State-of-the-art rule mining algorithms rely on summarizing the training set into efficient data structures which allow to quickly answer arbitrary conjunctive queries about the data. The key limitation of such techniques is their memory consumption. Pre-post code trees (PPC-trees) which are the basis of several efficient association and classification rule mining algorithms, are only constructed as an intermediate representation and subsequently converted into a much more efficient N-lists structure. In this paper, we introduce partial pre-post code trees (P3C-trees), which are based on the idea that partial trees are iteratively constructed, and immediately converted into N-lists. This tight integration of these phases allows to avoid the memory bottleneck of a full PPC-tree construction, and thus enables these algorithms to tackle the memory scalability problem posed by large-scale datasets. Our experiments with big datasets confirm that the memory used by P3C-tree is orders of magnitude smaller than the memory consumed by PPC-tree, and the generated N-lists are also more effective than alternative structures such as Tidset or Diffset. Moreover, the N-list construction can also be considerably sped up with the P3C-tree structure.

Uncrewed ground vehicle (UGV) applications, such as warehouse operations, assembly-line production, infrastructure inspection, surveillance, precision farming, and search & rescue, can benefit from shared control, in which a human can semi-automatically control the UGV when needed and let it operate fully-automatically, when desired. Many algorithms have been developed to permit a UGV to semi-autonomously conduct tasks, either individually, or in a group. However, a complete semi-autonomous system that works wherever, and whenever, needed is far from being implemented. Here, we develop a human-robot shared controller for the supervisory control of one or more UGVs by a single person. The shared controller blends an automatic control input with a human control input. The automatic control input consists of a trajectory tracking controller and a control barrier function based input term for collision avoidance. A joystick is used to provide the human control input. Human intent is measured employing a Lyapunov-like storage function, which is used in a convex function based blending law that continuously varies the magnitude of the control inputs coming from the human and the machine. The approach permits us to theoretically prove the asymptotic stability of the closed-loop system. The shared controller is validated using both a physical robot in a cluttered indoor environment, and a hardware-in-the-loop simulated robot operating in virtual warehouse environment.

Abstract Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop. These locally optimal rules are collected and then filtered for a final rule set, which is much larger than the sets learned by conventional rule learning algorithms. A new example is classified by selecting the best among the rules that cover this example. In our experiments on small to very large datasets, the approach’s average classification accuracy is higher than that of state-of-the-art rule learning algorithms. Moreover, the algorithm is highly efficient and can inherently be processed in parallel without affecting the learned rule set and so the classification accuracy. We thus believe that it closes an important gap for large-scale classification rule induction.

BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macromolecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling. RESULTS: We performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an [Formula: see text]score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the [Formula: see text]-score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an [Formula: see text]-score of 91% on the test data in contrast to 64% of the 3D-UNet. The better [Formula: see text]-score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the [Formula: see text]-score is boosted as network depth is increased which is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available. CONCLUSION: Quantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macromolecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet.

Mobile service robots deployed in populated environments like train stations, airports or offices are not only required to move safely, but also in socially acceptable ways. In order to achieve this, robots need to be able to track people within their vicinity. This work presents an approach to tracking people from a mobile robot platform, incorporating a novel approach to human motion prediction. Unfavorable viewing angles, motion blur and ever-changing light conditions are constant issues for sensors on mobile vehicles. Therefore, a system is needed which increases the tracking quality of humans in order to cope with a low detection rate. The scientific contribution of this paper lies in a precise human model for tracking which utilizes historical spatial data of pedestrians from previous detection and from simulation. The model is embedded into a particle-filter based tracking approach designed for the use on a moving platform and is able to incorporate a variety of person detectors. Experiments conducted prove that the proposed method increases tracking quality, especially at a low detection rate.

Autonomous vehicles are, in contrast to classical automated guided vehicles (AGVs), less predictable in their behavior and drive time. Therefore, the issue of how to efficiently control these vehicles arises, because autonomous agents need to be coordinated and not controlled, to give autonomous behaviors and actions space. The scientific contribution of this paper is a novel approach, based on prioritized planning to target this issue as well as an open source framework for evaluation and comparison. Prioritized planning has the disadvantage of being neither optimal nor complete, however, it has the advantage of being computationally feasible. This work utilizes prioritized planning to significantly increase the set of feasible scenarios through collision prevention: by locally finding alternative routes and adding them to the search graph. The paper clearly formulates the extensions needed and delineates the approach's limits, as it is neither optimal nor complete. More importantly, however, our method calculates routes for each vehicle with inter-vehicle synchronization, enabling vehicles to execute the plan in a distributed fashion without centralized control, thereby allowing autonomous behavior. Finally, results are verified by comparing our Multi Robot Router (MRR) proposed in this work to classical approaches. The software developed as well as the test sets are publicly available for ROS and the simulation environment.

The significant PVT variations seen with modern technologies make synchronous design inefficient. Asynchronous design with its flexible timing is a promising alternative, but prototyping is difficult on the available FPGA platforms which are clock centric and do not provide the required functional primitives like mutual exclusion or Muller C-elements. The solutions proposed in the literature work nicely in principle, but cannot safely handle metastability issues that are inevitable at interfaces even in asynchronous designs. In this paper we propose a reliable implementation of a Schmitt-trigger, which allows to safely convert potential intermediate voltage levels that result from metastability into late transitions that can be reliably handled in the asynchronous domain. Beyond the actual circuit we also discuss the associated routing constraints to make the circuit work properly in spite of the uncertain routing within FPGAs. Furthermore we propose a procedure for an "in situ reliability assessment" of the specific Schmitt-trigger element under consideration, which also applies to metastability containment with high-or low-threshold inverters only. Our proof of concept is based on experimental results for both Xilinx and Altera FPGA platforms.