Utility Estimation in Large Preference Graphs Using A* Search

Robust multivariate time series forecasting is crucial in many cyberphysical and Internet of Things applications. Existing state-of-the-art robust forecasting models decompose time series into independent functions covering trends and periodicities. However, these independent functions fail to capture correlations among multiple time series, thereby reducing prediction accuracy. Moreover, existing robust forecasting models treat certain abrupt but normal changes, e.g., caused by holidays, as outliers because they occur infrequently and have data distributions that resemble those of outliers. This exacerbates model bias and reduces prediction accuracy. This paper aims to capture correlations across multiple time series and abrupt but normal changes, thereby improving prediction accuracy. We employ weak labels to partition the dataset into source and target domains. Then, we propose the Domain Adversarial Robust Forecaster (DARF). This forecasting model is based on adversarial domain adaptation and includes two novel modules: Correlated Robust Forecaster (CORF) and Domain Critic. Specifically, CORF constitutes an encoder-decoder framework proficient at robust multivariate time series forecasting, and Domain Critic works to reduce data bias. Extensive experiments and discussions show that DARF is capable of state-of-the-art forecasting accuracy.

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CFt)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R2) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CFt (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R2 averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CFt, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CFt, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.

Solar-based power generation presents challenges for system and grid operators due to the intermittent nature of power supply. Predicting the performance of photovoltaic (PV) power plants and rooftop systems can often be challenging due to difficulties in data collection and incoherencies in interconnected systems. Following the hierarchical aggregation structure from geographical and temporal similarities between PV systems, we suggest a simplified approach to predicting the performance of individual installations and evaluating the impact of these hypothetical installations on the overall grid. We use the hierarchical nature of power generation and ascertain weather datasets to predict the performance of new or existing systems for locations with unmeasured input data. We demonstrate an approach that could improve grid stability by using a hierarchical model on publicly available datasets on utility and rooftop installations. Ensemble machine learning algorithms are trained with 16 weeks of known hourly input training features to form a baseline model for known locations. The prediction accuracy is then directly compared for locations with known and unknown input features, both on a granular and subregion level. We observe a reduction in prediction accuracy by 6–8% using the hierarchical approach. The accuracy of the hierarchical model can be further enhanced beyond our work by increasing the training dataset temporally, as well as by augmenting nested layers of the hierarchy.

Natural or artificial selection could shape genetic architecture, e.g., the relationship between minor allele frequency (MAF) and the effect sizes of causal variants (CVs). This study aimed to investigate the impact of the MAF-effect size relationship (as a selection signature, S) on genomic prediction and heritability estimation in livestock, using both simulated data (Holstein) and real datasets (Holstein and pigs). We evaluated the performance of two models: (1) selection-adjusted genomic best linear unbiased prediction (GBLUP-S), and (2) MAF-stratified selection-adjusted genomic best linear unbiased prediction (GBLUP-SMS). Simulation results demonstrated that for traits under strong negative selection (S < -1), both GBLUP-S and GBLUP-SMS outperformed classic GBLUP. The prediction accuracy of GBLUP-S improved by 0.011-0.031, while GBLUP-SMS achieved a gain of 0.005-0.025. Furthermore, GBLUP-SMS exhibited lower sensitivity to variations in S-values, whereas GBLUP-S heavily relied on accurate S specification. When the true S was matched, GBLUP-SMS generated more unbiased (or comparable) heritability estimates and higher prediction accuracy relative to GBLUP-S. Critically, mismatched S in GBLUP-S led to increased bias in heritability estimates and reduced prediction accuracy. Cross-validation with real phenotypic data from Holsteins and pigs demonstrated that implementing selection-adjust methods improved prediction accuracy by 0.015 for FP in Holsteins and 0.01 for T1 in pigs, while enhancing the unbiasedness of heritability estimates across all traits. Negative selection signatures were identified for cattle (S = -0.5) and pig T1, T2, and T3 (S = -1.5, -1, and -2, respectively). These findings advance the theoretical framework of GBLUP-based genomic prediction and heritability estimation.

The importance of an accurate branch prediction mechanism has been well documented. Since the introduction of gshare and the observation that aliasing in the PHT is a major factor in reducing prediction accuracy, several schemes have been proposed to reduce aliasing in the PHT. All these schemes are aimed at maximizing the prediction accuracy with the fewest resources. In this paper we introduce Yet Another Global Scheme (YAGS)-a new scheme to reduce the aliasing in the PHT-that combines the strong points of several previous schemes. YAGS introduces tags into the PHT that allows it to be reduced without sacrificing key branch outcome information. The size reduction more than offsets the cost of the tags. Our experimental results show that YAGS gives better prediction accuracy for the SPEC95 benchmark suite than several leading prediction schemes, for the same cost. It also performs better than the other schemes in the presence of a context switch. Finally, YAGS displays good results for the go benchmark, which is of special interest since it has a large number of static branches and reflects situations where aliasing in the PHT can be a problem.

The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

Differential pulse code modulation (DPCM) algorithms are widely used to compress gray-scale pictures. A critical element in a DPCM algorithm is the accurate prediction of the pixel gray-levels in the input picture. The switched predictors used in modern DPCM algorithms are generally accurate when the input picture is free of noise. However, even small amounts of noise in the input picture will cause a substantial reduction in prediction accuracy and in turn a reduction in compression efficiency. In this paper we describe a novel fuzzy rule-based predictor for use in a lossless DPCM algorithm. For noisy pictures, the accuracy of the new predictor is consistently higher than the accuracy of standard switched predictors.

The tensile strength of components fabricated through additive manufacturing processes is of paramount importance for their implementation in practical engineering applications. However, the intricacy of the process parameters renders the prediction of tensile strength a formidable challenge. In this scholarly work, a predictive model for the tensile strength of 316L stainless steel components produced via SLM was developed through the synergistic integration of CNN and RF. The model was trained on a dataset comprising 42 datasets and subsequently validated against 12 sets of experimental data. The model's predictive performance was quantified using MSE and MAE, which were recorded as 0.00295 and 0.0344, respectively. These values represent a reduction of 3.28% and 31.88% when compared to the predictive accuracy achieved by employing CNN in isolation. Furthermore, the correlation coefficient achieved a substantial increase of 74.18%, reaching a value of 0.9576, which is indicative of a high degree of accuracy in the model's predictive outcomes. With the same sample size, the incorporation of relative density and Vickers hardness as additional input conditions resulted in a reduction in prediction accuracy. The tensile strength prediction model presented herein demonstrates the capability for high-precision prediction even with small datasets, thereby offering a theoretical framework that may guide future endeavors in the prediction of mechanical properties for a broader spectrum of materials.

SummaryLinear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this short-coming is usually resolved by complete-case analysis or imputation. Both workarounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness-induced reduction in prediction accuracy and rely on (unrealistic) assumptions about the missing mechanism. Here, we derive adaptive predictor-set linear model (aps-lm), capable of making predictions for incomplete data without the need for imputation. It is derived by using a predictor-selection operation, the Moore-Penrose pseudoinverse and the reduced QR-decomposition. aps-lm is an LR generalization that inherently handles missing values. It is applied on a reference dataset, where complete predictors and outcome are available, and yields a set of privacy-preserving parameters. In a second stage, these are shared for making predictions of the outcome on external datasets with missing entries for predictors without imputation. Moreover, aps-lm computes prediction errors that account for the pattern of missing values even under extreme missingness. We benchmark aps-lm in a simulation study. aps-lm showed greater prediction accuracy and reduced bias compared to popular imputation strategies under a wide range of scenarios including variation of sample size, goodness-of-fit, missing value type and covariance structure. Finally, as a proof-of-principle, we apply aps-lm in the context of epigenetic aging clocks, linear models that predict a person’s biological age from epigenetic data with promising clinical applications.

ABSTRACTOne of the most important factors affecting genomic prediction accuracy appears to be training population (TP) composition. The objective of this study was to evaluate the effect of genomic relationship on genomic prediction accuracy and determine if adding increasingly unrelated individuals to a TP can reduce prediction accuracy. To accomplish this, a population of barley (Hordeum vulgare L.) lines from the University of Minnesota (lines denoted as MN) and North Dakota State University (lines denoted as ND) breeding programs were used for model training. Predictions were validated using two independent sets of progenies derived from MN × MN crosses and ND × ND crosses. Predictive ability sharply decreased with decreasing relationship between the TP and validation population (VP). More importantly, it was observed that adding increasingly unrelated individuals to the TP can actually reduce predictive ability compared with smaller TPs consisting of highly related individuals only. Reported results are possibly conditional on the relatively low marker density (342 single nucleotide polymorphisms [SNPs]) used. Nevertheless, these findings suggest plant breeding programs desiring to use genomic selection could benefit from focusing on good phenotyping of smaller TPs closely related to the selection candidates rather than developing large and diverse TPs.

Predicting high-dimensional dynamical systems with irregular time steps presents significant challenges for current data-driven algorithms. These irregularities arise from missing data, sparse observations, or adaptive computational techniques, reducing prediction accuracy. To address these limitations, we propose a novel method: a Physics-Spatiotemporal Masked Autoencoder. This method integrates convolutional autoencoders for spatial feature extraction with masked autoencoders optimised for irregular time series, leveraging attention mechanisms to reconstruct the entire physical sequence in a single prediction pass. The model avoids the need for data imputation while preserving the physical integrity of the system. Here, ’physics’ refers to high-dimensional fields generated by underlying dynamical systems, rather than the enforcement of explicit physical constraints or PDE residuals. We evaluate this approach on multiple simulated datasets and real-world ocean temperature data. The results demonstrate that our method achieves significant improvements in prediction accuracy, robustness to nonlinearities, and computational efficiency over traditional convolutional and recurrent network methods. The model shows potential for capturing complex spatiotemporal patterns without requiring domain-specific knowledge, with applications in climate modelling, fluid dynamics, ocean forecasting, environmental monitoring, and scientific computing.

Background/Objectives: Subjective refraction is the clinical gold standard but is time-consuming and examiner-dependent. Most artificial intelligence (AI)-based approaches rely on specialized imaging or biometric data not routinely available. This study aimed to predict subjective refraction using only routine, non-cycloplegic autorefraction and keratometric data and to identify factors associated with reduced prediction accuracy. Methods: This retrospective study included 1856 eyes from 1006 patients. A multi-output histogram gradient-boosting model predicted subjective spherical equivalent, cylindrical power, and astigmatic axis. Performance was evaluated on an independent test dataset using R2 and mean absolute error, with circular statistics for axis prediction. Prediction failure was assessed using clinically relevant tolerance thresholds (sphere/cylinder ≤ 0.50 D; axis ≤ 10°) and multivariable logistic regression. Results: The model achieved high accuracy for spherical and cylindrical prediction (R2 = 0.987 and 0.933; MAE = 0.126 D and 0.137 D). Astigmatic axis prediction demonstrated strong circular agreement (ρ = 0.898), with a mean absolute angular error of 4.65° (median, 0.96°). Axis errors were higher in eyes with low cylinder magnitude (<0.75 D) and oblique astigmatism. In multivariable analysis, steeper keratometry (K2; OR = 7.25, 95% CI 1.62–32.46, p = 0.010) and greater objective cylindrical power (OR = 2.79, 95% CI 1.87–8.94, p = 0.032) were independently associated with poor prediction. Conclusions: A machine-learning model based solely on routine, non-cycloplegic autorefractor and keratometric measurements can accurately estimate subjective refraction, supporting AI as a complementary decision-support tool rather than a replacement for conventional subjective refraction.

Microarray data can be used to detect diseases and predict responses to therapies through classification models. However, the high dimensionality and low sample size of such data result in many computational problems such as reduced prediction accuracy and slow classification speed. In this paper, we propose a novel family of nonnegative least-squares classifiers for high-dimensional microarray gene expression and comparative genomic hybridization data. Our approaches are based on combining the advantages of using local learning, transductive learning, and ensemble learning, for better prediction performance. To study the performances of our methods, we performed computational experiments on 17 well-known data sets with diverse characteristics. We have also performed statistical comparisons with many classification techniques including the well-performing SVM approach and two related but recent methods proposed in literature. Experimental results show that our approaches are faster and achieve generally a better prediction performance over compared methods.

Two thirds of human water use is linked to agricultural practices including crop irrigation. Furthermore, excess irrigation leads to drainage problems. For this reason, reduced irrigation strategies need to be implemented to protect water resources. However, low irrigation may lead to crop water stress. A fast and inexpensive way to predict the necessary amount of irrigation required is by a model-based approach. With this approach, it is possible to explore the relation between irrigation, crop water stress and drainage. However, parameter uncertainty can reduce prediction accuracy. Therefore, the aims of this research were: (1) to develop and test a methodology that allows the analysis of uncertainty sources in irrigation strategies (2) to identify how much irrigation can be reduced while maintaining a low risk of crop stress, and (3) to explore the influence of uncertainty in soil parameters and evapotranspiration on model predictions. Results from a realistic case considered in this study indicated that, while maintaining a low risk of crop stress (<1 %), it is possible to reduce drainage (by 88 %) and water use (22 %) for a conventional irrigation strategy. This reduction is dependent on the type of risk aversion strategy and is specific for a case scenario where variations are certain.

Accurate trajectory prediction is critical for ensuring the safety of autonomous vehicles (AVs) in complex traffic environments. Recently, fully perception-based trajectory prediction methods have gained significant attention due to their capability to holistically model interactions within traffic scenarios. However, existing methods struggle to address occlusion in real-world traffic conditions and are constrained by fixed perception radius, leading to either excessive computational overhead or reduced prediction accuracy. These limitations pose a critical challenge in balancing predictive performance and computational efficiency in multi-agent trajectory forecasting. To tackle these issues, we propose a novel occlusion-aware trajectory prediction framework, named OIPAR. Specifically, OIPAR first applies vectorized normalization to environmental data and integrates occlusion reasoning with trajectory prediction, enabling efficient handling of occlusions. Moreover, it introduces an adaptive perception radius iteration module that dynamically adjusts the perception range to enhance prediction rationality. Our experimental results demonstrate that OIPAR offers flexible configurability, allowing the adoption of adaptive strategies to accommodate diverse and complex traffic environments.