Curriculum Innovation: Incorporating the Kern Engineering Entrepreneurial Network (KEEN) Framework into Online Discussions

The purpose of this study was to respond to the following research question: How does the Kern Engineering Entrepreneurial Network (KEEN) framework build interest in technical topic areas, impact student learning outcomes, and develop the entrepreneurial mindset when applied to the engineering classroom? The KEEN framework was developed to combine the entrepreneurial mindset with engineering education to produce a more valuable, strategically prepared engineer, rather than simply an “obedient engineer”. The framework proposes that the entrepreneurial mindset of students is increased by promoting curiosity, encouraging connections, and creating value. The results from this work provide insight into the impact and implications resulting from applying the KEEN framework to the engineering classroom via online discussions

Optimal Distributed Power Generation Under Network-Load Constraints

In electrical power networks nowadays more and more customers are becoming power-producers, mainly because of the development of novel components for decentralized power generation (solar panels, small wind turbines and heat pumps). This gives rise to the question how many units of each type (solar panel, small wind turbine or central heating power units) can be inserted into any transmission line in the network, such that under given distributions on the typical production and consumption over time, the maximum loads on the lines and components will not be exceeded. In this paper, we present a linear programming model for maximizing the amount of decentralized power generation while respecting the load limitations of the network. We describe a prototype showing that for an example network the maximization problem can be solved efficiently. We also modeled the case were the power consumption and decentralized power generation are considered as stochastic variables, which is inherently more complex

Combining Hebbian and reinforcement learning in a minibrain model

A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of `path interference', which makes that the neural net quickly forgets previously learned input-output relations is tackled by adding a Hebbian term (proportional to the learning rate $\eta$) to the reinforcement term (proportional to $\rho$) in the learning rule. It is shown that the number of learning steps is reduced considerably if $1/4 < \eta/\rho < 1/2$, i.e., if the Hebbian term is neither too small nor too large compared to the reinforcement term

Embedded 45° micro-mirror for out-of-plane coupling in optical PCBs

We present an embedded 45° micro-mirror that can be used to couple light out-of-plane of the optical layer. The discrete micro-mirror is inserted in a micro-cavity into the optical layer. Loss measurements at receiver side show a mirror loss as low as 0.35dB

Reionization history constraints from neural network based predictions of high-redshift quasar continua

Observations of the early Universe suggest that reionization was complete by $z\sim6$, however, the exact history of this process is still unknown. One method for measuring the evolution of the neutral fraction throughout this epoch is via observing the Ly$\alpha$ damping wings of high-redshift quasars. In order to constrain the neutral fraction from quasar observations, one needs an accurate model of the quasar spectrum around Ly$\alpha$, after the spectrum has been processed by its host galaxy but before it is altered by absorption and damping in the intervening IGM. In this paper, we present a novel machine learning approach, using artificial neural networks, to reconstruct quasar continua around Ly$\alpha$. Our QSANNdRA algorithm improves the error in this reconstruction compared to the state-of-the-art PCA-based model in the literature by 14.2% on average, and provides an improvement of 6.1% on average when compared to an extension thereof. In comparison with the extended PCA model, QSANNdRA further achieves an improvement of 22.1% and 16.8% when evaluated on low-redshift quasars most similar to the two high-redshift quasars under consideration, ULAS J1120+0641 at $z=7.0851$ and ULAS J1342+0928 at $z=7.5413$, respectively. Using our more accurate reconstructions of these two $z>7$ quasars, we estimate the neutral fraction of the IGM using a homogeneous reionization model and find $\bar{x}_\mathrm{HI} = 0.25^{+0.05}_{-0.05}$ at $z=7.0851$ and $\bar{x}_\mathrm{HI} = 0.60^{+0.11}_{-0.11}$ at $z=7.5413$. Our results are consistent with the literature and favour a rapid end to reionization

Multi-mode ultra-strong coupling in circuit quantum electrodynamics

With the introduction of superconducting circuits into the field of quantum optics, many novel experimental demonstrations of the quantum physics of an artificial atom coupled to a single-mode light field have been realized. Engineering such quantum systems offers the opportunity to explore extreme regimes of light-matter interaction that are inaccessible with natural systems. For instance the coupling strength $g$ can be increased until it is comparable with the atomic or mode frequency $\omega_{a,m}$ and the atom can be coupled to multiple modes which has always challenged our understanding of light-matter interaction. Here, we experimentally realize the first Transmon qubit in the ultra-strong coupling regime, reaching coupling ratios of $g/\omega_{m}=0.19$ and we measure multi-mode interactions through a hybridization of the qubit up to the fifth mode of the resonator. This is enabled by a qubit with 88% of its capacitance formed by a vacuum-gap capacitance with the center conductor of a coplanar waveguide resonator. In addition to potential applications in quantum information technologies due to its small size and localization of electric fields in vacuum, this new architecture offers the potential to further explore the novel regime of multi-mode ultra-strong coupling.Comment: 15 pages, 9 figure

Biologically inspired learning in a layered neural net

A feed-forward neural net with adaptable synaptic weights and fixed, zero or non-zero threshold potentials is studied, in the presence of a global feedback signal that can only have two values, depending on whether the output of the network in reaction to its input is right or wrong. It is found, on the basis of four biologically motivated assumptions, that only two forms of learning are possible, Hebbian and Anti-Hebbian learning. Hebbian learning should take place when the output is right, while there should be Anti-Hebbian learning when the output is wrong. For the Anti-Hebbian part of the learning rule a particular choice is made, which guarantees an adequate average neuronal activity without the need of introducing, by hand, control mechanisms like extremal dynamics. A network with realistic, i.e., non-zero threshold potentials is shown to perform its task of realizing the desired input-output relations best if it is sufficiently diluted, i.e. if only a relatively low fraction of all possible synaptic connections is realized