Search

The diversity of California's teaching force continues to lag behind its student population. While students of Color make up 78% of the state's K-12 population, Teachers of Color and Indigenous Teachers (TOCIT) comprise just 34% of the teaching workforce (California Department of Education, 2018), a statistic that has dominated the teacher shortage narrative. Although there is promise in the fact that teacher education program (TEP) enrollment is more diverse than the state's current educator workforce, it's still 27% less diverse than the state's K-12 students. As demand for greater representation increases, so have the initiatives to recruit and retain more racially diverse teachers. In the past few years, California has spent billions in an effort to diversify its teacher workforce, including Assembly Bill 520, which allocated $15 million to be distributed to school districts to develop and implement programs that diversify teaching staffs, and Assembly Bill 130, which appropriated $350 million over the next five years to create or expand Teacher Residency Programs, a pathway that has been shown to recruit and retain higher numbers of TOCIT (California Legislative Information, 2023). This study sets out to (1) explore how current policies, structures, practices, attitudes, and ideologies across the pipeline and profession impede the recruitment and retention of TOCIT, and (2) better understand the racialized experiences and perspectives of pre-service and in-service Teachers of Color and Indigenous Teachers. The following paper is based on a mix of qualitative and quantitative data collected from system leaders in teacher preparation, pre-service teachers, in-service teachers, and former teachers over the course of approximately one year: spring 2021 to summer 2022.

Hot Jupiters are giant planets subject to intense stellar radiation. The physical and chemical properties of their atmosphere makes them the most amenable targets for the atmospheric characterization. In this paper we analyze the photometry collected during the secondary eclipses of the hot Jupiter WASP-3 b by CHEOPS, TESS and Spitzer. Our aim is to characterize the atmosphere of the planet by measuring the secondary eclipse depth in several passbands and constrain the planetary dayside spectrum. Our update of the stellar and planetary properties is consistent with previous works. The analysis of the occultations returns an eclipse depth of 92+-21 ppm in the CHEOPS passband, 83+-27 ppm for TESS and >2000 ppm in the IRAC 1-2-4 Spitzer passbands. Using the eclipse depths in the Spitzer bands we propose a set of likely emission spectra which constrain the emission contribution in the \cheops and TESS passbands to approximately a few dozens of parts per million. This allowed us to measure a geometric albedo of 0.21+-0.07 in the CHEOPS passband, while the TESS data lead to a 95\% upper limit of $\sim$0.2. WASP-3 b belongs to the group of ultra-hot Jupiters which are characterized by low Bond albedo (<0.3+-0.1), as predicted by different atmospheric models. On the other hand, it unexpectedly seems to efficiently recirculate the absorbed stellar energy, unlike similar highly irradiated planets. To explain this inconsistency, we propose that other energy recirculation mechanisms may be at play other than advection (for example, dissociation and recombination of H_2). Another possibility is that the observations in different bandpasses probe different atmospheric layers, making the atmospheric analysis difficult without an appropriate modeling of the thermal emission spectrum of WASP-3 b, which is not feasible with the limited spectroscopic data available to date.

We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learning models to classify them and evaluate their performance. Our study builds on previous work by examining how different machine learning models, parameters, and data preprocessing techniques impact classification accuracy. We test convolutional and recurrent neural networks, as well as six classical algorithms: random forest, support vector machines, naive Bayes, logistic regression, k-nearest neighbors, and eXtreme gradient boosting. All models, except naive Bayes, achieve over 90 per cent accuracy on our dataset. Additionally, we assess the impact of approximating the GW signal using the general relativistic effective potential (GREP) on EOS classification. We find that models trained on GREP data exhibit low classification accuracy. However, normalizing time by the peak signal frequency, which partially compensates for the absence of the time dilation effect in GREP, leads to a notable improvement in accuracy., Comment: Submitted to Machine Learning: Science and Technology

Compact conductively cooled SRF industrial linacs can provide unique parameters of the electron beam for industrial applications. (up to 10MeV, 1MW). For ERDC project we designed normal conducting RF injector with thermal RF gridded gun integrated in first cell of multi-cell cavities. For design of the RF gun we used MICHELLE software to simulate and optimize parameters of the beam. Output file was converted to ASTRA format and most beam dynamic simulations in multi-cell normal conducting cavity and cryomodule were performed by using ASTRA software. For cross-checking we compare results of MICHELLE and AS-TRA in first few cells. At the end of injector beam reach ~250keV energy which allow to trap bunch in acceleration regime without losses in TESLA like 1.3 GHz cavity. Short solenoid at the end of injector allow to regulate transverse beam size in cryomodule to match beam to extraction system and also reduce charge losses in accelerator., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

The thermionic gridded gun is described which generates short electron bunches for further acceleration in a Nb3Sn conduction cooled SRF linac. The gun is built into the first cavity of the 250 keV injector [1] for the 20 kW, 10 MeV, 1.3 GHz CW conduction cooled one-cavity linac. The beam current is 2 mA. The RF gun design is presented as well as the results of perveance measurements, which are in a good agreement with the design parameters. The design of the RF resonator of the gun is presented also. The beam generated by the gun is matched to the injector to provide lack of current interception in the SRF cavity., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

When eliciting forecasts from a group of experts, it is important to reward predictions so that market participants are incentivized to tell the truth. Existing mechanisms partially accomplish this but remain susceptible to groups of experts colluding to increase their expected reward, meaning that no aggregation of predictions can be fully trusted to represent the true beliefs of forecasters. This paper presents two novel scoring mechanisms which elicit truthful forecasts from any group of experts, even if they can collude or access each other's predictions. The key insight of this approach is a randomization component which maintains strict properness but prevents experts from coordinating dishonest reports in advance. These mechanisms are strictly proper and do not admit expected arbitrage, resolving an open question in the field.

In differential privacy (DP) mechanisms, it can be beneficial to release "redundant" outputs, in the sense that a quantity can be estimated by combining different combinations of privatized values. Indeed, this structure is present in the DP 2020 Decennial Census products published by the U.S. Census Bureau. With this structure, the DP output can be improved by enforcing self-consistency (i.e., estimators obtained by combining different values result in the same estimate) and we show that the minimum variance processing is a linear projection. However, standard projection algorithms are too computationally expensive in terms of both memory and execution time for applications such as the Decennial Census. We propose the Scalable Efficient Algorithm for Best Linear Unbiased Estimate (SEA BLUE), based on a two step process of aggregation and differencing that 1) enforces self-consistency through a linear and unbiased procedure, 2) is computationally and memory efficient, 3) achieves the minimum variance solution under certain structural assumptions, and 4) is empirically shown to be robust to violations of these structural assumptions. We propose three methods of calculating confidence intervals from our estimates, under various assumptions. We apply SEA BLUE to two 2010 Census demonstration products, illustrating its scalability and validity., Comment: 21 pages before references and appendices, 35 pages total, 2 figures and 6 tables

K2-24 is a planetary system composed of two transiting low-density Neptunians locked in an almost perfect 2:1 resonance and showing large TTVs, i.e., an excellent laboratory to search for signatures of planetary migration. Previous studies performed with K2, Spitzer and RV data tentatively claimed a significant non-zero eccentricity for one or both planets, possibly high enough to challenge the scenario of pure disk migration through resonant capture. With 13 new CHEOPS light curves (seven of planet -b, six of planet -c), we carried out a global photometric and dynamical re-analysis by including all the available literature data as well. We got the most accurate set of planetary parameters to date for the K2-24 system, including radii and masses at 1% and 5% precision (now essentially limited by the uncertainty on stellar parameters) and non-zero eccentricities $e_b=0.0498_{-0.0018}^{+0.0011}$, $e_c=0.0282_{-0.0007}^{+0.0003}$ detected at very high significance for both planets. Such relatively large values imply the need for an additional physical mechanism of eccentricity excitation during or after the migration stage. Also, while the accuracy of the previous TTV model had drifted by up to 0.5 days at the current time, we constrained the orbital solution firmly enough to predict the forthcoming transits for the next ~15 years, thus enabling an efficient follow-up with top-level facilities such as JWST or ESPRESSO., Comment: 19 pages, 11 figures, 8 tables. Accepted for publication in A&A on September 4, 2024. Typos corrected

Chromium ditelluride, CrTe$_2$, is an attractive candidate van der Waals material for hosting 2D magnetism. However, how the room-temperature ferromagnetism of the bulk evolves as the sample is thinned to the single-layer limit has proved controversial. This, in part, reflects its metastable nature, vs. a series of more stable self-intercalation compounds with higher relative Cr:Te stoichiometry. Here, exploiting a recently-developed method for enhancing nucleation in molecular beam epitaxy growth of transition-metal chalcogenides, we demonstrate the selective stabilisation of high-coverage CrTe$_2$ and Cr$_{2+\varepsilon}$Te$_3$ epitaxial monolayers. Combining X-ray magnetic circular dichroism, scanning tunnelling microscopy, and temperature-dependent angle-resolved photoemission, we demonstrate that both compounds order magnetically with a similar Tc. We find, however, that monolayer CrTe$_2$ forms as an anti-ferromagnetic metal, while monolayer Cr$_{2+\varepsilon}$Te$_3$ hosts an intrinsic ferromagnetic semiconducting state. This work thus demonstrates that control over the self-intercalation of metastable Cr-based chalcogenides provides a powerful route for tuning both their metallicity and magnetic structure, establishing the Cr-Te system as a flexible materials class for future 2D spintronics.

Markerless motion capture offers several benefits over traditional marker-based systems by eliminating the need for physical markers, which are prone to misplacement and artifacts. Utilizing computer vision and deep learning algorithms, markerless systems can directly detect human body landmarks, reducing manual processing and errors associated with marker placement. These systems are adaptable, able to track user-defined features, and practical for real-world applications using consumer-grade devices such as smartphone cameras. This study compares the performance of OpenCap, a markerless motion capture system, with traditional marker-based systems in assessing cycling biomechanics. Ten healthy adults participated in experiments to capture sagittal hip, knee, and ankle kinematics and dynamics using both methods. OpenCap used videos from smartphones and integrated computer vision and musculoskeletal simulations to estimate 3D kinematics. Results showed high agreement between the two systems, with no significant differences in kinematic and kinetic measurements for the hip, knee, and ankle. The correlation coefficients exceeded 0.98, indicating very strong consistency. Errors were minimal, with kinematic errors under 4 degrees and kinetic errors below 5 Nm. This study concludes that OpenCap is a viable alternative to marker-based motion capture, offering comparable precision without extensive setup for hip (flexion/extension), knee (flexion/extension), and ankle (dorsiflexion/plantarflexion) joints. Future work should aim to enhance the accuracy of ankle joint measurements and extend analyses to 3D kinematics and kinetics for comprehensive biomechanical assessments.

We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs exists, that for 3D geometries is largely unexplored. Here, we attempt to bridge this gap by proposing the Geo2Seq, which converts molecular geometries into $SE(3)$-invariant 1D discrete sequences. Geo2Seq consists of canonical labeling and invariant spherical representation steps, which together maintain geometric and atomic fidelity in a format conducive to LMs. Our experiments show that, when coupled with Geo2Seq, various LMs excel in molecular geometry generation, especially in controlled generation tasks.

We use Lie sphere geometry to describe two large categories of generalized Voronoi diagrams that can be encoded in terms of the Lie quadric, the Lie inner product, and polyhedra. The first class consists of diagrams defined in terms of extremal spheres in the space of Lie spheres, and the second class includes minimization diagrams for functions that can be expressed in terms of affine functions on a higher-dimensional space. These results unify and generalize previous descriptions of generalized Voronoi diagrams as convex hull problems. Special cases include classical Voronoi diagrams, power diagrams, order $k$ and farthest point diagrams, Apollonius diagrams, medial axes, and generalized Voronoi diagrams whose sites are combinations of points, spheres and half-spaces. We describe the application of these results to algorithms for computing generalized Voronoi diagrams and find the complexity of these algorithms., Comment: Comments are welcome

We report the first observation of the nearby giant radio galaxy NGC 315 using a global VLBI array consisting of 22 radio antennas located across five continents, including high-sensitivity stations, at 22 GHz. Utilizing the extensive $(u,v)$-coverage provided by the array, coupled with the application of a recently developed super-resolution imaging technique based on the regularized maximum likelihood method, we were able to transversely resolve the NGC 315 jet at parsec scales for the first time. Previously known for its central ridge-brightened morphology at similar scales in former VLBI studies, the jet now clearly exhibits a limb-brightened structure. This finding suggests an inherent limb-brightening that was not observable before due to limited angular resolution. Considering that the jet is viewed at an angle of $\sim50^\circ$, the observed limb-brightening is challenging to reconcile with the magnetohydrodynamic models and simulations, which predict that the Doppler-boosted jet edges should dominate over the non-boosted central layer. The conventional jet model that proposes a fast spine and a slow sheath with uniform transverse emissivity may pertain to our observations. However, in this model, the relativistic spine would need to travel at speeds of $\Gamma\gtrsim6.0-12.9$ along the de-projected jet distance of (2.3-10.8) $\times 10^3$ gravitational radii from the black hole. We propose an alternative scenario that suggests higher emissivity at the jet boundary layer, resulting from more efficient particle acceleration or mass loading onto the jet edges, and consider prospects for future observations with even higher angular resolution., Comment: 25 pages, 12 figures, 1 table, accepted for publication in the Astrophysical Journal Letters

We introduce GAPS (Guitar-Aligned Performance Scores), a new dataset of classical guitar performances, and a benchmark guitar transcription model that achieves state-of-the-art performance on GuitarSet in both supervised and zero-shot settings. GAPS is the largest dataset of real guitar audio, containing 14 hours of freely available audio-score aligned pairs, recorded in diverse conditions by over 200 performers, together with high-resolution note-level MIDI alignments and performance videos. These enable us to train a state-of-the-art model for automatic transcription of solo guitar recordings which can generalise well to real world audio that is unseen during training., Comment: ISMIR 2024

The First Large Absorption Survey in HI (FLASH) is a large-area radio survey for neutral hydrogen in the redshift range 0.41$, and appear to be a mixture of intervening and associated systems. The overall detection rate for HI absorption lines in the Pilot Surveys (0.3 to 0.5 lines per ASKAP field) is a factor of two below the expected value. There are several possible reasons for this, but one likely factor is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper will discuss the host galaxies of the HI absorption systems identified here., Comment: 46 pages, 25 figures, 10 tables. Submitted to PASA

The LHS 6343 system consists of a resolved M-dwarf binary with an evolved, negligibly irradiated brown dwarf, LHS 6343 C, orbiting the primary star. Such brown dwarf eclipsing binaries present rare and unique opportunities to calibrate sub-stellar evolutionary and atmosphere models since mass, radius, temperature and luminosity can be directly measured. We update this brown dwarf's mass (62.6+/-2.2 MJup) and radius (0.788+/-0.043 RJup) using empirical stellar relations and a Gaia DR3 distance. We use Hubble Space Telescope/WFC3 observations of an LHS 6343 C secondary eclipse to obtain a NIR emission spectrum, which matches to a spectral type of T1.5+/-1. We combine this spectrum with existing Kepler and Spitzer/IRAC secondary eclipse photometry to perform atmospheric characterization using the ATMO-2020, Sonora-Bobcat and BT-Settl model grids. ATMO-2020 models with strong non-equilibrium chemistry yield the best fit to observations across all modelled bandpasses while predicting physical parameters consistent with Gaia-dependant analogs. BT-Settl predicts values slightly more consistent with such analogs but offers a significantly poorer fit to the WFC3 spectrum. Finally, we obtain a semi-empirical measurement of LHS 6343 C's apparent luminosity by integrating its observed and modelled spectral energy distribution. Applying knowledge of the system's distance yields a bolometric luminosity of log(Lbol/Lsun) = -4.77+/-0.03 and, applying the Stefan-Boltzmann law for the known radius, an effective temperature of 1303+/-29 K. We also use the ATMO-2020 and Sonora-Bobcat evolutionary model grids to infer an age for LHS 6343 C of 2.86 +0.40-0.33 Gyr and 3.11 +0.50-0.38 Gyr respectively., Comment: Accepted for publication in ApJ

Guitar tablatures enrich the structure of traditional music notation by assigning each note to a string and fret of a guitar in a particular tuning, indicating precisely where to play the note on the instrument. The problem of generating tablature from a symbolic music representation involves inferring this string and fret assignment per note across an entire composition or performance. On the guitar, multiple string-fret assignments are possible for most pitches, which leads to a large combinatorial space that prevents exhaustive search approaches. Most modern methods use constraint-based dynamic programming to minimize some cost function (e.g.\ hand position movement). In this work, we introduce a novel deep learning solution to symbolic guitar tablature estimation. We train an encoder-decoder Transformer model in a masked language modeling paradigm to assign notes to strings. The model is first pre-trained on DadaGP, a dataset of over 25K tablatures, and then fine-tuned on a curated set of professionally transcribed guitar performances. Given the subjective nature of assessing tablature quality, we conduct a user study amongst guitarists, wherein we ask participants to rate the playability of multiple versions of tablature for the same four-bar excerpt. The results indicate our system significantly outperforms competing algorithms., Comment: Reviewed pre-print accepted for publication at ISMIR 2024

We present a rapid stellar core-collapse waveform emulator built using a deep convolutional generative adversarial network (DCGAN). The DCGAN was trained on the Richers \textit{et al.~}\cite{richers:2017} waveform catalogue to learn the structure of rotating stellar core-collapse gravitational-wave signals and generate realistic waveforms. We show that the DCGAN learns the distribution of the training data reasonably well, and that the waveform emulator produces signals that appear to have the key features of core-collapse, bounce, early post-bounce, and ringdown oscillations of the early proto-neutron star. The pre-trained DCGAN can therefore be used as a phenomenological model for rotating stellar core-collapse gravitational-waves., Comment: 12 pages, 7 figures

Using data from nearly 1.2 million Black SAT takers, we estimate the impacts of initially enrolling in an Historically Black College and University (HBCU) on educational, economic, and financial outcomes. We control for the college application portfolio and compare students with similar portfolios and levels of interest in HBCUs and non-HBCUs who ultimately make divergent enrollment decisions -- often enrolling in a four-year HBCU in lieu of a two-year college or no college. We find that students initially enrolling in HBCUs are 14.6 percentage points more likely to earn a BA degree and have 5 percent higher household income around age 30 than those who do not enroll in an HBCU. Initially enrolling in an HBCU also leads to $12,000 more in outstanding student loans around age 30. We find that some of these results are driven by an increased likelihood of completing a degree from relatively broad-access HBCUs and also relatively high-earning majors (e.g., STEM). We also explore new outcomes, such as credit scores, mortgages, bankruptcy, and neighborhood characteristics around age 30.

We present a calculation of the vector-isovector timelike form-factors of the pion and the kaon using lattice quantum chromodynamics. We calculate two-point correlation functions with $m_\pi \sim 280$ MeV, extracting both the finite-volume spectrum and matrix elements for these states created from the vacuum by a vector current. After determining the coupled-channel $\pi\pi, K\overline{K}$ scattering amplitudes, we perform the necessary correction for the significant finite-volume effects present in the current matrix elements, leading to the timelike form-factors. We find these to be dominated by the presence of the $\rho$ resonance, and we extract its decay constant by an analytic continuation of the amplitudes to the resonance pole. In addition, the spacelike pion form-factor is determined on the same lattice configurations, and a dispersive parameterization is used to simultaneously describe the spacelike and elastic timelike regions.

We report the spectroscopic confirmation and fundamental properties of TOI-757 b, a mini-Neptune on a 17.5-day orbit transiting a bright star ($V = 9.7$ mag) discovered by the TESS mission. We acquired high-precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine its mass. We also acquired space-borne transit photometry with the CHEOPS space telescope to place stronger constraints on the planet radius, supported with ground-based LCOGT photometry. WASP and KELT photometry were used to help constrain the stellar rotation period. We also determined the fundamental parameters of the host star. We find that TOI-757 b has a radius of $R_{\mathrm{p}} = 2.5 \pm 0.1 R_{\oplus}$ and a mass of $M_{\mathrm{p}} = 10.5^{+2.2}_{-2.1} M_{\oplus}$, implying a bulk density of $\rho_{\text{p}} = 3.6 \pm 0.8$ g cm$^{-3}$. Our internal composition modeling was unable to constrain the composition of TOI-757 b, highlighting the importance of atmospheric observations for the system. We also find the planet to be highly eccentric with $e$ = 0.39$^{+0.08}_{-0.07}$, making it one of the very few highly eccentric planets among precisely characterized mini-Neptunes. Based on comparisons to other similar eccentric systems, we find a likely scenario for TOI-757 b's formation to be high eccentricity migration due to a distant outer companion. We additionally propose the possibility of a more intrinsic explanation for the high eccentricity due to star-star interactions during the earlier epoch of the Galactic disk formation, given the low metallicity and older age of TOI-757., Comment: Accepted for publication in MNRAS; 26 pages, 14 figures, 6 tables

In modern High Energy Physics (HEP) experiments, triggers perform the important task of selecting, in real time, the data to be recorded and saved for physics analyses. As a result, trigger strategies play a key role in extracting relevant information from the vast streams of data produced at facilities like the Large Hadron Collider (LHC). As the energy and luminosity of the collisions increase, these strategies must be upgraded and maintained to suit the experimental needs. This whitepaper compiled by the SMARTHEP Early Stage Researchers presents a high-level overview and reviews recent developments of triggering practices employed at the LHC. The general trigger principles applied at modern HEP experiments are highlighted, with specific reference to the current trigger state-of-the-art within the ALICE, ATLAS, CMS and LHCb collaborations. Furthermore, a brief synopsis of the new trigger paradigm required by the upcoming high-luminosity upgrade of the LHC is provided.

One way to increase confidence in the outputs of Large Language Models (LLMs) is to support them with reasoning that is clear and easy to check -- a property we call legibility. We study legibility in the context of solving grade-school math problems and show that optimizing chain-of-thought solutions only for answer correctness can make them less legible. To mitigate the loss in legibility, we propose a training algorithm inspired by Prover-Verifier Game from Anil et al. (2021). Our algorithm iteratively trains small verifiers to predict solution correctness, "helpful" provers to produce correct solutions that the verifier accepts, and "sneaky" provers to produce incorrect solutions that fool the verifier. We find that the helpful prover's accuracy and the verifier's robustness to adversarial attacks increase over the course of training. Furthermore, we show that legibility training transfers to time-constrained humans tasked with verifying solution correctness. Over course of LLM training human accuracy increases when checking the helpful prover's solutions, and decreases when checking the sneaky prover's solutions. Hence, training for checkability by small verifiers is a plausible technique for increasing output legibility. Our results suggest legibility training against small verifiers as a practical avenue for increasing legibility of large LLMs to humans, and thus could help with alignment of superhuman models.

We report on frequency tripling of high energy, high repetition rate ns pulses from a cryogenically gas cooled multi-slab Yb:YAG laser system Bivoj/DiPOLE, using a type-I phase matched LBO crystal for second harmonic generation and type-II phase matched LBO crystal for sum frequency generation. We achieved conversion to 343 nm with stable energy of 50 J at repetition rate of 10 Hz and conversion efficiency of 53%.

We present HUP-3D, a 3D multi-view multi-modal synthetic dataset for hand-ultrasound (US) probe pose estimation in the context of obstetric ultrasound. Egocentric markerless 3D joint pose estimation has potential applications in mixed reality based medical education. The ability to understand hand and probe movements programmatically opens the door to tailored guidance and mentoring applications. Our dataset consists of over 31k sets of RGB, depth and segmentation mask frames, including pose related ground truth data, with a strong emphasis on image diversity and complexity. Adopting a camera viewpoint-based sphere concept allows us to capture a variety of views and generate multiple hand grasp poses using a pre-trained network. Additionally, our approach includes a software-based image rendering concept, enhancing diversity with various hand and arm textures, lighting conditions, and background images. Furthermore, we validated our proposed dataset with state-of-the-art learning models and we obtained the lowest hand-object keypoint errors. The dataset and other details are provided with the supplementary material. The source code of our grasp generation and rendering pipeline will be made publicly available., Comment: https://conferences.miccai.org/2024/en/

The PHENIX experiment measured the centrality dependence of two-pion Bose-Einstein correlation functions in $\sqrt{s_{_{NN}}}=200$~GeV Au$+$Au collisions at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The data are well represented by L\'evy-stable source distributions. The extracted source parameters are the correlation-strength parameter $\lambda$, the L\'evy index of stability $\alpha$, and the L\'evy-scale parameter $R$ as a function of transverse mass $m_T$ and centrality. The $\lambda(m_T)$ parameter is constant at larger values of $m_T$, but decreases as $m_T$ decreases. The L\'evy scale parameter $R(m_T)$ decreases with $m_T$ and exhibits proportionality to the length scale of the nuclear overlap region. The L\'evy exponent $\alpha(m_T)$ is independent of $m_T$ within uncertainties in each investigated centrality bin, but shows a clear centrality dependence. At all centralities, the L\'evy exponent $\alpha$ is significantly different from that of Gaussian ($\alpha=2$) or Cauchy ($\alpha=1$) source distributions. Comparisons to the predictions of Monte-Carlo simulations of resonance-decay chains show that in all but the most peripheral centrality class (50%-60%), the obtained results are inconsistent with the measurements, unless a significant reduction of the in-medium mass of the $\eta'$ meson is included. In each centrality class, the best value of the in-medium $\eta'$ mass is compared to the mass of the $\eta$ meson, as well as to several theoretical predictions that consider restoration of $U_A(1)$ symmetry in hot hadronic matter., Comment: 401 authors from 75 institutions, 20 pages, 15 figures, 2 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

It has been known for many years that methods inspired by string theory, such as the worldline formalism, allow one to write down integral representations that combine large numbers of Feynman diagrams of different topologies. However, to make this fact useful for state-of-the-art calculations one has to confront non-standard integration problems where neither the known integration techniques for Feynman diagrams nor algebraic manipulation programs are of much help. Here I will give a progress report on this long-term project focussing on photon amplitudes at one and two loops, in vacuum and in external fields., Comment: 13 pages, 3 figures, talk given by Christian Schubert at Loops and Legs in Quantum Field Theory (LL2024), 14-19 April 2024, Wittenberg, Germany

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting. Advancements in ubiquitous technology and natural language processing make it possible to realize more objective, real-time assessment of clinical interactions and in turn provide more timely feedback to clinicians about their communication effectiveness. In this paper, we propose CommSense, a computational sensing framework that combines smartwatch audio and transcripts with natural language processing methods to measure selected ``best-practice'' communication metrics captured by wearable devices in the context of palliative care interactions, including understanding, empathy, presence, emotion, and clarity. We conducted a pilot study involving N=40 clinician participants, to test the technical feasibility and acceptability of CommSense in a simulated clinical setting. Our findings demonstrate that CommSense effectively captures most communication metrics and is well-received by both practicing clinicians and student trainees. Our study also highlights the potential for digital technology to enhance communication skills training for healthcare providers and students, ultimately resulting in more equitable delivery of healthcare and accessible, lower cost tools for training with the potential to improve patient outcomes., Comment: 30 pages, accepted by ACM CSCW 2024, to appear in PACM HCI

This paper presents a novel approach to level set estimation for any function/simulation with an arbitrary number of continuous inputs and arbitrary numbers of continuous responses. We present a method that uses existing data from computer model simulations to fit a Gaussian process surrogate and use a newly proposed Markov Chain Monte Carlo technique, which we refer to as Smoothed Approximate Bayesian Computation to sample sets of parameters that yield a desired response, which improves on ``hard-clipped" versions of ABC. We prove that our method converges to the correct distribution (i.e. the posterior distribution of level sets, or probability contours) and give results of our method on known functions and a dam breach simulation where the relationship between input parameters and responses of interest is unknown. Two versions of S-ABC are offered based on: 1) surrogating an accurately known target model and 2) surrogating an approximate model, which leads to uncertainty in estimating the level sets. In addition, we show how our method can be extended to multiple responses with an accompanying example. As demonstrated, S-ABC is able to estimate a level set accurately without the use of a predefined grid or signed distance function., Comment: 14 pages, 12 figures

Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development. This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait. The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models. These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models., Comment: arXiv admin note: substantial text overlap with arXiv:2312.00169