Explainable robotics applied to bipedal walking gait development

Explainability is becoming an important topic in artificial intelligence (AI). A well explainable system can increase the trust in the application of that system. The same holds for robotics where the walking gait controller can be some AI system. We will show that a simple and explainable controller that enables an energy efficient walking gait and can handle uneven terrains, can be developed by a well structured design method. The main part of the controller consist of three simple neural networks with 4, 6 and 8 neurons. So, although creating a stable and energy efficient walking gait is a complex problem, it can be generated without some deep neural network or some complex mathematical model

Propagating Surface Plasmon Polaritons: Towards Applications for Remote-Excitation Surface Catalytic Reactions

Plasmonics is a well-established field, exploiting the interaction of light and metals at the nanoscale; with the help of surface plasmon polaritons, remote-excitation can also be observed by using silver or gold plasmonic waveguides. Recently, plasmonic catalysis was established as a new exciting platform for heterogeneous catalytic reactions. Recent reports present remote-excitation surface catalytic reactions as a route to enhance the rate of chemical reactions, and offer a pathway to control surface catalytic reactions. In this review, we focus on recent advanced reports on silver plasmonic waveguide for remote-excitation surface catalytic reactions. First, the synthesis methods and characterization techniques of sivelr nanowire plasmonic waveguides are summarized, and the properties and physical mechanisms of plasmonic waveguides are presented in detail. Then, the applications of plasmonic waveguides including remote excitation fluorescence and SERS are introduced, and we focus on the field of remote-excitation surface catalytic reactions. Finally, forecasts are made for possible future applications for the remote-excitation surface catalysis by plasmonic waveguides in living cells

Differential selection on gene translation efficiency between the filamentous fungus Ashbya gossypii and yeasts

<p>Abstract</p> <p>Background</p> <p>The filamentous fungus <it>Ashbya gossypii </it>grows into a multicellular mycelium that is distinct from the unicellular morphology of its closely related yeast species. It has been proposed that genes important for cell cycle regulation play central roles for such phenotypic differences. Because <it>A. gossypii </it>shares an almost identical set of cell cycle genes with the typical yeast <it>Saccharomyces cerevisiae</it>, the differences might occur at the level of orthologous gene regulation. Codon usage patterns were compared to identify orthologous genes with different gene regulation between <it>A. gossypii </it>and nine closely related yeast species.</p> <p>Results</p> <p>Here we identified 3,151 orthologous genes between <it>A. gossypii </it>and nine yeast species. Two groups of genes with significant differences in codon usage (gene translation efficiency) were identified between <it>A. gossypii </it>and yeasts. 333 genes (Group I) and 552 genes (Group II) have significantly higher translation efficiency in <it>A. gossypii </it>and yeasts, respectively. Functional enrichment and pathway analysis show that Group I genes are significantly enriched with cell cycle functions whereas Group II genes are biased toward metabolic functions.</p> <p>Conclusion</p> <p>Because translation efficiency of a gene is closely related to its functional importance, the observed functional distributions of orthologous genes with different translation efficiency might account for phenotypic differentiation between <it>A. gossypii </it>and yeast species. The results shed light on the mechanisms for pseudohyphal growth in pathogenic yeast species.</p

Traditional Chinese Medicine Improves Activities of Daily Living in Parkinson's Disease

We evaluated the effects of a traditional Chinese medicine (TCM), named Zeng-xiao An-shen Zhi-chan 2 (ZAZ2), on patients with Parkinson's disease (PD). Among 115 patients with idiopathic PD enrolled (mean age, 64.7 ± 10.2 years old), 110 patients (M = 65, F = 45; mean age, 64.9 ± 10.7 years old) completed the study. Patients took either ZAZ2 (n = 59) or placebo granule (n = 56) in a blind manner for 13 weeks while maintaining other anti-Parkinson medications unchanged. All participants wore a motion logger, and we analyzed the power-law temporal autocorrelation of the motion logger records taken on 3 occasions (before, one week, and 13 weeks after the drug administration). Drug efficacy was evaluated with the conventional Unified Parkinson Disease Rating Scale (UPDRS), as well as the power-law exponent α, which corresponds to the level of physical activity of the patients. ZAZ2 but not placebo granule improved the awake-sleep rhythm, the UPDRS Part II, Part II + III, and Part IV scores, and the α values. The results indicate that ZAZ2 improved activities of daily living (ADL) of parkinsonism and, thus, is a potentially suitable drug for long-term use

Intention Understanding in Human-Robot Interaction Based on Visual-NLP Semantics

With the rapid development of robotic and AI technology in recent years, human-robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human-robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures

Say What You Are Looking At: An Attention-Based Interactive System for Autistic Children

Gaze-following is an effective way for intention understanding in human–robot interaction, which aims to follow the gaze of humans to estimate what object is being observed. Most of the existing methods require people and objects to appear in the same image. Due to the limitation in the view of the camera, these methods are not applicable in practice. To address this problem, we propose a method of gaze following that utilizes a geometric map for better estimation. With the help of the map, this method is competitive for cross-frame estimation. On the basis of this method, we propose a novel gaze-based image caption system, which has been studied for the first time. Our experiments demonstrate that the system follows the gaze and describes objects accurately. We believe that this system is competent for autistic children’s rehabilitation training, pension service robots, and other applications.</jats:p