Scattered light is one of the most common sources of noise in the Laser Interferometer Gravitational Wave observatories (LIGO). Light scattering is a highly nonlinear process through which motion at low frequencies gets up-converted and creates noise in a higher-frequency band in the detector data. From the beginning of the fourth observation run, many glitches appeared in the data of LIGO Livingston detector in the frequency range 10–40 Hz, and the morphology of these glitches suggested that they were produced by scattered light. From our analysis, we identified two different populations of scattered light glitches, one group having higher signal-to-noise ratio (SNR) than the other. The glitches of the high-SNR group were solely modulated by microseismic ground motion (ground motion in 0.1–1.0 Hz), and in this paper, we present models of possible coupling mechanisms for these glitches. We also present results of a statistical correlation analysis based on our models, which indicates that the microseismic ground motion at the corner station along the X direction is the one most correlated with the noise that creates these high-SNR glitches. After installing baffles very close to the test mass mirrors, we have noticed a significant reduction in the rate and SNR of these glitches. The low-SNR glitches were primarily modulated by high-frequency (10–30 Hz) vertical ground motion at the corner station, and this motion was coupling through a specific vacuum chamber at the corner station. After installing an additional seismic isolation platform in that vacuum chamber, these glitches have disappeared.

The uncertainty principle constitutes a cornerstone of quantum mechanics. However, its conventional formulations do not adequately describe uncertainty in scenarios involving conditional measurement. In this work, we propose uncertainty relations based on conditional variance and conditional expectation. We define the conditional expectation operator $E\left(\widehat{A}\right|\widehat{B})$, its dual operator $E\left(\widehat{B}\right|\widehat{A})$, and a corresponding conditional covariance term ${\mathcal{R}}_{\text{corr}}$. We demonstrate that even for noncommuting observables $\widehat{A}$ and $\widehat{B}$, their conditional expectation operators can possess a nonvanishing commutator. This leads to a strengthened uncertainty relation applicable to both pure and mixed states. In addition to the standard term arising from the noncommutativity of the observables themselves, our relation incorporates three distinct contributions: one from the noncommutativity of the conditional expectations, one from quantum coherence, and one from the conditional covariance. These contributions collectively yield a significantly tighter lower bound. Numerical simulations verify the generality and enhanced tightness of the proposed relation. Our results provide a refined theoretical tool for the joint estimation of multiple observables in composite quantum systems, with potential applications in quantum precision metrology and secure quantum key distribution.

Detecting weak field perturbations at an optical-vortex node is a fundamental challenge across wave physics, as the local signal contrast typically scales quadratically with the impurity amplitude when no reference field is used. Here, we propose a complementary, spatially resolved diagnostic that exploits the strongly nonlinear response of an electromagnetically induced transparency (EIT) medium to transduce weak topological impurities into probe transmission. Using a vortex beam with a weak coherent admixture as a concrete realization, we show theoretically that EIT coherence converts the weak field that lifts the nominal vortex node into a highly sensitive, macroscopic transmission contrast. A resonant weak-probe model with representative cold-${}^{87}\mathrm{Rb}$ parameters predicts a per-exposure detection floor for the impurity amplitude at the level of a few $\times {10}^{-3}$, relative to a separately characterized, preaveraged pure-vortex reference. We quantify the intrinsic and experimental limits of this response, analyzing its dependence on coupling strength, ground-state decoherence, optical depth, two-photon detuning, and Maxwell-Boltzmann thermal motion. While not a complete, basis-independent measurement of modal purity, this method provides a calibrated, local all-optical diagnostic for weak coherent field admixtures at a known vortex node.

We investigate the collective behavior of motile rods immersed in a monolayer of apolar rods confined between vertically vibrating plates using numerical simulations. We uncover an antidiffusive phenomenon whereby motile rods segregate from the apolar medium and form anisotropic flocks whose size increases with the medium concentration. Remarkably, enhanced segregation leads to a reduction of the global polar order, giving rise to an anomalous dependence of the polar order on the medium concentration and the angular noise of the motile rods. The flock structure is strongly influenced by the anisotropy of the medium rods. For small aspect ratios, the flocks are elongated perpendicular to the mean direction of motion, whereas for larger aspect ratios they elongate along the direction of motility. We rationalize the emergence of segregation-induced disorder using a minimal mean-field model.

We investigate the role of stochastic resetting in non-Markovian systems, where memory effects arise due to slow relaxation, rugged energy landscapes, disordered environments, and molecular crowding. Using the celebrated continuous-time random walk framework, we analyze first-passage processes with multiple competing outcomes and examine how resetting can selectively enhance desired outcomes. We characterize the efficiency of resetting through conditional mean first-passage times and demonstrate that its impact is highly sensitive to the underlying waiting-time statistics. Furthermore, we derive an inequality that quantifies how resetting controls fluctuations in conditional first-passage times, revealing regimes where variability is significantly suppressed. Our results provide a systematic understanding of how long-term memory influences competitive first-passage outcomes and establish resetting as a powerful control mechanism beyond the conventional Markovian setting.

We propose a microscopic realization of the idle-level quantum heat engine architecture based on a two-qubit non-Hermitian XY model with a staggered imaginary magnetic field. The energy spectrum naturally separates into two working levels coupled to the external field and two field-decoupled idle levels entirely independent of it. Tuning the non-Hermitian parameter drives the system from a dissipative accelerator regime into a genuine heat engine regime, while simultaneously enhancing both the net work output and the cycle efficiency. The efficiency enhancement originates from the compression of the idle-level gap, which redistributes level occupations and progressively suppresses the idle-level heat current, signaling the approach to the reverse heat-flow regime that underlies the idle-level engine principle. We map the full thermodynamic phase diagram identifying engine, refrigerator, accelerator, and heater regimes, and decompose the heat current into working-level and idle-level contributions to clarify the microscopic origin of the performance gain. The model is implementable in trapped-ion and NMR quantum simulators. These results establish non-Hermiticity as a versatile control knob for idle-level quantum thermal machines.

We study the refrigerator working between a finite-sized cold heat bath and an infinite-sized hot heat bath (environment) in the nonlinear response regime. We assume that the initial temperature $T_i$ of the finite-sized cold heat bath satisfies $T_i\le T_h$, where $T_h$ is the temperature of the hot heat bath. By consuming the input power, the refrigerator transfers the heat from a finite-sized cold heat bath to the hot heat bath. Hence, the temperature of the finite-sized cold heat bath decreases until it reaches the desired low temperature $T_f$. By minimizing the input work for the heat transport process, we derive the optimal path for temperature change. We calculate the coefficient of performance as a function of average input power. We also obtain the bounds for the coefficient of performance by applying the asymmetric dissipation limits. For the parameter values considered in this study, we observe that the relation between the coefficient of performance and the average input power strongly depends on the nature of the finite-sized heat cold bath.

Epitaxial strain provides a powerful, nonchemical route to tune the properties of functional materials by modifying the coupling between spin, charge, and lattice degrees of freedom. We investigate strain-controlled magnetic transitions within an anisotropic half-filled one-band Hubbard model in three dimensions. In this framework, strain is introduced through anisotropic hopping processes between nearest- and next-nearest-neighbor sites. Using a semiclassical Monte Carlo (s-MC) approach, we construct ground-state phase diagrams in the nonperturbative regime, which show how uniaxial hopping anisotropy stabilizes distinct magnetic ground states: Compressive strain drives a transition from a G-type to a C-type antiferromagnetic (AF) insulator, whereas tensile strain suppresses the C-type AF order, favoring an A-type AF phase. Furthermore, we examine the effect of the on-site Hubbard interaction strength ($U$) on these phase diagrams, revealing systematic evolution of the competing magnetic phases. Overall, our s-MC calculations emphasize that directional electronic anisotropy is a powerful tuning parameter for controlling competing magnetic orders in correlated systems, offering valuable insights for the design of strain-controlled materials.

Complex numbers, intrinsic to the formulation of quantum theory, play a pivotal role in enabling advantages across a broad range of quantum information-processing tasks. Despite their fundamental importance, practical and scalable criteria for detecting quantum imaginarity remain relatively underexplored, particularly methods that enable its identification with reduced experimental overhead. In this work, we propose a realistic and experimentally feasible method to detect quantum imaginarity using moment-based approach. Our framework relies on experimentally accessible moments of the Kirkwood-Dirac quasiprobability distribution, enabling scalable detection in high-dimensional systems without requiring full state tomography. We then present illustrative examples to support our detection scheme. Finally, we present an interferometric scheme for measuring these moments, paving the way for experimental implementation of our detection protocol.

The temporal decay of triggered seismicity is commonly described by the Omori law $r\left(t\right)\sim {(t+c)}^{-p}$. I introduce a minimal phenomenological framework that relates this temporal decay to two concurrent processes: the spatial spreading of a triggering perturbation and the progressive depletion of elements that remain susceptible to failure. The model yields the scaling relation $p=1-\alpha d/2+\gamma$, where $\alpha$ characterizes spreading, $d$ is an effective spatial dimension, and $\gamma$ describes depletion or healing. This relation provides a joint consistency test rather than a unique inversion of the Omori exponent for material healing. Numerical simulations recover the predicted scaling. A comparison with published earthquake-pair statistics illustrates that slow spatial spreading can be identified, but also that spatial and temporal exponents cannot be combined unless their underlying observables and event selections are compatible. The main implication is that the Omori exponent alone cannot generally be interpreted as a direct measure of fault or reservoir healing; testing the proposed relation requires compatible estimates of spatial spreading, event-rate decay, effective geometry, and, where possible, an independent recovery observable.

We introduce the popularity-driven random walk graph convolutional network (PDRW-GCN), a topology-aware graph neural network that incorporates degree-biased diffusion to exploit the heavy-tailed connectivity of scale-free networks. By incorporating a structural bias parameter, $\beta$, the model dynamically reweights neighborhood propagation based on node degree. We identify a topology-dependent diffusion transition in heterophilic graph learning: Sparse heterophilic networks benefit from hub-amplified transport ($\beta >0$), whereas dense heterophilic networks require hub-suppressed diffusion ($\beta <0$) to prevent noisy neighborhood overmixing. Experiments across homophilic and heterophilic benchmark networks demonstrate consistent improvements in node classification performance measured by F1 score and receiver operating characteristic—area under the curve. Moreover, the model exhibits diminishing performance gains on non-scale-free topologies, indicating that $\beta$ acts primarily as a topology-dependent routing mechanism rather than a generic regularizer.

A theoretical model is presented for the reacceleration process of the bubble and the spike that follows after the velocity saturation phase in the nonlinear, single-mode, two-dimensional Rayleigh-Taylor instability in ideal media. A previous irrotational model is extended to include the vorticity effects consistently with the existing evidence that vorticity drains momentum from the translational motion. Contradictions and paradoxes arising from the current paradigm based on the acceleration by the vortices centrifugal force are solved. The model shows that the time when the reacceleration occurs depends on the density ratio between both fluids, and that it is determined by achieving the corresponding final widths of the bubble and the spike, which thereafter remains constant.

An exact microcanonical analysis of the one-dimensional Ising model is presented with emphasis on the emergence of negative temperatures. The number of states of the system is obtained as a function of the energy, the magnetization, and the number of spins. The Boltzmann and Gibbs entropy is computed, and the results are compared in the thermodynamic limit and for finite systems as well. According to the Boltzmann scenario, negative temperatures naturally appear as a consequence of the energy bounded spectrum. In this case, the Boltzmann entropy is not symmetric with the energy as soon as the magnetization is different from zero and does not vanish at the maximum allowed value of the energy. Along with entropy, the specific heat and magnetic susceptibility are analyzed as functions of the temperature. Although the specific heat is always positive, the symmetry is also broken in both branches of positive and negative temperatures for finite values of the magnetization. The susceptibility, in its turn, changes sign in the negative sector. In addition, the phase transition, which is present when the temperature goes to zero from the right, is totally suppressed when the temperature tends to zero from the left. Finite-size effects are also studied to understand the equivalence of the microcanonical and canonical formulations for this interacting model. Negative temperatures are not allowed according to the Gibbs formulation.

Perfect LOCC (local operations and classical communication) discrimination of locally indistinguishable quantum states usually requires maximally entangled states as a resource. Whether nonmaximally entangled resources can enable perfect LOCC discrimination of locally indistinguishable states remains an interesting area of research. The states in the three-qubit Greenberger-Horne-Zeilinger (GHZ)-SLOCC (stochastic local operation and classical communication) class can display various entanglement distribution patterns, including cases with no bipartite entanglement in any reduced subsystems, or instances where bipartite entanglement occurs across one, two, or all three reduced subsystems. Significant research has explored how such states can be used in entanglement-assisted discrimination tasks. In this paper, we investigate the distinguishability of orthogonal product states using GHZ-SLOCC class entangled states, and we identify two nonmaximal GHZ-SLOCC states that facilitate perfect distinguishability. Additionally, we analyze the relationship between the probability of error and the amount of bipartite and multiparty entanglement. Our analysis focuses on how the distribution of entanglement affects the accuracy of state discrimination.

Analytic continuation from imaginary-time Green’s functions to real-frequency spectra is a central ill-posed inverse problem in quantum many-body physics. We show that the thermal kernel admits an analytical generalized singular-value structure once its purely dynamical part is separated from the statistical weight imposed by the heat bath. The dynamical kernel is the imaginary-bandwidth continuation of Slepian’s finite Fourier transform and is governed by the same Sturm-Liouville algebra that yields prolate spheroidal wave functions. Fermionic and bosonic statistics then enter as gauge transformations of the frequency-space inner product, giving a generalized singular system and self-adjoint effective potentials in the corresponding weighted space. The Shannon number, $N_c=\beta {\omega }_{\max }/\pi$, fixes the upper information capacity of this pure Laplace channel. Finally, the optimal sampling points are obtained as eigenvalues of a Legendre colleague matrix, giving a deterministic compressed-sensing grid without iterative root searches.

The actin cytoskeleton is an inherently disordered active system. The actomyosin cortex and reconstituted actomyosin systems are globally disordered, yet undergo transitions between distinct disordered states as parameters like motor and crosslinker concentration and filament length and rigidity change. In cells, these changes are related to genetic mutations or differences in cell state and dictate fundamental biological processes. However, we do not have well-established methods to detect and classify differences in disordered polymer networks. Image-based morphology techniques provide a noninvasive, high-throughput method of extracting information about a system. In this work, we simulate biopolymer networks under varying conditions and develop and use morphological descriptors to construct trajectories in morphospace. Using statistical analysis, we find that morphological descriptors are able to distinguish between different trajectories of the system, including differences not apparent to the eye. However, no single descriptor alone is able to capture all the differences in the simulated trajectories. Nematic order parameters typically perform the worst for our simulations, while curvature and texture descriptors can collectively distinguish between dynamic trajectories. This work helps develop quantification of cytoskeleton dynamics for classification and data-driven modeling.

The realization of spin-based devices remains one of the central goals of spintronic research. Single-molecule magnets constitute an important class of nanoscale magnetic systems with significant potential for spintronic applications, where individual molecules can serve as fundamental building blocks of functional devices. In this work, we systematically investigate a family of $4d$ and $3d$ transition-metal metallocenes using first-principles density functional theory. Among the seven $4d$ metallocenes considered, only Mo and Rh metallocenes undergo Jahn-Teller distortions and exhibit uniaxial anisotropy with energy barriers of approximately 20 K. Similarly, among the $3d$ metallocenes studied in this work, only Cr and Co metallocenes undergo Jahn-Teller distortions and display uniaxial anisotropy, although with smaller barriers below 10 K. All remaining metallocenes exhibit easy-plane anisotropy. We find that the magnetic anisotropy energy does not increase monotonically with the number of $d$ electrons; instead, it is governed primarily by the orbital ordering of the transition-metal $d$ states. Our calculations further show that the Jahn-Teller distortion induces transverse anisotropy, leading to zero-field magnetization tunneling across the energy barrier, with the weakest tunneling rate for Mo metallocene. For the Mo metallocene, the magnetic anisotropy energy increases to approximately 60 K in cationic charge states, although the magnetic anisotropy changes from uniaxial to easy plane. In this work, we also investigate the influence of ligand size on the structural stability of metallocenes and establish practical guidelines for constructing reliable molecular models for first-principles studies. Finally, we also propose that metallocenes with easy-plane anisotropy could serve as magnetic sensing elements, highlighting their potential beyond memory applications.

We employ the polar decomposition of the Dirac field to describe it as an effective spinorial fluid. We then construct a $(1+1+2)$ covariant formalism for the Dirac field that avoids the introduction of tetrad fields and Clifford matrices. Within this framework, we analyze the conditions under which a self-gravitating Dirac field can be consistently embedded in locally rotationally symmetric (LRS) space-times of types I, II, and III. In accordance with the LRS symmetry requirements, we extend a previous work by assuming that the velocity and spin vector fields of the Dirac field lie in the planes defined pointwise by the generators of the timelike and spacelike congruences, which underlie the $(1+1+2)$ decomposition. We present some analytical and numerical solutions to illustrate the applicability of the proposed framework.

Half-metallic ferromagnets exhibit a gap in the density of states for one spin projection while remaining gapless for the opposite spin. We show that in helimagnets an unusual half-metallic state can exist, where the spin projection that experiences the gap is determined by the direction of the wave vector. This state originates from the nontrivial topology of the band structure, specifically from the dispersion forming a multisheeted covering over the Brillouin zone. We present two-dimensional tight-binding models for $p$-wave and $f$-wave half metals. These structures can be realized in crystalline and van der Waals systems. The complex band structure of the unusual $p$-wave half metal in nanostructures is also discussed. In a quantum well, a standing wave with a helical spin structure is formed, and a persistent spin current exists.

Living systems display organized, context-dependent dynamics through a restricted repertoire of persistent functional states. At mesoscopic scales, where biological motion is classical, dissipative, and noisy, such discreteness cannot be attributed to microscopic quantum effects alone. Here, we formulate a mesoscopic theory of functional closure for thermodynamically open episodes in which regulatory organization confines the dynamics to an approximately invariant informational class. Within a constrained open Hamilton-Jacobi framework, exact dissipative composability requires the complete irreversible realization of a biological act—including bulk, interfacial, regulatory, composite, and unresolved internal contributions—to admit an exhaustive, nonredundant thermodynamic partition. In an approximately isothermal regime, causal accumulation of the total irreversible power defines a unique positive dissipative-action scale, ${\hslash }_{\mathrm{B}}$. This act-dependent scale sets the resolution of distinguishable functional realizations, while closure defines the domain on which a global additive action invariant can be constructed. If the nontrivial closure spectrum has a least positive action scale, its admissible values form an exact lattice. Quantization in biological dissipative-action units occurs only when this primitive closure scale saturates the independently constructed thermodynamic resolution, so that the spectrum is resolved in integer multiples of ${\hslash }_{\mathrm{B}}$. Coherent functional modes are the closure-compatible, persistent, and action-resolved realizations of these discrete classes. The theory predicts clustering of completed trajectories in actionlike observables and separates state correlation, hidden dissipation, and information-geometric distinguishability from the exact composability of irreversible physical cost.