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In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the context of single jets. In this paper, we expand the scope to full proton-proton and electron-positron collision events and study reconstruction quality using metrics at the particle, jet, and event levels. Rather than operating on the entire event in a single pass, we train HGPflow on smaller partitions to avoid potentially learning long-range correlations related to the physics process. We demonstrate that this approach is feasible and that on most metrics, HGPflow outperforms both traditional particle flow algorithms and a machine learning-based benchmark model.

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space., Comment: 204 pages, 100+ figures, 30+ tables

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Stacking van der Waals magnets holds promise for creating new hybrid materials with properties that do not exist in bulk materials. Here we investigate orthogonally twisted stacks of the van der Waals antiferromagnet CrSBr, aiming to exploit an extreme misalignment of magnetic anisotropy across the twisted interface.Using nitrogen-vacancy centre microscopy, we construct vector maps of the magnetisation, and track their evolution under an external field, in a range of twisted compensated and uncompensated configurations differing by the number of layers. We show that twisted stacking consistently modifies the local magnetic switching behaviour of constituent flakes, and that these modifications are spatially non-uniform. In the case of compensated component flakes (even number of layers), we demonstrate that the combination of dipolar coupling and stacking-induced strain can reduce the switching field by over an order of magnitude. Conversely, in uncompensated component flakes (odd number of layers), we observe indications of a non-zero interlayer exchange interaction between twisted flakes during magnetization reversal, which can persistently modify magnetic order. This work highlights the importance of spatial imaging in investigating stacking-induced magnetic effects, particularly in the case of twistronics where spatial variation is expected and can be conflated with structural imperfections., Comment: 6 main text pages, 4 figures plus 8 supplementary information pages, 9 figures

This paper studies how groups of robots can effectively navigate through a crowd of agents. It quantifies the performance of platooning and less constrained, greedy strategies, and the extent to which these strategies disrupt the crowd agents. Three scenarios are considered: (i) passive crowds, (ii) counter-flow crowds, and (iii) perpendicular-flow crowds. Through simulations consisting of up to 200 robots, we show that for navigating passive and counter-flow crowds, the platooning strategy is less disruptive and more effective in dense crowds than the greedy strategy, whereas for navigating perpendicular-flow crowds, the greedy strategy outperforms the platooning strategy in either aspect. Moreover, we propose an adaptive strategy that can switch between platooning and greedy behavioral states, and demonstrate that it combines the strengths of both strategies in all the scenarios considered., Comment: 14 pages, 7 figures, to be published in DARS 2024

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.

A recent line of work in mechanistic interpretability has focused on reverse-engineering the computation performed by neural networks trained on the binary operation of finite groups. We investigate the internals of one-hidden-layer neural networks trained on this task, revealing previously unidentified structure and producing a more complete description of such models that unifies the explanations of previous works. Notably, these models approximate equivariance in each input argument. We verify that our explanation applies to a large fraction of networks trained on this task by translating it into a compact proof of model performance, a quantitative evaluation of model understanding. In particular, our explanation yields a guarantee of model accuracy that runs in 30% the time of brute force and gives a >=95% accuracy bound for 45% of the models we trained. We were unable to obtain nontrivial non-vacuous accuracy bounds using only explanations from previous works., Comment: 23 pages, 4 figures

Log-linear exponential random graph models are a specific class of statistical network models that have a log-linear representation. This class includes many stochastic blockmodel variants. In this paper, we focus on $\beta$-stochastic blockmodels, which combine the $\beta$-model with a stochastic blockmodel. Here, using recent results by Almendra-Hern\'{a}ndez, De Loera, and Petrovi\'{c}, which describe a Markov basis for $\beta$-stochastic block model, we give a closed form formula for the maximum likelihood degree of a $\beta$-stochastic blockmodel. The maximum likelihood degree is the number of complex solutions to the likelihood equations. In the case of the $\beta$-stochastic blockmodel, the maximum likelihood degree factors into a product of Eulerian numbers.

Phylogenetic networks describe the evolution of a set of taxa for which reticulate events have occurred at some point in their evolutionary history. Of particular interest is when the evolutionary history between a set of just three taxa has a reticulate event. In molecular phylogenetics, substitution models can model the process of evolution at the genetic level, and the case of three taxa with a reticulate event can be modelled using a substitution model on a mixed graph called a 3-sunlet. We investigate a class of substitution models called group-based phylogenetic models on 3-sunlet networks. In particular, we investigate the discrete geometry of the parameter space and how this relates to the dimension of the phylogenetic variety associated to the model. This enables us to give a dimension formula for this variety for general group-based models when the order of the group is odd.

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.

We present a novel strategy for obtaining optimal probe states and measurement schemes in a class of noiseless multiparameter estimation problems with symmetry among the generators. The key to the framework is the introduction of a set of quantum metrology conditions, analogous to the quantum error correction conditions of Knill and Laflamme, which are utilized to identify probe states that saturate the multiparameter quantum Cram\'{e}r-Rao bound. Similar to finding two-dimensional irreps for encoding a logical qubit in error correction, we identify trivial irreps of finite groups that guarantee the satisfaction of the quantum metrology conditions. To demonstrate our framework, we analyze the SU(2) estimation with symmetric states in which three parameters define a global rotation of an ensemble of $N$ qubits. For even $N$, we find that tetrahedral symmetry and, with fine-tuning, $S_{3}$ symmetry, are minimal symmetry groups providing optimal probe states for SU(2) estimation, but that the quantum metrology conditions can also be satisfied in an entanglement-assisted setting by using a maximally entangled state of two spin-$N/2$ representations for any $N$. By extending the multiparameter method of moments to non-commuting observables, we use the quantum metrology conditions to construct a measurement scheme that saturates the multiparameter quantum Cram\'{e}r-Rao bound for small rotation angles., Comment: Comments are Welcome!

Dual Active Galactic Nuclei (dual AGNs), a phase in some galaxy mergers during which both central supermassive black holes (SMBHs) are active, are expected to be a key observable stage leading up to SMBH mergers. Constraining the population of dual AGNs in both the nearby and high-z universe has proven to be elusive until very recently. We present a multi-wavelength follow-up campaign to confirm the nature of a sample of 20 candidate dual AGNs at cosmic noon (z~2) from the VODKA sample. Through a combination of Hubble Space Telescope (HST) and Very Large Array (VLA) imaging, we refute the possibility of gravitational lensing in all but one target. We find evidence of dual AGNs in four systems, while seven exhibit single AGN in galaxy pairs, either through strong radio emission or ancillary emission line data. The remaining systems are either confirmed as quasar-star superpositions (six) or non-lensed pairs (two) that require further investigations to establish AGN activity. Among the systems with radio detections, we find a variety of radio spectral slopes and UV/optical colors suggesting that our sample contains a range of AGN properties, from obscured radio-quiet objects to those with powerful synchrotron-emitting jets. This study presents one of the largest dedicated multi-wavelength follow-up campaigns to date searching for dual AGNs at high redshift. We confirm several of the highest-z systems at small physical separations, thus representing some of the most evolved dual AGN systems at the epoch of peak quasar activity known to date., Comment: 26 pages, 8 figures, submitted to ApJ, includes appendix of 9 additional figures and 18 tables

We present Gemini/GMOS and HST/STIS optical spectra for 27 dual quasar candidates selected based on their variability-induced astrometric noise or double detections in Gaia (the VODKA project). From this follow-up, we spectroscopically identify 10 star superpositions and 8 dual/lensed quasars. Among the remaining targets, 2 are likely dual/lensed quasars based on additional radio imaging, while the rest are quasars with unknown companions. Notably, WISE J1649+0812 is a newly confirmed dual quasar with a projected separation of 5 kpc at $z=1.39$ and a significant velocity offset of 183$\pm$76 km/s, highlighting the utility of narrow emission lines in identifying genuine dual quasars. Without prior photometric or spectroscopic selection, we find the star contamination rate to be 37-63%, while the dual/lensed quasar fraction is $\gtrsim$ 30% in the follow-up VODKA sample. However, when combined with existing unresolved spectra and spatially-resolved two-band color cuts, the dual/lensed quasar fraction can be increased to $\gtrsim$ 67%. High signal-to-noise ratio spectra ($\gtrsim$ 20 per spectral element) with adequate spectral resolution ($R \gtrsim$ 1000) are essential for identifying faint absorption lines in foreground stars and detecting dual quasars through velocity offsets., Comment: 14 pages, 6 figures, 4 tables, submitted to ApJ, comments are welcome

Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics, where the spatial resolution of calorimeters has a crucial impact. This study explores the integration of super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance the granularity of calorimeter data and suppress noise. We find that this software preprocessing step can significantly improve reconstruction quality without physical changes to detectors. To demonstrate the impact of our approach, we propose a novel particle flow model that offers enhanced particle reconstruction quality and interpretability. These advancements underline the potential of super-resolution to impact both current and future particle physics experiments.

Natural environments pose significant challenges for autonomous robot navigation, particularly due to their unstructured and ever-changing nature. Hiking trails, with their dynamic conditions influenced by weather, vegetation, and human traffic, represent one such challenge. This work introduces a novel approach to autonomous hiking trail navigation that balances trail adherence with the flexibility to adapt to off-trail routes when necessary. The solution is a Traversability Analysis module that integrates semantic data from camera images with geometric information from LiDAR to create a comprehensive understanding of the surrounding terrain. A planner uses this traversability map to navigate safely, adhering to trails while allowing off-trail movement when necessary to avoid on-trail hazards or for safe off-trail shortcuts. The method is evaluated through simulation to determine the balance between semantic and geometric information in traversability estimation. These simulations tested various weights to assess their impact on navigation performance across different trail scenarios. Weights were then validated through field tests at the West Virginia University Core Arboretum, demonstrating the method's effectiveness in a real-world environment.

A family of subsets $\mathcal{F} \subseteq \mathcal{P}(\{1, 2, \ldots, n\})$ has the disparate union property if any two disjoint subfamilies $\mathcal{F}_1, \mathcal{F}_2 \subseteq \mathcal{F}$ have distinct unions $\bigcup \mathcal{F}_1 \neq \bigcup \mathcal{F}_2$; what is the maximal size of a family with the disparate union property? Is there a simple and efficiently computable characterization of size-maximal families? This paper highlights a class of partially-ordered semirings -- difference ordered semirings with a multiplicatively absorbing element -- and shows it is common and easily constructed. We prove that a suitably modified definition of linear independence for semimodules over such semirings enjoys the same maximality property as for vector spaces, and can furthermore be efficiently detected by the bideterminant. These properties allow us to extend dimension argument in extremal combinatorics and provide simple and direct solutions to the puzzles above.

Limbless organisms of all sizes use undulating patterns of self-deformation to locomote. Geometric mechanics, which maps deformations to motions, provides a powerful framework to formalize and investigate the theoretical properties and limitations of such modes of locomotion. However, the inherent level of abstraction poses a challenge when bridging the gap between theory or simulations and laboratory experiments. We investigate the challenges of modeling motion trajectories of an undulating robotic locomotor by comparing experiments and simulations performed with a variational integrator. Despite the extensive simplifications that the model based on a geometric variation principle entails, the simulations show good agreement on average. Notably, our approach merely requires the knowledge of the \emph{dissipation metric} -- a Riemannian metric on the configuration space, which can in practice be approximated by means closely resembling \emph{resistive force theory}.

This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques. The key contributions include developing ML models that accurately predict various roughness values and identifying redundant sensors, particularly those for measuring normal cutting force. Our experiments show that removing certain sensors can reduce costs without sacrificing predictive accuracy, highlighting the potential of explainable machine learning to improve cost-effectiveness in machining., Comment: arXiv admin note: substantial text overlap with arXiv:2403.18731

Pruning neural networks (NNs) can streamline them but risks removing vital parameters from safe reinforcement learning (RL) policies. We introduce an interpretable RL method called VERINTER, which combines NN pruning with model checking to ensure interpretable RL safety. VERINTER exactly quantifies the effects of pruning and the impact of neural connections on complex safety properties by analyzing changes in safety measurements. This method maintains safety in pruned RL policies and enhances understanding of their safety dynamics, which has proven effective in multiple RL settings.

Reinforcement learning (RL) policies may exhibit unsafe behavior and are hard to explain. We use counterfactual large language model reasoning to enhance RL policy safety post-training. We show that our approach improves and helps to explain the RL policy safety.

This study reports the successful fabrication of high-positive refractive index change waveguides, exceeding 0.02 in fluoride glasses, marking a significant advancement in integrated optical components for visible to mid-infrared applications. This research overcomes longstanding challenges in direct-write photonics and therefore enables the realization of true 3D geometries in optical elements, access to novel visible lasing wavelengths typically suppressed in high phonon hosts, and the miniaturization of mid-infrared optical devices. The investigation into the waveguides' origin attributes the exceptionally high index change to material densification driven by the migration of specific elements, mainly barium, within the glass composition. These waveguides, characterized by low insertion losses, and highly customizable V-numbers evidenced by multimode operation at 3.5 um, offers substantial potential for chip laser technology and the creation of advanced optical devices for sensing and spectroscopy., Comment: 12 pages, 9 figures

Planar scanning probe microscopy is a recently emerging alternative approach to tip-based scanning probe imaging. It can scan an extended planar sensor, such as a polished bulk diamond doped with magnetic-field-sensitive nitrogen-vacancy (NV) centers, in nanometer-scale proximity of a planar sample. So far, this technique has been limited to optical near-field microscopy, and has required nanofabrication of the sample of interest. Here we extend this technique to magnetometry using NV centers, and present a modification that removes the need for sample-side nanofabrication. We harness this new ability to perform a hitherto infeasible measurement - direct imaging of the three-dimensional vector magnetic field of magnetic vortices in a thin film magnetic heterostructure, based on repeated scanning with NV centers with different orientations within the same scanning probe. Our result opens the door to quantum sensing using multiple qubits within the same scanning probe, a prerequisite for the use of entanglement-enhanced and massively parallel schemes.

Membrane-based sensors are an important market for microelectromechanical systems (MEMS). Two-dimensional (2D) materials, with their low mass, are excellent candidates for suspended membranes to provide high sensitivity, small footprint sensors. The present work demonstrates pressure sensors employing large-scale-synthesized 2D platinum diselenide (PtSe${_2}$) films as piezoresistive membranes supported only by a thin polymer layer. We investigate three different synthesis methods with contrasting growth parameters and establish a reliable high yield fabrication process for suspended PtSe${_2}$/PMMA membranes across sealed cavities. The pressure sensors reproducibly display sensitivities above 6 x 10${^4}$ kPa${^{-1}}$. We show that the sensitivity clearly depends on the membrane diameter and the piezoresistive gauge factor of the PtSe${_2}$ film. Reducing the total device size by decreasing the number of membranes within a device leads to a significant increase in the area-normalized sensitivity. This allows the manufacturing of pressure sensors with high sensitivity but a much smaller device footprint than the current state-of-the-art MEMS technology. We further integrate PtSe${_2}$ pressure sensors with CMOS technology, improving the technological readiness of PtSe${_2}$-based MEMS and NEMS devices., Comment: 42 pages

The layered molecular conductors $\kappa$-(BEDT-TTF)$_2$X are a perfect experimental platform for studying the physics of the Mott transition and related exotic electronic states. In these materials, the subtle balance between various instabilities of the normal metallic state can be efficiently changed by applying a very moderate external pressure or by subtle chemical modifications, e.g. by a replacement of the insulating anion X$^{-}$, frequently referred to as ``chemical pressure''. A crucially important but still unsettled issue is an exact understanding of the influence of physical and chemical pressure on the electronic structure. Here, we use magnetic quantum oscillations to explore in a broad pressure range the behavior of the key parameters governing the Mott physics, the electronic correlation strength ratio $U/t$ and the spin frustration ratio $t'/t$ in two $\kappa$ salts, the ambient-pressure antiferromagnetic insulator with X = Cu[N(CN)$_2$]Cl and the ambient-pressure superconductor with X = Cu(NCS)$_2$. Our analysis shows that pressure effectively changes not only the conduction bandwidth but also the degree of spin frustration, thus weakening both the electronic correlation strength and the magnetic ordering instability. At the same time, we find that the replacement of the anion Cu[N(CN)$_2$]Cl$^-$ by Cu(NCS)$_2^-$ results in a significant increase of the frustration parameter $t'/t$, leaving the correlation strength essentially unchanged., Comment: 13 pages,including 7 Figures. Supplemental Material is available upon request

Despite recent advances in multi-view hair reconstruction, achieving strand-level precision remains a significant challenge due to inherent limitations in existing capture pipelines. We introduce GroomCap, a novel multi-view hair capture method that reconstructs faithful and high-fidelity hair geometry without relying on external data priors. To address the limitations of conventional reconstruction algorithms, we propose a neural implicit representation for hair volume that encodes high-resolution 3D orientation and occupancy from input views. This implicit hair volume is trained with a new volumetric 3D orientation rendering algorithm, coupled with 2D orientation distribution supervision, to effectively prevent the loss of structural information caused by undesired orientation blending. We further propose a Gaussian-based hair optimization strategy to refine the traced hair strands with a novel chained Gaussian representation, utilizing direct photometric supervision from images. Our results demonstrate that GroomCap is able to capture high-quality hair geometries that are not only more precise and detailed than existing methods but also versatile enough for a range of applications., Comment: Accepted by SIGGRAPH Asia 2024

We present new scaling relations for the isotropic phase-space distribution functions (DFs) and energy distributions of simulated dark matter haloes. These relations are inspired by those for the singular isothermal sphere with density profile $\rho(r)\propto r^{-2}$, for which the DF satisfies $f(E) \propto r_{\max}^{-2}(E)$ and the energy distribution satisfies $dM/dE \propto r_{\max}(E)$, with $r_{\max}(E)$ being the radius where the gravitational potential equals energy $E$. For the simulated haloes, we find $f(E)\propto r_{\max}^{-2.08}(E)$ and $dM/dE \propto r_{\max}(E)$ across broad energy ranges. In addition, the proportionality coefficients depend on the gravitational constant and the parameters of the best-fit Navarro-Frenk-White density profile. These scaling relations are satisfied by haloes over a wide mass range and provide an efficient method to approximate their DFs and energy distributions. Understanding the origin of these relations may shed more light on halo formation., Comment: 9 pages, 4 figures, comments are welcome

This study explored the impact of the 95 Phonics Core Program® (95 PCP) on student literacy achievement across third graders who started the school year at different ability levels. The primary focus of this report was to investigate the relationship between changes in phonics scores with the beginning and end-of-year i-Ready® assessments and outcomes on State of Texas Assessments of Academic Readiness (STAAR®). To this end, i-Ready scores were collected from 4 elementary schools in the Southside Independent School District in San Antonio, Texas, during the 2022-2023 academic school year. Third graders from all schools were aggregated by beginning-of-year (BOY) placement level in terms of grade-level skills (kindergarten, first grade, second grade, third grade). Outcomes were expressed in terms of gains on scale scores and performance levels in STAAR. The analysis compared EOY performance levels for students who grew different amounts in their phonics skills during the school year. The analysis progressed through the following questions, each adding a piece to the overall picture.

Suspended membranes of monoatomic graphene exhibit great potential for applications in electronic and nanoelectromechanical devices. In this work, a "hot and dry" transfer process is demonstrated to address the fabrication and patterning challenges of large-area graphene membranes on top of closed, sealed cavities. Here, "hot" refers to the use of high temperature during transfer, promoting the adhesion. Additionally, "dry" refers to the absence of liquids when graphene and target substrate are brought into contact. The method leads to higher yields of intact suspended monolayer CVD graphene and artificially stacked double-layer CVD graphene membranes than previously reported. The yield evaluation is performed using neural-network-based object detection in SEM images, ascertaining high yields of intact membranes with large statistical accuracy. The suspended membranes are examined by Raman tomography and AFM. The method is verified by applying the suspended graphene devices as piezoresistive pressure sensors. Our technology advances the application of suspended graphene membranes and can be extended to other two-dimensional (2D) materials., Comment: 30 pages of manuscript plus 17 pages of Supporting Information

Astrophotonics represents a cutting-edge approach in observational astronomy. This paper explores the significant advancements and potential applications of astrophotonics, highlighting how photonic technologies stand to revolutionise astronomical instrumentation. Key areas of focus include photonic wavefront sensing and imaging, photonic interferometry and nulling, advanced chip fabrication methods, and the integration of spectroscopy and sensing onto photonic chips. The role of single-mode fibres in reducing modal noise, and the development of photonic integral field units (IFUs) and arrayed waveguide gratings (AWGs) for high-resolution, spatially resolved spectroscopy will be examined. As part of the Sydney regional-focus issue, this review aims to detail some of the current technological achievements in this field as well as to discuss the future trajectory of astrophotonics, underscoring its potential to unlock important new astronomical discoveries., Comment: Published version available at https://opg.optica.org/ao/fulltext.cfm?uri=ao-63-24-6393&id=554618

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

With the emergence of time-critical applications in modern communication networks, there is a growing demand for proactive network adaptation and quality of service (QoS) prediction. However, a fundamental question remains largely unexplored: how can we quantify and achieve more predictable communication systems in terms of performance? To address this gap, this paper introduces a theoretical framework for defining and analyzing predictability in communication systems, with a focus on the impact of observations for performance forecasting. We establish a mathematical definition of predictability based on the total variation distance between forecast and marginal performance distributions. A system is deemed unpredictable when the forecast distribution, providing the most comprehensive characterization of future states using all accessible information, is indistinguishable from the marginal distribution, which depicts the system's behavior without any observational input. This framework is applied to multi-hop systems under Markovian conditions, with a detailed analysis of Geo/Geo/1 queuing models in both single-hop and multi-hop scenarios. We derive exact and approximate expressions for predictability in these systems, as well as upper bounds based on spectral analysis of the underlying Markov chains. Our results have implications for the design of efficient monitoring and prediction mechanisms in future communication networks aiming to provide deterministic services.

Two-dimensional materials are extraordinarily sensitive to external stimuli, making them ideal for studying fundamental properties and for engineering devices with new functionalities. One such stimulus, strain, affects the magnetic properties of the layered magnetic semiconductor CrSBr to such a degree that it can induce a reversible antiferromagnetic-to-ferromagnetic phase transition. Given the pervasiveness of non-uniform strain in exfoliated two-dimensional magnets, it is crucial to understand its impact on their magnetic behavior. Using scanning SQUID-on-lever microscopy, we directly image the effects of spatially inhomogeneous strain on the magnetization of layered CrSBr as it is polarized by a field applied along its easy axis. The evolution of this magnetization and the formation of domains is reproduced by a micromagnetic model, which incorporates the spatially varying strain and the corresponding changes in the local interlayer exchange stiffness. The observed sensitivity to small strain gradients along with similar images of a nominally unstrained CrSBr sample suggest that unintentional strain inhomogeneity influences the magnetic behavior of exfoliated samples and must be considered in the design of future devices., Comment: 15 pages, 4 figures, 1 supplementary section

Recent research has seen notable progress in the development of linkage-based artificial hands. While previous designs have focused on adaptive grasping, dexterity and biomimetic artificial skin, only a few systems have proposed a lightweight, accessible solution integrating tactile sensing with a compliant linkage-based mechanism. This paper introduces OPENGRASP LITE, an open-source, highly integrated, tactile, and lightweight artificial hand. Leveraging compliant linkage systems and MEMS barometer-based tactile sensing, it offers versatile grasping capabilities with six degrees of actuation. By providing tactile sensors and enabling soft grasping, it serves as an accessible platform for further research in tactile artificial hands., Comment: Accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems, 14-18 October 2024

Recent years have seen a tremendous improvement in the quality of video generation and editing approaches. While several techniques focus on editing appearance, few address motion. Current approaches using text, trajectories, or bounding boxes are limited to simple motions, so we specify motions with a single motion reference video instead. We further propose to use a pre-trained image-to-video model rather than a text-to-video model. This approach allows us to preserve the exact appearance and position of a target object or scene and helps disentangle appearance from motion. Our method, called motion-textual inversion, leverages our observation that image-to-video models extract appearance mainly from the (latent) image input, while the text/image embedding injected via cross-attention predominantly controls motion. We thus represent motion using text/image embedding tokens. By operating on an inflated motion-text embedding containing multiple text/image embedding tokens per frame, we achieve a high temporal motion granularity. Once optimized on the motion reference video, this embedding can be applied to various target images to generate videos with semantically similar motions. Our approach does not require spatial alignment between the motion reference video and target image, generalizes across various domains, and can be applied to various tasks such as full-body and face reenactment, as well as controlling the motion of inanimate objects and the camera. We empirically demonstrate the effectiveness of our method in the semantic video motion transfer task, significantly outperforming existing methods in this context., Comment: Preprint. All videos in this paper are best viewed as animations with Acrobat Reader by pressing the highlighted frame of each video

To address the rural broadband challenge and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity wireless x-haul and access platforms across a rural area of diameter over 30 km. With both software-defined radios and programmable COTS systems and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across user equipment, base stations, edge, and cloud, ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as O-RAN, NextG, and agriculture applications. Here we present the design principles and implementation strategies of ARA, characterize its performance and heterogeneity, and highlight example wireless and application experiments uniquely enabled by ARA., Comment: 17 pages, 18 figures

Let $S$ be a compact hyperbolic surface of genus $g\geq 2$ and let $I(S) = \frac{1}{\mathrm{Vol}(S)}\int_{S} \frac{1}{\mathrm{Inj}(x)^2 \wedge 1} dx$, where $\mathrm{Inj}(x)$ is the injectivity radius at $x$. We prove that for any $k\in \{1,\ldots, 2g-3\}$, the $k$-th eigenvalue $\lambda_k$ of the Laplacian satisfies \begin{equation*} \lambda_k \geq \frac{c k^2}{I(S) g^2} \, , \end{equation*} where $c>0$ is some universal constant. We use this bound to prove the heat kernel estimate \begin{equation*} \frac{1}{\mathrm{Vol}(S)} \int_S \Big| p_t(x,x) -\frac{1}{\mathrm{Vol}(S)} \Big | ~dx \leq C \sqrt{ \frac{I(S)}{t}} \qquad \forall t \geq 1 \, , \end{equation*} where $C<\infty$ is some universal constant. These bounds are optimal in the sense that for every $g\geq 2$ there exists a compact hyperbolic surface of genus $g$ satisfying the reverse inequalities with different constants., Comment: 18 pages, 2 figures

By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficiently precise details. Although recent diffusion-based MDE approaches exhibit appealing detail extraction ability, they still struggle in geometrically challenging scenes due to the difficulty of gaining robust geometric priors from diverse datasets. To leverage the complementary merits of both worlds, we propose BetterDepth to efficiently achieve geometrically correct affine-invariant MDE performance while capturing fine-grained details. Specifically, BetterDepth is a conditional diffusion-based refiner that takes the prediction from pre-trained MDE models as depth conditioning, in which the global depth context is well-captured, and iteratively refines details based on the input image. For the training of such a refiner, we propose global pre-alignment and local patch masking methods to ensure the faithfulness of BetterDepth to depth conditioning while learning to capture fine-grained scene details. By efficient training on small-scale synthetic datasets, BetterDepth achieves state-of-the-art zero-shot MDE performance on diverse public datasets and in-the-wild scenes. Moreover, BetterDepth can improve the performance of other MDE models in a plug-and-play manner without additional re-training.

GLINT is a nulling interferometer downstream of the SCExAO extreme-adaptive-optics system at the Subaru Telescope (Hawaii, USA), and is a pathfinder instrument for high-contrast imaging of circumstellar environments with photonic technologies. GLINT is effectively a testbed for more stable, compact, and modular instruments for the era of 30m-class telescopes. GLINT is now undergoing an upgrade with a new photonic chip for more achromatic nulls, and for phase information to enable fringe tracking. Here we provide an overview of the motivations for the GLINT project and report on the design of the new chip, the on-site installation, and current status., Comment: Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama, Japan), Optical and Infrared Interferometry and Imaging IX

Large language model (LLM) chatbots are susceptible to biases and hallucinations, but current evaluations of mental wellness technologies lack comprehensive case studies to evaluate their practical applications. Here, we address this gap by introducing the MHealth-EVAL framework, a new role-play based interactive evaluation method designed specifically for evaluating the appropriateness, trustworthiness, and safety of mental wellness chatbots. We also introduce Psyfy, a new chatbot leveraging LLMs to facilitate transdiagnostic Cognitive Behavioral Therapy (CBT). We demonstrate the MHealth-EVAL framework's utility through a comparative study of two versions of Psyfy against standard baseline chatbots. Our results showed that Psyfy chatbots outperformed the baseline chatbots in delivering appropriate responses, engaging users, and avoiding untrustworthy responses. However, both Psyfy and the baseline chatbots exhibited some limitations, such as providing predominantly US-centric resources. While Psyfy chatbots were able to identify most unsafe situations and avoid giving unsafe responses, they sometimes struggled to recognize subtle harmful intentions when prompted in role play scenarios. Our study demonstrates a practical application of the MHealth-EVAL framework and showcases Psyfy's utility in harnessing LLMs to enhance user engagement and provide flexible and appropriate responses aligned with an evidence-based CBT approach.

Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance. Methods: DICOM structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration and interactive filtering capabilities that simplifies the process of assembling multi-modal datasets. Results: In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data includes DICOM data (i.e. computed tomography images, electrocardiography scans) as well as annotations (i.e. calcification segmentations, pointsets and pacemaker dependency), and metadata (i.e. prosthesis and diagnoses). Conclusion: Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for clinical studies. The graphical interface as well as example structured report templates will be made publicly available.

Transformer-based language models have shown state-of-the-art performance on a variety of natural language understanding tasks. To achieve this performance, these models are first pre-trained on general corpus and then fine-tuned on downstream tasks. Previous work studied the effect of pruning the training set of the downstream tasks on the performance of the model on its evaluation set. In this work, we propose an automatic dataset pruning method for the training set of fine-tuning tasks. Our method is based on the model's success rate in correctly classifying each training data point. Unlike previous work which relies on user feedback to determine subset size, our method automatically extracts training subsets that are adapted for each pair of model and fine-tuning task. Our method provides multiple subsets for use in dataset pruning that navigate the trade-off between subset size and evaluation accuracy. Our largest subset, which we also refer to as the winning ticket subset, is on average $3 \times$ smaller than the original training set of the fine-tuning task. Our experiments on 5 downstream tasks and 2 language models show that, on average, fine-tuning on the winning ticket subsets results in a $0.1 \%$ increase in the evaluation performance of the model., Comment: 28 pages, 17 figures. Accepted at the Third Conference on Lifelong Learning Agents (CoLLAs 2024)

Asgard/NOTT is an ERC-funded project hosted at KU Leuven and is part of a new visitor instrumental suite, called Asgard, under preparation for the Very Large Telescope Interferometer (VLTI). Leveraging nulling capabilities and the long VLTI baselines, it is optimized for high-contrast imaging of the snow line region around young nearby main-sequence stars. This will enable the characterization of the atmosphere of young giant exoplanets and warm/hot exozodiacal dust with spectroscopy in the L'-band (3.5-4.0$\mu$m). In this work, we present the first lab assembly of the instrument done at KU Leuven and the technical solutions to tackle the challenge of performing nulling in the mid-infrared despite the thermal background. The opto-mechanical design of the warm optics and the injection system for the photonic chip are described. The alignment procedure used to assemble the system is also presented. Finally, the first experimental results, including fringes and null measurements, are given and confirm the adequacy of the bench to test and optimize the Asgard/NOTT instrument., Comment: 13 pages (incl. 14 figures); Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama; Japan), Optical and Infrared Interferometry and Imaging IX

NOTT (formerly Hi-5) is the L'-band (3.5-4.0~microns) nulling interferometer of Asgard, an instrument suite in preparation for the VLTI visitor focus. The primary scientific objectives of NOTT include characterizing (i) young planetary systems near the snow line, a critical region for giant planet formation, and (ii) nearby main-sequence stars close to the habitable zone, with a focus on detecting exozodiacal dust that could obscure Earth-like planets. In 2023-2024, the final warm optics have been procured and assembled in a new laboratory at KU Leuven. First fringes and null measurements were obtained using a Gallium Lanthanum Sulfide (GLS) photonic chip that was also tested at cryogenic temperatures. In this paper, we present an overall update of the NOTT project with a particular focus on the cold mechanical design, the first results in the laboratory with the final NOTT warm optics, and the ongoing Asgard integration activities. We also report on other ongoing activities such as the characterization of the photonic chip (GLS, LiNbO3, SiO), the development of the exoplanet science case, the design of the dispersion control module, and the progress with the self-calibration data reduction software., Comment: 11 pages (incl. 5 figures); Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama; Japan), Optical and Infrared Interferometry and Imaging VI

Federated learning (FL) is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often involve inherent challenges such as partially labeled datasets, where not all clients possess expert annotations of all labels of interest, leaving large portions of unlabeled data unused. In this study, we conduct the largest federated cardiac CT imaging analysis to date, focusing on partially labeled datasets ($n=8,124$) of Transcatheter Aortic Valve Implantation (TAVI) patients over eight hospital clients. Transformer architectures, which are the major building blocks of current foundation models, have shown superior performance when trained on larger cohorts than traditional CNNs. However, when trained on small task-specific labeled sample sizes, it is currently not feasible to exploit their underlying attention mechanism for improved performance. Therefore, we developed a two-stage semi-supervised learning strategy that distills knowledge from several task-specific CNNs (landmark detection and segmentation of calcification) into a single transformer model by utilizing large amounts of unlabeled data typically residing unused in hospitals to mitigate these issues. This method not only improves the predictive accuracy and generalizability of transformer-based architectures but also facilitates the simultaneous learning of all partial labels within a single transformer model across the federation. Additionally, we show that our transformer-based model extracts more meaningful features for further downstream tasks than the UNet-based one by only training the last layer to also solve segmentation of coronary arteries. We make the code and weights of the final model openly available, which can serve as a foundation model for further research in cardiac CT imaging.