We study contact 3-manifolds $Y$ with a special global frame inspired by Cartan's structure equations. This frame is dual to a generalized Finsler structure defined by Bryant. We present some examples and rigidity results on the class of manifolds whose frame satisfies certain natural conditions on a scalar function $K\colon Y\to \mathbb{R}$, related to the frame. This function realizes the curvature when $Y$ is the unit tangent bundle with respect to a metric on a surface. As applications, we obtain sharp estimates for the action of a Reeb orbit in terms of this scalar function, under the assumption that the frame satisfies specific conditions. In particular, we recover a classical upper bound on the systole of positively curved metrics on $S^2$ due to Toponogov.

It is demonstrated the fundamental role of the equivalence principle in gravity for the Yukawa coupling between scalar and fermion fields. The Kibble-Zurek mechanism for formation of topological defects as vortexes and monopoles breaks down in system with a global gauge symmetry only. At the same time, the different vacuums can occur, which are separated be domain walls. The equivalence principle makes the strong violation of CP invariance impossible. Thus the axion hypothesis becomes redundant.

We present a theoretical framework for recovering the amplitude and carrier phase of a single received RF field with a Rydberg-atom receiver, without injecting an RF local oscillator (LO) into the atoms. The key enabling mechanism is a static DC bias applied to the vapor cell: by Stark-mixing a near-degenerate Rydberg pair, the bias activates an otherwise absent upper optical pathway and closes a phase-sensitive loop within a receiver driven only by the standard probe/coupling pair and the received RF field. For a spatially uniform bias, we derive an effective four-level rotating-frame Hamiltonian of Floquet form and show that the periodic steady state obeys an exact harmonic phase law, so that the $n$th probe harmonic carries the factor $e^{inΦ_S}$. This yields direct estimators for the signal phase and amplitude from a demodulated probe harmonic, with amplitude recovery obtained by inverting an injective harmonic response map. In the high-SNR regime, we derive explicit RMSE laws and use them to identify distinct phase-optimal and amplitude-optimal bias-controlled mixing angles, together with a weighted joint-design criterion and a balanced compromise angle that equalizes the fractional phase and amplitude penalties. We then extend the analysis to nonuniform DC bias through quasistatic spatial averaging and show that bias inhomogeneity reduces coherent gain for phase readout while also reshaping the amplitude-response slope. Numerical examples validate the phase law, illustrate response-map inversion and mixing-angle trade-offs, and quantify the penalties induced by bias nonuniformity. The results establish a minimal route to coherent Rydberg reception of a single RF signal without an auxiliary RF LO in the atoms.

We study approximation algorithms for satisfiable and nearly satisfiable instances of ordering constraint satisfaction problems (ordering CSPs). Ordering CSPs arise naturally in ranking and scheduling, yet their approximability remains poorly understood beyond a few isolated cases. We introduce a general framework for designing approximation algorithms for ordering CSPs. The framework relaxes an input instance to an auxiliary ordering CSP, solves the relaxation, and then applies a randomized transformation to obtain an ordering for the original instance. This reduces the search for approximation algorithms to an optimization problem over randomized transformations. Our main technical contribution is to show that the power of this framework is captured by a structured class of transformations, which we call strong IDU transformations: every transformation used in the framework can be replaced by a strong IDU transformation without weakening the resulting approximation guarantee. We then classify strong IDU transformations and show that optimizing over them reduces to an explicit optimization problem whose dimension depends only on the maximum predicate arity $k$ and the desired precision $δ> 0$. As a consequence, for any finite ordering constraint language, we can compute a strong IDU transformation whose guarantee is within $δ$ of the best guarantee achievable by the framework, in time depending only on $k$ and $δ$. The framework applies broadly and yields nontrivial approximation guarantees for a wide class of ordering predicates.

This note outlines a mean-field approach to dynamic optimal transport problems based on the recently proposed McKean-Pontryagin maximum principle. Key aspects of the proposed methodology include i) avoidance of sampling over stochastic paths, ii) a fully variational approach leading to constrained Hamiltonian equations of motion, and iii) a unified treatment of deterministic and stochastic optimal transport problems. We also discuss connections to well-known dynamic formulations in terms of forward-backward stochastic differential equations and extensions beyond classical entropic-regularized transport problems.

4D-STEM-based orientation and phase mapping has enabled rapid microstructure quantification that can be directly combined with standard TEM- and STEM-based imaging modes. Typically, orientation mapping is coupled with beam precession (i.e. precession electron diffraction) to achieve high indexing rates, adding to the cost and often decreasing the spatial resolution of the approach. This paper introduces a new post processing approach modeled after the non-local pattern averaging and reindexing algorithm developed for the electron backscatter diffraction community, wherein post-collection, patterns are averaged using a distance similarity parameter. Results from Ni and Au thin films show that indexing rates can be significantly improved using this post-processing technique due to improved signal-to-noise ratios in the diffraction patterns. Interestingly, the highest indexing rates are achieved in samples heavily damaged via ion irradiation, suggesting that averaging over curved lattices further improves indexing rates.

We give a precise asymptotic formula for the number of $n\times 4t$ partial Hadamard matrices in the regimes $t/n^3\to\infty$ and $t/n^3\toΘ$ for sufficiently large fixed $Θ$. This strengthens earlier results of de~Launey and Levin, who obtained the asymptotic for $t/n^{12}\to\infty$, and of Canfield, who extended this to $t/n^4\to\infty$.

We present results for unpolarized and polarized semi-inclusive deep-inelastic scattering mediated by electroweak gauge bosons at next-to-next-to-leading order (NNLO) in perturbative quantum chromodynamics. The results include all relevant structure functions arising from both neutral current (NC) and charged current (CC) interactions, incorporating contributions from all partonic channels with full flavor dependence. These corrections are crucial for improving the theoretical precision. A detailed numerical analysis of the NNLO corrections demonstrates their phenomenological importance, revealing sizable effects and a significant reduction in residual scale dependence in the kinematic range probed by the future Electron-Ion-Collider. These results will serve as a critical input for future global extractions of parton distributions functions and fragmentation functions.

We investigate stability of a new class of heteroclinic cycles that we call heteroclinic cycles of type Y. The cycles can be regarded as a generalisation of heteroclinic cycles of type Z introduced in [Podvigina, Nonlinearity 25, 2012]. The type Y cycles differ from the cycles of type Z in the following: The trajectories comprising a cycle of type Y belong to flow-invariant subspaces that can be of different dimensions. Unlike in the most studies of the stability of heteroclinic cycles, we do not require that the eigenvalues of the linearisations of the dynamical system near the equilibria are distinct. Instead of the common assumption that the cycles are robust, we prescribe flow-invariance of certain subspaces. Similarly to type Z cycles, asymptotic stability and fragmentary asymptotic stability of type Y cycles is determined by the eigenvalues and eigenvectors of transition matrices. The matrices are products of basic transition matrices that depend on the eigenvalues of linearisations and the dimensions of the contracting subspaces.

We study the geometric and dynamical structure induced by the return map associated with domains in the class \(\mathcal{O}_{C}\). This map, defined through a geometric round-trip between the convex core and the outer boundary, generates a discrete dynamical system on the boundary \(\partial C\). Building on previous results establishing global convergence of the return dynamics, we show that equilibria of the return map coincide with the critical points of the thickness function. This identification allows us to apply Morse-theoretic tools to derive global constraints on the dynamics. In particular, we obtain lower bounds on the number of equilibria in terms of the Betti numbers of \(\partial C\), as well as a global balance relation governed by the Euler characteristic. We further analyze the local behavior of the return map near equilibria. Using the differentiability of the return map inherited from the radial and reciprocal constructions, we derive a first-order expansion in which the linearization is governed by the Hessian of the thickness function and an operator arising from the geometry of the return map. This leads to an operator-valued generalization of the previously observed scalar structure, revealing that the dynamics behaves as an anisotropic gradient-like iteration rather than a purely isotropic descent. Near nondegenerate minima, we prove a quantitative descent estimate and local linear convergence under a spectral condition. Under aligned nonlocal geometry, the sign of the curvature gap between the convex core and the outer boundary determines whether the induced dynamics is contracting, neutral, or expanding in each principal direction. Finally, we discuss extensions beyond the Morse setting, including the Morse-Bott case, and highlight connections between the geometry of the domain, the topology of \(\partial C\), and the structure of the induced dynamics.

In this paper, we construct additively graceful signed graphs S from a given graph G that may be additively graceful or not be additively graceful. We also show the construction of additively graceful signed graphs from additively graceful signed graphs. We find the values of m, n in non-divisible sum graph, denoted as G(m, n), that admit additively graceful labeling.

Godunov-type methods, which obtain numerical fluxes through local Riemann problems at cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics. Exact Riemann solvers faithfully solve the underlying equations, but can be computationally expensive due to the iterative root-finding procedures they often require. Consequently, most practical computations rely on classical approximate Riemann solvers, such as Rusanov and Roe, which trade accuracy for computational speed. Neural networks have recently shown promise as an alternative for approximating exact Riemann solvers, but most existing approaches are data-driven or impose weak constraints. This may result in problems with maintaining balanced states, symmetry breaking, and conservation errors when integrated into a Godunov-type scheme. To address these issues, we propose a hard-constrained neural Riemann solver (HCNRS) and enforce five constraints: positivity, consistency, mirror symmetry, Galilean invariance, and scaling invariance. Numerical experiments are carried out for the shallow water and ideal-gas Euler equations on standard benchmark problems. In the absence of hard constraints, violations of the well-balanced property, mass conservation, and symmetry are observed. Notably, in the Euler implosion problem, the exact Riemann solver with MUSCL-Hancock captures the jet structure well, whereas the Rusanov flux is too diffusive and smears it out. HCNRS accurately reproduces the solution obtained by the exact Riemann solver. In contrast, an unconstrained neural formulation lacks mirror symmetry, which makes the solution depend on the choice of flux normal direction. As a result, the jet is either shifted or lost, along with diagonal symmetry.

Background: Renal blood flow (RBF) is an important marker of kidney health, but noninvasive assessment is not routinely used in clinical imaging. We evaluated the feasibility and physiologic validity of quantifying renal transport of Rubidium-82 (K1) during standard myocardial perfusion imaging (MPI) PET. Methods: We studied 126 patients (age 60 +/- 12 years; 48% male; 51% Black) undergoing clinically indicated rest and stress Rb-82 MPI, in whom at least one kidney was partially visualized within the axial field of view. Volumes of interest were drawn over the visible renal cortex. K1 was estimated using a one-tissue compartment model with arterial input functions (AIF) derived from either the left ventricle (LV) or abdominal aorta. Results: LV-derived AIF produced physiologic and internally consistent flow estimates, whereas aorta-derived AIF systematically overestimated K1 and flow. LV-based measurements were therefore used for all analyses. K1 demonstrated nonlinear flow dependence consistent with the Renkin-Crone extraction model, plateauing at higher perfusion states. Renal K1 and flow declined progressively with worsening kidney function, from 1.24 +/- 0.35 ml/min/g (eGFR >= 60) to 0.53 +/- 0.22 ml/min/g (eGFR < 15; P < 0.0001). Only patients with preserved eGFR showed significant hyperemic augmentation. ROC analysis demonstrated excellent discrimination for reduced kidney function (AUC > 0.90). Conclusion: Opportunistic renal K1 quantification during routine Rb-82 PET is feasible, physiologically consistent, and strongly associated with kidney function.

This study examines the evolution of Intelligent and Secure Smart Hospital Ecosystems using a Scoping Review with Bibliometric Analysis (ScoRBA) to map research patterns, identify gaps, and derive policy implications. Analyzing 891 journal articles from Scopus (2006-2025) through co-occurrence analysis, network visualization, overlay analysis, and the Enhanced Strategic Diagram (ESD), the study applies the PAGER framework to link Patterns, Advances, Gaps, Research directions, and Evidence-based policy implications. Findings reveal three interrelated clusters: AI-driven intelligent healthcare systems, decentralized privacy-preserving digital health ecosystems, and scalable cloud-edge infrastructures, showing a convergence toward integrated ecosystem architectures where intelligence, trust, and infrastructure reinforce each other. Despite progress in AI, blockchain, and cloud computing, gaps remain in interoperability, real-world implementation, governance, and cross-layer integration. Emerging themes such as explainable AI, federated learning, and privacy mechanisms highlight areas needing further research. Policy-relevant recommendations focus on coordinated governance, scalable infrastructure, and secure data ecosystems, particularly for developing country contexts. The study bridges bibliometric evidence with actionable policies, supporting informed decision-making in smart hospital development.

In General Relativity, constructing exact traversable wormholes coupled to electromagnetic fields typically requires complex Non-Linear Electrodynamics (NED). We demonstrate that Unimodular Gravity (UG) elegantly resolves this limitation. By relaxing energy-momentum conservation, UG introduces a dynamical cosmological term, $Λ(x)$, enabling a semi-classical energy exchange between matter and the vacuum. Exploiting this mechanism, we construct exact Scalar-Maxwell-$Λ(x)$ wormholes. We show that, provided the shape function $b(r)$ satisfies specific geometric conditions, these exact spacetimes can be fully supported by a phantom scalar field and standard linear Maxwell electrodynamics. This approach entirely bypasses NED, highlighting UG as a powerful framework for modeling non-trivial topologies with simplified, well-understood classical fields.

We prove that minimal Dirac operators on the half-line are self-modeling, which means that such an operator is determined by its arbitrary unitary copy uniquely up to a transformation (shape equivalence) which changes its potential by a constant factor of modulus one. This result is obtained using the wave functional model of the minimal matrix Schrödinger operator on the half-line.

Unlike electromagnetic telescopes, gravitational-wave (GW) detectors cannot produce pretty pictures, but we can convert GW signals into sound. I compute what the Universe actually sounds like by averaging over $\sim10^6$ synthetic compact binary coalescence events occurring throughout 2026. The result: a soothing, low-frequency rumble, perfect for sleeping, meditation, or contemplating the violent nature of spacetime. This is the $Universal\ harmony$, audio file included!

This paper studies the problem of hedging and pricing a European call option under proportional transaction costs, from two complementary perspectives. We first derive the optimal hedging strategy under CARA utility, following the stochastic control framework of Davis et al. (1993), characterising the no-transaction band via the Hamilton-Jacobi-Bellman Quasi-Variational Inequality (HJBQVI) and the Whalley-Wilmott asymptotic approximation. We then adopt a deep hedging approach, proposing two architectures that build on the No-Transaction Band Network of Imaki et al. (2023): NTBN-Delta, which makes delta-centring explicit, and WW-NTBN, which incorporates the Whalley-Wilmott formula as a structural prior on the bandwidth and replaces the hard clamp with a differentiable soft clamp. Numerical experiments show that WW-NTBN converges faster, matches the stochastic control no-transaction bands more closely, and generalises well across transaction cost regimes. We further apply both frameworks to the bull call spread, documenting the breakdown of price linearity under transaction costs.

A strong converse bound for the classical identification capacity of a quantum channel is an upper bound on the asymptotic identification rate of classical messages sent through the channel, such that, above this rate, the probability of an identification error necessarily converges to one. Converse bounds for identification are notoriously difficult to obtain for fully quantum channels. The only previously known converse bound, due to Atif, Pradhan and Winter [Int.~J.~Quantum Inf.~22(5):2440013, 2024], has the unsatisfactory feature of remaining strictly positive even for a completely noisy channel, for which identification is clearly impossible. We derive strong (and hence also weak) converse bounds, for the qubit depolarizing channel with noise parameter $p$, that vanish as $p\to 1$, thereby yielding the correct behavior in the completely noisy limit. Moreover, in the setting of simultaneous classical identification under the constraint of complete product measurements, our converse bound matches the corresponding achievability bound, and establishes that in this case the identification capacity equals the classical capacity of the channel.

Mathematical models of natural and man-made systems often have many adjustable parameters that must be estimated from multiple, potentially conflicting datasets. Rather than reporting a single best-fit parameter vector, it is often more informative to generate an ensemble of parameter sets that collectively map out the trade-offs among competing objectives. This paper presents ParetoEnsembles.jl, an open-source Julia package that generates such ensembles using Pareto Optimal Ensemble Techniques (POETs), a simulated-annealing-based algorithm that requires no gradient information. The implementation corrects the original dominance relation from weak to strict Pareto dominance, reduces the per-iteration ranking cost from $O(n^2 m)$ to $O(nm)$ through an incremental update scheme, and adds multi-chain parallel execution for improved front coverage. We demonstrate the package on a cell-free gene expression model fitted to experimental data and a blood coagulation cascade model with ten estimated rate constants and three objectives. A controlled synthetic-data study reveals parameter identifiability structure, with individual rate constants off by several-fold yet model predictions accurate to 7%. A five-replicate coverage analysis confirms that timing features are reliably covered while peak amplitude is systematically overconfident. Validation against published experimental thrombin generation data demonstrates that the ensemble predicts held-out conditions to within 10% despite inherent model approximation error. By making ensemble generation lightweight and accessible, ParetoEnsembles.jl aims to lower the barrier to routine uncertainty characterization in mechanistic modeling.

We compute the homotopy groups at each unital abelian C*-algebra $C(T)$ in the Morita $3$-category of abelian C*-algebras, C*-algebras with central maps, C*-correspondences, and adjointable bimodule maps. We describe these groups in terms of the topological data of the underlying compact Hausdorff space $T$. We also compute the actions of the first homotopy group on the second and third homotopy groups in terms of these topological invariants of $T$.

We introduce a systematic method for expanding general spin-glass Hamiltonians in terms of Mattis interactions, providing a novel perspective for understanding the fundamental differences between short-range Edwards-Anderson (EA) and mean-field Sherrington-Kirkpatrick (SK) spin glasses. By iteratively extracting patterns from the coupling matrix, we expand the original spin-glass system into a Hopfield-like model (a series of Mattis interactions) plus a residual system. Our analysis reveals profound distinctions between EA and SK models: while EA models in two and three dimensions break into isolated subconnected sections after expansion, the SK model exhibits remarkable self-similar behavior, with the residual system preserving the mean-field structure and Gaussian statistics throughout the expansion process. This self-similarity manifests in exponential decay of residual matrix norms and expansion coefficients, reflecting the inherent mean-field nature of the SK model. Furthermore, we demonstrate that pattern expansion can identify ultra-low energy excitations in EA models, revealing excitations with energies that decrease rapidly with expansion step. Through connected component analysis, we quantify the size-energy relationship of these independent excitation clusters, opening new avenues for understanding the low-energy landscape of spin glasses and providing insights into the nature of metastable states.

Nanocomposites comprised of insulated magnetic single-domain particles are promising candidates for high-frequency, eddy current free, soft magnetic materials, but tend to suffer from low magnetic susceptibility ($<20$). Particle alignment has been proposed to increase nanocomposite susceptibility and reduce magnetic losses but experimental verification has been lacking. Here, magnetic nanocomposites containing 3-57 vol\% field-aligned 11$\pm$3 nm maghemite particles in a poly-vinyl matrix were investigated for potential use as high-frequency inductor core materials. The particles were aligned by a homogenous static alignment field during nanocomposite drying, fixating the particle orientation. Particle aggregation was disproved by small-angle scattering. The dependence of the alignment field strength and particle concentration on the nanocomposite's susceptibility and hysteresis losses were investigated from DC up to 922 kHz by vibrating sample magnetometry, AC-susceptibility and high-frequency hysteresis measurements. Nanocomposite susceptibility increased super-linearly with particle fraction due to weak particle interactions. Alignment of the particles increased the nanocomposite susceptibility from 21 to 50 for samples with a particle content of 57 vol\%. Hence, the synergy between particle alignment and interaction allows for a higher than expected susceptibility of nanocomposites. The results show that magnetically aligning particles in a nanocomposite reduces magnetic losses when using well-dispersed single-domain superparamagnetic nanoparticles. Measured nanocomposite susceptibility could be modelled by a combination of directional dependent Debye-models including mean-field interaction effects and partial particle alignment. Measured susceptibility of 50 is among the highest obtained for nanocomposites, making it a relevant candidate for applications in power electronics.

This paper introduces a performative scenario optimization framework for decision-dependent chance-constrained problems. Unlike classical stochastic optimization, we account for the feedback loop where decisions actively shape the underlying data-generating process. We define performative solutions as self-consistent equilibria and establish their existence using Kakutani's fixed-point theorem. To ensure computational tractability without requiring an explicit model of the environment, we propose a model-free, scenario-based approximation that alternates between data generation and optimization. Under mild regularity conditions, we prove that a stochastic fixed-point iteration, equipped with a logarithmic sample size schedule, converges almost surely to the unique performative solution. The effectiveness of the proposed framework is demonstrated through an emerging AI safety application: deploying performative guardrails against Large Language Model (LLM) jailbreaks. Numerical results confirm the co-evolution and convergence of the guardrail classifier and the induced adversarial prompt distribution to a stable equilibrium.

In this article, we investigate vacuum leakage detection problems in composite manufacturing. Our approach uses Voronoi diagrams, a well-known structure in discrete geometry. The Voronoi diagram of the vacuum connection positions partitions the component surface. We use this partition to narrow down potential leak locations to a small area, making an efficient manual search feasible. To further reduce the search area, we propose refined Voronoi diagrams. We evaluate both variants using a novel dataset consisting of several hundred one- and two-leak positions along with their corresponding flow values. Our experimental results demonstrate that Voronoi-based predictive models are highly accurate and have the potential to resolve the leakage detection bottleneck in composite manufacturing.

The van der Waerden simplicial complex, denoted ${\tt vdw}(n,k)$, is the simpicial complex whose facets correspond to the arithmetic progressions of length $k$ in the set $\{1,\ldots,n\}$. We study the Lefschetz properties of the Artinian ring $A(n,k) = K[x_1,\ldots,x_n]/(I_{{\tt vdw}(n,k)} + \langle x_1^2,\ldots,x_n^2\rangle)$ where $I_{{\tt vdw}(n,k)}$ is the associated Stanley--Reisner ideal. If $k=1,2$ or $n-1$, the ring $A(n,k)$ will have the Weak Lefschetz Property for all $n > k$. When $k=3$, we classify the rings $A(n,3)$ that have the Weak Lefschetz Property. We conjecture that $A(n,k)$ fails to have the Weak Lefschetz Property if $n \gg k \geq 3$ and $k$ odd. We also classify when ${\tt vdw}(n,k)$ is a pseudo-manifold, which allows us to show that $A(n,k)$ satisfies the Weak Lefschetz Property in some degrees by using a result of Dao and Nair.

This study explores the use of INT8-based emulation for accelerating traditional FP64-based HPC workloads on modern GPU architectures. Through SCILIB-Accel automatic BLAS offload tool for cache-coherent Unified Memory Architecture, we emulate FP64 matrix multiplications in the LSMS CPU application in the MuST suite without code changes. We find that accuracy depends on both arithmetic precision and the properties of the operator, which can be dealt with through tunable precision emulation. Unlike traditional mixed-precision approaches, this method preserves original algorithms while optimizing hardware utilization. We showcase the potential of improving accuracy and performance at the same time. This work highlights the potential of AI-driven hardware to transform HPC, advocating for adaptive precision strategies in future scientific computing.

Entangled photon pairs are a ubiquitous resource in quantum technologies, used in quantum key distribution and quantum networking as well as fundamental tests of non-locality. For scalable quantum networks, pairs that are indistinguishable in all unentangled degrees of freedom are essential, as they enable high-fidelity entanglement swapping across network nodes. To date the most-studied sources of "swappable" entangled photon pairs have been based on spontaneous parametric down-conversion (SPDC) in non-linear crystals. However, the probabilistic nature and unavoidable trade-off between brightness and unwanted multi-photon emission limits their performance in lossy channels. Here, we demonstrate a high-fidelity source of "swappable" entangled photon pairs using a semiconductor quantum dot (QD) coupled to a tunable microcavity. By actively modulating the QD emission between orthogonal polarisation states, delaying one path in a low-loss Herriott cell, and recombining the two on a balanced beam splitter, we generate entangled photon pairs with a fidelity of $96.1\pm0.5$ %. We identify and mitigate fidelity-limiting factors, achieving a maximum fidelity of $98.1\pm0.5$ % through time-resolved post-selection. The scheme suppresses residual multi-photon events concentrated near the excitation pulse and has only a modest impact on the rate. Furthermore, the photons are mutually indistinguishable, enabling efficient entanglement swapping. Our results establish semiconductor QDs as a viable platform for quantum network-compatible swappable entangled photon pair generation, with feasible entanglement generation rates exceeding 0.5 Gpairs/s.

We estimate the Hollyfeld Gambit for the Powerball lottery and its return on investment compared to present and extrapolated federal funding for astrophysical grants. Using a Monte Carlo estimation of rate of return for the Powerball, we conclude a Hollyfeld Gambit is a better bet than a federal grant by the end of the decade if current trends hold.

The nature of Sagittarius A* (Sgr A*) has been the subject of intense study and debate for over half a century. Herein, we present the first successful interview with an astrophysical object, exploring the perspective of this supermassive black hole and, in doing so, challenging the traditional observational paradigm of astrophysics. Rather than treating astrophysical systems as purely passive entities characterized through indirect measurements, we introduce an interaction-based framework via a therapeutic-style interview enabled by the ARMCHAIR communication methodology. Using structured, psychotherapeutic dialogue, we probe Sgr A*'s responses to key aspects of its astrophysical characterization, including eating habits, its name, and concerns about privacy. These exchanges offer an alternative lens through which to interpret familiar observational phenomena. This work highlights potential limitations in strictly reductionist approaches and suggests a modest expansion of standard astrophysical methodology to leave room for considering how the objects we study might feel about the attention they receive.