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We leverage JWST data from the COSMOS-Web Survey in order to provide updated measurements on the auto-power spectrum of the now resolved Cosmic Infrared Background (CIB) and its coherence with the unresolved soft Cosmic X-ray Background (CXB) observed by Chandra at z > 6. Maps of the CIB in the F277W and F444W NIRCam filters are constructed with sources fainter than AB mag = 25 and cross-correlated with the CXB in the [0.5-2] keV band. We find that on scales between 1 and 1000'' the CIB-CXB cross-power in both NIRCam filters is statistically significant with signal-to-noise ratios (S/N) of 4.80 and 6.20 respectively from redshifts 0 < z < 13. In our high-z (6 < z < 13) interval we find coherence in both filters with a S/N of 7.32 and 5.39 respectively. These results suggest that there are X-ray emitting galaxies resolved by JWST, including star-forming galaxies (SFGs) and active galactic nuclei (AGNs). We fit the large-scale biasing of the IR sources producing the CIB as a function of z with results consistent with prior measurements and place constraints on the CXB flux and biasing at low- and high-z. The CXB flux measurements presented in this study suggest that approximately 94% of the [0.5-2] keV CXB is resolved, and this value is consistent within 2$\sigma$ with the complete resolution of the [0.5-2] keV CXB., Comment: 12 pages, 4 figures, 1 table. Submitted to ApJ

We study the topology of Vietoris--Rips complexes of finite grids on the torus. Let $T_{n,n}$ be the grid of $n\times n$ points on the flat torus $S^1\times S^1$, equipped with the $l^1$ metric. Let $\mathrm{VR}(T_{n,n};k)$ be the Vietoris--Rips simplicial complex of this torus grid at scale $k\ge 0$. For $n\ge 7$ and small scales $2\le k\le \frac{n-1}{3}$, the complex $\mathrm{VR}(T_{n,n};k)$ is homotopy equivalent to the torus. For large scales $k\ge 2\lfloor\frac{n}{2}\rfloor$, the complex $\mathrm{VR}(T_{n,n};k)$ is a simplex and hence contractible. Interesting topology arises over intermediate scales $\frac{n-1}{3}

Bayesian optimization (BO) has emerged as a powerful tool for guiding experimental design and decision-making in various scientific fields, including materials science, chemistry, and biology. However, despite its growing popularity, the complexity of existing BO libraries and the steep learning curve associated with them can deter researchers who are not well-versed in machine learning or programming. To address this barrier, we introduce Honegumi, a user-friendly, interactive tool designed to simplify the process of creating advanced Bayesian optimization scripts. Honegumi offers a dynamic selection grid that allows users to configure key parameters of their optimization tasks, generating ready-to-use, unit-tested Python scripts tailored to their specific needs. Accompanying the interface is a comprehensive suite of tutorials that provide both conceptual and practical guidance, bridging the gap between theoretical understanding and practical implementation. Built on top of the Ax platform, Honegumi leverages the power of existing state-of-the-art libraries while restructuring the user experience to make advanced BO techniques more accessible to experimental researchers. By lowering the barrier to entry and providing educational resources, Honegumi aims to accelerate the adoption of advanced Bayesian optimization methods across various domains., Comment: 7 pages, 3 figures, 1 table

This paper explores opportunities and challenges for data-driven advocacy to support home care workers, an often overlooked group of low-wage, frontline health workers. First, we investigate what data to collect and how to collect it in ways that preserve privacy and avoid burdening workers. Second, we examine how workers and advocates could use collected data to strengthen individual and collective advocacy efforts. Our qualitative study with 11 workers and 15 advocates highlights tensions between workers' desires for individual and immediate benefits and advocates' preferences to prioritize more collective and long-term benefits. We also uncover discrepancies between participants' expectations for how data might transform advocacy and their on-the-ground experiences collecting and using real data. Finally, we discuss future directions for data-driven worker advocacy, including combining different kinds of data to ameliorate challenges, leveraging advocates as data stewards, and accounting for workers' and organizations' heterogeneous goals., Comment: Accepted to CHI 2025

Snapping shrimp produce bubbles that emit light when they collapse. When a bubble collapses so strongly that it emits light, the light emission is usually called sonoluminescence; in the case of the shrimp, it is called "shrimpoluminescence". The bubble collapses so fast that no heat can escape and the gas trapped in the bubble becomes hot enough to ionize. Light is emitted through electron-ion bremsstrahlung, electron-atom bremsstrahlung, and electron-ion recombination. In this paper, we study the dynamics of a sonoluminescing bubble and learn how to calculate the spectrum of emitted light, allowing us to explain the physical mechanisms of shrimpoluminescence., Comment: Submitted to the physics department at the University of Colorado in Feb. 2022 to satisfy qualifying exam requirements during my PhD

In a recent study, Dannen et al. surveyed a large parameter space to study the transition from efficient to inefficient line driving. They found that when the line force significantly weakens due to ionization, the winds are variable, with a characteristic frequency comparable to the Lamb cut-off frequency of a stratified atmosphere, {\omega}c. In this work, we present a set of simulations and perturbation analyses that elucidate the variability source and characteristics. We found that the line force adds wave energy and amplifies perturbations with frequencies near {\omega}c. This selective amplification results from the coupling between the natural tendency of velocity perturbations to grow in a stratified atmosphere and the dependence of the line force on the velocity gradient, per the Castor-Abbott-Klein line-driven wind theory. We also found that the variability stems from self-excitation that occurs in the exponential atmosphere due to the non-linearity introduced by the absolute value of the velocity gradient in the line force prescription. We conclude that self-consistently calculating ionization is insufficient for modeling the dynamics in the subsonic atmosphere. Instead future wind models should relax the Sobolev approximation, or model the radiative transfer to capture the dynamics and instabilities at the base of the wind., Comment: 15 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.

It is a distinct possibility that spin fluctuations are the pairing interactions in a wide range of unconventional superconductors. In the case of the high-transition-temperature (high-$T_c$) cuprates, in which superconductivity emerges upon doping an antiferromagnetic Mott-insulating state, spin correlations might furthermore drive unusual pseudogap phenomena. Here we use polarized and unpolarized magnetic neutron scattering to study the simple tetragonal cuprate $\mathrm{HgBa}_{2}\mathrm{CuO}_{4+\delta}$ at very low doping ($T_c \approx 55$ K, hole concentration $p \approx 0.064$). In stark contrast to prior results for other underdoped cuprates, we find no evidence of incommensurate spin-density-wave, charge-spin stripe, or $q = 0$ magnetic order. Instead, the antiferromagnetic response in both the superconducting and pseudogap states is gapped below $\Delta_\mathrm{AF} \approx 6$ meV, commensurate over a wide energy range, and disperses above about 55 meV. Given the documented model nature of $\mathrm{HgBa}_{2}\mathrm{CuO}_{4+\delta}$, which exhibits high structural symmetry and minimal point disorder effects, we conclude that the observed behavior signifies the unmasked response of the quintessential $\mathrm{CuO}_{2}$ planes near the Mott-insulating state. These results for $\mathrm{HgBa}_{2}\mathrm{CuO}_{4+\delta}$ can therefore be expected to serve as a benchmark for a refined theoretical understanding of the cuprates., Comment: Main text: 18 pages, 6 figures. Supplement: 17 pages, 10 figures

Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these challenges, we introduce Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes to accelerate building ML for health applications. The models cover various modalities and domains, including radiology (X-rays and computed tomography), histopathology, dermatological imaging, and audio. These models provide domain specific embeddings that facilitate AI development with less labeled data, shorter training times, and reduced computational costs compared to traditional approaches. In addition, we utilize a common interface and style across these models, and prioritize usability to enable developers to integrate HAI-DEF efficiently. We present model evaluations across various tasks and conclude with a discussion of their application and evaluation, covering the importance of ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting. This technical report will be updated over time as more modalities and features are added., Comment: 16 pages, 8 figures

Multimodal learning, which involves integrating information from various modalities such as text, images, audio, and video, is pivotal for numerous complex tasks like visual question answering, cross-modal retrieval, and caption generation. Traditional approaches rely on modality-specific encoders and late fusion techniques, which can hinder scalability and flexibility when adapting to new tasks or modalities. To address these limitations, we introduce a novel framework that extends the concept of task reformulation beyond natural language processing (NLP) to multimodal learning. We propose to reformulate diverse multimodal tasks into a unified next-frame prediction problem, allowing a single model to handle different modalities without modality-specific components. This method treats all inputs and outputs as sequential frames in a video, enabling seamless integration of modalities and effective knowledge transfer across tasks. Our approach is evaluated on a range of tasks, including text-to-text, image-to-text, video-to-video, video-to-text, and audio-to-text, demonstrating the model's ability to generalize across modalities with minimal adaptation. We show that task reformulation can significantly simplify multimodal model design across various tasks, laying the groundwork for more generalized multimodal foundation models., Comment: 10 pages, 10 figures

Small-scale Brightenings (SBs) are commonly observed in the transition region that separates the solar chromosphere from the corona. These brightenings, omnipresent in active region patches known as "moss" regions, could potentially contribute to the heating of active region plasma. In this study, we investigate the properties of SB events in a moss region and their associated chromospheric dynamics, which could provide insights into the underlying generation mechanisms of the SBs. We analyzed the data sets obtained by coordinated observations using the Interface Region Imaging Spectrograph and the Goode Solar Telescope at Big Bear Solar Observatory. We studied 131 SB events in our region of interest and found that 100 showed spatial and temporal matches with the dynamics observed in the chromospheric H$\alpha$ images. Among these SBs, 98 of them were associated with spicules that are observed in H$\alpha$ images. Furthermore, detailed analysis revealed that one intense SB event corresponded to an Ellerman Bomb (EB), while another SB event consisted of several recurring brightenings caused by a stream of falling plasma. We observed that H$\alpha$ far wings often showed flashes of strong brightening caused by the falling plasma, creating an H$\alpha$ spectral profile similar to an EB. However, 31 of the 131 investigated SB events showed no noticeable spatial and temporal matches with any apparent features in H$\alpha$ images. Our analysis indicated that the predominant TR SB events in moss regions are associated with chromospheric phenomena primarily caused by spicules. Most of these spicules display properties akin to dynamic fibrils., Comment: 12 pages, 7 figures, accepted for publication in the ApJ

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.

Large-scale solar eruptions often include ejection of a filament, a solar flare, and expulsion of a coronal mass ejection (CME). Unravelling the magnetic processes that build up the free energy for these eruptions and trigger that energy's release in the eruption is a continuing challenge in solar physics. Such large-scale eruptions are comparatively infrequent, with the moderate level ones (say, GOES M-class events) occurring perhaps once every few days on average during active-activity times, and much less frequently during quieter times. In contrast, solar coronal jets, which are long (~50,000 km), narrow (less than about 10,000 km), transient (~10--20 min) plasma spires with bright bases and that are seen in soft X-rays and EUV, occur much more frequently, likely several hundred times per day independent of large-scale solar activity level. Recent studies indicate that coronal jets are small-scale versions of large-scale eruptions, often produced by eruption of a small-scale "miniflament," that results in a "miniflare" analogous to a larger typical solar flare, and that sometimes produces a CME analogue (a "narrow CME" or "white-light jet"). Under the assumption that jets are small-scale eruptions, their higher occurrence frequency and faster build-up evolution reveals perhaps fundamental aspects of all eruptions that are not as easy to discern in the more-complex magnetic environment and the slower build up to the larger eruptions. Therefore, the study of coronal jets can provide insights into the onset mechanism of CME-producing large-scale eruptions.

The current paradigm for the co-evolution of galaxies and their supermassive black holes postulates that dust-obscured active galactic nuclei (AGNs) represent a transitional phase towards a more luminous and unobscured state. However, our understanding of dusty AGNs and their host galaxies at early cosmic times is inadequate due to observational limitations. Here, we present JWST observations of CID-931, an X-ray-detected AGN at a spectroscopic redshift of $z_{\rm spec}=4.91$. Multiband NIRCam imaging from the COSMOS-Web program reveals an unresolved red core, similar to JWST-discovered dusty AGNs. Strikingly, the red core is surrounded by at least eight massive star-forming clumps spread over $1.\!\!^{\prime\prime}6 \approx 10~{\rm kpc}$, each of which has a stellar mass of $10^9-10^{10}M_\odot$ and $\sim0.1-1~{\rm kpc}$ in radius. The whole system amounts to $10^{11}M_\odot$ in stellar mass, higher than typical star-forming galaxies at the same epoch. In this system, gas inflows and/or complex merger events may trigger clump formation and AGN activity thus leading to the rapid formation of a massive galaxy hosting a supermassive black hole. Future follow-up observations will provide new insights into the evolution of the galaxy-black hole relationship during such transitional phases in the early universe., Comment: 13 pages, 10 figures, submitted to PASJ

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.

In-situ diffuse neutron scattering experiments revealed that when electric current is passed through single crystals of rutile TiO2 under conditions conducive to flash sintering, it induces the formation of parallel planes of oxygen vacancies. Specifically, a current perpendicular to the c-axis generates planes normal to the (132) reciprocal lattice vector, whereas currents aligned with the c-axis form planes normal to the (132) and to the (225) vector. The concentration of defects increases with incresing current. The structural modifications are linked to the appearance of signatures of interacting Ti3+ moments in magnetic susceptibility, signifying a structural collapse around the vacancy planes. Electrical conductivity measurements of the modified material reveal several electronic transitions between semiconducting states (via a metal-like intermediate state) with the smallest gap being 27 meV. Pristine TiO2 can be restored by heating followed by slow cooling in air. Our work suggests a novel paradigm for achieving switching of electrical conductivity related to the flash phenomenon

While systems analysis has been studied for decades in the context of control theory, it has only been recently used to improve the convergence of Denoising Diffusion Probabilistic Models. This work describes a novel improvement to Third- Order Langevin Dynamics (TOLD), a recent diffusion method that performs better than its predecessors. This improvement, abbreviated TOLD++, is carried out by critically damping the TOLD forward transition matrix similarly to Dockhorn's Critically-Damped Langevin Dynamics (CLD). Specifically, it exploits eigen-analysis of the forward transition matrix to derive the optimal set of dynamics under the original TOLD scheme. TOLD++ is theoretically guaranteed to converge faster than TOLD, and its faster convergence is verified on the Swiss Roll toy dataset and CIFAR-10 dataset according to the FID metric., Comment: 5 pages, 2 figures, 2 tables, submitted to ICASSP 2025

Reliable integration of photonic integrated circuits (PICs) into quantum sensors has the potential to drastically reduce sensor size, ease manufacturing scalability, and improve performance in applications where the sensor is subject to high accelerations, vibrations, and temperature changes. In a traditional quantum sensor assembly, free-space optics are subject to pointing inaccuracies and temperature-dependent misalignment. Moreover, the use of epoxy or sealants for affixing either free-space optics or PICs within a sensor vacuum envelope leads to sensor vacuum degradation and is difficult to scale. In this paper, we describe the hermetic integration of a PIC with a vacuum envelope via anodic bonding. We demonstrate utility of this assembly with two proof-of-concept atom-interrogation experiments: (1) spectroscopy of a cold-atom sample using a grating-emitted probe; (2) spectroscopy of alkali atoms using an evanescent field from an exposed ridge waveguide. This work shows a key process step on a path to quantum sensor manufacturing scalability, Comment: 8 pages, 7 figures

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

We report seven examples of a long-ignored type of confined solar flare eruption that does not fit the standard model for confined flare eruptions. Because they are confined eruptions, do not fit the standard model, and unexpectedly erupt in ostensibly inert magnetic arches, we have named them stealth non-standard-model confined flare eruptions. Each of our flaring magnetic arches stems from a big sunspot. We tracked each eruption in full-cadence UV and EUV images from the Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) in combination with magnetograms from SDO's Helioseismic and Magnetic Imager (HMI). We present the onset and evolution of two eruptions in detail: one of six that each make two side-by-side main flare loops, and one that makes two crossed main flare loops. For these two cases, we present cartoons of the proposed pre-eruption field configuration and how sudden reconnection makes the flare ribbons and flare loops. Each of the seven eruptions is consistent with being made by sudden reconnection at an interface between two internal field strands of the magnetic arch, where they cross at a small (10 - 20 degrees) angle. These stealth non-standard-model confined flare eruptions therefore plausibly support the idea of E. N. Parker for coronal heating in solar coronal magnetic loops by nanoflare bursts of reconnection at interfaces of internal field strands that cross at angles of 10 - 20 degrees., Comment: 26 pages, 7 figures, 2 movies (contact authors for the movies), accepted for publication in ApJ

Polyelectrolyte brushes consist of a set of charged linear macromolecules each tethered at one end to a surface. An example is the glycocalyx which refers to hair-like negatively charged sugar molecules that coat the outside membrane of all cells. We consider the transport and equilibrium distribution of ions, and the resulting electrical potential, when such a brush is immersed in a salt buffer containing monovalent cations (sodium and/or potassium). The Gouy-Chapman model for ion screening at a charged surface captures the effects of the Coulombic force that drives ion electrophoresis and diffusion, but neglects non-Coulombic forces and ion pairing. By including the distinct binding affinities of these counter-ions with the brush, and their so-called Born radii, which account for Born forces acting on them when the permittivity is non-uniform, we propose modified Poisson-Nernst-Planck continuum models that show the distinct profiles that may result depending on those ion-specific properties., Comment: 16 pages, 10 figures. Submitted to Physical Review E

Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitively unimpaired participants from two large Alzheimers studies at University of Wisconsin-Madison. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed on memory, processing speed and executive function. Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. Association of ADI with cognitive scores and MSN features were examined using linear regression and mediation analysis. ADI showed negative association with category fluency,implicit learning speed, story recall and modified pre-clinical Alzheimers cognitive composite scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Our preliminary findings suggest differences in local cortical organization by neighborhood disadvantage, which partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods.

The paper presents structures and techniques aimed towards co-designing scalable asynchronous and decentralized dynamic graph processing for fine-grain memory-driven architectures. It uses asynchronous active messages, in the form of actions that send ``work to data'', with a programming and execution model that allows spawning tasks from within the data-parallelism combined with a data-structure that parallelizes vertex object across many scratchpad memory-coupled cores and yet provides a single programming abstraction to the data object. The graph is constructed by streaming new edges using novel message delivery mechanisms and language constructs that work together to pass data and control using abstraction of actions, continuations and local control objects (LCOs) such as futures. It results in very fine-grain updates to a hierarchical dynamic vertex data structure, which subsequently triggers a user application action to update the results of any previous computation without recomputing from scratch. In our experiments we use BFS to demonstrate our concept design, and document challenges and opportunities.

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.

Next-frame prediction is a useful and powerful method for modelling and understanding the dynamics of video data. Inspired by the empirical success of causal language modelling and next-token prediction in language modelling, we explore the extent to which next-frame prediction serves as a strong foundational learning strategy (analogous to language modelling) for inducing an understanding of the visual world. In order to quantify the specific visual understanding induced by next-frame prediction, we introduce six diagnostic simulation video datasets derived from fundamental physical laws created by varying physical constants such as gravity and mass. We demonstrate that our models trained only on next-frame prediction are capable of predicting the value of these physical constants (e.g. gravity) without having been trained directly to learn these constants via a regression task. We find that the generative training phase alone induces a model state that can predict physical constants significantly better than that of a random model, improving the loss by a factor of between 1.28 to 6.24. We conclude that next-frame prediction shows great promise as a general learning strategy to induce understanding of the many `laws' that govern the visual domain without the need for explicit labelling., Comment: 7 Figures, 12 Pages, 1 Table

We study the formation of four coronal mass ejections (CMEs) originating from homologous blowout jets. All of the blowout jets originated from NOAA active region (AR) 11515 on 2012 July 2, within a time interval of $\approx$14 hr. All of the CMEs were wide (angular widths $\approx$95$-$150$^\circ$), and propagated with speeds ranging between $\approx$300$-$500 km s$^{-1}$ in LASCO coronagraph images. Observations at various EUV wavelengths in Solar Dynamics Observatory/Atmospheric Imaging Assembly images reveal that in all the cases, the source region of the jets lies at the boundary of the leading part of AR 11515 that hosts a small filament before each event. Coronal magnetic field modeling based on nonlinear force free extrapolations indicate that in each case the filament is contained inside of a magnetic flux rope that remains constrained by overlying compact loops. The southern footpoint of each filament is rooted in the negative polarity region where the eruption onsets occur. This negative-polarity region undergoes continuous flux changes, including emergence and cancellation with opposite polarity in the vicinity of the flux rope, and the EUV images reveal brightening episodes near the filament's southeastern footpoint before each eruption. Therefore, these flux changes are likely the cause of the subsequent eruptions. These four homologous eruptions originate near adjacent feet of two large-scale loop systems connecting from that positive-polarity part of the AR to two remote negative-polarity regions, and result in large-scale consequences in the solar corona., Comment: 24 pages, 11 Figures, 1 Table, Accepted for publication in the Astrophysical Journal

The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other application areas indicates that strong benchmarks are essential for the development of intelligent systems. We present Personal Health Interfaces Leveraging HUman-MAchine Natural interactions (PhilHumans), a holistic suite of benchmarks for machine learning across different Healthcare settings - talk therapy, diet coaching, emergency care, intensive care, obstetric sonography - as well as different learning settings, such as action anticipation, timeseries modeling, insight mining, language modeling, computer vision, reinforcement learning and program synthesis

Martin-L\"of's identity types provide a generic (albeit opaque) notion of identification or "equality" between any two elements of the same type, embodied in a canonical reflexive graph structure $(=_A, \mathbf{refl})$ on any type $A$. The miracle of Voevodsky's univalence principle is that it ensures, for essentially any naturally occurring structure in mathematics, that this the resultant notion of identification is equivalent to the type of isomorphisms in the category of such structures. Characterisations of this kind are not automatic and must be established one-by-one; to this end, several authors have employed reflexive graphs and displayed reflexive graphs to organise the characterisation of identity types. We contribute reflexive graph lenses, a new family of intermediate abstractions lying between families of reflexive graphs and displayed reflexive graphs that simplifies the characterisation of identity types for complex structures. Every reflexive graph lens gives rise to a (more complicated) displayed reflexive graph, and our experience suggests that many naturally occurring displayed reflexive graphs arise in this way. Evidence for the utility of reflexive graph lenses is given by means of several case studies, including the theory of reflexive graphs itself as well as that of polynomial type operators. Finally, we exhibit an equivalence between the type of reflexive graph fibrations and the type of univalent reflexive graph lenses., Comment: 52 pages