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Legged robots have achieved impressive feats in dynamic locomotion in challenging unstructured terrain. However, in entertainment applications, the design and control of these robots face additional challenges in appealing to human audiences. This work aims to unify expressive, artist-directed motions and robust dynamic mobility for legged robots. To this end, we introduce a new bipedal robot, designed with a focus on character-driven mechanical features. We present a reinforcement learning-based control architecture to robustly execute artistic motions conditioned on command signals. During runtime, these command signals are generated by an animation engine which composes and blends between multiple animation sources. Finally, an intuitive operator interface enables real-time show performances with the robot. The complete system results in a believable robotic character, and paves the way for enhanced human-robot engagement in various contexts, in entertainment robotics and beyond.

We present an ab initio theory of the non-resonant Raman scattering process in crystals at finite temperature in direct comparison with experiments. The theory incorporates the scattering geometry and polarization dependence of the Raman process and the small but finite wave vectors of the phonons for correctly describing the scattering with longitudinal optical (LO) modes in optically anisotropic solids. We implement the theory for first-order Raman scattering and showcase the approach for wurtzite Gallium Nitride and the complex chalcogenide perovskite BaZrS3 in comparison to experiment. We subsequently discuss several common estimates for second-order Raman scattering in complex materials, and highlight similarities and differences to established theoretical approaches and simulation protocols both from phonon theory and molecular dynamics., Comment: 16 pages, 8 figures

As of December 2024, the ARC-AGI benchmark is five years old and remains unbeaten. We believe it is currently the most important unsolved AI benchmark in the world because it seeks to measure generalization on novel tasks -- the essence of intelligence -- as opposed to skill at tasks that can be prepared for in advance. This year, we launched ARC Prize, a global competition to inspire new ideas and drive open progress towards AGI by reaching a target benchmark score of 85\%. As a result, the state-of-the-art score on the ARC-AGI private evaluation set increased from 33\% to 55.5\%, propelled by several frontier AGI reasoning techniques including deep learning-guided program synthesis and test-time training. In this paper, we survey top approaches, review new open-source implementations, discuss the limitations of the ARC-AGI-1 dataset, and share key insights gained from the competition.

This study investigates the spatial evolution of a zero pressure gradient turbulent boundary layer (TBL) imposed by a square-wave (SqW) of steady spanwise wall-forcing, which varies along the streamwise direction ($x$). The SqW wall-forcing is imposed experimentally via a series of streamwise periodic belts running in opposite spanwise directions, following the methodology of Knoop et al., (Exp Fluids, vol 65, 2024), with the streamwise extent increased to beyond $\sim 11$ times the boundary layer thickness (${\delta}_o$) in the present study. This unique setup is leveraged to investigate the influence of viscous-scaled wavelength of SqW wall-forcing on the turbulent drag reduction (DR) efficacy, for $\lambda^+_x = $ 471 (sub-optimal), 942 (near-optimal), and 1884 (post-optimal conditions), at fixed viscous-scaled wall-forcing amplitude, $A^+ = 12$, and friction Reynolds number, $Re_\tau = 960$. The DR mechanisms associated with SqW wall-forcing are elucidated by comparing against established DR mechanisms for traditionally studied sinusoidal forcing (SinW), based on analysis of the streamwise-phase variation of the Stokes strain rate (SSR). The analysis reveals SqW forcing to be a combination of two distinctly different/decoupled SSR states: (i) strong impulses of SSR at half- ($\lambda_x$/2) and full-phase ($\lambda_x$) locations associated with reversal in spanwise forcing directions leading to significant turbulence attenuation, and (ii) near-zero SSR over the remainder of forcing phase that enables turbulence recovery (when wall-forcing magnitudes and direction remain constant). While present experiments are limited to low Reynolds number TBLs, the results are extrapolated to depict a conceptual picture of SqW forcing efficacy in high Reynolds number TBLs (corresponding to a broader scale hierarchy).

We describe a theoretical and computational approach to calculate the vibrational, elastic, and thermal properties of materials from the low-temperature quantum regime to the high-temperature anharmonic regime. This approach is based on anharmonic lattice dynamics and the Boltzmann transport equation. It relies on second and third-order force constant tensors estimated by fitting temperature-dependent empirical potentials (TDEP) from path-integral quantum simulations with a first-principles machine learning Hamiltonian. The temperature-renormalized harmonic force constants are used to calculate the elastic moduli and the phonon modes of materials. Harmonic and anharmonic force constants are combined to solve the phonon Boltzmann transport equation to compute the lattice thermal conductivity. We demonstrate the effectiveness of this approach on bulk crystalline silicon in the temperature range from 50 to 1200~K, showing substantial improvement in the prediction of the temperature dependence of the target properties compared to experiments., Comment: 31 pages, 9 figures

Quantum Inspire has taken important steps to enable quantum applications by developing a setting that allows the execution of hybrid algorithms. Currently, the setting uses a classical server (HPC node) co-located with the quantum computer for the high frequency coupling needed by hybrid algorithms. A fast task manager (dispatcher) has been developed to orchestrate the interaction between the server and the quantum computer. Although successful, the setting imposes a specific hybrid job-structure. This is most likely always going to be the case and we are currently discussing how to make sure this does not hamper the uptake of the setting. Furthermore, first steps have been taken towards the integration with the Dutch National High-Performance Computing (HPC) Center, hosted by SURF. As a first approach we have setup a setting consisting of two SLURM clusters, one in the HPC (C1) and the second (C2) co-located with Quantum Inspire API. Jobs are submitted from C1 to C2. Quantum Inspire can then schedule with C2 the jobs to the quantum computer. With this setting, we enable control from both SURF and Quantum Inspire on the jobs being executed. By using C1 for the jobs submission we remove the accounting burden from Quantum Inspire. By having C2 co-located with Quantum Inspire API, we make the setting more resilient towards network failures. This setting can be extended for other HPC centers to submit jobs to Quantum Inspire backends., Comment: 6 pages, 3 figures

We report a comparative study of temperature-dependent photoluminescence and structural dynamics of two perovskite semiconductors, the chalcogenide BaZrS$_3$ (BZS) and the halide CsPbBr$_3$ (CPB). These materials have similar crystal structures and direct band gaps, but we find that they have quite distinct optoelectronic and vibrational properties. Both materials exhibit thermally-activated non-radiative recombination, but the non-radiative recombination rate in BZS is between two and four orders of magnitude faster than in CPB. Raman spectroscopy reveals that the effects of phonon anharmonicity are far more pronounced in CPB than in BZS. Further, although both materials feature a large dielectric response due to low-energy polar optical phonons, the phonons in CPB are substantially lower in energy than in BZS. Our results suggest that electron-phonon coupling in BZS is more effective at non-radiative recombination than in CPB, and that BZS may also have a substantially higher concentration of non-radiative recombination centers than CPB. The low defect concentration in CPB may be related to the ease of lattice reconfiguration, typified by anharmonic bonding. It remains to be seen to what extent these differences are inherent to the chalcogenide and halide perovskites and to what extent they can be affected by materials processing; comparing BZS single-crystals and thin films provides reason for optimism., Comment: Main text - 12 pages with 5 figures and 1 table. Supplemental text - 16 pages with 6 figures and 5 tables

Network-level traffic condition forecasting has been intensively studied for decades. Although prediction accuracy has been continuously improved with emerging deep learning models and ever-expanding traffic data, traffic forecasting still faces many challenges in practice. These challenges include the robustness of data-driven models, the inherent unpredictability of traffic dynamics, and whether further improvement of traffic forecasting requires more sensor data. In this paper, we focus on this latter question and particularly on data from loop detectors. To answer this, we propose an uncertainty-aware traffic forecasting framework to explore how many samples of loop data are truly effective for training forecasting models. Firstly, the model design combines traffic flow theory with graph neural networks, ensuring the robustness of prediction and uncertainty quantification. Secondly, evidential learning is employed to quantify different sources of uncertainty in a single pass. The estimated uncertainty is used to "distil" the essence of the dataset that sufficiently covers the information content. Results from a case study of a highway network around Amsterdam show that, from 2018 to 2021, more than 80\% of the data during daytime can be removed. The remaining 20\% samples have equal prediction power for training models. This result suggests that indeed large traffic datasets can be subdivided into significantly smaller but equally informative datasets. From these findings, we conclude that the proposed methodology proves valuable in evaluating large traffic datasets' true information content. Further extensions, such as extracting smaller, spatially non-redundant datasets, are possible with this method., Comment: 13 pages, 5 figures

An excessive demand of vehicles to a motorway bottleneck leads to traffic jams. Motorbikes are narrow and can drive next to each other in a lane, or in-between lanes in low speeds. This paper analyses the resulting traffic characteristics and presents numerical scheme for a macroscopic traffic flow model for these two classes. The behavior included is as follows. If there are two motorbikes behind each other, they can travel next to each other in one lane, occupying the space of one car. Also, at low speeds of car traffic, they can go in between the main lanes, creating a so-called filtering lane. The paper numerically derives functions of class-specific speeds as function of the density of both classes, incorporating flexible lane usage dependent on the speed. The roadway capacity as function of the motorbike fraction is derived, which interesting can be in different types of phases (with motorbikes at higher speeds or not). We also present a numerical scheme to analyse the dynamics of this multi-class system. We apply the model to an example case, revealing the properties of the traffic stream , queue dynamics and class specific travel times. The model can help in showing the relative advantage in travel time of switching to a motorbike.

We present an experimental realisation of spatial spanwise forcing in a turbulent boundary layer flow, aimed at reducing the frictional drag. The forcing is achieved by a series of spanwise running belts, running in alternating spanwise direction, thereby generating a steady spatial square-wave forcing. SPIV in the streamwise-wall-normal plane is used to investigate the impact of actuation on the flow in terms of turbulence statistics, drag performance characteristics, and spanwise velocity profiles, for a non-dimensional wavelength of $\lambda_x^+ = 397$. We confirm that a significant flow control effect can be realised with this type of forcing. The scalar fields of the higher-order turbulence statistics show a strong attenuation of stresses and production of turbulence kinetic energy over the first belt already, followed by a more gradual decrease to a steady-state energy response over the second belt. The streamwise velocity in the near-wall region is reduced, indicative of a drag-reduced flow state. The profiles of the higher-order turbulence statistics are attenuated up to a wall-normal height of $y^+ \approx 100$, with a maximum streamwise stress reduction of 45% and a reduction of integral turbulence kinetic energy production of 39%, for a non-dimensional actuation amplitude of $A^+ = 12.7$. An extension of the classical laminar Stokes layer theory is introduced, to describe the non-sinusoidal boundary condition that corresponds to the current case. The spanwise velocity profiles show good agreement with this extended theoretical model. The drag reduction was estimated from a linear fit in the viscous sublayer in the range $2 \leq y^+\leq 5$. The results are found to be in good qualitative agreement with the numerical implementations of Viotti et al. (2009), matching the drag reduction trend with $A^+$, and reaching a maximum of 20%.

This paper introduces decentralized control concepts for drones using differential game theory. The approach optimizes the behavior of an ego drone, assuming the anticipated behavior of the opponent drones using a receding horizon approach. For each control instant, the scheme computes the Nash equilibrium control signal which is applied for the control period. This results in a multi-drone conflict resolution scheme that is applied to all drones considered. The paper discusses the approach and presents the numerical algorithm, showing several examples that illustrate the performance of the model. We examine at the behavior of the ego drone, and the resulting collective drone flow operations. The latter shows that while the approach aims to optimize the operation cost of the ego drone, the experiments provide evidence that resulting flow operations are very efficient due to the self-organization of various flow patterns. The presented work contributes to the state of the art in providing a generic approach to multi-drone conflict resolution with good macroscopic flow performance characteristics. The approach enables relatively straightforward inclusion of error due to sensing and communication. The approach also allows for including different risk levels (e.g., for malfunctioning of sensor and communication technology), priority rules, regulations, and higher-level control signals (e.g., routing, dynamic speed limits)., Comment: Submitted to the TRB Annual Meeting 2024

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Autonomous Vehicles (AVs). However, trajectories collected by AVs are noisy and not readily applicable for studying CF behaviour. This paper extracts and enhances two categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset. First, CF pairs are selected based on specific rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the raw CF data is corrected and enhanced via motion planning, Kalman filtering, and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following segments are obtained, with a total driving distance of 150k+ km. A diversity assessment shows that the processed data cover complete CF regimes for calibrating CF models. This open and ready-to-use dataset provides the opportunity to investigate the CF behaviours of following AVs vs. HVs from real-world data. It can further facilitate studies on exploring the impact of AVs on mixed urban traffic., Comment: 6 pages, 9 figures

Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface. This potential determines many materials properties and simulation techniques usually require its gradients, in particular forces and stress for molecular dynamics, and heat flux for thermal transport properties. Recently developed potentials feature high body order and can include equivariant semi-local interactions through message-passing mechanisms. Due to their complex functional forms, they rely on automatic differentiation (AD), overcoming the need for manual implementations or finite-difference schemes to evaluate gradients. This study demonstrates a unified AD approach to obtain forces, stress, and heat flux for such potentials, and provides a model-independent implementation. The method is tested on the Lennard-Jones potential, and then applied to predict cohesive properties and thermal conductivity of tin selenide using an equivariant message-passing neural network potential., Comment: 9 pages, 2 figures, 6 tables, excluding supplement (3 pages, 3 figures, 2 tables). Additional information at https://marcel.science/glp

The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. Machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales at a fraction of the cost. In this paper, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semi-local interactions beyond the initial interaction cutoff. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency. The approach is demonstrated and validated by calculating the thermal conductivity of zirconium dioxide across temperatures., Comment: 6 pages, 3 figures, excluding supplement (12 pages, 10 figures), v2: fixed figures. Additional information at https://marcel.science/gknet

The anharmonicity of atomic motion limits the thermal conductivity in crystalline solids. However, a microscopic understanding of the mechanisms active in strong thermal insulators is lacking. In this letter, we classify 465 experimentally known materials with respect to their anharmonicity and perform fully anharmonic ab initio Green-Kubo calculations for 58 of them, finding 28 thermal insulators with $\kappa < 10$ W/mK including 6 with ultralow $\kappa \lesssim 1$ W/mK. Our analysis reveals that the underlying strong anharmonic dynamics is driven by the exploration of meta-stable intrinsic defect geometries. This is at variance with the frequently applied perturbative approach, in which the dynamics is assumed to evolve around a single stable geometry., Comment: 5 pages, 4 figures

Ab initio Green-Kubo (aiGK) simulations of heat transport in solids allow for assessing lattice thermal conductivity in anharmonic or complex materials from first principles. In this work, we present a detailed account of their practical application and evaluation with an emphasis on noise reduction and finite-size corrections in semiconductors and insulators. To account for such corrections, we propose strategies in which all necessary numerical parameters are chosen based on the dynamical properties displayed during molecular dynamics simulations in order to minimize manual intervention. This paves the way for applying the aiGK method in semi-automated and high-throughput frameworks. The proposed strategies are presented and demonstrated for computing the lattice thermal conductivity at room temperature in the mildly anharmonic periclase MgO, and for the strongly anharmonic marshite CuI., Comment: 13 pages, 9 figures