and Pb-Pb collisions at LHC energies were measured by the CMS and ALICE Collaborations, respectively. An improved Tsallis distribution in the two-cylinder model is used to describe the pseudorapidity spectra. We consider the rapidity shift at the longitudinal direction in the geometrical picture of the collisions. The calculated results are in agreement with the experimental data. The gap between the projectile cylinder and the target cylinder increases with the centralities. The rapidity shifts in the cylinders also increase with

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Zhao, Yusheng, Junyu Luo, Xiao Luo, et al. "Attention Bootstrapping for Multi-Modal Test-Time Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22849–57. https://doi.org/10.1609/aaai.v39i21.34446.

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Test-time adaptation aims to adapt a well-trained model to potential distribution shifts at test time using only unlabeled test data, without access to the original training data. While previous efforts mainly focus on a single modality, test-time distribution shift in the multi-modal setting is more complex and calls for new solutions. This paper tackles the problem of multi-modal test-time adaptation by proposing a novel method named Attention Bootstrapping with Principal Entropy Minimization (ABPEM). We observe that test-time distribution shift causes misalignment across modalities, leading

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Omoruyi, Frederick A., Ifunanya Lydia Omeje, Ifeanyi Charles Anabike, and Okechukwu J. Obulezi. "A New Variant of Rama Distribution with Simulation Study and Application to Blood Cancer Data." European Journal of Theoretical and Applied Sciences 1, no. 4 (2023): 389–409. http://dx.doi.org/10.59324/ejtas.2023.1(4).36.

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In this paper, we propose a new lifetime distribution with flexibility in modeling than its parent distribution. The new distribution is a variant of the Rama distribution having a positive shift parameter. We call the proposed distribution Shifted Rama (SR) distribution. Mathematical and statistical characteristics such as crude moments, central moment, coefficient of variation, index of dispersion, conditional moment, mean residual life function, mean deviation, Bonferroni and Lorenz curve, and the order statistics are derived. Furthermore, reliability measures like survival function, hazard

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Frederick, A. Omoruyi, Lydia Omeje Ifunanya, Charles Anabike Ifeanyi, and J. Obulezi Okechukwu. "A New Variant of Rama Distribution with Simulation Study and Application to Blood Cancer Data." European Journal of Theoretical and Applied Sciences 1, no. 4 (2023): 389–409. https://doi.org/10.59324/ejtas.2023.1(4).36.

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In this paper, we propose a new lifetime distribution with flexibility in modeling than its parent distribution. The new distribution is a variant of the Rama distribution having a positive shift parameter. We call the proposed distribution Shifted Rama (SR) distribution. Mathematical and statistical characteristics such as crude moments, central moment, coefficient of variation, index of dispersion, conditional moment, mean residual life function, mean deviation, Bonferroni and Lorenz curve, and the order statistics are derived. Furthermore, reliability measures like survival function, hazard

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Das, Nandini. "Process control for categorical (ordinal) data." International Journal of Engineering, Science and Technology 14, no. 2 (2022): 34–40. http://dx.doi.org/10.4314/ijest.v14i2.4.

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Quality improvement is playing the key role in the success of a business. Reduction of variability is the main step for improvement of quality. Control charts are developed for the purpose of monitoring the quality characteristics with the aim of reducing variability. In many industries instead of continuous variable categorical (ordinal) data are used to measure the quality characteristics of interest. Hence developing control charts techniques for monitoring ordinal data has become a recent research focus. Quality control practitioners often face a problem to select the appropriate technique

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Goodarzi, Payman, Julian Schauer, and Andreas Schütze. "Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems." Sensors 25, no. 3 (2025): 831. https://doi.org/10.3390/s25030831.

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Detecting distribution and domain shifts is critical in decision-sensitive applications, such as industrial monitoring systems. This paper introduces a novel, robust multi-sensor ensemble framework that integrates principles of automated machine learning (AutoML) to address the challenges of domain shifts and variability in sensor data. By leveraging diverse model architectures, hyperparameters (HPs), and decision aggregation strategies, the proposed framework enhances adaptability to unnoticed distribution shifts. The method effectively handles tasks with various data properties, such as the

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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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Huang, Huiqun, Sihong He, and Fei Miao. "CUQDS: Conformal Uncertainty Quantification Under Distribution Shift for Trajectory Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17422–30. https://doi.org/10.1609/aaai.v39i16.33915.

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Trajectory prediction models that can infer both future trajectories and their associated uncertainties of the target vehicles is crucial for safe and robust navigation and path planning of autonomous vehicles. However, the majority of existing trajectory prediction models have neither considered reducing the uncertainty as one objective during the training stage nor provided reliable uncertainty quantification during inference stage, especially under potential distribution shift. Therefore, in this paper, we propose the Conformal Uncertainty Quantification under Distribution Shift framework,

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Martínez-Martínez, Josué, Olivia Brown, and Rajmonda Caceres. "Towards Robustness to Natural Variations and Distribution Shift (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23579–81. http://dx.doi.org/10.1609/aaai.v38i21.30481.

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This research focuses on improving the robustness of machine learning systems to natural variations and distribution shifts. A design trade space is presented, and various methods are compared, including adversarial training, data augmentation techniques, and novel approaches inspired by model-based robust optimization formulations.

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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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Alexander, Chinago B., and Vincent E. Weli. "Statistical Analyses of Rainfall Distribution: A Check on Climatic Shift." Journal of Progress in Engineering and Physical Science 2, no. 1 (2023): 1–12. http://dx.doi.org/10.56397/jpeps.2023.03.01.

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The study Statistical Analyse of Rainfall Distribution: A check on Climate Shift is concerned with using table values and graph to visually compare rainfall distribution over different climatic cycles. To achieve this, rainfall data from Nigeria Meteorological Agency archive was extracted for analysis. The study observed that only two months had mean rainfall less than 51mm. It was also observed that rainfall occurrence decreases over the years, as indicated by the negative trend line. The study among other things discovered that the double maxima as a result of August dry spell has reduced si

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Weinman, J. A., F. S. Marzano, W. J. Plant, A. Mugnai, and N. Pierdicca. "Rainfall observation from X-band, space-borne, synthetic aperture radar." Natural Hazards and Earth System Sciences 9, no. 1 (2009): 77–84. http://dx.doi.org/10.5194/nhess-9-77-2009.

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Abstract. Satellites carrying X-band Synthetic Aperture Radars (SAR) have recently been launched by several countries. These provide new opportunities to measure precipitation with higher spatial resolution than has heretofore been possible. Two algorithms to retrieve precipitation from such measurements over land have been developed, and the retrieved rainfall distributions were found to be consistent. A maritime rainfall distribution obtained from dual frequency (X and C-band) data was used to compute the Differential Polarized Phase Shift. The computed Differential Polarized Phase Shift com

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Zhou, Fang Bin, and Yun Kai Guo. "Analysis on Difference of Contaminated Normal Distribution PDF." Applied Mechanics and Materials 409-410 (September 2013): 1661–66. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1661.

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As a very important distribution, contaminated normal distribution play a great role in data processing. The probability density function (PDF) feature of the contaminated normal distribution was investigated. The Kullback-Leibler distance is suggested for measuring PDF difference between mean shift model and variance inflation model. Numerical calculations show that the PDF difference of two kinds of model is related to mean shift parameter λ and the variance inflation factor α closely when the main distribution is the standard normal distribution and the relationship is nonlinear proportiona

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Zainab, Muhammad Jibrin, A. B. Zoramawa, Ahmed Audu, and Ahmad I. Tambuwal. "Performance of Modified Exponentially Weighted Moving Average (M-EWMA) Control Charts Using Transformed F-Distribution." Asian Journal of Probability and Statistics 27, no. 3 (2025): 176–87. https://doi.org/10.9734/ajpas/2025/v27i3733.

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The Modified Exponentially Weighted Moving Average (M_EWMA) control chart is a novel statistical tool designed to enhance process monitoring, particularly in scenarios involving F-distributed data. This research investigates its effectiveness by analyzing control limits, process variability, and shift detection capabilities. Results indicate that the M_EWMA chart offers stable control limits and low variance under normal conditions, ensuring a reliable framework for maintaining process stability. The study evaluates the impact of the smoothing parameter λ on the chart's sensitivity. Smaller λ

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van der Kooij, Jeroen, Sascha M. M. Fässler, David Stephens, Lisa Readdy, Beth E. Scott, and Beatriz A. Roel. "Opportunistically recorded acoustic data support Northeast Atlantic mackerel expansion theory." ICES Journal of Marine Science 73, no. 4 (2015): 1115–26. http://dx.doi.org/10.1093/icesjms/fsv243.

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Abstract Fisheries independent monitoring of widely distributed pelagic fish species which conduct large seasonal migrations is logistically complex and expensive. One of the commercially most important examples of such a species in the Northeast Atlantic Ocean is mackerel for which up to recently only an international triennial egg survey contributed to the stock assessment. In this study, we explore whether fisheries acoustic data, recorded opportunistically during the English component of the North Sea International Bottom Trawl Survey, can contribute to an improved understanding of mackere

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Culpeper, Jonathan, and Merja Kytö. "Data in historical pragmatics." Journal of Historical Pragmatics 1, no. 2 (2000): 175–99. http://dx.doi.org/10.1075/jhp.1.2.03cul.

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In this paper we examine four speech-related text types in terms of how linguistically close they are to spoken face-to-face interaction. Our “conversational” diagnostics include lexical repetitions, question marks (as an indicator of question-answer adjacency pairs), interruptions, and several single word interactive features (first- and second-person pronouns, private verbs and demonstrative pronouns). We discuss the nature of these diagnostics and then consider their distribution across our text types and across the period 1600 to 1720. We reveal: (1) a differential distribution across our

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Deng, Junli, Haoyuan Yao, and Ping Shi. "Enhanced 3D Pose Estimation in Multi-Person, Multi-View Scenarios through Unsupervised Domain Adaptation with Dropout Discriminator." Sensors 23, no. 20 (2023): 8406. http://dx.doi.org/10.3390/s23208406.

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Data-driven pose estimation methods often assume equal distributions between training and test data. However, in reality, this assumption does not always hold true, leading to significant performance degradation due to distribution mismatches. In this study, our objective is to enhance the cross-domain robustness of multi-view, multi-person 3D pose estimation. We tackle the domain shift challenge through three key approaches: (1) A domain adaptation component is introduced to improve estimation accuracy for specific target domains. (2) By incorporating a dropout mechanism, we train a more reli

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Yan, Yuguang, Mingkui Tan, Yanwu Xu, et al. "Oversampling for Imbalanced Data via Optimal Transport." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5605–12. http://dx.doi.org/10.1609/aaai.v33i01.33015605.

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The issue of data imbalance occurs in many real-world applications especially in medical diagnosis, where normal cases are usually much more than the abnormal cases. To alleviate this issue, one of the most important approaches is the oversampling method, which seeks to synthesize minority class samples to balance the numbers of different classes. However, existing methods barely consider global geometric information involved in the distribution of minority class samples, and thus may incur distribution mismatching between real and synthetic samples. In this paper, relying on optimal transport

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Vargas, David A., Juan F. De Villena, Valeria Larios, et al. "Data-Mining Poultry Processing Bio-Mapping Counts of Pathogens and Indicator Organisms for Food Safety Management Decision Making." Foods 12, no. 4 (2023): 898. http://dx.doi.org/10.3390/foods12040898.

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Bio-mapping studies play an important role, as the data collected can be managed and analyzed in multiple ways to look at process trends, find explanations about the effect of process changes, activate a root cause analysis for events, and even compile performance data to demonstrate to inspection authorities or auditors the effect of certain decisions made on a daily basis and their effects over time in commercial settings not only from the food safety perspective but also from the production side. This study presents an alternative analysis of bio-mapping data collected throughout several mo

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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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Li, Yang, Han Meng, Zhenyu Bi, Ingolv T. Urnes, and Haipeng Chen. "Population Aware Diffusion for Time Series Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18520–29. https://doi.org/10.1609/aaai.v39i17.34038.

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Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data at the individual level, but pay less attention to preserving the population-level properties on the entire dataset. Such population-level properties include value distributions for each dimension and distributions of certain functional dependencies (e.g., cross-correlation, CC) between different dimensions. For instance, when generating house energy consumption TS data, the value distributions of the outside temper

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