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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.

Disturbances to the climate system, both natural and anthropogenic, have far reaching impacts that are not always easy to identify or quantify using traditional climate science analyses or causal modeling techniques. In this paper, we develop a novel technique for discovering and ranking the chain of spatio-temporal downstream impacts of a climate source, referred to herein as a source-impact pathway, using Random Forest Regression (RFR) and SHapley Additive exPlanation (SHAP) feature importances. Rather than utilizing RFR for classification or regression tasks (the most common use case for RFR), we propose a fundamentally new RFR-based workflow in which we: (i) train random forest (RF) regressors on a set of spatio-temporal features of interest, (ii) calculate their pair-wise feature importances using the SHAP weights associated with those features, and (iii) translate these feature importances into a weighted pathway network (i.e., a weighted directed graph), which can be used to trace out and rank interdependencies between climate features and/or modalities. We adopt a tiered verification approach to verify our new pathway identification methodology. In this approach, we apply our method to ensembles of data generated by running two increasingly complex benchmarks: (i) a set of synthetic coupled equations, and (ii) a fully coupled simulation of the 1991 eruption of Mount Pinatubo in the Philippines performed using a modified version 2 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv2). We find that our RFR feature importance-based approach can accurately detect known pathways of impact for both test cases.

The goal of this project is to construct an estimator for the masses of supermassive black holes in active galactic nuclei (AGNs) based on the broad Halpha emission line. We make use of published reverberation mapping data. We remeasure all Halpha time lags from the original data as we find that often the reverberation measurements are improved by detrending the light curves. We produce mass estimators that require only the Halpha luminosity and the width of the Halpha emission line as characterized by either the FWHM or the line dispersion. It is possible, on the basis of a single spectrum covering the Halpha emission line, to estimate the mass of the central supermassive black hole in AGNs, taking into account all three parameters believed to affect mass measurement: luminosity, line width, and Eddington ratio. The typical formal accuracy in such estimates is of order 0.2-0.3 dex., Comment: 15 pages, 9 figures, submitted to A&A

A seemingly simple, yet widely applicable subroutine in automated train scheduling is the insertion of a new train path to a timetable in a railway network. We believe it to be the first step towards a new train-rerouting framework in case of large disturbances or maintenance works. Other applications include handling ad-hoc requests and modifying train paths upon request from railway undertakings. We propose a fast and scalable path-insertion algorithm based on dynamic programming that is able to output multiple suitable paths. Our algorithm uses macroscopic data and can run on railway networks with any number of tracks. We apply the algorithm on the line from G\"oteborg S\"aven\"as to the Norwegian border at Kornsj\"o. For a time window of seven hours, we obtain eight suitable paths for a freight train within 0.3 seconds after preprocessing.

Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait (FoG) critical. However, existing symptom monitoring technologies are power-hungry, rely on extensive amounts of labeled data, and operate in controlled settings. These shortcomings limit real-world deployment of the technology. This work presents LIFT-PD, a computationally-efficient self-supervised learning framework for real-time FoG detection. Our method combines self-supervised pre-training on unlabeled data with a novel differential hopping windowing technique to learn from limited labeled instances. An opportunistic model activation module further minimizes power consumption by selectively activating the deep learning module only during active periods. Extensive experimental results show that LIFT-PD achieves a 7.25% increase in precision and 4.4% improvement in accuracy compared to supervised models while using as low as 40% of the labeled training data used for supervised learning. Additionally, the model activation module reduces inference time by up to 67% compared to continuous inference. LIFT-PD paves the way for practical, energy-efficient, and unobtrusive in-home monitoring of PD patients with minimal labeling requirements., Comment: 11 pages

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

Entangled polymers are an important class of materials for their toughness, processability, and functionalizability. However, physically detailed modeling of highly entangled polymers can prove challenging, especially as one considers additional layers of physical or chemical complexity. To address these challenges, we present a series of generalizations for the useful "non-stretching" approximation, using asymptotic methods to formalize and expand the analysis. First, we rederive the popular non-stretching Rolie Poly model and extend it second order, reintroducing effects from finite chain stretching. Then, we extended the non-stretching framework to other special cases, accounting for flow-induced disentanglement, polydispersity, and reversible scission reactions. Benchmark calculations confirm that non-stretching models derived via systematic asymptotic methods provide excellent and improvable approximations for the rheology of well-entangled polymer constitutive equations with finite-time stretch relaxation dynamics., Comment: 19 pages, 2 figures

The non-stretching approximation of polymer rheology simplifies a constitutive equation but fundamentally changes its behavior in fast flows, and the circumstances under which fast flows emerge cannot always be predicted a-priori. In this paper, we consider two simple flows for which shear rates are bounded in the original RP model but diverge to infinity in finite time for the non-stretching RP model. The disparity between the full and non-stretching models can be resolved by extending the non-stretching approximation to second order in accuracy., Comment: 9 pages, 5 figures

This study introduces a novel multiagent ensemble method powered by LLMs to address a key challenge in ML - data labeling, particularly in large-scale EHR datasets. Manual labeling of such datasets requires domain expertise and is labor-intensive, time-consuming, expensive, and error-prone. To overcome this bottleneck, we developed an ensemble LLMs method and demonstrated its effectiveness in two real-world tasks: (1) labeling a large-scale unlabeled ECG dataset in MIMIC-IV; (2) identifying social determinants of health (SDOH) from the clinical notes of EHR. Trading off benefits and cost, we selected a pool of diverse open source LLMs with satisfactory performance. We treat each LLM's prediction as a vote and apply a mechanism of majority voting with minimal winning threshold for ensemble. We implemented an ensemble LLMs application for EHR data labeling tasks. By using the ensemble LLMs and natural language processing, we labeled MIMIC-IV ECG dataset of 623,566 ECG reports with an estimated accuracy of 98.2%. We applied the ensemble LLMs method to identify SDOH from social history sections of 1,405 EHR clinical notes, also achieving competitive performance. Our experiments show that the ensemble LLMs can outperform individual LLM even the best commercial one, and the method reduces hallucination errors. From the research, we found that (1) the ensemble LLMs method significantly reduces the time and effort required for labeling large-scale EHR data, automating the process with high accuracy and quality; (2) the method generalizes well to other text data labeling tasks, as shown by its application to SDOH identification; (3) the ensemble of a group of diverse LLMs can outperform or match the performance of the best individual LLM; and (4) the ensemble method substantially reduces hallucination errors. This approach provides a scalable and efficient solution to data-labeling challenges., Comment: 27 pages, 13 figures. Under journal review

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. This paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

The goal of the FNAL E989 experiment is to measure the muon magnetic anomaly to unprecedented accuracy and precision at the Fermi National Accelerator Laboratory. To meet this goal, the time and space averaged magnetic environment in the muon storage volume must be known to better than 70 ppb. A new pulsed proton nuclear magnetic resonance (NMR) magnetometer was designed and built at the University of Washington, Seattle to track the temporal stability of the 1.45T magnetic field in the muon storage ring at this precision. It consists of an array of 378 petroleum jelly based NMR probes that are embedded in the walls of muon storage ring vacuum chambers and custom electronics built with readily available modular radio frequency (RF) components. We give NMR probe construction details and describe the functions of the custom electronic subsystems. The excellent performance metrics of the magnetometer are discussed where after 8 years of operation, the median single shot resolution of the array of probes remains at 11 ppb., Comment: 19 pages, 20 figures

Global localization is a fundamental capability required for long-term and drift-free robot navigation. However, current methods fail to relocalize when faced with significantly different viewpoints. We present ROMAN (Robust Object Map Alignment Anywhere), a robust global localization method capable of localizing in challenging and diverse environments based on creating and aligning maps of open-set and view-invariant objects. To address localization difficulties caused by feature-sparse or perceptually aliased environments, ROMAN formulates and solves a registration problem between object submaps using a unified graph-theoretic global data association approach that simultaneously accounts for object shape and semantic similarities and a prior on gravity direction. Through a set of challenging large-scale multi-robot or multi-session SLAM experiments in indoor, urban and unstructured/forested environments, we demonstrate that ROMAN achieves a maximum recall 36% higher than other object-based map alignment methods and an absolute trajectory error that is 37% lower than using visual features for loop closures. Our project page can be found at https://acl.mit.edu/ROMAN/., Comment: 8 pages, 7 figures

LIFT-PD is an innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer. It minimizes the reliance on large labeled datasets by applying a Differential Hopping Windowing Technique (DHWT) to address imbalanced data during training. Additionally, an Opportunistic Inference Module is used to reduce energy consumption by activating the model only during active movement periods. Extensive testing on publicly available datasets showed that LIFT-PD improved precision by 7.25% and accuracy by 4.4% compared to supervised models, while using 40% fewer labeled samples and reducing inference time by 67%. These findings make LIFT-PD a highly practical and energy-efficient solution for continuous, in-home monitoring of PD patients., Comment: 2pages, 2 figures, submitted in BHI'24

Periorbital segmentation and distance prediction using deep learning allows for the objective quantification of disease state, treatment monitoring, and remote medicine. However, there are currently no reports of segmentation datasets for the purposes of training deep learning models with sub mm accuracy on the regions around the eyes. All images (n=2842) had the iris, sclera, lid, caruncle, and brow segmented by five trained annotators. Here, we validate this dataset through intra and intergrader reliability tests and show the utility of the data in training periorbital segmentation networks. All the annotations are publicly available for free download. Having access to segmentation datasets designed specifically for oculoplastic surgery will permit more rapid development of clinically useful segmentation networks which can be leveraged for periorbital distance prediction and disease classification. In addition to the annotations, we also provide an open-source toolkit for periorbital distance prediction from segmentation masks. The weights of all models have also been open-sourced and are publicly available for use by the community., Comment: 10 pages, 7 figures, 3 Tables

Periorbital distances and features around the eyes and lids hold valuable information for disease quantification and monitoring of surgical and medical intervention. These distances are commonly measured manually, a process that is both subjective and highly time-consuming. Here, we set out to developed three deep-learning methods for segmentation and periorbital distance prediction, and also evaluate the utility of periorbital distances for disease classification. The MAE of our deep learning predicted distances was less than or very close to the error observed between trained human annotators. We compared our models to the current state-of-the-art (SOTA) method for periorbital distance prediction and found that our methods outperformed SOTA on all of our datasets on all but one periorbital measurement. We also show that robust segmentation can be achieved on diseased eyes using models trained on open-source, healthy eyes, and that periorbital distances have can be used as high-quality features in downstream classification models. Leveraging segmentation networks as intermediary steps in classification has broad implications for increasing the generalizability of classification models in ophthalmic plastic and craniofacial surgery by avoiding the out-of-distribution problem observed in traditional convolutional neural networks., Comment: 20 pages, 10 figures, 9 tables

This work reports the fabrication and characterization of a NiOx/\b{eta}-Ga2O3 heterojunction diode (HJD) that uses a metallic nickel (Ni) target to deposit NiOx layers via reactive RF magnetron sputtering and lift-off processing with >3 kV breakdown voltage, record-low reverse current leakage under high reverse bias, and high junction electric fields (>3.34 MV/cm). The heterojunction diodes are fabricated via bilayer NiOx sputtering followed by self-aligned mesa-etching for field-termination on both large (1-mm2) and small area (100-{\mu}m diameter) devices. The HJD exhibits a ~135 A/cm2 forward current density at 5 V with a rectifying ratio of ~1010. The minimum differential specific on-resistance is measured to be 17.26 m{\Omega} cm2. The breakdown voltage on 100-{\mu}m diameter pads was measured to be greater than 3 kV with a noise floor-level reverse leakage current density (10-8~10-6 A/cm2) until 3 kV, accomplishing a parallel-plane junction electric field to be at least 3.34 MV/cm at 3 kV with a power figure of merit (PFOM) >0.52 GW/cm2. Temperature-dependent forward current density-voltage (J-V) measurements are performed from room temperature (25 C) to 200 C which showed a temperature coefficient of resistance ({\alpha}) equaling 1.56, higher than that of \b{eta}-Ga2O3 Schottky barrier diodes (SBDs), indicating potential conductivity degradation within NiOx at elevated temperatures., Comment: 6 pages, 5 figures, APL Journal

The Dark Energy Spectroscopic Instrument (DESI) collaboration measured a tight relation between the Hubble constant ($H_0$) and the distance to the Coma cluster using the fundamental plane (FP) relation of the deepest, most homogeneous sample of early-type galaxies. To determine $H_0$, we measure the distance to Coma by several independent routes each with its own geometric reference. We measure the most precise distance to Coma from 12 Type Ia Supernovae (SNe Ia) in the cluster with mean standardized brightness of $m_B^0=15.712\pm0.041$ mag. Calibrating the absolute magnitude of SNe Ia with the HST distance ladder yields $D_{\textrm Coma}=98.5\pm2.2$ Mpc, consistent with its canonical value of 95--100 Mpc. This distance results in $H_0=76.5 \pm 2.2$ km/s/Mpc from the DESI FP relation. Inverting the DESI relation by calibrating it instead to the Planck+$\Lambda$CDM value of $H_0=67.4$ km/s/Mpc implies a much greater distance to Coma, $D_{\textrm Coma}=111.8\pm1.8$ Mpc, $4.6\sigma$ beyond a joint, direct measure. Independent of SNe Ia, the HST Key Project FP relation as calibrated by Cepheids, Tip of the Red Giant Branch from JWST, or HST NIR surface brightness fluctuations all yield $D_{\textrm Coma}<$ 100 Mpc, in joint tension themselves with the Planck-calibrated route at $>3\sigma$. From a broad array of distance estimates compiled back to 1990, it is hard to see how Coma could be located as far as the Planck+$\Lambda$CDM expectation of $>$110 Mpc. By extending the Hubble diagram to Coma, a well-studied location in our own backyard whose distance was in good accord well before the Hubble Tension, DESI indicates a more pervasive conflict between our knowledge of local distances and cosmological expectations. We expect future programs to refine the distance to Coma and nearer clusters to help illuminate this new, local window on the Hubble Tension., Comment: v2 - team name fixed

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

We present a weak lensing analysis of the galaxy cluster Abell 2390 at z = 0.23 using second moment shape measurements made in 411 short 60s exposures. The exposures are obtained in three broadband photometric filters (g, r, i) using WIYN-ODI. Shape measurement in individual exposures is done using a moment matching algorithm. Forced measurement is used when the moment matching algorithm fails to converge at low signal to noise ratio (SNR). The measurements made in individual images are combined using inverse error weight to obtain accurate shape of sources and hence recover shear. We use PhoSim simulations to validate shear measurements recovered by our pipeline. We find the mass of Abell 2390 is in agreement with previously published results. We also find the E-Mode maps show filamentary structures consistent with baryonic structures and recovers most clusters/groups of galaxies found using Optical and X-Ray data. Thus we demonstrate the feasibility of using Weak Lensing to map large scale structure of the universe. We also find the central portion of the cluster has a bimodal mass distribution and the relative orientation of the peaks are similar to X-Ray. We discuss earlier research on this galaxy cluster and show that a late stage merger accounts for all the observed data., Comment: Accepted in ApJ

Let $\mathfrak{p} \subset V$ be a polytope and $\xi \in V_{\mathbb{C}}^*$. We obtain an expression for $I(\mathfrak{p}; \alpha) := \int_{\mathfrak{p}} e^{\langle \alpha, x \rangle} dx$ as a sum of meromorphic functions in $\alpha \in V^*_{\mathbb{C}}$ parametrized by the faces $\mathfrak{f}$ of $\mathfrak{p}$ on which $\langle \xi, x \rangle$ is constant. Each term only depends on the local geometry of $\mathfrak{p}$ near $\mathfrak{f}$ (and on $\xi$) and is holomorphic at $\alpha = \xi$. When $\langle \xi, \cdot \rangle$ is only constant on the vertices of $\mathfrak{p}$ our formula reduces to Brion's formula. Suppose $\mathfrak{p}$ is a rational polytope with respect to a lattice $\Lambda$. We obtain an expression for $S(\mathfrak{p}; \alpha) := \sum_{\lambda \in \mathfrak{p} \cap \Lambda} e^{\langle \alpha, \lambda \rangle}$ as a sum of meromorphic functions parametrized by the faces $\mathfrak{f}$ on which $e^{\langle \xi, x \rangle} = 1$ on a finite index sublattice of $\text{lin}(\mathfrak{f}) \cap \Lambda$. Each term only depends on the local geometry of $\mathfrak{p}$ near $\mathfrak{f}$ (and on $\xi$ and $\Lambda$) and is holomorphic at $\alpha = \xi$. When $e^{\langle \xi, \cdot \rangle} \neq 1$ at any non-zero lattice point on a line through the origin parallel to an edge of $\mathfrak{p}$, our formula reduces to Brion's formula, and when $\xi = 0$, it reduces to the Ehrhart quasi-polynomial. Our formulas are particularly useful for understanding how $I(\mathfrak{p}(h); \xi)$ and $S(\mathfrak{p}(h); \xi)$ vary in a family of polytopes $\mathfrak{p}(h)$ with the same normal fan. When considering dilates of a fixed polytope, our formulas may be viewed as polytopal analogues of Laplace's method and the method of stationary phase. Such expressions naturally show up in analysis on symmetric spaces and affine buildings., Comment: 44 pages; fixed lots of typos, improved exposition, added a figure

The use of in-situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these sensors provide near real-time data for science, the data are prone to technical anomalies that can undermine the trustworthiness of the data and the accuracy of statistical inferences, particularly in spatial and temporal analyses. Here we propose a framework for detecting anomalies in sensor data recorded in stream networks, which takes advantage of spatial and temporal autocorrelation to improve detection rates. The proposed framework involves the implementation of effective data imputation to handle missing data, alignment of time-series to address temporal disparities, and the identification of water quality events. We explore the effectiveness of a suite of state-of-the-art statistical methods including posterior predictive distributions, finite mixtures, and Hidden Markov Models (HMM). We showcase the practical implementation of automated anomaly detection in near-real time by employing a Bayesian recursive approach. This demonstration is conducted through a comprehensive simulation study and a practical application to a substantive case study situated in the Herbert River, located in Queensland, Australia, which flows into the Great Barrier Reef. We found that methods such as posterior predictive distributions and HMM produce the best performance in detecting multiple types of anomalies. Utilizing data from multiple sensors deployed relatively near one another enhances the ability to distinguish between water quality events and technical anomalies, thereby significantly improving the accuracy of anomaly detection. Thus, uncertainty and biases in water quality reporting, interpretation, and modelling are reduced, and the effectiveness of subsequent management actions improved.

In this paper we explore independently for the first time three chemical potentials (baryon $\mu_B$, charged $\mu_Q$, and strange $\mu_S$) in the Chiral mean-field (CMF) model. We designed and implemented \texttt{CMF++}, a new version of the CMF model rewritten in \texttt{C++} that is optimized, modular, and well-documented. \texttt{CMF++} has been integrated into the MUSES Calculation Engine as a free and open source software module. The runtime improved in more than 4 orders of magnitude across all 3 chemical potentials, when compared to the legacy code. Here we focus on the zero temperature case and study stable, as well as metastable and unstable, vacuum, hadronic, and quark phases, showing how phase boundaries vary with the different chemical potentials. Due to the significant numerical improvements in \texttt{CMF++}, we can calculate for the first time high-order susceptibilities within the CMF framework to study the properties of the quark deconfinement phase transition. We found phases of matter that include a light hadronic phase, strangeness-dominated hadronic phase, and quark deconfinement within our $\mu_B$, $\mu_S$, $\mu_Q$ phase space. The phase transitions are of first, second (quantum critical point), and third order between these phases and we even identified a tricritical point., Comment: 63 pages, 22 figures

We report on the successful numerical evolution of the compactified hyperboloidal initial value problem in general relativity using generalized harmonic gauge. We work in spherical symmetry, using a massless scalar field to drive dynamics. Our treatment is based on the dual-foliation approach, proceeding either by using a height function or by solving the eikonal equation to map between frames. Both are tested here with a naive implementation and with hyperboloidal layers. We present a broad suite of numerical evolutions, including pure gauge perturbations, constraint violating and satisfying data with and without scalar field matter. We present calculations of spacetimes with a regular center. For black hole spacetimes we use excision to remove part of the black hole interior. We demonstrate both pointwise and norm convergence at the expected rate of our discretization. We present evolutions in which the scalar field collapses to form a black hole. Evolving nonlinear scalar field perturbations of the Schwarzschild spacetime, we recover the expected quasinormal frequencies and tail decay rates from linear theory., Comment: 21 pages, 10 figures

Plasma detachment involves interactions of the plasma with impurities and neutral particles, leading to significant losses of plasma power, momentum, and particles. Accurate mapping of plasma emissivity in the divertor and X-point region is essential for assessing the relationship between particle flux and radiative detachment. The recently validated InfraRed Video Bolometer (IRVB) diagnostic, in MAST-U enables this mapping with higher spatial resolution than more established methods like resistive bolometers. In previous preliminary work, the evolution of radiative detachment was characterised in L-mode (power entering the scrape-off layer, PSOL ~0.4MW). With a conventional divertor the inner leg consistently detached ahead of the outer leg, and radiative detachment preceded particle flux detachment. This work presents results also from the third MAST-U experimental campaign, fuelled from the low field side instead of the high field side, including Ohmic and beam heated L-mode shots (with a power exiting the core up to PSOL ~1-1.5MW). The radiation peak moves upstream from the target at lower upstream densities than the ion target flux roll-over (typically considered the detachment onset), while the inner leg detaches before the outer one. The movement of the radiation is in partial agreement with the expectations from the DLS model, predicting a sudden shift from the target to the X-point. The energy confinement is found to be related to detachment, but there seems to be some margin between the radiation on the inner leg reaching the X-point and confinement being affected, a beneficial characteristic if it could be extrapolated to future reactors. For increasing PSOL the particle flux roll over is almost unaffected, while radiative detachment occurs at higher density in both legs, but much higher on the outer, suggesting an uneven distribution of the power exiting the core., Comment: Submitted to Nuclear Materials and Energy

After shipment to the Daresbury Lab and return to Fermilab, the prototype HB650 cryomodule underwent another phase of 2K RF testing to ascertain any performance issues that may have arisen from the transport of the cryomodule. While measurements taken at room temperature after the conclusion of shipment indicated that there were no negative impacts on cavity alignment, beamline vacuum, or cavity frequency, testing at 2K was required to validate other aspects such as tuner operation, cavity coupling, cryogenic system integrity, and cavity performance. Results of this latest round of limited 2K testing will be presented., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

It is often said that well-entangled and fast-breaking living polymers (such as wormlike micelles) exhibit a single relaxation time in their reptation dynamics, but the full story is somewhat more complicated. Understanding departures from single-Maxwell behavior is crucial for fitting and interpreting experimental data, but in some limiting cases numerical methods of solving living polymer models can struggle to produce reliable predictions/interpretations. In this work, we develop an analytic solution for the shuffling model of living polymers. The analytic solution is a converging infinite series, and it converges fastest in the fast-breaking limit where other methods can struggle., Comment: 11 pages, 2 figures

Hybrid organic-inorganic semiconductors with strong electron-phonon interactions provide a programmable platform for developing a variety of electronic, optoelectronic, and quantum materials by controlling these interactions. However, in current hybrid semiconductors, such as halide perovskites, anharmonic vibrations with rapid dephasing hinder the ability to coherently manipulate phonons. Here, we report the observation of long-lived coherent phonons in lead organic chalcogenides (LOCs), a new family of hybrid two-dimensional semiconductors. These materials feature harmonic phonon dynamics despite distorted lattices, combining long phonon dephasing times with tunable semiconducting properties. Dephasing time as long as 75 ps at 10 K, with up to 500 cycles of phonon oscillation between scattering events, was observed, corresponding to a dimensionless harmonicity parameter more than an order of magnitude larger than that of halide perovskites. The phonon dephasing time is significantly influenced by anharmonicity and centrosymmetry, both of which can be tuned through the design of the organic ligands thanks to the direct bonding between the organic and inorganic motifs. This research opens new opportunities for the manipulation of electronic properties with coherent phonons in hybrid semiconductors.

Fusion is a potentially transformational energy technology, which promises limitless clean energy. Yet, it requires continued scientific and technological development to realize its potential. The conditions necessary for fusion energy gain in terms of the product of plasma pressure $P$ and confinement time $\tau$ have been known for many decades. An underappreciated fact is that pulsed magnetic fusion has demonstrated $P \tau$ performance on par with laser-driven ICF and tokamaks despite receiving only a small fraction of investment relative to those concepts. In light of this demonstrated performance, well-established scaling relations, and opportunities for further innovations, here we advocate for pulsed magnetic fusion as the most attractive path towards commercialization of fusion energy.

At the low-redshift end ($z<0.05$) of the Hubble diagram with Type Ia Supernovae (SNe Ia), the contribution to Hubble residual scatter from peculiar velocities is of similar size to that due to the standardization of the SN Ia light curve. A way to improve the redshift measurement of the SN host galaxy is to utilize the average redshift of the galaxy group, effectively averaging over small-scale/intracluster peculiar velocities. One limiting factor is the fraction of SN host galaxies in galaxy groups, previously found to be 30% using (relatively incomplete) magnitude-limited galaxy catalogs. Here, we do the first analysis of N-body simulations to predict this fraction, finding $\sim$66% should have associated groups and group averaging should improve redshift precision by $\sim$120 km s$^{-1}$. Furthermore, using spectroscopic data from the Anglo-Australian Telescope, we present results from the first pilot program to evaluate whether or not 23 previously unassociated SN Ia hosts belong in groups. We find that 91% of these candidates can be associated with groups, consistent with predictions from simulations given the sample size. Combining with previously assigned SN host galaxies in Pantheon+, we demonstrate improvement in Hubble residual scatter equivalent to 145 km s$^{-1}$, also consistent with simulations. For new and upcoming low-$z$ samples from, for example, the Zwicky Transient Facility and the Rubin Observatory's Legacy Survey of Space and Time, a separate follow-up program identifying galaxy groups of SN hosts is a highly cost-effective way to enhance their constraining power., Comment: 17 pages, 12 figures. Submitted to ApJ

In this article, we investigate the in-situ growth of Al$_2$O$_3$ on $\beta$-Ga$_2$O$_3$ using metal-organic chemical vapor deposition (MOCVD) at a high temperature of 800{\deg}C. The Al$_2$O$_3$ is grown within the same reactor as the $\beta$-Ga$_2$O$_3$, employing trimethylaluminum (TMAl) and O$_2$ as precursors without breaking the vacuum. We characterize the shallow and deep-level traps through stressed capacitance-voltage (C-V) and photo-assisted C-V methods. The high-temperature deposited dielectric demonstrates an impressive breakdown field of approximately 10 MV/cm. Furthermore, we evaluate the reliability and lifetime of the dielectrics using time-dependent dielectric breakdown (TDDB) measurements. By modifying the dielectric deposition process to include a high-temperature (800{\deg}C) thin interfacial layer and a low-temperature (600{\deg}C) bulk layer, we report a 10-year lifetime under a stress field of 3.5 MV/cm along a breakdown field of 7.8 MV/cm.

Understanding how people behave in strategic settings--where they make decisions based on their expectations about the behavior of others--is a long-standing problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on these data predicts people's choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioral model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior.

Pd$_3$Bi$_2$Se$_2$ is a rare realization of a superconducting metal with a non-zero $Z_2$ topological invariant. We report the growth of high-quality single crystals of layered Pd$_3$Bi$_2$Se$_2$ with a superconducting transition at $T_c$ ~ 0.80 K and upper critical fields of ~10 mT and ~5 mT for the in-plane and out-of-plane directions, respectively. Our density functional theory (DFT) calculations reveal three pairs of doubly degenerate bands crossing the Fermi level, all displaying clear three-dimensional dispersion consistent with the overall low electronic anisotropy (<2). The multiband electronic nature of Pd$_3$Bi$_2$Se$_2$ is evident in magneto-transport measurements, yielding a sign-changing Hall resistivity at low temperatures. The magnetoresistance is non-saturating and follows Kohler's scaling rule. We interpret the magneto-transport data in terms of open orbits that are revealed in the DFT-calculated Fermi surface. de Haas-van Alphen (dHvA) oscillation measurements using torque magnetometry on single crystals yield four frequencies for out-of-plane fields: $F_\alpha = (150 \pm 26)$T, $F_\beta = (293 \pm 10)$T, $F_\gamma = (375 \pm 20)$T, and $F_\eta = (1017 \pm 12)$T, with the low frequency dominating the spectrum. Through the measurement of angular dependent dHvA oscillations and DFT calculations, we identify the $F_\alpha$ frequency with an approximately ellipsoidal electron pocket centered on the $L_2$ point of the Brillouin zone. Lifshitz-Kosevich analysis of the dHvA oscillations reveals a small cyclotron effective mass: $m^* = (0.11 \pm 0.02) m_0$ and a nontrivial Berry phase for the dominant orbit. The presence of nontrivial topology in a bulk superconductor positions Pd$_3$Bi$_2$Se$_2$ as a potential candidate for exploring topological superconductivity., Comment: 30 pages, 5 figures