Papers

We report on a Marangoni flow-driven pathway for non-equilibrium transport of metal ions across an oil-water interface. Under specific conditions, we show that the adsorption of a metallic species onto the extractant-decorated oil-water interface creates interfacial tension gradients that give rise to Marangoni flows. Strikingly, we observe that these flows are accompanied by removal of metal from the aqueous phase and that the extraction kinetics follow a non-diffusive behavior. The extraction rates are significantly higher - at least a threefold increase - when Marangoni flows are present. We show that the strength of the interfacial instability is coupled to the lowering of the interfacial tension- which is metal-dependent and therefore can lead to rate-mediated selective extractions. Overall, our study shows a fundamentally different paradigm for liquid-liquid extractions opening up a range of new directions of inquiry and future technologies.

In this paper, we study the equation which we call Manton's exotic vortex equation. The equation arose in Manton's study of a deformation of the classical vortex equation [Ma]. We first investigate the existence problem for Manton's exotic vortex equation and establish existence results. We then construct the moduli space of solutions. Finally, we establish a dimensional reduction for Manton's exotic vortex equation. More precisely, we establish a correspondence between solutions of Manton's exotic vortex equation and pseudo-Hermitian-Yang–Mills connections with pseudo-Hermitian metrics of signature (1,1).

We present a model in which asymptotically de Sitter universes of various types, and de Sitter (dS) radii $R_n$, live in the interiors of black holes in a maximally entropic flat $p = \rho$ Friedmann-Robertson-Walker universe. These dS universes clearly have many quantum states. We argue that they decay and equilibrate with the maximal entropy universe on time scales of order $\alpha_n R_n {\rm ln} (R_n /\delta_n)$, where $\alpha_n$ are model-dependent dimensionless constants and $\delta_n$ are the initial distances between the shell, satisfying the Israel junction conditions, and the dS horizon. These are time-scales as viewed by a detector following a trajectory far from the would-be cosmological horizon. These times are all exponentially shorter than dS recurrence times, which have no meaning in this model. It is impossible for a detector inside one of the dS universes to determine whether it is actually part of such a structure. This model could be used as the basis for claiming that certain constants of nature or cosmological initial conditions were chosen to have mathematically unnatural values because other values could not lead to any form of intelligent life.

We report on the phase behavior of solutions of a chromonic liquid crystal, disodium cromoglycate (DSCG) resulting from the presence of DNA that acts as a depletant. We show that DNA (both single and double stranded) can induce phase condensation of DSCG at volume fractions a thousand-fold lower than a commonly studied depletant of similar size, namely polyethylene glycol. Twist angle characterization via polarized optical microscopy reveals DNA introduces macroscopic chirality within the condensed phase, despite being present only in the continuous phase- as confirmed by imaging of fluorescently tagged DNA. UV-vis absorption spectroscopy reveals that the introduction of chirality reduces the absorption coefficient strongly, indicating that induced chirality leads to considerable increase in the average lengths of DSCG aggregates. We also show that DNA at concentrations (~nmol/L) typical for a post amplification event (such as PCR) can induce an isotropic-biphasic phase transition of DSCG enabling a fast optical method to report the presence of DNA. Overall, these findings establish DNA as exceptionally efficient depletants that regulate both phase condensation and chirality transfer in lyotropic liquid crystals and thereby providing a simple optical platform for detection of amplified DNA.

We investigate the algebraic and topological properties of the unitary group $U(4)$ subjected to a non-standard continuous symmetry breaking cascade down to an $SU(2)_H$ subgroup. By explicitly embedding the unbroken $SU(2)_H$ symmetry into the lower-right quadrant of the fundamental matrix representation, we demonstrate that the resulting 12-dimensional quotient space, $SU(4)/SU(2)_H$, undergoes a strictly asymmetric geometric partition. This specific breaking pattern uniquely isolates an 8-dimensional sector that retains closed commutation relations, forming a rigorous $\mathfrak{su}(3)$ Lie subalgebra. Concurrently, it isolates a 4-dimensional off-diagonal sector that fails to close algebraically, generating a purely non-linear topological coset. By analyzing the global spherical fibrations of the manifold, we show that this non-linear territory reduces to a $\mathbb{C}P^1 \cong S^2$ base space. Consequently, continuous mappings from a 3-dimensional spatial boundary are classified by the third homotopy group, $\pi_3(S^2) \cong \mathbb{Z}$, guaranteeing absolute vacuum stability via the integer-quantized Hopf invariant. Ultimately, this framework demonstrates that a non-simple unitary group can organically provide a mathematically rigorous geometric template capable of simultaneously accommodating both continuous gauge symmetries and stable, non-perturbative topological sectors.

We detected acoustic phonons in graphite and diamond by Raman spectroscopy supported by density functional theory calculations. The activation of these normally forbidden Raman modes was achieved via lattice amorphization in case of graphite and by boron doping in case of diamond. The doping-induced Raman signal in diamond was identified with substitutional boron of tetrahedral symmetry via its dependences on excitation wavelength and polarization. Comparison of the Raman spectra of amorphized graphite and heavily boron-doped diamond suggests the emergence of graphitic-like disorder in the diamond lattice. The reported approach is not limited to carbon and can be extended to a wide range of other materials.

We study spatial solitons and their interaction dynamics in nonlinear optical media with spatially engineered refractive index and competing cubic-quintic (CQ) nonlinear profiles, using the variational approximation (VA), the hybrid variational approximation (HVA), and direct numerical simulations. The model was implemented in a symmetric step-index planar dielectric waveguide with a core exhibiting competing CQ nonlinearity and cladding layers possessing only a cubic nonlinear response. For stationary states, the Gaussian VA predicts two types of $N(\mu)$ characteristic curves, where $N$ is soliton norm and $\mu$ is propagation constant, separated by a boundary surface in parameter space, and numerical calculations reveal the same two types. Below this surface, the VA agrees with the numerical results mainly at low powers, while pronounced deviations in the profile and stability appear at high powers. Above the surface, the variational and numerical curves retain the same qualitative form, and a super-Gaussian ansatz accurately describes the high-power flat-top solitons. Soliton collisions produce four post-interaction regimes: Oscillation, Molecular, Splitting, and Breakup. The HVA reproduces the first three over a broad power range, including collisions involving flat-top solitons. Its main limitation arises in the Breakup regime, where strong radiation leaves the guiding region and cannot be represented by the adopted HVA ansatz. Nevertheless, for solitons associated with the second type of characteristic curves, the HVA still captures the breakup dynamics qualitatively. Thus, the HVA provides an efficient description of complex soliton interactions at a substantially lower computational cost than direct numerical simulations.

One of the fundamental questions we ask in cosmology is what the spatial curvature of the universe is. Experimental observations show that the universe is very close to being spatially flat. However, new results, mainly from measurements by the Planck satellite, indicate that the curvature parameter might be positive. We will, however, examine completely new theoretical results that suggest that we actually live in a closed Universe.

We study ball-by-ball win probability (WP) for second-innings run chases in Twenty20 cricket, built as an exactly solvable Markov model: we estimate a single object, the per-ball outcome distribution over {0,...,6, wicket}, and derive WP for every game state by backward induction over the acyclic (balls, wickets, runs-required) chase graph. This construction makes WP an exact martingale, which in turn makes leverage (how much a ball can swing WP) and win probability added (WPA) well-defined and exactly attributable; we use them to confirm that finishers and death bowlers occupy the highest-leverage moments. We then show the model's WP is systematically miscalibrated, and that this is not incidental. Localizing the error, we rule out tail-thinning and marginal mis-estimation (the model's per-ball outcome distribution matches the empirical one to a total variation of at most 0.02 at every required run rate). The only remaining cause is unmodelled dependence given the state, and we identify it: a permutation-null decomposition shows short-range sequential run-scoring persistence (roughly 3-5 balls; innings-level heterogeneity contributes only about 18%; wickets, if anything, anti-cluster). A block-bootstrap simulator that injects the real dependence while holding the marginals fixed closes 26% of the calibration gap (replicated over six seeds), saturating at block lengths of about 20 balls, consistent with the measured correlation range; this is a constructive lower bound that confirms the diagnosis. The result is a structural tradeoff relative to the (balls, wickets, runs) state description: exact leverage requires the martingale, the martingale requires conditional ball independence on that state, and that independence is what miscalibrates the WP. Exactly attributable leverage and well-calibrated WP cannot be obtained from the same object over this state.

Reconstructing the analytic structure of a function from finite datasets is a fundamental problem across theoretical, numerical, and experimental physics. While Padé approximants provide a natural framework, finite-information effects, as well as statistical and systematic uncertainties, may obscure the underlying analytic structure and limit reconstruction reliability. In this work, we reinterpret the appearance of Froissart doublets not merely as numerical artifacts but as diagnostic objects carrying information about the analytic consistency of the input data. Accordingly, we develop a general Padé-based algorithm that exploits the dynamics of Froissart doublets along Padé sequences to identify localized inconsistencies and iteratively reconstruct the analytic structure most compatible with the data. The method requires no model for the origin of the inconsistencies and distinguishes genuine analytic features from spurious structures induced by finite-information effects. We validate it using Stieltjes functions, realistic pseudo-experimental datasets with statistical and systematic uncertainties, and general holomorphic functions. The complete algorithm is provided as a supplementary Mathematica notebook in an open GitLab repository.

Organic photovoltaic (OPV) materials are promising candidates for distributed solar energy in tropical regions, yet existing virtual screening tools report static power conversion efficiency (PCE) values at standard testing conditions (STC) that fail to capture the temperature-driven performance degradation experienced under real deployment conditions. Here we introduce a Climate-Native computational framework that forecasts the annual PCE profile of OPV donor molecules under geographically realistic operating conditions. The framework combines GFN2-xTB molecular dynamics with an equivariant graph neural network surrogate ($268$ Neyman-stratified CEP molecules; $120,600$ training geometries; $\sim 1050\times$ speedup over explicit quantum chemistry) and sequential deep learning models trained on annual time series anchored in NASA POWER climate data for Douala, Cameroon, and validated by zero-shot transfer to Yaoundé and Maroua. Applied to $\sim 30,000$ molecules from the Harvard Clean Energy Project (CEP) and validated against $350$ HOPV15 experimental device measurements, the framework demonstrates that sequential models trained on full molecular dynamics trajectories outperform time-averaged baselines ($35\%$-$48\%$ relative MAE improvement over static baselines), confirming that thermal conformational dynamics carry information beyond mean geometry. We further introduce a seasonal stability score that reranks OPV candidates by performance consistency under tropical conditions, identifying molecules whose deployment suitability differs substantially from their static PCE ranking.

Autonomous quantum thermal machines have recently been proposed as physics-based computing substrates where logical inputs and outputs are encoded in temperature gradients. While such “thermodynamic neurons” exhibit a clear trade-off between computational fidelity and heat dissipation, the fundamental information-theoretic limits of temperature-encoded computation remain uncharacterized. Here, we derive rigorous bounds linking average error probability, channel capacity, and entropy production for finite-capacity thermal reservoirs operating far from equilibrium. We prove that the minimal dissipation required to achieve a target average error probability $\langle \xi \rangle$ diverges as $\langle \xi \rangle$ approaches a fundamental minimum error floor $\epsMin$ imposed by finite-reservoir thermal fluctuations. We further establish a thermodynamic channel capacity that saturates at high dissipation, and quantify the minimal dissipation required for cascaded networks to maintain target fidelity. We demonstrate that the required dissipation grows linearly $\mathcal{O}(L)$ with network depth for shallow networks, but diverges as the network depth $L$ approaches a fundamental maximum limit $L_{\max} = \epsNet/\epsMin$ imposed by the minimum error floor. Furthermore, under strong noise amplification conditions, the required dissipation can grow up to $\mathcal{O}(L^3)$. Our framework bridges stochastic thermodynamics, finite-time information theory, and autonomous computation, providing rigorous design principles for energy-efficient analog thermodynamic hardware.

Quantum mechanics is difficult to teach because its conceptual content and mathematical notation usually arrive together. Rudolph's misty-state language was designed to decouple those burdens; basis states are visual objects, clouds represent superposition, gates act by elementary replacement rules, and destructive interference appears as cancellation rather than as matrix calculation. An elementary “misty-state” language can make quantum circuits accessible to students before they master complex linear algebra. Development presented here was initiated/led by the first author. The contribution is not a replacement for complete graphical calculi such as ZX or sum-over-paths. It is a source-specific bridge from an intuitive educational notation to a mathematically explicit, executable, and falsifiable semantics.

The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability. Dynamical downscaling is computationally prohibitive, when applied to extensive coastlines, predictive ensembles, or long time periods. Therefore, this work presents a statistical downscaling of sea surface temperature (SST) from the seasonal coupled ocean-atmosphere forecast system (ACCESS-S2) using machine learning techniques. This study proposes a novel deep learning framework that uses a U-Net to generate an initial high-resolution SST estimate, which is subsequently refined using a residual corrective approach. The target SST fields are derived from the Regional Ocean Modeling System (ROMS). This two step approach called Residual Corrective Neural Network (RCNN) progressively refines initial U-Net predictions by incorporating dynamically scaled residuals at each step, enabling accurate capture of broad patterns and fine-grained features such as eddies and fronts. We also introduce a custom loss-assisted RCNN variant to improve performance during extreme events, which may be absent from training data due to climate-driven shifts in SST extremes. The framework efficiently downscales SST along the west coast of Australia. A 2011 marine heatwave case study shows that the RCNN improves ACCESS-S2 SST predictions by increasing horizontal resolution from 25 km to 2 km, enabling identification of fine-scale anomalies unresolved in the ACCESS-S2 dataset. This balance between computational efficiency and accuracy supports applications in coastal impact assessment and marine ecosystem studies.

Phase singularities are topological defects around which the phase winds by an integer multiple of $2\pi$. In entangled multiphoton states, they can emerge nonlocally in the joint wavefunction rather than in the field of either subsystem alone. Here we show that the quantum Gouy phase generates nonlocal phase singularities (NPSs) in the two-dimensional longitudinal propagation space of entangled photon pairs. We consider photon pairs produced via spontaneous parametric down-conversion pumped by a Laguerre–Gaussian beam, with the signal and idler photons propagating independently over different longitudinal distances. The accumulated radial-mode-dependent Gouy phases induce destructive interference among biphoton spatial-mode components, producing isolated intensity nulls with quantized phase winding. For a pump with radial mode number $p$, the NPS topological charges have magnitude $p$, whereas their propagation positions and charge signs are governed by the Rayleigh ranges of the pump and phase-matching functions. We further identify the radial-mode structure required for their formation, showing that separable biphoton states cannot support isolated longitudinal NPSs. Our results extend nonlocal singular optics from transverse spatial correlations to longitudinal propagation dynamics and establish propagation distance as a coordinate space for topological structures in entangled photon pairs.

Ensemble forecasting aims to sample the conditional distribution of outcomes; whether AI forecast ensembles do this correctly in a joint sense remains largely untested. We train a diffusion model for probabilistic subseasonal coastal sea level forecasts at eight US East Coast tide gauge stations, with sea level derived from reanalysis, and find that marginal and joint forecast quality decouple: positive skill at every station and lead time marginally, while joint spatial structure is worse than climatological draws. A shuffle-based permutation decomposition reveals this failure is invisible to the energy score but detected by the variogram score. Lorenz-96 experiments across 0.7-170 equivalent years show the gap persists regardless of training volume and is reproduced by a linear baseline, indicating structural inadequacy of the learned distribution. A dynamical ensemble does not replicate the failure while a deterministic emulator does, suggesting it is specific to learned emulators rather than ensemble forecasting generally.

Recent work of Colbrook and Stepaniants produced the first counterexamples to the planar Pompeiu and Schiffer conjectures and introduced the conformal fixed-disc, disk-polynomial, and validated-tail machinery used here. By adapting this framework to the complementary Dirichlet endpoint, we disprove the unrestricted planar Berenstein conjecture. Specifically, we construct a bounded simply connected domain $\Omega$ with real-analytic Jordan boundary, which is not a disc and for which there exist $k\in(27.4381178838,27.4381198839)$ and a nonzero real-valued function $u\in C^\omega(\overline\Omega)$ satisfying $(\Delta+k^2)u=0$ in $\Omega$, with $u=0, \partial_\nu u=\text{constant}\ne0$ on $\partial\Omega$. Thus the overdetermined Dirichlet–Neumann data do not characterize the disc without an additional sign assumption on $u$. The domain has dihedral symmetry of order $26$, but is neither a disc nor centrally symmetric, and the corresponding eigenfunction changes sign. Equivalently, its boundary arclength measure satisfies $\widehat{\sigma_{\partial\Omega}}(k\omega)=0$ for $\omega\in\mathbb S^1$. After conformally transferring to the unit disc, exact support identities and quantitative disk-polynomial estimates yield rigorous control of the infinite-dimensional tail. A Newton–Kantorovich argument then reduces existence to finitely many explicit inequalities, which are certified using interval arithmetic. The extension from the Pompeiu–Schiffer problem is not formal. The earlier construction absorbs both boundary conditions into a single inverse-Laplacian equation. At the Dirichlet endpoint considered here, the nonzero Neumann datum forces the harmonic source modes to remain, producing a coupled interior–boundary system involving the full zero-Dirichlet inverse and its Neumann trace, together with a separate sign-recovery problem.

We present a real-time linear-response approach to solving the dynamical Bethe-Salpeter equation (BSE). The polarization part of the screened interaction is obtained from time-dependent Hartree propagation of the one-particle density matrix within an orbital basis-set representation. The frequency convolution defining the dynamical kernel is evaluated as a product in time, avoiding numerical integration and storage of the full screened Coulomb operator. The resulting nonlinear eigenvalue problem is then solved directly, going beyond the static screening approximation with full-frequency dependence in the screened interaction. We validate the method by calculating the low-lying singlet and triplet excitation energies of water, formaldehyde, and acetylene. While the deterministic scaling remains $\mathcal{O}(N^6)$, the time propagation formulation is readily compatible with grid-based stochastic sampling methods, which will open the possibility for dynamical BSE calculations of very large systems.

We ask whether any one-parameter structural correction to the radial acceleration relation (RAR) can be uniquely recovered from SPARC rotation curves, and answer with an identifiability audit: each candidate is benchmarked against per-galaxy nuisance freedom, with predictive scoring against mass-only and data-quality baselines. In the full sample (N = 126) the answer is no: a hybrid compactness term improves the fit, but zero-point freedom absorbs the gain, and in cross-validation the model fails to out-predict a mass-only baseline and loses to a quality-flag baseline. One regime retains structural information: in gas-dominated, low-acceleration disks – where MOND's strict locality and $\Lambda$CDM feedback models diverge most sharply – the RAR residual correlates with compactness ($r=0.46$, $p=1.3\times10^{-4}$), remains significant under hierarchical partial pooling ($\beta=0.23$, $p=1.7\times10^{-5}$; N = 63), and survives canonical joint control for quality, sampling, mass, inclination error, and first-order pressure support ($r=0.30$, $p=0.02$). All significant results pass a Benjamini-Hochberg correction over the declared 27-test family. Three limits temper that survival: it is not significant under rank-based control over the widest proxy set; it resides in faint dwarfs independent surveys do not reach; and after mass control it is shared across the mass-size manifold. Pressure support brackets the interpretation – isotropic drift correction absorbs a quarter of the amplitude, while a Jeans treatment overcorrects resolved cases – leaving the physical origin undetermined. The audit's product is the extraction limit: claimed corrections must clear the 0.106 dex per-galaxy nuisance floor, a mass-only baseline, and data-quality stratification.

Even though metastable false vacuum decay is ubiquitous in physics, its underlying dynamics are still not well understood. Dissipative state preparation in moiré quantum materials provides an exceptional setting for exploring this physics since it allows the possibility of generating exotic quantum states that are not the ground state of the system Hamiltonian. Motivated by recent experiments demonstrating steady-state optical orientation of the spin-valley degree of freedom of holes, here we investigate dynamics of itinerant and Chern ferromagnets in the presence of an opposing magnetic field. Optical pumping using a circularly polarized Laguerre-Gauss beam allows us to deterministically prepare a true vacuum bubble embedded inside a metastable state. Depending on its initial size controlled by the pump power, we observe that the bubble collapses or expands due to an interplay between domain wall and bulk dynamics. For external magnetic fields comparable to the coercive field of ferromagnetism, we observe up to two-orders-of-magnitude prolongation of the spin polarization decay time at commensurate fillings corresponding to integer and fractional Chern insulator states. Our experiments reveal that the nonequilibrium dynamics of the ferromagnetic domains is substantially more sensitive to the precise filling factor around Chern insulator states than standard transport or optical measurements.

We develop a multimode quantum model of optical-axis misalignment in a simplified representation of the differential mode of an Advanced LIGO interferometer. The system is described as two coupled optical resonators corresponding to the signal extraction cavity and the arm cavity. Analytical expressions for the mode-mixing coefficients are derived, establishing a direct connection between geometrical perturbations of optical elements and multimode optical dynamics. Using the developed formalism, we investigate the influence of optical-axis misalignment on quantum-noise-limited sensitivity and injected squeezed states. We show that misalignment between the signal extraction cavity and the arm cavity leads to significant sensitivity degradation, frequency-dependent rotation of the squeezing ellipse, and a reduction of the observable squeezing in the fundamental spatial mode. At the same time, the injected squeezing is not destroyed but redistributed among coupled spatial modes. The resulting mode coupling generates quantum correlations between the fundamental and higher-order modes and leads to spatial-mode entanglement verified using the positive partial transpose criterion. Our results demonstrate that optical-axis misalignment should be treated as a coherent multimode quantum process rather than as a simple optical loss mechanism.

The term “partition logic” denotes two constructions at different levels. In automaton and generalized-urn models, selected partitions generate Boolean event algebras whose contextwise union forms a concrete pasted event structure; in Ellerman's framework, whole partitions are classifications governed by refinement and partition operations. For a finite set $U$, Ore's correspondence maps each generator $\pi$ to its Boolean algebra $\BA(\pi)$, but it neither identifies the pasted carrier with $\Part(U)$ nor makes pasting a partition operation. It yields $\BA(\pi\wedge\sigma)=\BA(\pi)\cap\BA(\sigma)$ and $\BA(\pi\vee\sigma)=\langle\BA(\pi)\cup\BA(\sigma)\rangle_{\rm BA}$, where $\langle\cdot\rangle_{\rm BA}$ denotes Boolean-algebra generation. Thus meet captures the common event algebra, whereas join gives the ambient Boolean closure. Chinese-lantern, Firefly, and triangular examples distinguish shared events, atomic intertwining, and inherited concrete order. Ellerman's direct-sum decompositions (DSDs) provide a vector-space analogue: component projections of an orthogonal DSD resolve the identity and encode exclusive outcomes, but its components are not equivalence classes of vectors. Gleason and Kochen–Specker applications require globally context-consistent valuations on those projections.

The motion of a rigid body interacting with a wave leaves spectral signatures, with the Doppler shift as the dominant contribution. Since any motion can be decomposed into translational and rotational components, rotational Doppler provides additional information about the object's dynamics. In the electromagnetic domain, rotating objects generally produce rotational Doppler, or micro-Doppler, signals determined by the symmetry and spatial structure of the scattering process. For objects that are subwavelength or comparable in size to the wavelength, the response is typically dominated by the lowest dipolar scattering channel, so the leading spectral component commonly appears at twice the angular frequency. Here, we introduce the concept of artificially engineered rotational micro-Doppler by designing a compact, strongly scattering structure that operates through a high-order multipolar cascade of resonances, thereby producing a giant enhancement. Magneto-electric arrays composed of strongly coupled electric and magnetic resonators are optimized in the GHz range using a covariance matrix adaptation genetic algorithm to maximize the micro-Doppler frequency. Unlike conventional higher-order multipole designs used in superscatterers for a specific angle of incidence and polarization, our approach jointly optimizes excitation and scattering under radar-relevant conditions for a rotating blade. The resulting arrays exhibit a giant rotational meta-micro-Doppler response, exceeding the dipolar limit by two orders of magnitude and mapping rotations of tens of hertz into the kilohertz range. Beyond its fundamental significance, this mapping has practical value because it shifts rotor micro-Doppler signatures well above slow-moving radar clutter, thereby improving the detectability of slow motion.

Polaritons formed by strong coupling between excitons and confined electromagnetic modes underpin emerging nanophotonic technologies, yet platforms based on isotropic metals or optical microcavities provide limited control over propagation direction. Here we predict that monolayer MoS$_2$ on the in-plane hyperbolic conductor MoOCl$_2$ supports hyperbolic exciton-plasmon polaritons governed by crystallographic direction, slab thickness, and hyperbolic mode order. Using an anisotropic transfer-matrix model coupled to a dissipative three-level Hamiltonian, we obtain wavevector-dependent anticrossings between the MoOCl$_2$ plasmons and the spin-orbit-split A and B excitons of MoS$_2$, yielding lower, middle, and upper polariton branches with coupling energies $g_A$ = 64.7 meV and $g_B$ = 61.2 meV. Rotating the in-plane wavevector tunes the plasmon-exciton detuning, while increasing the MoOCl$_2$ thickness activates higher-order hyperbolic Fabry-Pérot modes. Their coupling follows an approximate effective-mode-volume scaling. Strong coupling persists for excitonic linewidths up to 50 meV, with the normalised spectral-resolution ratio remaining above unity. These results establish MoS$_2$/MoOCl$_2$ as a lithography-free platform for directional and multimode exciton-plasmon polariton engineering in the visible spectral range.

Transformer layers generate state-dependent interaction networks: token representations determine the attention matrix, which in turn updates the representations. We study this feedback in a minimal normalized self-attention dynamics and identify the overlap gap as the central quantity governing its attractor structure in the thermodynamic limit. When tokens form internally aligned clusters and their similarity to members of the same cluster exceeds that to every other cluster by a nonvanishing amount, inter-cluster attention is exponentially suppressed as the dimension increases. This mechanism produces a high-dimensional manifold of clustered fixed points, ranging from a few macroscopic clusters to extensive microscopic fragmentation, and also controls their stability against perturbations. Starting from an unstructured Gaussian state, we find that clustered states nucleate from the diffuse background only above a finite threshold in attention sharpness, giving rise to a dynamical attention-condensation transition.

Spacecraft rendezvous on elliptical orbits must hold a safety corridor under navigation noise, unmodelled perturbations, and thrust errors driven by propellant mass uncertainty. This paper develops a tube-based model predictive controller for the Yamanaka-Ankersen linear time-varying dynamics, carrying constraint-tightening tube methods from circular to elliptical orbits. A horizon-dependent, element-wise error bound propagates the actual closed-loop matrices along the prediction window, so early prediction steps keep nearly the full corridor that a constant-width tube would surrender. A multiplicative-to-additive conversion folds mass and thrust uncertainty into the same tightening recursion, and a Perron-Frobenius spectral-radius condition supplies a computable certificate that the tightening converges, with an explicit input-to-state stability gain. In a paired Monte Carlo campaign on a Mars sample-return orbit, the tube controller cuts mean corridor violations by more than an order of magnitude against a nominal predictive baseline at comparable fuel cost. A nonlinear truth-model test with oblateness perturbations well beyond the assumed disturbance budget leaves the corridor unviolated, and when the design envelope is exceeded the tightened problem becomes infeasible and the controller reverts to a saturated linear fallback, making the loss of guarantee explicit rather than silent. The construction requires only element-wise arithmetic and an open-source quadratic-programming solver.

During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resulting in increased cumulative X-ray dose. To mitigate this, low-dose cone-beam CT (CBCT) is routinely acquired during treatment delivery. However, CBCT image quality remains insufficient for accurate dose calculation and adaptive radiotherapy planning due to increased scatter, noise, beam hardening, and reconstruction related artifacts. This study develops a supervised deep learning based CBCT to CT synthesis framework using a conditional denoising diffusion probabilistic model (DDPM), where the generation of a CT-based planning for accurate positioning and dose calculation is obtained using generative models with low dose CBCT imaging. Beyond demonstrating CBCT to CT synthesis, the primary objective is to investigate how the representation of CBCT input data, either standard clinical DICOM CBCT images or filtered back-projection (FDK) reconstructions from raw projection data, affects the performance of diffusion based CT synthesis. The overarching aim is to assess whether physics aware CBCT representations better support CT-equivalent image quality while maintaining reduced imaging dose in radiotherapy workflows.

Moiré transition metal dichalcogenide (TMD) heterobilayers are commonly modeled by a continuum theory that yields topologically trivial bands, in contrast to their homobilayer counterparts which host topological bands and fractional Chern insulators (FCI). We show this conclusion is an artifact of neglecting the pseudomagnetic field generated by lattice relaxation, an effect intrinsic to every moiré material. We develop a continuum model that resolves relaxation into three channels: a modified moiré potential with higher Fourier harmonics, a pseudoelectric (scalar deformation) potential, and a pseudomagnetic (vector) potential. Using WSe$_2$/WS$_2$ as a prototype, we find that the pseudomagnetic field alone gaps the third and fourth valence bands with Chern numbers $\pm 1$ over a broad range of twist angle and lattice mismatch, while the moiré potential correction and pseudoelectric potential narrow the bandwidth and enhance the bandgaps, which survive many-body interactions using neural-network variational Monte Carlo calculations. Relaxation also smoothens the Berry curvature and quantum metric relative to the rigid model, moving the band closer to the ideal Chern limit, beneficial for the quantum anomalous Hall effect, FCI states, and flat-band superconductivity when filled to higher bands. Our work establishes a new framework that connects first-principles calculations, through the continuum model, to many-body observables. Using this framework, we show moiré heterobilayers as a new class of topological materials whose topology is driven entirely by intrinsic lattice relaxation.

High-resolution laser spectroscopy of the $^{85}\mathrm{Rb}_2$, $^{85}\mathrm{Rb}^{87}\mathrm{Rb}$, and $^{87}\mathrm{Rb}_2$ isotopologues has been performed in a supersonic molecular beam using a continuous-wave (cw) tunable diode laser. A total of 958 rovibronic transitions were recorded up to $7~\mathrm{cm}^{-1}$ below the vibrational band heads of the $X^1\Sigma_g^+(v''=0)\rightarrow B^1\Pi_u(v'=1,2)$ and $X^1\Sigma_g^+(v''=1)\rightarrow B^1\Pi_u(v'=1)$ bands, with a spectral resolution of $3.3\times10^{-4}$~cm$^{-1}$. Although restricted to the $v'=1$ and $v'=2$ vibrational levels of the excited $B^1\Pi_u$ state, the measurements extend previous work by Amiot and Vergès [Chemical Physics Letters 274, 91 (1997)] through substantially higher resolution and dense low-$J'$ rotational data for all three isotopologues. A global least-squares analysis combining the new and published data, yields improved Dunham coefficients for the excited $B^1\Pi_u$ state, significantly refines rotational and rovibrational coupling constants. In addition, the $\Lambda$-doubling constants of $B^1\Pi_u$ state were determined for the three isotopologues.

The emergent spectra of giant planets peak at thermal infrared wavelengths (3-5um) and possess atmospheric features in this regime that trace carbon chemistry, vertical mixing, clouds, and composition. However, this spectral region is challenging to access from the ground, and with JWST generally requires an independent pointing for each target. SPHEREx is providing all-sky spectra of millions of sources from 0.75-5um, enabling both individual and population-level studies of isolated and widely bound substellar objects. In this work we present a SPHEREx spectrophotometric atlas of 94 young (<300Myr) low-gravity L and T dwarfs to study their atmospheric diversity in the relatively unexplored wavelength range from 3-5um. These include free-floating planetary-mass objects in a range of ages, masses, and temperatures regularly probed with JWST and which will be accessible with upcoming instruments such as Keck/SCALES and ELT/METIS. We compute synthetic JWST/NIRCam and MKO photometry and colors across 22 filters, and examine trends in physically-motivated sets of colors, absolute magnitudes, and spectral types. We find that the lowest-gravity objects generally have the reddest 3-5um colors and tend to be brighter than field L dwarfs for a given spectral type, extending previous patterns originally established at near-infrared wavelengths. Compared to the low-gravity sample of free-floating objects, some giant planets closely follow the low-gravity locus, while others show larger scatter and more extreme colors, pointing to differences in cloud properties, temperature, metallicity, or vertical mixing. The SPHEREx data extraction code developed in this work is made available together with the extracted spectra and synthetic photometry.