In this work, we investigate the unjamming transition in a three-dimensional granular system composed of frictional spheres, in which the packing fraction is systematically reduced by random particle extractions. Using Discrete Element Method (DEM) simulations, we analyze the evolution of key micro-mechanical quantities, such as the interparticle forces, the coordination number and the overall packing density as a function of the interparticle friction coefficient. Our results reveal friction-dependent relationships on structural as well as mechanical observables, and exhibit trends that are qualitatively consistent with observations reported in dense granular systems. These trends persist despite the very different driving mechanism considered here. This paper is part of the thematic issue \emph{``Sand, silos and asteroids: clustering challenges in granular materials research''}.

Zonal flows and turbulence spreading play important roles in magnetic fusion plasma confinement, yet their coupling mechanisms remain elusive. Using global nonlinear gyrokinetic simulations, we show that turbulence spreading transports zonal flow radially, extending into the linearly stable regions after local nonlinear saturation of turbulence. Theoretical understanding has been gained by analyzing the simulation results in the context of a momentum theorem in toroidal plasmas [T.S. Hahm \textit{et al.}, Phys. Plasmas \textbf{31}, 032310 (2024)] which is an extension of the Charney-Drazin non-acceleration theorem [J.G. Charney and P.G. Drazin, J. Geophys. Res. \textbf{66}, 83 (1961)]. It indicates a direct relation between turbulence-driven enstrophy transport and perpendicular momentum generation.

Functional geometry is a framework using concepts from geometry to understand the invariance of amplitudes in quantum field theory under a large class of field redefinitions, including those involving derivatives. It is inspired by recursion relations among correlation functions, where higher-point functions depend iteratively upon smaller correlators. Previous work has shown that, with suitable modifications, these correlation functions become covariant under field redefinitions, provided they are evaluated at the physical ``on-shell" point. In this paper, we show how to further modify correlation functions in massless scalar field theories to achieve ``off-shell" covariance. We investigate the conditions required for the framework to work and discuss the geometric interpretation of this construction -- which prioritizes the covariant transformation of observables under field redefinitions over the role of a metric tensor and its derivatives. While analogous modifications may exist for massive theories, we show that framework developed here does not extend straightforwardly to that case.

Let $G$ be a graph and $S \subseteq V(G)$. In the cycle convexity, we say that $S$ is \textit{cycle convex} if for any $u\in V(G)\setminus S$, the induced subgraph of $S\cup\{u\}$ contains no cycle that includes $u$. The \textit{cycle convex hull} of $S$, denoted by $\hullc (S)$, is the smallest cycle convex set containing $S$. A set $S \subseteq V(G)$ is said to be \textit{Carathéodory independent} if there exists a vertex $u \in \hullc(S) $ such that $u \notin\displaystyle \bigcup_{a \in S} \hullc (S \setminus \{a\}) $, and the Carathéodory number $\car(G)$ is the maximum size of such a set. In this paper, we prove that given a graph $G$ and $k \in \mathbb{N}$, deciding whether $\car(G) \geq k$ is \NP-complete, even when $G$ is bipartite. On the other hand, we derive exact values and constant upper bounds for several graph classes, leading to polynomial-time algorithms. Some of them include forests, cycles, complete graphs, complete multipartite, split, and $P_4$-sparse graphs. In addition, we present a characterization of $n$-vertex graphs $G$ with extremal values near to $n$, including $\car(G) = n-1$ and $\car(G) = n-2$. Furthermore, we investigate the behavior of the Carathéodory number under graph products such as the strong, lexicographic, and Cartesian products.

It has been shown that Cantor bubble Julia sets can appear in the dynamics of polynomials and their singular perturbations. In this paper, we present a criterion that guarantees the existence of Cantor bubble Julia sets for certain rational maps with attracting or parabolic fixed points. Moreover, we construct other Cantor bubble Julia sets, including those with high-periodic attracting cycles and those with Hausdorff dimension two. Finally, we give a sufficient condition for Cantor bubble Julia sets to be quasisymmetrically equivalent to Cantor round bubbles.

We consider a super-Brownian motion $\{X_t, t\geq 0\}$ in a random environment described by a centered Gaussian field $\{W(t,x),t\geq 0, x\in\mathbb{R}^d\}$ whose correlation function is given by $\mathcal{C} (x,y)(t \wedge s)$. The process takes values in $\mathcal{M}(\mathbb{R}^d)$, the space of Radon measures on $\mathbb{R}^d$. It can be characterized through a conditional Laplace transform by a parabolic stochastic partial differential equation driven by $W(t, x)$. Suppose that $\mathcal{C} (x, y)\leq g(x-y)$ for some bounded positive function $g$ on $\mathbb{R}^d$ and the initial distribution of process $X$ is the Lebesgue measure $m$ on $\mathbb{R}^d$. We prove that for dimension $d\geq 3$, whenever $$ \sup_{x\in \mathbb{R}^d} \int_{\mathbb{R}^d} |x-y|^{2-d} g(y)dy< \frac{8 (d-2) π^{d/2}}{d 2^d Γ\left(d/2-1\right)}, $$ the distribution of $X_t$ converges weakly as $t \to \infty$ to a non-trivial invariant probability distribution $π^m$ on $\mathcal{M}(\mathbb{R}^d)$ with mean measure $m$. This result in particular gives an affirmative answer to Conjecture 1.4 of Mytnik and Xiong (Electron. J. Probab. 12: 1349-1378 (2007)). We further show that given $ Θ\in C^β(\mathbb{R}^d)$ $(β>1)$, when $\mathcal{C}(x,y)= a Θ(x-y)$ with $a$ being large enough, the superprocess $X$ suffers local extinction.

Let $k$ be a perfect field of characteristic $p>0$, and let $d$ be a positive integer not divisible by $p$. We define a non-empty Zariski open subset $U$ of the space of polynomials of degree $d$, and for $f(x)\in U(k)$, we compute the $a$-number of the curve defined by $y^p-y=f(x)$. This $a$-number realizes a lower bound given by Booher and Cais, so the latter is tight. Our result also implies that the bound of Booher and Cais for minimal $a$-numbers of Artin-Schreier covers of ordinary curves is tight.

Field-driven phenomena, from flash sintering to electromigration, exhibit threshold fields spanning six orders of magnitude. We show their product with the onset activation coherence length is a universal critical activation voltage, Vc =0.1-2.7 V. Vc represents the threshold electrical work required to resonantly couple to the universal phonon damping peak where lattice softening is maximized. This invariant unifies macroscopic thermal instabilities with the nanoscale Blech limit, establishing a universal phenomenological law for field-lattice coupling across 17 crystal families

Constrained combinatorial optimization problems are frequently reformulated as quadratic unconstrained binary optimization (QUBO) models in order to leverage emerging quantum optimization algorithms such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA). However, standard QUBO formulations enforce inequality constraints through slack variables and quadratic penalties, which can significantly increase the problem size and distort the optimization landscape. In this work, we propose a slack-free penalty formulation for constrained binary optimization that eliminates auxiliary slack variables and preserves the feasibility structure of the original problem. The proposed approach introduces a nonlinear custom penalty function to enforce inequality constraints directly in the objective function. To address the computational challenges associated with evaluating nonlinear penalties in variational quantum algorithms, we employ the finite-sampling method that avoids the exponential complexity required by exact expectation computation. Furthermore, we integrate the Conditional Value-at-Risk (CVaR) objective to improve optimization robustness and guide the search toward high-quality solutions. The proposed framework is evaluated on instances of the multi-dimensional knapsack problem, a classical benchmark in combinatorial optimization. We showcase that the proposed custom-penalty formulation combined with CVaR sampling achieves improved optimality gaps and more consistent performance compared with conventional slack-based QUBO formulations. The results suggest that careful penalty design can play a critical role in enabling quantum and hybrid quantum-classical algorithms for constrained optimization problems that arise in operations research.

In this work, we investigate a decaying dark matter scenario and its associated indirect detection signatures. The model consists of a scalar singlet with a lifetime exceeding the age of the Universe. Stability is ensured by a $Z_2$ symmetry imposed on the Lagrangian, allowing decay through a non-minimal gravitational coupling. The decay of dark matter produces Standard Model particles, which subsequently yield products such as gamma rays, neutrinos, and charged particles. We computed the gamma-ray and neutrino fluxes generated by this candidate in the Milky Way and in 14 dwarf spheroidal galaxies, as well as the corresponding expected number of events in selected experiments, using dedicated numerical tools. Results are presented for three benchmark masses and three coupling values consistent with cosmological constraints, showing that the predicted signals can be observable in specific regions of parameter space.

A physics-informed neural network (PINN) is developed, for the first time, to learn the time-dependent quasi-static magnetohydrodynamic (MHD) equations in axisymmetric tokamak geometry, without any experimental or synthetic data. The initial study considered an ITER-like tokamak and found that a PINN, after careful treatment, was capable of learning the solution to the MHD system and predict a vertically displacing plasma, where general agreement with ground truth simulation was observed. The proof-of-principle demonstration highlights the potential of physics-constrained deep learning to learn complex plasma behavior.

The generation of attosecond pulses (1 as=10-18 s) has enabled real-time observation and manipulation of coherent electron dynamics, yet their low peak power has hindered the development of advanced attosecond pump-probe spectroscopy and attosecond nonlinear metrology. Here we overcome this limitation by generating 1.64 uJ, 263 as isolated attosecond pulses with a peak power of 6.2 GW, the highest pulse energy and peak power reported for a tabletop isolated attosecond source. This is achieved by combining a 2.1 TW, few-cycle (8.3 fs) two-color synthesizer with a loose focusing geometry that enables macroscopic phase-matching. The synthesizer features a stabilized carrier-envelope phase and an actively synchronized relative time delay between the two-color channels, ensuring high stability and reproducibility. This robust tabletop attosecond source enables nonlinear effect experiments that were previously inaccessible with lower-power IAPs, establishing a foundation for advanced attosecond spectroscopy and nonlinear metrology.

AWS Lambda terminates containers with an uncatchable SIGKILL signal when a function exceeds its configured timeout. When a Spark-on-AWS-Lambda (SoAL) job is killed between Phase 1 (data upload) and Phase 2 (metadata commit) of a write, the result is silent data loss: orphaned Parquet files accumulate on S3 while the table's committed state remains unchanged and standard monitoring raises no alert. We characterize this vulnerability across Delta Lake and Apache Iceberg through 860 controlled kill-injection experiments at three dataset sizes. A SIGKILL landing in the inter-phase gap produced silent data loss in 100% of trials for both formats. We then present SafeWriter, a language-level wrapper that arms a watchdog thread 30 seconds before the Lambda timeout, triggers a format-native rollback via SQL, and records a checkpoint document on S3. SafeWriter converted every tested kill scenario into a clean, detectable rollback with under 100 ms added to normal write paths.

Detecting cold molecular gas in metal-poor starbursts remains a major challenge. Low carbon and oxygen abundances hinder CO formation, while low dust content reduces shielding against UV photodissociation. Consequently, CO, the main tracer of molecular gas, becomes faint or undetectable. We study the spatial distribution and kinematics of cold molecular gas in Mrk 996, a nearby low-mass Wolf-Rayet galaxy hosting a dense, low-metallicity (about 1/5 solar) and nitrogen-enriched nuclear starburst with complex ionized gas kinematics. Using ALMA observations of CO(1-0) and CO(2-1), we map the morphology and kinematics of the molecular gas and compare them with optical and UV data, tracing the ionized gas and young stellar populations. We detect compact CO clouds within 800 pc of the starburst, spatially offset from the nuclear super star cluster (SSC) and the most highly ionized regions. The CO lines are narrow and supersonic, exhibiting velocity gradients with a mild global blueshift, indicating dynamically perturbed gas without evidence for fast outflows, in contrast with the highly ionized phase. The global CO(2-1)/CO(1-0) ratio is low (R21 ~ 0.3), consistent with subthermal excitation. The millimeter continuum peaks at the SSC, while CO emission is displaced toward obscured regions, suggesting it traces dense shielded clumps. ALMA recovers about half of the single-dish flux, indicating the presence of extended, low-surface-brightness molecular gas. Using a metallicity-dependent CO-to-H2 conversion factor, we infer a molecular gas mass of a few 10^7 solar masses. The molecular gas is only weakly coupled to the stellar feedback that dominates the ionized phase. Our results support a multiphase scenario in which dense molecular clumps survive in shielded regions, while CO is photodissociated in their envelopes, leaving a significant CO-dark H2 component (Abridged).

We report the first detection of radio emission from the Type II supernova SN 2007it, located at a distance of 12.2 Mpc in NGC 5530. The observations were obtained with the Australian Telescope Compact Array (ATCA) more than 18 yr after the explosion as part of the Rebrightening in Interacting Supernova Emission (RISE) program, which monitors nearby core-collapse supernovae for late-time interaction with dense circumstellar material. SN 2007it was detected on 2026 April 8 (08:00-12:00 UTC) at 5.5 GHz with a flux density of $3.30 \pm 0.13$ mJy and at 9.0 GHz with $3.54 \pm 0.24$ mJy. Its non-detection in publicly available 0.88 GHz ASKAP data from 2026 January 11 suggests either rapidly rising emission or significant internal absorption at lower frequencies. We assess the prospects for detection at other wavelengths and encourage coordinated follow-up observations across the radio, optical, X-ray, and $γ$-ray band

While it is well known that the restricted isometry property (RIP) guarantees uniform sparse recovery from noisy linear measurements, uniform recovery of structured signals from nonlinear observations remains much less understood. This paper shows that the restricted approximate invertibility condition (RAIC) provides a unified approach to this end. Particularly, uniform recovery is achieved by projected gradient descent (PGD) with gradients obeying RAIC for all signals. As an application, under a large class of piecewise Lipschitz link functions (possibly discontinuous), we develop a uniform recovery theory for Gaussian single-index model by establishing the uniform RAIC for the gradient of the (scaled) $\ell_2$ loss via a covering argument. The theory generalizes the nonuniform recovery guarantees due to Plan and Vershynin (2016); Oymak and Soltanolkotabi (2017) and exhibits additional error terms that can be interpreted as the cost of uniform recovery. Intriguingly, in the three canonical settings of (a) sparse recovery via PGD with $\ell_0$ projection (i.e., iterative hard thresholding (IHT)), (b) sparse recovery via PGD with $\ell_1$ projection, and (c) recovering approximately sparse signals via PGD with $\ell_1$ projection, the additional error terms are negligible and in turn our uniform recovery error rates are at the same order of existing nonuniform ones, up to log factors. Our results hence improve on Genzel and Stollenwerk (2023). Under the specific nonlinearity of 1-bit quantization, we use a VC dimension argument to show that the uniform recovery error of IHT is at the same order of the nonuniform recovery error, with no loss of log factor. In addition, we show that the robustness of PGD to noise and corruption can be incorporated elegantly by bounding a single additional random process that captures the gradient mismatch.

Inference for time series of networks often relies on accurate vertex correspondence between network realizations at different times. In practice, however, such vertex alignments can be misspecified or unknown. We study the impact of vertex alignment on changepoint localization for dynamic networks through two illustrative models, each with a similar changepoint, with the key distinction being whether changepoint information is contained in marginal or joint distributions of the time-varying latent positions. We compare localization techniques ranging from the simple network statistic of average degree to the modern procedure of Euclidean mirrors. In one model, vertex misalignment causes little error, and in the other, it impairs localization in ways that cannot be corrected through graph matching or optimal transport, which we show are closely related in this setting. Our results demonstrate that robust network inference necessitates reckoning with the subtle interplay of marginal and joint information in the observed network time series.

Recent discoveries in VR have opened up scope for designing physical tools and controllers to enhance immersion, through perceived reality. In a virtually simulated sports scenario it is challenging to immerse user because most of the available controllers are unable to bridge the user experience in the real world to the actions in the virtual world. My research is to identify HCI problems in existing VR controllers, design a physical controller prototype with realistic tangible mapping, trying to solve the existing problems and evaluate it in a designed VR game for skating. Its immersiveness would be graded on Likert scale on parameters like perceived interactivity and reality, spatial presence and enjoyment. The evaluation will be done after trial runs and feedback sessions by playing the game with the designed controller and comparing it with ones available in the market. The findings will help people understand what all parameters we should consider while designing futuristic controllers, customized for a particular sport.

Infectious disease dynamics operate across multiple biological scales, with within-host viral dynamics being a key driver of between-host transmission. However, while models that explicitly link these scales exist, none have been developed with statistical inference as a primary goal. In this paper we propose a multiscale model that jointly captures heterogeneous individual-level viral load trajectories and stochastic household transmission, and develop efficient inference methods to fit it to data. Since full joint inference is computationally difficult, we employ a cut approach that passes information from the within-host to the between-host model but not vice versa. This enables the data on viral loads to inform the transmission parameters such as the infection times and symptom onset thresholds. We evaluate the framework on simulated household outbreak data, assessing parameter recovery, computational efficiency, and the effect of viral load sampling frequency on inference quality. Parameter recovery is unbiased when the sampling frequency of the viral loads is high enough. When sampling is sparse, some bias is introduced, but incorporating external viral load data can mitigate this.

This study strengthens the foundations of multi-venue market modeling by attempting an independent replication of Wah and Wellman's 2016 model of latency arbitrage in a fragmented market. We find that faithful replication is hindered by missing implementation details in the original paper and limited quantitative reporting. We demonstrate that increasing the number of simulation runs beyond the original design allows for the creation of bootstrap confidence intervals to support rigorous tests of quantitative alignment, compensating for lacking distributional information (e.g. variance). We also demonstrate that increased complexity across the modeled scenarios corresponds with increased difficulty aligning to the original results. We draw on a codebase released by the original authors in connection with a later paper to recover additional implementation details; however, we reject quantitative alignment between that codebase and the published results. Combining information from the paper and the released code, we achieve relational equivalence for most metrics but reject quantitative alignment for model settings where latency is non-zero. We show that many of the qualitative takeaways from the original paper on the effects of market fragmentation and latency arbitrage are sensitive to the specifics of a `greedy strategy' extension given to the zero-intelligence (ZI) trader agents. Under an alternative interpretation of this strategy, we find that market fragmentation decreases execution times in all experiments and increases trader welfare in most experiments. Finally, to facilitate future replication, critique, and extension, we provide an ODD (Overview, Design concepts, Details) protocol for our implementations of the model.

We present new regular black hole solutions in general relativity (GR) within a static, spherically symmetric framework governed by a variable equation of state, following the approach of [Class. Quant. Grav. 42, 025024 (2025)]. The matter supporting these geometries is identified as a purely magnetic monopole configuration of the Maxwell-Faraday tensor in the context of nonlinear electrodynamics (NLED). We explicitly reconstruct the corresponding NLED Lagrangian and analyze the asymptotic and central behaviors of the solutions. The geometric structure is examined through the metric functions, the regularity of the Kretschmann scalar, and the profiles of energy density and pressures, including a discussion of the resulting energy conditions. Using Event Horizon Telescope observations of Sgr A$^*$, we constrain the model parameters by comparing the predicted size of the central dark region with the inferred observational images, taking into account the effective geometry experienced by photons in the presence of NLED. Finally, we investigate the dynamical stability of these configurations under scalar perturbations by computing the quasinormal mode spectrum and performing a time-domain analysis.

Personalization has traditionally depended on platform-specific user models that are optimized for prediction but remain largely inaccessible to the people they describe. As LLM-based assistants increasingly mediate search, shopping, travel, and content access, this arrangement may be giving way to a new personalization stack in which user representation is no longer confined to isolated platforms. In this paper, we argue that the key issue is not simply that large language models can enhance recommendation quality, but that they reconfigure where and how user representations are produced, exposed, and acted upon. We propose a shift from hidden platform profiling toward governable personalization, where user representations may become more inspectable, revisable, portable, and consequential across services. Building on this view, we identify five research fronts for recommender systems: transparent yet privacy-preserving user modeling, intent translation and alignment, cross-domain representation and memory design, trustworthy commercialization in assistant-mediated environments, and operational mechanisms for ownership, access, and accountability. We position these not as isolated technical challenges, but as interconnected design problems created by the emergence of LLM agents as intermediaries between users and digital platforms. We argue that the future of recommender systems will depend not only on better inference, but on building personalization systems that users can meaningfully understand, shape, and govern.

While antiferromagnetic magnons in two-dimensional (2D) materials hold immense promise for high-frequency spintronics, achieving their efficient active control remains a critical challenge. Here, we propose a universal mechanism for the nonvolatile ferroelectric (FE) switching of antiferromagnetic magnon transport in 2D multiferroic lattices. Our mechanism relies on coupling the magnon geometric phase to the FE-induced sublattice asymmetry in exchange and Dzyaloshinskii-Moriya interactions. This explicitly breaks the exact compensation of opposite-chirality magnons inherent to collinear antiferromagnets, lifting their spin degeneracy and inducing a highly tunable net Berry curvature. Crucially, reversing the FE polarization deterministically swaps these magnetic asymmetries, which completely inverts the net magnon Berry curvature and the resulting anomalous thermal Hall conductivity. Using first-principles and linear spin-wave theory, we rigorously validate this geometric-phase-driven mechanism in single-layer CuCr2Se4. Our findings establish a robust paradigm for coupling multiferroicity with the magnon geometric phase, paving the way for nonvolatile and electrically switchable antiferromagnetic magnonics.

Scientific machine learning is increasingly being spoken of as universal emulators for classical numerical solvers for multi-scale partial differential equations, but most apparent successes can be explained by facts that also define their limits. Many successful benchmarks live on low-dimensional solution manifolds where any competent reduced model will interpolate well. More fundamentally, neural surrogates systematically under-resolve high-frequency content due to spectral bias, and coarse-graining compounds this problem through irreversible information loss. In many multi-scale problems, no architecture or training procedure can fully recover what the coarse representation discards. Two simple examples are used to characterize spectral bias, coarse-graining and error accumulation. We discuss why medium-range weather prediction on reanalysis data sits in a favorable sweet spot and why this will not generalize to genuinely chaotic multi-scale scenarios. We identify domains where neural surrogates offer genuine value, propose further research on neural-classical hybrids, and call for better reporting standards.

Extreme emission-line galaxies (EELGs) probe chemical enrichment in low-mass, bursty systems where star formation, feedback, and gas accretion are poorly constrained. Using DESI DR1, we select 23 nearby EELGs with detections of 19 ionic species (S/N $\geq$ 4), stellar masses $ M_* \geq 10^7 M_{\odot}$, and extreme H$α$ and [O III] 5007 equivalent widths (EW $\geq$ 500 Angstrom). We infer non-parametric star-formation histories and fit a Bayesian single-zone chemical-evolution model to O, N, Ne, S, and Ar, allowing time-dependent star-formation efficiency, outflow mass loading, and evolving inflow metallicity. We find short depletion timescales and large mass-loading factors, indicating rapid gas cycling in a burst-driven, non-equilibrium regime, with depletion times below Kennicutt-Schmidt expectations. Star-formation efficiency and outflows are well constrained, while inflow metallicity is weaker due to degeneracies with metal production. Abundance ratios isolate physical drivers: star-formation efficiency sets evolutionary tracks, outflows regulate metal retention and X/O normalization, and inflow metallicity sets baseline enrichment. N/O strongly constrains burst timing and gas flows, Ne/O remains nearly invariant, and S/O and Ar/O show intermediate sensitivity. These results demonstrate that multi-element abundances provide a direct probe of baryon-cycle processes in extreme low-mass starbursts.

This work investigates the effectiveness of the Back-and-Forth Nudging (BFN) data assimilation algorithm, specifically its performance when employing the Azouani-Olson-Titi (AOT) continuous data assimilation downscaling nudging algorithm, for recovering initial conditions of dissipative dynamical systems. Contrary to previous reports in the literature, we show that, for several systems of interest, one can construct initial conditions that BFN cannot reliably recover. Our key finding is the construction of infinitely many distinct solutions for certain dissipative systems that share identical spatially sparse observational data. Since these observations are indistinguishable, no data assimilation method relying only on them can differentiate between these solutions or recover the correct initial condition. We illustrate these pathological initial conditions for the Lorenz 1963 model and several 1D partial differential equations: the heat equation, viscous linear transport, and viscous and inviscid Burgers equations. Our analytical results are supported by numerical simulations. To address the numerical ill-posedness of backward-in-time iterations, an essential step of BFN for dissipative models, we introduce a regularized backward step, the Voigt-regularized BFN (VBFN). We investigate its performance for the 2D Navier-Stokes equations and the viscous 1D KdV equation, comparing it with standard BFN and Diffusive BFN (dBFN). While VBFN improves numerical stability and reduces model bias relative to dBFN, it still cannot reconstruct the unobserved fine spatial scales of the reference solution. This reinforces our main conclusion: even with regularization, BFN-type algorithms are limited in recovering the full state, and in particular the initial data, from sparse spatial observations.

The Cassini-Huygens mission detected large negative ions in Titan's ionosphere at pressures as low as $10^{-6}$ torr. These ions ultimately polymerize to form Titan's complex organic haze particles, which are observed throughout the atmosphere and potentially on the surface. Laboratory analogs of these hazes, known as tholins, have been used to study Titan's aerosols; however, most are produced at much higher pressures. The influence of formation pressures on key physical properties -- such as particle size, density, surface energy, and mechanical strength -- remains poorly constrained. These properties govern the haze's aggregation efficiency, radiative behavior, and surface-atmosphere interactions, shaping Titan's climate and surface. To investigate the effects of formation pressure, we generate tholins using a newly developed cold plasma discharge system. A 95% nitrogen and 5% methane gas mixture is exposed to plasma at two pressures, 1 torr and 0.125 torr. For both samples, we measure the production rate, particle size, morphology, density, surface free energy, Young's modulus, and nanoindentation hardness. While particle size, morphology, surface energy, and Young's modulus are similar across both pressures, tholins produced at lower pressure exhibited a threefold lower production rate, but a higher density and nanoindentation hardness. These variations likely reflect pressure-dependent changes in chemical structure, porosity, and mechanical strength. Because Titan's hazes form at much lower pressures than investigated here, actual haze particles are potentially even denser and mechanically stronger than our analogs, with implications for aerosol aggregation, aeolian and fluvial transport, and surface modification on Titan.

We present early science results from the MAUVE (Multiphase Astrophysics to Unveil the Virgo Environment) program which targets 40 Virgo Cluster galaxies to investigate the effect of environment on the interstellar medium (ISM) at ~100 pc scales. From 12 galaxies in the MAUVE-MUSE early sample, we find systematically elevated line ratios compared to PHANGS-MUSE field disks, with higher medians of [N II]/H$α$ (0.75 vs. 0.50), [S II]/H$α$ (0.57 vs. 0.49), and [O III]/H$β$ (1.04 vs. 0.68). Spatially resolved BPT diagrams show 74% of MAUVE-MUSE spaxels ionized by sources other than H II regions, versus 61% in the field, and we find these ionization differences to be closely coupled to broadened kinematics. 44% of MAUVE-MUSE spaxels exceed H$α$ $σ_{LOS} = 40$ km/s (vs. 26% in the field), driven mainly by non-star-forming gas with $σ_{LOS}$ between 40 and 80 km/s, consistent with enhanced contribution of diffuse ionized gas (DIG). A subdominant tail of 5% of spaxels at $σ_{LOS} > 100$ km/s, largely absent in PHANGS-MUSE (1%), points to shocks or turbulent mixing layers from intracluster interactions. Our results show that environmental quenching primarily suppresses star formation, unveiling DIG as the dominant ionized component in cluster disks. The elevated line ratios and broadened kinematics observed in the MAUVE sample reflect the physical state of the ISM in the absence of vigorous star formation, rather than widespread direct environmental excitation. The observed shock-like emission provides an additional, secondary contribution likely driven by active interactions with the intracluster medium.

Let $k$ be a field of characteristic $p,$ and $f : X \to S$ a smooth proper morphism of smooth $k$-schemes. Katz's formula gives a relationship between the Kodaira--Spencer map of $f,$ and an invariant called the $p$-curvature of the Gauss--Manin connection associated to $f.$ Recently, Lam--Litt proved a variant of Katz's formula in non-abelian Hodge theory, and suggested that it should be possible to give a more conceptual proof of their formula using the stacky approach to $p$-adic Hodge theory. In this article, we realize their suggestion, explaining how the rather concrete phenomena observed by Katz and Lam--Litt can be explained in a conceptual way using sheared de Rham stacks, as developed by Bhatt--Kanaev--Vologodsky--Zhang and Drinfeld (though we prove a slightly different statement than Lam--Litt do). We do not assume the reader has any background in the theory of de Rham stacks.

The realization of on-chip polarization beam splitters robust to fabrication imperfections remains a key challenge for polarization-sensitive photonic integration. We demonstrate a topologically protected polarization beam splitter based on a Floquet-engineered microring lattice implemented on a CMOS-compatible silicon nitride platform. By tailoring the dispersion of inter-ring coupling, the lattice supports complementary trivial and topological band gaps for orthogonal eigenpolarizations. At telecom wavelengths, TE modes propagate via a topological edge state while TM modes are suppressed by a trivial gap; this behavior reverses at shorter wavelengths. We measure extinction ratios of 16-20 dB for the protected port and 10-20 dB for the non-protected port, with insertion loss of 2 dB at long wavelengths. Reduced TM extinction at shorter wavelengths is attributed to suboptimal input coupling. We further identify spectral regions where both polarizations exhibit nontrivial band gaps, enabling polarization-independent edge transport and establishing a Floquet dual-polarization topological regime. Because operation is governed by band topology rather than geometric fine-tuning, the device shows intrinsic robustness to defects. These results establish polarization-tailored topological lattices as a scalable platform for robust beam splitting, routing, and interconnects in classical and quantum photonic systems.