The problem of detecting new signals in the presence of an unknown background is ubiquitous in scientific discoveries and is especially prominent in the physical sciences. Most solutions proposed thus far to address the problem focus on estimating the background distribution and using that estimate to infer the signal. By studying the geometry of the problem, this article demonstrates that estimating the background distribution is somewhat unnecessary for inferring the signal intensity. Instead, it suffices to estimate a single parameter, referred to as the compensator, to account for the incomplete knowledge on the background, substantially simplifying the problem's complexity and enabling proper uncertainty propagation. Such a compensator is shown to govern the conservativeness of the inference, both in the proposed setup and in likelihood-based approaches.

LEGEND-200 requires high-purity liquid argon for effective background discrimination. In this paper, we present the design, construction, and performance of a dedicated liquid argon purification system, along with the procurement and purification of liquid argon for filling the LEGEND-200 cryostat to its total capacity of 91 t. The purifier is based on copper catalyst and molecular sieve to remove oxygen and water. Starting with liquid argon of 5.5 quality, featuring an effective scintillation light triplet lifetime $τ_t$ of about 0.9 $μ$s, we achieved a final purity corresponding to $τ_t$ = 1.3 $μ$s. After complete filling of the LEGEND-200 cryostat, the measured effective triplet lifetime was 1.16 $μ$s. The notable reduction is caused by a residual nitrogen impurity introduced by an accidentally spoiled liquid argon delivery. An excessive nitrogen influx was prevented by the LEGEND Liquid Argon Monitoring Apparatus (LLAMA), which served as one of the three independent purity monitors during the filling campaign.

Digital lifestyle coaching systems must personalize peer support as user behavior and engagement evolve while preventing personally identifiable information (PII) and sensitive health information from leaking into analytics and AI pipelines. This creates a practical tension: personalization requires longitudinal linkability, while privacy engineering requires minimization, separation, and controlled re-identification. We present PRISM-Coach, a stakeholder-centered architecture and adaptive peer-group assignment method for privacy-preserving lifestyle coaching. PRISM-Coach separates each user into four bounded views: Identity, Operational, Learning, and Coaching, each with distinct access controls and risk profiles. Building on this separation, the system uses vault-based controlled identity restoration, a privacy-constrained contextual bandit to assign users to eligible peer groups under coach-capacity and stability constraints, and a human-in-the-loop coaching assistant that generates de-identified summaries and draft messages without sending raw PII or PHI to external AI services. We instantiate PRISM-Coach in a commercially deployed lifestyle coaching platform and evaluate it using three years of telemetry from approximately 2,800 users and an in-app needs assessment survey. At the population level, daily check-in adherence increases from 0.35 to 0.68, and engagement rises to 1.35 baseline. In a matched 19-week comparison window, the AI-enabled workflow achieves adherence of 0.74 versus 0.48 under static grouping and higher average weight loss: 5.2 kg versus 3.1 kg. Survey results show that 82% report positive perceived benefit, and 92% report increased privacy confidence after transparency disclosures. These results position PRISM-Coach as a practical blueprint for privacy-by-design adaptive learning systems in everyday wellness.

Given balls and boxes both enumerated by the positive integers, we consider a sequential allocation of the balls into the boxes. We fix $\ell \ge 2$. Proceeding in increasing order of box labels, assign to each box the next $r$ smallest balls for some $ 1\leq r\leq\ell$. Given an integer $k\ge 3$, is there a natural number $N$ such that in any placement of $N$ balls into boxes, there exist $k$ balls whose labels and box labels each form a $k$-term arithmetic progression? We address this question by identifying abelian power-free fixed points of morphisms over a binary alphabet. We present sufficient conditions under which a morphism is abelian $k$-power-free. Our conditions extend Dekking's result over a binary alphabet and offer a weaker, yet more effective alternative to Carpi's. Combining Dekking's result with the template method of Currie and Rampersad, we develop a sieve technique that significantly reduces the number of parents that must be examined to establish abelian power-freeness. We then identify a binary morphism that is abelian 16-power free (but not abelian $15$-power free) with an abelian 14-power free fixed point, demonstrating the strength of our technique in verifying abelian power-freeness. Furthermore, we give a binary morphism which is not abelian power-free, yet has an abelian $5$-power free fixed point. These results offer novel examples of morphisms whose fixed points exhibit stronger abelian power-freeness than the corresponding morphisms.

The origin of ω Centauri remains one of the key open problems in stellar dynamics and chemical evolution. Its large abundance spreads and multiple populations suggest a formation history more complex than that of a typical globular cluster. We investigate whether the chemical sub-populations identified in APOGEE DR17 also exhibit distinct spatial and kinematic signatures. We analyse a sample of APOGEE DR17 red-giant stars using a Gaussian Mixture Model in an eight-dimensional chemical-abundance space. The resulting chemical components are combined with Gaia proper motions and APOGEE line-of-sight velocities to derive intrinsic mean velocities and velocity dispersions in all three observable directions. We measure both global kinematic quantities and radial profiles for each chemically defined group, extending from the inner regions to ~ 4 half-light radii. The Gaussian Mixture Model identifies five chemical components, which, when examined through their radial cumulative distributions, naturally group into two broader families characterised by lower and higher aluminium enrichment. The two families differ significantly in their spatial and kinematic properties: the Al-rich stars are more centrally concentrated and exhibit stronger radial anisotropy than the Al-poor stars, which remain closer to isotropy over the radial range probed. Despite these significant differences, the two populations share a common rotation pattern. This work represents the first chemo-dynamical study of ω Cen linking detailed chemical tagging to internal kinematics from the inner regions to the cluster outskirts. A formation path involving both hierarchical assembly within a dwarf-galaxy potential and centrally concentrated, chemically enriched star formation offers a natural explanation for the observed chemo-dynamical complexity.

The design of structures and vehicles subject to fluid-structure interaction (FSI) often requires high-fidelity coupled analysis. While the design variables pertain to the structure, the computational cost is dominated by the fluid solver, making iterative optimization prohibitively expensive. This paper presents an adaptive multi-fidelity optimization method combining high-fidelity FSI analysis with a lightweight surrogate for fluid-induced loads and a decision model that selects between surrogate and high-fidelity fluid evaluations. During optimization, completed FSI analyses incrementally update a non-intrusive surrogate model based on nearest-neighbor search and radial interpolation. A hybrid Lagrangian-Eulerian mapping function is developed to transfer fluid loads between structural designs. The evolution of surface orientation is handled by decomposing the traction vectors into local orthonormal bases. An adaptive Gaussian process regression model is employed to predict surrogate error and quantify uncertainty, allowing risk-aware selection of when coupled analysis is required. As design evaluations cluster near the optimum, the accuracy of the surrogate model naturally improves, thereby reducing the reliance on the fluid solver. It requires no offline training, preserves the high-fidelity structural model in all design evaluations, and ensures that the final design is evaluated by high-fidelity FSI analysis. The fundamental idea is justified theoretically using a simplified model problem, which shows that the leading-order error is a monotonically increasing, concave, and bounded function of the fluid added mass. The framework is demonstrated on two benchmark problems. For shape optimization of a flexible panel under shock loading, results show an $80\%$ reduction in computational cost while maintaining accuracy within $2.3\%$ of fully high-fidelity FSI optimization.

Ensuring data quality in cloud-native Extract-Load-Transform (ELT) pipelines is increasingly challenging due to heterogeneous data sources, evolving schemas, and multi-backend execution environments. This paper presents a unified, multi-layer testing framework that integrates orchestration-level validation, declarative dbt tests, large language model (LLM)-generated semantic tests, and cross-store consistency checking between DuckDB and Snowflake, orchestrated through Apache Airflow. Controlled anomaly-injection experiments demonstrate that a manual-only baseline detected 7 of 16 injected anomalies. In contrast, both a manually expanded comparator and the proposed LLM-augmented configuration detected all 16, representing a 128.57% relative improvement in detection rate over the baseline. Post-migration cross-store validation confirmed exact agreement across all three curated tables. Of 25 LLM-generated test assertions, 9 were classified as useful, 4 as redundant, and 12 as executable but low-value. The complete workflow executed in 106.58 seconds across eight instrumented pipeline stages. These results demonstrate that LLM-driven semantic test synthesis can materially strengthen validation coverage while remaining operationally practical for production ELT environments.

While standard minimal coupling works well for Dirac fermions, its application to scalar fields features a known ``peculiarity'': the term linear in $A_μ$ does not coincide with the conserved Noether current of the interacting theory. We recently proposed choosing a different principle for electromagnetic interactions, namely a linear coupling $A_μJ^μ$ with $J^μ$ a (globally) conserved current, accepting the consequence that one must abandon full local gauge invariance in the electromagnetic sector and adopt an extended electrodynamics (of Aharonov--Bohm type) that can couple consistently to non-locally-conserved currents. We present the physical motivations offered for proposing the modified coupling and discuss general consequences of reducing gauge invariance. We then focus on the central condensed-matter claim: for bosonic charged condensates, the modified framework predicts a rescaled magnetic penetration depth $λ\to λ/\sqrt{2}$, while leaving other key qualitative features of superconducting electrodynamics and the type-I/type-II distinction unchanged (up to an equivalent rescaling of the GL parameter). Finally, we analyze experimental data for a London-length consistency check based on independent measurements of the ratio $n_s/m^\ast$ between carrier density and effective mass. We compare for five materials an ``optical'' penetration depth $λ_{\mathrm{opt}}$ inferred from IR/THz superfluid spectral weight with a ``magnetic'' depth $λ_{\mathrm{mag}}$ obtained independently (LE-$μ$SR, TF-$μ$SR, microwave methods, etc.). Data for Nb, YBCO and Ba(Fe,Co)$_2$As$_2$ confirm the hypothesis $λ_{\mathrm{opt}}>λ_{\mathrm{mag}}$, with a ratio not far from 1.4; data for Pb are inconclusive while data for MgB$_2$ indicate $λ_{\mathrm{opt}}\simeqλ_{\mathrm{mag}}$ as predicted by the standard theory.

At the end of its discovery apparition, the $\sim$60 m near-Earth object 2024 YR4 was associated with a non-zero probability of lunar impact during its 2032 December 22 close approach. While posing no threat to Earth, a lunar impact of this scale could have consequences for Earth-orbiting infrastructure, as well as for human exploration on and around the Moon. We present new JWST/NIRCam observations from 2026 February 18 and 26 that extend the observational arc by eight months, reduce the uncertainty in the 2032 lunar encounter by a factor $>$30, and constitute the faintest detection of a near-Earth object to date, reaching $V \sim 30.5$ -- beyond the $V \sim 27$ ground-based limit. The updated orbit solution yields a predicted miss distance of $22{\,}900 \pm 800$ km (1$σ$) from the center of the Moon, thus ruling out a lunar impact. Despite challenges due to the limited number of reference stars and saturation and trailing effects, we derive astrometric positions with three independent analysis methods, demonstrating consistency at the $\lesssim$50 mas level. These observations extend the orbital arc at epochs when the object is not accessible from the ground, advancing the timeline for hazard assessment by two years relative to the next feasible ground-based recovery. This capability is critical in an emerging regime of planetary defense characterized by the discovery of decameter-scale objects by next-generation surveys. These objects are far more common but rapidly become inaccessible to ground-based follow-up. In this regime, hazard assessment can become follow-up-limited, requiring targeted space-based observations, such as those demonstrated here, to reliably constrain impact probabilities on operationally relevant timescales.

Hypergraph partitioning is a recurring NP-hard problem in engineering; its efficient solution at scale hinges on parallelism. This work proposes a GPU-centric algorithm for multi-level hypergraph partitioning aimed at a specific set of problem constraints: limited size and distinct inbound hyperedges per partition. Manipulating hypergraphs requires deeply nested traversals and concurrent decision-making; our constraints impose further set operations amidst that. In turn, we design algorithms around the GPU's hierarchical parallelism and our problem's specifics. When forming partitions, we materialize the hypergraph's incidence structure and unique neighborhoods in memory to exploit set sparsity and batch node-pairing scores in shared memory. Upon refining partitions, we chain node moves into improving paths and cycles, checking their validity via cumulative set size variations reduced in parallel over moves. Thus, our dominant kernels exhibit a span linear in local hypergraph parameters. Results show an average 380x speedup and a 1.2-2.0x reduction in connectivity compared to a sequential multi-level partitioner. With minor changes, we also support k-way balanced partitioning, running 5x faster than CPU methods with a ~5% quality loss for k=2, outperforming an existing GPU partitioner at comparable runtime, with no measurable overhead from the added constraints handling logic.

Electromagnetic duality is a symmetry of the source-free Einstein-Maxwell equations that rotates electric and magnetic fields while leaving the stress-energy tensor invariant. We present the first fully nonlinear realization of this symmetry in dynamical strong-gravity regimes by performing numerical relativity simulations of charged black hole mergers across a continuous duality family. Starting from electrically charged binaries, we generate dyonic and magnetically charged configurations via duality rotations and evolve them within a common numerical framework. We find that all dual configurations exhibit identical spacetime dynamics, while the emitted electromagnetic radiation is related by a rotation of its polarization equal to the duality angle. Our results demonstrate a degeneracy of gravitational observables under electromagnetic duality and provide a concrete mapping between dual configurations at the level of radiation, establishing electromagnetic duality as an organizing principle for dynamical Einstein-Maxwell solutions.

The energy extraction from a magnetized Reissner-Nordström black hole is analyzed within the framework of the electric Penrose mechanism. The presence of an external magnetic field induces an axisymmetric configuration and an ergosphere (the region where energy extraction is possible) arises, allowing for negative energy states even in an otherwise static spacetime. By analyzing the decay of particles at turning points of the radial motion, we derive the general expression for the efficiency of the process in terms of the metric coefficients and the electromagnetic potential. This formulation provides a direct criterion for identifying the ergoregions and we show that the magnetic field acts as a control parameter that governs both the configuration of the ergosphere and the efficiency of the process. In particular, analytical expressions for the critical magnetic fields that determine the onset and suppression of energy extraction are determined. Our results extend previous analysis of the electric Penrose process for magnetized configurations and clarify the role of the external field in enhancing or inhibiting energy extraction from charged black holes.

We extend the tensor-product direct solver from the Laplacian to the Schrödinger operator $-Δ+ V$. When the potential $V_1$ is separable, the operator $-Δ+ V_1$ is inverted or exponentiated at cost $O(N^{1+1/d})$ in $d$ dimensions via per-axis eigendecomposition. On a single NVIDIA A100 GPU, this costs less than one second for $10^9$ degrees of freedom in 3D. For non-separable potentials $V = V_1 + V_2$, the same solver provides a preconditioner $(-Δ+ V_1)^{-1}$ for the preconditioned conjugate gradient (PCG) method and a propagator for operator-splitting time integrators. For bounded $V_2$, we prove that the preconditioned operator has a bounded condition number and a clustered spectrum with at most finitely many outlier eigenvalues, independently of the mesh size, and also independently of the domain size when $V_1$ is a confining potential. This explains the mesh- and domain-independent PCG iteration counts observed in practice. We apply this method to ground state computation via inverse iteration for linear problems and via the $a_u$ gradient flow for Gross--Pitaevskii energy in 3D, and also Hamiltonian simulation via the approximated qHOP and Magnus-2 splitting methods from 3D to 9D on a single NVIDIA GH200 GPU.

We introduce the Dynamical Cluster Assembly Framework (D-CAF), an AMUSE-based framework designed to connect embedded star formation histories to the dynamical evolution of young stellar systems. We model star formation through the gradual formation of stars inside an evolving background potential, where the global gas evolution is extracted from realistic magneto-hydrodynamical (MHD) simulations. In this first work, we focus on the global evolution of the natal gas and its dynamical imprint on the stellar population. Across all explored MHD setups, we find that the gas continues to collapse while stars are forming, increasing both the central concentration and velocity scale of the embedded stellar population before gas expulsion. Using a controlled grid of direct $N$-body simulations, we show that this embedded evolution strongly regulates both the survival and later expansion of young stellar systems. In particular, gas contraction shortens the stellar crossing time prior to gas expulsion, making the same gas-removal timescale effectively more adiabatic for the stars. We find that the present-day expansion of stellar associations still preserves information about the embedded dynamical state reached during formation. The expansion rate is limited by the velocity scale reached before gas expulsion, while the efficiency with which this velocity field is transformed into expansion depends on the gas-expulsion timescale. Finally, we show that some commonly used expansion diagnostics can directly trace the physical expansion rate of young stellar systems when full kinematic information is available, opening the possibility of using stellar kinematics to constrain the dynamical conditions of embedded star formation.

We introduce a variational notion of essential spectrum for the Dirichlet $p-$Laplacian. We then extend the classical Persson Theorem to this nonlinear setting. This result provides a geometric characterization of the bottom of the essential spectrum, in terms of the sharp $L^p$ Poincaré constant ``at infinity''. We also show that in the case $p=2$ our construction of the essential spectrum is perfectly consistent with the classical theory. Finally, as an example, we compute the full spectrum of the Dirichlet $p-$Laplacian on a rectilinear strip: it is purely essential, with no embedded eigenvalues. The arguments of the proofs are elementary and new already for the linear case $p=2$.

Elemental abundances in the Milky Way disc trace its star-formation and enrichment history, but predicting these abundances from theory is limited by uncertain nucleosynthetic yields and poorly constrained chemical evolution models. Large surveys provide many abundances that enable multi-dimensional insight. However, having so much data available complicates joint visualisation and physical interpretation. Here, we examine the element abundances of 70,057 red giant stars from the Milky Way Mapper survey ([Fe/H] $> -1$), using 16 elements (O,~Mg,~Al,~Si,~S,~K,~Ca,~Ti,~V, ~Cr, Mn,~Fe,~Co,~Ni,~Ce,~Nd). To tackle the challenges of joint-interpretation of these elements, we build a generative data-driven model, expressing each star's abundance vector as a linear combination of a few ($4$) latent nucleosynthetic patterns. These patterns are shared among the population but vary in fraction between stars. The model accurately generates the measured abundances, with $χ^2 < 3$ (5) for $\sim$ 80\% (95\%) of stars. Model failures, where stars' abundances are not generated by the latent basis reveal accreted material and the role of multiple channels of metal-poor disk enrichment. We associate the recovered patterns, which represent high-precision ($σ_P \sim 3$\%) nucleosynthetic channels, with specific enrichment sources; (early and late) core-collapse supernovae, supernovae Type Ia, and asymptotic giant branch stars. We subsequently explore how the dominance of enrichment channels varies across age, metallicity and spatial extent of the disk, and show that enrichment patterns tightly couple to orbital properties. Mean pattern fractions vary smoothly with enrichment, and change rapidly across the valley between the high- and low-$α$ sequences. Our results provide a framework for improving our understanding of Galactic evolution in the Milky Way.

It is a well known fact that the eikonal equation is well posed in complete length spaces. Among the studied notions of solutions in the literature, there is one that can be defined in any metric space using the local (descent) slope and considering pointwise solutions: functionals such that their slope coincides with the prescribed data at every point of the domain. In this work we explore the question ``Can we characterize the class of compact metric spaces in which every slope eikonal equation (under standard assumptions) always admits a pointwise solution?''. We provide a purely metric characterization of these spaces, as well as some interesting examples and counterexamples that illustrate the reach and limitations of the concept.

The paper describes a new method for estimating the poles of an ARMA model using higher-order crossings. The method involves transforming counts of crossing events into estimates of ARMA poles via the autocorrelation domain. An important advantage of the method is that the crossing counts are the only features that need to be stored from the original data. The poles of an ARMA model of a control loop correspond to the roots of the characteristic equation and are thus useful for evaluating control performance.

A ball rolled over a shallow step will experience an increase in velocity along the direction perpendicular to the step. This causes a deflection in the ball's trajectory. In this paper we derive the equations that describe the motion of a ball rolled over a shallow step and present the results of our experimental test. This simple demonstration can be used in any classroom where the physics teacher has access to a ball and a stack of papers. Prior work has shown that a ball rolled over an edge can maintain its speed, as is commonly assumed, but it can also experience an increase or even decrease in speed. The ball can either roll without slipping while it is in contact with the edge, or else begin to slip before it leaves the edge. In this paper we will consider the case where the ball rolls without slipping the entire time it is in contact with the step edge, then contacts a lower platform. We work with shallow step heights relative to the radius of the ball so that the motion of the ball is easy to observe at all times, and so that the ball does not bounce when it encounters the lower platform. These shallow step heights mean that we can assume the ball does not slip as it moves over the edge.

We prove that for many pairs $H_1, H_2$ of root subgroups of the automorphism group $\text{Aut}(\mathbb{C}^2)$ the diagonal action of the group generated by $H_1, H_2$ on $(\mathbb{C}^2)^m$ has an open orbit for any positive integer $m$. The result is based on the study of the Lie algebra of polynomials in two variables with the standard Poisson bracket.

We present calculations of the millielectronvolt-scale x-ray scattering spectra of multilayered dynamic-compression targets comprising an unstructured ablator layer and a crystalline, textured sample layer. Our model builds on the classic formulation of x-ray thermal diffuse scattering by Warren [B. E. Warren, Acta Crystallogr. 6, 803 (1953)] and includes both elastic and first-order (single-phonon) inelastic scattering contributions to the dynamic structure factor $S(\mathbf{q},ω)$. We focus on the umklapp scattering regime (i.e., at momentum transfers outside the first Brillouin zone) where the ablator scattering that threatens to overwhelm the inelastic scattering from the crystalline layer of interest is suppressed. We show that, despite the considerably more complex structure of the inelastic scattering spectra in this intermediate-$q$ regime, it is still possible to reliably deduce the temperature of the crystal using Dornheim's Laplace-transform--based formalism [Dornheim et al., Phys. Plasmas 30, 042707 (2023)], regardless of the details of the sample's texture.

Entropy stabilization of the compressible Euler system is achieved by adapting the averages that are applied to the density and internal energy variables. The approach achieves non-linear robustness despite the use of simplified symmetric means (e.g., arithmetic, geometric, or harmonic evaluations), including their related expansions for asymptotic entropy conservation. The proposed formulation works via centralized convective terms and can naturally adhere to additional structures of the flow equations such as kinetic-energy- and pressure-equilibrium-preservation.

The $α$-quantile of a stochastic process $M_{t,α}$ has been introduced in Miura (Hitotsubashi J Commerce Manag 27(1):15-28, 1992), and important distributional results have been derived in Akahori (Ann Appl Probab 5(2):383-388, 1995), Dassios (Ann Appl Probab 5(2):389-398, 1995) and Yor (J Appl Probab 32(2):405-416, 1995), with special attention given to the problem of pricing $α$-quantile options. We straightforwardly extend the classical monodimensional setting to $\mathbb{R}^d$ by introducing the hyperplane $α$-quantile, and we find an explicit functional continuity set of the $α$-quantile as a functional mapping $\mathbb{R}^d$-valued cadlag functions to $\mathbb{R}$. This specification allows us to use continuous mapping and assert that if a $\mathbb{R}^d$-valued cadlag stochastic process $X$ a.s. belongs to such continuity set, then $X^n \Rightarrow X$ (i.e., weakly in the Skorokhod sense) implies $M_{t,α}(X^n) \to^\textrm{w} M_{t,α}(X)$ (i.e., weakly) in the usual sense. We further the discussion by considering the conditions for convergence of a 'random time' functional of $M_{t,α}$, the first time at which the $α$-quantile has been hit, applied to sequences of cadlag functions converging in the Skorokhod topology. The Brownian distribution of this functional is studied, e.g., in Chaumont (J Lond Math Soc 59(2):729-741, 1999) and Dassios (Bernoulli 11(1):29-36, 2005). We finally prove the fact that if the limit process of a sequence of cadlag stochastic processes is a multidimensional Brownian motion with nontrivial covariance structure, such random time functional applied to the sequence of processes converges, jointly with the $α$-quantile, weakly in the usual sense.

In this short paper, I recount some early history of transaction research (including some of my own), explain why transaction research continues to this day (even though it seems to be a solved problem), and speculate about its future. This is an extended version of the paper that appeared in the Companion of the 2025 International Conference on Management of Data (SIGMOD-Companion '25).

Persistent self-criticism--harsh evaluative self-talk--can undermine illustrators' performance and well-being. Traditional interventions draw on psychotherapeutic approaches (e.g., compassion training) but sit outside the illustration workflow, requiring time, facilitation, and skill transfer. We propose an in-workflow alternative: evaluative off-centering, a mechanism redirecting self-critical evaluation away from an inherently self-evaluative task (like illustration) by embedding it in an alternative activity. We instantiate evaluative off-centering in Art Card Game (ACG) that integrates illustration into a card customization game: players illustrate cards that become playable assets in a head-to-head battle. In a four-day randomized controlled study with hobbyist and professional illustrators (N=38), ACG outperformed a control condition with identical illustration constraints but no evaluative off-centering mechanisms (e.g. multiplayer, gameplay), yielding significantly higher pride in produced artwork and activity enjoyment. Pride and enjoyment--positive affect states linked to lower self-criticism--help explain how ACG reduces self-criticism. We discuss design implications for creativity support tools that apply evaluative off-centering across creative domains.

We explore a continuous-time quantum walk starting at a single vertex on the discrete path and cycle with a cubic nonlinearity. Such nonlinearities arise in Bose-Einstein condensates described by the Gross-Pitaevskii equation or by nonlinear optical waveguide arrays. We analytically prove that the nonlinear quantum walk can be trapped to arbitrary fidelity depending on the coefficient of the nonlinear term. This contrasts with linear quantum walks, which are known for spreading quickly in one dimension. We propose that this trapping can be used for timing in quantum state transfer, where a qubit is held at a node until it is ready to be transferred, and it can also be held again at the receiving node. This scheme can also be interpreted as a form of quantum memory, with the trap and transfer corresponding to the storage and release of quantum information.

The jellyfish Clytia hemisphaerica possesses astounding regenerative capacities and is able to close even large wounds within a few hours. This rapid pace of wound closure raises the question whether tissue mechanics, rather than tissue restructuring or cell proliferation, might be underlying the process. We tested this possibility by asking if simple pre-strains within the jellyfish umbrella would be capable of initiating wound closure in a jellyfish body geometry. To this end, we employed an in silico spring lattice model, a coarse-grained model of elastic materials which has previously been established to study tissue mechanics problems. We found that, using radially contractile (but not radially extensile) strains, wound closure can indeed be initiated across a wide range of conditions. This is even true for large cut sizes and, hence, small pieces of remaining tissue material, in good agreement with the experimental findings. Finally, we derived an analytical expression for the expected amount of achievable closure as a function of the residual material angle. These results establish important foundations for further investigations of the biophysics underpinning jellyfish regeneration.

A design and implementation of "in-cell" magnetic refrigeration to achieve sub-10 mK temperatures T in cryogen-free dilution refrigerators is presented. The ultra low temperatures below 5 mK are attained in finite magnetic fields B up to 1 T. The holding time below 5 mK varies between about 3 to 30 hours, depending on the final magnetic field after demagnetization process. The developed technique can be used to study low dimensional devices at ultra low electron temperatures in the High B/T regime.

The "Make America Healthy Again" (MAHA) movement has created a complex ideological ecosystem within online communities, where advocacy for healthier lifestyles and whole-food diets coexists with vaccine skepticism and anti-science attitudes. Understanding how these interconnected beliefs interact, overlap, and evolve is critical for public health communication and intervention. We uncover the functional overlaps, network structures, engagement patterns, opinion dynamics, and linguistic differences across the full spectrum of MAHA ideologies. Using large-scale Reddit data spanning six years, we identified 12 MAHA-adjacent themes, including mainstream topics such as exercise, whole food, and screen use, as well as contentious topics such as vaccines, masks, GMOs, fluoride, and others. We developed a tree-based few-shot LLM pipeline to classify stances (pro, anti, neutral) across all themes, then computed user-level opinion scores to examine cross-theme interactions and opinion shifts over time. We find that MAHA-aligned users exhibit strong cross-theme bundling and coherent network structure, whereas anti-MAHA users do not bundle beyond chance. MAHA users cluster in a few mainstream subreddits, but post in a wide ecosystem of MAHA-related communities. During the pandemic, anti-fluoride and anti-mask posters transitioned into anti-vaccination posts, and later moved to broader anti-science narratives, suggesting that vaccine skepticism may serve as an entry point into wider anti-science engagement. Pro- and anti-MAHA communities also exhibit distinct psycholinguistic profiles, reflecting deeper ideological and rhetorical divides.

Weak shock theory based on cylindrical blast waves has been used to interpret meteor infrasound, but it has not been systematically benchmarked against a non-ablating hypersonic source with independently known parameters. The objective of this study is not to propose a new theoretical framework, but to evaluate the operational validity of the existing suite of blast radius formulations against a high-fidelity ground truth dataset. The OSIRIS-REx Sample Return Capsule reentry on 24 September 2023 provides such a benchmark because the capsule geometry, trajectory, and infrasound emission points are constrained from mission data and ray tracing, reducing source-side uncertainty associated with ablation. Using observations from 39 infrasound stations, this benchmarking study evaluates six published blast radius (R_0) formulations and three weak-shock transition coefficients (C) within a stratified atmospheric propagation model to predict signal period and peak overpressure. The benchmarking identifies the Sakurai formulation as the best-performing formulation for non-ablating bodies, with the Jones/Plooster formulation performing comparably when a physically appropriate C is adopted. Sakurai and Jones/Plooster yield linear-period median absolute percentage residuals of 9% and 11%, respectively. The period predictions show only weak sensitivity to C at these propagation distances. The Mach-diameter approximation commonly used in meteor studies overestimates R_0 by more than a factor of 3 in the absence of ablation. These results establish a performance baseline for applying cylindrical blast wave theory to non-ablating hypersonic bodies and demonstrate that the signal period is a robust observable for constraining R_0.