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Positive human–agent relationships can effectively improve human experience and performance in human–machine systems or environments. The characteristics of agents that enhance this relationship have garnered attention in human–agent or human–robot interactions. In this study, based on the rule of the persona effect, we study the effect of an agent's social cues on human–agent relationships and human performance. We constructed a tedious task in an immersive virtual environment, designing virtual partners with varying levels of human likeness and responsiveness. Human likeness encompassed appearance, sound, and behavior, while responsiveness referred to the way agents responded to humans. Based on the constructed environment, we present two studies to explore the effects of an agent's human likeness and responsiveness to agents on participants' performance and perception of human–agent relationships during the task. The results indicate that when participants work with an agent, its responsiveness attracts attention and induces positive feelings. Agents with responsiveness and appropriate social response strategies have a significant positive effect on human–agent relationships. These results shed some light on how to design virtual agents to improve user experience and performance in human–agent interactions. [ABSTRACT FROM AUTHOR]

In this study, we report an effective approach to tune the crystallization, microstructure and charge transport of solution-processed organic semiconductors by blending with a conjugated polymer additive poly(3-hexylthiophene) (P3HT). When 6,13-bis(triisopropylsilylethynyl) pentacene (TIPS pentacene) was used as a model semiconductor material to mix with different amount of P3HT, their intermolecular interactions led to distinctive TIPS pentacene film morphologies, including randomly-oriented crystal ribbons, elongated needles with enhanced long-range order, and grass-like curved microwires with interlinkages. Each type of morphology was found to further correlate to considerably different charge transport and device performance. As compared to pristine TIPS pentacene devices, bottom-gate, top-contact OTFTs with 2% in weight P3HT additive showed a 2-fold and 5-fold improvement of average field-effect mobility and performance consistency (defined as the ratio of average mobility to the standard deviation), respectively. The improvement in transistor electrical performance can be attributed to the combined effect of enhanced crystal orientation and uniformity, as well as increased areal coverage. This work can be applied beyond the particular example demonstrated in this study and to tune the charge transport of other small-molecule organic semiconductors in general. [ABSTRACT FROM AUTHOR]

Electrochemical metallization (ECM) cell kinetics are strongly determined by the electrolyte and can hardly be altered after the cell has been fabricated. Solid-state property tunable electrolytes in response to external stimuli are therefore desirable to introduce additional operational degree of freedom to the ECM cells, enabling novel applications such as multistate memory and reconfigurable computation. In this work, we use Ge2Sb2Te5(GST) as the electrolyte material whose solid state is switched from the amorphous(a) to the crystalline(c) phase thermally. Electrical heating too is readily achievable. The resistive switching characteristics of the cells with different GST phases are examined. The magnitude of the high resistance, the SET voltage and the on/off ratio are found to be considerably affected by the solid phase of GST, whereas the magnitude of the low resistance is least affected. Moreover, a transition from volatile to nonvolatile SET switching is only observed for c-GST based cell under prolonged voltage sweep, but not for a-GST based cell. This work provides a springboard for more studies on the manipulation of the ECM cell kinetics by tunable electrolyte and the resulting unprecedented device functionalities. [ABSTRACT FROM AUTHOR]

The emerging memristor-based neuromorphic engineering promises an efficient computing paradigm. However, the lack of both internal dynamics in the previous feedforward memristive networks and efficient learning algorithms in recurrent networks, fundamentally limits the learning ability of existing systems. In this work, we propose a framework to support complex learning functions by introducing dedicated learning algorithms to a bio-plausible recurrent memristive network with internal dynamics. We fabricate iron oxide memristor-based synapses, with well controllable plasticity and a wide dynamic range of excitatory/inhibitory connection weights, to build the network. To adaptively modify the synaptic weights, the comprehensive recursive least-squares (RLS) learning algorithm is introduced. Based on the proposed framework, the learning of various timing patterns and a complex spatiotemporal pattern of human motor is demonstrated. This work paves a new way to explore the brain-inspired complex learning in neuromorphic systems. [ABSTRACT FROM AUTHOR]