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We study the problem of clock synchronization in a networked system with arbitrary starts for all nodes. We consider a synchronous network of $n$ nodes, where each node has a local clock that is an integer counter. Eventually, clocks must be all equal and increase by one in each round modulo some period $P$. The purpose of this paper is to study whether clock synchronization can be achieved with bounded memory, that is every node maintains a number of states that does not depend on the network size. In particular, we are interested in clock synchronization algorithms which work in dynamic networks, i.e., tolerate that communication links continuously fail and come-up. We first focus on self-stabilizing solutions for clock synchronization, and prove that there is no such algorithm that is bounded memory, even in the case of static networks. More precisely, we show a lower bound of $n+1$ states at each node required to achieve clock synchronization in static strongly connected networks with at most $n$ nodes, and derive a lower bound of $n-2$ rounds on synchronization time, in the worst case. We then prove that, when the self-stabilizing requirement is removed, the impossibility of clock synchronization with bounded memory still holds in the dynamic setting: every solution for the clock synchronization problem in dynamic networks with at most $n$ nodes requires each node to have $\Omega(\log n)$ states.

Resistive pulse sensing has been widely used to characterize and count single particles in solution moving through channels under an electric bias, with nanoscale pores more recently providing enough spatial resolution for nucleic acid sequencing at the single-molecule level. At its core, this technique relies on measuring the drop in ionic current through the pore induced by the passage of a molecule and, through conductance models, translating the blockage signal to molecular dimensions. However, there exists no model considering the resistive contributions of the pore exterior, i.e. the access regions, when obstructed by a molecule. This is becoming increasingly important for low aspect ratio pores, with the advent of 2D materials and ultrathin membranes. In this work, a general method by which to model the resistance of the access regions of a pore in the presence of an insulating obstruction is presented. Thin oblate spheroidal slices are used to partition access regions and infer their conductance when blocked by differently shaped objects. We show that our model accurately estimates the blocked-state conductance of 2D and finite-length pores as a function of the distance from the pore in the presence of simple obstructions geometries (e.g. cylindrical and spherical objects) or complex structures (i.e. sequence of simple obstruction sub-units). The model is further shown to capture off-axis effects by predicting deeper blockages for obstructions offset from the pore's central axis. A web-based tool is created to predict the electrical signatures of a wide range of molecule geometries translocating through differently shaped pores. The introduced model will help guide experimental designs and thus presents a straightforward way to extend the quantification of the resistive pulse technique at the nanoscale.

Inspired by its central role in many biological processes, the transport of biopolymers across nanoscale pores is at the heart of a single-molecule sensing technology aimed at nucleic acid and protein sequencing, as well as biomarker detection. When electrophoretically driven through a pore by an electric potential gradient, a translocating polymer hinders the flow of ions, producing a transient current blockage signature that can be mapped to physicochemical properties of the polymer. Although investigated theoretically and by simulations, few experimental studies have attempted to validate the predicted transport properties, mainly due to the complex nature of the non-equilibrium translocation process. Here, we elucidate these fundamental concepts by constructing a patterned DNA nanostructure whose current signatures allow measurement of the instantaneous velocity throughout the translocation process. With simple physical insights from polymer and fluid dynamics, we show how the resulting molecular velocity profiles can be used to investigate the nanoscale forces at play and their dependence on experimental parameters such as polymer length, pore size and voltage. These results allow testing of theoretical models and outline their limitations. In addition to bridging experiment and theory, knowledge of the velocity fluctuation and force scaling during passage can assist researchers in designing nanopore experiments with optimized sensing performance.

We present a method utilizing atomic diffraction patterns and statistical analysis tools to infer the Casimir-Polder interaction between Argon atoms and a silicon nitride nanograting. The quantum model that supports the data is investigated in detail, as are the roles of nanograting geometry, finite size effects, slit width opening angles, and Lennard-Jones potentials. Our findings indicate that the atom-surface potential strength parameter is C3 = 6.87 $\pm$ 1.18 meV.nm3. This value is primarily constrained by the knowledge of the nanograting geometry. The high sensitivity of our method paves the way for precise determination of the Casimir-Polder potential and exploration of new short-distance forces.

On a closed analytic manifold $(M,g)$, let $\phi_i$ be the eigenfunctions of $\Delta_g$ with eigenvalues $\lambda_i^2$ and let $f:=\prod \phi_{k_j}$ be a finite product of Laplace-Beltrami eigenfunctions. We show that $\left\langle f, \phi_i \right\rangle_{L^2(M)}$ decays exponentially as soon as $\lambda_i > C \sum \lambda_{k_j}$ for some constant $C$ depending only on $M$. Moreover, by using a lower bound on $\| f \|_{L^2(M)} $, we show that $99\%$ of the $L^2$-mass of $f$ can be recovered using only finitely many Fourier coefficients.

Ultracold atomic gases hold unique promise for space science by capitalizing on quantum advantages and extended freefall, afforded in a microgravity environment, to enable next-generation precision sensors. Atom interferometers are a class of quantum sensors which can use freely falling gases of atoms cooled to sub-photon-recoil temperatures to provide unprecedented sensitivities to accelerations, rotations, and gravitational forces, and are currently being developed for space-based applications in gravitational, earth, and planetary sciences, as well as to search for subtle forces that could signify physics beyond General Relativity and the Standard Model. NASA's Cold Atom Lab (CAL) operates onboard the International Space Station as a multi-user facility for studies of ultracold atoms and to mature quantum technologies, including atom interferometry, in persistent microgravity. In this paper, we report on path-finding experiments utilizing ultracold $^{87}$Rb atoms in the CAL atom interferometer, which was enabled by an on-orbit upgrade of the CAL science module: A three-pulse Mach-Zehnder interferometer was studied to understand limitations from the influence of ISS vibrations. Additionally, Ramsey shear-wave interferometry was used to manifest interference patterns in a single run that were observable for over 150 ms free-expansion time. Finally, the CAL atom interferometer was used to remotely measure the photon recoil from the atom interferometer laser as a demonstration of the first quantum sensor using matter-wave interferometry in space., Comment: 14 pages, 5 figures

The industry of the future, also known as Industry 5.0, aims to modernize production tools, digitize workshops, and cultivate the invaluable human capital within the company. Industry 5.0 can't be done without fostering a workforce that is not only technologically adept but also has enhanced skills and knowledge. Specifically, collaborative robotics plays a key role in automating strenuous or repetitive tasks, enabling human cognitive functions to contribute to quality and innovation. In manual manufacturing, however, some of these tasks remain challenging to automate without sacrificing quality. In certain situations, these tasks require operators to dynamically organize their mental, perceptual, and gestural activities. In other words, skills that are not yet adequately explained and digitally modeled to allow a machine in an industrial context to reproduce them, even in an approximate manner. Some tasks in welding serve as a perfect example. Drawing from the knowledge of cognitive and developmental psychology, professional didactics, and collaborative robotics research, our work aims to find a way to digitally model manual manufacturing skills to enhance the automation of tasks that are still challenging to robotize. Using welding as an example, we seek to develop, test, and deploy a methodology transferable to other domains. The purpose of this article is to present the experimental setup used to achieve these objectives.

We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data including high frequency globally aligned 3D trajectories, scene point cloud, per-frame 3D eye gaze vector and time aligned speech transcription. In this paper, we demonstrate a few exemplar research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation. AEA is an open source dataset that can be downloaded from https://www.projectaria.com/datasets/aea/. We are also providing open-source implementations and examples of how to use the dataset in Project Aria Tools https://github.com/facebookresearch/projectaria_tools., Comment: Dataset website: https://www.projectaria.com/datasets/aea/

Light-matter interactions are frequently perceived as predominantly influenced by the electric optical field, with the magnetic component of light often overlooked. Nonetheless, the magnetic aspect plays a pivotal role in various optical processes, including chiral light-matter interactions, photon-avalanching, and forbidden photochemistry, underscoring the significance of manipulating magnetic processes in optical phenomena. Here, we explore the ability to control the magnetic light and matter interactions at the nanoscale. In particular, we demonstrate experimentally, using a plasmonic nanostructure, the transfer of energy from the optical magnetic field to a nanoparticle, thanks to the deep subwavelength magnetic confinement allowed by our nano-antenna. This control is made possible by the particular design of our plasmonic nanostructure, which has been optimized to spatially separate the electric and magnetic fields of the localized plasmon. Furthermore, by studying the spontaneous emission from the Lanthanide-ions doped nanoparticle, we observe that the optical field distributions are not spatially correlated with the electric and magnetic near-field quantum environments of this antenna, which seemingly contradicts the reciprocity theorem. We demonstrate that this counter-intuitive observation is in fact, the result of the different optical paths followed by the excitation and emission of the ions, which forbids a direct application of that theorem.

We present a theoretical model of matter-wave diffraction through a material nanostructure. This model is based on the numerical solution of the time-dependent Schr{\"o}dinger equation, which goes beyond the standard semi-classical approach. In particular, we consider the dispersion force interaction between the atoms and the material, which is responsible for high energy variations. The effect of such forces on the quantum model is investigated, along with a comparison with the semi-classical model. In particular, for atoms at low velocity and close to the material surface, the semi-classical approach fails, while the quantum model accurately describes the expected diffraction pattern. This description is thus relevant for slow and cold atom experiments where increased precision is required, e.g. for metrological applications.

The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.

The sensitivity of atom interferometers depends on their ability to realize long pulse separation times and prevent loss of contrast by limiting the expansion of the atomic ensemble within the interferometer beam through matter-wave collimation. Here we investigate the impact of atomic interactions on collimation by applying a lensing protocol to a $^{39}$K Bose-Einstein condensate at different scattering lengths. Tailoring interactions, we measure energies corresponding to $340 \pm 12$ pK in one direction. Our results are supported by an accurate simulation, which allows us to extrapolate a 2D ballistic expansion energy of $438 \pm 77$ pK. Based on our findings we propose an advanced scenario, which enables 3D expansion energies below $16$ pK by implementing an additional pulsed delta-kick. Our results pave the way to realize ensembles with more than $1\times10^5$ atoms and 3D energies in the two-digit pK range in typical dipole trap setups without the need for micro-gravity or long baseline environments.

We prove upper and lower bounds for the number of zeroes of linear combinations of Schr\"odinger eigenfunctions on metric (quantum) graphs. These bounds are distinct from both the interval and manifolds. We complement these bounds by giving non-trivial examples for the lower bound as well as sharp examples for the upper bound. In particular, we show that even tree graphs differ from the interval with respect to the nodal count of linear combinations of eigenfunctions. This stands in distinction to previous results which show that all tree graphs have to same eigenfunction nodal count as the interval.

We present an efficient method for modeling the single and double ionization dynamics of the H$_2$ molecule in ultrashort intense laser fields. This method is based on a semi-analytical approach to calculate the time-dependent single and double molecular ionization rates and on a numerical approach to describe the vibrational motion that takes place in the intermediate molecular ion H$_2^+$. This model allows for the prediction of the single and double ionization probabilities of the H$_2$ molecule to be made over a wide range of frequencies and laser intensities with limited computational time, while providing a realistic estimate of the energy of the products of the dissociative ionization and of the Coulomb explosion of the H$_2$ molecule. The effect of vibrational dynamics on ionization yields and proton kinetic energy release spectra is demonstrated and, in the case of the latter, discussed in terms of basic strong-field molecular fragmentation mechanisms.

Egocentric, multi-modal data as available on future augmented reality (AR) devices provides unique challenges and opportunities for machine perception. These future devices will need to be all-day wearable in a socially acceptable form-factor to support always available, context-aware and personalized AI applications. Our team at Meta Reality Labs Research built the Aria device, an egocentric, multi-modal data recording and streaming device with the goal to foster and accelerate research in this area. In this paper, we describe the Aria device hardware including its sensor configuration and the corresponding software tools that enable recording and processing of such data.

Atom interferometers allow determining inertial effects to high accuracy. Quantum-projection noise as well as systematic effects impose demands on large atomic flux as well as ultra-low expansion rates. Here we report on a high-flux source of ultra-cold atoms with free expansion rates near the Heisenberg limit directly upon release from the trap. Our results are achieved in a time-averaged optical dipole trap and enabled through dynamic tuning of the atomic scattering length across two orders of magnitude interaction strength via magnetic Feshbach resonances. We demonstrate BECs with more than $6\times 10^{4}$ particles after evaporative cooling for $170$ ms and their subsequent release with a minimal expansion energy of $4.5$ nK in one direction. Based on our results we estimate the performance of an atom interferometer and compare our source system to a high performance chip-trap, as readily available for ultra-precise measurements in micro-gravity environments.

The capability to reach ultracold atomic temperatures in compact instruments has recently been extended into space. Ultracold temperatures amplify quantum effects, while free-fall allows further cooling and longer interactions time with gravity - the final force without a quantum description. On Earth, these devices have produced macroscopic quantum phenomena such as Bose-Einstein condensation (BECs), superfluidity, and strongly interacting quantum gases. Quantum sensors interfering the superposition of two ultracold atomic isotopes have tested the Universality of Free Fall (UFF), a core tenet of Einstein's classical gravitational theory, at the $10^{-12}$ level. In space, cooling the elements needed to explore the rich physics of strong interactions and preparing the multiple species required for quantum tests of the UFF has remained elusive. Here, utilizing upgraded capabilities of the multi-user Cold Atom Lab (CAL) instrument within the International Space Station (ISS), we report the first simultaneous production of a dual species Bose-Einstein condensate in space (formed from $^{87}$Rb and $^{41}$K), observation of interspecies interactions, as well as the production of $^{39}$K ultracold gases. We have further achieved the first space-borne demonstration of simultaneous atom interferometry with two atomic species ($^{87}$Rb and $^{41}$K). These results are an important step towards quantum tests of UFF in space, and will allow scientists to investigate aspects of few-body physics, quantum chemistry, and fundamental physics in novel regimes without the perturbing asymmetry of gravity.

We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured using Aria glasses with extensive object, environment, and human level ground truth. This ADT release contains 200 sequences of real-world activities conducted by Aria wearers in two real indoor scenes with 398 object instances (324 stationary and 74 dynamic). Each sequence consists of: a) raw data of two monochrome camera streams, one RGB camera stream, two IMU streams; b) complete sensor calibration; c) ground truth data including continuous 6-degree-of-freedom (6DoF) poses of the Aria devices, object 6DoF poses, 3D eye gaze vectors, 3D human poses, 2D image segmentations, image depth maps; and d) photo-realistic synthetic renderings. To the best of our knowledge, there is no existing egocentric dataset with a level of accuracy, photo-realism and comprehensiveness comparable to ADT. By contributing ADT to the research community, our mission is to set a new standard for evaluation in the egocentric machine perception domain, which includes very challenging research problems such as 3D object detection and tracking, scene reconstruction and understanding, sim-to-real learning, human pose prediction - while also inspiring new machine perception tasks for augmented reality (AR) applications. To kick start exploration of the ADT research use cases, we evaluated several existing state-of-the-art methods for object detection, segmentation and image translation tasks that demonstrate the usefulness of ADT as a benchmarking dataset.

We prove that every nodal domain of an eigenfunction of the Laplacian of eigenvalue $\lambda$ on a $d$-dimensional closed Riemannian manifold contains a ball of radius $c\lambda^{-1/2}(\log\lambda)^{-(d-2)/2}$. This ball is centered at a point at which the eigenfunction attains its maximum in absolute value within the nodal domain., Comment: 15 pages. v2: Presentation of proof of main theorem significantly simplified following Sasha Logunov's suggestion

We present a highly efficient method for the numerical solution of coupled Gross-Pitaevskii equations describing the evolution dynamics of a multispecies mixture of Bose-Einstein condensates in time-dependent potentials. This method, based on a grid-scaling technique, compares favorably to a more standard but much more computationally expensive solution based on a frozen-resolution grid. It allows an accurate description of the long-time behavior of interacting, multi-species quantum mixtures including the challenging problem of long free expansions relevant for microgravity and space experiments. We demonstrate a successful comparison to experimental measurements of a binary Rb-K mixture recently performed with the payload of a sounding rocket experiment.