Strong and Reversible Adhesion of Interlocked 3D-Microarchitectures

Diverse physical interlocking devices have recently been developed based on one-dimensional (1D), high-aspect-ratio inorganic and organic nanomaterials. Although these 1D nanomaterial-based interlocking devices can provide reliable and repeatable shear adhesion, their adhesion in the normal direction is typically very weak. In addition, the high-aspect-ratio, slender structures are mechanically less durable. In this study, we demonstrate a highly flexible and robust interlocking system that exhibits strong and reversible adhesion based on physical interlocking between three-dimensional (3D) microscale architectures. The 3D microstructures have protruding tips on their cylindrical stems, which enable tight mechanical binding between the microstructures. Based on the unique 3D architectures, the interlocking adhesives exhibit remarkable adhesion strengths in both the normal and shear directions. In addition, their adhesion is highly reversible due to the robust mechanical and structural stability of the microstructures. An analytical model is proposed to explain the measured adhesion behavior, which is in good agreement with the experimental results

Selective deep convolutional neural network for low cost distorted image classification

Neural networks trained using images with a certain type of distortion should be better at classifying test images with the same type of distortion than generally-trained neural networks, given other factors being equal. Based on this observation, an ensemble of convolutional neural networks (CNNs) trained with different types and degrees of distortions is used. However, instead of simply classifying test images of unknown distortion types with the entire ensemble of CNNs, an extra tiny CNN is specifically trained to distinguish between the different types and degrees of distortions. Then, only the dedicated CNN for that specific type and degree of distortion, as determined by the tiny CNN, is activated and used to classify a possibly distorted test image. This proposed architecture, referred to as a \textit{selective deep convolutional neural network (DCNN)}, is implemented and found to result in high accuracy with low hardware costs. Detailed simulations with realistic image distortion scenarios using three popular datasets show that memory, MAC operations, and energy savings of up to 93.68%, 93.61%, and 91.92%, respectively, can be achieved with almost no reduction in image classification accuracy. The proposed selective DCNN scores up to 2.18x higher than the state-of-the-art DCNN model when evaluated using NetScore, a comprehensive metric that considers both CNN performance and hardware cost. In addition, it is shown that even higher hardware cost reduction can be achieved when selective DCNN is combined with previously proposed model compression techniques. Finally, experiments conducted with extended types and degrees of image distortion show that selective DCNN is highly scalable.11Ysciescopu

Flexible and Shape-Reconfigurable Hydrogel Interlocking Adhesives for High Adhesion in Wet Environments Based on Anisotropic Swelling of Hydrogel Microstructures

This study presents wet-responsive, shape-reconfigurable, and flexible hydrogel adhesives that exhibit strong adhesion under wet environments based on reversible interlocking between reconfigurable microhook arrays. The experimental investigation on the swelling behavior and structural characterization of the hydrogel microstructures reveal that the microhook arrays undergo anisotropic swelling and shape transformation upon contact with water. The adhesion between the interlocked microhook arrays is greatly enhanced under wet conditions because of the hydration-triggered shape reconfiguration of the hydrogel microstructures. Furthermore, wet adhesion monotonically increases with water-exposure time. A maximum adhesion force of 79.9 N cm-2 in the shear direction is obtained with the hydrogel microhook array after 20 h of swelling, which is 732.3% greater than that under dry conditions (i.e., 9.6 N cm-2). A simple theoretical model is developed to describe the measured adhesion forces. The results are in good agreement with the experimental data

Mesoscopic transport in KSTAR plasmas: avalanches and the E×BE \times B staircase

The self-organization is one of the most interesting phenomena in the non-equilibrium complex system, generating ordered structures of different sizes and durations. In tokamak plasmas, various self-organized phenomena have been reported, and two of them, coexisting in the near-marginal (interaction dominant) regime, are avalanches and the $E \times B$ staircase. Avalanches mean the ballistic flux propagation event through successive interactions as it propagates, and the $E \times B$ staircase means a globally ordered pattern of self-organized zonal flow layers. Various models have been suggested to understand their characteristics and relation, but experimental researches have been mostly limited to the demonstration of their existence. Here we report detailed analyses of their dynamics and statistics and explain their relation. Avalanches influence the formation and the width distribution of the $E \times B$ staircase, while the $E \times B$ staircase confines avalanches within its mesoscopic width until dissipated or penetrated. Our perspective to consider them the self-organization phenomena enhances our fundamental understanding of them as well as links our findings with the self-organization of mesoscopic structures in various complex systems

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Accurate hardware-efficient logarithm circuit

111Nsciescopu

Multipliers with Approximate 4-2 Compressors and Error Recovery Modules

Approximate multiplication is a common operation used in approximate computing methods for high performance and low power computing. Power-efficient circuits for approximate multiplication can be realized with an approximate 4-2 compressor. This letter presents a novel design that uses a modification of a previous approximate 4-2 compressor design and adds an error recovery module. The proposed design, even with the additional error recovery module, is more accurate, requires less hardware, and consumes less power than previously proposed 4-2 compressor-based approximate multiplier designs. © 2009-2012 IEEE.110Nsciescopu

DMC: Differentiable Model Compression for Hardware-Efficient Convolutional Neural Network

Hardware-efficient CNN model design can be divided into two stages: training of a large baseline network to achieve high accuracy and applying model compression to create a smaller network, at the possible expense of a slight reduction in accuracy. This paper proposes a new differential model compression (DMC) method based on bilevel optimization to find the importance of channels in a pretrained CNN. Experimental results show that, for model compression for an image classification task, DMC requires only 12 GPU minutes to achieve a similar compression ratio, but with increased image classification accuracy, when cmpared to the previous best method.1