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Abstract Recently artificial intelligence (AI) and machine learning (ML) models have demonstrated remarkable progress with applications developed in various domains. It is also increasingly discussed that AI and ML models and applications should be transparent, explainable, and trustworthy. Accordingly, the field of Explainable AI (XAI) is expanding rapidly. XAI holds substantial promise for improving trust and transparency in AI-based systems by explaining how complex models such as the deep neural network (DNN) produces their outcomes. Moreover, many researchers and practitioners consider that using provenance to explain these complex models will help improve transparency in AI-based systems. In this paper, wemore »conduct a systematic literature review of provenance, XAI, and trustworthy AI (TAI) to explain the fundamental concepts and illustrate the potential of using provenance as a medium to help accomplish explainability in AI-based systems. Moreover, we also discuss the patterns of recent developments in this area and offer a vision for research in the near future. We hope this literature review will serve as a starting point for scholars and practitioners interested in learning about essential components of provenance, XAI, and TAI.« less

Across forests, photosynthesis and woody growth respond to different climate cues.

Fibrin is the main component of blood clots. The mechanical properties of fibrin are therefore of critical importance in successful hemostasis. One of the divalent cations released by platelets during hemostasis is Zn²⁺; however, its effect on the network structure of fibrin gels and on the resultant mechanical properties remains poorly understood. Here, by combining mechanical measurements with three-dimensional confocal microscopy imaging, we show that Zn²⁺can tune the fibrin network structure and alter its mechanical properties. In the presence of Zn²⁺, fibrin protofibrils form large bundles that cause a coarsening of the fibrin network due to an increase in fibermore »diameter and reduction of the total fiber length. We further show that the protofibrils in these bundles are loosely coupled to one another, which results in a decrease of the elastic modulus with increasing Zn²⁺concentrations. We explore the elastic properties of these networks at both low and high stress: At low stress, the elasticity originates from pulling the thermal slack out of the network, and this is consistent with the thermal bending of the fibers. By contrast, at high stress, the elasticity exhibits a common master curve consistent with the stretching of individual protofibrils. These results show that the mechanics of a fibrin network are closely correlated with its microscopic structure and inform our understanding of the structure and physical mechanisms leading to defective or excessive clot stiffness.

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Sampling based planning is an important step for long-range navigation for an autonomous vehicle. This work proposes a GPU-accelerated sampling based path planning algorithm which can be used as a global planner in autonomous navigation tasks. A modified version of the generation portion for the Probabilistic Road Map (PRM) algorithm is presented which reorders some steps of the algorithm in order to allow for parallelization and thus can benefit highly from utilization of a GPU. The GPU and CPU algorithms were compared using a simulated navigation environment with graph generation tasks of several different sizes. It was found that themore »GPU-accelerated version of the PRM algorithm had significant speedup over the CPU version (up to 78×). This results provides promising motivation towards implementation of a real-time autonomous navigation system in the future.« less