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We use data from the applications North Carolina public school districts and charter schools submitted for Elementary and Secondary School Emergency Relief (ESSER) to investigate the sense that educational leaders made of the pandemic as it unfolded. LEAs understood the pandemic as a multifaceted problem. Nearly all applications addressed four problems: (1) public health, (2) academics and learning loss, (3) student and community well-being, and (4) instructional access. However, we document considerable variation in problem emphasis over time, across LEAs, and across organizational sector. The pandemic was not a single organizational problem, but many simultaneous problems posed in varying and shifting combinations. We argue this multi-faceted organizational view should be a starting point for assessments of LEAs' pandemic response.

The HAWC Observatory collected 6 years of extensive data, providing an ideal platform for long-term monitoring of blazars in the Very High Energy (VHE) band, without bias towards specific flux states. HAWC continuously monitors blazar activity at TeV energies, focusing on sources with a redshift of {z \lt 0.3}, based on the Third Fermi-LAT Catalog of High-Energy sources. We specifically focused our analysis on Mrk 421 and Mrk 501, as they are the brightest blazars observed by the HAWC Observatory. With a dataset of 2143 days, this work significantly extends the monitoring previously published, which was based on 511 days of observation. By utilizing HAWC data for the VHE {\gamma}-ray emission in the 300 GeV to 100 TeV energy range, in conjunction with Swift-XRT data for the 0.3 to 10 keV X-ray emission, we aim to explore potential correlations between these two bands. For Mrk 501, we found evidence of a long-term correlation. Additionally, we identified a period in the light curve where the flux was very low for more than two years. On the other hand, our analysis of Mrk 421 measured a strong linear correlation for quasi-simultaneous observations collected by HAWC and Swift-XRT. This result is consistent with a linear dependence and a multiple-zone synchrotron self-Compton model to explain the X-ray and the {\gamma}-ray emission. Finally, as suggested by previous findings, we confirm a harder-when-brighter behavior in the spectral evolution of the flux properties for Mrk 421. These findings contribute to the understanding of blazar emissions and their underlying mechanisms.

Context. Supernova remnants are one potential source class considered a PeVatron (i.e. capable of accelerating cosmic rays above PeV energies). The shock fronts produced after the explosion of the supernova are ideal regions for particle acceleration. IC 443 is a supernova remnant that has been studied extensively at different wavelengths. We study this region using very-high-energy gamma-ray data. Aims. We explore the region of IC 443 using 2966 days of gamma-ray data from the HAWC observatory. We study the emission of this supernova remnant and search for signatures that would show acceleration of (hadronic) cosmic rays at the PeV range. Methods. We use the maximum likelihood estimation and a likelihood ratio test to perform a multi-source fitting search. We find the best-fit morphology and spectrum of the IC 443 region above $\sim$300 GeV that best describes the HAWC data. Results. We observe a point source located at ($\alpha$=94.42$^{\circ}$, $\delta$=22.35$^{\circ}$) that we associate with IC 443. The measured spectrum is a simple power law with an index of -3.14$\pm$0.18, which is consistent with previous TeV observations. We also find a new extended component in the region whose emission is described by a simple power law with an index of -2.49$\pm$0.08 and which we call HAWC J0615+2213. Conclusions. Although we cannot confirm that IC 443 is a hadronic PeVatron, we do not find any sign that the spectrum has a cut off at tens of TeV energies, with the spectrum extending to $\sim$30 TeV. Furthermore, we find a new extended source in the region. While we show evidence that this new source might be a new TeV halo, we defer a detailed analysis of this new source to another publication.

A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and masks that can be prohibitively expensive to create. We propose a pipeline to automatically generate synthetic data sets to train these classifiers, leveraging modern drones' ability to survey terrain automatically and the ability to automatically calculate landing safety masks from terrain models derived from such surveys. We then train a U-Net on the synthetic data set, test it on real-world data for validation, and demonstrate it on our drone platform in real-time., Comment: 14 pages, 5 figures

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

Selecting representatives based on voters' preferences is a fundamental problem in social choice theory. While cardinal utility functions offer a detailed representation of preferences, ordinal rankings are often the only available information due to their simplicity and practical constraints. The metric distortion framework addresses this issue by modeling voters and candidates as points in a metric space, with distortion quantifying the efficiency loss from relying solely on ordinal rankings. Existing works define the cost of a voter with respect to a candidate as their distance and set the overall cost as either the sum (utilitarian) or maximum (egalitarian) of these costs across all voters. They show that deterministic algorithms achieve a best-possible distortion of 3 for any metric when considering a single candidate. This paper explores whether one can obtain a better approximation compared to an optimal candidate by relying on a committee of $k$ candidates ($k \ge 1$), where the cost of a voter is defined as its distance to the closest candidate in the committee. We answer this affirmatively in the case of line metrics, demonstrating that with $O(1)$ candidates, it is possible to achieve optimal cost. Our results extend to both utilitarian and egalitarian objectives, providing new upper bounds for the problem. We complement our results with lower bounds for both the line and 2-D Euclidean metrics.

In this paper, we study questions of definability and decidability for infinite algebraic extensions ${\bf K}$ of $\mathbb{F}_p(t)$ and their subrings of $\mathcal{S}$-integral functions. We focus on fields ${\bf K}$ satisfying a local property which we call $q$-boundedness. This can be considered a function field analogue of prior work of the first author which considered algebraic extensions of $\mathbb{Q}$. One simple consequence of our work states that if ${\bf K}$ is a $q$-bounded Galois extension of $\mathbb{F}_p(t)$, then for infinitely many non-constant $u$ the integral closure $\mathcal{O}_{\bf K}$ of $\mathbb{F}_p[u]$ inside ${\bf K}$ is first-order definable in ${\bf K}$. Under the additional assumption that the constant subfield of ${\bf K}$ is infinite, it follows that both $\mathcal{O}_{\bf K}$ and ${\bf K}$ have undecidable first-order theories, and that $\mathbb{F}_p[w]$ is definable in ${\bf K}$ for every non-constant $w$ in ${\bf K}$. Our primary tools are norm equations and the Hasse Norm Principle, in the spirit of Rumely. Our paper has an intersection with a recent arXiv preprint by Mart\'inez-Ranero, Salcedo, and Utreras, although our definability results are more extensive and undecidability results are much stronger., Comment: 30 pages. This version has significantly improved results, including the new Proposition 6.13, Corollaries 6.14, 8.5, and 9.3, and the all-new Section 10

We prove a sharp upper bound on the number of boundary lattice points of a rational polygon in terms of its denominator and the number of interior lattice points, generalizing Scott's inequality. We then give sharp lower and upper bounds on the area in terms of the denominator, the number of interior lattice points, and the number of boundary lattice points, which can be seen as a generalization of Pick's formula. Minimizers and maximizers are described in detail. As an application, we derive bounds for the coefficients of Ehrhart quasipolymials of half-integral polygons., Comment: 16 pages, 6 figures

We explore potential explanations for why using large-$N_c$ ($N_c$ is the number of colors) scaling to determine the relative size of few-nucleon low-energy operators agrees with experiment even when dynamical $\Delta$'s are not explicitly included. Given that the large-$N_c$ analysis is predicated on the nucleons and $\Delta$'s being degenerate, this is a curious result. We show that for purely $S$-wave interactions the relationships dictated by large-$N_c$ scaling are unaffected whether the $\Delta$ is included or not. In the case of higher partial waves that do not mix with $S$-waves, the impact of the $\Delta$ is perturbative, which makes the agreement with naive ($\Delta$-less) large-$N_c$ ordering unsurprising. For higher partial waves that mix with $S$-waves, the nucleon and $\Delta$ would need to decouple to get agreement with naive large-$N_c$ ordering. We find all $NN$, $\Delta N$, and $\Delta\Delta$ low energy coefficients for leading-order baryon-baryon scattering in $\Delta$-full pionless effective field theory in terms of the two independent parameters dictated by the SU($2F$) spin-flavor symmetry that arises in the $N_c \rightarrow \infty$ limit. Because of recent lattice QCD results and experimental interest, we extend our analysis to the three-flavor case to study $\Omega\Omega$ scattering. We show that in the unitary limit (where scattering lengths become infinite) one of the two SU($2F$) parameters is driven to zero, resulting in enhanced symmetries, which agree with those found in spin-1/2 entanglement studies., Comment: 34 pages, two figures

Very-high-energy (0.1-100 TeV) gamma-ray emission was observed in HAWC data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2,565 days of data from the High Altitude Water Cherenkov (HAWC) observatory. Our analysis reports the detection of a point-like source in the east lobe at a significance of $6.6\,\sigma$ and in the west lobe at a significance of $8.2\,\sigma$. For each jet lobe, we localize the gamma-ray emission and identify a best-fit position. The locations are close to the X-ray emission sites "e1" and "w1" for the east and west lobes, respectively. We analyze the spectral energy distributions and find that the energy spectra of the lobes are consistent with a simple power-law $\text{d}N/\text{d}E\propto E^{\alpha}$ with $\alpha = -2.44^{+0.13+0.04}_{-0.12-0.04}$ and $\alpha = -2.35^{+0.12+0.03}_{-0.11-0.03}$ for the east and west lobes, respectively. The maximum energy of photons from the east and west lobes reaches 56 TeV and 123 TeV, respectively. We compare our observations to various models and conclude that the very-high-energy gamma-ray emission can be produced by a population of electrons that were efficiently accelerated.

The online bipartite matching problem, extensively studied in the literature, deals with the allocation of online arriving vertices (items) to a predetermined set of offline vertices (agents). However, little attention has been given to the concept of class fairness, where agents are categorized into different classes, and the matching algorithm must ensure equitable distribution across these classes. We here focus on randomized algorithms for the fair matching of indivisible items, subject to various definitions of fairness. Our main contribution is the first (randomized) non-wasteful algorithm that simultaneously achieves a $1/2$ approximation to class envy-freeness (CEF) while simultaneously ensuring an equivalent approximation to the class proportionality (CPROP) and utilitarian social welfare (USW) objectives. We supplement this result by demonstrating that no non-wasteful algorithm can achieve an $\alpha$-CEF guarantee for $\alpha > 0.761$. In a similar vein, we provide a novel input instance for deterministic divisible matching that demonstrates a nearly tight CEF approximation. Lastly, we define the ``price of fairness,'' which represents the trade-off between optimal and fair matching. We demonstrate that increasing the level of fairness in the approximation of the solution leads to a decrease in the objective of maximizing USW, following an inverse proportionality relationship., Comment: Accepted to NeurIPS 2024

We present algorithms for classifying rational polygons with fixed denominator and number of interior lattice points. Our approach is to first describe maximal polygons and then compute all subpolygons, where we eliminate redundancy by a suitable normal form. Executing our classification, we obtained a dataset of several billions of polygons covering a wide variety of cases., Comment: 23 pages, 10 figures

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension of the spatial morphology of a microquasar, SS 433, to TeV gamma rays \cite{abeysekara2018very} localizes the acceleration of electrons at shocks in the jet far from the black hole \cite{hess2024ss433}. Here we report TeV gamma-ray emission from another microquasar, V4641~Sgr, which reveals particle acceleration at similar distances from the black hole as SS~433. Additionally, the gamma-ray spectrum of V4641 is among the hardest TeV spectra observed from any known gamma-ray source and is detected up to 200 TeV. Gamma rays are produced by particles, either electrons or hadrons, of higher energies. Because electrons lose energy more quickly the higher their energy, such a spectrum either very strongly constrains the electron production mechanism or points to the acceleration of high-energy hadrons. This observation suggests that large-scale jets from microquasars could be more common than previously expected and that microquasars could be a significant source of Galactic cosmic rays. high energy gamma-rays also provide unique constraints on the acceleration mechanisms of extra-Galactic cosmic rays postulated to be produced by the supermassive black holes and relativistic jets of quasars. The distance to quasars limits imaging studies due to insufficient angular resolution of gamma-rays and due to attenuation of the highest energy gamma-rays by the extragalactic background light.

This paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike traditional approaches, which address these problems using parameter-sharing, we consider an alternative methodology based on irreducible representations and Schur's lemma. Using this methodology, we obtain an alternative derivation for existing models like DeepSets, 2-IGN graph equivariant networks, and Deep Weight Space (DWS) networks. The derivation for DWS networks is significantly simpler than that of previous results. Next, we extend our approach to unaligned symmetric sets, where equivariance to the wreath product of groups is required. Previous works have addressed this problem in a rather restrictive setting, in which almost all wreath equivariant layers are Siamese. In contrast, we give a full characterization of layers in this case and show that there is a vast number of additional non-Siamese layers in some settings. We also show empirically that these additional non-Siamese layers can improve performance in tasks like graph anomaly detection, weight space alignment, and learning Wasserstein distances. Our code is available at \href{https://github.com/yonatansverdlov/Irreducible-Representations-of-Deep-Weight-Spaces}{GitHub}.

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report various information needs regarding buprenorphine treatment on social media platforms like Reddit. However, the key challenge is to characterize these needs. In this study, we propose a theme-based framework to curate and analyze large-scale data from social media to characterize self-reported treatment information needs (TINs). Methods: We collected 15,253 posts from r/Suboxone, one of the largest Reddit sub-community for buprenorphine products. Following the standard protocol, we first identified and defined five main themes from the data and then coded 6,000 posts based on these themes, where one post can be labeled with applicable one to three themes. Finally, we determined the most frequently appearing sub-themes (topics) for each theme by analyzing samples from each group. Results: Among the 6,000 posts, 40.3% contained a single theme, 36% two themes, and 13.9% three themes. The most frequent topics for each theme or theme combination came with several key findings - prevalent reporting of psychological and physical effects during recovery, complexities in accessing buprenorphine, and significant information gaps regarding medication administration, tapering, and usage of substances during different stages of recovery. Moreover, self-treatment strategies and peer-driven advice reveal valuable insights and potential misconceptions. Conclusions: The findings obtained using our proposed framework can inform better patient education and patient-provider communication, design systematic interventions to address treatment-related misconceptions and rumors, and streamline the generation of hypotheses for future research.

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Trustworthiness assessment is an essential step to assure that interdependent systems perform critical functions as anticipated, even under adverse conditions. In this paper, a holistic trustworthiness assessment framework for ultra-wideband self-localization is proposed, including attributes of reliability, security, privacy, and resilience. Our goal is to provide guidance for evaluating a system's trustworthiness based on objective evidence, so-called trustworthiness indicators. These indicators are carefully selected through the threat analysis of the particular system. Our approach guarantees that the resulting trustworthiness indicators correspond to chosen real-world threats. Moreover, experimental evaluations are conducted to demonstrate the effectiveness of the proposed method. While the framework is tailored for this specific use case, the process itself serves as a versatile template, which can be used in other applications in the domains of the Internet of Things or cyber-physical systems., Comment: 20 pages, accepted for MDPI Sensors, also check associated dataset https://doi.org/10.5281/zenodo.12705892

Supernova remnants (SNRs) are believed to be capable of accelerating cosmic rays (CRs) to PeV energies. SNR G106.3+2.7 is a prime PeVatron candidate. It is formed by a head region, where the pulsar J2229+6114 and its boomerang-shaped pulsar wind nebula are located, and a tail region containing SN ejecta. The lack of observed gamma ray emission from the two regions of this SNR has made it difficult to assess which region would be responsible for the PeV CRs. We aim to characterize the very-high-energy (VHE, 0.1-100 TeV) gamma ray emission from SNR G106.3+2.7 by determining the morphology and spectral energy distribution of the region. This is accomplished using 2565 days of data and improved reconstruction algorithms from the HAWC Observatory. We also explore possible gamma ray production mechanisms for different energy ranges. Using a multi-source fitting procedure based on a maximum-likelihood estimation method, we evaluate the complex nature of this region. We determine the morphology, spectrum, and energy range for the source found in the region. Molecular cloud information is also used to create a template and evaluate the HAWC gamma ray spectral properties at ultra-high-energies (UHE, >56 TeV). This will help probe the hadronic nature of the highest-energy emission from the region. We resolve one extended source coincident with all other gamma ray observations of the region. The emission reaches above 100~TeV and its preferred log-parabola shape in the spectrum shows a flux peak in the TeV range. The molecular cloud template fit on the higher energy data reveals that the SNR's energy budget is fully capable of producing a purely hadronic source for UHE gamma rays.

HESS J1809-193 is an unidentified TeV source, first detected by the High Energy Stereoscopic System (H.E.S.S.) Collaboration. The emission originates in a source-rich region that includes several Supernova Remnants (SNR) and Pulsars (PSR) including SNR G11.1+0.1, SNR G11.0-0.0, and the young radio pulsar J1809-1917. Originally classified as a pulsar wind nebula (PWN) candidate, recent studies show the peak of the TeV region overlapping with a system of molecular clouds. This resulted in the revision of the original leptonic scenario to look for alternate hadronic scenarios. Marked as a potential PeVatron candidate, this region has been studied extensively by H.E.S.S. due to its emission extending up-to several tens of TeV. In this work, we use 2398 days of data from the High Altitude Water Cherenkov (HAWC) observatory to carry out a systematic source search for the HESS J1809-193 region. We were able to resolve emission detected as an extended component (modelled as a Symmetric Gaussian with a 1 $\sigma$ radius of 0.21 $^\circ$) with no clear cutoff at high energies and emitting photons up-to 210 TeV. We model the multi-wavelength observations for the region HESS J1809-193 using a time-dependent leptonic model and a lepto-hadronic model. Our model indicates that both scenarios could explain the observed data within the region of HESS J1809-193.

This research investigates the potential use of a blockchain-based Public Key Infrastructure (PKI) within an organization and compares it to conventional PKI systems. The goal is to assess the advantages and disadvantages of both approaches in order to determine the feasibility of employing blockchain technology for a decentralized PKI. The study will also evaluate the impact of current legal frameworks, such as the Cyber Resilience Act (CRA) and NIS-2 Directive. The study will examine various implementations of blockchain PKIs based on factors such as security, performance, and platform. The results indicate that blockchain-based PKIs can overcome the limitations of conventional PKIs by decentralizing the trust anchor, providing greater security. Blockchain technology allows for the immutable and transparent management of certificates, making tampering significantly more challenging. Additionally, blockchain-based PKIs offer enhanced mechanisms for identifying and addressing certificate misconduct.

We report an observation of ultra-high energy (UHE) gamma rays from the Galactic Center region, using seven years of data collected by the High-Altitude Water Cherenkov (HAWC) Observatory. The HAWC data are best described as a point-like source (HAWC J1746-2856) with a power-law spectrum ($\mathrm{d}N/\mathrm{d}E=\phi(E/26 \,\text{TeV})^{\gamma}$), where $\gamma=-2.88 \pm 0.15_{\text{stat}} - 0.1_{\text{sys}} $ and $\phi=1.5 \times 10^{-15}$ (TeV cm$^{2}$s)$^{-1}$ $\pm\, 0.3_{\text{stat}}\,^{+0.08_{\text{sys}}}_{-0.13_{\text{sys}}}$ extending from 6 to 114 TeV. We find no evidence of a spectral cutoff up to $100$ TeV using HAWC data. Two known point-like gamma-ray sources are spatially coincident with the HAWC gamma-ray excess: Sgr A$^{*}$ (HESS J1745-290) and the Arc (HESS J1746-285). We subtract the known flux contribution of these point sources from the measured flux of HAWC J1746-2856 to exclude their contamination and show that the excess observed by HAWC remains significant ($>$5$\sigma$) with the spectrum extending to $>$100 TeV. Our result supports that these detected UHE gamma rays can originate via hadronic interaction of PeV cosmic-ray protons with the dense ambient gas and confirms the presence of a proton PeVatron at the Galactic Center.

The first TeV gamma-ray source with no lower energy counterparts, TeV J2032+4130, was discovered by HEGRA. It appears in the third HAWC catalog as 3HWC J2031+415 and it is a bright TeV gamma-ray source whose emission has previously been resolved as 2 sources: HAWC J2031+415 and HAWC J2030+409. While HAWC J2030+409 has since been associated with the \emph{Fermi-LAT} Cygnus Cocoon, no such association for HAWC J2031+415 has yet been found. In this work, we investigate the spectrum and energy-dependent morphology of HAWC J2031+415. We associate HAWC J2031+415 with the pulsar PSR J2032+4127 and perform a combined multi-wavelength analysis using radio, X-ray, and $\gamma$-ray emission. We conclude that HAWC J2031+415 and, by extension, TeV J2032+4130 are most probably a pulsar wind nebula (PWN) powered by PSR J2032+4127.

We give the first non-trivial decremental dynamic embedding of a weighted, undirected graph $G$ into $\ell_p$ space. Given a weighted graph $G$ undergoing a sequence of edge weight increases, the goal of this problem is to maintain a (randomized) mapping $\phi: (G,d) \to (X,\ell_p)$ from the set of vertices of the graph to the $\ell_p$ space such that for every pair of vertices $u$ and $v$, the expected distance between $\phi(u)$ and $\phi(v)$ in the $\ell_p$ metric is within a small multiplicative factor, referred to as the \emph{distortion}, of their distance in $G$. Our main result is a dynamic algorithm with expected distortion $O(\log^3 n)$ and total update time $O\left((m^{1+o(1)} \log^2 W + Q \log n)\log(nW) \right)$, where $W$ is the maximum weight of the edges, $Q$ is the total number of updates and $n, m$ denote the number of vertices and edges in $G$ respectively. This is the first result of its kind, extending the seminal result of Bourgain to the growing field of dynamic algorithms. Moreover, we demonstrate that in the fully dynamic regime, where we tolerate edge insertions as well as deletions, no algorithm can explicitly maintain an embedding into $\ell_p$ space that has a low distortion with high probability., Comment: Accepted to ICML 2024 (15 pages, 3 figures)

Sharing and receiving information plays a pivotal role in science and technology. Quantum communication relies on the principles of quantum mechanics to transmit information in a nonclassical manner. Existing quantum communication protocols are commonly based on shared entangled states between sender and receiver, while the transmitting medium is classical. We here demonstrate that information transfer may be enhanced in a quantum correlated medium without entanglement distribution. We concretely show that nonclassical correlations, with nonzero discord, between the first two spins of a spin chain that acts as a quantum wire can increase the information flow and reduce the propagation time. We relate this effect to the breaking of the spatial symmetry of the out-of-time-order correlator that characterizes the spread of information through the medium.

Sharpness-Aware Minimization (SAM) has emerged as a promising alternative optimizer to stochastic gradient descent (SGD). The originally-proposed motivation behind SAM was to bias neural networks towards flatter minima that are believed to generalize better. However, recent studies have shown conflicting evidence on the relationship between flatness and generalization, suggesting that flatness does fully explain SAM's success. Sidestepping this debate, we identify an orthogonal effect of SAM that is beneficial out-of-distribution: we argue that SAM implicitly balances the quality of diverse features. SAM achieves this effect by adaptively suppressing well-learned features which gives remaining features opportunity to be learned. We show that this mechanism is beneficial in datasets that contain redundant or spurious features where SGD falls for the simplicity bias and would not otherwise learn all available features. Our insights are supported by experiments on real data: we demonstrate that SAM improves the quality of features in datasets containing redundant or spurious features, including CelebA, Waterbirds, CIFAR-MNIST, and DomainBed., Comment: 25 pages, 10 figures, 2 tables

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

We need to look at our shoelaces as we first learn to tie them but having mastered this skill, can do it from touch alone. We call this phenomenon "sensory scaffolding": observation streams that are not needed by a master might yet aid a novice learner. We consider such sensory scaffolding setups for training artificial agents. For example, a robot arm may need to be deployed with just a low-cost, robust, general-purpose camera; yet its performance may improve by having privileged training-time-only access to informative albeit expensive and unwieldy motion capture rigs or fragile tactile sensors. For these settings, we propose "Scaffolder", a reinforcement learning approach which effectively exploits privileged sensing in critics, world models, reward estimators, and other such auxiliary components that are only used at training time, to improve the target policy. For evaluating sensory scaffolding agents, we design a new "S3" suite of ten diverse simulated robotic tasks that explore a wide range of practical sensor setups. Agents must use privileged camera sensing to train blind hurdlers, privileged active visual perception to help robot arms overcome visual occlusions, privileged touch sensors to train robot hands, and more. Scaffolder easily outperforms relevant prior baselines and frequently performs comparably even to policies that have test-time access to the privileged sensors. Website: https://penn-pal-lab.github.io/scaffolder/, Comment: ICLR 2024 Spotlight version

Consumer-grade drones have become effective multimedia collection tools, spring-boarded by rapid development in embedded CPUs, GPUs, and cameras. They are best known for their ability to cheaply collect high-quality aerial video, 3D terrain scans, infrared imagery, etc., with respect to manned aircraft. However, users can also create and attach custom sensors, actuators, or computers, so the drone can collect different data, generate composite data, or interact intelligently with its environment, e.g., autonomously changing behavior to land in a safe way, or choosing further data collection sites. Unfortunately, developing custom payloads is prohibitively difficult for many researchers outside of engineering. We provide guidelines for how to create a sophisticated computational payload that integrates a Raspberry Pi 5 into a DJI Matrice 350. The payload fits into the Matrice's case like a typical DJI payload (but is much cheaper), is easy to build and expand (3D-printed), uses the drone's power and telemetry, can control the drone and its other payloads, can access the drone's sensors and camera feeds, and can process video and stream it to the operator via the controller in real time. We describe the difficulties and proprietary quirks we encountered, how we worked through them, and provide setup scripts and a known-working configuration for others to use., Comment: 5 pages, 3 figures, 1 table, workshop paper, version 1 (preprint)

The High-Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory located on the side of the Sierra Negra volcano in Mexico, has been fully operational since 2015. The HAWC collaboration has recently significantly improved their extensive-air-shower reconstruction algorithms, which has notably advanced the observatory performance. The energy resolution for primary gamma rays with energies below 1~TeV was improved by including a noise-suppression algorithm. Corrections have also been made to systematic errors in direction fitting related to the detector and shower plane inclinations, $\mathcal{O}(0.1^{\circ})$ biases in highly inclined showers, as well as enhancements to the core reconstruction. The angular resolution for gamma rays approaching the HAWC array from large zenith angles ($> 37^{\circ}$) has improved by a factor of four at the highest energies ($> 70$~TeV) as compared to previous reconstructions. The inclusion of a lateral distribution function fit to the extensive air shower footprint on the array to separate gamma-ray primaries from cosmic-ray ones, based on the resulting $\chi^{2}$ values, improved the background rejection performance at all inclinations. At large zenith angles, the improvement in significance is a factor of four compared to previous HAWC publications. These enhancements have been verified by observing the Crab Nebula, which is an overhead source for the HAWC Observatory. We show that the sensitivity to Crab-like point sources ($E^{-2.63}$) with locations overhead to 30$^{\circ}$ zenith is comparable or less than 10\% of the Crab Nebula's flux between 2 and 50~TeV. Thanks to these improvements, HAWC can now detect more sources, including the Galactic Center.