The non quantum relativistic version of the proof of Feynman for the Maxwell equations is discussed in a framework with a minimum number of hypotheses required. From the present point of view it is clear that the classical equations of motion corresponding to the gauge field interactions can be deduced from the minimal coupling rule, and we claim here resides the essence of the proof of Feynman.

The Grothendieck constant $K_{G}$ is a fundamental quantity in functional analysis, with important connections to quantum information, combinatorial optimization, and the geometry of Banach spaces. Despite decades of study, the value of $K_{G}$ is unknown. The best known lower bound on $K_{G}$ was obtained independently by Davie and Reeds in the 1980s. In this paper we show that their bound is not optimal. We prove that $K_{G} \ge K_{DR} + 10^{-12}$, where $K_{DR}$ denotes the Davie-Reeds lower bound. Our argument is based on a perturbative analysis of the Davie-Reeds operator. We show that every near-extremizer for the Davie-Reeds problem has $Ω(1)$ weight on its degree-3 Hermite coefficients, and therefore introducing a small cubic perturbation increases the integrality gap of the operator.

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.

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 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.

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.

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!

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.

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 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.

The addition of Kounterterms to Einstein gravity leads to a finite action for asymptotically anti-de Sitter (AdS) spaces with a conformally flat boundary. In that sense, it provides a partial renormalization for AdS gravity when compared to standard holographic techniques, where the mismatch is given in terms of nontrivial conformal properties of the boundary. On the other hand, this method has the clear advantage that the variation of the action has a closed form in an arbitrary dimension. In this work, it is shown how to extract holographic information on conformal anomalies from the variation in $(2n+1)$-dimensional Einstein-AdS plus Kounterterms. Remarkably enough, a considerable part of the Weyl anomaly can be worked out for any odd dimension.

We present a stochastic approach to calculate the full statistics of classical voltage fluctuations across an arbitrary, nonlinear, dissipative device embedded in a circuit in the presence of a bias. We show how the feedback resulting from the circuit, made of an ohmic resistor and a capacitor, affects the cumulants of the voltage, and in particular resolves Brillouin's paradox to satisfy thermodynamics. We apply our results to the case of a tunnel junction and a diode.

Yes.................

We report a scalable molecular beam epitaxy strategy to achieve a low density of O-band electrically tunable InAs/InGaAs quantum dots (QDs) on GaAs(001) substrates. Our approach is based on a gradient deposition of InAs in the sub-ML regime and subsequent capping with an InGaA strain-reducing layer to redshift the emission wavelength. For different growth conditions, we investigate the optical properties of the dots using photoluminescence mapping and correlate with structural properties determined by scanning transmission electron microscopy. Using a surface roughness modulation technique and synchronizing InAs sub-monolayer deposition cycles with substrate rotation, we control the dot density and position low-density regions (< 1 QD per um^2) on the substrate. Hyperspectral imaging is used to map the spatial and spectral characteristics of many individual dots in the low-density region, confirming that our approach is universally applicable to conventional MBE growth on (001) surfaces. Finally, we tune the QD emission wavelength within the O-band using electric fields and demonstrate single-photon emission with g(2)(0) = 0.020(14).

We explore the capability of a Large Language Model (LLM) to perform specific computations in mathematical physics: the task is to compute the coordinate Bethe Ansatz solution of selected integrable spin chain models. We select three integrable Hamiltonians for which the solutions were unpublished; two of the Hamiltonians are actually new. We observed that the LLM semi-autonomously solved the task in all cases, with a few mistakes along the way. These were corrected after the human researchers spotted them. The results of the LLM were checked against exact diagonalization (performed by separate programs), and the derivations were also checked by the authors. The Bethe Ansatz solutions are interesting in themselves. Our second model manifestly breaks left-right invariance, but it is PT-symmetric, therefore its solution could be interesting for applications in Generalized Hydrodynamics. And our third model is solved by a special form of the nested Bethe Ansatz, where the model is interacting, but the nesting level has a free fermionic structure lacking $U(1)$-invariance. This structure appears to be unique and it was found by the LLM. We used ChatGPT 5.2 Pro and 5.4 Pro by OpenAI.

We propose new analytical tools for describing growth-rate distributions generated by stationary time-series. Our analysis shows how deviations from normality are not pathological behaviour, as suggested by some traditional views, but instead can be accounted for by clean and general statistical considerations. In contrast, strict normality is the effect of specific modelling choices. Systems characterized by stationary Gamma or heavy-tailed abundance distributions produce log-growth-rate distributions well described by a generalized logistic distribution, which can describe tent-shaped or nearly normal datasets and serves as a useful null model for these observables. These results prove that, for large enough time lags, in practice, growth-rate distributions cease to be time-dependent and exhibit finite variance. Based on this analysis, we identify some key stylized macroecological patterns and specific stochastic differential equations capable of reproducing them. A pragmatic workflow for heuristic selection between these models is then introduced. This approach is particularly useful for systems with limited data-tracking quality, where applying sophisticated inference methods is challenging.

We present galactic constellations: charming shapes in large cosmological surveys. By exploring a dense subset of DESI's first data release, we discover distinctive constellations including "Pisces Grandis", "The DESI Stick Woman", and "W". We additionally develop a public website for anyone to explore DESI data, find their own constellations, and share their creations: see cmlamman.github.io/galactic-constellations. Early users of the site discovered 93 constellations. We analyze the size of these constellations as an unconventional probe of homogeneity, finding consistency with the cosmological principle and Lambda-CDM.

Macroscopic dark matter with dominating strong interactions, supposed to be composites, represents an alternative to the most popular WIMP particles. Predicted in various models as strangelets, nuclearites, nuggets, having different internal structures and properties, but not yet observed experimentally, these forms of dark matter are associated with the existence of a large number of still unexplained observations. Nuggets, initially predicted by Witten, were reconsidered from the point of view of their internal structure and further theorized in 2003 by Zhitnitsky as axion quark nuggets and axion antiquark nuggets, as being made of quarks in a superconducting colour state, in the core, an electrosphere of electrons or positrons and a domain wall that maintain the stability of the macros with an incredible density, mass in the gram range and radius on the order of micrometers. If the existence of $\rm{AQNs}$ and $\rm{A\bar{Q}Ns}$ is demonstrated, two major open problems in physics could be addressed simultaneously: they would constitute viable dark matter candidates and, at the same time, provide a natural mechanism for restoring matter-antimatter symmetry in the Universe. The experimental evidence of the $\rm{AQNs}$ and $\rm{A\bar{Q}Ns}$ is a challenge for current and future experiments. The present study demonstrates that if these macroscopic systems exist, axions produced by $\rm{A\bar{Q}N}$s could be detected by the next generation of neutrino physics experiments using liquid noble gases, due to their huge active volumes.

The $1+1$ dimensional $Z_2$ gauge theory is the simplest model that allows for quantum computation or quantum simulation to probe the fundamental aspects of a gauge theory coupled with dynamical fermions. To reliably benchmark such a system, it is crucial to understand the non-unitary quantum dynamics arising from the underlying non-Hermitian evolution and to model the effects of quantum measurements. This work focuses on monitoring ultra-local physical observables for a $\mathbb Z_2$ gauge theory. Tensor network calculations are performed to dynamically probe entanglement entropy at larger lattice sizes. In this work, we report that continuously monitoring local and diagonal observables (electric and mass energy densities) in the computational basis demonstrates the absence of any measurement-induced phase transition, as indicated by the system-size independence of the late-time saturation value of the bipartite entanglement entropy.

The Large Magellanic Cloud (LMC) exhibits vigorous high-mass star formation, including the HII regions 30~Dor that is the most active site of star formation in the local group. The present paper focuses on the Giant Molecular Cloud (GMC) in the HII region N113 in the central part of the LMC. Based on the $^{12}$CO($J$ =1-0) and $^{13}$CO($J$ = 1-0) data at a resolution of approximately 0.2 pc taken with ALMA+APEX, we reveal that the GMC consists of two filamentary structures each of approximately 10 pc in length, forming a V-shape pattern with a vertex angle of 90 degrees. The filamentary structures host high-mass young stellar objects in gravitationally bound dense gas. Large-scale HI gas data covering 100 pc reveal two distinct velocity components separated by more than 40 km s$^{-1}$, that correspond to the low velocity (L-) and disk (D-) HI components of the LMC. The L-component appears to be located in a cavity-like distribution of the D-component, and the CO filaments are positioned at the cavity's edge. We find evidence for the L-component to fit the cavity by a 53 pc displacement, and suggest that collisional compression of the HI gas during the last 1.3 Myr triggered the GMC formation and the high-mass star formation. This lends support for the large scale collision driven by the tidal interaction is playing a role in evolution of interstellar medium in N113.

Reducing the non-Clifford cost of fault-tolerant quantum circuits is a central challenge in quantum compilation, since T gates are typically far more expensive than Clifford operations in error-corrected architectures. For Clifford+T circuits, minimizing T-count remains a difficult combinatorial problem even for highly structured algebraic optimizers. We introduce VarTODD, a policy-parameterized variant of FastTODD in which the correctness-preserving algebraic transformations are left unchanged while candidate generation, pooling, and action selection are exposed as tunable heuristic components. This separates the quality of the algebraic rewrite system from the quality of the search policy. On standard arithmetic benchmarks, fixed hand-designed VarTODD policies already match or improve strong FastTODD baselines, including reductions from 147 to 139 for GF(2^9) and from 173 to 163 for GF(2^10) in the corresponding benchmark branches. As a proof of principle for automated tuning, we then optimize VarTODD policies with GigaEvo, an LLM-guided evolutionary framework, and obtain additional gains on harder instances, reaching 157 for GF(2^10) and 385 for GF(2^16). These results identify policy optimization as an independent and practical lever for improving algebraic T-count reduction, while LLM-guided evolution provides one viable way to exploit it.