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We study the gradient flow of the Allen-Cahn equation with fixed boundary contact angle in Euclidean domains for initial data with bounded energy. Under general assumptions, we establish both interior and boundary convergence properties for the solutions and associated energy measures. Under various boundary non-concentration assumptions, we show that, for almost every time, the associated limiting varifolds satisfy generalised contact angle conditions and have bounded first variation, as well as deducing that the trace of the limit of the solutions coincides with the limit of their traces. Moreover, we derive an Ilmanen type monotonicity formula, for initial data with bounded energy, valid for the associated energy measures up to the boundary., Comment: 24 pages, 2 figures

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.

This study introduces a new framework for the artificial intelligence-assisted characterization of Gram-stained whole-slide images (WSIs). As a test for the diagnosis of bloodstream infections, Gram stains provide critical early data to inform patient treatment. Rapid and reliable analysis of Gram stains has been shown to be positively associated with better clinical outcomes, underscoring the need for improved tools to automate Gram stain analysis. In this work, we developed a novel transformer-based model for Gram-stained WSI classification, which is more scalable to large datasets than previous convolutional neural network (CNN) -based methods as it does not require patch-level manual annotations. We also introduce a large Gram stain dataset from Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire, USA) to evaluate our model, exploring the classification of five major categories of Gram-stained WSIs: Gram-positive cocci in clusters, Gram-positive cocci in pairs/chains, Gram-positive rods, Gram-negative rods, and slides with no bacteria. Our model achieves a classification accuracy of 0.858 (95% CI: 0.805, 0.905) and an AUC of 0.952 (95% CI: 0.922, 0.976) using five-fold nested cross-validation on our 475-slide dataset, demonstrating the potential of large-scale transformer models for Gram stain classification. We further demonstrate the generalizability of our trained model, which achieves strong performance on external datasets without additional fine-tuning.

When designing a public transportation network in a country, one may want to minimise the sum of travel duration of all inhabitants. This corresponds to a purely utilitarian view and does not involve any fairness consideration, as the resulting network will typically benefit the capital city and/or large central cities while leaving some peripheral cities behind. On the other hand, a more egalitarian view will allow some people to travel between peripheral cities without having to go through a central city. We define a model, propose algorithms for computing solution networks, and report on experiments based on real data., Comment: 32 pages, 18 figures

We introduce the task of mixed-view panorama synthesis, where the goal is to synthesize a novel panorama given a small set of input panoramas and a satellite image of the area. This contrasts with previous work which only uses input panoramas (same-view synthesis), or an input satellite image (cross-view synthesis). We argue that the mixed-view setting is the most natural to support panorama synthesis for arbitrary locations worldwide. A critical challenge is that the spatial coverage of panoramas is uneven, with few panoramas available in many regions of the world. We introduce an approach that utilizes diffusion-based modeling and an attention-based architecture for extracting information from all available input imagery. Experimental results demonstrate the effectiveness of our proposed method. In particular, our model can handle scenarios when the available panoramas are sparse or far from the location of the panorama we are attempting to synthesize.

Hadronic resonances emerge from strong interactions encoding the dynamics of quarks and gluons. The structure of these resonances can be probed by virtual photons parameterized in transition form factors. In this study, twelve $N^*$ and $\Delta$ transition form factors at the pole are extracted from data with the center-of-mass energy from $\pi N$ threshold to $1.8\,{\rm GeV}$, and the photon virtuality $0\leq Q^2/{\rm GeV}^2\leq 8$. For the first time, these results are determined from a simultaneous analysis of more than one state, i.e., $\sim 10^5$ $\pi N$, $\eta N$, and $K\Lambda$ electroproduction data. In addition, about $ 5\cdot 10^4$ data in the hadronic sector as well as photoproduction serve as boundary conditions. For the $\Delta(1232)$ and $N(1440)$ states our results are in qualitative agreement with previous studies, while the transition form factors at the poles of some higher excited states are estimated for the first time. Realistic uncertainties are determined by further exploring the parameter space., Comment: Text: 6 pages, 4 figures. Supplemental Material: 3 pages, 2 figures

Single-energy partial-wave analysis has often been applied as a way to fit data with minimal model dependence. However, remaining unconstrained, partial waves at neighboring energies will vary discontinuously because the overall amplitude phase cannot be determined through single-channel measurements. This problem can be mitigated through the use of a constraining penalty function based on an associated energy-dependent fit. However, the weight given to this constraint results in a biased fit to the data. In this paper, for the first time, we explore a constraining function which does not influence the fit to data. The constraint comes from the overall phase found in multi-channel fits which, in the present study, are the Bonn-Gatchina and J\"ulich-Bonn multi-channel analyses. The data are well reproduced and weighting of the penalty function does not influence the result. The method is applied to $K \Lambda$ photoproduction data and all observables can be maximally well reproduced. While the employed multi-channel analyses display very different multipole amplitudes, we show that the major difference between two sets of multipoles can be related to the different overall phases., Comment: 11 pages, 5 figures

This work addresses the task of modeling spatiotemporal traffic patterns directly from overhead imagery, which we refer to as image-driven traffic modeling. We extend this line of work and introduce a multi-modal, multi-task transformer-based segmentation architecture that can be used to create dense city-scale traffic models. Our approach includes a geo-temporal positional encoding module for integrating geo-temporal context and a probabilistic objective function for estimating traffic speeds that naturally models temporal variations. We evaluate our method extensively using the Dynamic Traffic Speeds (DTS) benchmark dataset and significantly improve the state-of-the-art. Finally, we introduce the DTS++ dataset to support mobility-related location adaptation experiments., Comment: European Conference on Computer Vision (ECCV) 2024

In this paper we hope to identify how the typical or standard artificial intelligence development process encourages or facilitates the creation of racialized technologies. We begin by understanding Sylvia Wynter's definition of the biocentric Man genre and its exclusion of Blackness from humanness. We follow this with outlining what we consider to be the typical steps for developing an AI-based technology, which we have broken down into 6 stages: identifying a problem, development process and management tool selection, dataset development and data processing, model development, deployment and risk assessment, and integration and monitoring. The goal of this paper is to better understand how Wynter's biocentric Man is being represented and reinforced by the technologies we are producing in the AI lifecycle and by the lifecycle itself; we hope to identify ways in which the distinction of Blackness from the "ideal" human leads to perpetual punishment at the hands of these technologies. By deconstructing this development process, we can potentially identify ways in which humans in general have not been prioritized and how those affects are disproportionately affecting marginalized people. We hope to offer solutions that will encourage changes in the AI development cycle., Comment: Published in CSCW '23 Conference Proceedings. 7 pages, 1 figure

Breast cancer is the most common malignancy affecting women worldwide and is notable for its morphologic and biologic diversity, with varying risks of recurrence following treatment. The Oncotype DX Breast Recurrence Score test is an important predictive and prognostic genomic assay for estrogen receptor-positive breast cancer that guides therapeutic strategies; however, such tests can be expensive, delay care, and are not widely available. The aim of this study was to develop a multi-model approach integrating the analysis of whole slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low and high risk. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation, complemented by a logistic regression model that analyzes clinicopathologic data for classification into two risk categories. This method was trained and tested on 993 hematoxylin and eosin-stained whole-slide images of breast cancers with corresponding clinicopathological features that had prior Oncotype DX testing. The model's performance was evaluated using an internal test set of 198 patients from Dartmouth Health and an external test set of 418 patients from the University of Chicago. The multi-model approach achieved an AUC of 0.92 (95 percent CI: 0.88-0.96) on the internal set and an AUC of 0.85 (95 percent CI: 0.79-0.90) on the external cohort. These results suggest that with further validation, the proposed methodology could provide an alternative to assist clinicians in personalizing treatment for breast cancer patients and potentially improving their outcomes., Comment: 16 pages, 4 figures and 4 tables

Background: Electronic health records (EHRs) are a data source for opioid research. Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes. Objectives: Our goals were 1) to identify problematic opioid use from a full range of clinical notes; and 2) to compare the characteristics of patients identified as having problematic opioid use, exclusively documented in clinical notes, to those having documented ICD opioid use disorder diagnostic codes. Materials and Methods: We developed and applied a natural language processing (NLP) tool to the clinical notes of a patient cohort (n=222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. We compared the demographic and clinical characteristics of patients identified only through NLP, to those of patients identified through ICD codes. Results: NLP exclusively identified 57,331 patients; 6,997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, more care encounters, and less likely to be married. Patients in the NLP and ICD groups had substantially elevated comorbidity levels compared to patients not documented as experiencing problematic opioid use. Conclusions: NLP is a feasible approach for identifying problematic opioid use not otherwise recorded by ICD codes. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes., Comment: 17 pages, 4 figures, 8 tables

We consider a sequence of bubble converging minimal hypersurfaces, or H-CMC hypersurfaces, in compact Riemannian manifolds without boundary, of dimension 4, 5, 6 or 7, and prove upper semicontinuity of index plus nullity, for such a bubble converging sequence. This complements the previously known lower semicontinuity of index obtained by Buzano--Sharp, and Bourni--Sharp--Tinaglia. The strategy of our proof is to analyse a weighted eigenvalue problem along our sequence of hypersurfaces. This strategy is inspired by the recent work of Da Lio--Gianocca--Rivi\`{e}re. A key aspect of our proof is making use of a Lorentz--Sobolev inequality to study the behaviour of these weighted eigenfunctions on the neck regions along the sequence, as well as the index and nullity of our non-compact bubbles., Comment: Revised document, in which we have: corrected minor typos, added extra references, shortened preliminary section on bubble convergence, extended various statements on minimal hypersurfaces in Euclidean space to higher dimensions (under appropriate assumptions on their total curvature), and added a final section on Jacobi fields on the higher dimensional catenoid

Topological crystalline insulators (TCIs) are classified by topological invariants defined with respect to the crystalline symmetries of their gapped bulk. The bulk-boundary correspondence then links the topological properties of the bulk to robust observables on the edges, e.g., the existence of robust edge modes or fractional charge. In one dimension, TCIs protected by reflection symmetry have been realized in a variety of systems where each unit cell has spatially distributed degrees of freedom (SDoF). However, these realizations of TCIs face practical challenges stemming from the sensitivity of the resulting edge modes to variations in edge termination and to the local breaking of the protective spatial symmetries by inhomogeneity. Here we demonstrate topologically protected edge states in a mono-atomic, orbital-based TCI that mitigates both of these issues. By collapsing all SDoF within the unit cell to a singular point in space, we eliminate the ambiguity in unit cell definition and hence remove a prominent source of boundary termination variability. The topological observables are also more tolerant to disorder in the orbital energies. To validate this concept, we experimentally realize a lattice of mechanical resonators where each resonator acts as an "atom" that harbors two key orbital degrees of freedom having opposite reflection parity. Our measurements of this system provide direct visualization of the $sp$-hybridization between orbital modes that leads to a non-trivial band inversion in the bulk. Furthermore, as the spatial width of the resonators is tuned, one can drive a transition between a topological and trivial phase. In the future we expect our approach can be extended to realize orbital-based obstructed atomic insulators and TCIs in higher dimensions.

The low-lying structure of $^{13}$Be has remained an enigma for decades. Despite numerous experimental and theoretical studies, large inconsistencies remain. Being both unbound, and one neutron away from $^{14}$Be, the heaviest bound beryllium nucleus, $^{13}$Be is difficult to study through simple reactions with weak radioactive ion beams or more complex reactions with stable-ion beams. Here, we present the results of a study using the $^{12}$Be(d,p)$^{13}$Be reaction in inverse kinematics using a 9.5~MeV per nucleon $^{12}$Be beam from the ISAC-II facility. The solid deuteron target of IRIS was used to achieve an increased areal thickness compared to conventional deuterated polyethylene targets. The Q-value spectrum below -4.4~MeV was analyzed using a Bayesian method with GEANT4 simulations. A three-point angular distribution with the same Q-value gate was fit with a mixture of $s$- and $p$-wave, $s$- and $d$-wave, or pure $p$-wave transfer. The Q-value spectrum was also compared with GEANT simulations obtained using the energies and widths of states reported in four previous works. It was found that our results are incompatible with works that revealed a wide $5/2^+$ resonance but shows better agreement with ones that reported a narrower width., Comment: 10 pages, 5 figures

A unified Chew-Mandelstam description of single-pion photoproduction data, together with pion- and eta-hadroproduction data, has been extended to include measurements carried out over the last decade. We consider photo-decay amplitudes evaluated at the pole with particular emphasis on ng couplings and the influence of weighting on our fits. Both energy-dependent and single-energy analysis (energy-binned data) are considered., Comment: 30 pages, 18 figures, and 14 tables

Exclusive electroproduction reactions provide an access to the structure of excited baryons. To extract electroproduction multipoles encoding this information, the J\"ulich-Bonn-Washington (JBW) analysis framework is extended to the analysis of differential cross sections in $K\Lambda$ electroproduction. This update enlarges the scope of previous coupled-channel analyses of pions and eta mesons, with photoproduction reactions as boundary condition in all analyzed electroproduction reactions. Polarization observables are predicted and compared to recent CLAS data. The comparison shows the relevance of these data to pin down baryon properties., Comment: 21 pages, 13 figures

To date GW170817, produced by a binary neutron star (BNS) merger, is the only gravitational wave event with an electromagnetic (EM) counterpart. It was associated with a prompt short gamma-ray burst (GRB), an optical kilonova, and the afterglow of a structured, off-axis relativistic jet. We model the prospects for future mergers discovered in gravitational waves to produce detectable afterglows. Using a model fit to GW170817, we assume all BNS mergers produce jets with the same parameters, and model the afterglow luminosity for a full distribution of observer angles, ISM densities, and distances. We find that in the LIGO/Virgo/KAGRA O4 run, 30% - 45% of BNS mergers with a well-localized counterpart will have an afterglow detectable with current instrumentation in the X-ray, radio and optical. Without a previously detected counterpart, 10% - 15% will have an afterglow detectable by wide-area radio and optical surveys, compared to only about 5% - 12% of events expected to have bright (on-axis) gamma-ray emission. Most afterglows that are detected will be from off-axis jets. Further in the future, in the A+ era (O5), 40% - 50% of mergers will have afterglows detectable with next-generation X-ray and radio instruments. Future wide-area radio survey instruments, particularly DSA-2000, could detect 40% of afterglows, even without a kilonova counterpart. Finding and monitoring these afterglows will provide valuable insight into the structure and diversity of relativistic jets, the rate at which mergers produce jets, and constrain the angle of the mergers relative to our line of sight., Comment: Accepted by MNRAS

The double-spin-polarization observable $\mathbb{E}$ for $\vec{\gamma}\vec{p}\to p\pi^0$ has been measured with the CEBAF Large Acceptance Spectrometer (CLAS) at photon beam energies $E_\gamma$ from 0.367 to $2.173~\mathrm{GeV}$ (corresponding to center-of-mass energies from 1.240 to $2.200~\mathrm{GeV}$) for pion center-of-mass angles, $\cos\theta_{\pi^0}^{c.m.}$, between -0.86 and 0.82. These new CLAS measurements cover a broader energy range and have smaller uncertainties compared to previous CBELSA data and provide an important independent check on systematics. These measurements are compared to predictions as well as new global fits from The George Washington University, Mainz, and Bonn-Gatchina groups. Their inclusion in multipole analyses will refine our understanding of the single-pion production contribution to the Gerasimov-Drell-Hearn sum rule and improve the determination of resonance properties., Comment: 11 pages, 5 figures

New high-statistics total cross section data for $\gamma p\to J/\psi p$ from the GLUonic EXcitation (GlueX) experiment are fitted in a search for the exotic $P_c(4312)^+$ state observed by the Large Hadron Collider beauty (LHCb) collaboration. The integrated luminosity of this GlueX experiment was about $320~\mathrm{pb^{-1}}$. The fits show that destructive interference involving an $S$-wave resonance and associated non-resonance background produces a sharp dip structure about $75~\mathrm{MeV}$ below the LHCb mass, in the same location as a similar structure is seen in the data. Limitations of the employed model and the need for improved statistics are discussed., Comment: 4 pages, 3 figures, 2 tables