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The vertical evolution of galactic discs is governed by the sub-structures within them. We examine the diversity of kinematic sub-structure present in the first 12 galaxies observed from the GECKOS survey, a VLT/MUSE large programme providing a systematic study of 35 edge-on, Milky Way-mass disc galaxies. Employing the nGIST analysis pipeline, we derive the mean line-of-sight stellar velocity ($V_{\star}$), velocity dispersion ($\sigma_{\star}$), skew ($h_{3}$), and kurtosis ($h_{4}$) for the sample, and examine 2D maps and 1D line profiles. Visually, the majority of this sample (8/12) are found to possess boxy-peanut bulges and host the corresponding kinematic structure predicted for stellar bars viewed in projection. Four galaxies exhibit strong evidence for the presence of nuclear discs, including central $h_{3}$-$V_{\star}$ anti-correlations, `croissant'-shaped central depressions in $\sigma_{\star}$ maps, strong gradients in $h_{3}$, and positive $h_{4}$ plateaus over the expected nuclear disc extent. The strength of the $h_{3}$ feature corresponds to the size of the nuclear disc, measured from the $h_{3}$ turnover radius. We can explain the features within the kinematic maps of all sample galaxies via disc structure(s) alone. We do not find any need to invoke the existence of dispersion-dominated bulges. Obtaining the specialised data products for this paper and the broader GECKOS survey required significant development of existing integral field spectroscopic (IFS) analysis tools. Therefore, we also present the nGIST pipeline: a modern, sophisticated, and easy-to-use pipeline for the analysis of galaxy IFS data. We conclude that the variety of kinematic sub-structures seen in GECKOS galaxies requires a contemporary view of galaxy morphology, expanding on the traditional view of galaxy structure, and uniting the kinematic complexity observed in the Milky Way with the extragalactic., Comment: 32 pages (9 of which are appendix), 26 figures, submitted to A&A. Comments welcome!

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

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

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.

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

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.

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

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.

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.

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

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.

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

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.

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

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

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.

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

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 .

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.

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.

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.

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

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

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

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

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

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