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We deploy the new Arkenstone galactic wind model in cosmological simulations for the first time, allowing us to robustly resolve the evolution and impact of high specific energy winds. In a (25$\,h^{-1}\,$Mpc)$^3$ box we perform a set of numerical experiments that systematically vary the mass and energy loadings of such winds, finding that their energy content is the key parameter controlling the stellar to dark matter mass ratio. Increasing the mass loading, at fixed energy, actually results in mildly enhanced star formation, counter to prevailing wisdom but in agreement with recent analytic models. Of the simple parametrisations that we test, we find that an energy loading that scales inversely with halo mass best matches a wide range of observations, and can do so with mass loadings drastically lower than those in most previous cosmological simulations. In this scenario, much less material is ejected from the interstellar medium. Instead, winds both heat gas in the circumgalactic medium, slowing infall onto the galaxy, and also drive shocks beyond the virial radius, preventing accretion onto the halo in the first place. We have not yet tied the mass and energy loadings to high-resolution simulations (a key goal of the Learning the Universe collaboration); however, we can already report that a much lower fraction of the available supernova energy is needed in preventative galaxy regulation than required by ejective wind feedback models such as IllustrisTNG., Comment: Submitted to MNRAS. 19 pages, 14 figures. This is a Learning the Universe publication

Various works have aimed at combining the inference efficiency of recurrent models and training parallelism of multi-head attention for sequence modeling. However, most of these works focus on tasks with fixed-dimension observation spaces, such as individual tokens in language modeling or pixels in image completion. To handle an observation space of varying size, we propose a novel algorithm that alternates between cross-attention between a 2D latent state and observation, and a discounted cumulative sum over the sequence dimension to efficiently accumulate historical information. We find this resampling cycle is critical for performance. To evaluate efficient sequence modeling in this domain, we introduce two multi-agent intention tasks: simulated agents chasing bouncing particles and micromanagement analysis in professional StarCraft II games. Our algorithm achieves comparable accuracy with a lower parameter count, faster training and inference compared to existing methods., Comment: 11 pages, 10 figures

Creation and storage of datasets are often overlooked input costs in machine learning, as many datasets are simple image label pairs or plain text. However, datasets with more complex structures, such as those from the real time strategy game StarCraft II, require more deliberate thought and strategy to reduce cost of ownership. We introduce a serialization framework for StarCraft II that reduces the cost of dataset creation and storage, as well as improving usage ergonomics. We benchmark against the most comparable existing dataset from \textit{AlphaStar-Unplugged} and highlight the benefit of our framework in terms of both the cost of creation and storage. We use our dataset to train deep learning models that exceed the performance of comparable models trained on other datasets. The dataset conversion and usage framework introduced is open source and can be used as a framework for datasets with similar characteristics such as digital twin simulations. Pre-converted StarCraft II tournament data is also available online., Comment: 14 pages, 7 figures

Metals in the diffuse, ionized gas at the boundary between the Milky Way's interstellar medium (ISM) and circumgalactic medium (CGM), known as the disk-halo interface (DHI), are valuable tracers of the feedback processes that drive the Galactic fountain. However, metallicity measurements in this region are challenging due to obscuration by the Milky Way ISM and uncertain ionization corrections that affect the total hydrogen column density. In this work, we constrain the ionization corrections to neutral hydrogen column densities using precisely measured electron column densities from the dispersion measure of pulsars that lie in the same globular clusters as UV-bright targets with high-resolution absorption spectroscopy. We address the blending of absorption lines with the ISM by jointly fitting Voigt profiles to all absorption components. We present our metallicity estimates for the DHI of the Milky Way based on detailed photoionization modeling to the absorption from ionized metal lines and ionization-corrected total hydrogen columns. Generally, the gas clouds show a large scatter in metallicity, ranging between $0.04-3.2\ Z_{\odot}$, implying that the DHI consists of a mixture of gaseous structures having multiple origins. We estimate the inflow and outflow timescales of the DHI ionized clouds to be $6 - 35$ Myr. We report the detection of an infalling cloud with super-solar metallicity that suggests a Galactic fountain mechanism, whereas at least one low-metallicity outflowing cloud ($Z < 0.1\ Z_{\odot}$) poses a challenge for Galactic fountain and feedback models., Comment: 28 pages, 15 figures, accepted for publication in ApJ

Algorithmic efficiency is essential to reducing energy use and time taken for computational problems. Optimizing efficiency is important for tasks involving multiple resources, for example in stochastic calculations where the size of the random ensemble competes with the time-step. We define the complexity order of an algorithm needing multiple resources as the exponent of inverse total error with respect to the total resources used. The optimum order c is predicted for cases involving competing, factorable resources. As an illustration, this is applied to computing averages in a stochastic differential equation, with numerical examples that agree with earlier literature, as well as with the more general analytic theory that is obtained here.

We describe AthenaK: a new implementation of the Athena++ block-based adaptive mesh refinement (AMR) framework using the Kokkos programming model. Finite volume methods for Newtonian, special relativistic (SR), and general relativistic (GR) hydrodynamics and magnetohydrodynamics (MHD), and GR-radiation hydrodynamics and MHD, as well as a module for evolving Lagrangian tracer or charged test particles (e.g., cosmic rays) are implemented using the framework. In two companion papers we describe (1) a new solver for the Einstein equations based on the Z4c formalism and (2) a GRMHD solver in dynamical spacetimes also implemented using the framework, enabling new applications in numerical relativity. By adopting Kokkos, the code can be run on virtually any hardware, including CPUs, GPUs from multiple vendors, and emerging ARM processors. AthenaK shows excellent performance and weak scaling, achieving over one billion cell updates per second for hydrodynamics in three-dimensions on a single NVIDIA Grace Hopper processor and with a typical parallel efficiency of 80% on 65536 AMD GPUs on the OLCF Frontier system. Such performance portability enables AthenaK to leverage modern exascale computing systems for challenging applications in astrophysical fluid dynamics, numerical relativity, and multimessenger astrophysics., Comment: Submitted to ApJS

High-velocity outflows are ubiquitous in compact, massive (M$_* \sim$ 10$^{11}$ M$_{\odot}$), z $\sim$ 0.5 galaxies with extreme star formation surface densities ($\Sigma_{SFR} \sim$ 2000 M$_{\odot}$ yr$^{-1}$ kpc$^{-2}$). We have previously detected and characterized these outflows using MgII absorption lines. To probe their full extent, we present Keck/KCWI integral field spectroscopy of the [OII] and MgII emission nebulae surrounding all of the 12 galaxies in this study. We find that [OII] is more effective than MgII in tracing low surface brightness, extended emission in these galaxies. The [OII] nebulae are spatially extended beyond the stars, with radial extent R$_{90}$ between 10 and 40 kpc. The nebulae exhibit non-gravitational motions, indicating galactic outflows with maximum blueshifted velocities ranging from -335 to -1920 km s$^{-1}$. The outflow kinematics correlate with the bursty star formation histories of these galaxies. Galaxies with the most recent bursts of star formation (within the last $<$ 3 Myr) exhibit the highest central velocity dispersions ($\sigma >$ 400 km s$^{-1}$), while the oldest bursts have the lowest-velocity outflows. Many galaxies exhibit both high-velocity cores and more extended, slower-moving gas indicative of multiple outflow episodes. The slower, larger outflows occurred earlier and have decelerated as they propagate into the CGM and mix on timescales $>$ 50 Myr., Comment: Accepted for publication in The Astrophysical Journal

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of Springel & Hernquist (2003, hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially-resolved simulations of the turbulent, multiphase, star-forming ISM have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the Pressure-Regulated Feedback-Modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the "native" simulation outputs with post-processed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step towards implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations., Comment: 33 pages, 12 figures, Accepted for publication in ApJ. This is a Learning the Universe Publication. All codes and data used to produce this work can be found at the following $\href{https://github.com/sultan-hassan/tng50-post-processing-prfm}{GitHub \,Link.}$

Arkenstone is a new scheme that allows multiphase, stellar feedback-driven winds to be included in coarse resolution cosmological simulations. The evolution of galactic winds and their subsequent impact on the circumgalactic medium are altered by exchanges of mass, energy, momentum, and metals between their component phases. These exchanges are governed by complex, small-scale physical processes that cannot be resolved in cosmological simulations. In this second presentation paper, we describe Arkenstone's novel cloud particle approach for modelling unresolvable cool clouds entrained in hot, fast winds. This general framework allows models of the cloud-wind interaction, derived from state-of-the-art high-resolution simulations, to be applied in a large-scale context. In this work, we adopt a cloud evolution model that captures simultaneous cloud mass loss to and gain from the ambient hot phase via turbulent mixing and radiative cooling, respectively. We demonstrate the scheme using non-cosmological idealized simulations of a galaxy with a realistic circumgalactic medium component, using the Arepo code. We show that the ability of a high-specific energy wind component to perform preventative feedback may be limited by heavy loading of cool clouds coupled into it. We demonstrate that the diverging evolution of clouds of initially differing masses leads to a complex velocity field for the cool phase and a cloud mass function that varies both spatially and temporally in a non-trivial manner. These latter two phenomena can manifest in the simulation because of our choice of a Lagrangian discretisation of the cloud population, in contrast to other proposed schemes. This is a Learning the Universe publication., Comment: Submitted to MNRAS. 27 pages, 11 figures. This is a Learning the Universe publication

Spatial distributions in current, temperature, state-of-charge and degradation across the plane of large format lithium-ion battery pouch cells can significantly impact their performance, especially at high C-rates. In this paper, a method to smooth out these spatial distributions by grading the electrode microstructure in-the-plane is proposed. A mathematical model of a large format pouch cell is developed and validated against both temperature and voltage experimental data. An analytic solution for the optimal graded electrode that achieves a uniform current distribution across the pouch cell is then derived. The model predicts that the graded electrodes could significantly reduce the likelihood of lithium plating in large format pouch cells, with grading increasing the C-rate at which plating occurs from 2.75C to 5.5C. These results indicate the potential of designing spatially varying electrode architectures to homogenise the response of large format pouch cells and improve their high rate performance.

Most numerical simulations of galaxy formation and evolution are unable to properly resolve the turbulent cascade at or below the resolution scale and turbulence models are required to capture the motion of eddies on those unresolved scales. In this study, we investigate the impact of turbulent metal diffusion models on multiphase outflows originating from dwarf galaxies ($M_{\rm halo} \sim 10^{10} - 10^{11}$ M$_\odot$). We use our state-of-the-art numerical model for the formation of single stars and non-equilibrium cooling and hydrogen chemistry. Our simulations are carried out at a mass resolution of $\sim$1 M$_{\odot}$, where the individual supernova explosions are resolved in terms of hot-phase generation and momentum input. We find that mass, energy, and metal loading factors are only weakly affected by the inclusion of a metal diffusion model. The metal enrichment factor at low altitude above the galactic disk is higher by around 20 per cent when the metal diffusion model is included. Specifically, we find more efficient cooling in the cold interstellar medium, as higher amounts of metals are kept in the cold dense phase. The most striking effect of the metal diffusion model is that, without metal diffusion, there is more rapid cooling in the hot phase and a reduced sound speed by a factor of two. Specifically, we find that the hot phase is more metal enriched in the case without metal diffusion leading to more rapid (over) cooling of that phase which is consistent with the higher sound speed we find in the runs with metal diffusion., Comment: 19 pages, 11 figures, submitted to ApJ, comments welcome!

In real analysis, the Lojasiewicz inequalities, revitalized by Leon Simon in his pioneering work on singularities of energy minimizing maps, have proven to be monumental in differential geometry, geometric measure theory, and variational problems. These inequalities provide specific growth and stability conditions for prescribed real-analytic functions, and have found applications to gradient flows, gradient systems, and as explicated in this paper, vector bundles over compact Riemannian manifolds. In this work, we outline the theory of functionals and variational problems over vector bundles, explore applications to arbitrary real-analytic functionals, and describe the energy functional on $S^{n-1}$ as a functional over a vector bundle., Comment: 21 pages, 4 figures

We use the GIBLE suite of cosmological zoom-in simulations of Milky Way-like galaxies with additional super-Lagrangian refinement in the circumgalactic medium (CGM) to quantify the origin and evolution of CGM cold gas clouds. The origin of $z$\,$=$\,$0$ clouds can be traced back to recent ($\lesssim$\,$2$\,Gyr) outflows from the central galaxy ($\sim$\,45\,$\%$), condensation out of the hot phase of the CGM in the same time frame ($\sim$\,45\,$\%$), and to a lesser degree to satellite galaxies ($\lesssim$\,5\,$\%$). We find that in-situ condensation results from rapid cooling around local over-densities primarily seeded by the dissolution of the previous generation of clouds into the hot halo. About $\lesssim$\,10\,$\%$ of the cloud population is long lived, with their progenitors having already assembled $\sim$\,$2$\,Gyr ago. Collective cloud-cloud dynamics are crucial to their evolution, with coalescence and fragmentation events occurring frequently ($\gtrsim$\,20\,Gyr$^{-1}$). These interactions are modulated by non-vanishing pressure imbalances between clouds and their interface layers. The gas content of clouds is in a constant state of flux, with clouds and their surroundings exchanging mass at a rate of \mbox{$\gtrsim$\,$10^3$\,M$_\odot$\,Myr$^{-1}$}, depending on cloud relative velocity and interface vorticity. Furthermore, we find that a net magnetic tension force acting against the density gradient is capable of inhibiting cloud-background mixing. Our results show that capturing the distinct origins of cool CGM clouds, together with their physical evolution, requires high-resolution, cosmological galaxy formation simulations with both stellar and supermassive black hole feedback-driven outflows., Comment: Submitted to A&A

In this survey, we examine the properties of the top dimensional singular set $\text{sing}_{*}(u)$, including several theorems, geometric properties, and relation to homogeneous degree zero minimizers., Comment: 8 pages, 1 figure

The multihop relay wireless networks have gained traction due to the emergence of Reconfigurable Intelligent Surfaces (RISs) which can be used as relays in high frequency range wireless network, including THz or mmWave. To select paths in these networks, the transmission performance plays the key network in these networks. In this paper, we enhance and greatly simplify the path selection in multihop relay RIS enabled wireless networks with what we refer to as interference graphs. Interference graphs are created based on SNR model, conical and cylindrical beam shapes in the transmission and the related interference model. Once created, they can be simply and efficiently used to select valid paths, without overestimation of the effect of interference. The results show that decreased ordering of conflict selections in the graphs yields the best results, as compared to conservative approach that tolerates no interference., Comment: This paper is uploaded here for research community, thus it is for non-commercial purposes

Many people expect robots to move fairly quietly, or make pleasant "beep boop" sounds or jingles similar to what they have observed in videos of robots. Unfortunately, this expectation of quietness does not match reality, as robots make machine sounds, known as 'consequential sounds', as they move and operate. As robots become more prevalent within society, understanding the sounds produced by robots and how these sounds are perceived by people is becoming increasingly important for positive human robot interactions (HRI). This paper investigates how people respond to the consequential sounds of robots, specifically how robots make a participant feel, how much they like the robot, would be distracted by the robot, and a person's desire to colocate with robots. Participants were shown 5 videos of different robots and asked their opinions on the robots and the sounds they made. This was compared with a control condition of completely silent videos. The results in this paper demonstrate with data from 182 participants (858 trials) that consequential sounds produced by robots have a significant negative effect on human perceptions of robots. Firstly there were increased negative 'associated affects' of the participants, such as making them feel more uncomfortable or agitated around the robot. Secondly, the presence of consequential sounds correlated with participants feeling more distracted and less able to focus. Thirdly participants reported being less likely to want to colocate in a shared environment with robots., Comment: 16 pages (5 supplementary), 9 figures

We present a new simulation suite for the star-forming interstellar medium (ISM) in galactic disks using the TIGRESS-NCR framework. Distinctive aspects of our simulation suite are: (1) sophisticated and comprehensive numerical treatments of essential physical processes including magnetohydrodynamics, self-gravity, and galactic differential rotation, as well as photochemistry, cooling, and heating coupled with ray-tracing UV radiation transfer and resolved supernova feedback and (2) wide parameter coverage including metallicity over $Z'\equiv Z/Z_\odot\sim0.1-3$, gas surface density $\Sigma_{\rm gas}\sim5-150 M_{\odot}{\rm pc^{-2}}$, and stellar surface density $\Sigma_{\rm star}\sim 1-50 M_{\odot}{\rm pc^{-2}}$. The range of emergent star formation rate surface density is $\Sigma_{\rm SFR}\sim 10^{-4}-0.5 M_{\odot}{\rm kpc^{-2}yr^{-1}}$ and ISM total midplane pressure is $P_{\rm tot}/k_B=10^3-10^6{\rm cm^{-3}K}$, with $P_{\rm tot}$ equal to the ISM weight $W$. For given $\Sigma_{\rm gas}$ and $\Sigma_{\rm star}$, we find $\Sigma_{\rm SFR} \propto Z'^{0.3}$. We provide an interpretation based on the pressure-regulated feedback-modulated (PRFM) star formation theory. We characterize feedback modulation in terms of the yield $\Upsilon$, defined as the ratio of each stress to $\Sigma_{\rm SFR}$. The thermal feedback yield varies sensitively with both weight and metallicity as $\Upsilon_{\rm th}\propto W^{-0.46}Z'^{-0.53}$, while the combined turbulent and magnetic feedback yield shows weaker dependence $\Upsilon_{\rm turb+mag}\propto W^{-0.22}Z'^{-0.18}$. The reduction in $\Sigma_{\rm SFR}$ at low metallicity is due mainly to enhanced thermal feedback yield, resulting from reduced attenuation of UV radiation. With the metallicity-dependent calibrations we provide, PRFM theory can be used for a new subgrid star formation prescription in cosmological simulations where the ISM is unresolved., Comment: Resubmitted to ApJ after minor revision

With neural networks being used to control safety-critical systems, they increasingly have to be both accurate (in the sense of matching inputs to outputs) and robust. However, these two properties are often at odds with each other and a trade-off has to be navigated. To address this issue, this paper proposes a method to generate an approximation of a neural network which is certifiably more robust. Crucially, the method is fully convex and posed as a semi-definite programme. An application to robustifying model predictive control is used to demonstrate the results. The aim of this work is to introduce a method to navigate the neural network robustness/accuracy trade-off.

Energy minimizing maps (E.M.M.s) play a central role in the calculus of variations, partial differential equations (PDEs), and geometric analysis. These maps are often embedded into $C^\infty$ Riemannian manifolds to minimize the Dirichlet Energy functional under certain prescribed conditions. For understanding physical phenomena where systems naturally evolve to states of minimal energy, the geometric analysis of these maps has provided elucidating insights. This paper explores the geometric and analytic properties of energy minimizing maps, tangent maps, and the singular set (sing(u)). We begin by establishing key concepts from analysis, including the Sobolev Space $W^{1,2}$ harmonic functions, and Hausdorff dimension. Significant results about the density function, its upper semi-continuity, and the compactness theorem for tangent maps, and theorems for homogeneous degree zero minimizers are presented. Also analyzed in detail is the singular set (sing(u)), its Hausdorff dimension, and geometric structure. We conclude with open problems that are rich in research potential, and the far-reaching implications if these problems are to be solved.

In this paper we study the filamentary substructure of 3.3 $\mu$m PAH emission from JWST/NIRCam observations in the base of the M82 star-burst driven wind. We identify plume-like substructure within the PAH emission with widths of $\sim$50 pc. Several of the plumes extend to the edge of the field-of-view, and thus are at least 200-300 pc in length. In this region of the outflow, the vast majority ($\sim$70\%) of PAH emission is associated with the plumes. We show that those structures contain smaller scale "clouds" with widths that are $\sim$5-15 pc, and they are morphologically similar to the results of "cloud-crushing" simulations. We estimate the cloud-crushing time-scales of $\sim$0.5-3 Myr, depending on assumptions. We show this time scale is consistent with a picture in which these observed PAH clouds survived break-out from the disk rather than being destroyed by the hot wind. The PAH emission in both the midplane and the outflow is shown to tightly correlate with that of Pa$\alpha$ emission (from HST/NICMOS data), at the scale of both plumes and clouds, though the ratio of PAH-to-Pa$\alpha$ increases at further distances from the midplane. Finally, we show that the outflow PAH emission is suppressed in regions of the M82 wind that are bright in X-ray emission. Overall, our results are broadly consistent with a picture in which cold gas in galactic outflows is launched via hierarchically structured plumes, and those small scale clouds are more likely to survive the wind environment when collected into the larger plume structure., Comment: Submitted to MNRAS

We present a study of spin-unpolarized and spin-polarized two-dimensional uniform electron liquids using variational and diffusion quantum Monte Carlo (VMC and DMC) methods with Slater-Jastrow-backflow trial wave functions. Ground-state VMC and DMC energies are obtained in the density range $1 \leq r_\text{s} \leq 40$. Single-particle and many-body finite-size errors are corrected using canonical-ensemble twist-averaged boundary conditions and extrapolation of twist-averaged energies to the thermodynamic limit of infinite system size. System-size-dependent errors in Slater-Jastrow-backflow DMC energies caused by partially converged VMC energy minimization calculations are discussed. We find that, for $1 \leq r_\text{s} \leq 5$, optimizing the backflow function at each twist lowers the twist-averaged DMC energy at finite system size. However, nonsystematic system-size-dependent effects remain in the DMC energies, which can be partially removed by extrapolation from multiple finite system sizes to infinite system size. We attribute these nonsystematic effects to the close competition between fluid and defected crystal phases at different system sizes at low density. The DMC energies in the thermodynamic limit are used to parameterize a local spin density approximation correlation functional for inhomogeneous electron systems. Our zero-temperature phase diagram shows a single transition from a paramagnetic fluid to a hexagonal Wigner crystal at $r_\text{s}=35(1)$, with no region of stability for a ferromagnetic fluid.

Model predictive control (MPC) for linear systems with quadratic costs and linear constraints is shown to admit an exact representation as an implicit neural network. A method to "unravel" the implicit neural network of MPC into an explicit one is also introduced. As well as building links between model-based and data-driven control, these results emphasize the capability of implicit neural networks for representing solutions of optimisation problems, as such problems are themselves implicitly defined functions., Comment: 13 pages

We present the results of direct-method metallicity measurements in the disk and outflow of the low-metallicity starburst galaxy NGC 1569. We use Keck Cosmic Web Imager observations to map the galaxy across 54$\arcsec$ (800 pc) along the major axis and 48$\arcsec$ (700 pc) along the minor axis with a spatial resolution of 1$\arcsec$ ($\sim$15 pc). We detect common strong emission lines ([\ion{O}{III}] $\lambda$5007, H$\beta$, [\ion{O}{II}] $\lambda$3727) and the fainter [\ion{O}{III}] $\lambda$4363 auroral line, which allows us to measure electron temperature ($T_e$) and metallicity. Theory suggests that outflows drive metals out of the disk driving observed trends between stellar mass and gas-phase metallicity. Our main result is that the metallicity in the outflow is similar to that of the disk, $Z_{\rm out} / Z_{\rm ISM} \approx 1$. This is consistent with previous absorption line studies in higher mass galaxies. Assumption of a mass-loading factor of $\dot{M}_{\rm out}/{\rm SFR}\sim3$ makes the metal-loading of NGC 1569 consistent with expectations derived from the mass-metallicity relationship. Our high spatial resolution metallicity maps reveal a region around a supermassive star cluster (SSC-B) with distinctly higher metallicity and higher electron density, compared to the disk. Given the known properties of SSC-B the higher metallicity and density of this region are likely the result of star formation-driven feedback acting on the local scale. Overall, our results are consistent with the picture in which metal-enriched winds pollute the circumgalactic medium surrounding galaxies, and thus connect the small-scale feedback processes to large-scale properties of galaxy halos., Comment: 15 pages, 11 figures, accepted by MNRAS

We use a cosmological zoom-in simulation of a Milky Way-like galaxy to study and quantify the topology of magnetic field lines around cold gas clouds in the circumgalactic medium (CGM). This simulation is a new addition to Project GIBLE, a suite of cosmological magnetohydrodynamical simulations of galaxy formation with preferential super-Lagrangian refinement in the CGM, reaching an unprecedented (CGM) gas mass resolution of $\sim$ $225$ M$_\odot$. To maximize statistics and resolution, we focus on a sample of $\sim$ $200$ clouds with masses of $\sim$ $10^6$ M$_\odot$. The topology of magnetic field lines around clouds is diverse, from threading to draping, and there is large variation in the magnetic curvature ($\kappa$) within cloud-background interfaces. We typically find little variation of $\kappa$ between upstream and downstream cloud faces, implying that strongly draped configurations are rare. In addition, $\kappa$ correlates strongly with multiple properties of the interface and the ambient background, including cloud overdensity and relative velocity, suggesting that cloud properties impact the topology of interface magnetic fields., Comment: Accepted for publication (A&A Letters); 5 pages, 4 figures

We measure resolved (kiloparsec-scale) outflow properties in a sample of 10 starburst galaxies from the DUVET sample, using Keck/KCWI observations of H$\beta$ and [OIII]~$\lambda$5007. We measure $\sim450$ lines-of-sight that contain outflows, and use these to study scaling relationships of outflow velocity ($v_{\rm out}$), mass-loading factor ($\eta$; mass outflow rate per SFR) and mass flux ($\dot{\Sigma}_{\rm out}$; mass outflow rate per area) with co-located SFR surface density ($\Sigma_{\rm SFR}$) and stellar mass surface density ($\Sigma_{\ast}$). We find strong, positive correlations of $\dot{\Sigma}_{\rm out} \propto \Sigma_{\rm SFR}^{1.2}$ and $\dot{\Sigma}_{\rm out} \propto \Sigma_{\ast}^{1.7}$. We also find shallow correlations between $v_{\rm out}$ and both $\Sigma_{\rm SFR}$ and $\Sigma_{\ast}$. Our resolved observations do not suggest a threshold in outflows with $\Sigma_{\rm SFR}$, but rather we find that the local specific SFR ($\Sigma_{\rm SFR}/\Sigma_\ast$) is a better predictor of where outflows are detected. We find that outflows are very common above $\Sigma_{\rm SFR}/\Sigma_\ast\gtrsim 0.1$~Gyr$^{-1}$ and rare below this value. We argue that our results are consistent with a picture in which outflows are driven by supernovae, and require more significant injected energy in higher mass surface density environments to overcome local gravity. The correlations we present here provide a statistically robust, direct comparison for simulations and higher redshift results from JWST., Comment: 14 pages, 7 figures, plus 4 figures in appendix, submitted to MNRAS

Cross-lingual open domain question answering (CLQA) is a complex problem, comprising cross-lingual retrieval from a multilingual knowledge base, followed by answer generation in the query language. Both steps are usually tackled by separate models, requiring substantial annotated datasets, and typically auxiliary resources, like machine translation systems to bridge between languages. In this paper, we show that CLQA can be addressed using a single encoder-decoder model. To effectively train this model, we propose a self-supervised method based on exploiting the cross-lingual link structure within Wikipedia. We demonstrate how linked Wikipedia pages can be used to synthesise supervisory signals for cross-lingual retrieval, through a form of cloze query, and generate more natural questions to supervise answer generation. Together, we show our approach, \texttt{CLASS}, outperforms comparable methods on both supervised and zero-shot language adaptation settings, including those using machine translation., Comment: EMNLP 2024 Main

Phasor measurement units (PMUs) provide a high-resolution view of the power system at the locations where they are placed. As such, it is desirable to place them in bulk in low voltage distribution circuits. However, the power consumption of a PMU/micro-PMU is in the order of Watts (W) that results in them requiring an external power supply, which in turn increases the overall cost. This work details the hardware design of a PMU capable of measuring and reporting voltage and current phasors for a single-phase system at an average power consumption of only 30.8 mW -- one to two orders of magnitude lower than existing academic and commercial PMUs. This enables the proposed PMU to run for two weeks using an 11-Wh battery or indefinitely if paired with an inexpensive solar panel. A test-bench developed in accordance with the 2018 IEC/IEEE 60255-118-1 PMU Standard confirms the accuracy of this PMU. Given its low power consumption, the proposed design is expected to accelerate adoption of PMUs in modern distribution grids., Comment: 6 pages, 4 figures, Presented at Texas Power and Energy Conference (TPEC) 2024

We present a novel bi-directional Transformer architecture (BiXT) which scales linearly with input size in terms of computational cost and memory consumption, but does not suffer the drop in performance or limitation to only one input modality seen with other efficient Transformer-based approaches. BiXT is inspired by the Perceiver architectures but replaces iterative attention with an efficient bi-directional cross-attention module in which input tokens and latent variables attend to each other simultaneously, leveraging a naturally emerging attention-symmetry between the two. This approach unlocks a key bottleneck experienced by Perceiver-like architectures and enables the processing and interpretation of both semantics ('what') and location ('where') to develop alongside each other over multiple layers -- allowing its direct application to dense and instance-based tasks alike. By combining efficiency with the generality and performance of a full Transformer architecture, BiXT can process longer sequences like point clouds, text or images at higher feature resolutions and achieves competitive performance across a range of tasks like point cloud part segmentation, semantic image segmentation, image classification, hierarchical sequence modeling and document retrieval. Our experiments demonstrate that BiXT models outperform larger competitors by leveraging longer sequences more efficiently on vision tasks like classification and segmentation, and perform on par with full Transformer variants on sequence modeling and document retrieval -- but require $28\%$ fewer FLOPs and are up to $8.4\times$ faster., Comment: Accepted at NeurIPS 2024; Code and models will be available at https://github.com/mrkshllr/BiXT

In the modern landscape of wireless communications, multi-hop, high-bandwidth, indoor Terahertz (THz) wireless communications are gaining significant attention. These systems couple Reconfigurable Intelligent Surface (RIS) and relay devices within the emerging 6G network framework, offering promising solutions for creating cell-less, indoor, and on-demand mesh networks. RIS devices are especially attractive, constructed by an array of reflecting elements that can phase shifts, such that the reflecting signals can be focused, steered, and the power of the signal enhanced towards the destination. This paper presents an in-depth, analytical examination of how path allocation impacts interference within such networks. We develop the first model which analyzes interference based on the geometric parameters of beams (conic, cylindrical) as they interact with RIS, User Equipment (UE), and relay devices. We introduce a transmission scheduling heuristic designed to mitigate interference, alongside an efficient optimization method to maximize throughput. Our performance results elucidate the interference's effect on communication path quality and highlight effective path selection strategies with throughput maximization., Comment: This paper is uploaded here for research community, thus it is for non-commercial purposes

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics. The aim of this paper is to highlight the limitations of this approach; it is shown that re-tuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness.

While significant advancements have been made in video question answering (VideoQA), the potential benefits of enhancing model generalization through tailored difficulty scheduling have been largely overlooked in existing research. This paper seeks to bridge that gap by incorporating VideoQA into a curriculum learning (CL) framework that progressively trains models from simpler to more complex data. Recognizing that conventional self-paced CL methods rely on training loss for difficulty measurement, which might not accurately reflect the intricacies of video-question pairs, we introduce the concept of uncertainty-aware CL. Here, uncertainty serves as the guiding principle for dynamically adjusting the difficulty. Furthermore, we address the challenge posed by uncertainty by presenting a probabilistic modeling approach for VideoQA. Specifically, we conceptualize VideoQA as a stochastic computation graph, where the hidden representations are treated as stochastic variables. This yields two distinct types of uncertainty: one related to the inherent uncertainty in the data and another pertaining to the model's confidence. In practice, we seamlessly integrate the VideoQA model into our framework and conduct comprehensive experiments. The findings affirm that our approach not only achieves enhanced performance but also effectively quantifies uncertainty in the context of VideoQA.