Acoustic Phonon-Assisted Resonant Tunneling via Single Impurities

We perform the investigations of the resonant tunneling via impurities embedded in the AlAs barrier of a single GaAs/AlGaAs heterostructure. In the $I(V)$ characteristics measured at 30mK, the contribution of individual donors is resolved and the fingerprints of phonon assistance in the tunneling process are seen. The latter is confirmed by detailed analysis of the tunneling rates and the modeling of the resonant tunneling contribution to the current. Moreover, fluctuations of the local structure of the DOS (LDOS) and Fermi edge singularities are observed.Comment: accepted in Phys. Rev.

Few-Shot Bayesian Imitation Learning with Logical Program Policies

Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. We represent policies as logical combinations of programs drawn from a domain-specific language (DSL), define a prior over policies with a probabilistic grammar, and derive an approximate Bayesian inference algorithm to learn policies from demonstrations. In experiments, we study five strategy games played on a 2D grid with one shared DSL. After a few demonstrations of each game, the inferred policies generalize to new game instances that differ substantially from the demonstrations. Our policy learning is 20--1,000x more data efficient than convolutional and fully convolutional policy learning and many orders of magnitude more computationally efficient than vanilla program induction. We argue that the proposed method is an apt choice for tasks that have scarce training data and feature significant, structured variation between task instances.Comment: AAAI 202

Plasma Nanoscience: from Nano-Solids in Plasmas to Nano-Plasmas in Solids

The unique plasma-specific features and physical phenomena in the organization of nanoscale solid-state systems in a broad range of elemental composition, structure, and dimensionality are critically reviewed. These effects lead to the possibility to localize and control energy and matter at nanoscales and to produce self-organized nano-solids with highly unusual and superior properties. A unifying conceptual framework based on the control of production, transport, and self-organization of precursor species is introduced and a variety of plasma-specific non-equilibrium and kinetics-driven phenomena across the many temporal and spatial scales is explained. When the plasma is localized to micrometer and nanometer dimensions, new emergent phenomena arise. The examples range from semiconducting quantum dots and nanowires, chirality control of single-walled carbon nanotubes, ultra-fine manipulation of graphenes, nano-diamond, and organic matter, to nano-plasma effects and nano-plasmas of different states of matter.Comment: This is an essential interdisciplinary reference which can be used by both advanced and early career researchers as well as in undergraduate teaching and postgraduate research trainin

Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning

In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. These non-stationarities are difficult to detect and cope with due to their continuous nature. Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them. We propose Reactive Exploration to track and react to continual domain shifts in lifelong reinforcement learning, and to update the policy correspondingly. To this end, we conduct experiments in order to investigate different exploration strategies. We empirically show that representatives of the policy-gradient family are better suited for lifelong learning, as they adapt more quickly to distribution shifts than Q-learning. Thereby, policy-gradient methods profit the most from Reactive Exploration and show good results in lifelong learning with continual domain shifts. Our code is available at: https://github.com/ml-jku/reactive-exploration.Comment: CoLLAs 202

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

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

An Architectural Approach to Ensuring Consistency in Hierarchical Execution

Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of knowledge can lead to difficulty in maintaining logical consistency in asserted knowledge. We explore the problematic consequences of persistent assumptions in the reasoning process and introduce novel potential solutions. Having implemented one of the possible solutions, Dynamic Hierarchical Justification, its effectiveness is demonstrated with an empirical analysis