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We investigate the central density structure of dark matter halos in cold dark matter (CDM) and self-interacting dark matter (SIDM) models using simulations that are part of the Feedback In Realistic Environments (FIRE) project. For simulated halos of dwarf galaxy scale ($M_{\rm halo}(z=0)\approx 10^{10}\,M_\odot$), we study the central structure in both dissipationless simulations and simulations with full FIRE-2 galaxy formation physics. As has been demonstrated extensively in recent years, both baryonic feedback and self-interactions can convert central cusps into cores, with the former process doing so in a manner that depends sensitively on stellar mass at fixed $M_{\rm halo}$. Whether the two processes (baryonic feedback and self-interactions) are distinguishable, however, remains an open question. Here we demonstrate that, compared to feedback-induced cores, SIDM-induced cores transition more quickly from the central region of constant density to the falling density at larger radial scales. This result holds true even when including identical galaxy formation modeling in SIDM simulations as is used in CDM simulations, since self-interactions dominate over galaxy formation physics in establishing the central structure of SIDM halos in this mass regime. The change in density profile slope as a function of radius therefore holds the potential to discriminate between self-interactions and galaxy formation physics as the driver of core formation in dwarf galaxies., Comment: 11 pages, 7 figures

From $>1000$ orbits of HST imaging, we present deep homogeneous resolved star color-magnitude diagrams that reach the oldest main sequence turnoff and uniformly measured star formation histories (SFHs) of 36 dwarf galaxies ($-6 \ge M_V \ge -17$) associated with the M31 halo, and for 10 additional fields in M31, M33, and the Giant Stellar Stream. From our SFHs we find: i) the median stellar age and quenching epoch of M31 satellites correlate with galaxy luminosity and galactocentric distance. Satellite luminosity and present-day distance from M31 predict the satellite quenching epoch to within $1.8$ Gyr at all epochs. This tight relationship highlights the fundamental connection between satellite halo mass, environmental history, and star formation duration. ii) There is no difference between the median SFH of galaxies on and off the great plane of Andromeda satellites. iii) $\sim50$\% of our M31 satellites show prominent ancient star formation ($>12$ Gyr ago) followed by delayed quenching ($8-10$ Gyr ago), which is not commonly observed among the MW satellites. iv) A comparison with TNG50 and FIRE-2 simulated satellite dwarfs around M31-like hosts show that some of these trends (dependence of SFH on satellite luminosity) are reproduced in the simulations while others (dependence of SFH on galactocentric distance, presence of the delayed-quenching population) are weaker or absent. We provide all photometric catalogs and SFHs as High-Level Science Products on MAST., Comment: Accepted for publication on ApJ. 47 pages, 24 figures, 12 tables. Corresponding HLSP data can be retrieved at: https://archive.stsci.edu/hlsp/m31-satellites

We explore covert communication of qubits over an arbitrary quantum channel. Covert communication conceals the transmissions in the channel noise, ensuring that an adversary is unable to detect their presence. We show the achievability of a $\textit{square root law}$ (SRL) for quantum covert communication similar to that for classical: $M(n)\propto\sqrt{n}$ qubits can be transmitted covertly and reliably over $n$ uses of a general quantum channel. We lower bound $M(n)$ with and without assistance from a two-way covert classical channel. In the former case, we quantify the number of classical covert bits sufficient for our protocol.

This report investigates Training Data Attribution (TDA) and its potential importance to and tractability for reducing extreme risks from AI. First, we discuss the plausibility and amount of effort it would take to bring existing TDA research efforts from their current state, to an efficient and accurate tool for TDA inference that can be run on frontier-scale LLMs. Next, we discuss the numerous research benefits AI labs will expect to see from using such TDA tooling. Then, we discuss a key outstanding bottleneck that would limit such TDA tooling from being accessible publicly: AI labs' willingness to disclose their training data. We suggest ways AI labs may work around these limitations, and discuss the willingness of governments to mandate such access. Assuming that AI labs willingly provide access to TDA inference, we then discuss what high-level societal benefits you might see. We list and discuss a series of policies and systems that may be enabled by TDA. Finally, we present an evaluation of TDA's potential impact on mitigating large-scale risks from AI systems.

We utilize the cosmological volume simulation, FIREbox, to investigate how a galaxy's environment influences its size and dark matter content. Our study focuses on approximately 1,200 galaxies (886 central and 332 satellite halos) in the low-mass regime, with stellar masses between $10^6$ to $10^9$ $M_{\odot}$. We analyze the size-mass relation ($r_{50} - M_{\star}$), inner dark matter mass-stellar mass ($M^{50}_{\rm DM} - M_{\star}$) relation, and the halo mass-stellar mass ($M_{\rm halo} - M_{\star}$) relation. At fixed stellar mass, we find the galaxies experiencing stronger tidal influences, indicated by higher Perturbation Indices (PI $>$ 1) are generally larger and have lower masses relative to their counterparts with lower Perturbation Indices (PI $<$ 1). Applying a Random Forest regression model, we show that both the environment (PI) and halo mass ($M_{rm halo}$) are significant predictors of a galaxy's relative size and dark matter content. Notably, because $M_{\rm halo}$ is also strongly affected by the environment, our findings indicate that environmental conditions not only influence galactic sizes and relative inner dark matter content directly, but also indirectly through their impact on halo mass. Our results highlight a critical interplay between environmental factors and halo mass in shaping galaxy properties, affirming the environment as a fundamental driver in galaxy formation and evolution., Comment: 17 pages, 9 figures; submitted to ApJ

Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions by formulating $\ell_{1}$-norm minimization problems, solving these problems offline pointwise in time can lead to non-smooth reference commands, resulting in abrupt transitions. This study addresses this limitation by proposing a time-varying optimization method that explicitly considers time dependence. By leveraging a prediction-correction interior-point time-varying optimization framework, the proposed method solves an ordinary differential equation to update reference commands continuously over time, enabling smooth reference command generation in real time. Numerical simulations with a two-dimensional Lift+Cruise vehicle validate the effectiveness of the proposed method, demonstrating its ability to generate smooth reference commands online., Comment: 10 pages, 7 figures, AIAA SciTech 2025 Forum

Alcoved polytopes are convex polytopes which are the closure of a union of alcoves in an affine Coxeter arrangement. They are rational polytopes, and therefore have Ehrhart quasipolynomials. We describe a method for computing the generating function of the Ehrhart quasipolynomial, or Ehrhart series, of any alcoved polytope via a particular shelling order of its alcoves. We also conjecture a connection between Early's decorated ordered set partitions and this shelling order for the hypersimplex $\Delta_{2,n}$.

The "Report on the Condition of Education" is an annual report from the National Center for Education Statistics (NCES) that is mandated by the United States Congress. Using data from NCES and other sources, NCES compiles a set of "indicators" of the condition of education in the United States at all levels, from prekindergarten through postsecondary, as well as labor force outcomes and international comparisons. The full contents of the Condition of Education Indicator System can be accessed online or by downloading PDFs for the individual indicators. The "Report on the Condition of Education 2024" comprises key findings from the Indicator System. This summary report provides a brief overview of information available on various topics as well as direct links to the online versions of indicators discussed. This year's report also includes select content from sources outside the Indicator System, such as the National Assessment of Educational Progress (NAEP) and the Integrated Postsecondary Education System (IPEDS).

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined., Comment: 25 pages, 3 figures, Accepted for publication at Knowledge Engineering Review (KER)

This essay examines how Artificial Intelligence (AI) systems are becoming more integral to international affairs by affecting how global governors exert power and pursue digital sovereignty. We first introduce a taxonomy of multifaceted AI payoffs for governments and corporations related to instrumental, structural, and discursive power in the domains of violence, markets, and rights. We next leverage different institutional and practice perspectives on sovereignty to assess how digital sovereignty is variously implicated in AI-empowered global governance. States both seek sovereign control over AI infrastructures in the institutional approach, while establishing sovereign competence through AI infrastructures in the practice approach. Overall, we present the digital sovereignty stakes of AI as related to entanglements of public and private power. Rather than foreseeing technology companies as replacing states, we argue that AI systems will embed in global governance to create dueling dynamics of public/private cooperation and contestation. We conclude with sketching future directions for IR research on AI and global governance., Comment: 21 pages, 2 tables

Flexible photovoltaics with a lightweight and adaptable nature that allows for deployment on curved surfaces and in building facades have always been a goal vigorously pursued by researchers in thin-film solar cell technology. The recent strides made in improving the sunlight-to-electricity conversion efficiency of kesterite Cu$_{2}$ZnSn(S, Se)$_{4}$ (CZTSSe) suggest it to be a perfect candidate. However, making use of rare Mo foil in CZTSSe solar cells causes severe problems in thermal expansion matching, uneven grain growth, and severe problems at the back contact of the devices. Herein, a strategy utilizing single-crystal graphene to modify the back interface of flexible CZTSSe solar cells is proposed. It will be shown that the insertion of graphene at the Mo foil/CZTSSe interface provides strong physical support for the subsequent deposition of the CZTSSe absorber layer, improving the adhesion between the absorber layer and the Mo foil substrate. Additionally, the graphene passivates the rough sites on the surface of the Mo foil, enhancing the chemical homogeneity of the substrate, and resulting in a more crystalline and homogeneous CZTSSe absorber layer on the Mo foil substrate. The detrimental reaction between Mo and CZTSSe has also been eliminated. Through an analysis of the electrical properties, it is found that the introduction of graphene at the back interface promotes the formation of a quasi-ohmic contact at the back contact, decreasing the back contact barrier of the solar cell, and leading to efficient collection of charges at the back interface. This investigation demonstrates that solution-based CZTSSe photovoltaic devices could form the basis of cheap and flexible solar cells.

Integral field spectroscopy (IFS) is a powerful tool for understanding the formation of galaxies across cosmic history. We present the observing strategy and first results of MSA-3D, a novel JWST program using multi-object spectroscopy in a slit-stepping strategy to produce IFS data cubes. The program observed 43 normal star-forming galaxies at redshifts $0.5 \lesssim z \lesssim 1.5$, corresponding to the epoch when spiral thin-disk galaxies of the modern Hubble sequence are thought to emerge, obtaining kpc-scale maps of rest-frame optical nebular emission lines with spectral resolution $R\simeq2700$. Here we describe the multiplexed slit-stepping method which is $>15$ times more efficient than the NIRSpec IFS mode for our program. As an example of the data quality, we present a case study of an individual galaxy at $z=1.104$ (stellar mass $M_{*} = 10^{10.3}~M_{\odot}$, star formation rate~$=3~M_{\odot}$ yr$^{-1}$) with prominent face-on spiral structure. We show that the galaxy exhibits a rotationally supported disk with moderate velocity dispersion ($\sigma = 36^{+5}_{-4}$~\kms), a negative radial metallicity gradient ($-0.020\pm0.002$~dex\,kpc$^{-1}$), a dust attenuation gradient, and an exponential star formation rate density profile which closely matches the stellar continuum. These properties are characteristic of local spirals, indicating that mature galaxies are in place at $z\sim1$. We also describe the customized data reduction and original cube-building software pipelines which we have developed to exploit the powerful slit-stepping technique. Our results demonstrate the ability of JWST slit-stepping to study galaxy populations at intermediate to high redshifts, with data quality similar to current surveys of the $z\sim0.1$ universe., Comment: 24 pages, 12 figures, 1 table; submitted to ApJ, comments welcome. Our custom designed data reduction pipeline (MSA3D) and example processed data-cube will be publicly released before the JWST Cycle 4 deadline. MSA3D package on GitHub: https://github.com/barisiciv/msa3d.git

This study analysed sprint kayak pacing profiles in order to categorise and compare an athlete's race profile throughout their career. We used functional principal component analysis of normalised velocity data for 500m and 1000m races to quantify pacing. The first four principal components explained 90.77% of the variation over 500m and 78.80% over 1000m. These principal components were then associated with unique pacing characteristics with the first component defined as a dropoff in velocity and the second component defined as a kick. We then applied a Hidden Markov model to categorise each profile over an athlete's career, using the PC scores, into different types of race profiles. This model included age and event type and we identified a trend for a higher dropoff in development pathway athletes. Using the four different race profile types, four athletes had all their race profiles throughout their careers analysed. It was identified that an athlete's pacing profile can and does change throughout their career as an athlete matures. This information provides coaches, practitioners and athletes with expectations as to how pacing profiles can be expected to change across the course of an athlete's career., Comment: 17 Pages, 7 Figures, 3 Tables

We examine how the federal government can enhance its AI emergency preparedness: the ability to detect and prepare for time-sensitive national security threats relating to AI. Emergency preparedness can improve the government's ability to monitor and predict AI progress, identify national security threats, and prepare effective response plans for plausible threats and worst-case scenarios. Our approach draws from fields in which experts prepare for threats despite uncertainty about their exact nature or timing (e.g., counterterrorism, cybersecurity, pandemic preparedness). We focus on three plausible risk scenarios: (1) loss of control (threats from a powerful AI system that becomes capable of escaping human control), (2) cybersecurity threats from malicious actors (threats from a foreign actor that steals the model weights of a powerful AI system), and (3) biological weapons proliferation (threats from users identifying a way to circumvent the safeguards of a publicly-released model in order to develop biological weapons.) We evaluate the federal government's ability to detect, prevent, and respond to these threats. Then, we highlight potential gaps and offer recommendations to improve emergency preparedness. We conclude by describing how future work on AI emergency preparedness can be applied to improve policymakers' understanding of risk scenarios, identify gaps in detection capabilities, and form preparedness plans to improve the effectiveness of federal responses to AI-related national security threats.

Recent observations with the James Webb Space Telescope (JWST) have uncovered unexpectedly high cosmic star formation activity in the early Universe, mere hundreds of millions of years after the Big Bang. These observations are often understood to reflect an evolutionary shift in star formation efficiency (SFE) caused by changing galactic conditions during these early epochs. We present FIREbox-HR, a high-resolution, cosmological hydrodynamical simulation from the Feedback in Realistic Environments project, which offers insights into the SFE of galaxies during the first billion years of cosmic time. FIREbox-HR re-simulates the cosmic volume (L = 22.1 cMpc) of the original FIREbox run with eight times higher mass resolution (m_b ~ 7800 M_sun), but with identical physics, down to z ~ 6. FIREbox-HR predicts ultraviolet (UV) luminosity functions in good agreement with available observational data. The simulation also successfully reproduces the observed cosmic UV luminosity density at z ~ 6 - 14, demonstrating that relatively high star formation activity in the early Universe is a natural outcome of the baryonic processes encoded in the FIRE-2 model. According to FIREbox-HR, the SFE - halo mass relation for intermediate mass halos (M_halo ~ 10^9 - 10^11 M_sun) does not significantly evolve with redshift and is only weakly mass-dependent. These properties of the SFE - halo mass relation lead to a larger contribution from lower mass halos at higher z, driving the gradual evolution of the observed cosmic UV luminosity density. A theoretical model based on the SFE - halo mass relation inferred from FIREbox-HR allows us to explore implications for galaxy evolution. Future observations of UV faint galaxies at z > 12 will provide an opportunity to further test these predictions and deepen our understanding of star formation during Cosmic Dawn., Comment: 28 pages, 14 figures, 5 tables, revised to match version accepted by MNRAS, Appendix D added, UV LF shown in Fig. 1 provided as txt file

Contact between languages has the potential to transmit vocabulary and other language features; however, this does not always happen. Here, an iterated learning model is used to examine, in a simple way, the resistance of languages to change during language contact. Iterated learning models are agent-based models of language change, they demonstrate that languages that are expressive and compositional arise spontaneously as a consequence of a language transmission bottleneck. A recently introduced type of iterated learning model, the Semi-Supervised ILM is used to simulate language contact. These simulations do not include many of the complex factors involved in language contact and do not model a population of speakers; nonetheless the model demonstrates that the dynamics which lead languages in the model to spontaneously become expressive and compositional, also cause a language to maintain its core traits even after mixing with another language., Comment: to appear ALIFE24

This is the revised edition of the 2023 "Report on the Condition of Education," which is a congressionally mandated annual report from the National Center for Education Statistics (NCES). Using the most recent data available (at the time this report was written) from NCES and other sources, the report contains key indicators on the condition of education in the United States at all levels, from prekindergarten through postsecondary, as well as labor force outcomes and international comparisons. There are core indicators that are updated every year and spotlight indicators that provide in-depth analyses on topics of interest to education agencies, policymakers, researchers, and the public. At the broadest level, the Condition of Education Indicator System is organized into five sections: family characteristics; preprimary, elementary, and secondary education; postsecondary education; population characteristics and economic outcomes; and international comparisons. The Report on the "Condition of Education 2023" encompasses key findings from the Condition of Education Indicator System. The full contents of the Indicator System can be accessed online through the website or by downloading PDFs for the individual indicators. [For the first edition of the report, see ED628286.]

The iterated learning model is an agent model which simulates the transmission of of language from generation to generation. It is used to study how the language adapts to pressures imposed by transmission. In each iteration, a language tutor exposes a na\"ive pupil to a limited training set of utterances, each pairing a random meaning with the signal that conveys it. Then the pupil becomes a tutor for a new na\"ive pupil in the next iteration. The transmission bottleneck ensures that tutors must generalize beyond the training set that they experienced. Repeated cycles of learning and generalization can result in a language that is expressive, compositional and stable. Previously, the agents in the iterated learning model mapped signals to meanings using an artificial neural network but relied on an unrealistic and computationally expensive process of obversion to map meanings to signals. Here, both maps are neural networks, trained separately through supervised learning and together through unsupervised learning in the form of an autoencoder. This avoids the computational burden entailed in obversion and introduces a mixture of supervised and unsupervised learning as observed during language learning in children. The new model demonstrates a linear relationship between the dimensionality of meaning-signal space and effective bottleneck size and suggests that internal reflection on potential utterances is important in language learning and evolution., Comment: 33pages. (v2->v3 - revisions following referees report; the paper is now in press with PLoS Complex Systems)

While galaxy rotation curves provide one of the most powerful methods for measuring dark matter profiles in the inner regions of rotation-supported galaxies, at the dwarf scale there are factors that can complicate this analysis. Given the expectation of a universal profile in dark matter-only simulations, the diversity of observed rotation curves has become an often-discussed issue in Lambda Cold Dark Matter cosmology on galactic scales. We analyze a suite of Feedback in Realistic Environments (FIRE) simulations of $10^{10}-10^{12}$ $M_\odot$ halos with standard cold dark matter, and compare the true circular velocity to rotation curve reconstructions. We find that, for galaxies with well-ordered gaseous disks, the measured rotation curve may deviate from true circular velocity by at most 10% within the radius of the disk. However, non-equilibrium behavior, non-circular motions, and non-thermal and non-kinetic stresses may cause much larger discrepancies of 50% or more. Most rotation curve reconstructions underestimate the true circular velocity, while some reconstructions transiently over-estimate it in the central few kiloparsecs due to dynamical phenomena. We further demonstrate that the features that contribute to these failures are not always visibly obvious in HI observations. If such dwarf galaxies are included in galaxy catalogs, they may give rise to the appearance of "artificial" rotation curve diversity that does not reflect the true variation in underlying dark matter profiles., Comment: 20 pages, 10 figures

The observationally-inferred size versus stellar-mass relationship (SMR) for low-mass galaxies provides an important test for galaxy formation models. However, the relationship relies on assumptions that relate observed luminosity profiles to underlying stellar mass profiles. Here we use the Feedback in Realistic Environments simulations of low-mass galaxies to explore how the predicted SMR changes depending on whether one uses star-particle counts directly or mock observations. We reproduce the SMR found in The Exploration of Local Volume Satellites survey remarkably well only when we infer stellar masses and sizes using mock observations. However, when we use star particles to directly infer stellar masses and half-mass radii, we find that our galaxies are too large and obey a SMR with too little scatter compared to observations. This discrepancy between the "true" galaxy size and mass and those derived in the mock observation approach is twofold. First, our simulated galaxies have higher and more varied MLRs at a fixed colour than those commonly-adopted, which tends to underestimate their stellar masses compared to their true, simulated values. Second, our galaxies have radially increasing MLR gradients therefore using a single MLR tends to under-predict the mass in the outer regions. Similarly, the true half-mass radius is larger than the half-light radius because the light is more concentrated than the mass. If our simulations are accurate representations of the real universe, then the relationship between galaxy size and stellar mass is even tighter for low-mass galaxies than is commonly inferred from observed relations., Comment: 11 pages, 7 figures, 1 table

We use FIRE-2 zoom simulations of Milky Way size disk galaxies to derive easy-to-use relationships between the observed circular speed of the Galaxy at the Solar location, $v_\mathrm{c}$, and dark matter properties of relevance for direct detection experiments: the dark matter density, the dark matter velocity dispersion, and the speed distribution of dark matter particles near the Solar location. We find that both the local dark matter density and 3D velocity dispersion follow tight power laws with $v_\mathrm{c}$. Using this relation together with the observed circular speed of the Milky Way at the Solar radius, we infer the local dark matter density and velocity dispersion near the Sun to be $\rho = 0.42\pm 0.06\ \mathrm{GeV}\,\mathrm{cm^{-3}}$ and $\sigma_{\rm 3D} = 280^{+19}_{-18}\,\mathrm{km\,s^{-1}}$. We also find that the distribution of dark matter particle speeds is well-described by a modified Maxwellian with two shape parameters, both of which correlate with the observed $v_{\rm c}$. We use that modified Maxwellian to predict the speed distribution of dark matter near the Sun and find that it peaks at a most probable speed of $257\,\mathrm{km\,s^{-1}}$ and begins to truncate sharply above $470\,\mathrm{km\,s^{-1}}$. This peak speed is somewhat higher than expected from the standard halo model, and the truncation occurs well below the formal escape speed to infinity, with fewer very-high-speed particles than assumed in the standard halo model., Comment: 25 pages, 12 figures, 3 tables; JCAP accepted version

We present NIRCam and NIRISS modules for DOLPHOT, a widely-used crowded field stellar photometry package. We describe details of the modules including pixel masking, astrometric alignment, star finding, photometry, catalog creation, and artificial star tests (ASTs). We tested these modules using NIRCam and NIRISS images of M92 (a Milky Way globular cluster), Draco II (an ultra-faint dwarf galaxy), and WLM (a star-forming dwarf galaxy). DOLPHOT's photometry is highly precise and the color-magnitude diagrams are deeper and have better definition than anticipated during original program design in 2017. The primary systematic uncertainties in DOLPHOT's photometry arise from mismatches in the model and observed point spread functions (PSFs) and aperture corrections, each contributing $\lesssim0.01$ mag to the photometric error budget. Version 1.2 of WebbPSF models, which include charge diffusion and interpixel capacitance effects, significantly reduced PSF-related uncertainties. We also observed minor ($\lesssim0.05$ mag) chip-to-chip variations in NIRCam's zero points, which will be addressed by the JWST flux calibration program. Globular cluster observations are crucial for photometric calibration. Temporal variations in the photometry are generally $\lesssim0.01$ mag, although rare large misalignment events can introduce errors up to 0.08 mag. We provide recommended DOLPHOT parameters, guidelines for photometric reduction, and advice for improved observing strategies. Our ERS DOLPHOT data products are available on MAST, complemented by comprehensive online documentation and tutorials for using DOLPHOT with JWST imaging data., Comment: 30 pages, 17 figures. Accepted to ApJS. Data products to be hosted on MAST. For DOLPHOT/JWST tutorials, see https://dolphot-jwst.readthedocs.io/en/latest/ . For more program and DOLPHOT info, see https://ers-stars.github.io

A kinematic misalignment of the stellar and gas components is a phenomenon observed in a significant fraction of galaxies. However, the underlying physical mechanisms are not well understood. A commonly proposed scenario for the formation of a misaligned component requires any pre-existing gas disc to be removed, via fly-bys or ejective feedback from an active galactic nucleus. In this Letter, we study the evolution of a Milky Way mass galaxy in the FIREbox cosmological volume that displays a thin, counter-rotating gas disc with respect to its stellar component at low redshift. In contrast to scenarios involving gas ejection, we find that pre-existing gas is mainly removed via the conversion into stars in a central starburst, triggered by a merging satellite galaxy. The newly-accreted, counter-rotating gas eventually settles into a kinematically misaligned disc. About 4.4 (8 out of 182) of FIREbox galaxies with stellar masses larger than 5e9 Msun at z=0 exhibit gas-star kinematic misalignment. In all cases, we identify central starburst-driven depletion as the main reason for the removal of the pre-existing co-rotating gas component, with no need for feedback from, e.g., a central active black hole. However, during the starburst, the gas is funneled towards the central regions, likely enhancing black hole activity. By comparing the fraction of misaligned discs between FIREbox and other simulations and observations, we conclude that this channel might have a non-negligible role in inducing kinematic misalignment in galaxies., Comment: 9 pages; 3 figures; submitted to ApJ Letters