Papers

Truncated shifted iYangians are a family of algebras expected to quantize certain components of affine Grassmannian islices. We introduce orientifold KLRW (oKLRW) algebras associated with quivers with involution and establish their faithful polynomial representations and diagrammatic bases. We also define KLR iYangians using double reflective KLR diagrams and construct diagrammatic realizations of the iGKLO homomorphisms. For integral parameters, we introduce interval oKLRW algebras and prove an equivalence between integral weight modules over truncated shifted iYangians and nilpotent modules over the corresponding interval oKLRW algebras.

(524366) 2001 XR$_{254}$ is a dynamically Cold Classical Kuiper Belt Object that is a nearly equal-sized wide binary whose upcoming mutual events season makes it a particularly valuable target for physical characterization. In advance of the mutual eclipse events, we conducted a ground-based photometric observing campaign between 2021 and 2026 using the Lowell Discovery Telescope to investigate the rotational and physical properties of the system, to refine predictions for its future mutual events, as well as provide a lightcurve we can use as a baseline to identify eclipse events superimposed with the rotation. We derive a double-peaked asymmetric rotational lightcurve with a period of 11.17$\pm$0.04 h and a lightcurve amplitude of 0.42$\pm$0.04 mag. The lightcurve presents a sharp V-shaped minimum consistent with the primary being a close/contact binary, possibly making this a triple system. Using the Keplerian mutual orbit solution, we model the upcoming mutual event season expected between $\sim$2031 and $\sim$2040 and present some individual events for future campaigns. 2001 XR$_{254}$ is one of the few wide binary Kuiper belt systems with a well-determined mutual orbit and a soon observable mutual eclipse event season. Mutual event observations of this possible triple system will provide a rare opportunity to improve component sizes, shapes, densities, and surface properties, offering insights into the formation of the Kuiper Belt.

In this paper, we calculate the tidal Love numbers of multi-state boson stars (MSBSs) composed of ground state and first excited state complex scalar fields. Under synchronized and nonsynchronized frequency conditions, the background solutions of MSBSs are classified into single-branch and double-branch types. The field functions, ADM mass, and binding energy of different solutions are discussed. We then calculate the quadrupolar ($\ell=2$) electric and magnetic tidal Love numbers for branches containing stable solutions. Our results show that the electric tidal Love numbers are initially positive and then suddenly transition to negative values. This phenomenon occurs when the parameters satisfy $\tilde{\mu}_1 > 0.891$ or $\tilde{\omega}_0 > 0.777$; for smaller values of these parameters, the electric Love numbers remain positive. The magnetic tidal Love numbers are always negative, with absolute values smaller than those of the electric tidal Love numbers.

In this work, we describe a new preprocessing tool for geometry-adaptive mesh refinement on hybrid unstructured meshes with a target application on immersed boundary methods. The tool has as input an unstructured, hybrid, and conforming mesh generated by an external mesh generation software, and the main goal is to refine this mesh around immersed geometries in such a way that the CFD solver using the immersed boundary method can simulate flow problems in an accurate and efficient manner. The input background mesh can be made of different types of elements, like tetrahedra, hexahedra, prisms, and pyramids, which, unlike Cartesian meshes, permit for a more flexible mesh. Hybrid unstructured meshes enable one to use the immersed boundary technology in a new class of flow problems where the full geometry is decomposed into a fixed geometry part and a changing geometry part. A body-fitted mesh is generated for the fixed geometry while for the changing one is used the immersed boundary method. We simulate several flow problems to test the new meshes, including subsonic flow past a cylinder and subsonic flow past an NACA0012 airfoil, both using finite volume and discontinuous Galerkin methods and solving the Navier-Stokes equations. As an industrial example of our mesh generation, we consider the simulation of a multi-element airfoil: in this case, a mesh generation software generates an unstructured conforming background mesh for the slat and main airfoil, while the flap is placed as immersed geometry in this body-fitted mesh. As accurate and efficient results are sought, this mesh is refined around the flap and then the subsonic flow at high-lift flow conditions is simulated with a finite volume method coupled with an immersed boundary method and using the Reynolds-averaged Navier-Stokes equations.

Event-activity selections in small nuclear collision systems couple collision geometry to the soft response accompanying a hard scattering. We examine this coupling in d+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV with PYTHIA 8.316 Angantyr, using direct photons, terminal pre-decay neutral pions, and anti-kT jets. A hard-bias factor compares the probability for a hard event to enter an activity class with an $N^{ND}_{coll}$ -weighted minimum-bias reference. The model predicts a depleted most-active class and an enhanced peripheral class for both photons and pions. Photon-tagged events also sample a smaller mean impact parameter and more nondiffractive subcollisions than pion-tagged or inclusive HardQCD events. Comparing fully correlated Angantyr events with an Ncoll-reweighted minimum-bias reference and a factorized diagnostic separates the explicit geometric contribution from residual hard-soft correlations. STAR-like and PHENIX-like particle-level activity proxies preserve the class ordering but give different class probabilities. The calculation is generator-level and does not identify a unique microscopic origin for the residual correlations.

Whole-cell simulation, modeling all of a cell's functional systems over its life cycle, is an outstanding challenge in computational biology. Even the simplest living cell contains thousands of interacting proteins and metabolites (on the order of trillions of atoms) whose full functional dynamics spans roughly five orders of magnitude in space (nm to $\mu$m) and nearly nineteen in time (fs to hours). Further, many of the governing physical and chemical properties remain incompletely characterized. Simulating such complex systems at fully atomistic resolution over a full cell cycle is computationally intractable on classical architectures, raising a central question: Can quantum computing offer a viable path to whole-cell simulations that integrate molecular- and systems-level complexity? This Perspective examines the potential of quantum computing across three hierarchical scales: atomistic-molecular modeling, metabolic and regulatory networks, and whole-cell spatial modeling. We present a complexity analysis comparing classical and quantum algorithms for representative biological problems, identifying regimes of substantial theoretical speedup under specified algorithmic assumptions. We highlight algorithmic developments designed to leverage both near-term exploratory and fault-tolerant quantum architectures, and discuss practical bottlenecks: data encoding overhead, system conditioning, measurement constraints, and hybrid quantum-HPC integration. Together, these results outline a roadmap for quantum-accelerated whole-cell modeling and the biological insights such multiscale frameworks may eventually enable.

Subgrid-scale (SGS) models for large-eddy simulations (LES) of geophysical turbulence typically need to balance dissipative regularization with backscatter, the upscale transfer of energy from unresolved to resolved scales. Dynamic mixed models (DMMs) combine functional eddy viscosity and structural closures through dynamically estimated coefficients that are least-squares optimal with respect to the Germano identity error (GIE). We show that this classical DMM least-squares estimation can be dominated by the structural component, thereby limiting the functional component's dissipative regularizing role. To address this limitation, we develop a modified Gram-based framework to construct a novel parametric family of fully-coupled, sequential, and fully-decoupled DMMs with tunable structural-functional balance. We evaluate the resulting closures using an idealized forced two-dimensional and $\beta$-plane turbulence framework with the Leith model and the fourth-order nonlinear gradient model. A priori results show that structurally-dominated models achieve strong agreement with the ideal SGS forcing and accurately reproduce local SGS energy exchange, including backscatter. However, in a posteriori tests, structurally dominated models exhibit noise-like artifacts with high-wavenumber spectral deviations, indicating insufficient net dissipation. In contrast, the sequential DMM in which the functional component is determined first and then corrected by the structural component retains much of the a priori structural accuracy while improving the a posteriori vorticity fields, spectra, and domain-averaged diagnostics. Spectral SGS energy and enstrophy-transfer analyses show that this sequential DMM permits backscatter at scales larger than the forcing scale with enhanced dissipation at smaller scales, thereby improving the balance between instantaneous structural fidelity and long-term accuracy.

If electronics drives only electrons to charge electrodes, natural systems learn by moving matter to evolve. Morphogenesis in sessile organisms can be seen both as a fabrication process and as an operative mechanism. However, intricating manufacturing and programming functionalities in electronic hardware is not conventional. In this study, we experimentally implement such a concept of an evolutionary electrical system using a neuro-inspired electronic nose as a model, to store a history of sensory data in the physical properties of the electrical interconnects of sensing elements. Triggered only by volatile molecule exposures, different sensing elements change instantaneously and reversibly their impedance, so pulse voltages enable the electrochemical growth of conducting polymer dendrites. The strength of the evolving interconnects is specific to the sensing materials and to the nature of volatile molecules to which they are exposed. The dendritic growths occur exclusively when exposed to volatile samples, and stop immediately after interrupting the exposure. The capability of such "passive memory" was also assessed by simulating a network architecture, which showed that this way of storing information should greatly diminish the fabrication complexity of a highly dense sensing array while realistically enabling its calibration to classify user-specific environment exposures. By demonstrating that memory in electronics can be a concept linked to manufacturing like in living organisms, this study shows that low material resources and low energy activation can be exploited for practical electronic applications in future-emerging sensing technologies.

We investigate two novel non-hexagonal boron nitride monolayers, pentaoctite-BN (PO-BN) and pop-BN (PP-BN), using first-principles calculations. Their structural, electronic, mechanical, vibrational, thermal, and optical properties are systematically analyzed to assess their stability and potential applications. Despite being metastable with respect to hexagonal BN, both polymorphs satisfy the criteria for dynamical, mechanical, and thermal stability, indicating that they are viable two-dimensional materials. Both systems are indirect-gap semiconductors whose electronic states near the band edges are dominated by out-of-plane pz orbitals. Their distinct pentagon-octagon ring networks also give rise to different in-plane elastic anisotropies. Many-body optical calculations reveal strong excitonic effects and pronounced polarization-dependent optical absorption, with lattice engineering shifting the optical response from the ultraviolet toward the visible and infrared regions. These findings demonstrate that engineering non-hexagonal lattice architectures provides an effective strategy for tuning the electronic and optical properties of two-dimensional BN, highlighting PO-BN and PP-BN as promising candidates for future optoelectronic and photonic applications.

While real-time simulation of Quantum Chromodynamics remains technologically out of reach, many simplified models for studying elements of QCD phenomenology are being investigated. In this work, we present a simple model, a spin-1 truncation of the Compact Abelian Higgs Model simulated on qutrit sites, in which confinement and string breaking are accessible to current simulation methods. In the low-energy regime of 1+1 dimensional scalar electrodynamics, the heavy modes are integrated out, producing a spin chain effective Hamiltonian in which Gauss' law is automatically satisfied. We study the spectrum of string-like excitations using DMRG methods on the order of 100 sites. We demonstrate that an added, local chemical potential, playing a role analogous to external charges, permits parameter-dependent measurements of physical features of interest like the string tension and effective meson mass. Varying the chemical potential also permits a characterization of string stability not assessed in prior studies of confining lattice models.

A coaxial gyromagnetic nonlinear transmission line (GNLTL) enables pulse-front compression and oscillation packet generation in the 0.3-10 GHz range. Numerical modeling of such systems typically employs a 2D axially symmetric FDTD scheme coupled with the parallel solution of full non-linearized Landau-Lifshitz-Gilbert equations. Previous studies have shown that using an insulating dielectric with a permittivity $\epsilon$>60 enables the generation of long-lasting oscillations via a Cherenkov-like effect. However, practical implementation is limited due to the severe high-frequency losses inherent in materials with such extreme permittivity, like water. To overcome this limitation and enhance performance, we propose a novel design utilizing a spiral central conductor to mimic a high-permittivity environment, with the ferrite placed inside the spiral. Due to the loss of axial symmetry, the FDTD model was extended to 3D coordinates. Simulations confirm that the spiral conductor configuration generates a prolonged oscillation packet similar to that of a high-permittivity dielectric waveguide, but with significantly reduced losses and improved energy transfer efficiency.

This article reviews the early history of our solar system from an astrobiological perspective and presents evidence from meteorites and astronomical observations. The purpose is to trace the formation of key molecules that participated in the building blocks of life. The Sun and its planetary system started from a section of a molecular cloud that collapsed into a protoplanetary disk. In the center of the protoplanetary disk, the protosun heated the surrounding material. The dust and gas inherited from the cloud remained pristine farther away from the protostar, while new compounds were created in the gas and on the icy mantles of the dust. The dust accreted into pebbles, pebbles formed planetesimals, and planetesimals collided and accreted pebbles to create planets. Meanwhile, the protosun became the Sun when its core reached the pressure and temperature required to transform hydrogen into helium. During this process, the Sun emitted high-energy radiation and particles that impacted the chemistry in the disk and the early evolution of the terrestrial planets.

We study the non-relativistic limit of Gaussian covariant hydrodynamics [1]. We argue that the condition of incompressibility provides additional symmetries matching relativistic hydrodynamics but incompressibility must break down at a “microscopic“ scale. We then develop the renormalization group equations for average and fluctuations w.r.t. that scale, to understand its effect on flows at intermolecular distances where hydrodynamics gives way to statistical mechanics. The resulting dynamics naturally incorporates spontaneous stochasticity as a macroscopic back reaction of statistical mechanics fluctuations, as well as features reminiscent of anomalous dissipation and “wild solutions“ as renormalization group counterterms. We frame these considerations into both a phenomenological discussion of the limits of applicability of fluid dynamics, and a discussion of where physics might shed some light on the mathematical issues associated with turbulence.

Multimode quantum processes are generally difficult to learn, due to the large dimensionality and complex entanglement structure beyond the Gaussian class. Here, we show that the fundamental obstruction is not non-Gaussianity itself, but the buildup of irreducible multimode non-Gaussian correlations. We establish a tractability frontier for bosonic unitary learning: a general $m$-mode unitary with input energy at most $E$ per mode requires at least $\Omega(E^{2m})$ channel uses, whereas two broad non-Gaussian families—$t$-doped Gaussian unitaries and Gaussian-entanglable unitaries—can be learned with resources polynomial in $m$. The latter can exhibit both extensive non-Gaussianity and strong multimode entanglement. Our forward-only protocols use coherent-state probes, Gaussian operations, local heterodyne detection, and classical post-processing to identify the global Gaussian mixing and reduce the remaining task to single- or few-mode learning. The analysis also yields a multimode quantum Darmois–Skitovich theorem showing that mode-spreading passive networks preserve product structure only for Gaussian input states, an almost-sure activation theorem for non-Gaussian processes showing that non-Gaussian unitaries yield non-Gaussian outputs for almost all coherent input states, and a method for learning unitaries from uncalibrated coherent probes. Our results identify that complexity of learning arises from irreducible mixing of non-Gaussianity and entanglement, rather than either resource alone.

The committor function describes the probability that a stochastic dynamical system, while transitioning between two metastable states, will first reach one state rather than the other. It is a central object in transition path theory, providing the accurate reaction rate and the characterization of the transition mechanism through reactive probability flux and the transition state ensemble. While the numerical methods for this function are well studied for over-damped Langevin dynamics where the friction damping coefficient goes to infinity, its computation in the general under-damped case remains an open challenge due to the lack of a variational structure in the underlying backward Kolmogorov equation. This restricts the quantitative investigation of the impact of the finite damping coefficient on rare transitions in real systems. In this work, by decomposing the infinitesimal generator of the underdamped Langevin dynamics into a self-adjoint and a skew-adjoint part, we develop an iterative scheme in which each subproblem has a variational form and can be solved efficiently using neural networks. Fundamentally, this approach can be viewed as a generalized Concus-Golub-Widlund (CGW) splitting method and each subproblem has a variational form, which can be solved efficiently. This iterative scheme also has the flexibility of introducing a preconditioning operator associated with a new overdamped Langevin dynamics, which naturally leads to the design of enhanced adaptive sampling for the subproblems. We validate the efficiency and accuracy of our method on benchmark systems including a one-dimensional double-well potential, the Muller-Brown potential, and alanine dipeptide. The proposed iterative variational framework provides a practical and extensible numerical tool for probing rare transition events in Langevin dynamics across varying damping coefficients.

Hexanal, a volatile organic compound (VOC), is a potential biomarker for the early detection of lung cancer. In this study, we developed a dual-mode flexible biosensor that integrates surface-enhanced Raman scattering (SERS) and colorimetric detection for the quantitative analysis of hexanal in human exhaled breath. The biosensor employs Ag nanocubes wrapped with Co-Ni layered double hydroxide (AgNCs@Co-Ni LDH) as a functional matrix, offering both superior SERS enhancement and efficient VOC adsorption properties. To achieve selective detection, AgNCs@Co-Ni LDH were incorporated into agarose hydrogels along with 3-methyl-2-benzothiazolinone hydrazone (MBTH). The resulting MBTH-AgNCs@Co-Ni LDH/hydrogel-patch facilitate the oxidation of hexanal, enabling simultaneous colorimetric and SERS signal generation, while producing acrizine, a blue-colored reaction product. This hydrogel-based dual-mode sensing platform exhibits high selectivity, excellent stability, and precision in SERS-based hexanal detection. The detection limit for the SERS method was determined to be as low as 3.34x10-13 M. Furthermore, developed and optimized compact CNN-based multi-terminal intelligent recognition system for enhanced hydrogel-patch detection through AI-driven, portable, and real-time colorimetric analysis. Therefore, this work not only enables the effective detection of hexanal in the exhaled breath of suspected lung cancer patients, underscoring its potential for early lung cancer screening, but also establishes a foundation for the development of multimodal hydrogel biosensors for broader applications in disease diagnosis.

We prove that the full spectrum of the graph $p$-Laplacian is finite for every finite graph and every real $p>1$. This resolves the problem of finiteness of the full graph spectrum posed by Amghibech (2003). More generally, for signed weighted graph $p$-Schrödinger operators with arbitrary real potentials, we obtain bounds in terms of the numbers of vertices and positive-weight edges, uniform in all coefficients and in $p$. On $n$ vertices with $m$ positive-weight edges, the logarithms of the spectral cardinality and of the total number of connected components of the normalised eigenvector sets are $O((n+m)^2)$; the sum over the full spectrum of their rational Betti numbers is at most $\exp(C(n+m)^2)$ for an absolute constant $C$. These topological bounds extend to homogeneous eigenproblems built from arbitrary finite families of linear forms, including generalised $p$-eigenvalue problems for matrix pairs. For the uniformly weighted $K_n$ and $p\ne2$, we identify the nonconstant eigenlines with the barycentres of the coordinate-hyperplane cell decomposition of $\mathbb RP^{n-2}$ and determine their local Morse data on either side of $p=2$. A renormalised logarithmic limit describes the transition at $p=2$. Positive integer weights then separate these critical values, giving at least $(3^n-2^{n+1}+3)/2$ distinct spectral values for every fixed $p\ne2$ and every $n\geq2$. By contrast, the maximal spectral cardinality at $p=2$ is $n$; at $p=4$, the maximal spectral cardinalities in the unsigned and general classes both have exponential growth rate exactly $3$. For $n\geq3$ the same examples resolve Amghibech's extremal question. The proof combines o-minimal and Pfaffian geometry with projective $L^p$-duality, Morse theory, critical groups, and tensor eigenvalue bounds.

A weak acoustic, entropy or vortical sheet can cross a finite-strength three-dimensional shock without producing a kink, yet it can deposit a discontinuity of curvature along the intersection curve. We derive a local Euler junction law that separates the active normal-plane scattering problem from its three-dimensional geometric realization. The scattering selects one scalar curvature amplitude, while the orientation and pre-existing shape of the shock determine the full curvature tensor and the downstream flow response. A controlled pair of saddle-shock interactions has identical incident and outgoing modal amplitudes but produces post-shock pressure-gradient vectors separated by 31.6 degrees and vorticity vectors separated by 25.6 degrees. For a perfect gas, acoustic incidence also possesses curvature-neutral branches: outgoing acoustic, entropy and shear waves remain finite while the curvature channel cancels. Convected entropy and in-plane vortical sheets deposit curvature with opposite signs, and their coefficients obey an exact relation imposed by total-enthalpy conservation and acoustic orthogonality; line-tangent vorticity is curvature-transparent. An expanding spherical shock provides an unsteady example with non-zero shock speed and non-zero intersection-line tracking speed, separating the smooth blast-wave acceleration from the deposited acceleration jump. Near an umbilic, the same junction selects or rotates the principal-curvature frame. The resulting law supplies the interface data needed to connect smooth curved-shock reconstructions across a continuously differentiable, piecewise twice-differentiable front.

We show that, in appropriate regimes, ultracold collisionally shielded molecules in an optical lattice realize a controlled extended Hubbard model, despite their large-radius hard-core repulsion. We compute the Hubbard parameters and show lattice depth and microwave-induced dipolar interactions provide flexible, independent control of on- and off-site interactions. This makes shielded molecules a powerful platform for extended Hubbard models and enables an interaction microscope that can distinguish inter-hole interactions in doped Mott insulators - crucial to superconductivity and strange metallicity - that are indistinguishable with quantum gas microscopy alone.

Despite fundamental differences in morphology, dimensionality, and microscopic transport, semicontinuous Au films and compacted CrO2 powders converge on a common network-level principle: the electrically relevant network is a condition-dependent subset of the physical contact network. In Au films near the geometrical percolation threshold, increasing bias reversibly shifts the low-temperature response from activated nearest-neighbour hopping with negative magnetoresistance to a weak-localization-like regime with positive magnetoresistance, consistent with broader electronic participation of the fixed island network. In CrO2 powder compacts, magnetic field mainly changes the spin-dependent resistances of existing intergranular junctions and thereby reorders competing current paths; temperature and measuring current can additionally expand or deplete the active network. Cohn's theorem provides a compact sensitivity principle for both cases: local resistance changes influence the measured response in proportion to the current carried by the affected links. The comparison identifies two limiting but interconvertible modes of reconfiguration - link enabling and link reweighting - and shows why geometrical connectivity alone is insufficient for interpreting transport in strongly inhomogeneous conductors.

Cosmological stasis can recur multiple times, and each occurrence imprints a characteristic feature on the inflationary gravitational wave background (IGWB). We extend the single-epoch spectral template to an arbitrary number $N$ of consecutive stasis epochs, deriving a closed-form $N$-epoch piecewise template that factorizes cleanly: the amplitude steps at each break are determined entirely by the local equation of state $w_s^{(i)}$ of that epoch, while the inter-epoch plateau levels encode the accumulated spectral tilt from all prior epochs. Each resolved spectral feature yields an independent test of the Bessel-function consistency relation $C^2 = C^2(\alpha)$ established in~\cite{BarenboimBurns:paper1}, and the probability that $N$ such tests are simultaneously satisfied by a non-stasis spectrum falls sharply with $N$, making a confirmed multi-epoch comb the strongest available discriminator against constant-$w$ alternatives. We apply the formalism to the triple-stasis scenario of~\cite{Dienes:2023ziv}, which chains three pairwise stasis mechanisms (matter/radiation, vacuum-energy/matter, vacuum-energy/radiation) sequentially. For each block we derive the conditions under which the epoch lies within the template's validity range $w_s > -1/3$. We show that the Fig.~3 benchmark of~\cite{Dienes:2023ziv} falls outside this range and identify parameter choices that are both physically realizable and accessible to the template. We validate the inter-plateau ratio predictions numerically for representative two- and three-block chains, and work out the touching-epoch limit in which consecutive blocks share a break frequency without an intervening radiation-dominated interval. Finally, we discuss the detectability of a variety of multi-epoch stasis scenarios and give the minimum detectable tensor-to-scalar ratio as a function of the number of recurrent stasis epochs.

Every solution of the hyperbolic Toda equation determines a classical Abelian monopole on a (2+1)-dimensional Einstein–Weyl space. We determine when its connection is also a source-free electromagnetic potential on a curved Lorentzian 3+1 spacetime with a spacelike Killing field. On the generic branch, Maxwell's equation classifies the admissible Killing-fibre scale and horizontal twist through one characteristic of the Toda field. A separated Liouville branch gives purely magnetic fields with considerably freer fibre geometry. The round spherical solution yields a monopole of unit charge with conserved flux on a contracting screen. The background metric here is neither assumed to be Einstein nor self-dual.

We study boundary free energies in disordered Edwards–Anderson Ising models. For rectangular strips of fixed width, we prove that the expected free-to-fixed boundary correction, divided by the tangential length, converges for every finite inverse temperature and at zero temperature. The proof combines a uniform almost-additivity estimate with product-measure concentration, yielding self-averaging along tangential intervals. We derive an exact finite-volume Gaussian interpolation identity and prove that a uniform exponential boundary-mixing condition implies boundary localization with an explicit exponential rate. The tangential pressure theorem extends to all dimensions and symmetric coupling laws with finite first moment; under a product-concentration hypothesis, self-averaging is obtained along tangential cubes. Finally, a subcritical open-bond criterion, verified explicitly for sparse signed couplings, gives low-temperature localization without assuming an infinite-volume Gibbs-state property.

Quantum light sources constitute a crucial physical resource for the construction of quantum networks. Despite remarkable recent progress, there remains a lack of systematic investigation into the bandwidth tunability of quantum light sources under fixed waveguide parameters. In this work, we demonstrate a broadband quantum light source in the 1.5 $\mu$m band with tunable bandwidth by changing the temperature of a piece of periodically poled lithium niobate waveguide. In our demonstration, the bandwidth of the quantum light source is tuned from 78.3 nm to 96.2 nm with a temperature change of 1 $^\circ$C . Under different bandwidths, the generation rates of correlated photon pairs are greater than 6.3 MHz with coincidence-to-accidental ratios consistently being no less than 608. The energy-time entanglement properties are measured by using the Franson interference with two-photon interference visibilities larger than 99.06%. Our results provide an effective method for developing the quantum light sources with tunable bandwidth which has great potential for building the large-scale quantum networks.

Robert Smithson's 1970 land artwork Spiral Jetty, located in the north arm of Utah's Great Salt Lake, has alternated between submergence and exposure during severe lake decline. We analyze 1,744 co-registered Landsat 4-9 and Sentinel-2 image chips spanning every year and calendar month from 1984 to 2025. A 14-feature complexity signature combines Shannon entropy, multiscale permutation entropy, fractal dimension, lacunarity, gray-level co-occurrence texture, intensity statistics, and ImageNet-pretrained ResNet50 features. These measurements are compared with a 42-year monthly climate and hydrology panel from NASA GISTEMP, USGS NWIS, Open-Meteo, and the Global Carbon Budget. Bootstrap analysis shows that Shannon entropy is a weak proxy and does not support an earlier small-sample claim of positive correlation with global temperature. By contrast, coarse-scale permutation entropy and mean intensity track lake elevation strongly, with Spearman correlations of 0.85 to 0.88 and 95 percent confidence intervals excluding zero. The third principal component of the ResNet50 embeddings emerges without supervision as an AI climate axis, correlating 0.86 with cumulative CO2 and -0.83 with lake elevation. Image complexity leads lake stage by about three years, with Pearson r = 0.58 at lag +3 and a 95 percent confidence interval of 0.40 to 0.73. STL decomposition reveals a non-monotonic trend that rises from 1984 to 2015 and declines sharply thereafter as the lake approaches record-low elevations. Partial correlations controlling for month and sensor confirm robustness to seasonal and sensor effects. These results refine the art-as-thermometer metaphor into an art-as-leading-indicator-of-hydrological-state interpretation. The dataset, feature pipeline, and analysis code are released as a public benchmark.

We investigate the strong gravitational lensing properties of the Lorentzian Euclidean black hole, a spacetime in which the horizon at $r=2M$ is not a coordinate singularity but a genuine surface of signature change, with the associated curvature singularities removed by two regularization parameters, $\rho$ and $k$. Starting from the null geodesic equations, we derive the photon sphere, the critical impact parameter, and the strong deflection limit coefficients, and use them to obtain the deflection angle and the full set of strong lensing observables, namely the angular position of the relativistic images, their angular separation, the relative magnification, and the differential time delay between successive images. We show that the photon sphere and critical impact parameter increase with $\rho$ and decrease with $k$, indicating that the two parameters have opposite effects on the optical geometry, and evaluate the resulting observables numerically for the supermassive black holes Sgr A* and M87*. Comparison with the Event Horizon Telescope shadow measurements shows that the Schwarzschild limit is mildly disfavored for Sgr A*, whereas M87* places $k$-dependent upper bounds on $\rho$, with the adopted fiducial values lying well below these limits. We further show that the shadow constrains only a combination of $\rho$ and $k$, and identify observables such as shadow circularity, higher order image time delays, and quasinormal mode spectra that are capable of breaking this degeneracy. These results identify gravitational lensing observables as effective probes of the Lorentzian–Euclidean scenario.

Autonomous and intelligent transportation systems operate in complex urban environments where safety depends on interactions among vehicle behavior, environmental conditions, and vulnerable road users (VRUs) such as pedestrians and cyclists. Most advanced driver assistance systems (ADAS) employ reactive mechanisms that activate only after hazards have emerged, a critical limitation underscored by rising VRU fatalities in the United States. This study introduces PRISM (Proactive Risk Intelligence and Safety Management), an agentic multi-model safety architecture that transitions from reactive crash avoidance to proactive, continuous risk management. PRISM employs inverse crash-probability modeling to convert binary crash classifiers into dynamic, interpretable safety scores. Three specialized models addressing trajectory kinematics, environmental risk, and VRU interaction operate concurrently, coordinated by a reasoning layer incorporating reinforcement learning, contextual memory, and feature-level attribution. The system provides graduated safety interventions across four tiers, from silent monitoring to emergency alerts. Unlike rule-based systems with static thresholds, PRISM dynamically adjusts safety parameters in real time. Validated across 1,296 scenarios from three naturalistic driving datasets without dataset-specific retraining, the system yielded a mean safety score of 68 out of 100, classified 77.6% of scenarios as advisory, and flagged a near-miss rate of 3.8%, with 11% of scenarios escalating to intervention or emergency response. Feature attribution consistently identified trajectory risk and VRU proximity as primary safety factors. PRISM provides a unified, interpretable framework for proactive transportation safety with emphasis on VRU risk reduction in dense urban environments.

As a cornerstone result of social choice and democracy the- ory, the Condorcet Jury Theorem (CJT) provides probabilistic guaran- tees for the correctness of majority decisions under idealized conditions, in particular assuming that voters are statistically independent. When generalizing the original CJT, a standard approach in the literature to model dependence introduces an external opinion leader (OL) whose in- fluence affects individual voting behavior. In this paper, we take the first steps toward a unified framework for modeling dependence by translat- ing the OL model into standard correlation measures, namely covariance and correlation coefficient. These translations build conceptual bridges between different dependence models in the voting literature and facili- tate the application of existing theoretical results across frameworks.

Artificial intelligence (AI) agents built on large language models increasingly work in teams, where one agent divides tasks or resources among others that the software marks with a team, a role or a similar label. A model that simply up-weights any recipient sharing a string with its own context would favor same-label peers, so a shared-label tilt alone says little about groups. Borrowing the minimal-group paradigm of social psychology, we had six language models divide 100 points among 19 peers that differed only in carrying one of two invented words, in groups of unequal size (32,400 decisions). Varying group size separates two effects. In five of the six models, at the most unequal split, a recipient with the rarer label received 1.3 to 3.3 times the points of one with the common label even when the decider was not told its own label: a rarity effect that involves no match to the decider. In four of them it accounts for 35% to 55% of the extra favoritism that agents in the smaller group show their own group, and for more in the fifth. Telling the decider its label adds an own-group effect, positive in every model and in five of the six indistinguishable from label matching. With the labels hidden, neither appears. Because the labels mean nothing, bias tests built on lists of named social groups are not designed to detect either effect.

The growing catalog of gravitational-wave events has revealed substantial diversity in the properties of compact-binary mergers. However, commonly used population-inference methods based on discrete posterior samples can struggle to constrain narrow population features, resulting in biased or unstable estimates of population hyperparameters. We first demonstrate this limitation using a toy population model by comparing parameter recovery with a continuous likelihood model against discrete approximations constructed from $10^3$, $10^4$, and $10^5$ samples. We then perform the same comparison using synthetic eccentric and multisource populations introduced in previous studies. Although the continuous and discrete approaches yield broadly consistent results, the continuous approximation more accurately recovers the parameters of narrow simulated populations. In particular, while both methods produce similar mass distributions, appreciable differences arise for narrowly distributed parameters such as spin and eccentricity. Our results indicate that the continuous approach provides more reliable inference for spin and eccentricity, whose narrow population distributions can be inadequately represented by finite sample sets. Continuous likelihood models therefore offer a valuable tool for improving population inference and extracting more robust information about the formation and evolution of compact-binary systems.