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We study the behavior of solutions to the incompressible 2d Euler equations near two canonical shear flows with critical points, the Kolmogorov and Poiseuille flows, with consequences for the associated Navier–Stokes problems. We exhibit a large family of new, non-trivial stationary states that are arbitrarily close to the Kolmogorov flow on the square torus $$\mathbb {T}^2$$ T 2 in analytic regularity. This situation contrasts strongly with the setting of some monotone shear flows, such as the Couette flow: there the linearized problem exhibits an “inviscid damping” mechanism that leads to relaxation of perturbations of the base flows back to nearby shear flows. Our results show that such a simple description of the long-time behavior is not possible for solutions near the Kolmogorov flow on $$\mathbb {T}^2$$ T 2 . Our construction of the new stationary states builds on a degeneracy in the global structure of the Kolmogorov flow on $$\mathbb {T}^2$$ T 2 , and we also show a lack of correspondence between the linearized description of the set of steady states and its true nonlinear structure. Both the Kolmogorov flow on a rectangular torus and the Poiseuille flow in a channel are very different. We show that the only stationary states near them must indeed be shears, even in relatively low regularity. In addition, we show that this behavior is mirrored closely in the related Navier–Stokes settings: the linearized problems near the Poiseuille and Kolmogorov flows both exhibit an enhanced rate of dissipation. Previous work by us and others shows that this effect survives in the full, nonlinear problem near the Poiseuille flow and near the Kolmogorov flow on rectangular tori, provided that the perturbations lie below a certain threshold. However, we show here that the corresponding result cannot hold near the Kolmogorov flow on $${\mathbb T}^2$$ T 2 .

Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this paper, we propose L1-NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline., 8 Pages, 6 figures, Accepted RAL 2021

Room-temperature realization of macroscopic quantum phases is one of the major pursuits in fundamental physics

Intertwining quantum order and non-trivial topology is at the frontier of condensed matter physics1–4. A charge-density-wave-like order with orbital currents has been proposed for achieving the quantum anomalous Hall effect5,6 in topological materials and for the hidden phase in cuprate high-temperature superconductors7,8. However, the experimental realization of such an order is challenging. Here we use high-resolution scanning tunnelling microscopy to discover an unconventional chiral charge order in a kagome material, KV3Sb5, with both a topological band structure and a superconducting ground state. Through both topography and spectroscopic imaging, we observe a robust 2 × 2 superlattice. Spectroscopically, an energy gap opens at the Fermi level, across which the 2 × 2 charge modulation exhibits an intensity reversal in real space, signalling charge ordering. At the impurity-pinning-free region, the strength of intrinsic charge modulations further exhibits chiral anisotropy with unusual magnetic field response. Theoretical analysis of our experiments suggests a tantalizing unconventional chiral charge density wave in the frustrated kagome lattice, which can not only lead to a large anomalous Hall effect with orbital magnetism, but also be a precursor of unconventional superconductivity. An unconventional chiral charge order is observed in a kagome superconductor by scanning tunnelling microscopy. This charge order has unusual magnetic tunability and intertwines with electronic topology.

Fault-tolerant control is crucial for safety-critical systems, such as quadrotors. State-of-art flight controllers can stabilize and control a quadrotor even when subjected to the complete loss of a rotor. However, these methods rely on external sensors, such as GPS or motion capture systems, for state estimation. To the best of our knowledge, this has not yet been achieved with only onboard sensors. In this paper, we propose the first algorithm that combines fault-tolerant control and onboard vision-based state estimation to achieve position control of a quadrotor subjected to complete failure of one rotor. Experimental validations show that our approach is able to accurately control the position of a quadrotor during a motor failure scenario, without the aid of any external sensors. The primary challenge to vision-based state estimation stems from the inevitable high-speed yaw rotation (over 20 rad/s) of the damaged quadrotor, causing motion blur to cameras, which is detrimental to visual inertial odometry (VIO). We compare two types of visual inputs to the vision-based state estimation algorithm: standard frames and events. Experimental results show the advantage of using an event camera especially in low light environments due to its inherent high dynamic range and high temporal resolution. We believe that our approach will render autonomous quadrotors safer in both GPS denied or degraded environments. We release both our controller and VIO algorithm open source., Comment: 8 pages, 10 figures

Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection without human intervention, the robots must be able to perch on the powerlines to recharge their batteries. Highly versatile perching capabilities are necessary to adapt to the variety of configurations and constraints that are present in real powerline systems. This paper presents a novel perching trajectory generation framework that computes perception-aware, collision-free, and dynamically-feasible maneuvers to guide the robot to the desired final state. Trajectory generation is achieved via solving a Nonlinear Programming problem using the Primal-Dual Interior Point method. The problem considers the full dynamic model of the robot down to its single rotor thrusts and minimizes the final pose and velocity errors while avoiding collisions and maximizing the visibility of the powerline during the maneuver. The generated maneuvers consider both the perching and the posterior recovery trajectories. The framework adopts costs and constraints defined by efficient mathematical representations of powerlines, enabling online onboard execution in resource-constrained hardware. The method is validated on-board an agile quadrotor conducting powerline inspection and various perching maneuvers with final pitch values of up to 180 degrees. The developed code is available online at: https://github.com/grvcPerception/pa_powerline_perching, IEEE Robotics and Automation Letters (2022)

Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty. To address these limitations, we propose a novel framework for uncertainty estimation. Based on Bayesian belief networks and Monte-Carlo sampling, our framework not only fully models the different sources of prediction uncertainty, but also incorporates prior data information, e.g. sensor noise. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. In addition, our framework has several desirable properties: (i) it is agnostic to the network architecture and task; (ii) it does not require changes in the optimization process; (iii) it can be applied to already trained architectures. We thoroughly validate the proposed framework through extensive experiments on both computer vision and control tasks, where we outperform previous methods by up to 23% in accuracy., Accepted for publication in the Robotics and Automation Letters 2020, and for presentation at the International Conference on Robotics and Automation (ICRA) 2020

Humans race drones faster than algorithms, despite being limited to a fixed camera angle, body rate control, and response latencies in the order of hundreds of milliseconds. A better understanding of the ability of human pilots of selecting appropriate motor commands from highly dynamic visual information may provide key insights for solving current challenges in vision-based autonomous navigation. This paper investigates the relationship between human eye movements, control behavior, and flight performance in a drone racing task. We collected a multimodal dataset from 21 experienced drone pilots using a highly realistic drone racing simulator, also used to recruit professional pilots. Our results show task-specific improvements in drone racing performance over time. In particular, we found that eye gaze tracks future waypoints (i.e., gates), with first fixations occurring on average 1.5 seconds and 16 meters before reaching the gate. Moreover, human pilots consistently looked at the inside of the future flight path for lateral (i.e., left and right turns) and vertical maneuvers (i.e., ascending and descending). Finally, we found a strong correlation between pilots eye movements and the commanded direction of quadrotor flight, with an average visual-motor response latency of 220 ms. These results highlight the importance of coordinated eye movements in human-piloted drone racing. We make our dataset publicly available., Comment: 8 pages, 6 figures

Autonomous car racing is a major challenge in robotics. It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling. Besides, the requirement of minimizing the lap time, which is a sparse objective, and the difficulty of collecting training data from human experts have also hindered researchers from directly applying learning-based approaches to solve the problem. In the present work, we propose a learning-based system for autonomous car racing by leveraging a high-fidelity physical car simulation, a course-progress proxy reward, and deep reinforcement learning. We deploy our system in Gran Turismo Sport, a world-leading car simulator known for its realistic physics simulation of different race cars and tracks, which is even used to recruit human race car drivers. Our trained policy achieves autonomous racing performance that goes beyond what had been achieved so far by the built-in AI, and, at the same time, outperforms the fastest driver in a dataset of over 50,000 human players., Comment: Accepted for Publication at the IEEE Robotics and Automation Letters (RA-L) 2021, and International Conference on Robots and Automation (ICRA) 2021

Within the framework of diffuse interface methods, we derive a pressure-based Baer-Nunziato type model well-suited to weakly compressible multiphase flows. The model can easily deal with different equation of states and it includes relaxation terms characterized by user-defined finite parameters, which drive the pressure and velocity of each phase toward the equilibrium. There is no clear notion of speed of sound, and thus, most of the classical low Mach approximation cannot easily be cast in this context. The proposed solution strategy consists of two operators: a semi-implicit finite-volume solver for the hyperbolic part and an ODE integrator for the relaxation processes. Being the acoustic terms in the hyperbolic part integrated implicitly, the stability condition on the time step is lessened. The discretization of non-conservative terms involving the gradient of the volume fraction fulfills by construction the non-disturbance condition on pressure and velocity to avoid oscillations across the multimaterial interfaces. The developed simulation tool is validated through one-dimensional simulations of shock-tube and Riemann-problems, involving water-aluminum and water-air mixtures, vapor-liquid mixture of water and of carbon dioxide, and almost pure flows. The numerical results match analytical and reference ones, except some expected discrepancies across shocks, which however remain acceptable (errors within some percentage points). All tests were performed with acoustic CFL numbers greater than one, and no stability issues arose, even for CFL greater than 10. The effects of different values of relaxation parameters and of different amount equations of state -- stiffened gas and Peng-Robinson -- were investigated., Comment: 49 pages, 27 figures

Here we describe a completely integrated and customizable microfluidic control and sensing architecture that can be readily implemented for laboratory or portable chemical or biological control and sensing applications. The compact platform enables control of 32 solenoid valves, a multitude of pumps and motors, a thermo-electric controller, a pressure controller, and a high voltage power supply. It also features two temperature probe interfaces, one relative humidity and ambient temperature sensor, two pressure sensors, and interfaces to an electrical conductivity sensor, flow sensor, and a bubble detector. The platform can be controlled via an onboard microcontroller and requires no proprietary software.

During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation.

Birth trauma affects millions of women and infants worldwide. Levator ani muscle avulsions can be responsible for long-term morbidity, associated with 13-36% of women who deliver vaginally. Pelvic floor injuries are enhanced by fetal malposition, namely persistent occipito-posterior (OP) position, estimated to affect 1.8-12.9% of pregnancies. Neonates delivered in persistent OP position are associated with an increased risk for adverse outcomes. The main goal of this work was to evaluate the impact of distinct fetal positions on both mother and fetus. Therefore, a finite element model of the fetal head and maternal structures was used to perform childbirth simulations with the fetus in the occipito-anterior (OA) and OP position of the vertex presentation, considering a flexible-sacrum maternal position. Results demonstrated that the pelvic floor muscles' stretch was similar in both cases. The maximum principal stresses were higher for the OP position, and the coccyx rotation reached maximums of 2.17[Formula: see text] and 0.98[Formula: see text] for the OP and OA positions, respectively. Concerning the fetal head, results showed noteworthy differences in the variation of diameters between the two positions. The molding index is higher for the OA position, with a maximum of 1.87. The main conclusions indicate that an OP position can be more harmful to the pelvic floor and pelvic bones from a biomechanical point of view. On the other side, an OP position can be favorable to the fetus since fewer deformations were verified. This study demonstrates the importance of biomechanical analyses to further understand the mechanics of labor.

Synthetic bone substitutes are used for regeneration of bone defects and for vertical or horizontal bone augmentation. With the development of new manufacturing methodologies such as additive manufacturing, not only can the outer shape of a scaffold (the macroarchitecture) be personalized, but also the microarchitecture, defined as the distribution of material within the scaffold, can be designed and optimized for bone ingrowth. Our study assessed the optimal pore-based microarchitecture for osteoconduction in comparison to vertical bone augmentation. Histological analysis of the samples from an osteoconduction and a bone augmentation model showed that the best microarchitecture to achieve vertical bone augmentation contained pores of 1.7 mm diameter and differed from the optimal microarchitecture for osteoconduction, which contained pores of 1.2 mm diameter. The extent of vertical bone augmentation correlated with increasing pore diameter. Moreover, the differences between the microarchitectures were highly significant for osteoconduction, but less significant for vertical bone augmentation. Thus, our results suggest that the microarchitectures of the scaffolds in vertical bone augmentation and in bone regeneration of a defect are crucial determinants of the depth of bone ingrowth and the area of bony regeneration. However, the microarchitecture appears to be more crucial in osteoconduction.

Chronic diseases can greatly benefit from bioelectronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired low-power implantable processing systems that can be interfaced with biological systems. These circuits, therefore, represent a promising additional tool in the tool-set of bioelectronic medicine. In this paper, we describe the main features of neuromorphic circuits that are ideally suited for continuously monitoring the physiological parameters of the body and interact with them in real-time. We propose examples of computational primitives that can be used for real-time pattern generation and present a neuromorphic implementation of neural oscillators for the generation of sequence activation patterns. We demonstrate the features of such systems with an implementation of a three-phase network that models the dynamics of the respiratory Central Pattern Generator (CPG) and the heart chambers rhythm, and that could be used to build an adaptive pacemaker., Comment: 4 pages; 7 figures; to be published in 10th International IEEE EMBS Conference On Neural Engineering (NER'21), Special Session on Bioelectronics medicine

Increasing mobility demands raise the pressure on existing transport networks. As the most used mode of transport, private cars have a particularly strong environmental impact and produce congestion. Ridesharing or carpooling, where a driver and several riders form a carpool, can help to address these issues by increasing the number of persons per car. Therefore, recent years have seen a strong interest in carpooling. However, there exists no effective method of integrating carpooling into transport trip planners as of now, mainly due to the fuzzy and flexible nature (e.g., no fixed stops, possibility of making detours) of carpooling. This hinders the acceptance of carpooling by the general public. This paper proposes a new method to merge public transport and carpooling networks for multimodal route planning, considering the fuzziness and flexibility brought by carpooling. It is based on the concept of drive-time areas and points of action. The evaluation with real-world data sets shows that, compared with the state-of-the-art method, the proposed method merges static (i.e., public transport) and dynamic/fuzzy (i.e., carpooling) networks better, while retaining the desired flexibility offered by the latter, and thus creates a higher interconnectivity between the networks. Meanwhile, the merged network enables multimodal route planning, which can provide users with trips from an origin to a destination using different combinations of modes.

Inert single-layer boron nitride (h-BN) grown on a catalytic metal may be functionalized with quaternary ammonium compounds (quats) that are widely used as nonreactive electrolytes. We observe that the quat treatment, which facilitates the electrochemical transfer of two-dimensional materials, involves a decomposition of quat ions and leads to covalently bound quat derivatives on top of the 2D layer. Applying tetraoctylammonium and h-BN on rhodium, the reaction product is top-alkylized h-BN as identified with high-resolution X-ray photoelectron spectroscopy. The alkyl chains are homogeneously distributed across the surface, and the properties thereof are well-tunable by the choice of different quats. The functionalization further weakens the 2D material-substrate interaction and promotes easy transfer. Therefore, the functionalization scheme that is presented enables the design of 2D materials with tailored properties and with the freedom to position and orient them as required. The mechanism of this functionalization route is investigated with density functional theory calculations, and we identify the proximity of the catalytic metal substrate to alter the chemical reactivity of otherwise inert h-BN layers.

The enhanced electrochemical activity of nanostructured materials is readily exploited in energy devices, but their utility in scalable and human-compatible implantable neural interfaces can significantly advance the performance of clinical and research electrodes. We utilize low-temperature selective dealloying to develop scalable and biocompatible one-dimensional platinum nanorod (PtNR) arrays that exhibit superb electrochemical properties at various length scales, stability, and biocompatibility for high performance neurotechnologies. PtNR arrays record brain activity with cellular resolution from the cortical surfaces in birds and nonhuman primates. Significantly, strong modulation of surface recorded single unit activity by auditory stimuli is demonstrated in European Starling birds as well as the modulation of local field potentials in the visual cortex by light stimuli in a nonhuman primate and responses to electrical stimulation in mice. PtNRs record behaviorally and physiologically relevant neuronal dynamics from the surface of the brain with high spatiotemporal resolution, which paves the way for less invasive brain−machine interfaces.

We present a novel computational approach, based on a parametrized reduced-order model, for accelerating the calculation of pressure drop along blood vessels. Vessel lumina are defined by a geometric parametrization using the discrete empirical interpolation method on control points located on the surface of the vessel. Hemodynamics are then computed using a reduced-order representation of the parametrized three-dimensional unsteady Navier-Stokes and continuity equations. The reduced-order model is based on an offline-online splitting of the solution process, and on the projection of a finite volume full-order model on a low-dimensionality subspace generated by proper orthogonal decomposition of pressure and velocity fields. The algebraic operators of the hemodynamic equations are assembled efficiently during the online phase using the discrete empirical interpolation method. Our results show that with this approach calculations can be sped up by a factor of about 25 compared to the conventional full-order model, while maintaining prediction errors within the uncertainty limits of invasive clinical measurement of pressure drop. This is of importance for a clinically viable implementation of noninvasive, medical imaging-based computation of fractional flow reserve.

In this letter, we study the effects that perception latency has on the maximum speed a robot can reach to safely navigate through an unknown cluttered environment. We provide a general analysis that can serve as a baseline for future quantitative reasoning for design tradeoffs in autonomous robot navigation. We consider the case where the robot is modeled as a linear second-order system with bounded input and navigates through static obstacles. Also, we focus on a scenario where the robot wants to reach a target destination in as little time as possible, and therefore cannot change its longitudinal velocity to avoid obstacles. We show how the maximum latency that the robot can tolerate to guarantee safety is related to the desired speed, the range of its sensing pipeline, and the actuation limitations of the platform (i.e., the maximum acceleration it can produce). As a particular case study, we compare monocular and stereo frame-based cameras against novel, low-latency sensors, such as event cameras, in the case of quadrotor flight. To validate our analysis, we conduct experiments on a quadrotor platform equipped with an event camera to detect and avoid obstacles thrown towards the robot. To the best of our knowledge, this is the first theoretical work in which perception and actuation limitations are jointly considered to study the performance of a robotic platform in high-speed navigation.

An open-source, low noise, low cost, and tunable transimpedance amplifier is presented. The compact circuit board requires few parts and costs less than $65 USD. The transimpedance amplifier is intended for low-light detection and operation with commercial photomultiplier tubes (PMTs). It provides a much more cost-effective acquisition tool compared to competitive products on the market. The system can easily be assembled and modified to suit specific current sensing applications. The amplifier features two variable gains and an adjustable DC offset to optimize dynamic range and suppress potential bias in the signal. With a target bandwidth of DC to 2 Hz and fourth-order Sallen-Key cutoff, the design is ideally suited for various applications in the field of analytical chemistry and biology, such as laser-induced fluorescence detection or chemiluminescence measurements.

The sensory hairs of the Venus flytrap (Dionaea muscipula Ellis) detect mechanical stimuli imparted by their prey and fire bursts of electrical signals called action potentials (APs). APs are elicited when the hairs are sufficiently stimulated and two consecutive APs can trigger closure of the trap. Earlier experiments have identified thresholds for the relevant stimulus parameters, namely the angular displacement $$\theta $$ θ and angular velocity $$\omega $$ ω . However, these experiments could not trace the deformation of the trigger hair’s sensory cells, which are known to transduce the mechanical stimulus. To understand the kinematics at the cellular level, we investigate the role of two relevant mechanical phenomena: viscoelasticity and intercellular fluid transport using a multi-scale numerical model of the sensory hair. We hypothesize that the combined influence of these two phenomena and $$\omega $$ ω contribute to the flytrap’s rate-dependent response to stimuli. In this study, we firstly perform sustained deflection tests on the hair to estimate the viscoelastic material properties of the tissue. Thereafter, through simulations of hair deflection tests at different loading rates, we were able to establish a multi-scale kinematic link between $$\omega $$ ω and the cell wall stretch $$\delta $$ δ . Furthermore, we find that the rate at which $$\delta $$ δ evolves during a stimulus is also proportional to $$\omega $$ ω . This suggests that mechanosensitive ion channels, expected to be stretch-activated and localized in the plasma membrane of the sensory cells, could be additionally sensitive to the rate at which stretch is applied.

We present the results of the simulation of two-phase CO

Nanoscale mapping of the distinct electronic phases characterizing the metal-insulator transition displayed by most of the rare-earth nickelate compounds is fundamental for discovering the true nature of this transition and the possible couplings that are established at the interfaces of nickelate-based heterostructures. Here, we demonstrate that this can be accomplished by using scanning transmission electron microscopy in combination with electron energy-loss spectroscopy. By tracking how the O K and Ni L edge fine structures evolve across two different NdNiO3/SmNiO3 superlattices, displaying either one or two metal- insulator transitions depending on the individual layer thickness, we are able to determine the electronic state of each of the individual constituent materials. We further map the spatial configuration associated with their metallic/insulating regions, reaching unit cell spatial resolution. With this, we estimate the width of the metallic/insulating boundaries at the NdNiO3/SmNiO3 interfaces, which is measured to be on the order of four unit cells.

Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, the operating conditions of current autonomous cars are mostly restricted to ideal scenarios. This means that driving in challenging illumination conditions such as night, sunrise, and sunset remains an open problem. In these cases, standard cameras are being pushed to their limits in terms of low light and high dynamic range performance. To address these challenges, we propose, DSEC, a new dataset that contains such demanding illumination conditions and provides a rich set of sensory data. DSEC offers data from a wide-baseline stereo setup of two color frame cameras and two high-resolution monochrome event cameras. In addition, we collect lidar data and RTK GPS measurements, both hardware synchronized with all camera data. One of the distinctive features of this dataset is the inclusion of high-resolution event cameras. Event cameras have received increasing attention for their high temporal resolution and high dynamic range performance. However, due to their novelty, event camera datasets in driving scenarios are rare. This work presents the first high-resolution, large-scale stereo dataset with event cameras. The dataset contains 53 sequences collected by driving in a variety of illumination conditions and provides ground truth disparity for the development and evaluation of event-based stereo algorithms., Comment: IEEE Robotics and Automation Letters

Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous "events". They offer significant advantages compared to standard cameras due to their high temporal resolution, high dynamic range and lack of motion blur. However, events only measure the varying component of the visual signal, which limits their ability to encode scene context. By contrast, standard cameras measure absolute intensity frames, which capture a much richer representation of the scene. Both sensors are thus complementary. However, due to the asynchronous nature of events, combining them with synchronous images remains challenging, especially for learning-based methods. This is because traditional recurrent neural networks (RNNs) are not designed for asynchronous and irregular data from additional sensors. To address this challenge, we introduce Recurrent Asynchronous Multimodal (RAM) networks, which generalize traditional RNNs to handle asynchronous and irregular data from multiple sensors. Inspired by traditional RNNs, RAM networks maintain a hidden state that is updated asynchronously and can be queried at any time to generate a prediction. We apply this novel architecture to monocular depth estimation with events and frames where we show an improvement over state-of-the-art methods by up to 30% in terms of mean absolute depth error. To enable further research on multimodal learning with events, we release EventScape, a new dataset with events, intensity frames, semantic labels, and depth maps recorded in the CARLA simulator.

SERS tags are a class of nanoparticles with great potential in advanced imaging experiments. The preparation of SERS tags however is complex, as they suffer from the high variability of the SERS signals observed even at the slightest sign of aggregation. Here, we developed a method for the preparation of SERS tags based on the use of gold nanostars conjugated with neutravidin. The SERS tags here obtained are extremely stable in all biological buffers commonly employed and can be prepared at a relatively large scale in very mild conditions. The obtained SERS tags have been used to monitor the expression of fibroblast activation protein alpha (FAP) on the membrane of primary fibroblasts obtained from patients affected by Crohn’s disease. The SERS tags allowed the unambiguous identification of FAP on the surface of cells thus suggesting the feasibility of semi-quantitative analysis of the target protein. Moreover, the use of the neutravidin–biotin system allows to apply the SERS tags for any other marker detection, for example, different cancer cell types, simply by changing the biotinylated antibody chosen in the analysis.

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit their capabilities. Autonomous operation with on-board sensing and computation has been limited to low speeds. State-of-the-art methods generally separate the navigation problem into subtasks: sensing, mapping, and planning. While this approach has proven successful at low speeds, the separation it builds upon can be problematic for high-speed navigation in cluttered environments. Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline. Here we propose an end-to-end approach that can autonomously fly quadrotors through complex natural and man-made environments at high speeds, with purely onboard sensing and computation. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. This direct mapping drastically reduces processing latency and increases robustness to noisy and incomplete perception. The sensorimotor mapping is performed by a convolutional network that is trained exclusively in simulation via privileged learning: imitating an expert with access to privileged information. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments that were never experienced during training: dense forests, snow-covered terrain, derailed trains, and collapsed buildings. Our work demonstrates that end-to-end policies trained in simulation enable high-speed autonomous flight through challenging environments, outperforming traditional obstacle avoidance pipelines., Comment: 16 pages (+7 supplementary)

Reliable perception during fast motion maneuvers or in high dynamic range environments is crucial for robotic systems. Since event cameras are robust to these challenging conditions, they have great potential to increase the reliability of robot vision. However, event-based vision has been held back by the shortage of labeled datasets due to the novelty of event cameras. To overcome this drawback, we propose a task transfer method to train models directly with labeled images and unlabeled event data. Compared to previous approaches, (i) our method transfers from single images to events instead of high frame rate videos, and (ii) does not rely on paired sensor data. To achieve this, we leverage the generative event model to split event features into content and motion features. This split enables efficient matching between latent spaces for events and images, which is crucial for successful task transfer. Thus, our approach unlocks the vast amount of existing image datasets for the training of event-based neural networks. Our task transfer method consistently outperforms methods targeting Unsupervised Domain Adaptation for object detection by 0.26 mAP (increase by 93%) and classification by 2.7% accuracy.

Today's autonomous drones have reaction times of tens of milliseconds, which is not enough for navigating fast in complex dynamic environments. To safely avoid fast moving objects, drones need low-latency sensors and algorithms. We departed from state-of-the-art approaches by using event cameras, which are bioinspired sensors with reaction times of microseconds. Our approach exploits the temporal information contained in the event stream to distinguish between static and dynamic objects and leverages a fast strategy to generate the motor commands necessary to avoid the approaching obstacles. Standard vision algorithms cannot be applied to event cameras because the output of these sensors is not images but a stream of asynchronous events that encode per-pixel intensity changes. Our resulting algorithm has an overall latency of only 3.5 milliseconds, which is sufficient for reliable detection and avoidance of fast-moving obstacles. We demonstrate the effectiveness of our approach on an autonomous quadrotor using only onboard sensing and computation. Our drone was capable of avoiding multiple obstacles of different sizes and shapes, at relative speeds up to 10 meters/second, both indoors and outdoors.

In this paper, we propose to augment image-based place recognition with structural cues. Specifically, these structural cues are obtained using structure-from-motion, such that no additional sensors are needed for place recognition. This is achieved by augmenting the 2D convolutional neural network (CNN) typically used for image-based place recognition with a 3D CNN that takes as input a voxel grid derived from the structure-from-motion point cloud. We evaluate different methods for fusing the 2D and 3D features and obtain best performance with global average pooling and simple concatenation. On the Oxford RobotCar dataset, the resulting descriptor exhibits superior recognition performance compared to descriptors extracted from only one of the input modalities, including state-of-the-art image-based descriptors. Especially at low descriptor dimensionalities, we outperform state-of-the-art descriptors by up to 90%., Comment: 8 pages, published in RA-L & IROS 2020

Marine demosponges of the Verongiida order are considered a gold-mine for bioinspired materials science and marine pharmacology. The aim of this work was to simultaneously isolate selected bromotyrosines and unique chitinous structures from A. aerophoba and to propose these molecules and biomaterials for possible application as antibacterial and antitumor compounds and as ready-to-use scaffolds for cultivation of cardiomyocytes, respectively. Among the extracted bromotyrosines, the attention has been focused on aeroplysinin-1 that showed interesting unexpected growth inhibition properties for some Gram-negative clinical multi-resistant bacterial strains, such as A. baumannii and K. pneumoniae, and on aeroplysinin-1 and on isofistularin-3 for their anti-tumorigenic activity. For both compounds, the effects are cell line dependent, with significant growth inhibition activity on the neuroblastoma cell line SH-SY5Y by aeroplysinin-1 and on breast cancer cell line MCF-7 by isofistularin-3. In this study, we also compared the cultivation of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) on the A. aerophoba chitinous scaffolds, in comparison to chitin structures that were pre-coated with Geltrex™, an extracellular matrix mimetic which is used to enhance iPSC-CM adhesion. The iPSC-CMs on uncoated and pure chitin structures started contracting 24 h after seeding, with comparable behaviour observed on Geltrex-coated cell culture plates, confirming the biocompatibility of the sponge biomaterial with this cell type. The advantage of A. aerophoba is that this source organism does not need to be collected in large quantities to supply the necessary amount for further pre-clinical studies before chemical synthesis of the active compounds will be available. A preliminary analysis of marine sponge bioeconomy as a perspective direction for application of biomaterials and secondary bioactive metabolites has been finally performed for the first time.

Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D properties of the world via interaction with the environment. Existing works for depth estimation require either massive amounts of annotated training data or some form of hard-coded geometrical constraint. This paper explores a new approach to learning depth perception requiring neither of those. Specifically, we train a specialized global-local network architecture with what would be available to a robot interacting with the environment: from extremely sparse depth measurements down to even a single pixel per image. From a pair of consecutive images, our proposed network outputs a latent representation of the observer's motion between the images and a dense depth map. Experiments on several datasets show that, when ground truth is available even for just one of the image pixels, the proposed network can learn monocular dense depth estimation up to 22.5% more accurately than state-of-the-art approaches. We believe that this work, despite its scientific interest, lays the foundations to learn depth from extremely sparse supervision, which can be valuable to all robotic systems acting under severe bandwidth or sensing constraints., Comment: Accepted for Publication at the IEEE Robotics and Automation Letters (RA-L) 2020, and International Conference on Intelligent Robots and Systems (IROS) 2020

Chiral crystals are materials whose lattice structure has a well-defined handedness due to the lack of inversion, mirror, or other roto-inversion symmetries. These crystals represent a broad, important class of quantum materials; their structural chirality has been found to allow for a wide range of phenomena in condensed matter physics, including skyrmions in chiral magnets, unconventional pairing in chiral superconductors, nonlocal transport and unique magnetoelectric effects in chiral metals, as well as enantioselective photoresponse. Nevertheless, while these phenomena have been intensely investigated, the topological electronic properties of chiral crystals have still remained largely uncharacterized. While recent theoretical advances have shown that the presence of crystalline symmetries can protect novel band crossings in 2D and 3D systems, we present a new class of Weyl fermions enforced by the absence of particular crystal symmetries. These "Kramers-Weyl" fermions are a universal topological electronic property of all nonmagnetic chiral crystals with spin-orbit coupling (SOC); they are guaranteed by lattice translation, structural chirality, and time-reversal symmetry, and unlike conventional Weyl fermions, appear at time-reversal-invariant momenta (TRIMs). We cement this finding by identifying representative chiral materials in the majority of the 65 chiral space groups in which Kramers-Weyl fermions are relevant to low-energy physics. By combining our analysis with the results of previous works, we determine that all point-like nodal degeneracies in nonmagnetic chiral crystals with relevant SOC carry nontrivial Chern numbers. We further show that, beyond the previous phenomena allowed by structural chirality, Kramers-Weyl fermions enable unusual phenomena, such as a novel electron spin texture, chiral bulk Fermi surfaces over large energy windows., Comment: Main text + Supplementary Material. Material predictions added. Extended theoretical analysis using band representation theory as well as possible signatures of Kramers-Weyl fermions. Title and author list updated

It is of interest to federal and state agencies to develop an advanced uniform method for estimation of vehicle miles traveled (VMT) on local roads which can be used as a guideline for agencies nationwide. The purpose of this study is to propose advanced innovative approaches for estimating VMT on local roads and analyze the feasibility of applying these methods. The paper presents a methodology and procedure for estimating local road VMT using GPS vehicle trajectory data and an all-street road network and expands these methodologies and results to determine the minimum required GPS sample size. The Federal Highway Administration and other transportation agencies may consider using these methodologies as a future guide to update VMT estimates with minimal additional cost requirements. The key finding of the research is that it is feasible to use new GPS vehicle trajectory data to estimate VMT on non-Federal Aid System roadways. The sample size of this data allows the application of this new method across the nation. The accuracy of this method was tested for the State of Maryland. Once such statewide GPS data is obtained by a given state, the methodology can be easily applied to that state as well.

We present a novel pressure-based method for weakly compressible multiphase flows, based on a non-equilibrium Baer and Nunziato-type model. Each component is described by its own thermodynamic model, thus the definition of a mixture speed of sound is not required. In this work, we describe the hyperbolic operator, without considering relaxation terms. The acoustic part of the governing equations is treated implicitly to avoid the severe restriction on the time step imposed by the CFL condition at low-Mach. Particular care is taken to discretize the non-conservative terms to avoid spurious oscillations across multi-material interfaces. The absence of oscillations and the agreement with analytical or published solutions is demonstrated in simplified test cases, which confirm the validity of the proposed approach as a building block on which developing more accurate and comprehensive methods.

Civilian drones are soon expected to be used in a wide variety of tasks, such as aerial surveillance, delivery, or monitoring of existing architectures. Nevertheless, their deployment in urban environments has so far been limited. Indeed, in unstructured and highly dynamic scenarios, drones face numerous challenges to navigate autonomously in a feasible and safe way. In contrast to traditional “map-localize-plan” methods, this letter explores a data-driven approach to cope with the above challenges. To accomplish this, we propose DroNet: a convolutional neural network that can safely drive a drone through the streets of a city. Designed as a fast eight-layers residual network, DroNet produces two outputs for each single input image: A steering angle to keep the drone navigating while avoiding obstacles, and a collision probability to let the UAV recognize dangerous situations and promptly react to them. The challenge is however to collect enough data in an unstructured outdoor environment such as a city. Clearly, having an expert pilot providing training trajectories is not an option given the large amount of data required and, above all, the risk that it involves for other vehicles or pedestrians moving in the streets. Therefore, we propose to train a UAV from data collected by cars and bicycles, which, already integrated into the urban environment, would not endanger other vehicles and pedestrians. Although trained on city streets from the viewpoint of urban vehicles, the navigation policy learned by DroNet is highly generalizable. Indeed, it allows a UAV to successfully fly at relative high altitudes and even in indoor environments, such as parking lots and corridors. To share our findings with the robotics community, we publicly release all our datasets, code, and trained networks.

This article describes the development of a compact, relatively low-cost, high compliance voltage laser driver that can provide the constant optical laser output required for a range of applications. The system contains an integrated, high-precision temperature controller that can be implemented with butterfly-style lasers containing an internal thermoelectric cooler. The laser parameters can be controlled manually or via an onboard microcontroller. Additionally, an adjustable over-current protection circuit safeguards the laser diode from potential damage.

This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i.e. 5–15 m above the ground). The 2 km dataset consists of time synchronized aerial high-resolution images, global position system and inertial measurement unit sensor data, ground-level street view images, and ground truth data. The dataset is ideal to evaluate and benchmark appearance-based localization, monocular visual odometry, simultaneous localization and mapping, and online three-dimensional reconstruction algorithms for micro aerial vehicles in urban environments.

The human hand is the most frequently used body part in activities of daily living. With its complex anatomical structure and the small size compared to the body, assessing the functional capability is highly challenging. The aim of this review was to provide a systematic overview on currently available 3D motion analysis based on skin markers for the assessment of hand function during activities of daily living. It is focused on methodology rather than results. A systematic review according to the PRISMA guidelines was performed. The systematic search yielded 1349 discrete articles. Of 147 articles included on basis of title, 123 were excluded after abstract review, and 24 were included in the full-text analysis with 13 key articles. There is still limited knowledge about hand and finger kinematics during activities of daily living. A standardization of the task is required in order to overcome the nonrepetitive nature and high variability of upper limb motion and ensure repeatability of task performance. To yield a progress in the analysis of human hand movements, an assessment of human kinematics including fingers, wrist, and thumb and an identification of relevant parameters that characterize a healthy motion pattern during functional tasks are needed.

In recent years, many approaches to visual-inertial odometry (VIO) have become available. However, they neither exploit the robot's dynamics and known actuation inputs, nor differentiate between the desired motion due to actuation and the unwanted perturbation due to external force. For many robotic applications, it is often essential to sense the external force acting on the system due to, for example, interactions, contacts, and disturbances. Adding a motion constraint to an estimator leads to discrepancy between the model-predicted motion and the actual motion. Our approach exploits this discrepancy and resolves it by simultaneously estimating the motion and external force. We propose a relative motion constraint combining the robot's dynamics and the external force in a preintegrated residual, resulting in a tightly coupled, sliding-window estimator exploiting all correlations among all variables. We implement our visual inertial model-based odometry system into a state-of-the-art VIO approach and evaluate it against the original pipeline without motion constraints both on simulated and real-world data. The results show that our approach increases the accuracy of the estimator up to 29% as compared to the original VIO, and it provides external force estimates at no extra computational cost. To the best of our knowledge, this is the first approach that exploits model dynamics by jointly estimating motion and external force. Our implementation will be made available open-source.

[Image: see text] In this study, we analyze how the octahedral tilts and rotations of thin films of LaNiO(3) and LaAlO(3) grown on different substrates, determined using synchrotron X-ray diffraction-measured half-integer Bragg peaks, depend upon the total film thickness. We find a striking difference between films grown on SrTiO(3) and LaAlO(3) substrates which appears to stem not only from the difference in epitaxial strain state but also from the level of continuity at the heterointerface. In particular, the chemically and structurally discontinuous LaNiO(3)/SrTiO(3) and LaAlO(3)/SrTiO(3) interfaces cause a large variation in the octahedral network as a function of film thickness whereas the rather continuous LaNiO(3)/LaAlO(3) interface seems to allow from just a few unit cells the formation of a stable octahedral pattern corresponding to that expected only given the applied biaxial strain.

IEEE Robotics and Automation Letters, 4 (2), ISSN:2377-3766

Spiking neural networks (SNNs) employing memristive synapses are capable of life-long online learning. Because of their ability to process and classify large amounts of data in real-time using compact and low-power electronic systems, they promise a substantial technology breakthrough. However, the critical issue that memristor-based SNNs have to face is the fundamental limitation in their memory capacity due to finite resolution of the synaptic elements, which leads to the replacement of old memories with new ones and to a finite memory lifetime. In this study we demonstrate that the nonlinear conductance dynamics of memristive devices can be exploited to improve the memory lifetime of a network. The network is simulated on the basis of a spiking neuron model of mixed-signal digital-analogue sub-threshold neuromorphic CMOS circuits, and on memristive synapse models derived from the experimental nonlinear conductance dynamics of resistive memory devices when stimulated by trains of identical pulses. The network learning circuits implement a spike-based plasticity rule compatible with both spike-timing and rate-based learning rules. In order to get an insight on the memory lifetime of the network, we analyse the learning dynamics in the context of a classical benchmark of neural network learning, that is hand-written digit classification. In the proposed architecture, the memory lifetime and the performance of the network are improved for memristive synapses with nonlinear dynamics with respect to linear synapses with similar resolution. These results demonstrate the importance of following holistic approaches that combine the study of theoretical learning models with the development of neuromorphic CMOS SNNs with memristive devices used to implement life-long on-chip learning.