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Abstract Supernova (SN) 2023ixf was discovered on 2023 May 19. The host galaxy, M101, was observed by the Hobby–Eberly Telescope Dark Energy Experiment collaboration over the period 2020 April 30–2020 July 10, using the Visible Integral-field Replicable Unit Spectrograph (3470 ≲λ≲ 5540 Å) on the 10 m Hobby–Eberly Telescope. The fiber filling factor within ±30″ of SN 2023ixf is 80% with a spatial resolution of 1″. Ther< 5.″5 surroundings are 100% covered. This allows us to analyze the spatially resolved preexplosion local environments of SN 2023ixf with nebular emission lines. The two-dimensional maps of the extinction and the star formation rate (SFR) surface density (Σ_SFR) show weak increasing trends in the radial distributions within ther< 5.″5 regions, suggesting lower values of extinction and SFR in the vicinity of the progenitor of SN 2023ixf. The median extinction and that of the surface density of SFR withinr< 3″ areE(B−V) = 0.06 ± 0.14, and ${\mathrm{\Sigma }}_{\mathrm{SFR}}={10}^{-5.44\pm 0.66}{M}_{☉}{\mathrm{yr}}^{-1}{\mathrm{arcsec}}^{-2}.$There is no significant change in extinction before and after the explosion. The gas metallicity does not change significantly with the separation from SN 2023ixf. The metal-rich branch of theR₂₃calculations indicates that the gas metallicity around SN 2023ixf is similar to the solar metallicity (∼Z_☉). The archival deep images from the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) show a clear detection of the progenitor of SN 2023ixf in thezband at 22.778 ± 0.063 mag, but nondetections in the remaining four bands of CFHTLS (u,g,r,i). The results suggest a massive progenitor of ≈22M_☉. 

Embodied Question Answering (EQA) is a relatively new task where an agent is asked to answer questions about its environment from egocentric perception. EQA as introduced in [8] makes the fundamental assumption that every question, e.g. “what color is the car?”, has exactly one target (“car”) being inquired about. This assumption puts a direct limitation on the abilities of the agent. We present a generalization of EQA – Multi-Target EQA (MT-EQA). Specifically, we study questions that have multiple targets in them, such as “Is the dresser in the bedroom bigger than the oven in the kitchen?”, where the agent has to navigate to multiple locations (“dresser in bedroom”, “oven in kitchen”) and perform comparative reasoning (“dresser” bigger than “oven”) before it can answer a question. Such questions require the development of entirely new modules or components in the agent. To address this, we propose a modular architecture composed of a program generator, a controller, a navigator, and a VQA module. The program generator converts the given question into sequential executable sub-programs; the navigator guides the agent to multiple locations pertinent to the navigation-related sub-programs; and the controller learns to select relevant observations along its path. These observations are then fed to the VQA module to predict the answer. We perform detailed analysis for each of the model components and show that our joint model can outperform previous methods and strong baselines by a significant margin. Project page: https://embodiedqa.org.