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1. Impact of synthesis method on the structure and function of high entropy oxides

Author: González-Rivas, Mario U., Aamlid, Solveig S., Rutherford, Megan R., Freese, Jessica, Sutarto, Ronny, Chen, Ning, Villalobos-Portillo, Edgar E., Castillo-Michel, Hiram, Kim, Minu, Takagi, Hidenori, Green, Robert J., and Hallas, Alannah M.
Subjects: Condensed Matter - Materials Science, Condensed Matter - Disordered Systems and Neural Networks
Abstract: The term sample dependence describes the troublesome tendency of nominally equivalent samples to exhibit different physical properties. High entropy oxides (HEOs) are a class of materials where sample dependence has the potential to be particularly profound due to their inherent chemical complexity. In this work, we prepare a spinel HEO of identical nominal composition by five distinct methods, spanning a range of thermodynamic and kinetic conditions: solid state, high pressure, hydrothermal, molten salt, and combustion syntheses. By structurally characterizing these five samples across all length scales with a variety of x-ray methods, we find that while the average structure is unaltered, the samples vary significantly in their local structures and their microstructures. The most profound differences are observed at intermediate length scales, both in terms of crystallite morphology and cation homogeneity. As revealed by x-ray fluorescence microscopy ideal cation homogeneity is achieved only in the case of combustion synthesis. These structural differences in turn significantly alter the observed functional properties, which we demonstrate via characterization of their magnetic response. While ferrimagnetic order is retained across all five samples, the sharpness of the transition, the size of the saturated moment, and the coercivity all show marked variations with synthesis method. We conclude that the chemical flexibility inherent to HEOs is complemented by strong synthesis method dependence, providing another axis along which to optimize these materials for a wide range of applications., Comment: 14 pages, 5 figures
Published: 2024
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2. Pressure-Induced Phase Transitions in Bilayer La$_3$Ni$_2$O$_7$

Author: Xu, Mingyu, Jose, Greeshma C., Rutherford, Aya, Wang, Haozhe, Zhang, Stephen, Cava, Robert J., Zhou, Haidong, Bi, Wenli, and Xie, Weiwei
Subjects: Condensed Matter - Materials Science, Condensed Matter - Superconductivity
Abstract: La$_3$Ni$_2$O$_7$ exists in two polymorphs: an unconventional structure with alternating layers of single- and triple-layered nickel-oxygen octahedra, and a classical double-layered Ruddlesden-Popper phase. In this study, we report the growth of single crystals of classical double-layered La$_3$Ni$_2$O$_7$ using the floating zone method. Structural characterization under pressures up to 15.4 GPa reveals a gradual transition from orthorhombic to tetragonal symmetry near 12 GPa. Additionally, we present pressure and field-dependent electrical resistance measurements under pressures as high as 27.4 GPa, from which we construct a phase diagram., Comment: 13+5 pages, 4+2 figures
Published: 2024

3. Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models

Author: Kim, Dahyun, Lee, Sukyung, Kim, Yungi, Rutherford, Attapol, and Park, Chanjun
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: The rapid advancement of large language models (LLMs) has highlighted the need for robust evaluation frameworks that assess their core capabilities, such as reasoning, knowledge, and commonsense, leading to the inception of certain widely-used benchmark suites such as the H6 benchmark. However, these benchmark suites are primarily built for the English language, and there exists a lack thereof for under-represented languages, in terms of LLM development, such as Thai. On the other hand, developing LLMs for Thai should also include enhancing the cultural understanding as well as core capabilities. To address these dual challenge in Thai LLM research, we propose two key benchmarks: Thai-H6 and Thai Cultural and Linguistic Intelligence Benchmark (ThaiCLI). Through a thorough evaluation of various LLMs with multi-lingual capabilities, we provide a comprehensive analysis of the proposed benchmarks and how they contribute to Thai LLM development. Furthermore, we will make both the datasets and evaluation code publicly available to encourage further research and development for Thai LLMs.
Published: 2024

4. Probing fermionic asymmetric dark matter cores using global neutron star properties

Author: Rutherford, Nathan, Prescod-Weinstein, Chanda, and Watts, Anna
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, High Energy Physics - Phenomenology, Nuclear Theory
Abstract: It is possible for asymmetric dark matter (ADM) to accumulate in neutron star interiors and affect their global properties. Considering the effects of this accumulation, neutron star mass-radius measurements can deliver new insights into the cold dense matter equation of state (EoS). In this paper, we employ Bayesian parameter estimation using real and synthetic neutron star mass-radius data to infer constraints on the combined baryonic matter and fermionic ADM EoS, where the fermionic ADM forms a core in the neutron star interior. Using currently available mass-radius data, we find that the lower bound of the ratio between ADM effective self-repulsion strength ($g_\chi/m_\phi$) and particle mass ($m_\chi$) can be constrained at the 68% (95%) credible level to $10^{-6.59}$ ($10^{-7.36}$). We also find that, if neutron star mass-radius measurement uncertainties are reduced to the 2% level, the constraints on lower bound on the ratio of $g_\chi/m_\phi$ to $m_\chi$ can be improved to $10^{-6.5}$ and $10^{-7.29}$ at the 68% and 95% credible levels, respectively. However, all other combinations, of $m_\chi$, $g_\chi$, and the ADM mass-fraction, $F_\chi$, (i.e., the ratio of the gravitational ADM mass to the gravitational mass of the neutron star) are unconstrained. Furthermore, in the pressure-energy density and mass-radius planes, the inferences which include the possibility of fermionic ADM cores are nearly identical with the inferences that neglect fermionic ADM for $F_\chi \leq 1.7\%$ and neutron star mass-radius uncertainties $\geq 2\%$. Therefore, we find that neutron star mass-radius measurements can constrain ADM in some scenarios and that the presence of ADM in neutron star cores is as equally consistent with current data as the absence of ADM., Comment: 26 pages, 8 figures
Published: 2024

5. Real-time fetAl brain and placental T2* mapping at 0.55T low-field MRI (RAT)

Author: Verdera, Jordina Aviles, Silva, Sara Neves, Tomi-Tricot, Raphael, Hall, Megan, Story, Lisa, Malik, Shaihan J, Hajnal, Joseph V, Rutherford, Mary A, and Hutter, Jana
Subjects: Physics - Medical Physics
Abstract: Purpose: To provide real-time quantitative organ-specific information - specifically placental and brain T2* - to allow optimization of the MR examination to the individual patient. Methods: A FIRE-based real-time setup segmenting placenta and fetal brain in real-time, performing T2* fitting and analysis and calculation of the centile was implemented. A nn-UNet were trained and tested on 2989 datasets for the fetal brain and a second one trained on 210 datasets for the placenta for automatic segmentation. T2* normal curves were obtained from 106 cases and prospective evaluation was performed on 10 cases between 35 and 39 weeks GA. Results: Quantitative brain and placental T2* maps and centiles were available in all prospective cases within 30 seconds. The robustness of the method was shown with intra-scan repeats (mean difference 1.04+/-12.39 ms for fetal brain and -3.15+/-8.88 ms for placenta) and direct validation with vendor-processed offline results (mean difference 1.62+/-4.33 ms for fetal brain and 0.16+/-6.19 ms for placenta). Discussion and Conclusion: Real-time available organ-specific quantitative information enables more personalized MR examinations, selection of the most pertinent sequences and thus the promise of reduced recalls and specific insights into tissue properties. The here enabled placental T2*, demonstrated in multiple recent studies to be a biomarker sensitive to a range of pregnancy complications, and fetal brain T2* will be explored in further studies in pregnancies with pre-eclampsia, growth restriction as a way of enabling future MR-guided fetal interventions.
Published: 2024

6. A construction for regular-graph designs

Author: Forbes, Anthony and Rutherford, Carrie
Subjects: Mathematics - Combinatorics, 05B25
Abstract: A regular-graph design is a block design for which a pair $\{a,b\}$ of distinct points occurs in $\lambda+1$ or $\lambda$ blocks depending on whether $\{a,b\}$ is or is not an edge of a given $\delta$-regular graph. Our paper describes a specific construction for regular-graph designs with $\lambda = 1$ and block size $\delta + 1$. We show that for $\delta \in \{2,3\}$, certain necessary conditions for the existence of such a design with $n$ points are sufficient, with two exceptions in each case and two possible exceptions when $\delta = 3$. We also construct designs of orders 105 and 117 for connected 4-regular graphs., Comment: 18 pages. The paper will be submitted to a journal. Some basic definitions have been lifted from other papers by the same authors
Published: 2024

7. The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal

Author: Xie, Huiyuan, Steffek, Felix, de Faria, Joana Ribeiro, Carter, Christine, and Rutherford, Jonathan
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: This paper explores the intersection of technological innovation and access to justice by developing a benchmark for predicting case outcomes in the UK Employment Tribunal (UKET). To address the challenge of extensive manual annotation, the study employs a large language model (LLM) for automatic annotation, resulting in the creation of the CLC-UKET dataset. The dataset consists of approximately 19,000 UKET cases and their metadata. Comprehensive legal annotations cover facts, claims, precedent references, statutory references, case outcomes, reasons and jurisdiction codes. Facilitated by the CLC-UKET data, we examine a multi-class case outcome prediction task in the UKET. Human predictions are collected to establish a performance reference for model comparison. Empirical results from baseline models indicate that finetuned transformer models outperform zero-shot and few-shot LLMs on the UKET prediction task. The performance of zero-shot LLMs can be enhanced by integrating task-related information into few-shot examples. We hope that the CLC-UKET dataset, along with human annotations and empirical findings, can serve as a valuable benchmark for employment-related dispute resolution.
Published: 2024

8. Strengthening the Teacher Workforce to Support Multilingual Learners: A Tool for State Educational Agencies

Author: National Comprehensive Center (NCC), Westat, Inc., Caroline E. Parker, Anne Partika, and Sara Rutherford-Quach
Abstract: One in 10 public school students in the United States are designated as English learners (ELs), an increase of more than 25 percent over the past 2 decades (National Center for Education Statistics, 2022). In nearly half of states, the proportion of students designated as ELs has more than doubled since 2000. Further, several states--such as Kentucky, Mississippi, North Dakota, and South Carolina--are serving substantial numbers of ELs for the first time in recent history (NCES, 2022). With these increases, many state educational agencies (SEAs) are grappling with shortages of certified teachers to provide bilingual instruction or English language development (ELD) support to their linguistically diverse student populations. This tool helps state leaders identify specific needs to recruit, retain, and support teachers who serve multilingual learners (MLs).
Published: 2024

9. No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery

Author: Rutherford, Alexander, Beukman, Michael, Willi, Timon, Lacerda, Bruno, Hawes, Nick, and Foerster, Jakob
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Robotics
Abstract: What data or environments to use for training to improve downstream performance is a longstanding and very topical question in reinforcement learning. In particular, Unsupervised Environment Design (UED) methods have gained recent attention as their adaptive curricula promise to enable agents to be robust to in- and out-of-distribution tasks. This work investigates how existing UED methods select training environments, focusing on task prioritisation metrics. Surprisingly, despite methods aiming to maximise regret in theory, the practical approximations do not correlate with regret but with success rate. As a result, a significant portion of an agent's experience comes from environments it has already mastered, offering little to no contribution toward enhancing its abilities. Put differently, current methods fail to predict intuitive measures of ``learnability.'' Specifically, they are unable to consistently identify those scenarios that the agent can sometimes solve, but not always. Based on our analysis, we develop a method that directly trains on scenarios with high learnability. This simple and intuitive approach outperforms existing UED methods in several binary-outcome environments, including the standard domain of Minigrid and a novel setting closely inspired by a real-world robotics problem. We further introduce a new adversarial evaluation procedure for directly measuring robustness, closely mirroring the conditional value at risk (CVaR). We open-source all our code and present visualisations of final policies here: https://github.com/amacrutherford/sampling-for-learnability.
Published: 2024

10. AutOmatic floW planning for fetaL MRI (OWL)

Author: Silva, Sara Neves, Woodgate, Tomas, McElroy, Sarah, Cleri, Michela, Clair, Kamilah St, Verdera, Jordina Aviles, Payette, Kelly, Uus, Alena, Story, Lisa, Lloyd, David, Rutherford, Mary A, Hajnal, Joseph V, Pushparajah, Kuberan, and Hutter, Jana
Subjects: Physics - Medical Physics
Abstract: Two subsequent deep learning networks, one localizing the fetal chest and one identifying a set of landmarks on a coronal whole-uterus balanced steady-state free precession scan, were trained on 167 and 71 fetal datasets across field strengths, acquisition parameters, and gestational ages and implemented in a real-time setup. Next, a phase-contrast sequence was modified to use the identified landmarks for planning. The OWL pipeline was evaluated retrospectively in 10 datasets and prospectively in 7 fetal subjects (gestational ages between 36+3 and 39+3 weeks). The prospective cases were additionally manually planned to enable direct comparison both qualitatively, by scoring the planning quality, and quantitatively, by comparing the indexed flow measurements. OWL enabled real-time fully automatic planning of the 2D phase-contrast scans in all but one of the prospective participants. The fetal body localization achieved an overall Dice score of 0.94+-0.05 and the cardiac landmark detection accuracy was 5.77+-2.91 mm for the descending aorta, 4.32+-2.44 mm for the spine, and 4.94+-3.82 mm for the umbilical vein. For the prospective cases, overall planning quality was 2.73/4 for the automated scans, compared to 3.0/4 for manual planning, and the flow quantitative evaluation showed a mean difference of -1.8% (range -14.2% to 14.9%) by comparing the indexed flow measurements obtained from gated automatic and manual acquisitions. Real-time automated planning of 2D phase-contrast MRI was effectively accomplished for 2 major vessels of the fetal vasculature. While demonstrated here on 0.55T, the achieved method has wider implications, and training across multiple field strengths enables generalization. OWL thereby presents an important step towards extending access to this modality beyond specialised centres., Comment: 12 pages
Published: 2024

11. To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling

Author: Rutherford, Saige, Wolfers, Thomas, Fraza, Charlotte, Harrnet, Nathaniel G., Beckmann, Christian F., Ruhe, Henricus G., and Marquand, Andre F.
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society
Abstract: Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent potential clinical risk. How the demographics of the reference class influence clinical interpretation of deviations is unknown. Using normative modeling, a method for building reference classes, we evaluate the fairness (racial bias) in reference models of structural brain images that are widely used in psychiatry and neurology. We test whether including race in the model creates fairer models. We predict self-reported race using the deviation scores from three different reference class normative models, to better understand bias in an integrated, multivariate sense. Across all of these tasks, we uncover racial disparities that are not easily addressed with existing data or commonly used modeling techniques. Our work suggests that deviations from the norm could be due to demographic mismatch with the reference class, and assigning clinical meaning to these deviations should be done with caution. Our approach also suggests that acquiring more representative samples is an urgent research priority.
Published: 2024

12. Ising-type quantum spin liquid state in PrMgAl$_{11}$O$_{19}$

Author: Li, N., Rutherford, A., Wang, Y. Y., Liang, H., Li, Q. J., Zhang, Z. J., Wang, H., Xie, W., Zhou, H. D., and Sun, X. F.
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: We have grown single crystals of PrMgAl$_{11}$O$_{19}$, an ideal triangular-lattice antiferromagnet, and performed magnetic susceptibility, specific heat and thermal conductivity measurements at low temperatures. The main results are as follows: (i) The temperature-dependent susceptibility shows a negligible in-plane response and the isothermal magnetization curves confirm the easy axis along the $c$ axis. (ii) The specific heat measurements reveal the absence of long-range magnetic order down to 60 mK, and the power-law temperature dependence indicates the existence of the gapless magnetic excitations in system. (iii) The ultralow-temperature thermal conductivity exhibits negligibly small residual term ($\kappa_0/T$) and strong spin-phonon scattering effect, suggesting that the spin excitations are also involved. Our results further demonstrate that PrMgAl$_{11}$O$_{19}$ is a rare quantum spin liquid candidate with Ising-like anisotropy., Comment: 8 pages, 5 figures
Published: 2024

13. Magnetic properties of RE2O2CO3 (RE = Pr, Nd, Gd, Tb, Dy, Ho, Er, Yb) with a rare earth-bilayer of triangular lattice

Author: Rutherford, Aya N., Xing, Chengkun, Zhou, Haidong, Huang, Qing, and Calder, Stuart
Subjects: Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science
Abstract: Polycrystalline samples of RE2O2CO3 (RE = Pr, Nd, Gd, Tb, Dy, Ho, Er, and Yb) with a unique rare-earth bilayer of triangular lattice were synthesized and studied by DC and AC magnetic susceptibility. Data reveals various magnetic ground states including (i) a nonmagnetic ground state for the Pr sample; (ii) long range magnetic ordering for the Nd, Gd, Tb, Dy, Ho, and Er samples. Besides the Gd sample, they exhibit field-induced spin state transitions. More interestingly, the series spin state transitions in the Nd and Dy samples could be attributed to the field-induced up-up-down (UUD) spin structure. Neutron powder diffraction (NPD) measurements of the Er sample suggest a spiral spin structure below its TN; and (iii) a short-range ordering for the Yb sample. The disrupted inter-layer interaction due to the shift of Yb3+ ions within the bilayer prevents long range magnetic ordering down to 30 mK and makes it another Yb-related triangular lattice antiferromagnet that has the potential to realize a quantum spin liquid state.
Published: 2024

14. Constraining the dense matter equation of state with new NICER mass-radius measurements and new chiral effective field theory inputs

Author: Rutherford, Nathan, Mendes, Melissa, Svensson, Isak, Schwenk, Achim, Watts, Anna L., Hebeler, Kai, Keller, Jonas, Prescod-Weinstein, Chanda, Choudhury, Devarshi, Raaijmakers, Geert, Salmi, Tuomo, Timmerman, Patrick, Vinciguerra, Serena, Guillot, Sebastien, and Lattimer, James M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, Nuclear Experiment, Nuclear Theory
Abstract: Pulse profile modeling of X-ray data from NICER is now enabling precision inference of neutron star mass and radius. Combined with nuclear physics constraints from chiral effective field theory ($\chi$EFT), and masses and tidal deformabilities inferred from gravitational wave detections of binary neutron star mergers, this has lead to a steady improvement in our understanding of the dense matter equation of state (EOS). Here we consider the impact of several new results: the radius measurement for the 1.42$\,M_\odot$ pulsar PSR J0437$-$4715 presented by Choudhury et al. (2024), updates to the masses and radii of PSR J0740$+$6620 and PSR J0030$+$0451, and new $\chi$EFT results for neutron star matter up to 1.5 times nuclear saturation density. Using two different high-density EOS extensions -- a piecewise-polytropic (PP) model and a model based on the speed of sound in a neutron star (CS) -- we find the radius of a 1.4$\,M_\odot$ (2.0$\,M_\odot$) neutron star to be constrained to the 95% credible ranges $12.28^{+0.50}_{-0.76}\,$km ($12.33^{+0.70}_{-1.34}\,$km) for the PP model and $12.01^{+0.56}_{-0.75}\,$km ($11.55^{+0.94}_{-1.09}\,$km) for the CS model. The maximum neutron star mass is predicted to be $2.15^{+0.14}_{-0.16}\,$$M_\odot$ and $2.08^{+0.28}_{-0.16}\,$$M_\odot$ for the PP and CS model, respectively. We explore the sensitivity of our results to different orders and different densities up to which $\chi$EFT is used, and show how the astrophysical observations provide constraints for the pressure at intermediate densities. Moreover, we investigate the difference $R_{2.0} - R_{1.4}$ of the radius of 2$\,M_\odot$ and 1.4$\,M_\odot$ neutron stars within our EOS inference., Comment: 23 pages, 15 figures; published version
Published: 2024
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15. A NICER View of the Nearest and Brightest Millisecond Pulsar: PSR J0437$\unicode{x2013}$4715

Author: Choudhury, Devarshi, Salmi, Tuomo, Vinciguerra, Serena, Riley, Thomas E., Kini, Yves, Watts, Anna L., Dorsman, Bas, Bogdanov, Slavko, Guillot, Sebastien, Ray, Paul S., Reardon, Daniel J., Remillard, Ronald A., Bilous, Anna V., Huppenkothen, Daniela, Lattimer, James M., Rutherford, Nathan, Arzoumanian, Zaven, Gendreau, Keith C., Morsink, Sharon M., and Ho, Wynn C. G.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, Nuclear Theory
Abstract: We report Bayesian inference of the mass, radius and hot X-ray emitting region properties - using data from the Neutron Star Interior Composition ExploreR (NICER) - for the brightest rotation-powered millisecond X-ray pulsar PSR J0437$\unicode{x2013}$4715. Our modeling is conditional on informative tight priors on mass, distance and binary inclination obtained from radio pulsar timing using the Parkes Pulsar Timing Array (PPTA) (Reardon et al. 2024), and we use NICER background models to constrain the non-source background, cross-checking with data from XMM-Newton. We assume two distinct hot emitting regions, and various parameterized hot region geometries that are defined in terms of overlapping circles; while simplified, these capture many of the possibilities suggested by detailed modeling of return current heating. For the preferred model identified by our analysis we infer a mass of $M = 1.418 \pm 0.037$ M$_\odot$ (largely informed by the PPTA mass prior) and an equatorial radius of $R = 11.36^{+0.95}_{-0.63}$ km, each reported as the posterior credible interval bounded by the 16% and 84% quantiles. This radius favors softer dense matter equations of state and is highly consistent with constraints derived from gravitational wave measurements of neutron star binary mergers. The hot regions are inferred to be non-antipodal, and hence inconsistent with a pure centered dipole magnetic field., Comment: 27 pages, 16 figures, + appendices. Accepted in ApJL. Data files and reproduction package available in Zenodo
Published: 2024
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16. Power handling in a highly-radiative negative triangularity pilot plant

Author: Miller, M. A., Arnold, D., Wigram, M., Nelson, A. O., Witham, J., Rutherford, G., Choudhury, H., Cummings, C., Paz-Soldan, C., and Whyte, D. G.
Subjects: Physics - Plasma Physics
Abstract: This work explores power handling solutions for high-field, highly-radiative negative triangularity (NT) reactors based around the MANTA concept \cite{rutherford_manta_2024}. The divertor design is kept as simple as possible, opting for a standard divertor with standard leg length. FreeGS is used to create an equilibrium for the boundary region, prioritizing a short outer leg length of only $\sim$50 cm ($\sim$40\% of the minor radius). The UEDGE code package is used for the boundary plasma solution, to track plasma temperatures and fluxes to the divertor targets. It is found that for $P_\mathrm{SOL}$ = 25 MW and $n_\mathrm{sep} = 0.96 \times 10^{20}$ m$^{-3}$, conditions consistent with initial core transport modeling, little additional power mitigation is necessary. For external impurity injection of just 0.13\% Ne, the peak heat flux density at the more heavily loaded outer targets falls to 7.8 MW/m$^{2}$, while the electron temperature $T_\mathrm{e}$ remains just under 5 eV. Scans around the parameter space reveal that even at densities lower than in the primary operating scenario, $P_\mathrm{SOL}$ can be increased up to 50 MW, so long as a slightly higher fraction of extrinsic radiator is used. With less than 1\% neon (Ne) impurity content, the divertor still experiences less than 10 MW/m$^{2}$ at the outer target. Design of the plasma-facing components includes a close-fitting vacuum vessel with a tungsten inner surface as well as FLiBe-carrying cooling channels fashioned into the VV wall directly behind the divertor targets. For the seeded heat flux profile, Ansys Fluent heat transfer simulations estimate that the outer target temperature remains at just below 1550\degree C. Initial scoping of advanced divertor designs shows that for an X-divertor, detachment of the outer target becomes much simpler, and plasma fluxes to the targets drop considerably with only 0.01\% Ne content.
Published: 2024

17. Learning Job Title Representation from Job Description Aggregation Network

Author: Laosaengpha, Napat, Tativannarat, Thanit, Piansaddhayanon, Chawan, Rutherford, Attapol, and Chuangsuwanich, Ekapol
Subjects: Computer Science - Computation and Language
Abstract: Learning job title representation is a vital process for developing automatic human resource tools. To do so, existing methods primarily rely on learning the title representation through skills extracted from the job description, neglecting the rich and diverse content within. Thus, we propose an alternative framework for learning job titles through their respective job description (JD) and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss to account for the bidirectional relationship between the job title and its description. We evaluated the performance of our method on both in-domain and out-of-domain settings, achieving a superior performance over the skill-based approach., Comment: to be published in Findings of the Association for Computational Linguistics: ACL 2024
Published: 2024

18. ThaiCoref: Thai Coreference Resolution Dataset

Author: Trakuekul, Pontakorn, Leong, Wei Qi, Polpanumas, Charin, Sawatphol, Jitkapat, Tjhi, William Chandra, and Rutherford, Attapol T.
Subjects: Computer Science - Computation and Language
Abstract: While coreference resolution is a well-established research area in Natural Language Processing (NLP), research focusing on Thai language remains limited due to the lack of large annotated corpora. In this work, we introduce ThaiCoref, a dataset for Thai coreference resolution. Our dataset comprises 777,271 tokens, 44,082 mentions and 10,429 entities across four text genres: university essays, newspapers, speeches, and Wikipedia. Our annotation scheme is built upon the OntoNotes benchmark with adjustments to address Thai-specific phenomena. Utilizing ThaiCoref, we train models employing a multilingual encoder and cross-lingual transfer techniques, achieving a best F1 score of 67.88\% on the test set. Error analysis reveals challenges posed by Thai's unique linguistic features. To benefit the NLP community, we make the dataset and the model publicly available at http://www.github.com/nlp-chula/thai-coref .
Published: 2024

19. First was to sit down and bring our minds together. A qualitative study on safer conception decision-making among HIV sero-different couples in Zimbabwe.

Author: Gitome, Serah, Musara, Petina, Chitukuta, Miria, Mhlanga, Felix, Mateveke, Bismark, Chirenda, Thandiwe, Mgodi, Nyaradzo, Mutero, Prisca, Matubu, Allen, Chareka, Gift, Chasakara, Charles, Murombedzi, Caroline, Makurumure, Tinei, Smith-Hughes, Carolyn, Bukusi, Elizabeth, Cohen, Craig, Shiboski, Stephen, Darbes, Lynae, Rutherford, George, Chirenje, Z, and Brown, Joelle
Subjects: HIV sero-different, PrEP, Zimbabwe, child-bearing, couples, decision-making, safer conception, semen washing, sero-discordant, vaginal insemination, Humans, Zimbabwe, Male, Female, Decision Making, Qualitative Research, Adult, HIV Infections, Fertilization, Pilot Projects, Pregnancy, HIV Seropositivity, Interviews as Topic, Communication
Abstract: Decision-making on childbearing and safer conception use in HIV sero-different couples involves an intricate balance of individual desires and perceived HIV acquisition risk. This paper addresses an important knowledge gap regarding HIV sero-different couples considerations and the relationship and power dynamics involved when deciding to use a safer conception method. Between February and June 2019, we conducted semi-structured in-depth interviews among 14 men and 17 women, representing 17 couples, who exited the SAFER study - a pilot study assessing the feasibility, acceptability and cost-effectiveness of a safer conception programme for HIV sero-different couples in Zimbabwe. All couples in SAFER were provided with a choice of safer conception methods and were followed for up to 12 months of pregnancy attempts and 3 months following pregnancy. While couples generally perceived their safer conception discussions to be easy and consensus-driven, the decision-making process also involved complex gender dynamics and trade-offs in relationship power, which resulted in differing interpretations of what constituted a joint or shared couple decision. Participants regarded effective couple communication as an essential component of and precursor to good safer conception conversations and requested additional training in couple communication. Couples relied on information from healthcare providers to kickstart their safer conception discussions. Safer conception programmes should address relationship power imbalances, promote effective couple communication and offer healthcare provider support to enable HIV sero-different couples to make informed choices about conception in a manner that upholds their safety and reproductive autonomy.
Published: 2024

20. Role Models in Action through YouTube Videos for Engineering Community College Students

Author: Hye Rin Lee, Teomara Rutherford, Paul Hanselman, Fernando Rodriguez, Kevin F. Ramirez, and Jacquelynne S. Eccles
Abstract: Community colleges provide broad access to a college degree due to their less expensive tuition, greater course time offerings, and more open admission policies compared to four-year universities as reported (Juszkiewicz, 2015). These institutions have great potential to diversify who chooses STEM, such as engineering. Such diverse representation is important, because students from different backgrounds can contribute new perspectives that foster creative and innovative approaches to solving problems. However, approximately 40% of the students who enter community college in the U.S. leave before graduation (Xu & Jaggars, 2011). Harnessing the power of social media to reduce attrition, we tested a pilot intervention in which community college students in engineering courses completed a reflection exercise after watching YouTube videos of former engineering students from similar backgrounds who successfully transferred to a four-year college. Quasi-experimental analyses (N = 537) show that students in the intervention received higher engineering course grades and had greater odds of enrolling in an engineering course in the subsequent term compared to those who were not in the intervention. Additionally, we find support for the hypothesis that the intervention has larger benefits for women. Results suggest that hearing from other students about their engineering experience can be a useful tool for improving community college students' choices and performance.
Published: 2024
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21. We chose PrEP because I wanted to be sure that this child my wife was going to conceive was indeed mine. Factors influencing the choice of safer conception methods and experiences with its use: a qualitative study among HIV sero-discordant couples in Zimbabwe.

Author: Brown, Joelle, Musara, Petina, Gitome, Serah, Chitukuta, Miria, Mataveke, Bismark, Chirenda, Thandiwe, Mgodi, Nyaradzo, Mutero, Prisca, Matubu, Allen, Chareka, Gift, Chasakara, Charles, Murombedzi, Caroline, Makurumure, Tinei, Hughes, Carolyn, Bukusi, Elizabeth, Cohen, Craig, Shiboski, Stephen, Darbes, Lynae, Rutherford, George, Chirenje, Z, and Mhlanga, Felix
Subjects: ART, HIV prevention, HIV-discordant couples, LMIC, PrEP, Safer conception, Semen washing, Vaginal insemination, Zimbabwe, sub-Saharan Africa, Humans, Zimbabwe, Male, Female, Adult, HIV Infections, Qualitative Research, Pre-Exposure Prophylaxis, Fertilization, Choice Behavior, Interviews as Topic, Middle Aged, Pilot Projects, Young Adult, HIV Seropositivity, Pregnancy
Abstract: BACKGROUND: Safer conception services are needed to minimize HIV transmission among HIV sero-discordant couples desiring pregnancy. Few studies have evaluated the choices couples make when they are offered multiple safer conception methods or real-world method acceptability. This paper addresses an important knowledge gap regarding factors that influence the choice of safer conception methods, couples actual experiences using safer conception methods, and why some couples switch safer conception methods. METHODS: Between February and June 2019, we conducted semi-structured in-depth interviews among 14 men and 17 women, representing 17 couples who exited the SAFER study-a pilot safer conception study for HIV sero-discordant couples in Zimbabwe that offered couples a choice of ART with monthly viral load monitoring (ART/VL), oral PrEP, vaginal insemination, and semen washing. All couples in SAFER had used at least two safer conception methods. RESULTS: We found that safer conception method choice often centered around a desire for intimacy, condomless sex, and certainty in the conception process, particularly for men. Method-related attributes such as familiarity, perceived ease of use, side effects, and perceived level of effectiveness in preventing HIV and achieving pregnancy influenced method choice, switching, and satisfaction. Concerns were expressed about each safer conception method and couples were willing to try different methods until they found method(s) that worked for them. The majority of participants reported having positive experiences using safer conception, especially those using ART/VL + PrEP, citing that they were able to attempt pregnancy for the first time with peace of mind and experienced joy and satisfaction from being able to achieve pregnancy safely. CONCLUSIONS: The differences in method preferences and experiences voiced by participants in this study and in other studies from the region point to the importance of having a variety of safer conception options in the service delivery package and addressing concerns about paternity, intimacy, and method-related attributes to enable HIV sero-discordant couples to safely achieve their reproductive goals.
Published: 2024

22. Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study

Author: Chen, Zhenlin, Abbadi, Sahar H El, Sherwin, Evan D, Burdeau, Philippine M, Rutherford, Jeffrey S, Chen, Yuanlei, Zhang, Zhan, and Brandt, Adam R
Subjects: Control Engineering, Mechatronics and Robotics, Engineering, Climate Action
Published: 2024

23. Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation

Author: Abbadi, Sahar H El, Chen, Zhenlin, Burdeau, Philippine M, Rutherford, Jeffrey S, Chen, Yuanlei, Zhang, Zhan, Sherwin, Evan D, and Brandt, Adam R
Subjects: Environmental Sciences, Environmental Management, Climate Action, Methane, Aircraft, Greenhouse Gases, Environmental Monitoring, Climate Change, Air Pollutants, remote sensing, controlled release, methane, oil and gas, climate change, energy
Abstract: Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.
Published: 2024

24. MANTA: A Negative-Triangularity NASEM-Compliant Fusion Pilot Plant

Author: MANTA Collaboration, Rutherford, G., Wilson, H. S., Saltzman, A., Arnold, D., Ball, J. L., Benjamin, S., Bielajew, R., de Boucaud, N., Calvo-Carrera, M., Chandra, R., Choudhury, H., Cummings, C., Corsaro, L., DaSilva, N., Diab, R., Devitre, A. R., Ferry, S., Frank, S. J., Hansen, C. J., Jerkins, J., Johnson, J. D., Lunia, P., van de Lindt, J., Mackie, S., Maris, A. D., Mandell, N. R., Miller, M. A., Mouratidis, T., Nelson, A. O., Pharr, M., Peterson, E. E., Rodriguez-Fernandez, P., Segantin, S., Tobin, M., Velberg, A., Wang, A. M., Wigram, M., Witham, J., Paz-Soldan, C., and Whyte, D. G.
Subjects: Physics - Plasma Physics
Abstract: The MANTA (Modular Adjustable Negative Triangularity ARC-class) design study investigated how negative-triangularity (NT) may be leveraged in a compact, fusion pilot plant (FPP) to take a ``power-handling first" approach. The result is a pulsed, radiative, ELM-free tokamak that satisfies and exceeds the FPP requirements described in the 2021 National Academies of Sciences, Engineering, and Medicine report ``Bringing Fusion to the U.S. Grid". A self-consistent integrated modeling workflow predicts a fusion power of 450 MW and a plasma gain of 11.5 with only 23.5 MW of power to the scrape-off layer (SOL). This low $P_\text{SOL}$ together with impurity seeding and high density at the separatrix results in a peak heat flux of just 2.8 MW/m$^{2}$. MANTA's high aspect ratio provides space for a large central solenoid (CS), resulting in ${\sim}$15 minute inductive pulses. In spite of the high B fields on the CS and the other REBCO-based magnets, the electromagnetic stresses remain below structural and critical current density limits. Iterative optimization of neutron shielding and tritium breeding blanket yield tritium self-sufficiency with a breeding ratio of 1.15, a blanket power multiplication factor of 1.11, toroidal field coil lifetimes of $3100 \pm 400$ MW-yr, and poloidal field coil lifetimes of at least $890 \pm 40$ MW-yr. Following balance of plant modeling, MANTA is projected to generate 90 MW of net electricity at an electricity gain factor of ${\sim}2.4$. Systems-level economic analysis estimates an overnight cost of US\$3.4 billion, meeting the NASEM FPP requirement that this first-of-a-kind be less than US\$5 billion. The toroidal field coil cost and replacement time are the most critical upfront and lifetime cost drivers, respectively.
Published: 2024

25. Thai Universal Dependency Treebank

Author: Sriwirote, Panyut, Leong, Wei Qi, Polpanumas, Charin, Thanyawong, Santhawat, Tjhi, William Chandra, Aroonmanakun, Wirote, and Rutherford, Attapol T.
Subjects: Computer Science - Computation and Language
Abstract: Automatic dependency parsing of Thai sentences has been underexplored, as evidenced by the lack of large Thai dependency treebanks with complete dependency structures and the lack of a published systematic evaluation of state-of-the-art models, especially transformer-based parsers. In this work, we address these problems by introducing Thai Universal Dependency Treebank (TUD), a new largest Thai treebank consisting of 3,627 trees annotated in accordance with the Universal Dependencies (UD) framework. We then benchmark dependency parsing models that incorporate pretrained transformers as encoders and train them on Thai-PUD and our TUD. The evaluation results show that most of our models can outperform other models reported in previous papers and provide insight into the optimal choices of components to include in Thai dependency parsers. The new treebank and every model's full prediction generated in our experiment are made available on a GitHub repository for further study.
Published: 2024

26. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author: Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
Published: 2024

27. Person-centred crisis support services as alternatives to emergency departments: a systematic scoping review

Author: Postorivo, Danielle, Parker, Stephen, Whiteford, Harvey, Papinczak, Zoe, and Rutherford, Zoe
Published: 2024
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28. Mapping cerebellar anatomical heterogeneity in mental and neurological illnesses

Author: Kim, Milin, Leonardsen, Esten, Rutherford, Saige, Selbæk, Geir, Persson, Karin, Steen, Nils Eiel, Smeland, Olav B., Ueland, Torill, Richard, Geneviève, Beckmann, Christian F., Marquand, Andre F., Andreassen, Ole A., Westlye, Lars T., Wolfers, Thomas, and Moberget, Torgeir
Published: 2024
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29. Mixture and Non-mixture Cure Models for Health Technology Assessment: What You Need to Know

Author: Latimer, Nicholas R. and Rutherford, Mark J.
Published: 2024
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30. Supportive care interventions for managing gastrointestinal symptoms following treatment for colorectal cancer: a systematic review

Author: Ju, Angela, Wiltink, Lisette, Walker, Jared, White, Kate, and Rutherford, Claudia
Published: 2024
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31. Using Bentonite to Reduce Neat Cement Grout Losses in Soil Nail Wall Applications

Author: Brockett, Sadie J., Ajmera, Beena, Rutherford, Cassandra, and Sturgill, Roy
Published: 2024
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32. Birthing a new maternal cognition literature: 10 recommendations for future research

Author: Orchard, Edwina R. and Rutherford, Helena J.V.
Published: 2024
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33. Generalized Pentagonal Geometries-II

Author: Forbes, A. D. and Rutherford, C. G.
Published: 2024
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34. Ending HIV Transmission in Australia: Expanding PrEP to Cisgender Women: A Scoping Review

Author: MacPhail, Catherine, Manlik, Kate, Dews, Hannah, Mao, Limin, and Rutherford, Alison
Published: 2024
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35. The SAMI Galaxy Survey: galaxy spin is more strongly correlated with stellar population age than mass or environment

Author: Croom, S. M., van de Sande, J., Vaughan, S. P., Rutherford, T. H., Lagos, C. P., Barsanti, S., Bland-Hawthorn, J., Brough, S., Bryant, J. J., Colless, M., Cortese, L., D'Eugenio, F., Fraser-McKelvie, A., Goodwin, M., Lorente, N. P. F., Richards, S. N., Ristea, A., Sweet, S. M., Yi, S. K., and Zafar, T.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We use the SAMI Galaxy Survey to examine the drivers of galaxy spin, $\lambda_{R_e}$, in a multi-dimensional parameter space including stellar mass, stellar population age (or specific star formation rate) and various environmental metrics (local density, halo mass, satellite vs. central). Using a partial correlation analysis we consistently find that age or specific star formation rate is the primary parameter correlating with spin. Light-weighted age and specific star formation rate are more strongly correlated with spin than mass-weighted age. In fact, across our sample, once the relation between light-weighted age and spin is accounted for, there is no significant residual correlation between spin and mass, or spin and environment. This result is strongly suggestive that present-day environment only indirectly influences spin, via the removal of gas and star formation quenching. That is, environment affects age, then age affects spin. Older galaxies then have lower spin, either due to stars being born dynamically hotter at high redshift, or due to secular heating. Our results appear to rule out environmentally dependent dynamical heating (e.g. galaxy-galaxy interactions) being important, at least within $1R_e$ where our kinematic measurements are made. The picture is more complex when we only consider high-mass galaxies ($M_*\gtrsim 10^{11}$M$_{\odot}$). While the age-spin relation is still strong for these high-mass galaxies, there is a residual environmental trend with central galaxies preferentially having lower spin, compared to satellites of the same age and mass. We argue that this trend is likely due to central galaxies being a preferred location for mergers., Comment: 24 pages, 9 figures. Accepted for publication in MNRAS
Published: 2024
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36. A Survey of Offline and Online Learning-Based Algorithms for Multirotor UAVs

Author: Sönmez, Serhat, Rutherford, Matthew J., and Valavanis, Kimon P.
Subjects: Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract: Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Navigation controllers endowed with different attributes and onboard sensor suites enable multirotor autonomous or semi-autonomous, safe flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the faster-than-exponential increase of available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been, and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environment in which multirotors operate. Learning algorithms combined with model-based control techniques have been proven beneficial when applied to multirotors. This survey summarizes published research since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories, and then, further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey are on online learning algorithms as applied to multirotors with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, and how, and how fast. The outcome of the survey offers a clear understanding of the recent state-of-the-art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real-time., Comment: 26 pages, 6 figures, 4 tables, Survey Paper
Published: 2024

37. The SAMI Galaxy Survey: Using Tidal Streams and Shells to Trace the Dynamical Evolution of Massive Galaxies

Author: Rutherford, Tomas H., van de Sande, Jesse, Croom, Scott M., Valenzuela, Lucas M., Remus, Rhea-Silvia, D'Eugenio, Francesco, Vaughan, Sam P., Zovaro, Henry R. M., Casura, Sarah, Barsanti, Stefania, Bland-Hawthorn, Joss, Brough, Sarah, Bryant, Julia J., Goodwin, Michael, Lorente, Nuria, Oh, Sree, and Ristea, Andrei
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Slow rotator galaxies are distinct amongst galaxy populations, with simulations suggesting that a mix of minor and major mergers are responsible for their formation. A promising path to resolve outstanding questions on the type of merger responsible, is by investigating deep imaging of massive galaxies for signs of potential merger remnants. We utilise deep imaging from the Subaru-Hyper Suprime Cam Wide data to search for tidal features in massive ($\log_{10}(M_*/M_{\odot}) > 10$) early-type galaxies (ETGs) in the SAMI Galaxy Survey. We perform a visual check for tidal features on images where the galaxy has been subtracted using a Multi-Gauss Expansion (MGE) model. We find that $31\pm 2$ percent of our sample show tidal features. When comparing galaxies with and without features, we find that the distributions in stellar mass, light-weighted mean stellar population age and H$\alpha$ equivalent width are significantly different, whereas spin ($\lambda_{R_e}$), ellipticity and bulge to total ratio have similar distributions. When splitting our sample in age, we find that galaxies below the median age (10.8 Gyr) show a correlation between the presence of shells and lower $\lambda_{R_e}$, as expected from simulations. We also find these younger galaxies which are classified as having "strong" shells have lower $\lambda_{R_e}$. However, simulations suggest that merger features become undetectable within $\sim 2-4$ Gyr post-merger. This implies that the relationship between tidal features and merger history disappears for galaxies with older stellar ages, i.e. those that are more likely to have merged long ago., Comment: Accepted for publication in MNRAS. 22 pages, 14 figures
Published: 2024
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38. A quantitative assessment of Geant4 for predicting the yield and distribution of positron-emitting fragments in ion beam therapy

Author: Chacon, Andrew, Rutherford, Harley, Hamato, Akram, Nitta, Munetaka, Nishikido, Fumihiko, Iwao, Yuma, Tashima, Hideaki, Yoshida, Eiji, Akamatsu, Go, Takyu, Sodai, Kang, Han Gyu, Franklin, Daniel R., Parodi, Katia, Yamaya, Taiga, Rosenfeld, Anatoly, Guatelli, Susanna, and Safavi-Naeini, Mitra
Subjects: Physics - Computational Physics, Physics - Medical Physics
Abstract: Purpose: To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting fragments produced along the beam path during carbon and oxygen ion therapy. Materials and Methods: Phantoms of polyethylene, gelatin or poly(methyl methacrylate) were irradiated with monoenergetic carbon and oxygen ion beams. Post-irradiation, 4D PET images were acquired and parent $^{11}$C, $^{10}$C and $^{15}$O radionuclides contributions in each voxel were determined from the extracted time activity curves. Experiments were simulated in Geant4 Monte Carlo versions 10-11.1, with three different fragmentation models: binary ion cascade (BIC), quantum molecular dynamics (QMD) and the Liege intranuclear cascade (INCL++) - 30 combinations. Total/parent isotope positron annihilation yields were compared between simulations and experiments using normalised mean squared error and Pearson cross-correlation coefficient. Depth of maximum/distal 50\% peak position yield were also compared. Results: Performance varied considerably across versions and models, with no one best predicting all positron-emitting fragments. BIC in Geant4 10.2 provided the best overall agreement with experimental results in the largest number of test cases. QMD consistently provided the best estimates of both the depth of peak positron yield (10.4 and 10.6) and the distal 50\%-of-peak point (10.2), while BIC also performed well and INCL generally performed the worst across most Geant4 versions. Conclusions: Best spatial prediction of annihilation yield and positron-emitting fragment production during carbon and oxygen ion therapy was found to be 10.2.p03 with BIC or QMD. These version/model combinations are recommended for future heavy ion therapy research., Comment: This manuscript has been submitted to IOP's Journal of Physics in Medicine and Biology, on the 5th of February 2024
Published: 2024

39. Fully automated planning for anatomical fetal brain MRI on 0.55T

Author: Silva, Sara Neves, McElroy, Sarah, Verdera, Jordina Aviles, Colford, Kathleen, Clair, Kamilah St, Tomi-Tricot, Raphael, Uus, Alena, Ozenne, Valery, Hall, Megan, Story, Lisa, Pushparajah, Kuberan, Rutherford, Mary A, Hajnal, Joseph V, and Hutter, Jana
Subjects: Physics - Medical Physics
Abstract: Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning-based detection of key brain landmarks on a whole-uterus EPI scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. Comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality and the resulting image quality was conducted. Results: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.21+-2.56 mm for the fetal eyes and 6.47+-3.23 for the cerebellum, planning quality was 2.44/3 (compared to 2.56/3 for manual planning) and diagnostic image quality was 2.14 compared to 2.07 for manual planning. Conclusions: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way towards simplifying the acquisition of fetal MRI thereby widening the availability of this modality in non-specialist centres., Comment: 17 pages, 8 figures, 1 table, MRM
Published: 2024

40. Design spectra for 6-regular graphs with 12 vertices

Author: Forbes, Anthony D. and Rutherford, Carrie G.
Subjects: Mathematics - Combinatorics, 05C51
Abstract: The design spectrum of a simple graph $G$ is the set of positive integers $n$ such that there exists an edgewise decomposition of the complete graph $K_n$ into $n(n - 1)/(2 |E(G)|)$ copies of $G$. We compute the design spectra for 7788 6-regular graphs with 12 vertices., Comment: 261 pages. The paper without the appendix will be submitted to a journal
Published: 2024

41. Cinematic Comanches: The Lone Ranger in the Media Borderlands

Author: Rutherford, Jack
Abstract: Book review
Published: 2024

42. Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach.

Author: Weng, Yingjie, Tian, Lu, Boothroyd, Derek, Lee, Justin, Zhang, Kenny, Lu, Di, Lindan, Christina, Bollyky, Jenna, Huang, Beatrice, Rutherford, George, Maldonado, Yvonne, and Desai, Manisha
Subjects: Humans, Likelihood Functions, Incidence, Longitudinal Studies, Pandemics, Computer Simulation, COVID-19
Abstract: Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.
Published: 2024

43. Cystatin C- and Creatinine-based Estimated GFR Differences: Prevalence and Predictors in the UK Biobank.

Author: Lees, Jennifer, Rutherford, Elaine, Mark, Patrick, Potok, O, Rifkin, Dena, Ix, Joachim, Shlipak, Michael, Estrella, Michelle, Chen, Debbie, Lu, Kaiwei, and Scherzer, Rebecca
Subjects: Chronic kidney disease, creatinine, cystatin C, estimated glomerular filtration rate, prediction
Abstract: RATIONALE & OBJECTIVE: Large differences between estimated glomerular filtration rate (eGFR) based on cystatin C (eGFRcys) and creatinine (eGFRcr) occur commonly. A comprehensive evaluation of factors that contribute to these differences is needed to guide the interpretation of discrepant eGFR values. STUDY DESIGN: Cohort study. SETTING & PARTICIPANTS: 468,969 participants in the UK Biobank. EXPOSURES: Candidate sociodemographic, lifestyle factors, comorbidities, medication usage, and physical and laboratory predictors. OUTCOMES: eGFRdiff, defined as eGFRcys minus eGFRcr, categorized into 3 levels: lower eGFRcys (eGFRdiff, less than -15 mL/min/1.73 m2), concordant eGFRcys and eGFRcr (eGFRdiff, -15 to 300 mg/g. Odds ratio estimates for these predictors were largely inverse of those in the lower eGFRcr group. The models area under the curve was 0.75 in the validation set, with good calibration (1.00). LIMITATIONS: Limited generalizability. CONCLUSIONS: This study highlights the multitude of demographic, lifestyle, and health characteristics that are associated with large eGFRdiff. The clinical model may identify individuals who are likely to have discrepant eGFR values and thus should be prioritized for cystatin C testing.
Published: 2024

44. Gemini: A Family of Highly Capable Multimodal Models

Author: Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
Published: 2023

45. Long-Range Structural Order in a Hidden Phase of Ruddlesden-Popper Bilayer Nickelate La$_3$Ni$_2$O$_7$

Author: Wang, Haozhe, Chen, Long, Rutherford, Aya, Zhou, Haidong, and Xie, Weiwei
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Materials Science
Abstract: The recent discovery of superconductivity in Ruddlesden-Popper bilayer nickelate, specifically La$_3$Ni$_2$O$_7$, has generated significant interest in the exploration of high-temperature superconductivity within this material family. In this study, we present the crystallographic and electrical resistivity properties of two distinct Ruddlesden-Popper nickelates: the bilayer La$_3$Ni$_2$O$_7$ (referred to as 2222-phase) and a previously uncharacterized phase, La$_3$Ni$_2$O$_7$ (1313-phase). The 2222-phase is characterized by a pseudo $F$-centered orthorhombic lattice, featuring bilayer perovskite [LaNiO$_3$] layers interspaced by rock salt [LaO] layers, forming a repeated ...2222... sequence. Intriguingly, the 1313-phase, which displays semiconducting properties, crystallizes in the $Cmmm$ space group and exhibits a pronounced predilection for a $C$-centered orthorhombic lattice. Within this structure, the perovskite [LaNiO$_3$] layers exhibit a distinctive long-range ordered arrangement, alternating between single- and trilayer configurations, resulting in a ...1313... sequence. This report contributes to novel insights into the crystallography and the structure-property relationship of Ruddlesden-Popper nickelates, paving the way for further investigations into their unique physical properties.
Published: 2023
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46. Multimodal Speech Emotion Recognition Using Modality-specific Self-Supervised Frameworks

Author: Patamia, Rutherford Agbeshi, Santos, Paulo E., Acheampong, Kingsley Nketia, Ekong, Favour, Sarpong, Kwabena, and Kun, She
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Emotion recognition is a topic of significant interest in assistive robotics due to the need to equip robots with the ability to comprehend human behavior, facilitating their effective interaction in our society. Consequently, efficient and dependable emotion recognition systems supporting optimal human-machine communication are required. Multi-modality (including speech, audio, text, images, and videos) is typically exploited in emotion recognition tasks. Much relevant research is based on merging multiple data modalities and training deep learning models utilizing low-level data representations. However, most existing emotion databases are not large (or complex) enough to allow machine learning approaches to learn detailed representations. This paper explores modalityspecific pre-trained transformer frameworks for self-supervised learning of speech and text representations for data-efficient emotion recognition while achieving state-of-the-art performance in recognizing emotions. This model applies feature-level fusion using nonverbal cue data points from motion capture to provide multimodal speech emotion recognition. The model was trained using the publicly available IEMOCAP dataset, achieving an overall accuracy of 77.58% for four emotions, outperforming state-of-the-art approaches
Published: 2023

47. Pattern of Multimorbidity in Middle-Aged and Older-Aged People with Mild Intellectual Disability in Australia

Author: Grace Rutherford, Rafat Hussain, and Kathleen Tait
Abstract: Background: Non-communicable diseases (NCDs), also known as chronic diseases, now constitute a major proportion of ill-health across most adult and older populations including in people with intellectual disability. The current paper is a comparative analysis of prevalence of NCDs across mid-aged and older-aged people with mild intellectual disability. Method: Comparative data comes from two cross-sectional surveys using similar methodology and timeframes. The analysis sample comprises mid-aged group (30-50 years, N = 291) and older-aged group (=60 years, N = 391). Results: People with mild intellectual disability start developing NCDs in early to mid-adulthood and increases with age. The mean number of NCDs in mid-aged group was 0.86 (SD, 0.84) compared to 3.82 in older group (SD, 2.67). Conclusion: There needs to be early identification and management of NCDs using relevant health promotion and preventative measures at optimal intervention points. The training of healthcare professionals needs improvement.
Published: 2024
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48. Towards Automated Multi-regional Lung Parcellation for 0.55-3T 3D T2w Fetal MRI

Author: Uus, Alena U., Zampieri, Carla Avena, Downes, Fenella, Collado, Alexia Egloff, Hall, Megan, Davidson, Joseph, Payette, Kelly, Verdera, Jordina Aviles, Grigorescu, Irina, Hajnal, Joseph V., Deprez, Maria, Aertsen, Michael, Hutter, Jana, Rutherford, Mary A., Deprest, Jan, Story, Lisa, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Link-Sourani, Daphna, editor, Abaci Turk, Esra, editor, Macgowan, Christopher, editor, Hutter, Jana, editor, Melbourne, Andrew, editor, and Licandro, Roxane, editor
Published: 2025
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49. Automatic Assessment of Fetal Multi-echo Diffusion Weighted Scans

Author: Bortolazzi, Antonia, Verdera, Jordina Aviles, Payette, Kelly, Silva, Sara Neves, Rutherford, Mary, Hajnal, Jo, Hutter, Jana, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Link-Sourani, Daphna, editor, Abaci Turk, Esra, editor, Macgowan, Christopher, editor, Hutter, Jana, editor, Melbourne, Andrew, editor, and Licandro, Roxane, editor
Published: 2025
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50. Automatic Disentanglement of Motion in Fetal Low Field MRI Scans

Author: Kitzberger, Michael, Silva, Sara Neves, Verdera, Jordina Aviles, Rojas, Diego Fajardo, Uus, Alena, Schulz-Heise, Susanne, Schmidt, Sandy, Schneider, Michael, Story, Lisa, Uder, Michael, Rutherford, Mary, Hutter, Jana, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Link-Sourani, Daphna, editor, Abaci Turk, Esra, editor, Macgowan, Christopher, editor, Hutter, Jana, editor, Melbourne, Andrew, editor, and Licandro, Roxane, editor
Published: 2025
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