加州理工学院

Line-Intensity Mapping (LIM) has emerged as a powerful technique for studying large-scale structure and the high-redshift universe, enabling three-dimensional maps of line emission across vast cosmological volumes. In this review, we summarize the LIM framework, its key scientific goals, and its future prospects. We describe the landscape of emission line tracers, theoretical modeling approaches, anticipated signals, and data-analysis methodologies. We also discuss experimental challenges, particularly those posed by astrophysical foregrounds, and review possible mitigation strategies. Further, we highlight a range of cross-correlation science cases, linking LIM with other cosmological surveys. Finally, we summarize current and upcoming experiments and early results, including recent first detections, while outlining the outlook for future discoveries. Specifically, LIM may offer new insights into galaxy formation and evolution and cosmology, while revealing the Epoch of Reionization, Cosmic Dawn, and possibly the Cosmic Dark Ages. LIM enables cosmological measurements that complement other probes and provide unique access to the high-redshift universe, potentially shedding light on dark matter, dark energy, and cosmic inflation.

We present a quantum spectral solver for the steady incompressible Stokes equations on a two-dimensional periodic domain. The method uses the Quantum Fourier Transform as a coherent change of basis and exploits the resulting spectral structure of the Stokes operator: the Laplacian becomes diagonal, while incompressibility is enforced mode by mode through a Helmholtz projection. In two dimensions, this projection is realized by a mode-dependent rotation from Cartesian velocity components to longitudinal–transverse coordinates, followed by component-conditioned inverse-Laplacian scaling. The velocity and pressure fields are encoded as quantum states over Fourier modes and physical components, and the corresponding spectral factors are implemented through polynomially encoded amplitude blocks. The construction extends recent quantum spectral methods in computational mechanics to an incompressible flow operator with explicit pressure–velocity splitting and divergence-free projection. The approach is also compatible with multiscale finite-element architectures in which quantum parallelism can simultaneously update all representative volume element (RVE) states. Numerical verification includes a steady vortex, a regularized periodic force-dipole benchmark, and an RVE-inspired Kolmogorov-like fluctuation benchmark. The latter illustrates how the circuit can recover a homogenized kinetic-energy observable without reconstructing the full velocity field, consistent with the role of averaged quantities in multiscale flow calculations. Subject to application-dependent assumptions about efficient state preparation and observable estimation, the circuit has polylogarithmic dependence on the grid resolution, with the polynomial degree and tile count appearing as explicit approximation and implementation parameters.

Fungi have repeatedly delivered transformative advances across industries, from medicine to food to materials, yet these breakthroughs have emerged from a narrow base of organisms and well-studied systems. Although mycology has taken a big leap forward in the past decades because of advances in genomics, multi-omics, engineering, phenotyping and environmental monitoring, much fungal diversity remains taxonomically and functionally under-characterised, and many sustainability-relevant innovations remain limited by lack of investment, sparse infrastructure, and fragmented governance. We now have technologies that make previously inaccessible fungal questions tractable, which can help unlock the next generation of fungal breakthroughs. Informed by a workshop held in December 2025 that convened 20 leading experts across fungal disciplines, here we lay out key bottlenecks to fungal research and innovation through three lenses: functional mycology, human and institutional capacity, and governance. We identify selected sustainability-relevant fungal frontiers and use these to surface the recurrent gaps that cut across domains. We propose four elements of coordinated action across research and development, governance, and advocacy that could contribute to unlocking the full potential of fungi for sustainability transitions.

Explicit incorporation of magnetic degrees of freedom in machine-learning interatomic potentials (magnetic MLIPs) plays a crucial role in the correct description of magnetic materials and their properties. An important ingredient for fitting of magnetic MLIPs is spin-polarized density functional theory (DFT) calculations with non-equilibrium magnetic moments, i.e. DFT calculations with constraints on magnetic moments. In this study, we present a workflow for active learning of magnetic Moment Tensor Potential (mMTP) during molecular dynamics (MD) simulations. Magnetic MTP and its active learning algorithm were implemented in the open-source spin-MLIP code, DFT soft-constrained spin-polarized calculations were performed with the VASP code, and MD simulations were conducted in the open-source LAMMPS code. We test our workflow on the Fe–Pd crystal. The dependencies of magnetization and phonon density of states (DOSs) on the volume of a supercell (or, pressure) are in good agreement with those calculated with DFT. Furthermore, the calculated DOSs correspond to the experimental ones.

We present a kernel-based methodology for constructing Lyapunov functions for nonlinear dynamical systems using approximate Koopman eigenfunctions. Our approach decomposes principal Koopman eigenfunctions into linear and nonlinear components, where the linear part is obtained from the system's linearization and the nonlinear part is computed by solving a partial differential equation using symmetric kernel collocation in reproducing kernel Hilbert spaces (RKHS). The resulting Lyapunov function is constructed as a quadratic form in the approximate eigenfunctions. We establish error bounds relating the approximation quality to the fill distance of collocation points and provide a certification procedure using continuous piecewise affine (CPA) methods. Numerical experiments on benchmark systems, including a polynomial system and the Duffing oscillator, demonstrate the effectiveness of our approach.