Robotic manipulation within dynamic environments presents challenges to precise control and adaptability. Traditional fixed-view camera systems face challenges adapting to change viewpoints and scale variations, limiting perception and manipulation precision. To tackle these issues, we propose the Active Vision-driven Robotic (AVR) framework, a teleoperation hardware solution that supports dynamic viewpoint and dynamic focal length adjustments to continuously center targets and maintain optimal scale, accompanied by a corresponding algorithm that effectively enhances the success rates of various operational tasks. Using the RoboTwin platform with a real-time image processing plugin, AVR framework improves task success rates by 5%-16% on five manipulation tasks. Physical deployment on a dual-arm system demonstrates in collaborative tasks and 36% precision in screwdriver insertion, outperforming baselines by over 25%. Experimental results confirm that AVR framework enhances environmental perception, manipulation repeatability (40% $\le $1 cm error), and robustness in complex scenarios, paving the way for future robotic precision manipulation methods in the pursuit of human-level robot dexterity and precision.

Imitation learning has emerged as a powerful paradigm for robot skills learning. However, traditional data collection systems for dexterous manipulation face challenges, including a lack of balance between acquisition efficiency, consistency, and accuracy. To address these issues, we introduce Exo-ViHa, an innovative 3D-printed exoskeleton system that enables users to collect data from a first-person perspective while providing real-time haptic feedback. This system combines a 3D-printed modular structure with a slam camera, a motion capture glove, and a wrist-mounted camera. Various dexterous hands can be installed at the end, enabling it to simultaneously collect the posture of the end effector, hand movements, and visual data. By leveraging the first-person perspective and direct interaction, the exoskeleton enhances the task realism and haptic feedback, improving the consistency between demonstrations and actual robot deployments. In addition, it has cross-platform compatibility with various robotic arms and dexterous hands. Experiments show that the system can significantly improve the success rate and efficiency of data collection for dexterous manipulation tasks.

Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to prevent production delays and reduce risks in the supply chains. However, information about supply chains, especially at the deeper levels, is often intransparent and incomplete, making it difficult to obtain precise predictions about prospective risks. By connecting different data sources, we model the supply network as a knowledge graph and achieve transparency up to tier-3 suppliers. To predict missing information in the graph, we apply state-of-the-art knowledge graph completion methods and attain a mean reciprocal rank of 0.4377 with the best model. Further, we apply graph analysis algorithms to identify critical entities in the supply network, supporting supply chain managers in automated risk identification.

Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which are expected to influence future situations. In temporal knowledge graphs, time information is integrated into the graph by equipping each edge with a timestamp or a time range. Embedding-based methods have been introduced for link prediction on temporal knowledge graphs, but they mostly lack explainability and comprehensible reasoning chains. Particularly, they are usually not designed to deal with link forecasting -- event prediction involving future timestamps. We address the task of link forecasting on temporal knowledge graphs and introduce TLogic, an explainable framework that is based on temporal logical rules extracted via temporal random walks. We compare TLogic with state-of-the-art baselines on three benchmark datasets and show better overall performance while our method also provides explanations that preserve time consistency. Furthermore, in contrast to most state-of-the-art embedding-based methods, TLogic works well in the inductive setting where already learned rules are transferred to related datasets with a common vocabulary.

The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link prediction problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approach outperforms several baseline methods.

Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells. Hematologists usually label and count microscopy images of blood cells manually. In many cases, however, cells in different maturity states are difficult to distinguish, and in combination with image noise and subjectivity, humans are prone to make labeling mistakes. This results in labels that are often not reproducible, which can directly affect the diagnoses. We introduce TIMELY, a probabilistic model that combines pseudotime inference methods with inhomogeneous hidden Markov trees, which addresses this challenge of label inconsistency. We show first on simulation data that TIMELY is able to identify and correct wrong labels with higher precision and recall than baseline methods for labeling correction. We then apply our method to two real-world datasets of blood cell data and show that TIMELY successfully finds inconsistent labels, thereby improving the quality of human-generated labels.

Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electronic health records, a great variety of data has been collected in clinical practice, enabling the evaluation of treatments and treatment policies based on observational data. In this paper, we focus on learning individualized treatment rules (ITRs) to derive a treatment policy that is expected to generate a better outcome for an individual patient. In our framework, we cast ITRs learning as a contextual bandit problem and minimize the expected risk of the treatment policy. We conduct experiments with the proposed framework both in a simulation study and based on a real-world dataset. In the latter case, we apply our proposed method to learn the optimal ITRs for the administration of intravenous (IV) fluids and vasopressors (VP). Based on various offline evaluation methods, we could show that the policy derived in our framework demonstrates better performance compared to both the physicians and other baselines, including a simple treatment prediction approach. As a long-term goal, our derived policy might eventually lead to better clinical guidelines for the administration of IV and VP.