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We consider 3XOR games with perfect commuting operator strategies. Given any 3XOR game, we show existence of a perfect commuting operator strategy for the game can be decided in polynomial time. Previously this problem was not known to be decidable. Our proof leads to a construction, showing a 3XOR game has a perfect commuting operator strategy iff it has a perfect tensor product strategy using a 3 qubit (8 dimensional) GHZ state. This shows that for perfect 3XOR games the advantage of a quantum strategy over a classical strategy (defined by the quantum-classical bias ratio) is bounded. This is in contrast to the general 3XOR case where the optimal quantum strategies can require high dimensional states and there is no bound on the quantum advantage. To prove these results, we first show equivalence between deciding the value of an XOR game and solving an instance of the subgroup membership problem on a class of right angled Coxeter groups. We then show, in a proof that consumes most of this paper, that the instances of this problem corresponding to 3XOR games can be solved in polynomial time.

 

Wildfires are an essential part of a healthy ecosystem, yet the expansion of the wildland-urban interface, combined with climatic changes and other anthropogenic activities, have led to the rise of wildfire hazards in the past few decades. Managing future wildfires and their multi-dimensional impacts requires moving from traditional reactive response to deploying proactive policies, strategies, and interventional programs to reduce wildfire risk to wildland-urban interface communities. Existing risk assessment frameworks lack a unified analytical method that properly captures uncertainties and the impact of decisions across social, ecological, and technical systems, hindering effective decision-making related to risk reduction investments. In this paper, a conceptual probabilistic wildfire risk assessment framework that propagates modeling uncertainties is presented. The framework characterizes the dynamic risk through spatial probability density functions of loss, where loss can include different decision variables, such as physical, social, economic, environmental, and health impacts, depending on the stakeholder needs and jurisdiction. The proposed approach consists of a computational framework to propagate and integrate uncertainties in the fire scenarios, propagation of fire in the wildland and urban areas, damage, and loss analyses. Elements of this framework that require further research are identified, and the complexity in characterizing wildfire losses and the need for an analytical-deliberative process to include the perspectives of the spectrum of stakeholders are discussed. 

Drone based wildfire detection and modeling methods enable high-precision, real-time fire monitoring that is not provided by traditional remote fire monitoring systems, such as satellite imaging. Precise, real-time information enables rapid, effective wildfire intervention and management strategies. Drone systems’ ease of deployment, omnidirectional maneuverability, and robust sensing capabilities make them effective tools for early wildfire detection and evaluation, particularly so in environments that are inconvenient for humans and/or terrestrial vehicles. Development of emerging drone-based fire monitoring systems has been inhibited by a lack of well-annotated, high quality aerial wildfire datasets, largely as a result of UAV flight regulations for prescribed burns and wildfires. The included dataset provides a collection of side-by-side infrared and visible spectrum video pairs taken by drones during an open canopy prescribed fire in Northern Arizona in 2021. The frames have been classified by two independent classifiers with two binary classifications. The Fire label is applied when the classifiers visually observe indications of fire in either RGB or IR frame for each frame pair. The Smoke label is applied when the classifiers visually estimate that at least 50% of the RGB frame is filled with smoke. To provide additional context to the main dataset’s aerial imagery, the provided supplementary dataset includes weather information, the prescribed burn plan, a geo-referenced RGB point cloud of the preburn area, an RGB orthomosaic of the preburn area, and links to further information. 

We present SAMI-H i, a survey of the atomic hydrogen content of 296 galaxies with integral field spectroscopy available from the SAMI Galaxy Survey. The sample spans nearly 4 dex in stellar mass ($M_\star = 10^{7.4}-10^{11.1}~ \rm M_\odot$), redshift z < 0.06, and includes new Arecibo observations of 153 galaxies, for which we release catalogues and H i spectra. We use these data to compare the rotational velocities obtained from optical and radio observations and to show how systematic differences affect the slope and scatter of the stellar-mass and baryonic Tully–Fisher relations. Specifically, we show that $\rm H\alpha$ rotational velocities measured in the inner parts of galaxies (1.3 effective radii in this work) systematically underestimate H i global measurements, with H i/$\rm H\alpha$ velocity ratios that increase at low stellar masses, where rotation curves are typically still rising and $\rm H\alpha$ measurements do not reach their plateau. As a result, the $\rm H\alpha$ stellar mass Tully–Fisher relation is steeper (when M⋆ is the independent variable) and has larger scatter than its H i counterpart. Interestingly, we confirm the presence of a small fraction of low-mass outliers of the $\rm H\alpha$ relation that are not present when H i velocity widths are used and are not explained by ‘aperture effects’. These appear to be highly disturbed systems for which $\rm H\alpha$ widths do not provide a reliable estimate of the rotational velocity. Our analysis reaffirms the importance of taking into account differences in velocity definitions as well as tracers used when interpreting offsets from the Tully–Fisher relation, at both low and high redshifts and when comparing with simulations.

 

We measure the molecular-to-atomic gas ratio,R_mol, and the star formation rate (SFR) per unit molecular gas mass, SFE_mol, in 38 nearby galaxies selected from the Virgo Environment Traced in CO (VERTICO) survey. We stack ALMA¹²CO (J= 2−1) spectra coherently using Hivelocities from the VIVA survey to detect faint CO emission out to galactocentric radiir_gal∼ 1.2r₂₅. We determine the scale lengths for the molecular and stellar components, finding a ∼3:5 relation compared to ∼1:1 in field galaxies, indicating that the CO emission is more centrally concentrated than the stars. We computeR_molas a function of different physical quantities. While the spatially resolvedR_molon average decreases with increasing radius, we find that the mean molecular-to-atomic gas ratio within the stellar effective radiusR_e,R_mol(r<R_e), shows a systematic increase with the level of Hi, truncation and/or asymmetry (H_Iperturbation). Analysis of the molecular- and the atomic-to-stellar mass ratios withinR_e,$R_{\star }^{\mathrm{mol}}(r<R_e)$and$R_{\star }^{\mathrm{atom}}(r<R_e)$, shows that VERTICO galaxies have increasingly lower$R_{\star }^{\mathrm{atom}}(r<R_e)$for larger levels of H_Iperturbation (compared to field galaxies matched in stellar mass), but no significant change in$R_{\star }^{\mathrm{m}\mathrm{o}\mathrm{l}}(r<R_e)$. We also measure a clear systematic decrease of the SFE_molwithinR_e, SFE_mol(r<R_e), with increasingly perturbed Hi. Therefore, compared to field galaxies from the field, VERTICO galaxies are more compact in CO emission in relation to their stellar distribution, but increasingly perturbed atomic gas increases theirR_moland decreases the efficiency with which their molecular gas forms stars.