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Physical therapy (PT) plays a crucial role in muscle injury recovery, but people struggle to adhere to and perform PT exercises correctly from home. To support challenges faced with in-home PT, augmented reality (AR) holds promise in enhancing patient's engagement and accuracy through immersive interactive visualizations. However, effectively leveraging AR requires a better understanding of patient needs during injury recovery. Through interviews with six individuals undergoing physical therapy, this paper introduces user-centered design considerations integrating AR and body motion data to enhance in-home PT for injury recovery. Our findings identify key challenges and propose design variables for future body-based visualizations of body motion data for PT.

Of the many applications of whispering-gallery mode (WGM) microresonators, Single-atom cavity-QED poses the most extreme demands on mode-volume and quality factor. Here we present a model-based procedure for the fabrication of small mode-volume ultrahigh Q silica WGM microresonators of varying geometries, from toroidal to micro-spheres. We experimentally demonstrate WGM resonators with ultra-high qualities as high as $1.7\times 10^8$ at 780nm. We present a theoretical model that allows tailoring the recipe to attain the desired geometry of the fabricated WGM microresonators., Comment: 15 pages, 9 figure

We present a new approach for simulating x-ray nanobeam Bragg coherent diffraction patterns based on the Takagi-Taupin equations. Compared to conventional methods, the current approach can be universally applied to any weakly strained system including semi-infinite crystals that diffract dynamically. It addresses issues such as the curved wavefront and re-divergence of the focused incident beam. We show excellent agreement against experimental data on a strained La0.7Sr0.3MnO3 thin film on SrTiO3 substrate, and a path to extracting physical information using automatic differentiation.

We present spiral resonators of thin film niobium (Nb) that exhibit large geometric inductance, high critical magnetic fields and high single photon quality factors. These low loss geometric inductors can be a compelling alternative to kinetic inductors to create high-impedance superconducting devices for applications that require magnetic fields. By varying the spiral pitch, we realize resonators with characteristic impedances ranging from 3.25-7.09 k{\Omega}. We measure the temperature and magnetic field dependent losses and find that the high-impedance resonators maintain an intrinsic quality factor above {\sim} 10^5 for parallel magnetic fields of up to 1 T. These properties make spiral Nb resonators a promising candidate for quantum devices that require circuit elements with high impedance and magnetic field resilience.

People with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients' motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions., Comment: 18 pages, 11 figures, Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA

It is now well established that a high-frequency electromagnetic dressing field within the off-resonance regime significantly modifies the electronic transport and optical properties on Dirac materials. Here, using light with circular polarization, we investigate its effect on the energy spectrum of tilted monolayer 1T$^\prime$MoS$_2$ which acquires two energy gaps associated with up- and down- pseudospin. We can adjust its electronic properties over a wider range by varying these two band gaps in contrast with graphene. With the use of the Lindhard approach for the frequency-dependent polarizability propagator, we have developed a rigorous theoretical formalism for employing the Floquet energy spectrum for investigating the many-body effects on the plasmon excitations, their lifetimes due to Landau damping and the exchange energy of tilted monolayer 1T$^\prime$MoS$_2$ under normal incidence of electromagnetic radiation at arbitrary temperature. The dressed states at very low temperature corresponding to circular polarization suppress the response of the system to the external probe. This gives rise to the weak but long lived plasmon excitations at small wavenumber $q$ when compared to the plasmon spectrum in this regime in the absence of irradiation. However, $\sqrt{qT}$-dependent plasmons are restored at high temperatures. Our calculations have shown that the tilting, anisotropy, direct and indirect band gaps lead to a reduced exchange energy, which has some potential applications such as, tunability of exciton polariton and plasmon excitations.

We describe a phase-retrieval-based imaging method to directly spatially resolve the vector lattice distortions in an extended crystalline sample by explicit coupling of independent Bragg ptychography data sets into the reconstruction process. Our method addresses this multi-peak Bragg ptychography (MPBP) inverse problem by explicit gradient descent optimization of an objective function based on modeling of the probe-lattice interaction, along with corrective steps to address spurious reconstruction artifacts. Robust convergence of the optimization process is ensured by computing exact gradients with the automatic differentiation capabilities of high-performance computing software packages. We demonstrate MPBP reconstruction with simulated ptychography data mimicking diffraction from a single crystal membrane containing heterogeneities that manifest as phase discontinuities in the diffracted wave. We show the superior ability of such an optimization-based approach in removing reconstruction artifacts compared to existing phase retrieval and lattice distortion reconstruction approaches.

With the maturity of deep learning, its use is emerging in every field. Also, as different types of GPUs are becoming more available in the markets, it creates a difficult decision for users. How can users select GPUs to achieve optimal performance for a specific task? Analysis of GPU architecture is well studied, but existing works that benchmark GPUs do not study tasks for networks with significantly larger input. In this work, we tried to differentiate the performance of different GPUs on neural network models that operate on bigger input images (256x256).

We study a combinatorial model of the quantum scalar field with polynomial potential on a graph. In the first quantization formalism, the value of a Feynman graph is given by a sum over maps from the Feynman graph to the spacetime graph (mapping edges to paths). This picture interacts naturally with Atiyah-Segal-like cutting-gluing of spacetime graphs. In particular, one has combinatorial counterparts of the known gluing formulae for Green's functions and (zeta-regularized) determinants of Laplacians., Comment: 72 pages, 26 figures

We address the question of counting maps between projective spaces such that images of cycles on the source intersect cycles on the target. In this paper we do it by embedding maps into quasimaps that form a projective space of their own. When a quasimap is not a map, it contains freckles (studied earlier) and/or scars, appearing when the complex dimension of the source is greater than one. We consider a lot of examples showing that freckle/scar calculus (using excess intersection theory) works. We also propose the "smooth conjecture" that may lead to computation of the number of maps by an integral over the space of quasimaps., Comment: 53 pages

An effective interdisciplinary study between machine learning and solar-thermal desalination requires a sufficiently large and well-analyzed experimental datasets. This study develops a modified dataset collection and analysis process for studying solar-thermal desalination by machine learning. Based on the optimized water condensation and collection process, the proposed experimental method collects over one thousand datasets, which is ten times more than the average number of datasets in previous works, by accelerating data collection and reducing the time by 83.3%. On the other hand, the effects of dataset features are investigated by using three different algorithms, including artificial neural networks, multiple linear regressions, and random forests. The investigation focuses on the effects of dataset size and range on prediction accuracy, factor importance ranking, and the model's generalization ability. The results demonstrate that a larger dataset can significantly improve prediction accuracy when using artificial neural networks and random forests. Additionally, the study highlights the significant impact of dataset size and range on ranking the importance of influence factors. Furthermore, the study reveals that the extrapolation data range significantly affects the extrapolation accuracy of artificial neural networks. Based on the results, massive dataset collection and analysis of dataset feature effects are important steps in an effective and consistent machine learning process flow for solar-thermal desalination, which can promote machine learning as a more general tool in the field of solar-thermal desalination.

The manipulation and control of nanoscale magnetic spin textures is of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.

Surface acoustic waves (SAW) and associated SAW devices are ideal for sensing, metrology, and connecting and controlling hybrid quantum devices. While the advances demonstrated to date are largely based on electromechanical coupling, a robust and customizable coherent optical coupling would unlock mature and powerful cavity optomechanical control techniques and an efficient optical pathway for long-distance quantum links. Here we demonstrate direct and robust coherent optical coupling to surface acoustic wave cavities through a Brillouin-like optomechanical interaction. In high-frequency SAW cavities designed with curved metallic acoustic reflectors deposited on crystalline substrates, SAW modes are efficiently optically accessed in piezo-active directions that can be accessed through traditionally electromechanical techniques as well as non-piezo-active directions that cannot. The non-contact nature of the optical technique enables controlled analysis of dissipation mechanisms and access to pristine mechanical resonators with record-level quality factors (>100,000 measured here). The exceptional control of the optical probe beams also enables detailed transverse spatial mode spectroscopy, for the first time. These advantages combined with simple fabrication, small size, large power handling, and strong coupling to quantum systems make SAW optomechanical platforms particularly attractive for sensing, material science, and hybrid quantum systems.

Because of their small size, low loss, and compatibility with magnetic fields and elevated temperatures, surface acoustic wave resonators hold significant potential as future quantum interconnects. Here, we design, fabricate, and characterize GHz-frequency surface acoustic wave resonators with the potential for strong capacitive coupling to nanoscale solid-state quantum systems, including semiconductor quantum dots. Strong capacitive coupling to such systems requires a large characteristic impedance, and the resonators we fabricate have impedance values above 100 $\Omega$. We achieve such high impedance values by tightly confining a Gaussian acoustic mode. At the same time, the resonators also have low loss, with quality factors of several thousand at millikelvin temperatures. These high-impedance resonators are expected to exhibit large vacuum electric-field fluctuations and have the potential for strong coupling to a variety of solid-state quantum systems.

We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography. Ptychography is a lensless method that is being used to image samples through a simultaneous numerical inversion of a large number of diffraction patterns from adjacent overlapping scan positions. This acquisition method can enable nanoscale imaging with x-rays and electrons, but this often requires very large experimental datasets and commensurately high turnaround times, which can limit experimental capabilities such as real-time experimental steering and low-latency monitoring. In this work, we introduce a software system that can automate ptychography data analysis tasks. We accelerate the data analysis pipeline by using a modified version of PtychoNN -- an ML-based approach to solve phase retrieval problem that shows two orders of magnitude speedup compared to traditional iterative methods. Further, our system coordinates and overlaps different data analysis tasks to minimize synchronization overhead between different stages of the workflow. We evaluate our workflow system with real-world experimental workloads from the 26ID beamline at Advanced Photon Source and ThetaGPU cluster at Argonne Leadership Computing Resources., Comment: 7 pages, 1 figure, to be published in High Performance Computing for Imaging Conference, Electronic Imaging (HPCI 2023)

With the continuing advances in scientific instrumentation, scanning microscopes are now able to image physical systems with up to sub-atomic-level spatial resolutions and sub-picosecond time resolutions. Commensurately, they are generating ever-increasing volumes of data, storing and analysis of which is becoming an increasingly difficult prospect. One approach to address this challenge is through self-driving experimentation techniques that can actively analyze the data being collected and use this information to make on-the-fly measurement choices, such that the data collected is sparse but representative of the sample and sufficiently informative. Here, we report the Fast Autonomous Scanning Toolkit (FAST) that combines a trained neural network, a route optimization technique, and efficient hardware control methods to enable a self-driving scanning microscopy experiment. The key features of our method are that: it does not require any prior information about the sample, it has a very low computational cost, and that it uses generic hardware controls with minimal experiment-specific wrapping. We test this toolkit in numerical experiments and a scanning dark-field x-ray microscopy experiment of a $WSe_2$ thin film, where our experiments show that a FAST scan of <25% of the sample is sufficient to produce both a high-fidelity image and a quantitative analysis of the surface distortions in the sample. We show that FAST can autonomously identify all features of interest in the sample while significantly reducing the scan time, the volume of data acquired, and dose on the sample. The FAST toolkit is easy to apply for any scanning microscopy modalities and we anticipate adoption of this technique will empower broader multi-level studies of the evolution of physical phenomena with respect to time, temperature, or other experimental parameters.

The present work focuses on a specific bouncing behaviour as a particle settling through a three-layer stratified fluid in the absence of neutral buoyant position, which was firstly discovered by Abaid, N., Adalsteinsson D., Agyapong A. & McLaughlin, R.M. (2004) in salinity-induced stratification. Both experiments and numerical simulations are carried out. In our experiments, illuminated by a laser sheet on the central plane of the particle, its bouncing behaviour is well captured. We find that the bouncing process starts after the wake detaches from the particle. The PIV results show that an upward jet is generated at the central axis behind the particle after the wake breaks. By conducting a force decomposition procedure, we quantify the enhanced drag caused by the buoyancy of the wake ($F_{sb}$) and the flow structure ($F_{sj}$). It is noted that $F_{sb}$ contributes primarily to the enhanced drag at the early stage, which becomes less dominant after the detachment of the wake. In contrast, $F_{sj}$ plays a pivotal role in reversing the particle's motion. We conjecture that the jet flow is a necessary condition for the occurrence of bouncing motion. Then, we examine the minimal velocities (negative values when bounce occurs) of the particle by varying the lower Reynolds number $Re_l$, the Froude number $Fr$ and the upper Reynolds number $Re_u$ within the ranges $1 \leq Re_l\leq 125$, $115 \leq Re_u\leq 356$ and $2 \leq Fr\leq 7$. We find that the bouncing behaviour is primarily determined by $Re_l$. In our experiments, the bouncing motion is found to occur below a critical lower Reynolds number around $Re^ \ast _{l}=30$. In the numerical simulations, the highest value for this critical number is $Re^ \ast _{l}=46.2$, limited in the currently studied parametric ranges.