Papers

Quantum optimal control is central to designing spin manipulation pulses. Gradient-based pulse optimization can be facilitated by either accelerating gradient evaluation or enhancing the convergence rate. In this work, we accelerated single-spin optimal control by combining the finite element method with the method of moving asymptotes. By treating discretized time as spatial coordinates, the Liouville - von Neumann equation was reformulated as a linear system, efficiently yielding a joint solution of the spin trajectory and control gradient. The method of moving asymptotes, relying on the ensemble fidelities and gradients, achieves rapid convergence for a target fidelity of 0.995.

We report cutting-edge performance results via mixed-precision spin adapted ab initio Density Matrix Renormalization Group (DMRG) electronic structure calculations utilizing the Ozaki scheme for emulating FP64 arithmetic through the use of fixed-point compute resources. By approximating the underlying matrix and tensor algebra with operations on a modest number of fixed-point representatives (“slices”), we demonstrate on smaller benchmark systems and for the active compounds of the FeMoco and cytochrome P450 (CYP) enzymes with complete active space (CAS) sizes of up to 113 electrons in 76 orbitals [CAS(113, 76)] and 63 electrons in 58 orbitals [CAS(63, 58)], respectively, that the chemical accuracy can be reached with mixed-precision arithmetic. We also show that, due to its variational nature, DMRG provides an ideal tool to benchmark accuracy domains, as well as the performance of new hardware developments and related numerical libraries. Detailed numerical error analysis and performance assessment are also presented for subcomponents of the DMRG algebra by systematically interpolating between double- and pseudo-half-precision. Our analyis represents the first quantum chemistry evaluation of FP64 emulation for correlated calculations capable of achieving chemical accuracy and emulation based on fixed-point arithmetic, and it paves the way for the utilization of state-of-the-art Blackwell technology in tree-like tensor network state electronic structure calculations, opening new research directions in materials sciences and beyond.

We report a significant advancement in ultra low power light-by-light phase modulation using open semiconductor microcavities in the strong light-matter coupling regime. We achieve cross-phase modulation of up to 247$\pm$17 mrad per particle between laser beams attenuated to single-photon average intensities. This breakthrough extends the potential for quantum information processing and nonlinear quantum optics in strongly coupled light-matter systems, setting a new benchmark in the field without relying on atom-like emitters. Our findings suggest promising new avenues for scalable quantum optical technologies.

Individual thresholds in social contagions capture what fraction of others must adopt a new product or behavior before an individual adopts it. Yet in a given choice context and population, we rarely know the empirical threshold distribution or what explains its heterogeneity. This limits the ability to predict diffusion dynamics and design effective network interventions. We address this by measuring thresholds with an incentivized online experiment in which participants make adoption decisions for products that vary in attractiveness and payoff uncertainty. The results show that product characteristics shift threshold distributions: lower attractiveness and higher uncertainty increase thresholds. Individual characteristics, however, remain essential for explaining threshold heterogeneity. We then use data-driven simulations to show that both product characteristics and individual characteristics moderate the effect of long ties in social contagions. In sum, the threshold distribution is the behavioral microfoundation of social contagion models and identifying empirical correlates of thresholds is crucial for understanding how network structure affects social contagions.

Resistive Plate Chambers detectors are extensively used in several domains of Physics. In High Energy Physics, they are typically operated in avalanche mode with a high-performance gas mixture based on Tetrafluoroethane (C2H2F4), a fluorinated high Global Warming Potential greenhouse gas. The RPC EcoGas@GIF++ Collaboration has pursued an intensive R&D activity to search for new gas mixtures with low environmental impact, fulfilling the performance expected for the LHC operations as well as for future and different applications. Here, results obtained with new eco-friendly gas mixtures based on Tetrafluoropropene and carbon dioxide, even under high-irradiation conditions, will be presented. Long-term aging tests carried out at the CERN Gamma Irradiation Facility will be discussed along with their possible limits and future perspectives.

We experimentally demonstrate a method for reconstructing the transverse 4D phase space of an electron beam at the time of emission from downstream diagnostics of the 4D phase space. This method does not rely on detailed knowledge of the beamline transport, besides assuming that linearity and symplecticity are satisfied. We apply this method to measure the transverse position and momentum phase space of electrons emitted from a spatially-structured alkali-antimonide cathode. This method can uncover local correlations between emission location and momentum spread. We formulate this method analytically and investigate resolution limits.

We designed a scheme for a neutral atom Rydberg blockade $C_Z$ gate based on the double sequence of adiabatic pulses applied symmetrically to both atoms and using counterdiabatic driving for Rydberg excitation. This provides a substantial reduction in the quantum gate operation time compared to previously proposed double adiabatic schemes, and makes our scheme competitive with modern time-optimal protocols for high-fidelity entangling gates with neutral atoms. Our approach creates a bridge between fully adiabatic and time-optimal gate schemes. The use of adiabatic passage reduces the sensitivity of gate fidelity to variations in laser intensity, while counterdiabatic driving provides short gate times. The intensity and phase profiles of the laser pulse acting on the atoms are described analytically depending only on the gate duration. We demonstrated the applicability of this scheme for single-photon and two-photon schemes of Rydberg excitation in rubidium and cesium atoms, and, for the first time, discussed the implementation of a $C_Z$ gate using three-photon excitation of rubidium atoms. In contrast to many modern $C_Z$ gate protocols, our scheme does not generate intrinsic single-qubit phase shifts, although they still appear in two-photon configuration. We also designed a numerically optimized amplitude-robust gate with an analytically defined phase profile of the laser pulse and compared its performance with the counteradiabatic gate scheme.

We present a higher-order boundary condition for atomistic simulations of dislocations that address the slow convergence of standard supercell methods. The method is based on a multipole expansion of the equilibrium displacement, combining continuum predictor solutions with discrete moment corrections. The continuum predictors are computed by solving a hierarchy of singular elliptic PDEs via a Galerkin spectral method, while moment coefficients are evaluated from force-moment identities with controlled approximation error. A key feature is the coupling between accurate continuum predictors and moment evaluations, enabling the construction of systematically improvable high-order boundary conditions. We thus design novel algorithms, and numerical results for screw and edge dislocations confirm the predicted convergence rates in geometry and energy norms, with reduced finite-size effects and moderate computational cost.

Single-electron detectors are a key component of electron microscopes and advanced electron optics experiments. We present a YAG:Ce scintillator-based single-electron detector with a spatial resolution of 1 micrometer at an electron energy of 17 keV. Single-electron detection events are identified with an efficiency and purity larger than 0.8 at an electron energy of 17 keV, reaching 0.96 at 30 keV. We show that the detector enables electron diffraction studies with a sample-detector distance comparable to the mean free path of electrons at atmospheric pressure, potentially enabling atmospheric electron diffraction studies.

The Kibble balance realizes the kilogram by linking mechanical and electrical quantities via a magnet system. In an improved BIPM-type magnet design by Tsinghua University, an open/close surface was incorporated, facilitating operation. However, an unavoidable mechanical air gap at the splitting plane introduces asymmetry in the magnetic flux density profile, degrading field uniformity. This study proposes a two-step yoke compensation method to restore symmetry by adjusting the upper outer yoke's inner radius and the splitting gap height. Finite element simulations show linear relationships between asymmetry and these parameters, enabling predictive compensation. Experimental results confirm that sequential tuning successfully eliminates asymmetry and recovers the designed uniform field range. The method provides an effective solution for enhancing magnetic field quality in openable Kibble balance magnets.

Manoeuvring untethered, centimetre-scale airborne structures has been a long-standing challenge. Active flight systems, relying on high-power-density actuators alongside mechanical and electronic components, are constrained by critical limitations in energy delivery and miniaturisation. In contrast, passive systems transported and distributed by the wind typically lack the capability for mid-air controlled manoeuvrability. Here we report an ultra-light (1.2 mg) hexagonal polymeric assembly capable of passive flight with optical control of its trajectory. This dandelion-inspired micro-flyer incorporates six radially arranged filamentous structures, of which morphology is dynamically controlled through photomechanical deformation by six independent soft actuators made of liquid crystalline elastomer thin films. Compared to the diaspore of the dandelion (Taraxacum officinale), micro-flyer demonstrate a similar terminal velocity (~0.5 m s-1), 45% better positional stability and nearly zero rotational rate (1.68 s-1; natural seeds: 50.8 s-1). Particle image velocimetry reveals that a stable asymmetric separated vortex ring underlies its flight stability, enabling mid-air steerability. When free-falling in a low-turbulent airstream, the light-driven hexapodal fliers demonstrate precise altitude control, reversible body flipping, pattern formation, interactive swarm, and controlled trajectories across three-dimensional space. The results show that responsive materials with light-induced asymmetry can bring about manoeuvrability in air, paving the way for agile, untethered controlled micro-fliers.

We present a comprehensive study of the electronic transitions in neutral cadmium (Cd I) with a focus on forbidden transitions, motivated by recent advances in laser technology and the growing relevance of cadmium in quantum gas research, precision metrology, and atom trapping. General analytic expressions are derived for transition matrix elements of all multipolar orders, formulated to be applicable for experimental use. Using configuration interaction combined with many-body perturbation theory, we calculate not only the previously reported contributions from electric dipole (E1) transitions, but also the electric quadrupole (E2), electric octupole (E3), magnetic dipole (M1), and magnetic quadrupole transitions (M2) that have not yet been investigated for cadmium. These matrix elements are then employed to determine the lifetimes of key excited states, particularly those pertinent to laser cooling and optical frequency standards, and to evaluate the long-range dispersion coefficient C6. The linewidths of the strongest transitions, along with the atomic energy levels, are compared with available experimental data to validate the accuracy of the simulations. Overall, the results are in good agreement, with the calculated energy levels exhibiting an average relative deviation of 0.3% from experiments. These values serve as benchmarks for both bosonic and fermionic isotopes, providing a foundation for future experimental and theoretical work in cadmium-based precision spectroscopy and cold-collision studies.

The DANish regional atmospheric ReAnalysis (DANRA) is a novel high-resolution (2.5 km) reanalysis dataset covering Denmark and its surrounding regions over a 34-year period (1990-2023). Denmark's complex coastline, with over 400 islands and an extensive 7,400 km coastline, means that most municipalities experience mixed land-sea variability. This complexity requires a regional climate reanalysis that can resolve fine-scale coastal and inland features, as well as their impact on climate variability. DANRA is based on the HARMONIE-AROME Numerical Weather Prediction (NWP) model and assimilates a comprehensive set of observations, with a particular focus on Denmark. Compared to global reanalyses such as the ECMWF Reanalysis v5 (ERA5), DANRA demonstrates superior performance in representing essential climate variables, including near-surface weather parameters during both extreme and ordinary conditions. We illustrate these improvements in the representation of several extreme weather cases over Denmark, such as the December 1999 hurricane-force storm, the July 2022 national temperature record, and the August 2007 cloudburst in South Jutland. DANRA is made to support climate adaptation, impact modelling, and the training of next-generation data-driven atmospheric forecasting models. DANRA is distributed as Zarr dataset freely accessible from an object store, maximizing its usability for climate adaptation, impact modelling, and data-driven research.

The design of the future European energy system depends heavily on how Europe balances its domestic hydrogen production against its reliance on imports. This study reveals that neither-extreme full self-sufficiency nor complete reliance on imports-is economically optimal through 2050. Using a high-resolution energy system model accounting for interannual weather variability, we find import cost thresholds favoring domestic production decrease from 3.0 EUR/kg (2030) to 2.5 EUR/kg (2050). However, the impact of weather is significant and can shift the optimal import share by up to 60 percentage points at constant prices. Strategies with a high import share minimize the need for domestic renewable energy and electrolyzers but require significant investment in long-distance transportation, backup electricity capacity, and storage. Conversely, achieving full self-sufficiency demands massive domestic infrastructure, including up to 1,315 GW of electrolysis by 2050. These findings highlight the critical need for diversified hydrogen strategies that balance cost, resilience, and energy sovereignty. Policy must prioritize flexible infrastructure accommodating both imports and scalable domestic production to navigate evolving market and climatic conditions.

Soliton gases are large ensembles of random solitons with distinct characteristics arising from integrable system dynamics. They have been widely studied in theory and experiments, and were observed in natural lagoons. However, it remains an open question whether they occur naturally in the open ocean. Nonlinear ocean states containing solitons have been observed in the literature, but the dominance of solitons over other wave components required for a soliton gas has not been demonstrated. Our study provides the first field evidence of soliton gas sea states in the deep ocean, measured in Taiwan waters. The soliton energy ratio derived from the nonlinear Fourier transform (NFT) is used as a key parameter to quantify how close sea states are to soliton gases. We identify eleven measurements with extremely high soliton energy ratios. They are characterized by short-period waves with relatively small wave heights, accompanied by extreme steepness and Benjamin Feir Index (BFI) values. These states are exceptionally rare, representing only 0.054% of our dataset. Since directional interference can artificially increase the estimated soliton energy ratio obtained from measured time series, we further apply a probabilistic directional filtering method to remove the directional interference. Three wave records from the Eluanbi station are found to retain high soliton energy ratios after the directional interference has been removed, confirming that they are indeed soliton gases.

The two leading models of isotropic magnetohydrodynamic (MHD) turbulence have competing predictions: $k^{-5/3}$ (Kolmogorov) and $k^{-3/2}$ (Iroshnikov-Kraichnan) scalings. This paper identifies the valid MHD turbulence model using high-resolution numerical and diagnostics-structure functions, intermittency exponents, and energy spectra and fluxes of imbalance MHD. The energy spectra of our forced MHD simulations on $8192^2$, $4096^2$, $1024^3$, and $512^3$ support Kolmogorov's k^{-5/3} spectrum over Iroshnikov-Kraichnan's k^{-3/2} spectrum, but the difference in the spectral exponents is small. However, the numerically computed third-order structure functions and intermittency exponents support Kolmogorov scaling in both two and three dimensions. Also, the energy fluxes of the imbalance MHD follow the predictions of Kolmogorov scaling. These results would help in better modelling of solar wind, solar corona, and dynamos.

The progress in understanding the optical and microscopic properties of polaritons relies on various optical cavities to confine electromagnetic radiation, which causes a demand for new platforms with higher $Q$-factors and better fabrication robustness. In this context, so called Mie voids – spherical cavities inside a dielectric medium, where the light confinement occurs due to refractive index contrast at the air-dielectric interface – present a substantial interest. Here, we theoretically study the resonant characteristics and polaritonic spectra of spherical Mie cavities loaded with resonant media, as well as address the inverted problem, where a Mie void is formed inside a resonant dispersive medium. We establish approximate analytical expressions for the $Q$-factors of Mie void cavities, find the parameter ranges of spherical voids leading to the regimes of weak and strong light-matter coupling and analyze the concomitant effects, such as $Q$-factor enhancement and spatial field localization, from the polaritonic perspective. Our result could be valuable for the design of new polaritonic systems.

Understanding the relationship between microscopic structure and macroscopic thermodynamic properties is a central challenge in the study of complex fluids. The Kirkwood-Buff (KB) theory offers an elegant and powerful framework for bridging this gap by relating integrals over pair correlation functions to measurable thermodynamic quantities. In multicomponent systems, KB integrals connect directly to derivatives of thermodynamic potentials, including chemical potentials derivatives, partial molar volumes, and isothermal compressibility. While several computational methods exist to estimate KB integrals from molecular simulations, their application often demands careful treatment of finite-size effects and explicit extrapolation to the thermodynamic limit. Recently, alternative strategies based on the analysis of partial structure factors in reciprocal space have been proposed. Unlike real-space approaches, reciprocal-space methods avoid the additional truncation artifacts associated with direct integration or fluctuations in subensemble. They evaluate density fluctuations across the entire simulation box, fully accounting for periodic boundary conditions rather than relying on subdomains. As a result, these methods offer a compelling alternative, providing enhanced numerical stability for estimating KB integrals in complex mixtures. In this work, we extend, compare and validate these methods using binary and quaternary Lennard-Jones mixtures, as well as realistic molecular systems such as hexane-ethanol, water-urea, and aqueous NaCl mixtures. Our results provide practical guidelines for computing KB integrals and associated thermodynamic properties from canonical ensemble simulations, including recommendations on reciprocal-space extrapolation, uncertainty estimation, and linear algebra formulations of thermodynamic derivatives.

The semi-empirical Pariser-Parr-Pople (PPP) Hamiltonian is reviewed for its ability to provide a minimal model of the chemistry of conjugated $\pi$-electron systems, and its current applications and limitations are discussed. From its inception, the PPP Hamiltonian has helped in the development of new computational approaches in instances where compute is constrained due to its inherent approximations that allow for an efficient representation and calculation of many systems of chemical and technological interest. The crucial influence of electron correlation on the validity of these approximations is discussed, and we review how PPP model exact calculations have enabled a deeper understanding of conjugated polymer systems. More recent usage of the PPP Hamiltonian includes its application in high-throughput screening activities to the inverse design problem, which we illustrate here for two specific fields of technological interest: singlet fission and singlet-triplet inverted energy gap molecules. Finally, we conjecture how utilizing the PPP model in quantum computing applications could be mutually beneficial.

Photonic platforms have emerged as versatile and powerful classical simulators of quantum dynamics, providing clean, controllable optical analogs of extended structured (i.e., crystalline) electronic systems. While most realizations to date have used only the fundamental mode in each site, recent advances in structured light - particularly the use of higher-order spatial modes, including those with orbital angular momentum - are enabling richer dynamics and new functionalities. These additional degrees of freedom facilitate the emulation of phenomena ranging from topological band structures and synthetic gauge fields to orbitronics. In this perspective, we discuss how exploiting the internal structure of higher-order modes is reshaping the scope and capabilities of photonic platforms for simulating quantum phenomena.

The yield strength plateau of two BCC refractory high entropy alloys (RHEAs) - MoNbTaVW and MoNbTaW was examined through hybrid Monte Carlo and molecular dynamics (MC/MD) simulations. By analyzing atomic diffusivities derived from vacancy formation and migration energies around the edge dislocation cores, the number of critical atomic swaps were calculated at different temperatures. Using hybrid MC/MD simulations of these critical swaps, we demonstrate that above 1400K, the stress required to move the dislocations gets saturated, indicating the effect of Dynamic Strain Ageing (DSA) via cross core motion. Further simulations on random solid solutions (0 MC swaps) revealed a similar plateau effect at the intermediate temperatures. This was attributed to the additional athermal stress arising from lattice distortions due to solid solution strengthening. Our findings suggest that the yield strength plateau results from an interplay between the DSA-driven diffusion process and athermal stress. Specifically, the plateau emerges from DSA mechanisms in the presence of atomic diffusion, whereas in the absence of diffusion, it is governed by athermal statistical lattice distortions. This dual mechanism framework provides a comprehensive explanation for the experimentally observed Yield strength behavior in RHEAs at intermediate temperatures.

Large-scale outbreaks of epidemics, misinformation, or other harmful contagions pose significant threats to human society, yet the fundamental question of whether an emerging outbreak will escalate into a major epidemic or naturally die out remains largely unaddressed. This problem is challenging, partially due to inadequate data during the early stages of outbreaks and also because established models focus on average behaviors of large epidemics rather than the stochastic nature of small transmission chains. Here, we introduce the first systematic framework for forecasting whether initial transmission events will amplify into major outbreaks or fade into extinction during early stages, when intervention strategies can still be effectively implemented. Using extensive data from stochastic spreading models, we developed a deep learning framework that predicts early-stage spreading outcomes in real-time. Validation across Erdős-Rényi and Barabási-Albert networks with varying infectivity levels shows our method accurately forecasts stochastic spreading events well before potential outbreaks, demonstrating robust performance across different network structures and infectivity scenarios.To address the challenge of sparse data during early outbreak stages, we further propose a pretrain-finetune framework that leverages diverse simulation data for pretraining and adapts to specific scenarios through targeted fine-tuning. The pretrain-finetune framework consistently outperforms baseline models, achieving superior performance even when trained on limited scenario-specific data. To our knowledge, this work presents the first framework for predicting stochastic take-off versus die-out. This framework provides valuable insights for epidemic preparedness and public health decision-making, enabling more informed early intervention strategies.

The simplest, algebraic quantum-electrodynamical corrections, due to the double-negative energy subspace and instantaneous interactions, are computed to the no-pair energy of two-spin-1/2-fermion systems. Numerical results are reported for two-electron atoms with a clamped nucleus and positronium-like genuine two-particle systems. The Bethe-Salpeter equation provides the theoretical framework, and numerical methods have been developed for its equal-time time-slice. In practice, it requires solving a sixteen-component eigenvalue equation with a two-particle Dirac Hamiltonian, including the appropriate interaction. The double-pair corrections can either be included in the interaction part of the eigenvalue equation or treated as a perturbation to the no-pair Hamiltonian. The numerical results have an $\alpha$ fine-structure constant dependence that is in excellent agreement with the known $\alpha^3E_\mathrm{h}\$-order double-pair correction of non-relativistic quantum electrodynamics.

The atomistic Landau-Lifshitz-Gilbert equation is challenged when modeling spintronic devices where Joule heating is significant, due to its core assumption of a constant magnetization magnitude. Based on a statistical framework that treats the magnetization magnitude as a dynamic variable coupled to a thermal bath, we derive a dynamic Landau-Lifshitz-Bloch-Slonczewski set of equations for torques, that captures the transient, heating-induced demagnetization that occurs during high-current operation. Integrating these dynamic equations and comparing them to their stochastic equivalents reveals that both the energy landscape and switching dynamics in high-anisotropy systems are similarly modified. This approach yields accurate and accelerated predictions of critical currents and switching times.

Physics-based atmosphere-land models with prescribed sea surface temperature have notable successes but also biases in their ability to represent atmospheric variability compared to observations. Recently, AI emulators and hybrid models have emerged with the potential to overcome these biases, but still require systematic evaluation against metrics grounded in fundamental atmospheric dynamics. Here, we evaluate the representation of four atmospheric variability benchmarking metrics in a fully data-driven AI emulator (ACE2-ERA5) and hybrid model (NeuralGCM). The hybrid model and emulator can capture the spectra of large-scale tropical waves and extratropical eddy-mean flow interactions, including critical levels. However, both struggle to capture the timescales associated with quasi-biennial oscillation (QBO, $\sim 28$ months) and Southern annular mode propagation ($\sim 150$ days). These dynamical metrics serve as an initial benchmarking tool to inform AI model development and understand their limitations, which may be essential for out-of-distribution applications (e.g., extrapolating to unseen climates).

Few-body physics explores quantum systems of a small number of particles, bridging the gap between single-particle and many-body regimes. To provide an accessible tool for such studies, we present FewBodyToolkit.jl, a Julia package for quantum few-body simulations. The package supports general two- and three-body systems in various spatial dimensions with arbitrary pair-interactions, and allows to calculate bound and resonant states. The implementation is based on the well-established Gaussian expansion method and we illustrate the package's capabilities through benchmarks and research examples. The package comes with documentation and examples, making it useful for research, teaching, benchmarking, and method development.

Urban intersections with mixed pedestrian and non-motorized vehicle traffic present complex safety challenges, yet traditional models fail to account for dynamic interactions arising from speed heterogeneity and collision anticipation. This study introduces the Time and Angle Based Social Force Model (TASFM), an enhanced framework extending the classical Social Force Model by integrating Time-to-Collision (TTC) metrics and velocity-angle-dependent tangential forces to simulate collision avoidance behaviors more realistically. Using aerial trajectory data from a high-density intersection in Shenzhen, China, we validated TASFM against real-world scenarios, achieving a Mean Trajectory Error (MTE) of 0.154 m (0.77% of the experimental area width). Key findings reveal distinct behavioral patterns: pedestrians self-organize into lanes along designated routes (e.g., zebra crossings), while non-motorized vehicles exhibit flexible path deviations that heighten collision risks. Simulations of three conflict types (overtaking, frontal/lateral crossing) demonstrate TASFM's capacity to replicate adaptive strategies like bidirectional path adjustments and speed modulation. The model provides actionable insights for urban planners, including conflict hotspot prediction and infrastructure redesign (e.g., segregated lanes), while offering a scalable framework for future research integrating motorized traffic and environmental variables. This work advances the understanding of mixed traffic dynamics and bridges the gap between theoretical modeling and data-driven urban safety solutions.

In recent electron-positron colliders designed and operated with very low emittance and high current, the energy density of the beam has increased significantly compared to earlier designs. Under these conditions, interactions between the beam and residual materials within the beam pipe exert considerable mutual influence. The residual materials are heated by the beam, leading to evaporation, charging, and possible fission, eventually resulting in a plasma state. Conversely, the beam undergoes emittance growth due to electromagnetic interactions with the resulting plasma. We investigate the effects of such dust-induced plasma on the beam through numerical simulations of these processes.

This paper is a follow-up to our earlier study, Natural Disasters in Canada (2017). We analyze the Canadian Disaster Database (CDD) to examine the frequency and severity of various natural disasters over the past 120 years and to identify emerging trends. We generate annual loss distributions for individual disaster types, as well as an aggregate annual loss distribution across all event types. Our analysis provides evidence that Canada is experiencing warmer and wetter conditions and indicates a substantial likelihood of extreme national-level losses.

Changing climate conditions threaten the natural permafrost thaw-freeze cycle, leading to year-round soil temperatures above 0{\deg}C. In Alaska, the warming of the topmost permafrost layer, known as the active layer, signals elevated greenhouse gas release due to high carbon storage. Accurate soil temperature prediction is therefore essential for risk mitigation and stability assessment; however, many existing approaches overlook the numerous factors driving soil thermal dynamics. This study presents a proof-of-concept latitude-based deep learning pipeline for modeling yearly soil temperatures across multiple depths. The framework employs dynamic reanalysis feature data from the ERA5-Land dataset, static geologic and lithological features, sliding-window sequences for seasonal context, a derived scenario signal feature for long-term climate forcing, and latitude band embeddings for spatial sensitivity. Five deep learning models were tested: a Temporal Convolutional Network (TCN), a Transformer, a 1-Dimensional Convolutional Long-Short Term Memory (Conv1DLSTM), a Gated-Recurrent Unit (GRU), and a Bidirectional Long-Short Term Memory (BiLSTM). Results showed solid recognition of latitudinal and depth-wise temperature discrepancies, with the GRU performing best in sequential temperature pattern detection. Bias-corrected CMIP5 RCP data enabled recognition of sinusoidal temperature trends, though limited divergence between scenarios were observed. This study establishes an end-to-end framework for adopting deep learning in active layer temperature modeling, offering seasonal, spatial, and vertical temperature context without intrinsic restrictions on feature selection.