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Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While the stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion with nominal convergence guarantees. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.

This paper presents a comprehensive review of both the theory and experimental successes of Quantum Chromodynamics, starting with its emergence as a well defined theory in 1972-73 and following developments and results up to the present day. Topics include a review of the earliest theoretical and experimental foundations; the fundamental constants of QCD; an introductory discussion of lattice QCD, the only known method for obtaining exact predictions from QCD; methods for approximating QCD, with special focus on effective field theories; QCD under extreme conditions; measurements and predictions of meson and baryon states; a special discussion of the structure of the nucleon; techniques for study of QCD at high energy, including treatment of jets and showers; measurements at colliders; weak decays and quark mixing; and a section on the future, which discusses new experimental facilities or upgrades currently funded. The paper is intended to provide a broad background for Ph.D. students and postdocs starting their career. Some contributions include personal accounts of how the ideas or experiments were developed., Comment: Invited volume for the EJPC; 567 pages if text/figures and 4783 references occupying about 160 additional pages. arXiv abstract abridged, for the complete abstract please see the full text

The scalar glueball is observed in a coupled-channel analysis of the $S$-wave amplitude from BESIII data on radiative $J/\psi$ decays and further data. Ten scalar isoscalar resonances were required to fit the data. Five of them were interpreted as mainly-singlet, five as mainly-octet resonances in SU(3). The yield of resonances showed a striking peak with properties expected from a scalar glueball. The $D$ wave amplitude in the BESIII data on radiative $J/\psi$ decays reveales a high-mass structure which can be described by a single Breit-Wigner or by the sum of three $\phi\phi$ resonances interpreted as tensor glueballs a long time ago. The structure - and further tensor resonances observed in radiative $J/\psi$ decays - are tentatively interpreted as tensor glueball. In $J/\psi$ decays into $\gamma\pi^0\pi^0\eta'$ several resonances are reported. The possibility is discussed that the pseudoscalar glueball might be hidden in these data., Comment: Contribution to XVth Quark confinement and the Hadron spectrum conference, August 1st - 6th, 2022, University of Stavanger, Norway

The Voyager spacecraft are providing the first in situ measurements of physical properties in the outer heliosphere beyond the heliopause. These data, together with data from the IBEX and HST spacecraft and physical models consistent with these data, now provide critical measurements of pressures in the heliosphere and surrounding interstellar medium. Using these data, we assemble the first comprehensive survey of total pressures inside and outside of the heliopause, in the interstellar gas surrounding the heliosphere, and in the surrounding Local Cavity to determine whether the total pressures in each region are in balance with each other and with the gravitational pressure exerted by the Galaxy. We inter-compare total pressures in each region that include thermal, non-thermal, plasma, ram, and magnetic pressure components. An important result is the role of dynamic (ram) pressure. Total pressure balance at the heliopause can only be maintained with a substantial contribution of dynamic pressure from inside. Also, total pressure balance between the outer heliosphere and pristine very local ISM (VLISM) and between the pristine VLISM and the Local Cavity requires large dynamic pressure contributions., Comment: 15 pages, 1 figure, Astrophysical Journal in press

Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed systems, training time could be reduced from several days to several hours for standard benchmark tasks. However, while rewards in simulated environments are well-defined and easy to compute, reward evaluation becomes the bottleneck in many real-world environments, e.g., in molecular optimization tasks, where computationally demanding simulations or even experiments are required to evaluate states and to quantify rewards. Therefore, training might become prohibitively expensive without an extensive amount of computational resources and time. We propose to alleviate this problem by replacing costly ground-truth rewards with rewards modeled by neural networks, counteracting non-stationarity of state and reward distributions during training with an active learning component. We demonstrate that using our proposed ACRL method (Actively learning Costly rewards for Reinforcement Learning), it is possible to train agents in complex real-world environments orders of magnitudes faster. By enabling the application of reinforcement learning methods to new domains, we show that we can find interesting and non-trivial solutions to real-world optimization problems in chemistry, materials science and engineering.

This is a contribution to the review "50 Years of Quantum Chromdynamics" edited by F. Gross and E. Klempt [arXiv:2212.11107], to be published in EPJC. The contribution reviews the new baryon resonances derived from photoproduction experiments. Implications of the new results for the interpretation of baryons are discussed., Comment: 11 pages, 7 figures, 2 tables

This is a contribution to the review "50 Years of Quantum Chromdynamics" edited by F. Gross and E. Klempt [arXiv:2212.11107], to be published in EPJC. The contribution remembers the early searches and explains how to find a glueball, based on its properties. The results of a coupled-channel analysis are presented that provides evidence for the scalar glueball and first hints for the tensor glueball. Data on radiative decays of $\psi(2S)$ and $\Upsilon(1S)$ show scalar intensity that is likely due to glueball production., Comment: 9 pages, 12 figures

This is a contribution to the review "50 Years of Quantum Chromdynamics" edited by F. Gross and E. Klempt [arXiv:2212.11107], to be published in EPJC. The contribution reviews the properties of baryons with one heavy flavor: the lifetimes of ground states and the spectrum of excited states. The importance of symmetries to understand the excitation spectrum is underlined. An overview of searches for pentaquarks is given., Comment: 10 pages, 8 tables, 7 figures

Three-body recombination is a chemical reaction where the collision of three atoms leads to the formation of a diatomic molecule. In the ultracold regime it is expected that the production rate of a molecule generally decreases with its binding energy $E_b$, however, its precise dependence and the physics governing it have been left unclear so far. Here, we present a comprehensive experimental and theoretical study of the energy dependency for three-body recombination of ultracold Rb. For this, we determine production rates for molecules in a state-to-state resolved manner, with the binding energies $E_b$ ranging from 0.02 to 77 GHz$\times h$. We find that the formation rate approximately scales as $E_b^{-\alpha}$, where $\alpha$ is in the vicinity of 1. The formation rate typically varies only within a factor of two for different rotational angular momenta of the molecular product, apart from a possible centrifugal barrier suppression for low binding energies. In addition to numerical three-body calculations we present a perturbative model which reveals the physical origin of the energy scaling of the formation rate. Furthermore, we show that the scaling law potentially holds universally for a broad range of interaction potentials., Comment: 15 pages, 13 figures

On its journey through the Galaxy, the Sun passes through diverse regions of the interstellar medium. High-resolution spectroscopic measurements of interstellar absorption lines in spectra of nearby stars show absorption components from more than a dozen warm partially ionized clouds within 15 pc of the Sun. The two nearest clouds - the Local Interstellar Cloud (LIC) and Galactic (G) cloud - move toward each other. Their bulk heliocentric velocities can be compared with the interstellar neutral helium flow velocity obtained from space-based experiments. We combine recent results from Ulysses, IBEX, and STEREO observations to find a more accurate estimate of the velocity and temperature of the very local interstellar medium. We find that, contrary to the widespread viewpoint that the Sun resides inside the LIC, the locally observed velocity of the interstellar neutral helium is consistent with a linear combination of the velocities of the LIC and G cloud, but not with either of these two velocities. This finding shows that the Sun travels through a mixed-cloud interstellar medium composed of material from both these clouds. Interactions between these clouds explain the substantially higher density of the interstellar hydrogen near the Sun and toward stars located within the interaction region of these two clouds. The observed asymmetry of the interstellar helium distribution function also supports this interaction. The structure and equilibrium in this region require further studies using in situ and telescopic observations., Comment: 13 pages, 5 figures, 1 table, accepted for publication in ApJL

In windy conditions, the air is turbulent. The strong and intermittent velocity variations of turbulence are invisible to flying animals. Nevertheless, flying animals, not much larger than the smallest scales of turbulence, manage to maneuver these highly fluctuating conditions quite well. Here we quantify honeybee flight with time-resolved three-dimensional tracking in calm conditions and controlled turbulent winds. We find that honeybee mean speed and acceleration are only weakly correlated with the strength of turbulence. In flight, honeybees accelerate slowly and decelerate rapidly, i.e., they break suddenly during turns and then accelerate again. While this behavior is observed in both calm and turbulent conditions, it is increasingly dominant under turbulent conditions where short straight trajectories are broken by turns and increased maneuvering. This flight-crash behavior is reminiscent of turbulence itself. Our observations may help the development of flight strategies for miniature flying robotics under turbulent conditions., Comment: 18 pages, 7 figures

A transversal in an $n \times n$ latin square is a collection of $n$ entries not repeating any row, column, or symbol. Kwan showed that almost every $n \times n$ latin square has $\bigl((1 + o(1)) n / e^2\bigr)^n$ transversals as $n \to \infty$. Using a loose variant of the circle method we sharpen this to $(e^{-1/2} + o(1)) n!^2 / n^n$. Our method works for all latin squares satisfying a certain quasirandomness condition, which includes both random latin squares with high probability as well as multiplication tables of quasirandom groups., Comment: 26 pages, 3 figures. Final version incorporating referee's comments. To appear in Proceedings of the London Mathematical Society

Planning a time-optimal trajectory for aerial robots is critical in many drone applications, such as rescue missions and package delivery, which have been widely researched in recent years. However, it still involves several challenges, particularly when it comes to incorporating special task requirements into the planning as well as the aerial robot's dynamics. In this work, we study a case where an aerial manipulator shall hand over a parcel from a moving mobile robot in a time-optimal manner. Rather than setting up the approach trajectory manually, which makes it difficult to determine the optimal total travel time to accomplish the desired task within dynamic limits, we propose an optimization framework, which combines discrete mechanics and complementarity constraints (DMCC) together. In the proposed framework, the system dynamics is constrained with the discrete variational Lagrangian mechanics that provides reliable estimation results also according to our experiments. The handover opportunities are automatically determined and arranged based on the desired complementarity constraints. Finally, the performance of the proposed framework is verified with numerical simulations and hardware experiments with our self-designed aerial manipulators.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs., Comment: 37 pages, 2 figures