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Harmonizing serology measurements is critical for identifying reference materials that permit standardization and comparison of results across different diagnostic platforms. However, the theoretical foundations of such tasks have yet to be fully explored in the context of antibody thermodynamics and uncertainty quantification (UQ). This has restricted the usefulness of standards currently deployed and limited the scope of materials considered as viable reference material. To address these problems, we develop rigorous theories of antibody normalization and harmonization, as well as formulate a probabilistic framework for defining correlates of protection. We begin by proposing a mathematical definition of harmonization equipped with structure needed to quantify uncertainty associated with the choice of standard, assay, etc. We then show how a thermodynamic description of serology measurements (i) relates this structure to the Gibbs free-energy of antibody binding, and thereby (ii) induces a regression analysis that directly harmonizes measurements. We supplement this with a novel, optimization-based normalization (not harmonization!) method that checks for consistency between reference and sample dilution curves. Last, we relate these analyses to uncertainty propagation techniques to estimate correlates of protection. A key result of these analyses is that under physically reasonable conditions, the choice of reference material does not increase uncertainty associated with harmonization or correlates of protection. We provide examples and validate main ideas in the context of an interlab study that lays the foundation for using monoclonal antibodies as a reference for SARS-CoV-2 serology measurements.

This is the second manuscript in a two-part series that uses diagnostic testing to understand the connection between prevalence (i.e. number of elements in a class), uncertainty quantification (UQ), and classification theory. Part I considered the context of supervised machine learning (ML) and established a duality between prevalence and the concept of relative conditional probability. The key idea of that analysis was to train a family of discriminative classifiers by minimizing a sum of prevalence-weighted empirical risk functions. The resulting outputs can be interpreted as relative probability level-sets, which thereby yield uncertainty estimates in the class labels. This procedure also demonstrated that certain discriminative and generative ML models are equivalent. Part II considers the extent to which these results can be extended to tasks in unsupervised learning through recourse to ideas in linear algebra. We first observe that the distribution of an impure population, for which the class of a corresponding sample is unknown, can be parameterized in terms of a prevalence. This motivates us to introduce the concept of linearly independent populations, which have different but unknown prevalence values. Using this, we identify an isomorphism between classifiers defined in terms of impure and pure populations. In certain cases, this also leads to a nonlinear system of equations whose solution yields the prevalence values of the linearly independent populations, fully realizing unsupervised learning as a generalization of supervised learning. We illustrate our methods in the context of synthetic data and a research-use-only SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA).

We use JWST/NIRSpec observations from the Assembly of Ultradeep Rest-optical Observations Revealing Astrophysics (AURORA) survey to constrain the shape of the nebular attenuation curve of a star-forming galaxy at z=4.41, GOODSN-17940. We utilize 11 unblended HI recombination lines to derive the attenuation curve spanning optical to near-infrared wavelengths (3751-9550 \r{A}). We then leverage a high-S/N spectroscopic detection of the rest-frame ultraviolet continuum in combination with rest-UV photometric measurements to constrain the shape of the curve at ultraviolet wavelengths. While this UV constraint is predominantly based on stellar emission, the large measured equivalent widths of H$\alpha$ and H$\beta$ indicate that GOODSN-17940 is dominated by an extremely young stellar population <10 Myr in age such that the UV stellar continuum experiences the same attenuation as the nebular emission. The resulting combined nebular attenuation curve spans 1400-9550 \r{A} and has a shape that deviates significantly from commonly assumed dust curves in high-redshift studies. Relative to the Milky Way, SMC, and Calzetti curves, the new curve has a steeper slope at long wavelengths ($\lambda>5000$ \r{A}) while displaying a similar slope across blue-optical wavelengths ($\lambda=3750-5000$ \r{A}). In the ultraviolet, the new curve is shallower than the SMC and Calzetti curves and displays no significant 2175 \r{A} bump. This work demonstrates that the most commonly assumed dust curves are not appropriate for all high-redshift galaxies. These results highlight the ability to derive nebular attenuation curves for individual high-redshift sources with deep JWST/NIRSpec spectroscopy, thereby improving the accuracy of physical properties inferred from nebular emission lines., Comment: 21 pages, 6 figures, 1 table, submitted to ApJ

On-chip magnetic field sensing with Nitrogen-Vacancy (NV) centers in diamond requires scalable integration of 3D waveguides into diamond substrates. Here, we develop a sensing array device with an ensemble of shallow implanted NV centers integrated with arrays of laser-written waveguides for excitation and readout of NV signals. Our approach enables an easy-to-operate on-chip magnetometer with a pixel size proportional to the Gaussian mode area of each waveguide. The performed continuous wave optically detected magnetic resonance on each waveguide gives an average dc-sensitivity value of $195 \pm 3 {nT}/\sqrt{Hz}$, which can be improved with lock-in-detection or pulsed-microwave sequences. We apply a magnetic field to separate the four NV crystallographic orientations of the magnetic resonance and then utilize a DC current through a straight wire antenna close to the waveguide to prove the sensor capabilities of our device. We reconstruct the complete vector magnetic field in the NV crystal frame using three different NV crystallographic orientations. By knowing the polarization axis of the waveguide mode, we project the magnetic field vector into the lab frame.

We report on the effects of cosmic ray interactions with the Kinetic Inductance Detector (KID) based focal plane array for the Terahertz Intensity Mapper (TIM). TIM is a NASA-funded balloon-borne experiment designed to probe the peak of the star formation in the Universe. It employs two spectroscopic bands, each equipped with a focal plane of four $\sim\,$900-pixel, KID-based array chips. Measurements of an 864-pixel TIM array shows 791 resonators in a 0.5$\,$GHz bandwidth. We discuss challenges with resonator calibration caused by this high multiplexing density. We robustly identify the physical positions of 788 (99.6$\,$%) detectors using a custom LED-based identification scheme. Using this information we show that cosmic ray events occur at a rate of 2.1$\,\mathrm{events/min/cm^2}$ in our array. 66$\,$% of the events affect a single pixel, and another 33$\,$% affect $<\,$5 KIDs per event spread over a 0.66$\,\mathrm{cm^2}$ region (2 pixel pitches in radius). We observe a total cosmic ray dead fraction of 0.0011$\,$%, and predict that the maximum possible in-flight dead fraction is $\sim\,$0.165$\,$%, which demonstrates our design will be robust against these high-energy events., Comment: 14 pages, 5 figures. Accepted for the publication in Journal of Low Temperature Physics (2024)

Future quantum networks will rely on the ability to coherently transfer optically encoded quantum information between different wavelength bands. Bragg-scattering four-wave mixing in optical fiber is a promising route to achieving this, but requires fibers with precise dispersion control and broadband transmission at signal, target and pump wavelengths. Here we introduce a photonic crystal fiber with a germanium-doped core featuring group velocity matching at 1550 nm, the telecoms C-band, and 920 nm, within the emission range of efficient single photon sources based on InAs quantum dots. With low chromatic walk-off and good optical guidance even at long wavelengths, large lengths of this fiber are used to achieve nanometer-scale frequency shifts between wavelengths around 920 nm with up to 79.4\% internal conversion efficiency, allowing dissimilar InAs dots to be interfaced. We also show how cascading this frequency conversion can be used to generate a frequency comb away from telecoms wavelengths. Finally, we use the fiber to demonstrate tunable frequency conversion of weak classical signals around 918 nm to the telecoms C-band.

We have used a combined sample of JADES and CEERS objects in order to constrain ionizing photon production efficiency ($\xi_{\rm ion}$) from JWST/NIRSpec and JWST/NIRCam data. We examine 163 objects at 1.06 < z < 6.71 with significant (3$\sigma$) spectroscopic detections of H$\alpha$ and H$\beta$ in order to constrain intrinsic H$\alpha$ luminosities corrected from nebular dust attenuation via Balmer decrements. We constrain dust-corrected UV luminosities from best-fit spectral-energy distribution modeling. We find a sample median log$_{10}$($\xi{\rm ion,0}$/erg Hz$^{-1}$) = $25.29^{+0.29}_{-0.37}$, assuming f$_{\rm esc}$=0 for the escape fraction of Lyman continuum emission. We find significant correlation between $\xi_{\rm ion,0}$ and z, with 17 objects at z > 4.64 having median log$_{10}$($\xi_{\rm ion,0}$/erg Hz$^{-1}$) = $25.38^{+0.38}){-0.38}$, with those below having log$_{10}$($\xi_{\rm ion,0}$/erg Hz$^{-1}$) = $25.24^{+0.30}_{-0.33}$. We also find significant, positive correlations between $\xi_{\rm ion,0}$ and LUV; W{\lambda}([O iii]); [O iii]{\lambda}5007/[O ii]{\lambda}{\lambda}3726, 3729; and inverse correlations with metallicity. In contrast with some previous results, we find no trends between $\xi_{\rm ion,0}$ and stellar mass, stellar dust attenuation, or UV slope. Applying a multivariate fit to $\xi_{\rm ion,0}$, z, and MUV to an empirically-motivated model of reionization, and folding in f$_{\rm esc}$ estimates from direct observations of the Lyman continuum at z ~ 3 from the Keck Lyman Continuum Spectroscopic survey, we find that the number of ionizing photons entering the IGM causes reionization to end at z ~ 5 - 7., Comment: 17 pages, 8 figures, submitted to ApJ

Effective collaboration between humans and AIs hinges on transparent communication and alignment of mental models. However, explicit, verbal communication is not always feasible. Under such circumstances, human-human teams often depend on implicit, nonverbal cues to glean important information about their teammates such as intent and expertise, thereby bolstering team alignment and adaptability. Among these implicit cues, two of the most salient and fundamental are a human's actions in the environment and their visual attention. In this paper, we present a novel method to combine eye gaze data and behavioral data, and evaluate their respective predictive power for human proficiency, trust, and intent. We first collect a dataset of paired eye gaze and gameplay data in the fast-paced collaborative "Overcooked" environment. We then train models on this dataset to compare how the predictive powers differ between gaze data, gameplay data, and their combination. We additionally compare our method to prior works that aggregate eye gaze data and demonstrate how these aggregation methods can substantially reduce the predictive ability of eye gaze. Our results indicate that, while eye gaze data and gameplay data excel in different situations, a model that integrates both types consistently outperforms all baselines. This work paves the way for developing intuitive and responsive agents that can efficiently adapt to new teammates., Comment: 7 pages, 5 figures, To be published in The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement., Comment: 90 pages, 22 figures, roadmap, neuromorphic

We report new radio observations of SDSS J090122.37+181432.3, a strongly lensed star-forming galaxy at $z=2.26$. We image 1.4 GHz (L-band) and 3 GHz (S-band) continuum using the VLA and 1.2 mm (band 6) continuum with ALMA, in addition to the CO(7-6) and CI(${\rm ^3P_2\rightarrow ^3\!P_1}$) lines, all at $\lesssim1.^{\prime\prime}7$ resolution. Based on the VLA integrated flux densities, we decompose the radio spectrum into its free-free (FF) and non-thermal components. The infrared-radio correlation (IRRC) parameter $q_{\rm TIR}=2.65_{-0.31}^{+0.24}$ is consistent with expectations for star forming galaxies. We obtain radio continuum-derived SFRs that are free of dust extinction, finding $\rm {620}_{-220}^{+280}\,M_\odot\,yr^{-1}$, $\rm {230}_{-160}^{+570}\,M_\odot\,yr^{-1}$, and $\rm {280}_{-120}^{+460}\,M_\odot\,yr^{-1}$ from the FF emission, non-thermal emission, and when accounting for both emission processes, respectively, in agreement with previous results. We estimate the gas mass from the CI(${\rm ^3P_2\rightarrow ^3\!P_1}$) line as $M_{\rm gas}=(1.2\pm0.2)\times10^{11}\,M_\odot$, which is consistent with prior CO(1-0)-derived gas masses. Using our new IR and radio continuum data to map the SFR, we assess the dependence of the Schmidt-Kennicutt relation on choices of SFR and gas tracer for $\sim{\rm kpc}$ scales. The different SFR tracers yield different slopes, with the IR being the steepest, potentially due to highly obscured star formation in J0901. The radio continuum maps have the lowest slopes and overall fidelity for mapping the SFR, despite producing consistent total SFRs. We also find that the Schmidt-Kennicutt relation slope is flattest when using CO(7-6) or CI(${\rm ^3P_2\rightarrow ^3\!P_1}$) to trace gas mass, suggesting that those transitions are not suitable for tracing the bulk molecular gas in galaxies like J0901., Comment: 28 pages, 13 figures, 9 tables

We present results on the emission-line properties of z=1.4-7.5 star-forming galaxies in the Assembly of Ultradeep Rest-optical Observations Revealing Astrophysics (AURORA) Cycle 1 JWST/NIRSpec program. Based on its depth, continuous wavelength coverage from 1--5 microns, and medium spectral resolution (R~1000), AURORA includes detections of a large suite of nebular emission lines spanning a broad range in rest wavelength. We investigate the locations of AURORA galaxies in multiple different emission-line diagrams, including traditional "BPT" diagrams of [OIII]/Hbeta vs. [NII]/Halpha, [SII]/Halpha, and [OI]/Halpha, and the "ionization-metallicity" diagram of [OIII]/[OII] (O32) vs. ([OIII]+[OII])/Hbeta (R23). We also consider a bluer rest-frame "ionization-metallicity" diagram introduced recently to characterize z>10 galaxies: [NeIII]/[OII] vs. ([NeIII]+[OII])/Hdelta; as well as longer-wavelength diagnostic diagrams extending into the rest-frame near-IR: [OIII]/Hbeta vs. [SIII]/[SII] (S32); and HeI/Pagamma and [SIII]/Pagamma vs. [FeII]/Pabeta. With a significant boost in signal-to-noise and large, representative samples of individual galaxy detections, the AURORA emission-line diagrams presented here definitively confirm a physical picture in which chemically-young, alpha-enhanced, massive stars photoionize the ISM in distant galaxies with a harder ionizing spectrum at fixed nebular metallicity than in their z~0 counterparts. We also uncover previously unseen evolution prior to z~2 in the [OIII]/Hbeta vs. [NII]/Halpha diagram, which motivates deep NIRSpec observations at even higher redshift. Finally, we present the first statistical sample of rest-frame near-IR emission-line diagnostics in star-forming galaxies at high redshift. In order to truly interpret rest-frame near-IR line ratios including [FeII], we must obtain better constraints on dust depletion in the high-redshift ISM., Comment: 17 pages, 8 figures, submitted to ApJ

Identifying multi-exciton states generated from singlet fission is key to understanding the carrier multiplication process, which presents a strategy for improving the efficiency of photovoltaics and bio-imaging. Broadband optically detected magnetic resonance is a sensitive technique to detect multi-exciton states. Here we report a dominant species emerging under intense light excitation corresponding to a weakly exchange coupled triplet pair located on adjacent molecules oriented by nearly 90 degrees, contrasting to the pi-stacked triplet pair under low excitation intensity. The weakly coupled species model precisely reproduces the intricate spin transitions in the Hilbert space of the triplet pair. Combining the magneto photoluminescence and high-magnetic field ODMR, we also identify a strongly exchange-coupled state of three triplet excitons formed by photoexcited V2, which manifests through the magnetic field induced level crossings between its quintet and triplet manifolds. The excellent agreement between the experimental Zeeman fan and the two-triplet spin Hamiltonian highlights the potential of multi-exciton states for quantum information processing.

We investigate the multi-phase structure of gas flows in galaxies. We study 80 galaxies during the epoch of peak star formation ($1.4\leq z\leq2.7$) using data from Keck/LRIS and VLT/KMOS. Our analysis provides a simultaneous probe of outflows using UV emission and absorption features and H$\alpha$ emission. With this unprecedented data set, we examine the properties of gas flows estimated from LRIS and KMOS in relation to other galaxy properties, such as star formation rate (SFR), star formation rate surface density ($\Sigma_{\rm SFR}$), stellar mass (M$_*$), and main sequence offset ($\Delta$MS). We find no strong correlations between outflow velocity measured from rest-UV lines and galaxy properties. However, we find that galaxies with detected outflows show higher averages in SFR, $\Sigma_{\rm SFR}$, and $\Delta$MS than those lacking outflow detections, indicating a connection between outflow and galaxy properties. Furthermore, we find a lower average outflow velocity than previously reported, suggesting greater absorption at the systemic redshift of the galaxy. Finally, we detect outflows in 49% of our LRIS sample and 30% in the KMOS sample, and find no significant correlation between outflow detection and inclination. These results may indicate that outflows are not collimated and that H$\alpha$ outflows have a lower covering fraction than low-ionization interstellar absorption lines. Additionally, these tracers may be sensitive to different physical scales of outflow activity. A larger sample size with a wider dynamic range in galaxy properties is needed to further test this picture., Comment: 20 pages, 9 figures, 5 tables, submitted to ApJ

Magnetic resonance coupling (MRC) is widely used for wireless power transfer (WPT) applications, but little work has explored how MRC phenomena could be exploited for sensing applications. This paper introduces, validates and evaluates the unique multi-resonant phenomena predicted by circuit theory for over-coupled inductive arrays, and presents eigen-formulae for calculating resonant frequencies and voltage modes within passively excited arrays. Finite-element simulations and experimental results demonstrate the validity of the multi-modal resonant principles for strongly-coupled inductor arrays. The results confirm the distinctive multi-modal resonant frequencies these arrays exhibit, corresponding to the specific magnetic excitation "modes" (comparable to vibrational modes in multi-degree-of-freedom systems). The theoretical and finite element models presented offer a framework for designing and optimizing novel inductive sensing arrays, capitalizing on the unique resonant effects of over-coupling and exploiting their potential magnetic field shaping.

Infrared emission features toward interstellar gas of the IC 348 star cluster in Perseus have been recently proposed to originate from the amino acid tryptophan. The assignment was based on laboratory infrared spectra of tryptophan pressed into pellets, a method which is known to cause large frequency shifts compared to the gas phase. We assess the validity of the assignment based on the original Spitzer data as well as new data from JWST. In addition, we report new spectra of tryptophan condensed in para-hydrogen matrices to compare with the observed spectra. The JWST MIRI data do not show evidence for tryptophan, despite deeper integration toward IC 348. In addition, we show that several of the lines attributed to tryptophan are likely due to instrumental artifacts. This, combined with the new laboratory data, allows us to conclude that there is no compelling evidence for the tryptophan assignment.

Immune events such as infection, vaccination, and a combination of the two result in distinct time-dependent antibody responses in affected individuals. These responses and event prevalences combine non-trivially to govern antibody levels sampled from a population. Time-dependence and disease prevalence pose considerable modeling challenges that need to be addressed to provide a rigorous mathematical underpinning of the underlying biology. We propose a time-inhomogeneous Markov chain model for event-to-event transitions coupled with a probabilistic framework for anti-body kinetics and demonstrate its use in a setting in which individuals can be infected or vaccinated but not both. We prove the equivalency of this approach to the framework developed in our previous work. Synthetic data are used to demonstrate the modeling process and conduct prevalence estimation via transition probability matrices. This approach is ideal to model sequences of infections and vaccinations, or personal trajectories in a population, making it an important first step towards a mathematical characterization of reinfection, vaccination boosting, and cross-events of infection after vaccination or vice versa., Comment: 27 pages, 7 figures

We present a sample of 341 "little red dots" (LRDs) spanning the redshift range $z\sim2-11$ using data from the CEERS, PRIMER, JADES, UNCOVER and NGDEEP surveys. These sources are likely heavily-reddened AGN that trace a previously-hidden phase of dust-obscured black hole growth in the early Universe. Unlike past use of color indices to identify LRDs, we employ continuum slope fitting using shifting bandpasses to sample the same rest-frame emission blueward and redward of the Balmer break. This approach allows us to identify LRDs over a wider redshift range and is less susceptible to contamination from galaxies with strong breaks that otherwise lack a rising red continuum. The redshift distribution of our sample increases at $z<8$ and then undergoes a rapid decline at $z\sim4.5$, which may tie the emergence, and obscuration, of these sources to the inside-out growth that galaxies experience during this epoch. We find that LRDs are 2-3 dex more numerous than bright quasars at $z\sim5-7$, but their number density is only 0.6-1 dex higher than X-ray and UV selected AGN at these redshifts. Within our sample, we have identified the first X-ray detected LRDs at $z=3.1$ and $z=4.66$. An X-ray spectral analysis confirms that these AGN are moderately obscured with $\log\,(N_{\rm H}/{\rm cm}^{2}$) of $23.3^{+0.4}_{-1.3}$ and $22.72^{+0.13}_{-0.16}$. Our analysis reveals that reddened AGN emission dominates their rest-optical light, while the rest-UV originates from their host galaxies. We also present NIRSpec follow-up spectroscopy of 17 LRDs that show broad emission lines consistent with AGN activity. The confirmed AGN fraction of our sample is $71\%$ for sources with F444W$<26.5$. In addition, we find three LRDs with narrow blue-shifted Balmer absorption features in their spectra, suggesting an outflow of high-density, low ionization gas from near the central engine of these faint, red AGN., Comment: 23 pages, 17 figures, submitted to ApJ

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits.

We use both chirped-pulse Fourier transform and frequency modulated absorption spectroscopy to study the rotational spectrum of 2-methoxyethanol in several frequency regions ranging from 8.7-500 GHz. The resulting rotational parameters permitted a search for this molecule in Atacama Large Millimeter/submillimeter Array (ALMA) observations toward the massive protocluster NGC 6334I as well as source B of the low-mass protostellar system IRAS 16293-2422. 25 rotational transitions are observed in the ALMA Band 4 data toward NGC 6334I, resulting in the first interstellar detection of 2-methoxyethanol. A column density of $1.3_{-0.9}^{+1.4} \times 10^{17}$ cm$^{-2}$ is derived at an excitation temperature of $143_{-39}^{+31}$ K. However, molecular signal is not observed in the Band 7 data toward IRAS 16293-2422B and an upper limit column density of $2.5 \times 10^{15}$ cm$^{-2}$ is determined. Various possible formation pathways--including radical recombination and insertion reactions--are discussed. We also investigate physical differences between the two interstellar sources that could result in the observed abundance variations., Comment: Accepted for publication in The Astrophysical Journal Letters

Swarm robotics promises adaptability to unknown situations and robustness against failures. However, it still struggles with global tasks that require understanding the broader context in which the robots operate, such as identifying the shape of the arena in which the robots are embedded. Biological swarms, such as shoals of fish, flocks of birds, and colonies of insects, routinely solve global geometrical problems through the diffusion of local cues. This paradigm can be explicitly described by mathematical models that could be directly computed and exploited by a robotic swarm. Diffusion over a domain is mathematically encapsulated by the Laplacian, a linear operator that measures the local curvature of a function. Crucially the geometry of a domain can generally be reconstructed from the eigenspectrum of its Laplacian. Here we introduce spectral swarm robotics where robots diffuse information to their neighbors to emulate the Laplacian operator - enabling them to "hear" the spectrum of their arena. We reveal a universal scaling that links the optimal number of robots (a global parameter) with their optimal radius of interaction (a local parameter). We validate experimentally spectral swarm robotics under challenging conditions with the one-shot classification of arena shapes using a sparse swarm of Kilobots. Spectral methods can assist with challenging tasks where robots need to build an emergent consensus on their environment, such as adaptation to unknown terrains, division of labor, or quorum sensing. Spectral methods may extend beyond robotics to analyze and coordinate swarms of agents of various natures, such as traffic or crowds, and to better understand the long-range dynamics of natural systems emerging from short-range interactions.

We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning and attitude estimates. This new capability overcomes the challenge of developing thermal semantic perception algorithms for field robots due to the lack of annotated thermal field datasets and the time and costs of manual annotation, enabling precise and rapid annotation of thermal data from field collection efforts at a massively-parallelizable scale. By incorporating a thermal-conditioned refinement step with visual foundation models, our approach can produce highly-precise semantic segmentation labels using low-resolution satellite land cover data for little-to-no cost. It achieves 98.5% of the performance from using costly high-resolution options and demonstrates between 70-160% improvement over popular zero-shot semantic segmentation methods based on large vision-language models currently used for generating annotations for RGB imagery. Code will be available at: https://github.com/connorlee77/aerial-auto-segment.

Quantum emitters, such as the negatively charged nitrogen-vacancy center in diamond, are attractive for quantum technologies such as nano-sensing, quantum information processing, and as a non-classical light source. However, it is still challenging to position individual emitters in photonic structures whilst preserving the spin coherence properties of the defect. In this paper, we investigate single and ensemble waveguide-integrated nitrogen-vacancy centers in diamond fabricated by femtosecond laser writing followed by thermal annealing. Their spin coherence properties are systematically investigated and are shown to be comparable to native nitrogen-vacancy centers in diamond. This method paves the way for the fabrication of coherent spins integrated within photonic devices., Comment: 9 pages, 5 figures

The negatively charged nitrogen-vacancy (NV$^{-}$) center in diamond has shown great potential in nanoscale sensing and quantum information processing due to its rich spin physics. An efficient coupling with light, providing strong luminescence, is crucial for realizing these applications. Laser-written waveguides in diamond promote NV$^{-}$ creation and improve their coupling to light but, at the same time, induce strain in the crystal. The induced strain contributes to light guiding but also affects the energy levels of NV$^{-}$ centers. We probe NV$^{-}$ spin states experimentally with the commonly used continuous-wave zero-field optically detected magnetic resonance (ODMR). In our waveguides, the ODMR spectra are shifted, split, and consistently asymmetric, which we attribute to the impact of local strain. To understand these features, we model ensemble ODMR signals in the presence of strain. By fitting the model results to the experimentally collected ODMR data, we determine the strain tensor components at different positions, thus determining the strain profile across the waveguide. This shows that zero-field ODMR spectroscopy can be used as a strain imaging tool. The resulting strain within the waveguide is dominated by a compressive axial component transverse to the waveguide structure, with a smaller contribution from vertical and shear strain components., Comment: 12 pages, 7 figures + Supplementary Information (6 pages, 3 figures)

Recent advancements in multimode Gottesman-Kitaev-Preskill (GKP) codes have shown great promise in enhancing the protection of both discrete and analog quantum information. This broadened range of protection brings opportunities beyond quantum computing to benefit quantum sensing by safeguarding squeezing -- the essential resource in many quantum metrology protocols. However, it is less explored how quantum sensing can benefit quantum error correction. In this work, we provide a unique example where techniques from quantum sensing can be applied to improve multimode GKP codes. Inspired by distributed quantum sensing, we propose the distributed two-mode squeezing (dtms) GKP codes that offer benefits in error correction with minimal active encoding operations. In fact, the proposed codes rely on a single (active) two-mode squeezing element and an array of beamsplitters that effectively distributes continuous-variable correlations to many GKP ancillae, similar to continuous-variable distributed quantum sensing. Despite this simple construction, the code distance achievable with dtms-GKP qubit codes is comparable to previous results obtained through brute-force numerical search [PRX Quantum 4, 040334 (2023)]. Moreover, these codes enable analog noise suppression beyond that of the best-known two-mode codes [Phys. Rev. Lett. 125, 080503 (2020)] without requiring an additional squeezer. We also provide a simple two-stage decoder for the proposed codes, which appears near-optimal for the case of two modes and permits analytical evaluation., Comment: 24 pages, 8 figures

The interaction of excitonic transitions with confined photonic modes enables tests of quantum physics and design of efficient optoelectronic devices. Here we study how key metrics such as Purcell factor, beta-factor and collection efficiency are determined by the non-cavity modes which exist in real devices, taking the well-studied micropillar cavity as an example. Samples with dots at different positions in the cavity field allow us to quantify the effect of the non-cavity modes and show that the zero-phonon line and the phonon-assisted emission into the cavity mode HE11 is suppressed by positioning dots at the field node., Comment: 5 pages, 4 figures