Supplementary data are available at Bioinformatics online.

Over the past few decades, plenty of visualization software for the structural analysis of disordered/complex systems has been developed, but the uniqueness and correctness of structural quantification for such systems are still challenging. This paper introduces a visualization analysis tool based on the largest standard cluster analysis (LaSCA), which satisfies the three essential requirements for general structural analysis: physical correctness, objective identification, and injective representation. The specific functionalities of LaSCA include the directed graph model of complex systems, novel structural parameters, topologically close-packed structures, arbitrary partial pair distribution functions, the identification of long-range ordered structures, the adaptive selection of graphical elements, the tracking display of atom ID, user-defined view angles, various options for atom selection, and so on. The program is efficiently based on OpenGL hardware acceleration, employing special algorithms to treat bonds as cylinders or lines and treat atoms as spheres, icosahedrons, tetrahedrons, or points. LaSCA can process more than 1.2 million atoms within 50 s on a PC with 1 GB memory and four cores (Intel Core i7-9700). It is robust and low-cost for surveying short-, medium-, and long-range ordered structures and tracking their evolutions.

Query processing over uncertain data is very important in many applications due to the existence of uncertainty in real-world data. In this paper, we propose a novel and important query for uncertain data, namely probabilistic top-(k, l) range (PTR) query, which retrieves l uncertain tuples that are expected to meet score range constraint [s 1 , s 2 ] and have the maximum top-k probabilities but no less than a given probability threshold q. In order to accelerate the PTR query, we present some effective pruning techniques to reduce the search space of PTR query, which are integrated seamlessly into an efficient PTR query procedure. Extensive experiments over both real-world and synthetic datasets verify the efficiency and effectiveness of our proposed approaches.

In this work, we present DiPPeR, a novel and fast 2D path planning framework for quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a scalable dataset of map images and corresponding end-to-end trajectories, an image-conditioned diffusion planner for mobile robots, and a training/inference pipeline employing CNNs. We validate our approach in several mazes, as well as in real-world deployment scenarios on Boston Dynamic's Spot and Unitree's Go1 robots. DiPPeR performs on average 70 times faster for trajectory generation against both search based and data driven path planning algorithms with an average of 80% consistency in producing feasible paths of various length in maps of variable size, and obstacle structure.

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