Abstract
Decision trees are a cornerstone of interpretable machine learning and are widely used for their simplicity and effectiveness in classification tasks. To address the growing need for models that can operate on continuous, unbounded data, decision trees have been reinvented for the data stream setting, where they must learn incrementally under constraints such as limited memory, evolving distributions, and delayed supervision. A critical component of these tree-based models, particularly those based on the Hoeffding Trees, is the split criterion, which determines how the input space is partitioned. This study introduces a new split criterion for stream-based Hoeffding trees, based on a unified five-parameter entropic formulation that generalizes several well-known measures, including Shannon, Gini, Tsallis, and Rényi entropies. While such formulations have been explored in batch learning, their application to streaming scenarios has not been made. By incorporating this criterion into a variety of established streaming classifiers and evaluating performance on standard benchmark datasets, we demonstrate consistent and statistically significant improvements over existing methods, including those implemented in the River library. Notably, we report gains of up to 40% in immediate evaluation metrics, along with consistent wins and some draws on the prequential Macro-F1, with no observed losses against baseline criteria. The generality of the approach introduces additional computational overhead and also enables greater expressiveness and adaptability in handling uncertainty and nonstationary data. This work advances the integration of information-theoretic principles into online learning and highlights the importance of efficient hyperparameter tuning and adaptive entropy selection in streaming environments.

Abstract
Data streams with varying feature spaces have received extensive attention recently, while the common concept drift in them remains underexplored. Unsupervised concept drift detectors can report potential drifts without class labels, making them suitable for practical scenarios where labeling is usually costly and difficult. However, existing unsupervised detectors usually operate under fixed feature spaces. To address this limitation, a Matching Degree Histogram-based unsupervised detector for data streams with Varying Feature Spaces (MDH-VFS) is proposed. Changes in input features are refined into four scenarios, specifying the sources of concept drifts in such data streams. Based on this, MDH-VFS monitors the distribution of each feature independently using the fix-slide windows model. A matching degree-based histogram (MD-Histogram) supporting online updating is proposed to model data distribution. MD-Histogram requires no prior distributions and captures data change more sensitively than traditional histograms. The dissimilarity between two MD-Histograms is measured by the Hellinger distance, and drift is detected using an adaptive thresholding strategy. Both the drift positions and drift features can be reported. Experimental results show that MDH-VFS can not only effectively detect drifts in data streams with varying feature spaces (achieving average F1-score/MCC above 77% and outperforming nine existing detectors with improvements of at least 43%), but also improve the classification performance of downstream learning algorithms (reaching a maximum average accuracy of 88% and yielding up to 7.23% improvement).

Abstract
Image reconstruction from incomplete measurements is a fundamental problem in signal and image processing, with applications ranging from medical imaging to computational photography. In recent years, deep learning approaches have shown promising performance, particularly when combined with physics-inspired formulations such as deep unrolling. This paper presents a systematic comparative study of classical interpolation and variational reconstruction methods, direct convolutional neural networks (CNNs), and unrolled data-consistency CNN architectures for image reconstruction under different sampling patterns. We consider three representative mask types: structured block masks, nonuniform masks, and random sampling patterns, with sampling ratios ranging from 10% to 50%. Experiments are conducted on the public BSDS500 image dataset, using a fixed grayscale preprocessing pipeline and a reproducible train/validation/test split. Experimental results demonstrate that reconstruction performance strongly depends on the sampling pattern. For random masks, the full unrolled DC-CNN achieves the best quantitative and qualitative performance, reaching a PSNR of 30.98 dB and an SSIM of 0.921 at 50% sampling. In contrast, for structured block and nonuniform masks, TV-based inpainting provides the strongest overall performance, showing that classical model-based reconstruction remains highly competitive when the sampling pattern contains spatially coherent missing regions. A block-size sensitivity analysis further confirms that the difficulty of structured-mask reconstruction is governed by the geometric severity of the missing region. Statistical analysis using paired tests with Holm correction confirms that the main performance differences are significant across the evaluated configurations. Furthermore, we show that a lightweight unrolled model with shared weights and reduced depth achieves a substantially lower parameter count and lower computational cost than the full unrolled architecture, although with reduced accuracy in the most favorable random-sampling cases. These findings provide practical insights into the relationship between sampling strategies and reconstruction performance, offering guidance for the design of efficient and robust learning-based reconstruction systems.

Abstract

Abstract
Car sharing, car clubs and short-term rentals could support the transition toward net zero but their success depends on them being financially sustainable for service providers and attractive to end users. Dynamic pricing could support this by incentivizing users while balancing supply and demand. We describe the usage of a round trip car sharing fleet by a continuous time Markov chain model, which reduces to a multi-server queuing model where hire duration is assumed independent of the hourly rental price. We present analytical and simulation optimization models that allow the development of dynamic pricing strategies for round trip car sharing systems; in particular identifying the optimal hourly rental price. The analytical tractability of the queuing model enables fast optimization to maximize expected hourly revenue for either a single fare system or a system where the fare depends on the number of cars on hire, while accounting for stochasticity in customer arrival times and durations of hire. Simulation optimization is used to optimize prices where the fare depends on the time of day or hire duration depends on price. We present optimal prices for a given customer population and show how the expected revenue and car availability depend on the customer arrival rate, willingness-to-pay distribution, dependence of the hire duration on price, and size of the customer population. The results provide optimal strategies for pricing of car sharing and inform strategic managerial decisions such as whether to use time-or state-dependent pricing and optimizing the fleet size.

Abstract
Synchronizing multimodal physiological data streams is a critical and growing challenge in biomedical engineering, particularly when data is collected from multiple devices. This review analyzes the recent state of the art in this field, based on a comprehensive search across five bibliographic databases that yielded 1176 publications. Of these, 60 were selected for in-depth analysis. Our review emphasizes the increasing importance of robust synchronization methodologies in multimodal physiological data analysis. We focused on several key aspects: the types of physiological data streams, the devices used for data collection, methods for measuring alignment latency, the synchronization techniques employed by researchers, and the technological readiness level (TRL) of each technique. Despite the valuable insights from the analyzed studies, a significant gap was identified: 58% of publications that used multiple devices did not assess synchronization latency. This omission is crucial, as latency measurement serves as a key performance indicator for benchmarking different approaches. This finding highlights the critical need for this systematic review and underscores the challenges ahead, as well as the urgent need for further research and development of synchronization techniques in these scenarios. We highlight the need for improvements in synchronization methods and emphasize the importance of accurate latency verification to enhance data acquisition, analysis, and the overall quality of research on multimodal physiology data streams.