A vibrating flow pump (VFP), which can generate oscillated blood flow (10-50 Hz/min), has been developed by our team for the artificial heart system. However, the flow pattern of this pump was different from that of the natural heart; therefore, it is important to analyze the effect of this oscillated blood flow on the circulatory regulatory system. To analyze the hemodynamics of high frequency oscillated blood flow as an entity, (not decomposed), nonlinear mathematical techniques were utilized. VFPs were implanted between the left atrium in animal experiments using adult goats. After the implantation procedure, the ascending aorta was clamped to constitute the complete left heart circulation with VFP. Using a nonlinear mathematical technique, an arterial blood pressure waveform was embedded into four-dimensional phase space and projected into three-dimensional phase space. The Lyapunov numerical method was used as an adjunct to graphic analysis of the state space. Phase portrait of the attractor showed a high dimension complex structure, suggesting deterministic chaos during natural circulation. However, phase portrait of the hemodynamics during oscillated blood flow showed a single circle with banding and a forbidden zone, similar to a limit-cycle attractor, suggesting a lower dimensional dynamic system. Positive Lyapunov exponent during oscillated blood flow suggests the existence of lower dimensional chaotic dynamics. These results suggest that the circulatory regulatory system during oscillated blood flow may be a lower dimensional homeochaotic state; thus, hemodynamic parameters must be carefully regulated when unexpected external stimuli are present.
In this paper, we present a compressed data structure for moving object trajectories in a road network, which are represented as sequences of road edges. Unlike existing compression methods for trajectories in a network, our method supports pattern matching and decompression from an arbitrary position while retaining a high compressibility with theoretical guarantees. Specifically, our method is based on FM-index, a fast and compact data structure for pattern matching. To enhance the compression, we incorporate the sparsity of road networks into the data structure. In particular, we present the novel concepts of relative movement labeling and PseudoRank, each contributing to significant reductions in data size and query processing time. Our theoretical analysis and experimental studies reveal the advantages of our proposed method as compared to existing trajectory compression methods and FM-index variants.
We consider a general task called partial Wasserstein covering with the goal of providing information on what patterns are not being taken into account in a dataset (e.g., dataset used during development) compared to another (e.g., dataset obtained from actual applications). We model this task as a discrete optimization problem with partial Wasserstein divergence as an objective function. Although this problem is NP-hard, we prove that it satisfies the submodular property, allowing us to use a greedy algorithm with a 0.63 approximation. However, the greedy algorithm is still inefficient because it requires solving linear programming for each objective function evaluation. To overcome this inefficiency, we propose quasi-greedy algorithms, which consist of a series of techniques for acceleration such as sensitivity analysis based on strong duality and the so-called C-transform in the optimal transport field. Experimentally, we demonstrate that we can efficiently fill in the gaps between the two datasets, and find missing scene in real driving scene datasets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.