Existing stretchable, transparent conductors are mostly electronic conductors. They limit the performance of interconnects, sensors, and actuators as components of stretchable electronics and soft machines. We describe a class of devices enabled by ionic conductors that are highly stretchable, fully transparent to light of all colors, and capable of operation at frequencies beyond 10 kilohertz and voltages above 10 kilovolts. We demonstrate a transparent actuator that can generate large strains and a transparent loudspeaker that produces sound over the entire audible range. The electromechanical transduction is achieved without electrochemical reaction. The ionic conductors have higher resistivity than many electronic conductors; however, when large stretchability and high transmittance are required, the ionic conductors have lower sheet resistance than all existing electronic conductors.

Anomaly detection is a significant and hence wellstudied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. ALAD builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize GAN training, which results in significantly improved anomaly detection performance. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.

The interplay of the polyelectrolyte-surface electrostatic and non-electrostatic interactions in the polyelectrolytes adsorption onto two charged objects -A self-consistent field study J. Chem. Phys. 137, 104904 (2012) Voltage-induced deformation in dielectric J. Appl. Phys. 112, 033519 (2012) Concentration and mobility of charge carriers in thin polymers at high temperature determined by electrode polarization modeling J. Appl. Phys. 112, 013710 (2012) Crystal phase dependent photoluminescence of 6,13-pentacenequinone J. Appl. Phys. 112, 013512 (2012) Frequency-dependent dielectric response model for polyimide-poly(vinilydenefluoride) multilayered dielectrics Appl.The dynamic performance of dielectric elastomer transducers and their capability of electromechanical energy conversion are affected by dissipative processes, such as viscoelasticity, dielectric relaxation, and current leakage. This paper describes a method to construct a model of dissipative dielectric elastomers on the basis of nonequilibrium thermodynamics. We characterize the state of the dielectric elastomer with kinematic variables through which external loads do work, and internal variables that measure the progress of the dissipative processes. The method is illustrated with examples motivated by existing experiments of polyacrylate very-high-bond dielectric elastomers. This model predicts the dynamic response of the dielectric elastomer and the leakage current behavior. We show that current leakage can be significant under large deformation and for long durations. Furthermore, current leakage can result in significant hysteresis for dielectric elastomers under cyclic voltage.

Sustainable natural rubber for soft generators opens up new possibilities for harvesting renewable resources. With this technology, ocean wave energy could become a cheap and clean resource for generation of electricity.

Far greater voltage-actuated deformation is achievable for a dielectric elastomer under equal-biaxial dead load than under rigid constraint usually employed. Areal strains of 488% are demonstrated. The dead load suppresses electric breakdown, enabling the elastomer to survive the snap-through electromechanical instability. The breakdown voltage is found to increase with the voltage ramp rate. A nonlinear model for viscoelastic dielectric elastomers is developed and shown to be consistent with the experimental observations.

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Motivation: The need for accurate and efficient tools for computational RNA structure analysis has become increasingly apparent over the last several years: RNA folding algorithms underlie numerous applications in bioinformatics, ranging from microarray probe selection to de novo non-coding RNA gene prediction.In this work, we present RAF (RNA Alignment and Folding), an efficient algorithm for simultaneous alignment and consensus folding of unaligned RNA sequences. Algorithmically, RAF exploits sparsity in the set of likely pairing and alignment candidates for each nucleotide (as identified by the CONTRAfold or CONTRAlign programs) to achieve an effectively quadratic running time for simultaneous pairwise alignment and folding. RAF's fast sparse dynamic programming, in turn, serves as the inference engine within a discriminative machine learning algorithm for parameter estimation.Results: In cross-validated benchmark tests, RAF achieves accuracies equaling or surpassing the current best approaches for RNA multiple sequence secondary structure prediction. However, RAF requires nearly an order of magnitude less time than other simultaneous folding and alignment methods, thus making it especially appropriate for high-throughput studies.Availability: Source code for RAF is available at:http://contra.stanford.edu/contrafold/Contact: chuongdo@cs.stanford.edu

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