The JMGT equation was put forward by Pedro Jordan~\cite{jordan2008nonlinear,jordan2014second}, also referring to earlier work by Moore and Gibson~\cite{moore1960propagation}, as well as Thompson~\cite{thompson} to amend the infinite speed of sound paradox of classical models of nonlinear acoustics such as the Westervelt and Kuznetsov's equation. Additionally to its physical significance (and of course related to it), it has given rise to a substantial body of mathematical literature -- possibly even more than the above mentioned classical models. In this paper, we aim to provide a systematic (though inevitably incomplete) overview %and indicate some potential open questions. thereby focusing on well-posedness analysis of initial value and time periodic problems, memory and fractional attenuation as well as singular limits and -- with one example each -- control and inverse problems.

This paper first demonstrates that applying standard incremental nonlinear dynamic inversion (INDI) with incremental control allocation (ICA) to input nonaffine systems relies on an untenable linear approximation of the actuator model. It then shows that avoiding this issue, while retaining the static control allocation paradigm, generally requires solving a nonlinear programming (NLP) problem. To address the associated online computational challenges, the paper subsequently presents a supervised learning-based approach. Numerical experiments on an example problem validate the identified limitations of standard INDI + ICA for input nonaffine systems, while also demonstrating that the proposed learning-based method provides an effective and computationally tractable alternative.

We develop a unified framework to characterize the power of higher-level algorithms for the constraint satisfaction problem (CSP), such as $k$-consistency, the Sherali-Adams LP hierarchy, and the affine IP hierarchy. As a result, solvability of a fixed-template CSP or, more generally, a Promise CSP by a given level is shown to depend only on the polymorphism minion of the template. Similarly, we obtain a minion-theoretic description of $k$-consistency reductions between Promise CSPs. We introduce a new hierarchy of SDP-like vector relaxations with vectors over $\mathbb Z_{p}$ in which orthogonality is imposed on $k$-tuples of vectors. Surprisingly, this relaxation turns out to be equivalent to the $k$-th level of the AIP-$\mathbb{Z}_p$ relaxation. We show that it solves the CSP of the dihedral group $\mathbf{D}_4$, the smallest CSP that fools the singleton BLP+AIP algorithm. Using this vector representation, we further show that the $p$-th level of the $\mathbb{Z}_p$ relaxation solves linear equations modulo $p^2$.

We investigate electron-positron pair production from the vacuum in presence of a strong oscillating electric field with a multi-pulse structure and variable inter-pulse delay. The pair production probabilities are computed by numerically solving the time-dependent Dirac equation. We analyze the resulting momentum distribution and the total number density of produced particles for different numbers of pulses and inter-pulse delays. In particular, we demonstrate the emergence of a characteristic time-domain multi-slit interference pattern in the pair production probability as a function of the inter-pulse delay.

Generative AI enables students to produce plausible code quickly. Producing working code is therefore no longer a reliable indicator of understanding. This is particularly problematic in non-computer-science programmes, where time constraints make it hard to balance conceptual foundations with sufficient application practice. Empirical studies of AI tutors, educational chatbots, and code-assistance systems report useful but often case-specific findings, while learning theory remains too abstract to directly guide course design. As a result, instructors lack a simple, reusable way to make learning intent explicit and translate it into concrete teaching structures and student learning behaviour. This paper contributes knowledge markers as a lightweight, AI-agnostic, course-level operationalisation for course design. The markers label learning units by their primary emphasis: (A) Application knowledge (implementation), (S) Structure knowledge (concepts and mental models), or (P) Procedure knowledge (systematic methods, decision making, and verification). We show how the labels can be embedded at fine granularity in open teaching artifacts (interactive website, PDF script, and notebooks), paired with communication elements and optional AI-usage guidance. We demonstrate the approach by analysing, redesigning, and descriptively evaluating an introductory programming course using marker distributions derived from the table of contents. The paper is design- and artifact-oriented and does not claim measured learning gains; empirical evaluation is future work.

Resource allocation problems across multiple contests are ubiquitous in adversarial settings, from military operations to market competition. While Colonel Blotto and General Lotto games have provided valuable theoretical foundations for such problems, their equilibrium characterizations typically permit resources to be arbitrarily allocated across all contests -- a flexibility that rarely aligns with practical constraints. This paper introduces a novel constrained variant of the General Lotto game where one player is restricted to allocating resources to only a single contest. In this model we provide lower and upper bounds on the security values for this constrained player, quantifying how the inability to distribute resources across multiple contests fundamentally changes optimal strategic behavior and performance guarantees. These findings contribute to a broader understanding of how operational constraints shape strategic outcomes in competitive resource allocation, with implications for decision-makers facing similar constraints in practice.

Machine learning techniques, notably various deep neural network methods, are instrumental in processing extensive and intricate data sets in engineering and scientific fields. This paper shows how deep neural networks can inversely design cascaded-mode converting systems, particularly the waveguide gratings that implement selective mode conversion upon reflection. Neural networks can map the grating's physical features to scattering parameters of the modes reflected from the grating. The trained networks can then be utilized to inversely design the gratings based on the desired values of the scattering parameters. The process of the inverse design involves using the technique of gradient descent of a defined loss function. Minimizing this loss function leads to calculating more accurate features fulfilling the desired scattering parameters.

We revisit a learning-induced tricritical point, at which three phases with strong, weak, and broken $Z_2$ symmetry meet, in the phase diagram of a deformed toric code wavefunction subjected to weak measurements. This setting is exactly dual to a classical Bayesian inference phase diagram of the $2D$ classical Ising model. Here we demonstrate that this tricritical point lies on a distinct $\textit{higher Nishimori line}$, which has an emergent gauge-invariant formulation, just like the ordinary Nishimori line but with a higher replica symmetry as a replica stat-mech model in the replica number $R\rightarrow2$ limit, where disorder is averaged according to the Born rule. As such, the learning tricritical point is in fact a $\textit{higher Nishimori critical point}$. Using this identification, we obtain a number of $\textit{exact results}$ at this $\textit{higher}$ Nishimori critical point; e.g., we show that the power-law exponent of the Edwards-Anderson correlation function is exactly equal to that of the spin correlation function at the unmeasured Ising critical point and verify this in numerical simulations. Using the tools of the proof of a $c$-effective theorem [arXiv:2507.07959], we show that the Casimir effective central charge $c_{\text{eff}}$ $\textit{decreases}$ under renormalization group (RG) flow from the $\textit{higher}$ Nishimori critical point to the unmeasured $2D$ Ising critical point, and is thus greater than $1/2$. This is corroborated by extensive numerical simulations finding $c_{\text{eff}} = 0.522(1)$. The analytical result also explains, with a physically motivated assumption, the numerically observed increase of the Casimir effective central charge under the RG flow from the ordinary Nishimori critical point to the clean Ising critical point in the random-bond Ising model. We also discuss $\textit{higher}$ Nishimori criticality in general dimensions $D>1$.

General relativity and quantum mechanics are incompatible at the Planck scale. This contention can be examined if a quantum computer is set to operate at a rate that exceeds the classical limit of one operation per Planck volume-time, or equivalently $2^{491}$ m$^{-3}$ s$^{-1}$. Here we quantify the relation between the logical qubit count and the extent to which classicality is challenged. We argue that 500 logical qubits are sufficient to reject theories confined to a laboratory. We account for the operational cost of computation and communication at all scales up to and including the observable universe, ultimately constrained by a 1600-logical-qubit computer. Remarkably, current plans for commercial quantum computers are projected to surpass this limit, thereby putting the quantum-gravity standoff to the test.

Grant recommendation systems remain one of the least explored areas within academic recommender systems, and existing proposals are typically tied to specific funding agencies or disciplinary domains. This paper presents an institution-level reproducible framework for matching researchers to funding opportunities by combining bibliometric profiling with semantic matching. Rather than representing each researcher through a single aggregated profile, the framework constructs multiple publication sets defined by bibliometric criteria such as authorship position and time window, each independently compared against funding calls using word embeddings. Within-researcher normalisation and percentile-based ranking transform cosine similarity scores into actionable recommendations. A case study applied to 3,013 researchers from the University of Granada and 291 Horizon Europe topics verify it and shows that the four indicators capture complementary signals.

Let $\mathcal{X}$ be an algebraic stack admitting a moduli space $\mathcal{X}_{\mathrm{mod}}$. We study the factorizations of the moduli space morphism $\mathcal{X}\rightarrow\mathcal{X}_{\mathrm{mod}}$ to construct intermediate stacks that simplify the stacky structure of $\mathcal{X}$ while retaining more structural information than $\mathcal{X}_{\mathrm{mod}}$. Under mild assumptions, we prove the existence of a universal morphism from $\mathcal{X}$ to stacks satisfying well-behaved `modular properties' (such as being Deligne-Mumford, having finite inertia, or being uniformizable), and show that this universal map is itself an adequate moduli space morphism. We achieve this by proving that ascending chains of adequate moduli space morphisms from a Noetherian stack stabilize if they are cohomologically affine or with target Deligne-Mumford stacks. Finally, we demonstrate that stabilization completely fails for general adequate moduli space morphisms. We construct a simple Noetherian, Deligne-Mumford stack admitting an infinite, non-stabilizing chain of adequate moduli space morphisms, whose limit is a non-algebraic fpqc stack.

Semi-analytic models (SAMs) have been treating galaxy populations as dynamical systems for $\gtrsim50$ years, but their evolution equations remain poorly constrained. We introduce sapphire, a modular, automatically differentiable, GPU-accelerated SAM written from scratch in JAX. For the first time, we compute exact Jacobian matrices of our nonlinear differential equations and show that they have interpretable, non-random structures, using the Pandya et al. (2023) physical model as an initial example. Both local and global sensitivity analyses reveal that supernova energy loading is a key astrophysical parameter for galaxy evolution. We use gradient descent and Hamiltonian Monte Carlo (HMC) to perform comprehensive mock parameter recovery tests. These indicate that the z=0 stellar-to-halo-mass relation alone does not contain enough information to infer many astrophysical parameters. Using observations of star-forming galaxies from the MaNGA survey and the Behroozi et al. (2019) empirical model as one baseline, we derive multiple posteriors assuming different combinations of data, including z=0 interstellar medium gas fractions and metallicities. The inferred physical parameters suggest that galaxies self-regulate their star formation primarily through preventative rather than ejective feedback. Both Fisher and HMC forecasts demonstrate the potential of sapphire to enable precision inference for galaxy formation, but more work is needed to expand its library of models. We discuss how our unique blend of differentiability, massive GPU parallelization, numerical robustness and principled Bayesian methods sets the stage for hybrid physics-informed, data-driven discovery of galaxy formation astrophysics and cosmology. We make sapphire publicly available at https://github.com/virajpandya/sapphire.

Understanding the multiplicity of stellar systems and the correlations between their hierarchical components provides crucial insights into star formation processes. If binary companions form independently in each component of a wide binary (WB), the fraction of quadruple systems, i.e., 2+2 configurations where both components are themselves close binaries (CBs), should equal the product of individual CB fractions. Using \textit{Gaia} DR3 radial velocity spectroscopy (RVS) data for WB systems, we measure the CB fraction $p$ and quadruple fraction $P_{2+2}$, suggesting an enhancement factor $κ= P_{2+2}/p^2 = 2.34_{-0.11}^{+0.12}$, significantly exceeding unity expected under a statistical model of independence. We confirm the significance of this excess by performing two sets of tests: (1) shuffling WB pairings while preserving the overall $ΔG$ distribution shows no significant enhancement, ruling out selection effects; (2) simulations preserving the spectral type (temperature-dependent) CB fraction also yield the same null excess. When examined as a function of WB separation, the enhancement remains strong at separations $\leq 5\,000$ AU, but shows a decline towards unity at the widest separations ($\geq 10\,000$ AU). An independent proper motion anomaly (PMa) consistency check confirms the enhancement, suggesting a similar value. We further find that the enhancement declines with increasing peculiar velocity, suggesting that dynamical processing in older or dynamically hotter populations may transform 2+2 quadruples into triples over time. Our results provide strong evidence for correlated binary formation processes operating in WB systems.

We present the first microscopic demonstration of a disorder-pinned hole Wigner crystal (WC), providing a natural explanation for the reentrant integer quantum Hall effect observed near $ν=2/3$, as well as its analogs in fractional Chern insulators. We further identify a novel crossover regime above filling $ν=2/3$ that connects this hole WC to an electron WC, characterized by a network-like electron density structure. To uncover these phenomena, we use neural-network variational Monte Carlo (NNVMC) with a disorder-aware self-attention neural quantum state that describes both fractional quantum Hall (FQH) liquids and Wigner crystals within a single unbiased variational framework. More broadly, our method establishes a unified phase diagram that exposes a fundamental asymmetry in crystallization across half-filling: near $ν=1/3$, increasing LL mixing and disorder both stabilize an electron WC, whereas near $ν=2/3$, the hole WC dominates at weak LL mixing and ultimately gives way to the electron WC at strong LL mixing.

We present a scenario for GeV-scale thermal dark matter that can only be tested with accelerator experiments. Dark matter is composed of dark pions arising from a confining strong interaction in the dark sector. The thermal relic density is obtained through the interplay of up-scatterings of dark pions to heavier dark mesons (the dark counterparts of the kaons and $η$), and decays of the unstable dark $η$ to Standard Model particles. This mechanism is analogous to a playground slide, where one climbs up first and then slides down with a release of energy. We illustrate the scenario with a minimal model based on the SU(3)/SO(3) coset, where dark matter is stabilized by a U(1) flavor symmetry. The correct relic density is obtained with dark meson mass splittings of 10% to 50% and a dark-$η$ lifetime shorter than $10^3\,\mathrm{m}/c$. Direct and indirect dark matter searches are mostly ineffective, as a consequence of the charge conjugation symmetry of the stabilizing U(1). The most striking signals arise at the LHC, from the production of dark showers containing long-lived dark $η$'s that decay to visible final states. These signatures crucially depend on the portal interaction connecting the dark sector to the Standard Model. We show that several well-known portals can complete the scenario above the weak scale, and outline the expected signals in each case.

We present the first implementation of an evolving dust grain size distribution (GSD) within a semi-analytic cosmological model (SAM) of galaxy evolution. This flexible model self-consistently accounts for stellar dust production, shattering, coagulation, accretion of gas-phase metals, and destruction in supernova-driven shocks and hot gas, successfully reproducing key observational constraints. The purpose of this paper is to present the key physical elements of this novel dust implementation in a SAM and to explore controlled numerical experiments to identify the mechanisms shaping the GSD and extinction law in galaxies. Our results show that the GSD evolves from a large-grain-dominated regime at high redshift to a flatter, MRN-like shape at low redshift. This transition occurs earlier for massive galaxies, at a characteristic metallicity determined by the galaxy depletion time. The resulting extinction curves show an increase of the UV/optical slope and a pronounced $2175$ A bump toward lower redshift, in good agreement with the extinction properties of the MW. Through numerical experiments, we find that once stars provide the initial reservoir of large grains, shattering and ISM accretion are the principal mechanisms driving the growth of small grains. When accretion is included, the model robustly reproduces the observed $z \approx 0$ dust masses, largely independent of the specific assumptions adopted for grain-size physics. The extinction properties of MW-like galaxies are also generally recovered, except in extreme cases, such as when grain velocities in turbulent media are assumed to be independent of grain size.

Tomographic Volumetric Additive Manufacturing (TVAM) enables rapid, layerless biofabrication; however, its application to thermoreversible hydrogels is often compromised by complex chemical kinetics. In this study, we identify and characterize a recurrent printing artifact - termed the Pandoro effect - manifesting as a truncated-cone distortion caused by premature polymerization at the vial bottom and inhibition at the top. We demonstrate that this phenomenon originates from a vertical oxygen gradient driven by the thermal hysteresis of resin preparation: heating depletes dissolved oxygen, while subsequent cooling induces diffusion-limited re-oxygenation from the air-resin interface. To mitigate this, we present a multi-tiered strategy. First, we introduce a coupled ray-optical and photochemical optimization model that rigorously accounts for spatially heterogeneous inhibitor concentrations. Unlike conventional threshold-based approaches, this differentiable framework explicitly simulates the spatiotemporal reaction-diffusion dynamics of oxygen depletion, allowing the inverse solver to predictively compensate for local inhibition gradients. Complementing this algorithmic correction, we validate two process-based interventions: the elimination of the air-resin interface and the control of headspace atmosphere. We demonstrate that these strategies effectively suppress the Pandoro effect, and are compatible with cell-laden resins. This work establishes guidelines for reproducible volumetric bioprinting and expands our open-source Dr.TVAM platform with advanced polymerization modeling capabilities.

We present a detailed, fully nonlinear study of binary systems involving one black hole and one boson star, considering the effects of both a quartic self-interaction and a solitonic potential for the scalar field. First, we show the importance of using initial data for which the boson star is in an equilibrated configuration to obtain accurate gravitational waveforms, and discuss methods to further improve constraint violations in the initial data. We then present a series of head-on collisions, showing that even in this simplified scenario the radiative efficiency varies significantly with the scalar potential chosen. In addition to this, we present a preliminary study of inspiral configurations, showing that an appropriate scalar self-interaction can suppress tidal disruption. We comment throughout on implications for attempts to build model-agnostic waveform template banks for exotic compact objects.

Discoveries of gamma-ray bursts (GRBs) have become commonplace in recent decades, totalling $\mathcal{O}(10^4)$ unique detections across various missions. However, there have been no confirmed discoveries of a gravitationally-lensed GRB, despite expected lensing rates of $\sim1$ in $10^{3}$. In light of this, we complete an archival search for lensed GRBs by cross-matching well-localised \emph{Swift}/XRT-detected bursts with a large all-sky sample of galaxy clusters as potential lenses. We find a total of 17 candidate lensed GRBs defined by a 2 arcminute search radius from a cluster in our sample. 14 of our candidates are either confirmed to be at higher redshifts than their cross-matched cluster, or are consistent with a higher redshift origin based on the Amati relation between $E_{p,i}$ and $E_{\rm iso}$ of GRBs, indicating they are, at some level, lensed by their nearby cluster. Using the Amati relation and the lens-GRB separation, we quantify the magnification experienced by each GRB. We find $μ< 10$ for all except for one candidate, GRB~071031, which is consistent with $μ> 10$, but is uncertain. Another candidate, GRB~050509B, does not have a directly measured redshift, but was previously assumed to be at the redshift of its nearby cluster, $z=0.225$. We produce a lens model of this cluster and show that GRB~050509B is consistent with $z>1$ and magnified by $μ\simeq2-6$. We present these findings in anticipation of future lensed GRB discoveries enabled by facilities such as the Vera C. Rubin Observatory in the coming years.

We present a parameterized ("toy") model for shock interaction and $γ$-ray emission in classical novae, in which a white dwarf envelope of mass $M_{\rm env}$ is removed over a timescale $τ$ (proportional to the nova speed class, $t_{2}$) in an outflow that accelerates on the same timescale to a terminal speed $v_{\rm f}$. Particle acceleration occurs at the reverse shock generated when the outflow collides with a thin, dense shell of slower material released earlier. Accelerated protons are then advected into the shell, where for typical ${ M_{\rm env}, τ, \text{and } v_{\rm f}}$ they radiate in the calorimetric limit, consistent with correlated optical and $γ$-ray emission seen in well-sampled novae. The maximum proton energy, set by a Hillas-like argument, scales with the thickness of the hot post-shock region. Recent work shows turbulent mixing of hot post-shock gas with cooler dense gas may limit this thickness to $\lesssim 10^{-4}$ of the shock radius, explaining low X-ray luminosities. Using this empirically motivated thickness, and assuming efficient magnetic amplification, we predict maximum proton energies $E_{\rm max} \sim 10$ GeV, consistent with $γ$-ray spectra of Fermi-detected novae near optical peak ($\sim τ$). However, as the shock and post-shock layer expand, $E_{\rm max}$ can grow to $\gtrsim 10$ TeV on timescales of a few $τ$, enabling potential detection by atmospheric Cherenkov telescopes. We encourage TeV follow-up of Fermi-detected novae weeks to months after the optical/GeV peak and quantify the most promising events.

Understanding the dynamics of systems evolving in complex and rugged energy landscapes is central across physics, economics, biology, and computer science. Disordered mean-field models provide a powerful framework, as exact Dynamical Mean-Field Equations (DMFE) can be derived. However, solving the DMFE -- a set of coupled integral-differential equations for two-time functions -- remains a major numerical challenge. So far, large-time solutions of DMFE rely either on analytical approaches, such as the Cugliandolo--Kurchan ansatz based on assumptions like weak ergodicity breaking (which is known to fail in some cases), or on numerical integrations that reliably reach times $O(10^3)$ and extend further only via poorly controlled approximations. Consequently, no general method currently exists to solve DMFE at very long times, limiting the study of slow dynamics in complex landscapes. We present \textsc{Dynamite} (DYNAmical Mean-fIeld Time Evolution solver), a high-performance framework for solving DMFE up to unprecedented times $t=O(10^7)$. It combines non-uniform interpolation, adaptive time stepping, and numerical `renormalization' of memory, enabling accurate evaluation of history integrals. Its asymptotic runtime is linear, with sublinear memory scaling. Stability and precision are ensured via an adaptive Runge--Kutta scheme and periodic sparsification of the past. \textsc{Dynamite} achieves orders-of-magnitude speedups over uniform-grid methods while maintaining accuracy and reproducibility on CPU and GPU architectures. Benchmarks on glassy mean-field models, including the mixed spherical $p$-spin system, demonstrate access to aging and relaxation regimes previously out of reach. The framework provides a reproducible and extensible foundation for studying long-memory dynamical systems.

Neutron stars, at their cores, are highly dense and, thus, are expected to have a number of exotic processes. This includes a possible phase transition to deconfined quark matter at the core, leading to a hybrid star. The quark matter is expected to additionally be color superconducting. The physics of superconductivity plays an important role in understanding the high density matter in the interiors of neutron/hybrid stars. At their high densities, additionally, both proton superconductivity and neutron superfluidity are expected. We study the effect of superconducting (quark/proton) matter, along with the internal magnetic field, leading to pressure anisotropy within hybrid stars. We aim to probe the effect of superconductivity, especially from color superconducting quarks, in hybrid star structure. We propose new phenomenological model anisotropy profiles within a one-dimensional framework. We model quark matter using the vector interaction enhanced Bag model, and hadron matter with the DD2 equation of state. A Maxwell construction joins both phases. We further investigate the possible observational signatures of these hybrid stars. These include mass enhancement and continuous gravitational waves, possibly arising from the anisotropy induced deformation, helping us further constrain our model and its physical parameters.

We present a solvable Hamiltonian that realizes an exact lattice chiral $U(1)_V \times U(1)_A$ symmetry. Nielsen-Ninomiya-type no-go theorems are evaded by using lattice bosons rather than fermions. The continuum limit is a compact boson field theory with an axion-like coupling. The $U(1)_V$ symmetry shifts the scalar, while $U(1)_A$ acts on local operators associated with short axion strings and is transmuted into a higher-form symmetry in the continuum limit. We demonstrate the chiral anomaly by showing that the lattice theta angle is shifted by an axial rotation when $U(1)_V$ is gauged. Gauging either $U(1)_V$ or $U(1)_A$ leads to lattice non-invertible and 2-group symmetries, respectively, matching the continuum picture.

Theories with warped extra dimensions, like the Randall-Sundrum (RS) model, exhibit a holographic phase transition from a hot, deconfined black brane phase to a cool, confined phase. The standard picture of a first-order, strongly supercooled phase transition is expected to change in variations where the extra dimension is smoothly cut off by a soft-wall curvature singularity, as opposed to a hard brane. To understand this situation, we consider a simple ansatz for the warped geometry which allows us to obtain analytical results while maintaining the essential behavior of a soft wall. Unlike RS with the usual Goldberger-Wise stabilization, the hot, black brane phase only exists above a minimum temperature, which is not much smaller than the critical temperature. We explore the dynamics of the phase transition across the range of possibilities for the asymptotic geometry of a soft wall. This involves calculating an effective 4D action for the location of the black brane horizon. Using the effective action, we show that the phase transition completes rapidly ($β/H$ of $10^{3\text{-}4}$ is typical) and with only slight supercooling. We compute the resulting gravitational wave signal for a TeV-scale transition, finding that it is accessible to future space-based interferometers.

We present JWST/MIRI mid-infrared integral field spectroscopy combined with ALMA CO(2-1) observations of the post-starburst galaxy ESO 420-G13, hosting a low-luminosity AGN. The unprecedented spatial and spectral resolution of MIRI enables a detailed study of the molecular and ionised gas kinematics, excitation, and energetics in the nuclear kiloparsec, revealing the impact of AGN feedback in a system with modest radiative output. Despite its faint radio and X-ray emission ($L_{2-10keV} \sim 10^{40}$ erg/s), ESO 420-G13 exhibits powerful kinetic feedback in the form of massive molecular and ionised gas outflows, with a total kinetic power of $\sim 1.5 \times 10^{41}$ erg/s. This corresponds to a jet-ISM coupling efficiency of ~3.8%, within the range observed in more powerful AGN. The feedback is driven by a previously undetected compact jet, traced by collimated coronal-line and extended X-ray emission to >870 pc from the nucleus. The interaction is strongest ~370 pc north of the nucleus, where a fast ionised gas stream emerges perpendicular to the jet axis, coinciding with a bend in the jet direction. Enhanced velocity dispersion in warm H2 surrounds this gas stream, consistent with an expanding molecular bubble. Massive molecular outflows are detected at its edges; the blueshifted outflow is devoid of CO emission, likely due to CO destruction in shocks or by cosmic rays from the jet-ISM interaction. About 5% of the central molecular reservoir has already been expelled, and the remaining gas is turbulent and warm, suggesting an ongoing phase of AGN-driven feedback in this post-starburst galaxy. Our results highlight the enormous potential of mid-IR imaging spectroscopy to uncover jet-driven feedback in low-luminosity AGN. Without the spatially resolved MIRI diagnostics, the kinetic power of the AGN in ESO 420-G13 and its role in shaping the host galaxy ISM would have remained hidden.

We use controlled N-body simulations to study the collisional exchange of energy between stars and dark matter in ultra-faint galaxies. We find that dynamical friction between stars and subsolar-mass dark matter particles results in the depletion of dark matter from the galaxies' centers, thereby transforming dark matter cusps into constant-density cores. The process is particularly effective in tidally limited galaxies with low stellar velocity dispersion. As high-mass stars sink toward the center of the dark matter halo, the dynamical-to-stellar mass ratio within the stellar half-light radius decreases monotonically. The stellar population of a dark matter-dominated galaxy is thereby compacted into a dense, baryon-dominated cluster, surrounded by a dark matter halo. Such a cluster would share the chemical composition of an ultra-faint galaxy, yet would be virtually dark matter-free within its half-light radius. We moreover find that the collisional cooling with dark matter particles provides an efficient pathway for the formation of stellar binaries in the contracting cluster. The contraction is eventually slowed down due to the decreasing central dark matter densities and the formation of stellar binaries. Our models highlight that the dynamical processes governing the faintest galaxies give rise to a rich phenomenology, blurring the line between the dynamics of globular clusters and galaxies.

Gas properties in the outer intracluster medium (ICM) are not well-constrained, as traditional probes lose sensitivity at Mpc distances. We show that the morphology of radio relics effectively encodes the power spectrum of the surrounding density fluctuations, and that they hence represent a new observational window. To demonstrate this, we use cosmologically motivated shock-tube simulations in which we systematically vary the coherence length, amplitude, and power-law slope of the upstream density power spectra. We then post-process our simulations with the cosmic ray electron spectral solver, Crest, thereby producing a suite of mock radio relics. We find that the downstream morphology of our simulated relics is independently sensitive to each of the aforementioned parameters. Specifically, we show that observed 'double strand' features can be formed by curved shock fronts in projection, and that the scale of these features maps directly to the fluctuation coherence length. Increasing the fluctuation amplitude, meanwhile, progressively lengthens the downstream extent of the relic, thus explaining why relics are observed to be broader than the idealised expectation. It also broadens the Mach number distribution across the shock, which simultaneously increases the integrated radio flux density and produces patchier emission. Finally, steepening the power-law slope makes 'double strand' features more likely, and additionally increases both the number of radio filaments oriented parallel to the shock front and their spacing. At higher Mach numbers, steepening the power-law slope can further lead to the production of curved radio filaments, which trace large eddies. We apply our analysis to the Toothbrush and Sausage relics, and find evidence for a typical fluctuation coherence length of ~500 kpc, a non-uniform amplitude, and power spectra that are steeper than a Kolmogorov-like scaling.

Large language model-based agents are increasingly being integrated into core battlefield functions, including intelligence analysis, data fusion, and battlefield management. This paper argues that the very features that make such agents operationally attractive, namely their capacity for initiative, interpretation, their goal-directedness, and dynamic memory, are the same features that render context-appropriate human judgment and control substantively ineffectual in those parts of the kill chain where agents operate. Drawing on specific use cases, the paper argues that by relocating initiative and interpretation, LLM-based agents displace human decision-making in ways that makes their use incompatible with the requirement of human judgment and control which is central to existing governance frameworks, like those proposed by the GGE-CCW and REAIM. The paper concludes that a subset of agentic AI applications, particularly those deployed for data fusion and battle management in lethal contexts, cannot be used justifiably on the battlefield under current and foreseeable conditions, and proposes two ways for the international governance community to respond to this challenge.

This paper studies the co-design of actuators, sensors, and communication in the distributed setting, where a networked plant is partitioned into subsystems each equipped with a sub-controller interacting with other sub-controllers. The objective is to jointly minimize control cost (measured by LQ cost) and material cost (measured by the number of actuators, sensors, and communication links used). We approach this using an evolutionary algorithm to selectively prune a baseline dense LQR controller. We provide convergence and stability analyses for this algorithm. For unstable plants, controller pruning is more likely to induce instability; we provide an algorithm modification to address this. The proposed methods is validated in simulations. One key result is that co-design of a 98-state swing equation model can be done on a standard laptop in seconds; the co-design outperforms naive controller pruning by over 50%.

Recent work by Pain [1] proposed a systematic approach to evaluating binomial sums involving reciprocals of binomial coefficients via Beta integrals. In particular, a parametric extension (Proposition 6.1) was introduced and claimed to admit a closed-form representation in terms of a terminating 2F1 hypergeometric function. Through a combination of internal logical consistency checks, integral derivation analysis, and exact symbolic computation, we definitively prove that this parametric identity is false.