We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a proximal gradient-type algorithm in the space of invertible matrices with joint learning of a prediction model through backpropagation. We illustrate the proposed algorithm on synthetic and real data experiments. In particular, owing to the additional supervision, we observe an increased success rate of the non-convex optimization and the improved interpretability of the independent components.

The discovery of equations from observational data is one of the fundamental pillars of the traditional scientific method. From the work of Johannes Kepler, who inferred the laws of planetary motion from meticulous astronomical observations [1] collected by Tycho Brahe [2], to Isaac Newton's theoretical formulations that consolidated classical mechanics, the process of identifying mathematical relationships underlying natural phenomena has historically been characterized by its manual nature, based essentially on systematic trial-and-error procedures. However, in recent decades, the advent of Big Data, characterized by the production of an immense volume of complex, mostly nonlinear, data, in several fields has driven a new search for physical laws. Faced with the need to analyze these data sets to understand their intrinsic structure and derive symbolic representations that capture the integral behavior of a system, the demand for advanced analytical methods has become growing and indispensable. With the emergence of modern computational techniques, this process has undergone a radical transformation, driving the widespread development and use of various regression techniques.

Current methods for stochastic hyperparameter learning in Gaussian Processes (GPs) rely on approximations, such as computing biased stochastic gradients or using inducing points in stochastic variational inference. However, when using such methods we are not guaranteed to converge to a stationary point of the true marginal likelihood. In this work, we propose algorithms for exact stochastic inference of GPs with kernels that induce a Reproducing Kernel Hilbert Space (RKHS) of moderate finite dimension. Our approach can also be extended to infinite dimensional RKHSs at the cost of forgoing exactness. Both for finite and infinite dimensional RKHSs, our method achieves better experimental results than existing methods when memory resources limit the feasible batch size and the possible number of inducing points.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an important resource for the identification of new therapeutic targets and cardioprotective drugs. After differentiation iPSC-CMs show an immature, fetal-like phenotype. Cultivation of iPSC-CMs in lipid-supplemented maturation medium (MM) strongly enhances their structural, metabolic and functional phenotype. Nevertheless, assessing iPSC-CM maturation state remains challenging as most methods are time consuming and go in line with cell damage or loss of the sample. To address this issue, we developed a non-invasive approach for automated classification of iPSC-CM maturity through interpretable artificial intelligence (AI)-based analysis of beat characteristics derived from video-based motion analysis. In a prospective study, we evaluated 230 video recordings of early-state, immature iPSC-CMs on day 21 after differentiation (d21) and more mature iPSC-CMs cultured in MM (d42, MM). For each recording, 10 features were extracted using Maia motion analysis software and entered into a support vector machine (SVM). The hyperparameters of the SVM were optimized in a grid search on 80 % of the data using 5-fold cross-validation. The optimized model achieved an accuracy of 99.5 $\pm$ 1.1 % on a hold-out test set. Shapley Additive Explanations (SHAP) identified displacement, relaxation-rise time and beating duration as the most relevant features for assessing maturity level. Our results suggest the use of non-invasive, optical motion analysis combined with AI-based methods as a tool to assess iPSC-CMs maturity and could be applied before performing functional readouts or drug testing. This may potentially reduce the variability and improve the reproducibility of experimental studies.

-- Staircase perception is critical for humanoid robots and mobility -impaired individuals, yet existing methods have low accuracy, lighting sensitivity, and texture dependency. To address this, we propose a novel polarization-visual fusion framework that achieves robust staircase detection and high -precision the three-dimensional (3D) reconstruction, establishing a paradigm of S taircase recognition -- Heterogeneous sensor calibration (monocular and TOF camera) -- Polarization 3D reconstruction . First, the staircase recognition algorithm based on YOLOv11 integrated with polarization-intensity contrast enhancement algorithm and point cloud segmentation is improved, reaching recognition accuracy of 98.7% 0.10% by suppressing reflections and correcting by r edundant information of point cloud. Then, an improved gray wolf optimizer with Levy flight and d ynamic weights enable s accurate heterogeneous sensor calibration ( 0.33 0.04 mm error) between heterogeneous-resolution cameras is employed . Finally, a method of fusing polarized binocular and TOF depth information to realize the 3D reconstruction of the staircase is proposed . Considering the ambiguity in polarization reconstruction and the data holes in binocular reconstruction, b inocular vision is used to correct polarization azimuth ambiguity, TOF is used to fill data holes from stereo matching. Experiments show our method achieves <0.2% reconstruction error at 0.5m - significantly outperforming binocular (surface distortion) and polarization-based (normal vector ambiguity) approaches. This technology provides accurate terrain adaptation for robot ic foothold planning. INTRODUCTION A s a general scene, the staircase interferes with the traversal of h umanoid robots, legged robots, lower limb disabilities, or visually impaired individuals due to its special physical structure. Accurate staircase recognition technology is a prerequisite for navigation and control, and staircase recognition technology has attracted the attention of man y scholars [1],[2],[3] . Staircase recognition is of great significance for the mode switching and foothold position calculation of robots, which can improve the overall performance of robots in stair case scenes. As a common terrain, stairs are very difficult for humanoid robots and people with lower limb disabilities or visual impairments. Therefore, it is of great significance to design a staircase scene perception algorithm. At present, the staircase recognition is mainly applied in the fields of rehabilitation medicine and humanoid robots [ 4 ].

Data has become a foundational asset driving innovation across domains such as finance, healthcare, and e-commerce. In these areas, predictive modeling over relational tables is commonly employed, with increasing emphasis on reducing manual effort through automated machine learning (AutoML) techniques. This raises an interesting question: can feature augmentation itself be automated and identify and utilize task-related relational signals? To address this challenge, we propose an end-to-end automated feature augmentation framework, ReCoGNN, which enhances initial datasets using features extracted from multiple relational tables to support predictive tasks. ReCoGNN first captures semantic dependencies within each table by modeling intra-table attribute relationships, enabling it to partition tables into structured, semantically coherent segments. It then constructs a heterogeneous weighted graph that represents inter-row relationships across all segments. Finally, ReCoGNN leverages message-passing graph neural networks to propagate information through the graph, guiding feature selection and augmenting the original dataset. Extensive experiments conducted on ten real-life and synthetic datasets demonstrate that ReCoGNN consistently outperforms existing methods on both classification and regression tasks.

-- Collaborative learning enhances the performance and adaptability of multi-robot systems in complex tasks but faces significant challenges due to high communication overhead and data heterogeneity inherent in multi-robot tasks. T o this end, we propose CoCoL, a Co mmunication efficient decentralized Co llaborative L earning method tailored for multi-robot systems with heterogeneous local datasets. Leveraging a mirror descent framework, CoCoL achieves remarkable communication efficiency with approximate Newton-type updates by capturing the similarity between objective functions of robots, and reduces computational costs through inexact sub-problem solutions. Furthermore, the integration of a gradient tracking scheme ensures its robustness against data heterogeneity. Experimental results on three representative multi-robot collaborative learning tasks show that the proposed CoCoL can significantly reduce both the number of communication rounds and total bandwidth consumption while maintaining state-of-the-art accuracy. These benefits are particularly evident in challenging scenarios involving non-IID (non-independent and identically distributed) data distribution, streaming data, and time-varying network topologies. I. INTRODUCTION Multi-robot systems offer the ability to tackle complex tasks through proper collaboration with enhanced efficiency, robustness, and flexibility compared to single-robot systems [1]. By sharing information, a team of robots can leverage collective knowledge to make more informed decisions and accomplish tasks in a coordinated manner.

This study proposes a generalizable encoding strategy that maps tactile sensor data to electrical stimulation patterns, enabling neural organoids to perform an open-loop artificial tactile Braille classification task. Human forebrain organoids cultured on a low-density microelectrode array (MEA) are systematically stimulated to characterize the relationship between electrical stimulation parameters (number of pulse, phase amplitude, phase duration, and trigger delay) and organoid responses, measured as spike activity and spatial displacement of the center of activity. Implemented on event-based tactile inputs recorded from the Evetac sensor, our system achieved an average Braille letter classification accuracy of 61 percent with a single organoid, which increased significantly to 83 percent when responses from a three-organoid ensemble were combined. Additionally, the multi-organoid configuration demonstrated enhanced robustness against various types of artificially introduced noise. This research demonstrates the potential of organoids as low-power, adaptive bio-hybrid computational elements and provides a foundational encoding framework for future scalable bio-hybrid computing architectures.

Feature transformation plays a critical role in enhancing machine learning model performance by optimizing data representations. Recent state-of-the-art approaches address this task as a continuous embedding optimization problem, converting discrete search into a learnable process. Although effective, these methods often rely on sequential encoder-decoder structures that cause high computational costs and parameter requirements, limiting scalability and efficiency. To address these limitations, we propose a novel framework that accomplishes automated feature transformation through four steps: transformation records collection, embedding space construction with a revised Generative Pre-trained Transformer (GPT) model, gradient-ascent search, and autoregressive reconstruction. In our approach, the revised GPT model serves two primary functions: (a) feature transformation sequence reconstruction and (b) model performance estimation and enhancement for downstream tasks by constructing the embedding space. Such a multi-objective optimization framework reduces parameter size and accelerates transformation processes. Experimental results on benchmark datasets show that the proposed framework matches or exceeds baseline performance, with significant gains in computational efficiency. This work highlights the potential of transformer-based architectures for scalable, high-performance automated feature transformation.

Data were collected through semi-structured interviews conducted in hospital settings. Each participant completed standardized clinical questionnaires, including the PHQ-9 and HAMD-24 scales [27], to assess depression severity. HAMD-24 scores are used to generate labels for the binary and ternary classification tasks, while PHQ-9 scores are used for the quinary classification task. To enable a more comprehensive participant profile, additional annotations are provided, including Big five personality traits (using a 10-point scale) [15], physical health conditions, financial stress levels, and the number of cohabiting family members, see Table II.