Percolation of fine particles (fines) in a static bed of larger particles is central to many industrial and natural processes. Noncohesive fines either pass through the bed or become trapped depending on multiple factors including particle sizes, friction and restitution coefficients, and size polydispersity. Here, we consider the additional factor of cohesion in the single-particle limit. We use the discrete element method to simulate gravity-driven percolation of non-self-interacting cohesive fine particles through a static bed of randomly packed large particles; fines interact with bed particles but not with each other. A large-to-fine particle diameter ratio of seven geometrically permits noncohesive fines to pass the narrowest pore throats formed by the large particles so they can freely percolate. However, sufficiently large cohesion and friction lead to nongeometric trapping. Fines are trapped when they fail to rebound after a collision, due to large cohesion, low restitution, and low collision velocity, and any subsequent rolling or sliding is insufficient to cause detachment. This establishes a sequence of local interactions—collision, adhesion, and postcontact motion—that governs the ultimate fate of a fine particle. A collisional model that incorporates a trapping probability per collision and a collision frequency predicts the trapping distance in the regime dominated by collision-induced trapping. For nonrebounding collisions, frictional effects are enhanced by cohesion and, when large enough, prevent the fine particle from subsequently detaching. A static equilibrium condition based on force balance predicts whether a fine particle remains stationary after contact. These results show that percolation of cohesive fine particles in the single-particle limit is not determined by geometric accessibility alone, but also by particle-scale interaction dynamics that can override geometric expectations.

Neural-network quantum states have been developed as an efficient method for solving quantum many-body problems, not only in lattice systems but also in systems of particles in continuous space. Here, we apply this approach to strongly interacting few-body problems in continuous space at unitarity: the Efimov states and associated few-body bound states. We extend the previous work [J. Phys. Soc. Jpn. 87, 074002 (2018)], in which the ground states of few-boson systems were obtained, to the first excited states with a projection method, and also to a mass-imbalanced fermionic system consisting of two identical fermions and a third particle. The obtained energies of the ground and first excited states of these systems agree well with previously reported results. Furthermore, the proposed approach also reproduces the discrete scale invariance between the ground and first excited states and the critical-mass behavior in mass-imbalanced fermionic systems.

We extend the concept of polygonal resonators to curved spaces through a unified model governed by a joint curvature parameter. This framework uncovers unique dynamics, including dissipative states defined by hyperbolic fixed points that yield phase diagrams in the curvature space. We find that stability transitions across geometric boundaries are characterized by sharp declines in the quality factor, a manifestation of chaotic instability that is overcome in the wave regime, highlighting the wave nature of wave chaos. A key discovery is the symmetry-driven asymptotic behavior in geodesic-sided cavities, revealing a profound connection between physical dynamics and underlying geometry. We also introduce a “non-Euclidean unit circle” as a general shape-invariant platform for optical simulation. These findings provide a robust framework for studying non-Euclidean wave chaos and point to strategies for designing integrated photonics with enhanced degrees of freedom.

X-ray free-electron lasers (XFELs) provide ultrashort, high-brightness pulses for probing matter on femtosecond and attosecond timescales, but their stochastic shot-to-shot fluctuations make x-ray absorption spectroscopy (XAS) challenging, as both incident and transmitted spectra are required to be measured on the same shot. Conventional approaches that rely on averaged reference spectra have limited accuracy, and full-shot characterizations are impractical. Here, we demonstrate a machine learning approach to bypass these limitations. An artificial neural network predicts the incident spectral shape from recorded diagnostics, effectively providing the equivalent of simultaneous incident spectrum measurements based on recorded machine diagnostics. Applied to XAS of Mylar at the oxygen K-edge, the method improves the accuracy in the pre-edge region ($<530.5\text{eV}$), reducing the root-mean-squared error from $0.0415$ to $0.0082\text{OD}$ (optical density). This approach remains highly robust with small target datasets and reduces experimental acquisition time, offering a strategy for more accurate XAS measurements in XFEL experiments where noninvasive, single-shot monitoring of the incident spectrum is not feasible.

We reveal interference patterns in photo-double-ionization spectra, produced by the interaction of argon atoms with an extreme-ultraviolet light wave packet in the form of a double pulse with variable pulse separation. These interference features provide direct evidence for nonseparable two-electron wavefunctions, an insight previously deemed beyond the reach of electron spectroscopy. This advance leverages the recently uncovered longitudinal coherence of undulator radiation, opening new avenues for attosecond-scale quantum interference experiments. The present undulator-based methodology is straightforwardly extendable into the x-ray regime, potentially offering a platform for investigating multielectron correlations associated with core-hole creation and decay.

Predicting the nematic alignment of nonspherical particles in sheared granular flows is essential for understanding the rheology, packing, and constitutive response of dense particulate media. While macroscopic fabric is typically attributed to complex multibody interactions, stress transmission, and dissipative collisions, empirical observations reveal that the steady-state nematic order parameter $S_2$ depends primarily on particle aspect ratio and remains remarkably insensitive to shear rate and interparticle friction. Here, we show that this leading-order alignment emerges directly from single-particle boundary geometry without resolving dynamical equations of motion. Assuming uniform contact probability along a particle perimeter, we derive an analytical transform linking local boundary curvature $\kappa \left(\theta \right)$ to the distribution of contact normals, $P\left(\theta \right)\propto 1/\kappa \left(\theta \right)$, which in turn geometrically constrains the phase space of admissible particle orientations. This minimal framework accurately predicts the magnitude of $S_2$ across the full continuum of aspect ratios for smooth ellipsoids as well as the singular limit of faceted rectangles and cylinders. Our analytical predictions capture the envelope of three-dimensional discrete element simulations and match laboratory measurements on sheared rice grains and glass cylinders across decadal variations in shear rate. By identifying particle geometry as the primary control parameter for granular alignment, this work provides a first-principles physical foundation for geometric saturation at the critical state, establishing a universal baseline upon which dynamical and frictional effects act as secondary modulations.

Nonreciprocal interactions are known to produce distinctive dynamics in active matter. To shed light on how the stationary state of such systems is affected by breaking reciprocity, we consider interacting particles propelled by persistent noise, where reciprocity is broken by a transverse force perpendicular to the gradient of the interaction energy. Focusing on the steady-state distribution of positions, we show that the nonreciprocal coupling helps keep the system at its stable configurations. Specifically, we demonstrate this effect for a variety of active systems whose stable configurations are energy minima, finding that the nonreciprocal coupling stiffens springs, aligns spins, and improves associative memory. In contrast, the transverse force does not change the stationary distribution of positions at all when the noise is thermal. Preliminary simulations suggest that this nonreciprocal coupling plays a similar stabilizing role in other active systems, such as those exhibiting motility-induced phase separation, whose stable configurations are not energy minima.

Photoinduced phase transition (PIPT) involves substantial changes in both electronic and crystallographic structures caused by electron excitations. Because PIPT is ultrafast and exhibits inhomogeneous behavior, its fundamental processes, in particular, how the ultrafast dynamics of photoexcited electrons trigger lattice deformation and the timescales involved, remain poorly understood. In this study, phase transition and its spatial separation in organic charge-transfer complexes was investigated using photoemission electron microscopy with femtosecond pulsed laser excitation. First, the thermally induced phase transition was confirmed by imaging the morphology change and also by observing the change of spectral shape around the Fermi level. Second, the escape time of photoexcited electrons or the heat generated by the laser pulses is estimated by observing the morphology change depending on the laser repetition rate. Finally, the dynamics of the photoexcited electrons and the subsequent morphology change are directly visualized. The results show that an anisotropic morphology change is initiated following the accumulation of electrons in unoccupied states over approximately a picosecond.

Time-varying external fields can program dipolar interactions in soft matter systems. Continuously rotating fields are widely used to promote isotropic self-assembly, but finite relaxation of an induced dipole generically introduces a phase lag that can drive the spinning of both individual particles and clusters. Alternating rotating fields, which reverse the rotation direction every half cycle, suppress net spinning, but need not yield a truly isotropic time-averaged interaction when the dipole relaxes with a finite timescale $\tau$. Here, we propose an extended alternating rotating field in which the field sweeps beyond $2\pi$ within each half cycle to compensate for relaxation-induced undersampling of dipole orientations near the reversal direction. Using paramagnetic colloids under time-varying magnetic fields as a representative realization of a generic first-order relaxation model, we determine the half-cycle sweep angle ${\varphi }_{\mathrm{iso}}$ required to obtain an isotropic cycle-averaged pair potential. The isotropy condition collapses onto a master curve controlled by the dimensionless parameter $\tau \omega$, and we derive accurate analytical approximations in the experimentally important small-$\tau \omega$ regime. We also analyze the residual anisotropy under conventional alternating rotating-field protocols and show that its nonmonotonic dependence on $\tau \omega$ underlies the single-peaked ${\varphi }_{\mathrm{iso}}\left(\tau \omega \right)$. Because the theory assumes only first-order relaxation, the framework applies broadly to field-responsive soft matter beyond magnetic colloids. It enables switching between isotropic and anisotropic cycle-averaged pair interactions while suppressing net rotation and provides a route to infer relaxation times.

We show that anisotropic spin interactions do not merely break spin-space group (SSG) symmetries, but instead twist them through cohomology invariants, yielding symmetry classes beyond subgroups of $\mathrm{O}\left(3\right)\times Isom\left({\mathbb{R}}^3\right)$. This requires redefining the spin-only group ${\mathcal{S}}_0$ in terms of proper spin rotations. Based on this unitary ${\mathcal{S}}_0$, we formulate a twisted SSG (tSSG) theory that captures the complete set of spin-space symmetries. We then study a spin-1 model with tSSG symmetry using linear flavor wave theory and find ${\mathbb{Z}}_2$ topological quadrupolar excitations defined on a spin Brillouin Klein bottle. Specifically, the quadrupolar excitations possess a momentum-space glide-reflection symmetry and the edge states exhibit a nonlocal momentum twist. These results establish the symmetry language required for interacting spin systems, whether realized in magnetic materials or on programmable quantum simulators, and open a route to unconventional magnetism.

Efficient optomechanical cooling typically requires high photon occupancy to maximize cooling power, a constraint that generally limits the degree of coherent quantum control available in the few-photon regime. Here, we investigate this trade-off by considering a strongly nonlinear cavity operated as a discrete quantum system. In the weak-coupling limit, we derive a general connection between the optomechanical damping rate and the cavity's dressed-state manifold. This framework reveals that the damping rate (determined by the population imbalance across dressed states) is directly tunable via the coherent manipulation tools, which are standard in circuit quantum electrodynamics. We illustrate this framework using a Josephson photonics architecture, where a dc-biased junction induces a photon blockade that truncates the cavity to an $N$-level system. By sacrificing raw cooling (or heating) power, this platform enables full quantum mechanical control over optomechanical properties, offering a versatile avenue for the quantum manipulation of mechanical modes.

The Eigen model is a prototypical toy model of evolution that is synonymous with the so-called error catastrophe: When mutation rates are sufficiently high, the genetic variant with the largest replication rate does not occupy the largest fraction of the total population because it acts as a source for the other variants. Here, we show that, in the stochastic version of the Eigen model, there is also a fidelity catastrophe. This arises due to the state-dependence of fluctuations and occurs when rates of mutation fall beneath a certain threshold, which we calculate. The result is a type of noise-induced multistability in which the system stochastically switches between short-lived regimes of effectively clonal behavior by different genetic variants. Most notably, when the number of possible variants—potentially $\sim 4^L$, with $L\gg 1$ the length of the genome—is significantly larger than the population size, there is only a vanishingly small interval of mutation rates for which the Eigen model is neither in the fidelity- nor error-catastrophe regimes.

We investigate the singular behavior of information flow near the Hopf bifurcation point by analyzing the learning rate as a specific measure of information flow. We study the Brusselator, a model system exhibiting the Hopf bifurcation. We first numerically compute the learning rate in the stationary regime and find that it remains finite even in the deterministic limit, suggesting that the learning rate can be quantified in deterministic dynamics through probabilistic descriptions. Linear analysis accurately reproduces the numerical results in the stationary regime but fails near the bifurcation point. To overcome this limitation, we employ the singular perturbation method, well known in deterministic bifurcation theory, and carry out the corresponding calculation explicitly for a stochastic system described by a Langevin equation. This allows us to evaluate the learning rate near the bifurcation point. We then theoretically derive its nonsmooth behavior in the deterministic limit. Our results demonstrate that changes in dynamical behavior are reflected in the learning rate and provide a basis for analyzing information processing in biochemical oscillations.

The evolution of a charged lepton in the field of an electromagnetic plane wave can be described as a superposition of Volkov states. Here, we demonstrate that imposing specific momentum correlations among Volkov states produces a spatiotemporally structured wave packet whose probability-density peak, evaluated on fixed-light-cone slices, travels at an arbitrary tailored velocity. This velocity can be chosen independently of both the field amplitude and the velocity expectation value. The imposed momentum correlations modify the expectation-value trajectory, providing a measurable signature of the arbitrary velocity within a physical observable.

The spatial distribution of cloud droplets is relevant for various microphysical processes, such as condensational growth, collision coalescence, riming and aggregation, and radiative transfer. Cloud droplets and other hydrometeors exhibit clustering at dissipation scales and above due to turbulence-induced preferential concentration. Here, we investigate the role of entrainment in modulating the scale-dependent clustering of cloud droplets and explore how this mechanism differs from inertial clustering. We have performed experiments in the Pi convection-cloud chamber, and imaged 2D droplet fields in the cloud-top region where dry-air entrained through an open cylindrical flange leads to an entrainment-mixing process. Captured images are processed to retrieve instantaneous two-dimensional velocity fields using particle image velocimetry, scale-dependent clustering is explored using the radial distribution function, and spatial nonuniformity is quantitatively identified through the Kolmogorov-Smirnov test. The analysis reveals that the observed droplet clustering is scale dependent and occurs intermittently with enhanced magnitude in the region influenced by entrainment. Furthermore, the strength of clustering and spatial nonuniformity increases with mean entrainment flow. Using a novel bias-mitigation method, we estimate that the strength of clustering (pair-correlation function) roughly doubles in the presence of entrainment (downdraft), compared to no-entrainment (updraft) regimes. Such an entrainment-clustering relationship could be used to help identify and decipher the role of entrainment in modulating localized clustering in natural atmospheric clouds.

Multiple phenotypic protein expressions arise from one genome represent variations in the protein relative abundance and their stoichiometry. A lack of definite compositional parts challenges the modeling of protein megacomplexes and cellular architectures. Despite the advance in protein structural predictions with Artificial Intelligence (AI), the mechanism of protein interactions and the emergence of megacomplexes they assemble remains unclear. Here, we present a statistical-physics framework of grand canonical ensemble to explore the protein interactions that drive the emergent assembly of a megacomplex using the observational mass spectrometry datasets including protein relative abundance and the cross-linked connections. Using chromatin remodeler megacomplex, INO80, as an example, we discovered a class of “divergent” protein that plays a critical role in orchestrating the assembly beyond nearest neighbors, dependent on the excluded volumes exerted by others. With the constraints of the excluded volumes by varying the volume fraction of INO80 subunits, these divergent subunits orchestrate and form clusters with selective components growing into configurationally distinct architectures. We propose a machinery view for the INO80 chromatin remodeler complex where each loosely associated subunits can be occasionally recruited for parts as attachment into a core assembly driven by excluded volumes. Our computational framework provides a mechanistic insight into taking the volume fractions as necessary physicochemical variables representing cell states to remodel the configurations of protein megacomplexes with structurally loose modules.

We present a theoretical scheme for a family of fast and high-fidelity two-qubit iswap gates between neutral atoms separated by more than $20\mu \mathrm{m}$, enabled by resonant dipole-dipole spin-exchange interactions between Rydberg states. The protocol harnesses coherent excitation-exchange-deexcitation dynamics between the qubit and the Rydberg states within a single and smooth laser pulse, in the presence of strong dipole-dipole interactions. We utilize optimal control methods to achieve theoretical gate fidelities and durations comparable to blockade-based gates in the presence of relevant noise, while extending the effective interaction range by an order of magnitude. This enables entanglement well beyond the blockade radius, offering a route toward fast, high-connectivity quantum processors.