Papers

The Earth's climate is an ensemble of interacting, spatially extended oscillatory media ('climate systems') whose slow-changing averages coexist with chaotic, high-frequency weather fluctuations in quasi-equilibrium. The limit of deterministic predictability (LDP) for any climate system is determined by its fastest-growing errors. However, recent findings show that the Indian Summer Monsoon Rainfall (ISMR) can be predicted up to 18 months in advance-far beyond its LDP. Using a model of two interacting oscillatory media, we show that this extended predictability arises from lag synchronization between ISMR and its predictor, the Global El Nino-Southern Oscillation (G-ENSO), to which it is strongly coupled. We introduce complex order parameters representing the internal dynamics of the two climate systems. Their spatiotemporal evolution is governed by coupled Complex Ginzburg-Landau Equations, producing aperiodic yet strongly correlated time series at long lead times. Our findings have far-reaching consequences in advancing seasonal prediction across climate systems.

Background and Objectives: Cryptogenic new-onset refractory status epilepticus (cNORSE) represents one of the most severe forms of status epilepticus, occurring in patients without prior neurological disease, and remaining of unknown aetiology despite extensive diagnostic evaluation. Emerging evidence supports a role for immune dysregulation in cNORSE; however, marked heterogeneity in inflammatory signatures has been reported, complicating the selection of targeted immunotherapies. Therefore, a critical need for tools facilitating the interpretation of cytokine panels exists. Methods: Building on the identification of distinct inflammatory groups of cNORSE patients using a graph clustering approach applied to a cohort of 62 patients with serum profiling of 96 cytokines, we tailored new models to quantify attribution probability to biologically validated clusters. Statistical assessment of the most informative model involved Monte-Carlo simulations and custom-developed parametric tests. Ultimately, we applied our framework to the implementation of a clinician-friendly interface for inflammatory profiling. Results: Our approach enables quick processing of several cytokine profiles, providing the most likely inflammatory cluster, associated attribution probability, and statistical confidence. For longitudinal assessments, the proposed method may also allow tracking the evolution of inflammatory trajectories over time. Conclusion: Systematic statistical characterization of the inflammatory heterogeneity in cNORSE requires the development of clinically actionable support tools. Our study offers a framework that may support personalized immunomodulatory strategies in cNORSE patients through clustering-based cytokine profiling.

Beam splitters allow us to superpose two continuous single mode quantum systems. To study the behaviour of beam splitters' strongly mode mixing dynamics we consider variable beam splitters acting on Wigner's phase space distribution, W , the evolution of which is governed by the continuity-equation {\partial \tau} W = - {\nabla} J. We derive the form of the corresponding Wigner current, J. J's form allows us to use a classical trajectories-approach to analyze the influence of the two modes on each other. We show that the dynamics for variable beam splitters amounts to a rotation confined within the plane of the two positions together with the same simultaneous rotation confined within the plane of the two momenta. In this way explicit and very transparent expressions for the rotated Wigner distributions and Wigner currents can be given in terms of classical trajectories. This helps us to gain deeper insights and perform geometrical analyses of the mixing of modes at beam splitters.

Selected configuration interaction methods achieve near-exact electronic structure calculations by iteratively constructing compact variational spaces, but their efficiency depends critically on the heuristics used to identify important determinants. Here, we introduce a data-driven selection framework that recasts determinant importance as a binary classification task and integrates a neural-network classifier into the iterative CI workflow through an active-learning loop. At each iteration, a random subset of candidate determinants is labelled via temporary diagonalisation, and the trained classifier guides selection of the remaining configurations. We demonstrate the utility of this framework for both classical and quantum CI methods by calculating the ground-state energy of a diatomic molecule. Our method achieves result parity with traditional configuration interaction methods at substantially lower computational cost: roughly a $\times 5$ reduction in memory and per-iteration cost for the classical cHCI variant, and convergence in markedly fewer iterations for the quantum-classical cSQD variant. These results establish classifier-assisted determinant selection as a lightweight, method-agnostic tool for compressing variational spaces and accelerating both classical and hybrid quantum-classical configuration interaction algorithms.

The Triangular mesh-based gyrokinetic code TRIMEG-C1 solves the gyrokinetic equations using the particle-in-cell scheme to simulate electromagnetic instabilities in tokamak plasmas. TRIMEG-C1 utilizes a high-order C1 finite element method, which captures the accurate physics with lower grid resolution than the C0 method. In this work, we focus on achieving a portable implementation on multiple graphics processing unit (GPU) architectures to accelerate the TRIMEG-C1 code for future physics studies. The OpenMP framework is chosen as the acceleration framework for GPU offloading on different hardware platforms, specifically, NVIDIA and AMD GPUs. The particle pushing procedure, as well as particle-to-grid operations have been adapted for GPU execution. A speedup of $\approx9$ for the particle pusher kernel is achieved on 2 AMD MI300A APUs (Accelerated Processing Unit) compared with 2 AMD 9754 CPUs. In addition, the efficiency of hybrid MPI-OpenMP offloading parallelization was assessed by oversubscribing GPU resources. The Ion Temperature Gradient (ITG) mode was simulated using the GPU implementation, and its correctness was verified by comparing the physics results in terms of the energy growth rate and the two-dimensional mode structures.

Photon-counting Computed Tomography (PCCT) is the most advanced Computed Tomography (CT) technology, offering significant improvements in image quality and diagnostic capabilities. However, since PCCT has only recently been adopted in the clinc, there are no publicly available PCCT image datasets for study. We therefore aim to synthesize PCCT spectral material-basis images from publicly available EID CT images. We propose a two-step deep learning model designed to synthesize photon-counting spectral material basis images from public Energy-Integrating Detector (EID) CT images. In the first step, we use a Denoising Diffusion Implicit Model (DDIM) to generate EID CT images from PCCT images. In the second step we use a U-Net with a Domain-Adversarial Neural Network to predict water and iodine maps from generated EID CT images. We also reconstruct basis images and virtual monoenergetic images (VMIs) from the predicted material-basis maps for evaluation. We evaluated the generated water and iodine maps as well as the 40 and 70 keV PCCT images in terms of Hounsfield Unit accuracy, modulation transfer function and noise power spectrum as well as qualitative image appearance. The reconstructed 40 and 70 keV PCCT images exhibit higher spatial resolution while preserving the anatomical structures and textures of the original EID CT images, thereby demonstrating the feasibility of the proposed approach. The proposed framework provides a feasible approach for synthesizing PCCT spectral material-basis images from conventional EID CT without requiring paired images. This method has the potential to provide large sets of synthetic training and evaluation data for PCCT algorithm development in data-limited environments.

An analytical formulation of the fluid-structure interaction of a flexible foil driven by an oscillating force actuating on its elastically mounted leading edge, so that it can heave, pitch and deform passively with the hydrodynamic forces, is used to investigate the aquatic locomotion of a body, responsible for the whole drag and thrusted by the oscillating flexible foil. The small-amplitude theoretical model is validated with previous theoretical and experimental results for a body propelled by a rigid plate oscillating with a prescribed heaving motion and passive pitch. The inclusion of passive heave and deformation allows to expand the parametric ranges for optimal self-propulsion conditions in terms of length travelled by flapping cycle (stride length) and locomotion efficiency. In addition to the known optimal locomotion condition localized near the resonance of the torsional spring on which the foil is elastically mounted, which here is modulated by its coupling with the resonances of the translational spring and of the structural deformation of the foil, another even better local optimal locomotion condition is found near the translational spring branch of the elastic support resonance that occurs at lower stiffnesses of both springs. Unlike the local maximum of efficiency close to the natural frequency associated with the torsional spring branch, which increases with the stiffness of the foil, being the highest for a rigid foil, the larger local maximum associated with the translational spring branch increases as the stiffness of the foil decreases.

Finite-size scaling and the renormalization group form the central toolkit for analyzing quantum phase transitions (QPTs). In this Letter, we introduce a novel Hilbert-space tailoring scheme to probe quantum critical phenomena. Applied to the second-order QPT of the one-dimensional (1D) XY model, our method yields precise critical points and exponents on lattices containing merely 50 unit cells. We further establish the universal applicability of this framework via investigations of the Berezinskii-Kosterlitz-Thouless transition in the 1D XXZ chain: critical parameters are recovered with as few as 12 lattice sites. This technique may open an alternative, efficient route to universally characterize QPT across many-body lattice systems.

This paper addresses non-physical artifact, specifically spontaneous droplet shrinkage, in Cahn-Hilliard-based simulations of immiscible two-phase microflows. That artifact arise from the energy functional's preference for reduced interfacial area, leading to shifts in equilibrium phase fractions away from the physical bounds of 0 and 1. A curvature-dependent correction is proposed that shifts the double-well potential to counteract this drift. This method is validated on the following test cases: an isolated droplet in a cubic cavity, drainage in a synthetic porous medium, and flow through a T-shaped junction. Results demonstrate significant suppression of phase fraction overshoot/undershoot and improved droplet volume conservation.

Hydrodynamic memory governs Brownian motion over a broad range of timescales, from acoustic wave propagation at short times to diffusive relaxation at long times. While confinement-induced corrections to Brownian diffusion are well established, how confinement modifies the full hydrodynamic response remains less explored. In this Letter, we use molecular-dynamics simulations of a neutrally buoyant colloidal particle in an explicit solvent to resolve the velocity autocorrelation function across a broad hydrodynamic spectrum. In the bulk, the simulations recover compressibility, added mass, the hydrodynamic long-time tail, and Stokes-Einstein diffusion without adjustable parameters. Near a rigid wall, the velocity correlations become anisotropic, their algebraic tails are modified, and the diffusion coefficients are reduced. Most importantly, the short-time dynamics reveals a pronounced enhancement of the effective added mass as the wall is approached. As such, the velocity autocorrelation function appears as a central quantity to bridge the zero-frequency mobility and the high-frequency inertial behaviour of a confined Brownian particle.

Principal component analysis (PCA) is widely used to characterize collective protein motions from molecular dynamics (MD) simulations. While PCA identifies the dominant modes of structural fluctuation, it does not reveal whether different principal components (PCs) causally influence each other. Here, we investigate this question using a recently introduced causal-discovery framework [Del Tatto et al, PNAS 2024], which allows to infer putative causal asymmetries between high-dimensional time series. We apply this approach to long-timescale MD trajectories of two proteins. By analyzing relationships among PCs, we construct directed networks describing how PCs influence one another across time scales. These directional relationships, whose existence is a necessary condition for the presence of a causal link, are not captured by conventional covariance-based analyses and provide information that is complementary to PCA and Time-lagged Independent Component Analysis (TICA). Our results suggest that our causal inference approach can uncover previously hidden aspects of the dynamical organization of protein motions and offer a new perspective on this very popular class of collective variables.

We compare two geometric stochastic frameworks for the two-dimensional Euler equations, being the circulation-preserving stochastic advection by Lie transport (SALT) and the energy-preserving stochastic forcing by Lie transport (SFLT) approaches. While preserving both circulation and energy is ideal, their simultaneous conservation restricts perturbations to a stochastic reparametrization of time. Consequently, a fundamental choice must be made between preserving structure or the kinetic energy. Analysis reveals that SALT is significantly more sensitive to high-frequency flow components, with noise effects scaling by $| \bk |^2$ relative to SFLT. This suggests that SALT acts as a localized perturbation sensitive to sharp gradients, while SFLT behaves as a more regularized global forcing. Numerical experiments on a traveling dipole, vortex merger, and forced-damped turbulence confirm that SALT introduces uncertainty localized near dynamically active vorticity gradients, whereas SFLT produces a more diffuse variance field spread across the domain. These results illustrate how the choice of geometric invariant fundamentally determines scale-sensitivity and spatial distribution of modeled uncertainty in vortex dynamics.

Instantaneous Quantum Polynomial-time (IQP) circuits are promising candidates for near-term quantum computing due to the conjectured classical hardness of their sampling task and their shallow, highly parallelizable structure. Despite extensive studies of their complexity-theoretic properties, the role of circuit connectivity in their trainability as variational ansatze remains poorly understood. In this work, we present an investigation of IQP circuits for Hamiltonian optimization that traces a mechanistic chain linking circuit connectivity, entanglement generation, and gradient scaling: increasing connectivity drives the circuit output toward Haar-like statistics and volume-law entanglement, and this increase in entanglement is consistently associated with an exponential decay of the gradient variance and degraded optimization performance, while sparser architectures retain stable gradients at the cost of global expressibility and entanglement capacity. This connectivity-entanglement-gradient mechanism produces a nontrivial connectivity-trainability trade-off, demonstrating that effective IQP-based Hamiltonian optimization requires a balance between global expressibility and trainability rather than maximal expressivity alone. In particular, on our benchmarks, a circuit generating no entanglement already matches the optimization performance of more highly connected circuits, and a circular topology retains entanglement capacity while preserving trainability, making it a broadly effective choice among the architectures considered.

Laser speckle is a powerful tool for precision metrology that enables highly sensitive measurements of light sources and subtle environmental perturbations. Many applications require operation in photon-limited regimes, for example when using low-power illumination or in spectral regions where sensitive detectors are unavailable. In these conditions, the structured speckle pattern that encodes the signal becomes challenging to disentangle from measurement noise, severely degrading performance. Here, we introduce a denoising framework to separate measurement noise from the underlying speckle structure in low-signal data. Using a hybrid pre-training and experimental fine-tuning strategy, the model is adapted using a small experimental dataset and integrates directly with existing speckle metrology pipelines. Applied to femtometre-scale wavelength sensing using an integrating sphere, the approach reduces root-mean-square error in low-signal conditions by up to 72% and enables accurate reconstruction where conventional speckle metrology fails.

Atomically sharp edges are essential for future high-index nanophotonic structures, yet conventional lithography and dry etching methods inevitably introduce edge roughness that limits optical confinement and reproducibility. Recently, anisotropic wet etching of multilayer van der Waals crystals, such as transition metal dichalcogenides (TMDs), has enabled crystallographically defined, atomically sharp zigzag edges, eliminating the edge-roughness problem. However, the process is intrinsically limited to confined geometries such as isolated triangular or hexagonal features dictated by crystal stacking symmetry. Here, we demonstrate a lithography-guided anisotropic etching framework that drives TMDs etching beyond isolated confined geometries by enforcing controlled interaction of neighboring etched nanoholes regions. In multilayer 2H-WS2, merging of anisotropic etch fronts enables sustained long-range propagation of zigzag facets, introducing a previously inaccessible 180-degree edge alignment and a crystallographically defined design space combining 120-degree and 180-degree junctions. Using this approach, we fabricate extended nanophotonic structures with ultrasharp sidewalls, including sub-100-nm-gap one-dimensional gratings, waveguides, defect-engineered photonic cavities, angle programmed photonic lattices, and diffractive zone plates. Back-focal-plane reflection spectroscopy of atomically sharp 1D periodic 2H-WS2 gratings demonstrates their photonic functionality, revealing symmetry-protected bound states in the continuum (SP-BICs) and strong exciton-photon coupling in multilayer WS2. Finally, we fabricate ultrathin, ultranarrow, and ultralong nanoribbons with record-high aspect ratios. Together, these results demonstrate edge merging as a generic route to fabricate edge-defined, atomically sharp nanophotonic and nanoelectronic architectures in layered van der Waals platforms.

We report the enhanced sensitivity of the Rydberg microwave (MW) receiver by exploiting the Doppler effect in a vapor cell. A two-photon Rydberg ladder scheme is implemented via the co-propagation of probe and coupling lasers, which enhances the Doppler effect. When an MW field is applied, microwave dressing modifies the velocity-dependent resonance condition, enabling stronger contributions from atoms with non-zero velocities and leading to an enhancement of the EIT transmission. Based on this mechanism, we achieve a sensitivity of $35.1\ \mathrm{nV\ cm^{-1}\ Hz^{-1/2}}$ using the heterodyne technique, which is 1.5 times better than that obtained in the counter-propagating configuration. Meanwhile, the required local oscillator (LO) field is reduced by a factor of 17.6 compared with the counter-propagating configuration, which is advantageous for applications requiring minimal radiation and low power consumption. Moreover, the co-propagating configuration is more amenable to integration or portable sensing platforms because multiple laser fields can be delivered through a single optical fiber.

The quest for the origin of life, especially in the metabolism-first scenario inspired by the celebrated Miller-Urey experiment, has triggered a research program dedicated to studying the emergence of complex dynamical behaviors in large chemical mixtures. Though autocatalysis, understood as the capacity of a reaction network to grow exponentially, has been recognized as a potential driver of instability and multistability, no quantitative theory has yet emerged, partly because of the lack of available kinetic data. We introduce a computational tool for large chemical reaction networks based on a scale-splitting algorithm inspired by Wilson's renormalization group. We focus on dilute regimes, where species of interest have low concentration, non-unimolecular reactions may be neglected, and the dynamics is close to linear. Depending on parameter thresholds, such networks can exhibit autocatalytic behavior. Our algorithm takes as input a network structure and outputs (1) a simplified effective graph containing the dominant reaction pathways, obtained through recursive coarse-graining; and (2) analytical formulas for the dynamics in terms of kinetic rates, called hierarchical formulas. These formulas are approximate but interpretable, accurate when scale separation is effective, and provide a reliable multiscale description of the dynamics. Their domains of validity define kinetic phases, each typically associated with a distinct pattern of chemical composition. We show on a simple example that this approach enables fast and reliable inference of kinetic rates from concentration time series. Hierarchical formulas have been implemented as a Python package and are illustrated on a simplified model of the formose reaction.

In this work, the ground-state energy of the lithium atom is systematically investigated using both time-independent perturbation theory and the variational method to provide a comprehensive pedagogical analysis of many-body atomic systems. The unperturbed Hamiltonian is initially constructed by neglecting electron-electron interactions, treating the system as three independent hydrogen-like electrons to yield a zeroth-order energy baseline of -275.51 eV. The antisymmetric fermionic nature of the exact wave function is rigorously enforced through the Slater determinant formalism. First-order perturbation theory is applied to evaluate static inter-electronic repulsion using exact Coulomb and exchange integrals, refining the energy state to -192.01 eV. To account for dynamical electronic correlation, second-order perturbation theory is computed numerically for virtual single-electron s-orbital transitions, leading to a total perturbative energy of -196.36 eV. A brief discussion of two-electron excitations is also included to encapsulate further physical realism within the framework. Furthermore, a non-orthogonal two-parameter variational approach is employed to model the shell-specific shielding effect. By optimizing the effective nuclear charges, the variational method establishes a superior upper bound energy of -201.187 eV. The results of both methods are comprehensively contrasted against each other and the reference baseline to provide critical insights into the nature of electron correlation and screening in multi-electron atoms.

Recent advances in dynamical low-rank approximation (DLRA) have demonstrated its effectiveness in high-dimensional simulations. However, existing DLRA algorithms still face significant challenges when handling systems that involve complex collision terms, including the pseudo-differential operator (${\rm \Psi}$) in the Wigner equation, a representative operator characterized by nonlocality. It is deserving to carry out a series of works to develop the DLRA algorithms for solving the Wigner equation. As the first step in this series of works, we propose an efficient DLRA algorithm for the Wigner equation, using a separable decomposition of the difference potential. We combine this separable assumption with two often-used truncations of ${\rm \Psi}$, namely $\mathcal{K}$-truncation and $\mathcal{Y}$-truncation, to obtain a kind of separated representation of ${\rm \Psi}$. Complexity analysis and several challenging experiments, including harmonic oscillators, Gaussian barrier scattering, electron-electron scattering, and a Helium-like system, all of which satisfy the separable assumption, confirm that the proposed DLRA algorithm has significant advantages, achieving a reduction in computational effort by one to two orders of magnitude in both runtime and memory requirements compared to the full-grid approach. It is worth noting that, even in the absence of a predetermined low-rank structure for the solution, DLRA can still serve as a numerical scheme that balances efficiency and accuracy.

We demonstrate that the Talbot length of periodic wave packets, long believed to be solely determined by their periodicity, is strongly affected by the source coherence in the nonlinear propagation regime. We reveal that reducing the source coherence – and consequently the speckle size of a periodic field – shortens the Talbot length and significantly improves the quality of the recurrent Talbot images, despite a fixed source periodicity. This effect arises from coherence-mediated nonlinear mode coupling, which alters a phase synchronization condition undergirding the wave packet revivals. Our findings expose a hidden role of coherence in governing Talbot revivals of random waves in the nonlinear regime, crucially informing the understanding and application of the Talbot effect in realistic media.

Accurate electric field estimation is critical for the design and optimization of Micro Pattern Gaseous Detectors (MPGDs). The nearly exact Boundary Element Method (neBEM) offers high precision field computation but is limited by long CPU runtime arising from its complex analytical formulations. This work presents a comprehensive optimization of the neBEM solver, focusing on a hybrid hardware acceleration strategy using OpenMP for multi-core CPUs and GPU acceleration using NVIDIA's CUDA. A key contribution is the new implementation of a dynamic space charge calculation, which has also been designed to be accelerated by CUDA. This primary acceleration is complemented by enhanced algorithmic optimizations to reduce the complexity of the problem. The proposed implementation achieves substantial speedups while preserving inherent accuracy of the solver. Simulations on staggered thick Gas Electron Multiplier geometries demonstrate agreement with other commercially available field solvers, verifying the fidelity of accelerated neBEM. Benchmarking tests show a significant speedup, enabling rapid yet precise simulations for complex MPGD configurations. These improvements make GPU-accelerated neBEM a practical tool for large-scale detector simulation.

The arm length stabilisation system is employed in gravitational-wave detectors to reduce the velocity of the mirrors such that the arm cavities can be brought onto resonance in a controlled manner required to attain the detector operating point. For future upgrades of current gravitational wave detectors such as A#, which will incorporate AlGaAs/GaAs coatings, the current frequency-doubled arm length stabilisation system is unsuitable due to excessive absorption of the frequency-doubled 532nm beam by the AlGaAs/GaAs coating. We propose a novel multi-wavelength arm length stabilisation scheme that uses both frequency-doubled and frequency-tripled beams. The 1596nm auxiliary locking beam is outside the absorption bands of AlGaAs/GaAs coating. It is frequencytripled to 532nm and phase-locked with the 1064nm science laser through its second harmonic at 532 nm. In a tabletop setup, we experimentally demonstrated the stable cavity detuning and robust cavity locking transition by controlling the 1596nm laser and 1064nm laser phase locked loop. This demonstration confirmed that the proposed novel arm length stabilisation scheme is compatible with future upgrades or third-generation gravitational wave detectors that use AlGaAs/GaAs-coated test masses.

Traditional time-domain prediction of vessel motions in irregular waves usually relies on superposing responses from many regular-wave components, which is computationally expensive for long-duration simulation and real-time applications. This issue is particularly relevant to unmanned surface vehicles (USVs), for which efficient and realistic motion prediction is needed for seakeeping assessment, simulation-based testing, and control-system development. This study applies an impulse response function (IRF)-based time-domain framework to predict vessel motions in short-crested irregular waves. Froude-Krylov, diffraction, and radiation loads are obtained from frequency-domain analysis and transformed into the time domain. Instantaneous responses are then evaluated directly through convolution-based force reconstruction, reducing the need for repeated regular-wave simulations. Weak nonlinear restoring effects are included by instantaneous wetted-surface pressure integration, and directional wave spectra are used to represent realistic sea states. The framework is validated against model-test measurements of an offshore supply vessel in long-crested beam irregular waves and full-scale measurements of a USV operating in real sea conditions. Predicted significant amplitudes, mean zero-crossing periods, standard deviations, and motion time histories agree well with measurements. The effect of directional-spectrum discretization is also examined. Results show that motion amplitudes are moderately sensitive to directional resolution, whereas motion periods are relatively insensitive. A 30 deg directional interval provides a practical balance between prediction accuracy and computational cost. The proposed framework offers an efficient tool for high-fidelity time-domain prediction of USV motions in realistic directional irregular seas.

Diffusive-convective (DC) staircases in the ocean are observed across a wide range of settings, but their formation, structure, and persistence are not fully understood. Theories for DC staircases are reviewed to identify mechanisms governing their development and evolution. Staircase evolution through layer merging and possibly interface splitting, including the relationship to background turbulence, is assessed. Oceanographic examples illustrate the variety of settings in which DC staircases are found, and how they can persist under weak turbulence but are disrupted when turbulence becomes sufficiently strong. Key open questions are identified, highlighting the challenge of linking small-scale processes to the large-scale coherence and persistence of DC staircases in the ocean.

Thermal control is one of the important technologies for fundamental science and thermal management. Among them, thermal oscillators have been in demands in the field of materials science and device application. In general, flexible frequency, amplitude, and waveform are needed for useful thermal oscillator, and the stability of the average temperature is also highly required. However, thermal oscillators based on an AC-current-driven heater require complicated control of input power to achieve the above-mentioned flexibility and stability of the outputs. Here, we demonstrate that magnetically-driven thermal oscillators fabricated using a metal-superconductor (Cu-Pb) joint achieve those requirements easily by tuning the applied magnetic field (H). A DC-current-driven heater is attached on the metal (Cu) side, and the superconductor (Pb) edge is attached to thermal bath. We use a sharp and huge change in thermal conductivity at the superconducting transition of the Pb wire to generate thermal oscillation at the Cu-wire side. A sine-shaped thermal oscillation with an amplitude of 180 mK and a frequency of 0.17 Hz is observed with highly stable average temperature. Furthermore, a larger amplitude is achieved in a square-shaped oscillation with a larger H amplitude. Our thermal oscillator with temperature stability, large amplitude, and relatively high frequency will work as a flexible AC heat source at cryogenic temperatures.

In many social communities, individuals can simultaneously trust and distrust the same source, a feature standard opinion-dynamics models often ignore. We formalize this ambivalence with Gated Network Credence, in which each directed relationship encodes distinct trust and distrust assessments. These jointly determine "net trust" – the willingness to rely on a source – and "uncertainty" – the conflict between trust and distrust within the same relationship. Agents update beliefs only when net trust exceeds a threshold and uncertainty falls below another, yielding an effective influence graph whose topology drives long-run belief states. Sweeping both thresholds uncovers four regimes – Accommodating, Evaluative, Friction-averse, and Guarded – that differ in openness to trust and conflict. Under the modeled family of trust-distrust coupling and prestige-biased trust allocation, the model exhibits a hub-periphery reversal: in the Evaluative regime, high-degree agents contribute more strongly to the limiting belief, whereas in the Friction-averse regime, stringent uncertainty filtering can disproportionately remove hub-directed influence channels and reduce their contribution to the collective equilibrium. This conditional pattern recurs across synthetic network topologies and two empirical network evaluations. Our results show that belief dynamics depend not only on network structure but also on how relational ambivalence between trust and distrust gates interpersonal influence.

Plasma wakefield acceleration in hollow plasma channels has emerged as a promising approach for positron acceleration, since an electron beam can drive wakes with a transversely uniform accelerating field and no intrinsic defocusing force for positrons. Recently, it was proposed that a transversely asymmetric electron beam can excite quadrupole-dominated wakefield in a hollow channel, enabling the formation of accelerating and focusing fields suitable for positrons. However, the self-consistent evolution and stability of such asymmetric drivers, which are crucial for sustaining a usable wake over long distances, remain insufficiently understood. In this work, we investigate the evolution modes of wakefield driven by asymmetric electron beams in hollow plasma channels using fully three-dimensional particle-in-cell simulations. We identify two distinct unstable scenarios: a reversal of quadrupole field polarity and continuous penetration of the driver into the plasma wall. By analyzing the transverse dynamics of the driver and the restoring forces provided by the channel ions, we establish simple physical criteria that ensure stable propagation. These results clarify the fundamental constraints governing asymmetric-driver evolution, provide practical guidance for realizing long-lived, quasisteady wakes in hollow plasma channels, and indicate that the scheme can preserve mm$\cdot$mrad-level normalized positron emittance.

This work presents a machine learning framework that leverages an autoencoder (AE) for the efficient modeling of FinFET. We first calibrated a BSIM-CMG model to generate a dataset of current-voltage (ID-VG) characteristics. This data was used to train an autoencoder that compresses full I-V curves into a low-dimensional latent space, which intrinsically encodes key device physics. A key innovation is the explicit incorporation of parameter such as drain to source voltage (VDS) as an input feature, enhancing the model ability to capture bias dependent variation. The trained model successfully reconstructs full I-V curves and directly extracts critical device metrics including threshold voltage (VTH), subthreshold slope (SS), and peak transconductance (gm). This approach demonstrates that data driven compact models, built from actual characterization data, can achieve high accuracy with minimal training data, providing a powerful tool for rapid device characterization, modelling and circuit level simulation.

Nanofluidic ionic oscillators based on the dynamic regulation of surface charges hold great promise for neuromorphic computing, biosensing, and ionic circuits. Here, by dynamically adjusting the local charge inversion on pore walls, we present a simple and effective strategy to achieve periodic current oscillations by harnessing the transient adsorption and desorption of Ca2+ ions in cylindrical mesopores under concentration gradients. Based on the combined precision current measurements and multiphysics simulations, we demonstrate that local overadsorption of Ca2+ ions may induce asymmetric bipolar charge distributions along the pore axis, which periodically reverses the direction of electroosmotic flow and modulates the local ion concentration inside the pore, generating highly regular current oscillations. Notably, both the oscillation frequency and the open-state probability of the pore vary nearly linearly with the applied voltage. Moreover, under dynamic voltage scanning, the system exhibits typical memristive hysteresis, and the switching between the open and closed states is highly reproducible. This work not only reveals the dynamic, heterogeneous surface charge regulation by divalent ions, but also provides a simple, material agnostic method for constructing ionic oscillators and memristors based on dynamic adsorption/desorption of multivalent ions.

We demonstrate broadband dual-comb cavity mode dispersion spectroscopy using mode-locked erbium-fiber frequency combs and a length-tunable optical cavity. The flat baseline and low-noise characteristics of cavity-mode-dispersion spectroscopy, combined with the broadband coverage of fiber frequency combs, provide highly precise spectral profiles that are difficult to obtain with conventional dual-comb spectroscopy. We measured the entire $\nu_1 + \nu_3$ band of acetylene in the $1.5~\mathrm{\mu m}$ region. The simultaneous fitting of multiple transitions agreed well with the measured spectra, yielding a relative standard deviation of 0.27 % for the retrieved acetylene pressure and a spectral fluctuation corresponding to an absorption coefficient of $1.4\times 10^{-6}~\mathrm{cm}^{-1}$. These results reveal the high precision achievable with the present method. Further sensitivity improvements are expected with a higher-finesse cavity.