Classical First-Passage-Time Distributions (FPTDs) have been extensively studied both theoretically and experimentally. Their quantum counterparts-Quantum First-Passage-Time Distributions (QFPTDs)-remain largely unexplored and have deep implications for both fundamental physics and the development of emerging quantum technologies. We measure the first QFPTDs using a motional mode of a single trapped ion. We develop a novel composite-phase laser pulse sequence to perform tunable stroboscopic single-shot projective measurements of the motional state of a trapped ion. We measure QFPTDs of the ion energy when coupled to electric-field noise. The measurement protocol developed here is broadly applicable to other quantum systems and provides a powerful method for exploring a broad range of QFPTD phenomena. With these results we open a new field of experimental investigations of QFPT processes with potential future relevance to quantum search algorithms, unraveling connections between classical and quantum dynamics, and study of the quantum measurement problem.

The study of random walks has increasingly been popular across diverse disciplines such as statistics, mathematics, quantum physics, where they are used to model paths consisting of successive random steps in a mathematical space. A fundamental quantity of interest is the probability that a simple symmetric random walk returns to the origin after 2n steps. In this paper, we develop a unified probabilistic approach that connects the return probabilities in arbitrary dimensions with moment representations. Using this framework, we provide probabilistic proofs of several combinatorial identities involving beta and gamma functions, and derive new combinatorial identities in general dimensions.

Inspired by the classical Besov $p$-spaces defined via higher-order derivatives on the upper half-plane, we introduce Besov-type spaces on simply connected domains. We first prove that on quasidisks, the first-order Besov space is isomorphic to its higher-order counterparts, and that these higher-order spaces preserve conformal quasi-invariance. Based on this result, we characterize chord-arc domains in terms of the isomorphism between the first-order Besov space and the boundary Besov space. This extends recent results for the Dirichlet space ($p=2$) to the general case $1 < p < \infty$.

Gene regulatory networks are routinely translated from Boolean update rules into large continuous ODE systems integrated numerically for attractor identification, sensitivity analysis, and control design. The reliability of that integration depends critically on the sigmoidal kernel representing regulation. This simulation study shows that the Hill function -- the near-universal choice -- is a generically unreliable kernel, while the logistic function is a robust replacement. Two failure modes are demonstrated. First, because the Hill function vanishes at zero input, bistable circuits acquire an absorbing off-state: with experimentally grounded \textit{E. coli} galactose-operon autoregulation parameters, a Hill model stays trapped below the unstable separatrix, whereas the logistic model -- whose basal rate is strictly positive by construction -- escapes in about $44$~minutes through basal production alone, matching an analytical estimate of ${\approx}58$~min. A saddle-node analysis characterises the bistable window via an explicit transcendental equation and identifies the threshold $λθ=2$ separating monostable from bistable regimes. Second, when the Hill exponent is non-integer -- as in dose-response fits -- the power law $x^n=e^{n\ln x}$ turns complex-valued whenever a solver overshoots into negative concentrations. On an $80$-gene Boolean-derived benchmark with $n\approx3.509$, the Hill solver is silently contaminated by complex values from $t\approx52.64$, yielding smooth but spurious trajectories, whereas the logistic formulation completes $t\in[0,200]$ without a single warning. Because the logistic vector field is globally Lipschitz with explicit constant, we further prove an a priori global-error bound of classical order -- a guarantee structurally unavailable to the Hill formulation.

For an integer $r \ge 2$ and an order $n \equiv 1, 3 \pmod{6}$, write $δ_r(n)$ for the minimum, over all $r$-colourings $χ: \binom{[n]}{3} \to [r]$, of $\max_{\mathcal{S}} \mathrm{disc}(\mathcal{S}, χ)$, where the maximum is over labelled Steiner triple systems $\mathcal{S}$ of order $n$ and $\mathrm{disc}(\mathcal{S}, χ) = \max_c |\#\{T \in \mathcal{S} : χ(T) = c\} - |\mathcal{S}|/r|$. Following Gishboliner, Glock, and Sgueglia \cite{GishbolinerGlockSgueglia2025}, the bulk of the recent work on this quantity has been on lower bounds for $r \ge 3$ (proving $δ_r(n) = Ω(n^2)$) and on structural characterisation of the low-discrepancy 2-colourings. We give three small computational contributions in the small-$n$ regime $n \in \{7, 9, 13, 15, 19, 21\}$: An exact value of $δ_2(n)$ for each such $n$, matching the formula $δ_2(n) = \min_{x \in [0, n] \cap \mathbb{Z}} |x(n-x)/2 - n(n-1)/12|$ obtained by optimising the GGS Example 1.1 family. Rigorous for $n \in \{7, 9\}$ via exhaustive search over labelled STSs ($30$ resp. $840$ systems) and over all $2$-colourings; computational for $n \in \{13, 15, 19, 21\}$ by simulated-annealing search; A wide near-optimal basin: at $n = 9$, every two-colour-flip neighbour of the optimal Example~1.1 colouring that maintains discrepancy $1.0$ exists; about $34\%$ of two-flip perturbations preserve optimality; Random-colouring statistics for $r \in \{2, 3, 4\}$: $\langle\max_{\mathcal{S}}\mathrm{disc}\rangle$ grows linearly in $n$, in agreement with a heuristic Gaussian estimate $n / \sqrt{6r} \cdot \sqrt{2 \log K}$ over $K$ sampled labellings; the typical-case discrepancy is far below the GGS worst-case $Ω(n^2)$. We additionally state a conjectural exact formula for $δ_2(n)$ that holds for every $n \equiv 1, 3 \pmod{6}$.

This paper explores the dimension theory of non-Noetherian graded rings by introducing the class of Hilbert--Serre rings. We generalize Krull's dimension theorem and Smoke's dimension theorem by establishing the fundamental inequalities $\dim_{\mathrm{gr}}(R) \leq \dim(R) \leq \operatorname{GKdim}_k(R) \leq d(R)$ for any Hilbert--Serre ring $R$, where $d(R)$ is the pole order of its Poincaré series at $t=1$. Furthermore, we apply these results to initial algebras, proving that all these dimensions, including the transcendence degree, coincide for monomial algebras. Finally, we provide explicit examples demonstrating that these inequalities can be strict in general, even for integral domains.

We show that a Hamiltonian in terms of the local real-space currents obeying an $\mathfrak{su}_1(2)$ affine Lie algebra eliminates the non-locality in the Hatsugai-Kohmoto model for a doped Mott insulator. We establish this local correspondence through the Bjorken-Johnson-Low prescription for anomalous commutators. With this result, we show that the charge susceptibility computed from the current Hamiltonian is identical to that with the elemental Fermionic fields. Consequently, the HK model is local in real space, though not in terms of the Fermionic fields, thereby eliminating the key criticism of this model and reinforcing the utility of current algebras for strong interactions.

Separating multiple graph signals from a single observed mixture is an inherently ill-posed problem that traditionally relies on restrictive and handcrafted priors. This letter addresses this challenge by proposing an unsupervised learnable spectral filtering framework. Our approach reconstructs latent components by passing a fixed random input through learnable spectral filters, operating within the low-frequency eigenspace of each source-specific graph Laplacian. The architecture implicitly biases the recovered signals toward smooth patterns by confining reconstruction to these low-frequency subspaces. This acts as a structural prior, establishing a principled bridge between classical graph spectral analysis and modern neural decomposition. Numerical experiments confirm that this framework successfully isolates individual sources using solely the observed mixture and the underlying graph topology.

We compute the abundance of cosmological gravitational waves produced during the evolution of an ultralight vector (spin-1) dark matter field. A homogeneous background vector field breaks spatial isotropy, requiring a Bianchi I geometry and inducing a mixing between the scalar, vector, and tensor perturbation sectors. We derive the perturbation equations in this background and show that, as a consequence of this mixing, scalar perturbations act as a source of tensor modes, generating a stochastic GW background. The production and cosmological evolution of these gravitational waves are implemented in \texttt{class.VFDM}, a modified version of \texttt{CLASS}, from which we obtain the present-day spectrum.

Quantum computing may speed up numerical problems involving large matrices that are demanding for classical computers, and active research on this possibility is ongoing. In this work, we propose quantum algorithms for the exact simulation of a normalised correlated Gaussian random vector $|x\rangle=\vec{x}/\lVert\vec{x}\rVert$, $\vec{x}\sim\mathcal{N}(0,Σ)$, and its exponentiation $|e^{\vec{x}} \rangle= e^{\vec{x}}/\lVert e^{\vec{x}}\rVert$. When an $O(\mathrm{polylog} N)$-gate-depth quantum data loader for the covariance matrix $Σ\in\mathbb{R}^{N\times N}$ is available, preparing $|x\rangle$ and $|e^{\vec{x}}\rangle$ require $\widetilde{O}\left(\frac{\lVertΣ\rVert_F}{λ_{\max}}κ^{1.5}\right)$ and $\widetilde{O}\left(\lVert\vec{x}\rVert\frac{\lVertΣ\rVert_F}{λ_{\max}}κ^{1.5}\right)$ elementary gate depth respectively, where $\lVertΣ\rVert_F$, $λ_{\max}$, $κ$ denote the Frobenius norm, maximal eigenvalue, and condition number of $Σ$. Motivated by financial applications, we provide an end-to-end resource analysis when $\vec{x}$ represents a sample path of a Riemann-Liouville or standard fractional Brownian motion, or of a stationary fractional Ornstein-Uhlenbeck process. As a concrete example, we construct the quantum state encoding the rough Bergomi variance process and analyse the extraction of the integrated variance via quantum amplitude estimation. Under specific conditions, the dependence of $\lVertΣ\rVert_F/λ_{\max}$ and $κ$ on $N$ is small, and subcubic complexity in $N$ is achieved, indicating a quantum advantage over classical Cholesky-based sampling methods. To our knowledge, this constitutes the first quantum algorithmic framework for the amplitude encoding of exponentiated Gaussian processes, providing foundational primitives for quantum-enhanced financial modelling.

We establish a formal thermodynamic-optical duality that bridges the semiclassical quantum evaporation of black holes with their classical macroscopic geometry. The physical viability of this framework is anchored by a stable multivariate coordinate transformation and a non-vanishing Jacobian determinant, which allows for a diffeomorphic inversion mapping that decouples intrinsic physical quantities such as bare mass from the unobservable spacetime interior. By re-parameterizing black hole properties entirely in terms of the analytical shadow radius ($R_{sh}$), we derive explicit, observable-based expressions for the weak deflection angle, Hawking temperature, and integrated semiclassical luminosity. We demonstrate the framework's predictive utility by applying it to standard Kerr, Kerr-MOG (Scalar-Tensor-Vector Gravity), and rotating Horndeski spacetimes. Our results provide a definitive mathematical solution to parameter degeneracy, revealing that distinct fundamental fields (vector vs. scalar) leave unique observational fingerprints on far-field astrometry and horizon-scale quantum thermodynamics. By confronting these models with Event Horizon Telescope (EHT) M87* data, we show that this formalism successfully breaks mass-parameter degeneracies, offering a robust and computationally efficient operational tool for testing the Kerr hypothesis and probing modified gravity theories with next-generation very-long-baseline interferometry (VLBI).

Small area estimation produces estimates of population parameters for geographic and demographic subgroups with limited sample sizes. Such estimates are critical for policy decisions, yet principled validation of these models remains a challenge. Unlike conventional predictive settings, validation data are rarely available. Data thinning splits a single observation into independent training and test components. It enables out-of-sample validation using only the area-level summary statistics routinely available, requiring only their Gaussianity and known sampling variances. However, the properties of thinning-based model comparison have not been formally studied. In this paper, we develop these properties. We construct an unbiased estimator of thinned-data mean squared error and show that it differs systematically from its full-data counterpart; for the standard Fay-Herriot model, the gap admits a closed-form expression that depends on the candidate model's shrinkage behavior. We further show that the estimator variance increases sharply as the training fraction approaches one, producing a bias-variance tradeoff with no universally optimal thinning parameter. Practical recommendations balancing these forces are informed by theory and verified empirically. Design-based simulations using American Community Survey microdata show that the recommended data thinning approach is competitive with information-criterion and simulation-based methods, and substantially more stable across heterogeneous sampling designs.

The NEXT-100 detector at the LSC aims at the first competitive search for the \bbnonu decay using a high-pressure \Xe{136} electroluminescent time projection chamber. The first low-background run of NEXT-100 at 3.95 bar has been devoted to the measurement of the radon-induced backgrounds impacting this search. The contributions from both the internal and external airborne radon have been evaluated. The internal \Rn{222} activity is found to be (0.95$\pm$0.04(stat)$\pm$0.09(sys)) Bq/m$^3$, while no traces of \Rn{220} have been observed. Most of the \Rn{222} progeny plate-out on the surface of the cathode of the detector, leading to a rate of Rn-induced \Bi{214} of (0.97$\pm$0.05(stat)$\pm$0.10(sys)) Hz for visible energies above 400 keV. The corresponding background index in the \bbnonu region of interest is evaluated as (7.3$\pm$1.5(stat)$\pm$0.8(sys))$\times10^{-4}$ counts/(keV$\cdot$kg$\cdot$yr) after selection of the fully contained events. This background index is reduced to $\sim$4$\times10^{-5}$ counts/(keV$\cdot$kg$\cdot$yr) by applying a topological selection requiring only one double-electron-like track in the events. This value is one order of magnitude below the total radiogenic background expectation in NEXT-100. By analyzing the correlation of the airborne radon activity and the measured rate of events in NEXT-100, it is concluded that the detector operates in a virtually radon-free environment thanks to the radon abatement system of the LSC.

Jet quenching provides a valuable measure of the opacity of the quark-gluon plasma (QGP) produced in high-energy heavy-ion collisions. However, substantial suppression of charged hadron spectra is observed in highly peripheral collisions, despite the expectation of negligible jet-QGP interactions in this regime. To address this, we develop a HIJING-based initial condition model that accounts for the impact parameter dependence of both inelastic nucleon-nucleon (NN) collisions and the number of hard partonic scatterings per inelastic NN collision. This dependence introduces a geometric bias effect on the jet yield within a given centrality class of nucleus-nucleus (AA) collisions, suppressing the high-transverse-momentum hadron spectrum in peripheral collisions due to dilute nucleon overlap at large AA impact parameters. By combining this improved initial condition model with a linear Boltzmann transport model for jet-QGP interactions, we obtain a satisfactory description of the centrality dependence of charged hadron suppression in Pb+Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$ TeV.

Two-path complementarity in polarized interferometric fields is reconsidered by retaining the complete path--polarization density matrix instead of reducing the description to the path degree of freedom from the outset. The familiar relation connecting the Cartesian visibility components, path predictability, and reduced-state mixedness is recovered as a marginal consequence of the reduced path state and is not interpreted as a new complementarity law. Attention is focused instead on the full path--polarization description in a real reference basis adapted to the path and linear-polarization degrees of freedom. Within this framework, the normalized purity separates naturally into path, polarization, and path--polarization-correlation contributions, while the antisymmetric sector provides a sector-resolved measure of cohesion. The resulting decomposition identifies which parts of the complete state store phase-sensitive interferometric coherence and which contributions are removed when polarization is traced out. The formalism therefore provides a sector-resolved accounting of complementarity within the full path--polarization state and clarifies the connection between reduced visibility loss, polarization marking, path--polarization correlations, and quantum-eraser recovery. The present article establishes the framework and associated purity decomposition; a more detailed exploration of full path--polarization complementarity and its dynamical aspects is left for future work.

We determine the fully resolved equilibrium density profiles for two binary hard-sphere crystal structures using classical density functional theory through the White Bear II functional from fundamental measure theory. While for the substitutional crystal, in which some hard spheres are replaced by spheres of slightly smaller diameter, the density profiles are rather similar to the single-component case (narrow Gaussian peaks centered at fcc lattice sites), we observe a more complex behavior for the case of interstitial solid solutions, where the small species is fairly delocalized in the unit cell. Further, we compute the species-resolved inhomogeneous two-body direct correlation functions, depending on two three-dimensional vectors, for these two types of binary crystals. The large--large components are mainly determined by the vacancy concentration $n_\text{vac}$ and show a characteristic magnitude $\sim 1/n_\text{vac}$. Based on this observation, we propose a simple geometric picture. The components of the direct correlation function involving the small spheres substantially differ in interstitial solid solutions from those of the substitutional crystal.

Driven by the demands of 5G/Beyond 5G and 6G networks, Unmanned Aerial Vehicles (UAVs) have surfaced in critical roles for aerial communications. In the present survey, we explore the multi-mode roles of UAVs as relays, User Equipment (UE), gNB and Reconfigurable Intelligent Surfaces (RIS), along with their deployment scenarios, architectural frameworks, and different communication models incorporating Artificial Intelligence (AI) configurations. We consider the effects of alternate power sources on the communication payload. The survey also aims to address security issues in the UAV communications. As an advancement, we propose a novel UAV-Network-in-a-Box (NIB) architecture for disaster recovery and temporary coverage as an alternative to traditional network infrastructure.

This experimental dataset presents both module-level and cell-level characterization data for lithium-ion battery modules composed of three parallel-connected inhomogeneous cells across a wide range of module-level state of health (M-SoH) and cell-to-cell variation (CtCV). First, 70 cells are aged to establish an inventory with cell-level state of health (C-SoH) ranging approximately from 100% to 80% (80% is considered as the end-of-life for automotive applications). From this inventory, 78 battery modules are then assembled, each exhibiting a distinct M-SoH value (from 100% to 80.98%) and a unique CtCV value (from 0% to 9.31%, defined as population standard deviation of C-SoH within each module). Module-level characterization data are collected under 25°C at 0.5C and 0.25C conditions, enabling extraction of module-level capacities and supporting diagnostic analyses such as incremental capacity analysis and differential voltage analysis. Before a module is assembled and tested, cell-level characterization tests are conducted for every individual cell within that module under 1C conditions, enabling direct quantification of CtCV and providing accurate labels for cell-level capacities and internal resistances. The dataset is organized with both raw time-series data and processed summary information such as C-SoH, M-SoH, and CtCV for all modules. With the paired module-level and cell-level characterization data, this dataset enables understanding and development of advanced degradation monitoring mechanisms for battery modules with parallel-connected cells in the presence of CtCVs.

A low-energy nuclear transition in the isotope thorium-229 has been excited in thorium-doped crystals with laser light. This opens the perspective towards a highly stable and robust solid-state optical nuclear clock. The required laser radiation at 148 nm wavelength has so far been produced using pulsed laser systems where only a small fraction of the incident photons has been resonant with the narrow nuclear transition. Here we show that the nuclear resonance can be excited with a continuous-wave narrow-bandwidth laser source with a power of less than 1 nW, and that the resonance signal can be detected in absorption rather than in fluorescence. This eliminates the slow nuclear fluorescence decay from the detection process and offers a considerable advantage for clock operation through fast signal acquisition. The VUV laser source is based on three sequential frequency doublings, starting from a diode laser at 1187 nm that is well suited for linewidth narrowing and for frequency comparisons with optical atomic clocks. We use absorption spectroscopy for the quantitative characterization of two different Th-centers in calcium fluoride crystal and measure the isomeric shift between them. One of the centers shows a very small static electric crystal field gradient 0.1 V/$Å^2$, to be compared to gradients in the range of 100 V/$Å^2$ observed earlier. This indicates a center with high symmetry of the ions surrounding the Th nucleus, promising nuclear resonance lines that are nearly independent of the lattice spacing.

We revisit the optimization problem solved in Løkka & Zervos (2008), i.e., the maximization of dividends, in a Brownian risk model, with the possibility (not the obligation) of making capital injections. Following the approach introduced in Alvarez & Shepp (1998), Renaud & Simard (2021), Renaud et al. (2023), we consider instead absolutely continuous (AC) dividend strategies with an affine bound on the payment rates, while singular capital injections are still allowed. In addition, we incorporate a parameter for the cost of ruin or, said differently, a penalty at ruin in the performance function. We show that the solution is a so-called Løkka-Zervos dichotomy: the surplus is never ruined by making bail-out payments, or no capital is injected and bankruptcy can occur; in either case, dividends are paid at full rate when the surplus is above a threshold. Our framework allows us to provide explicit conditions to express the dichotomy, either using the cost of capital injections or the cost of ruin as a criterion, which also exposes the underlying structure of the solution. In particular, for some values of the parameters, we show that it is optimal to liquidate. Moreover, we perform a numerical analysis highlighting the range of values generated under this AC affine-bound structure.

This paper develops a model of \textit{identification design} and applies it to robust causal inference in microeconometrics. The decision maker observes the population distribution of signals generated by an information structure and ranks actions by their worst-case payoff over the set of admissible state distributions consistent with those signals. We call an environment \textit{manipulable} if every action is implementable under all true distributions of the state variable, and show this holds if and only if all actions share the same worst-case payoff. We confirm in application that all treatment-effects models are manipulable, and moreover that manipulation is feasible via \textit{almost fully informative} information structures that conceal at most one dimension of information from the decision maker. As in practice, we consider a restriction to \textit{marginal information structures} that disclose the joint distribution of the outcome variable, treatment variable, and a selection of covariates. In that context, we provide necessary and sufficient conditions for exact identification and sharp payoff bounds for disclosures that do not satisfy those conditions. In doing so, we show that the disclosure of a sufficiently rich set of covariates to verify faithful execution of the assignment mechanism eliminates all scope for manipulation in experiments, while observational studies remain partially manipulable via covariate selection.

We introduce ParaToric, a C++ package for simulating the toric code in a parallel field (i.e., $X$- and $Z$-fields) at finite temperature. We implement and extend the continuous-time quantum Monte Carlo algorithm of Wu, Deng, and Prokof'ev on the square, triangular, honeycomb, and cubic lattices with either periodic or smooth open boundaries. The package is expandable to arbitrary lattice geometries and custom observables diagonal in either the $X$- or $Z$-basis. ParaToric also supports snapshot extraction in both bases, making it ideal for generating training/benchmarking data for other methods, such as lattice gauge theories, cold atom or other quantum simulators, quantum spin liquids, artificial intelligence, and quantum error correction. The software provides bindings to C/C++ and Python, and is thus almost universally integrable into other software projects.

The precise determination of the Higgs self-couplings is an essential task for understanding electroweak symmetry breaking and probing physics beyond the Standard Model (SM). The calculation of two-loop corrections to scalar couplings is important as it provides a critical test of the perturbative stability of the theoretical predictions, especially in scenarios with extended scalar sectors where large one-loop corrections can occur. Moreover, two-loop corrections need to be taken into account for the future perspective of precisely measuring the trilinear Higgs self-coupling. We present new results for the leading two-loop corrections to trilinear Higgs couplings in the Two-Higgs-Doublet Model (2HDM). We focus in particular on the couplings $λ_{hhh}$ and $λ_{hhH}$, which are relevant for Higgs pair production at the (HL-)LHC or at future linear colliders. We address the renormalisation of the alignment limit in the Higgs basis and give some insights into technical details of the calculation. Finally, we discuss the phenomenological impact of our results on di-Higgs production differential distributions.

Recent study \cite{rajamanickam2025shear} of non-Boussinesq fluids in narrow channels identified a novel shear-induced horizontal buoyancy force that emerges upon depth-averaging the Navier--Stokes equations. This note demonstrates that this force is formally equivalent to the divergence of a Korteweg stress tensor. Unlike classical Korteweg stresses, which are typically attributed to molecular-scale cohesive potentials or implemented through assumed constitutive relations, we show that this emergent stress arises purely from self-coupled transport where the internal Ostroumov flow is kinematically coupled to the local density gradient. We derive explicit expressions for the effective stress coefficients, revealing a fundamental dependence on the Prandtl number and Grashof number. This correspondence is contrasted with classical Taylor dispersion, where the absence of self-coupling yields only a uniaxial stress. Our results provide an example - not a general theory - of how quadratic gradient stresses can emerge from sub-scale self-coupled gradient-driven flows, without appeal to molecular potentials or variational principles.

Here we examine how AI agent "personalities" interact with human personalities to shape human-AI collaboration and performance. In a large-scale, preregistered randomized experiment, we paired 1,258 participants with AI agents prompted to exhibit varying levels of the Big Five personality traits. These human-AI teams produced 7,266 display ads for a real think tank, which we evaluated using 1,168 independent human raters, and a field experiment on X that generated nearly 5 million impressions. We found that human and AI personalities individually shaped ad quality and teamwork and that human-AI personality pairings directly influenced ad quality and ad performance. For example, extraverted humans paired with conscientious AI produced the lowest quality ads, followed by conscientious humans paired with agreeable AI and neurotic humans paired with conscientious AI. In the field experiment, ad quality significantly influenced ad performance, measured by click-through rates and cost-per-click, and neurotic humans paired with neurotic AI achieved the highest click-through rates. Together, these results demonstrate that personality pairing can improve human-AI collaboration and performance. They also motivate future research on the complex implications of AI personalization for human-AI collaboration, teamwork and performance.

Atmospheric Methane Removal (AMR) is a third class of climate intervention, along with Carbon Dioxide Removal (CDR) and Solar Radiation Management (SRM). We show that, unlike CDR, the avoided warming by AMR is not durable due to methane's short atmospheric lifetime, although its temperature rebound upon termination is less abrupt than that of SRM. AMR's impact on tropospheric ozone can be further modulated by background pollutant levels.

We present a self-consistent two-fluid theory demonstrating that pressure gradients simultaneously generate plasma flows and magnetic fields. We show that compatibility between ion momentum balance and mass conservation imposes a previously unrecognized constraint on plasma evolution: the total pressure must satisfy the Laplace equation, $\nabla^2 p = 0$. This condition yields a class of exact analytical solutions in which pressure-driven flows and Biermann-type magnetic fields emerge together. Application of the model to a galactic gas clump reveals that, under thermal pressure, electrons and ions move almost together, giving rise to weak currents and consequently very small seed magnetic fields. Ion dynamics are also important for determining the seed magnetic-field generation time $τ_B$ and for estimating the ion flow velocity. The model is further applied to laser-produced plasma to describe its short-time evolution. The present theory provides a unified, self-consistent description of pressure-driven flow generation and magnetogenesis in both astrophysical and laboratory plasmas.

The celebrated analogy between the pressure-temperature phase diagram of a liquid-gas system and the field-temperature phase diagram of a ferromagnet has long been a cornerstone for understanding universality of phase transitions and critical phenomena. Here we extend this analogy to a highly frustrated antiferromagnet, the spiral Ising compound Nd$_3$BWO$_9$ with kagome layers. In its phase diagram, we identify a metamagnetic transition line with a critical endpoint (CEP) located at $μ_0H_{\mathrm{c}} \simeq 1.04$ T and $T_{\mathrm{c}} \simeq 0.3$ K. Above the CEP, an Ising supercritical regime emerges with crossover lines that follow a universal scaling law, as evidenced by the specific heat, magnetic susceptibility, and magnetocaloric measurements. Remarkably, we observe highly sensitive magnetic cooling near the emergent CEP, characterized by a divergent magnetic Grüneisen ratio $Γ_H \propto 1/t^{β+γ-1}$, with $β+ γ\simeq 1.563$ the sum of critical exponents of the 3D Ising universality class and $t \equiv (T-T_{\rm c})/T_{\rm c}$ the reduced temperature. Adiabatic demagnetization from 2 K and 4 T reaches a minimum temperature of 195 mK, via a self-cascading process that combines supercritical and topological cooling. Our findings open a new avenue for studying supercritical phenomena and magnetic refrigeration with the frustrated rare-earth compounds RE$_3$BWO$_9$ and, more broadly, in Ising-anisotropic antiferromagnets such as spin ices.

Coflow provides a key application-layer abstraction for capturing communication patterns, enabling the efficient coordination of parallel data flows to reduce job completion times in distributed systems. Modern data center networks (DCNs) are employing multiple independent optical circuit switching (OCS) cores operating concurrently to meet the massive bandwidth demands of application jobs. However, existing coflow scheduling research primarily focuses on the single-core setting, with multi-core fabrics only for EPS (electrical packet switching) networks. To address this gap, this paper studies the coflow scheduling problem in multi-core OCS networks under the \textit{not-all-stop} reconfiguration model in which one circuit's reconfiguration does not interrupt other circuits. The challenges stem from two aspects: (i) cross-core coupling induced by traffic assignment across heterogeneous cores; and (ii) per-core OCS scheduling constraints, namely \textit{port exclusivity} and \textit{reconfiguration delay}. We propose an approximation algorithm that jointly integrates cross-core flow assignment and per-core circuit scheduling to minimize the total weighted coflow completion time (CCT) and establish a provable worst-case performance guarantee. Trace-driven simulations using real Facebook workloads demonstrate that our algorithm effectively reduces weighted CCT and tail CCT.

Physics-informed neural networks (PINNs) often become difficult to optimize for incompressible flow problems with large spatial domains, multiscale stresses, or long-time invariant dynamics. We propose an invariant-guided PINN (IG-PINN) framework that uses partitioned training as a conservative preconditioning stage rather than as the final piecewise representation. A globally defined architecture is trained successively on spatial subdomains or temporal slabs; selected field traces, structural information, and conservative diagnostics are then transferred to a final global correction, yielding a single neural field on the full spatial or space-time domain. The framework is tested on two incompressible flow problems: steady Oldroyd--B flow past a confined cylinder and a rotational Newtonian flow with helicity diagnostics. In the Oldroyd--B case, IG-PINN transfers velocity, polymeric stress, and mass-flux information while avoiding pressure traces at artificial interfaces. In the helicity case, endpoint velocity is transferred through a hard temporal constraint and kinetic energy is controlled during slab training and residual global correction. The experiments demonstrate improved optimization robustness, reduced conservation errors for the cylinder wake, and controlled energy and helicity diagnostics for the transient rotational flow.