Pseudorandom number generation based on controllable cellular automata

A novel Cellular Automata (CA) Controllable CA (CCA) is proposed in this paper. Further, CCA are applied in Pseudorandom Number Generation. Randomness test results on CCA Pseudorandom Number Generators (PRNGs) show that they are better than 1-d CA PRNGs and can be comparable to 2-d ones. But they do not lose the structure simplicity of 1-d CA. Further, we develop several different types of CCA PRNGs. Based on the comparison of the randomness of different CCA PRNGs, we find that their properties are decided by the actions of the controllable cells and their neighbors. These novel CCA may be applied in other applications where structure non-uniformity or asymmetry is desired

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

Development of a New Mixed Mode I-II-III Delamination Toughness Test

Optimal use of laminated composite materials can only be achieved if its most common failure mechanisms are well understood. One of the most common modes of failure in laminates is delamination, or the separation of adjacent material layers. Therefore, there is a need to be able to predict a laminated composite’s resistance to delamination growth due to the complex real-world application loadings that it may experience. These complex loadings are made up of three primary modes of delamination growth, known as modes I, II, and III. Test fixtures for a new mixed mode I-II-III delamination toughness test were designed, built, and used to perform exploratory experiments. The test utilizes laminated composite test specimens that are similar to those used in other established toughness tests. The specimen is approximately 25 mm wide, 150 mm long and between 3 and 6 mm thick. The new test fixture may be installed in a standard uniaxial tension load frame, and includes two screw driven actuators. Three separate loads are applied to the specimen: one through the test machine’s hydraulic actuator and two using the screw driven actuators. The relative amounts of mode I, II and III loading may be adjusted by varying the relative magnitudes of these three loads. The new test set-up was used to perform a series of mode I, mode III, and mixed mode I-III delamination toughness tests on unidirectional T800S/3900-2B graphite/epoxy specimens. These tests yielded promising results, but a limited amount of fixture modifications were required to reduce frictional resistance. A set of proposed fixture modifications were therefore devised and are described herein

From automata to animate beings: the scope and limits of attributing socialness to artificial agents

Understanding the mechanisms and consequences of attributing socialness to artificial agents has important implications for how we can use technology to lead more productive and fulfilling lives. Here, we integrate recent findings on the factors that shape behavioral and brain mechanisms that support social interactions between humans and artificial agents. We review how visual features of an agent, as well as knowledge factors within the human observer, shape attributions across dimensions of socialness. We explore how anthropomorphism and dehumanization further influence how we perceive and interact with artificial agents. Based on these findings, we argue that the cognitive reconstruction within the human observer is likely to be far more crucial in shaping our interactions with artificial agents than previously thought, while the artificial agent's visual features are possibly of lesser importance. We combine these findings to provide an integrative theoretical account based on the “like me” hypothesis, and discuss the key role played by the Theory‐of‐Mind network, especially the temporal parietal junction, in the shift from mechanistic to social attributions. We conclude by highlighting outstanding questions on the impact of long‐term interactions with artificial agents on the behavioral and brain mechanisms of attributing socialness to these agents

Microwave-induced nonequilibrium temperature in a suspended carbon nanotube

Antenna-coupled suspended single carbon nanotubes exposed to 108 GHz microwave radiation are shown to be selectively heated with respect to their metal contacts. This leads to an increase in the conductance as well as to the development of a power-dependent DC voltage. The increased conductance stems from the temperature dependence of tunneling into a one-dimensional electron system. The DC voltage is interpreted as a thermovoltage, due to the increased temperature of the electron liquid compared to the equilibrium temperature in the leads