Eduardo Veas

Objective. Augmented reality can provide digital information about physical entities presented within its real-world context. However, this information might disagree with the user’s expectations due to factual errors in the data or cognitive biases. Such incongruity can impair user experience and undermine trust in the AR system. To address this issue, we propose detecting inconsistencies between physical objects and digital information through hybrid brain-computer interfaces. Approach. We conducted two complementary experiments. First, we implemented a strategy that integrates eye-tracking and brain signals for incongruity detection in an offline study. Subsequently, we assessed our approach in an online study in which participants received immediate feedback on the classification. Main results. The grand average event-related potentials revealed consistent electroencephalographic responses to incongruent augmentations, specifically a centroparietal N400 effect, across both experiments. We could further distinguish between congruent and incongruent information with an average balanced accuracy of 70% in the online study. Significance. These findings demonstrate the feasibility of detecting incongruity online, allowing for autonomous system adaptation, like presenting information in a more accessible format or providing contextual support.

Background/Objectives: Pharmaceutical formulation involves designing a drug product by combining the properties of an active pharmaceutical ingredient (API) with suitable excipients and processing strategies to produce a safe, effective, and manufacturable dosage form. However, data in formulation science are often limited, expensive to generate, and frequently restricted by proprietary and confidentiality constraints. Interactive digital tools can support formulators during early drug product development by improving the structure, transparency, and efficiency of formulation decision-making. While the current system focuses on structured decision support, future extensions may incorporate machine-learning methods for recommendation and knowledge extraction. Methods: In this work, we developed the Formulation tool, an interactive decision-support and visualization system for formulation development based on a hierarchical formulation-strategy framework commonly used in pharmaceutical practice. The tool is designed to prioritize suitable formulation principles and associated processing routes, with oral solid formulation as the initial application domain. The evaluated scenarios also include pathway regions relevant to oral liquid formulations. Its modular architecture also makes it adaptable to other formulation scenarios. To assess practical applicability, the tool was evaluated in a structured expert study involving five expert users across six predefined formulation scenarios (n = 30 runs), covering three drugs under adult and pediatric conditions. Results: The tool showed agreement with the expected dosage-form families and overall formulation properties, with adult scenarios converging to oral solid regions and pediatric scenarios converging to oral liquid regions. At the downstream formulation-profile level, users converged either to the dominant expected pathway or to alternative feasible pathways within the same formulation region. Variability in full pathway completion was observed and was primarily associated with differences in user interaction behavior and exploratory usage patterns. The median task completion time was 113.5 s. Conclusions: In addition to organizing formulation knowledge, the Formulation tool records user interactions in a structured manner, which may support future learning from usage patterns. Because detailed downstream formulation constraints are often institution-specific and are typically not available in the public domain, the present evaluation focused on agreement at the dosage-form-family level and on overall formulation properties rather than on highly specialized constraint logic. The system is based on a constraint satisfaction problem (CSP) framework, which is well suited for modeling complex decision processes under explicit constraints. CSP has also been widely applied in intelligent scheduling systems, supporting its suitability for structured, constraint-rich decision-making tasks such as pharmaceutical formulation strategy development.

Objective: Augmented reality can provide digital information about physical entities presented within its real-world context. However, this information might disagree with the user’s expectations due to factual errors in the data or cognitive biases. Such incongruity can impair user experience and undermine trust in the AR system. To address this issue, we propose detecting inconsistencies between physical objects and digital information through hybrid brain-computer interfaces. Approach: We conducted two complementary experiments. First, we implemented a strategy that integrates eye-tracking and brain signals for incongruity detection in an offline study. Subsequently, we assessed our approach in an online study in which participants received immediate feedback on the classification. Main results: The grand average event-related potentials revealed consistent electroencephalographic responses to incongruent augmentations, specifically a centroparietal N400 effect, across both experiments. We could further distinguish between congruent and incongruent information with an average balanced accuracy of 70 % in the online study. Significance: These findings demonstrate the feasibility of detecting incongruity online, allowing for autonomous system adaptation, like presenting information in a more accessible format or providing contextual support.

Optical Music Recognition (OMR) for historical Chinese musical notations, such as suzipu and lülüpu, presents unique challenges due to high class imbalance and limited training data. This paper introduces significant advancements in OMR for Jiang Kui's influential collection Baishidaoren Gequ from 1202. In this work, we develop and evaluate a character recognition model for scarce imbalanced data. We improve upon previous baselines by reducing the Character Error Rate (CER) from 10.4% to 7.1% for suzipu, despite working with 77 highly imbalanced classes, and achieve a remarkable CER of 0.9% for lülüpu. Our models outperform human transcribers, with an average human CER of 15.9% and a best-case CER of 7.6%. We employ temperature scaling to achieve a well-calibrated model with an Expected Calibration Error (ECE) below 0.0162. Using a leave-one-edition-out cross-validation approach, we ensure robust performance across five historical editions. Additionally, we extend the KuiSCIMA dataset to include all 109 pieces from Baishidaoren Gequ, encompassing suzipu, lülüpu, and jianzipu notations. Our findings advance the digitization and accessibility of historical Chinese music, promoting cultural diversity in OMR and expanding its applicability to underrepresented music traditions.

Building accurate and reliable maps is a critical requirement for autonomous robots. In this paper, we propose UN3-Mapping, an implicit neural mapping method that enables high-quality 3D reconstruction with integrated uncertainty estimation. Our approach employs a hybrid representation: an implicit neural distance field models scene geometry, while an explicit gradient field, optimized from surface normals, derives non-projective signed distance labels from raw range data. These refined distance labels are then used to train our implicit map. For uncertainty estimation, we design an online learning framework to capture the reconstruction uncertainty in a self-supervised manner. Benefiting from the uncertainty-aware map, our method is capable of removing the dynamic obstacles with high uncertainty within the raw point cloud. Extensive experiments show that our approach outperforms existing methods in mapping accuracy and completeness while also exhibiting promising potential for dynamic object segmentation. The code will be released at https://github.com/tiev-tongji/UN3-Mapping.

Eye movements shape all visual input to the brain, making their understanding essential for studying perception and visual guidance in dynamic environments. Research on expert performance indicates that gaze coordination is a key feature of expertise in, for example, sports. Mobile eye tracking provides the opportunity to investigate gaze strategies supporting the skilled actions of an athlete and can deliver insight into the underlying perceptual-cognitive processes. We systematically observed the visual strategy of an expert racing driver performing a domain-representative task. Synchronized gaze, telemetry, and localization data from a high-grade simulator were analyzed to address four classes of research questions: oculomotor, scene analysis, timing, and point of vantage. The results (a) replicate the seminal tangent point orientation (pre-turn-in saccades), (b) describe both the oculomotor signature and timing signature of the steering with the head strategy, (c) identify a novel saccade strategy (pre-full-throttle saccades), and (d) reveal a previously unstudied spatial regularity in the serial organization of behavior: a tight localization of the points of vantage where the pre-turn-in saccades and pre-full-throttle saccades are made. The gaze strategies are not tied to specifics of the task and may be relevant for understanding expert performance in other fields with similar visuomotor and cognitive demands. The method of cross-examining an integrated dataset by multiple parametrizations itself complements traditional research designs with predefined task constraints and restrictions. We are not aware of any study that has simultaneously addressed all four kinds of research questions simultaneously.