Lateral Magnetically Modulated Multilayers by Combining Ion Implantation and Lithography

The combination of lithography and ion implantation is demonstrated to be a suitable method to prepare lateral multilayers. A laterally, compositionally, and magnetically modulated microscale pattern consisting of alternating Co (1.6 µm wide) and Co‐CoO (2.4 µm wide) lines has been obtained by oxygen ion implantation into a lithographically masked Au‐sandwiched Co thin film. Magnetoresistance along the lines (i.e., current and applied magnetic field are parallel to the lines) reveals an effective positive giant magnetoresistance (GMR) behavior at room temperature. Conversely, anisotropic magnetoresistance and GMR contributions are distinguished at low temperature (i.e., 10 K) since the O‐implanted areas become exchange coupled. This planar GMR is principally ascribed to the spatial modulation of coercivity in a spring‐magnet‐type configuration, which results in 180° Néel extrinsic domain walls at the Co/Co‐CoO interfaces. The versatility, in terms of pattern size, morphology, and composition adjustment, of this method offers a unique route to fabricate planar systems for, among others, spintronic research and applications.     

Complexes With Biologically Active Ligands. Part 6 Ni(II) Coordination Compounds of Hydrazine and Heterocyclic Sulfonamides as Inhibitors of the Zinc Enzyme Carbonic Anhydrase

Ternary Ni(II) complexes of hydrazine and eight heterocyclic sulfonamides possessing carbonic anhydrase (CA) inhibitory properties, were prepared and characterized by elemental analysis, spectroscopic, magnetic, thermogravimetric, and conductimetric measurements. The complexes behave as strong inhibitors for two isozymes (I and II) of carbonic anhydrase.     

Consequences and conjectures in preordered sets

In a preordered set, or preset, consequence operators in the sense of Tarski, defined on families of subsets, are introduced. From them, the corresponding sets of conjectures, hypotheses, speculations and refutations are considered, studying the relationships between these sets and those previously defined on ortholattices. All the concepts introduced are illustrated with three particular consequence operators, whose behavior is studied in detail. The results obtained are applied to the case of fuzzy sets endowed with the usual pointwise ordering.     

Deciding k-Colorability of P 5-Free Graphs in Polynomial Time

The problem of computing the chromatic number of a P ₅-free graph (a graph which contains no path on 5 vertices as an induced subgraph) is known to be NP-hard. However, we show that for every fixed integer k, there exists a polynomial-time algorithm determining whether or not a P ₅-free graph admits a k-coloring, and finding one, if it does.     

Non‐Rigid Puzzles

Shape correspondence is a fundamental problem in computer graphics and vision, with applications in various problems including animation, texture mapping, robotic vision, medical imaging, archaeology and many more. In settings where the shapes are allowed to undergo non‐rigid deformations and only partial views are available, the problem becomes very challenging. To this end, we present a non‐rigid multi‐part shape matching algorithm. We assume to be given a reference shape and its multiple parts undergoing a non‐rigid deformation. Each of these query parts can be additionally contaminated by clutter, may overlap with other parts, and there might be missing parts or redundant ones. Our method simultaneously solves for the segmentation of the reference model, and for a dense correspondence to (subsets of) the parts. Experimental results on synthetic as well as real scans demonstrate the effectiveness of our method in dealing with this challenging matching scenario.     

Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture Model

Programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.     

Opportunistic sampling-based active visual SLAM for underwater inspection

This paper reports on an active SLAM framework for performing large-scale inspections with an underwater robot. We propose a path planning algorithm integrated with visual SLAM that plans loop-closure paths in order to decrease navigation uncertainty. While loop-closing revisit actions bound the robot’s uncertainty, they also lead to redundant area coverage and increased path length. Our proposed opportunistic framework leverages sampling-based techniques and information filtering to plan revisit paths that are coverage efficient. We employ Gaussian process regression for modeling the prediction of camera registrations and use a two-step optimization procedure for selecting revisit actions. We show that the proposed method offers many benefits over existing solutions and good performance for bounding navigation uncertainty in long-term autonomous operations with hybrid simulation experiments and real-world field trials performed by an underwater inspection robot.     

Adaptive continuous‐space informative path planning for online environmental monitoring

Autonomous mobile robots are increasingly employed to take measurements for environmental monitoring, but planning informative, measurement‐rich paths through large three‐dimensional environments is still challenging. Designing such paths, known as the informative path planning (IPP) problem, has been shown to be NP‐hard. Existing algorithms focus on providing guarantees on suboptimal solutions, but do not scale well to large problems. In this paper, we introduce a novel IPP algorithm that uses an evolutionary strategy to optimize a parameterized path in continuous space, which is subject to various constraints regarding path budgets and motion capabilities of an autonomous mobile robot. Moreover, we introduce a replanning scheme to adapt the planned paths according to the measurements taken in situ during data collection. When compared to two state‐of‐the‐art solutions, our method provides competitive results at significantly lower computation times and memory requirements. The proposed replanning scheme enables to build models with up to 25% lower uncertainty within an initially unknown area of interest. Besides presenting theoretical results, we tailored the proposed algorithms for data collection using an autonomous surface vessel for an ecological study, during which the method was validated through three field deployments on Lake Zurich, Switzerland. Spatiotemporal variations are shown over a period of three months and in an area of 350 m × 350 m × 13 m. Whereas our theoretical solution can be applied to multiple applications, our field results specifically highlight the effectiveness of our planner for monitoring toxic microorganisms in a pre‐alpine lake, and for identifying hot‐spots within their distribution.