Thermalization and many-body localization in systems under dynamic nuclear polarization

We study the role of dipolar interactions in the standard protocol used to achieve dynamic nuclear polarization (DNP). In the so-called spin-temperature regime, where the interactions establish an effective thermodynamic behavior in the out-of-equilibrium stationary state, we provide numerical predictions for the level of hyperpolarization. We show that nuclear spins equilibrate to the effective spin-temperature established among the electron spins of radicals, as expected from the quantum theory of thermalization. Moreover, we present an analytical technique to estimate the spin temperature, and thus, the nuclear hyperpolarization in the steady state, as a function of interaction strength and quenched disorder. This reproduces both our numerical data and experimental results. Our central finding is that the nuclear hyperpolarization increases steadily upon reducing the interaction strength (by diluting the radical density). Interestingly, the highest polarization is reached at a point where the establishment of a spin temperature is just about to break down due to the incipient many-body localization transition in the electron spin system.Comment: 12 pages (+ 3 pages of appendix), 8 figure

Multiscale Coupling of One-dimensional Vascular Models and Elastic Tissues

We present a computational multiscale model for the efficient simulation of vascularized tissues, composed of an elastic three-dimensional matrix and a vascular network. The effect of blood vessel pressure on the elastic tissue is surrogated via hyper-singular forcing terms in the elasticity equations, which depend on the fluid pressure. In turn, the blood flow in vessels is treated as a one-dimensional network. Intravascular pressure and velocity are simulated using a high-order finite volume scheme, while the elasticity equations for the tissue are solved using a finite element method. This work addresses the feasibility and the potential of the proposed coupled multiscale model. In particular, we assess whether the multiscale model is able to reproduce the tissue response at the effective scale (of the order of millimeters) while modeling the vasculature at the microscale. We validate the multiscale method against a full scale (three-dimensional) model, where the fluid/tissue interface is fully discretized and treated as a Neumann boundary for the elasticity equation. Next, we present simulation results obtained with the proposed approach in a realistic scenario, demonstrating that the method can robustly and efficiently handle the one-way coupling between complex fluid microstructures and the elastic matrix. © 2021, The Author(s)

Multiscale coupling of one-dimensional vascular models and elastic tissues

We present a computational multiscale model for the efficient simulation of vascularized tissues, composed of an elastic three-dimensional matrix and a vascular network. The effect of blood vessel pressure on the elastic tissue is surrogated via hyper-singular forcing terms in the elasticity equations, which depend on the fluid pressure. In turn, the blood flow in vessels is treated as a one-dimensional network. The pressure and velocity of the blood in the vessels are simulated using a high-order finite volume scheme, while the elasticity equations for the tissue are solved using a finite element method. This work addresses the feasibility and the potential of the proposed coupled multiscale model. In particular, we assess whether the multiscale model is able to reproduce the tissue response at the effective scale (of the order of millimeters) while modeling the vasculature at the microscale. We validate the multiscale method against a full scale (three-dimensional) model, where the fluid/tissue interface is fully discretized and treated as a Neumann boundary for the elasticity equation. Next, we present simulation results obtained with the proposed approach in a realistic scenario, demonstrating that the method can robustly and efficiently handle the one-way coupling between complex fluid microstructures and the elastic matrix

Fully Onboard Low-Power Localization with Semantic Sensor Fusion on a Nano-UAV using Floor Plans

Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoor applications and for close proximity to humans. To enable autonomy, the nano-UAV must be able to self-localize in its operating environment. This is a particularly-challenging task due to the limited sensing and compute resources on board. This work presents an online and onboard approach for localization in floor plans annotated with semantic information. Unlike sensor-based maps, floor plans are readily-available, and do not increase the cost and time of deployment. To overcome the difficulty of localizing in sparse maps, the proposed approach fuses geometric information from miniaturized time-of-flight sensors and semantic cues. The semantic information is extracted from images by deploying a state-of-the-art object detection model on a high-performance multi-core microcontroller onboard the drone, consuming only 2.5mJ per frame and executing in 38ms. In our evaluation, we globally localize in a real-world office environment, achieving 90% success rate. We also release an open-source implementation of our work.Comment: Under review for ICRA 2024, 7 page

A fully digital bridge towards the realization of the farad from the quantum Hall effect

This paper presents the implementation of an electronic fully-digital impedance bridge optimized for RC comparisons with equal impedance magnitudes, together with an evaluation of the uncertainty. This bridge has been designed with the goal of realizing the farad directly from the quantum Hall effect with a bridge uncertainty component at the 1E-7 level. Thanks to its simple design, ease of operation and affordability, this bridge is suitable to be industrially manufactured. Together with the increasing availability of graphene quantum Hall resistance standards, this can provide an affordable quantum realization of the unit farad for metrology institutes and calibration centres. In this paper we present the uncertainty budget of an example measurement and the results of the validation of the bridge against a suitably modified version of the traceability chain of the Italian national standard of capacitance. The combined uncertainty of the bridge resulted from repeated measurements (overall measurement time of about 200 min) is 9.2 × 10^−8, suitable for the primary realization of the unit of capacitance from a quantized Hall resistance standard. The crosstalk among the channels of the electrical generator is the most significant uncertainty component, possibly reducible with internal shielding and filtering of the electronic generator

Engineering the substrate scope of the Fe(II) dependent halogenase WelO15

Selective halogenation is an important reaction for late-stage functionalisation of drug-like molecules. Performing halogenations under mild conditions using sodium chloride as the chlorine source has great potential for sustainable catalysis. The discovery of non-heme iron (NHI) and 2-oxoglutarate dependent halogenases, acting directly on a small organic molecule and not on acyl-carrier bound substrates,[1,2] has eliminated a major drawback of know NHI-halogenases. Hence, these enzymes represent attractive starting points for developing biocatalytic routs for selective, aliphatic chlorination, a paramount challenge in organic synthesis. The wild-types have a narrow natural substrate-scope and are unexplored for biocatalytic applications.[3] After solving the crystal structure of WelO15 from Westiella intricata, we used directed evolution to redesign the active site using a small-but-smart amino acid alphabet, thereby limiting the screening effort to a HPLC compatible throughput. New variants were found, able to chlorinate novel synthesized non-natural substrates. This study represents a first step towards milder, selective chlorination using biocatalysis. Please click Additional Files below to see the full abstract

Rotational and high-resolution infrared spectrum of HC3_3N: global ro-vibrational analysis and improved line catalogue for astrophysical observations

HC$_3$N is an ubiquitous molecule in interstellar environments, from external galaxies, to Galactic interstellar clouds, star forming regions, and planetary atmospheres. Observations of its rotational and vibrational transitions provide important information on the physical and chemical structure of the above environments. We present the most complete global analysis of the spectroscopic data of HC$_3$N. We have recorded the high-resolution infrared spectrum from 450 to 1350 cm$^{-1}$, a region dominated by the intense $\nu_5$ and $\nu_6$ fundamental bands, located at 660 and 500 cm$^{-1}$, respectively, and their associated hot bands. Pure rotational transitions in the ground and vibrationally excited states have been recorded in the millimetre and sub-millimetre regions in order to extend the frequency range so far considered in previous investigations. All the transitions from the literature and from this work involving energy levels lower than 1000 cm$^{-1}$ have been fitted together to an effective Hamiltonian. Because of the presence of various anharmonic resonances, the Hamiltonian includes a number of interaction constants, in addition to the conventional rotational and vibrational l-type resonance terms. The data set contains about 3400 ro-vibrational lines of 13 bands and some 1500 pure rotational lines belonging to 12 vibrational states. More than 120 spectroscopic constants have been determined directly from the fit, without any assumption deduced from theoretical calculations or comparisons with similar molecules. An extensive list of highly accurate rest frequencies has been produced to assist astronomical searches and data interpretation. These improved data, have enabled a refined analysis of the ALMA observations towards Sgr B2(N2).Comment: 35 pages, 14 figures, accepted for pubblication in ApJ Supplemen

Event-based Backpropagation for Analog Neuromorphic Hardware

Neuromorphic computing aims to incorporate lessons from studying biological nervous systems in the design of computer architectures. While existing approaches have successfully implemented aspects of those computational principles, such as sparse spike-based computation, event-based scalable learning has remained an elusive goal in large-scale systems. However, only then the potential energy-efficiency advantages of neuromorphic systems relative to other hardware architectures can be realized during learning. We present our progress implementing the EventProp algorithm using the example of the BrainScaleS-2 analog neuromorphic hardware. Previous gradient-based approaches to learning used "surrogate gradients" and dense sampling of observables or were limited by assumptions on the underlying dynamics and loss functions. In contrast, our approach only needs spike time observations from the system while being able to incorporate other system observables, such as membrane voltage measurements, in a principled way. This leads to a one-order-of-magnitude improvement in the information efficiency of the gradient estimate, which would directly translate to corresponding energy efficiency improvements in an optimized hardware implementation. We present the theoretical framework for estimating gradients and results verifying the correctness of the estimation, as well as results on a low-dimensional classification task using the BrainScaleS-2 system. Building on this work has the potential to enable scalable gradient estimation in large-scale neuromorphic hardware as a continuous measurement of the system state would be prohibitive and energy-inefficient in such instances. It also suggests the feasibility of a full on-device implementation of the algorithm that would enable scalable, energy-efficient, event-based learning in large-scale analog neuromorphic hardware