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Robots designed to interact with people in collaborative or social scenarios must move in ways that are consistent with the robot's task and communication goals. However, combining these goals in a naïve manner can result in mutually exclusive solutions, or infeasible or problematic states and actions. In this paper, we present Lively, a framework which supports configurable, real-time, task-based and communicative or socially-expressive motion for collaborative and social robotics across multiple levels of programmatic accessibility. Lively supports a wide range of control methods (i.e. position, orientation, and joint-space goals), and balances them with complex procedural behaviors for natural, lifelike motion that are effective in collaborative and social contexts. We discuss the design of three levels of programmatic accessibility of Lively, including a graphical user interface for visual design called LivelyStudio, the core library Lively for full access to its capabilities for developers, and an extensible architecture for greater customizability and capability. 

The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, broadly associated with immunity and diseases in humans and plants. To date, most studies describing cytoskeleton dynamics and function rely on qualitative/quantitative analyses of cytoskeletal images. While state-of-the-art, these approaches face general challenges: the diversity among filaments causes considerable inaccuracy, and the widely adopted image projection leads to bias and information loss. To solve these issues, we developed the Implicit Laplacian of Enhanced Edge (ILEE), an unguided, high-performance approach for 2D/3D-based quantification of cytoskeletal status and organization. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing biologically interpretable indices measuring the density, bundling, segmentation, branching, and directionality of the cytoskeleton. Our data demonstrated that ILEE resolves the defects of traditional approaches, enables the detection of novel cytoskeletal features, and yields data with superior accuracy, stability, and robustness. The ILEE toolbox is available for public use through PyPI and Google Colab.

 

Probe is the core component of an optical scanning probe microscope such as scattering-type scanning near-field optical microscopy (s-SNOM). Its ability of concentrating and localizing light determines the detection sensitivity of nanoscale spectroscopy. In this paper, a novel plasmonic probe made of a gradient permittivity material (GPM) is proposed and its nanofocusing performance is studied theoretically and numerically. Compared with conventional plasmonic probes, this probe has at least two outstanding advantages: First, it doesn't need extra structures for surface plasmon polaritons (SPPs) excitation or localized surface plasmon resonance (LSPR), simplifying the probe system; Second, the inherent nanofocusing effects of the conical probe structure can be further reinforced dramatically by designing the distribution of the probe permittivity. As a result, the strong near-field enhancement and localization at the tip apex improve both spectral sensitivity and spatial resolution of a s-SNOM. We also numerically demonstrate that a GPM probe as well as its enhanced nanofocusing effects can be realized by conventional semiconductor materials with designed doping distributions. The proposed novel plasmonic probe promises to facilitate subsequent nanoscale spectroscopy applications. 

Non-conservative dislocation climb plays a unique role in the plastic deformation and creep of crystalline materials. Nevertheless, the underlying atomic-scale mechanisms of dislocation climb have not been explored by direct experimental observations. Here, we report atomic-scale observations of grain boundary (GB) dislocation climb in nanostructured Au during in situ straining at room temperature. The climb of a edge dislocation is found to occur by stress-induced reconstruction of two neighboring atomic columns at the edge of an extra half atomic plane in the dislocation core. This is different from the conventional belief of dislocation climb by destruction or construction of a single atomic column at the dislocation core. The atomic route of the dislocation climb we proposed is demonstrated to be energetically favorable by Monte Carlo simulations. Our in situ observations also reveal GB evolution through dislocation climb at room temperature, which suggests a means of controlling microstructures and properties of nanostructured metals.

 

Autonomous vehicles (AVs) are on the verge of changing the transportation industry. Despite the fast development of autonomous driving systems (ADSs), they still face safety and security challenges. Current defensive approaches usually focus on a narrow objective and are bound to specific platforms, making them difficult to generalize. To solve these limitations, we propose AVMaestro, an efficient and effective policy enforcement framework for full-stack ADSs. AVMaestro includes a code instrumentation module to systematically collect required information across the entire ADS, which will then be feed into a centralized data examination module, where users can utilize the global information to deploy defensive methods to protect AVs from various threats. AVMaestro is evaluated on top of Apollo-6.0 and experimental results confirm that it can be easily incorporated into the original ADS with almost negligible run-time delay. We further demonstrate that utilizing the global information can not only improve the accuracy of existing intrusion detection methods, but also potentially inspire new security applications.