We present a spatially resolved study of cold molecular gas and dust in ten main-sequence galaxies at z=1.1-1.6, using observations of CO(4-3), CO(3-2), [C I](1-0), and dust continuum from the NOEMA3D survey. We find a widely presence of spatially extended molecular gas and dust, with sizes comparable to those of the stellar disk, in contrast to those of central-dominated starburst galaxies at similar redshifts. While various molecular gas tracers generally exhibit similar spatial distributions, the CO line (J=3-2 or J=4-3) remain the most effective for mapping molecular gas distribution and kinematics. In addition, the spatially resolved correlations between different molecular gas tracers exhibit about two times larger scatter than their galactic-integrated correlations, indicating that interstellar medium (ISM) conditions already deviate from global averages on scales of 3-6 kpc, likely reflecting the clumpy or inhomogeneous ISM in cosmic noon star-forming galaxies. Within our sample, both the molecular gas fraction and its depletion time are nearly constant across the galactic disks out to 2 Re, supporting a global linear Kennicutt-Schmidt law. The presence of extended molecular gas disks, along with regular stellar structures, small central bulges, and ordered cold gas kinematics, supports the idea that the evolution of main-sequence disk galaxies at cosmic noon is driven by steady gas accretion and transport through prominent spiral arms and/or bars. This process stands in contrast to the merger-driven stochastic gas accretion in compact starbursts.

Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is a powerful approach for modeling molten salts. However, standard exchange-correlation functionals often neglect dispersion interactions, introducing potential errors in property predictions. Dispersion corrections are commonly applied ad hoc to match experimental salt densities, but their systematic impact on predicting structure, thermophysical, and transport properties of salt remains unexamined. This study evaluates the impact of Grimme's DFT-D and nonlocal van der Waals (vdW-DF) corrections on molten fluorides of Group-I (LiF, NaF, KF) and Group-II (BeF$_2$, MgF$_2$, CaF$_2$), which are relevant to reactor applications. Results indicate that dispersion corrections have a minor effect on binding energies but significantly influence density predictions. Systematic benchmarking across compositions and temperatures reveals that semi-empirical dispersion models often produce more accurate densities compared to vdW-DF. Diffusion coefficients remain largely invariant to dispersion corrections at fixed densities, while coordination number distributions exhibit notable differences based on chosen dispersion. BeF$_2$, in particular, deviates from other fluorides, showing pronounced structural and dynamical differences in the absence of dispersion corrections. This highlights the necessity of dispersion effects for high-charge-density cations that promote intermediate- to long-range ordering. These findings provide a systematic framework for selecting dispersion models in molten salt simulations, improving density and structural predictions.

Charge-density-wave (CDW) order in correlated metals originates in a peaked electronic susceptibility at a finite wavevector $\mathbf Q_0$, set either by Fermi-surface features (nesting or saddle-point singularities) or by momentum-resolved electron-phonon coupling, or by a combination of the two. CDW precursor fluctuations can attenuate heat-carrying acoustic phonons even when long-range order is absent. We develop a Green's-function theory in which a damped-harmonic-oscillator propagator for a hybrid CDW--lattice soft mode at the ordering wavevector $\mathbf Q_0$ and a strain--intensity vertex obtained from an electron loop combine to give the acoustic phonon self-energy. The theory identifies two scattering channels: a local-intensity channel, controlled by a retarded composite CDW response and giving a narrow critical contribution when the CDW correlation length is large, and a texture (gradient) channel, which couples acoustic strain to spatial variations of the CDW envelope and, in a frozen-texture limit, reduces to a phenomenological form set by the measured diffraction peak weight and width. The same propagator fixes the lattice projection of a hybrid CDW--phonon soft pole measured by inelastic X-ray scattering, with an underdamped-to-overdamped crossover controlled by the distance to the CDW instability and a mass-tracking identity for the slow overdamped relaxation rate. The framework unifies diffraction, soft-mode spectroscopy, and thermal transport and applies broadly across CDW materials, including the transition-metal dichalcogenides, rare-earth tritellurides, kagome CDW compounds, and the cuprate fluctuating charge-order regime; we illustrate it by direct comparison with experimental IXS phonon softening and anomalous thermal transport in 2H-TaSe$_2$ at elevated temperatures.

Let $K$ be a complete discrete valuation field of characteristic $0$ with perfect residue field of characteristic $p>0$. Let $\mathbb{A}_K$ denote the imperfect coefficient ring of $(φ,Γ)$-modules defined by Jean-Marc Fontaine. We prove that the canonical map $W(k_{K_\infty})[[μ]]\rightarrow \mathbb{A}_K\cap A_\mathrm{inf}$ is an isomorphism, even if $K$ is ramified. This fact was remarked by Nathalie Wach without proof.

The quantum metric is a fundamental ingredient of band quantum geometry and has recently at tracted intense interest, with most of its transport signatures appearing in the intrinsic second order nonlinear conductivity. In the clean limit, previous works argued that linear response conductivity is insensitive to the quantum metric, while the Berry curvature yields an intrinsic anomalous Hall con tribution. Here we combine analytic derivations with new numerics to show that disorder modifies the linear response conductivity dominated by geometric conductivity which is determined by the real space quantum metric. Focusing on a two dimensional multi-flatband stub-pyrochlore lattice, we identify a critical delocalized regime sandwiched between flat band localization and Anderson localization, characterized by finite geometric conductivity. Upon including spin orbit coupling, this regime evolves into a diffusive metallic phase, constituting a two dimensional inverse Anderson transition. Moreover, exploiting the connection between the real space quantum metric marker and the Wannier function spread, we construct a bond-percolation model on a square lattice. The resulting percolation region quantitatively coincides the critical delocalized regime, the exponent of which supports a classical percolation universality class. These findings suggest that flat band de localization can be understood as a classical percolation of quantum metric puddles. This advances our understanding of quantum geometric contributions to transport and establishes linear response measurements as a new avenue for accessing the quantum metric.

We establish a deformation framework for highly symmetric solutions to the Einstein equations. In this framework, four-dimensional metrics are constructed from three-dimensional η-Einstein metrics admitting a deformation determined by a single function. Under this deformation, the resulting spacetime solves the Einstein equations with a string-cloud source. Within this framework , a wide range of symmetric spacetimes can be treated in a unified manner. These include FLRW, Kantowski-Sachs, and LRS Bianchi cosmological models (including Taub-NUT-(A)dS solutions), as well as Reissner-Nordström-(A)dS black holes admitting spherical, planar, or hyperbolic symmetry. In the cosmological setting, the deformation leaves the evolution equations for the scale factors unchanged, and hence the expansion history coincides with that of the corresponding undeformed models. For the deformed Reissner-Nordström-(A)dS black holes, the structure of Killing horizons is insensitive to the deformation.

Automatically translating system software from C to Rust is an appealing but challenging problem, as it requires whole-program reasoning to satisfy Rust's ownership and borrowing discipline. A key enabling step in whole-program translation is interface translation, which produces Rust declarations for the C program's top-level declarations (i.e., structs and function signatures), enabling modular and incremental code translation. This paper introduces correct, precise C-to-Rust interface translation, called &inator. &inator employs a novel constraint-based formulation of semantic equivalence and type correctness including borrow-checking rules to produce a Rust interface that is correct (i.e., the interface admits a semantics-preserving implementation in safe Rust) and precise (i.e., it uses the simplest, least costly types). Our results show &inator produces correct, precise Rust interfaces for real C programs, but support for certain C features and scaling to large programs are challenges left for future work. This work advances the state of the art by being the first correct, precise approach to C-to-Rust interface translation.

Kinetic Alfven waves (KAWs) are fundamental to energy transport and small-scale structure formation in the turbulent, magnetized interstellar medium (ISM). While first-order Korteweg--de Vries (KdV) models describe weakly nonlinear KAW solitons, they fail in strongly inhomogeneous environments where higher-order effects become significant. We investigate higher-order "dressed" kinetic Alfven (KA) solitons in a structured ISM (warm ionized medium, H II regions, stellar-wind bubbles, supernova remnants). Using a multi-component fluid model with superthermal electrons, we derive an inhomogeneous KdV-type equation with cubic nonlinearity, nonlinear-dispersive cross terms, and fifth-order dispersion. The dressed soliton has a $\operatorname{sech}^2$ core decorated by higher-order corrections. We classify soliton morphologies across the Galactic plane as a function of electron suprathermality $κ_e$. Five classes ($ψ_{\rm I}$--$ψ_{\rm V}$) evolve non-monotonically with $κ_e$: strongly suprathermal ($κ_e=1.6$) favour negative double-hump ($ψ_{\rm III}$); intermediate $κ_e$ produce layered sequences of $ψ_{\rm II}$, $ψ_{\rm I}$, $ψ_{\rm IV}$, $ψ_{\rm V}$; near-Maxwellian ($κ_e=3.1$) revert to KdV-like $ψ_{\rm I}$. Localised $ψ_{\rm V}$ appear as a red ring around the SWB shell and a red core inside the SNR, showing embedded structures actively generate distinct morphologies. First-order KdV theory is insufficient; dressed solitons are the natural nonlinear states. The ISM morphology selects soliton class by modulating leading vs. higher-order terms. $ψ_{\rm V}$ features link macroscopic ISM structures to kinetic-scale fluctuations, offering candidates for extreme scattering events and pulsar scintillation. The non-monotonic $κ_e$ dependence can constrain electron suprathermality from observations.

Deep learning has triggered explosive growth in the demand for specialized hardware processors, thus motivating the development of scalable and reconfigurable computing substrates. Optical processors offer a fundamentally different computing paradigm, combining massive parallelism and ultrahigh bandwidth with the potential for substantial energy savings. However, progress has been constrained by the absence of scalable and reconfigurable architectures that can implement a broad class of network architectures. Here, we introduce TRON, a scalable and trainable optoelectronic deep optical neural network that exploits a multi-scattering medium and a DMD as a learnable, high-dimensional dense optical matrix multiplier, processing with fixed and tunable optical operations. We perform in-situ optimization of the optical parameters involved in the scattering process, together with automated neural architecture search (NAS) and optimization directly on optics. The experimental results demonstrate that in-situ NAS is essential to discover architectures that adapt to both the task and hardware constraints, establishing a viable path towards large-scale optical processors for next-generation machine learning and data-intensive computing.

We present a determination of the scalar and tensor $Λ\to p$ transition form factors using lattice QCD. These form factors are relevant for semileptonic hyperon decays in the presence of extensions of the Standard Model that include scalar and tensor interactions. The calculation is carried out using a gauge ensemble of twisted mass fermions at the physical pion mass, following the same strategy as our recent study on vector and axial form factors for the same transition. We provide the complete set of form factors as functions of $q^2$ employing a model-independent parametrization. We examine their impact on searches for non-standard charged-current interactions via the muon-to-electron decay-rate ratio $R^{μe}=Γ(Λ\to pμ\barν_μ)/Γ(Λ\to pe\barν_e)$, where scalar and tensor contributions enter linearly and are helicity-enhanced relative to the electron channel. We compare this first-principles prediction for the decay-rate ratio with recent experimental measurements, thereby enabling improved constraints on non-standard charged-current interactions.

We present comprehensive photometric characterisation of 2025 FA$_{22}$, a Potentially Hazardous Asteroid (PHA) discovered on 29 March 2025 and observed during the seventh International Asteroid Warning Network (IAWN) coordinated campaign. The asteroid's close approach at 2 lunar distances on 18 September 2025 provided an opportunity for rapid physical characterisation in a simulated virtual impactor scenario. Photometric observations were conducted from 17 September to 1 October 2025, during which 2025 FA$_{22}$ traversed a $150^{\circ}$ arc and spanned solar phase angles from $\sim20^{\circ}$ to $\sim70^{\circ}$. This geometry enabled detailed physical characterisation, including determination of the spin axis and shape. Convex inversion yields a sidereal rotation period of $P_{\mathrm{sid}} = 13.07366 \pm 0.00076$ h and a spin axis at ecliptic coordinates $(λ, β) = (246^{\circ} \pm 9^{\circ}, 60^{\circ} \pm 9^{\circ})$. The absolute magnitude was derived as $H_{\mathrm{V}} = 21.39^{+0.07}_{-0.08}$ mag ($G_1 = 0.8228$, $G_2 = 0.0194$), with colour indices $B-V = 0.71 \pm 0.05$ mag, $V-R = 0.39 \pm 0.03$ mag, and $R-I = 0.39 \pm 0.04$ mag, consistent with X-complex classification in the Bus--DeMeo taxonomy. Assuming a geometric albedo of $p_v = 0.15^{+0.05}_{-0.04}$, representative of the moderate-albedo X-complex asteroids, we estimate $D_{\mathrm{eff}} = 181^{+31}_{-25}$ m, consistent with "China Compound Eye" radar dimensions of ${\sim}100 \times 320$ m ($D_{\mathrm{eff}} \sim 186$ m), which also reveal a contact-binary morphology consistent with the axis ratios $a:b:c \sim 2.68:1.96:1.00$ derived from our photometric shape model. The bilobate morphology is consistent with YORP-driven spin-up and deformation during a previous YORP cycle, with the current slow rotation possibly explained by internal reconfiguration or tidal braking during close Earth encounters.

Prefill-decode (PD) disaggregation has become the standard architecture for large-scale LLM serving, but in practice its deployment boundary is still determined by KVCache transfer. In conventional dense-attention models, prefill generates huge KVCache traffics that keep prefill and decode tightly coupled within a single high-bandwidth network domain, limiting heterogeneous deployment and resource elasticity. Recent hybrid-attention architectures substantially reduce KVCache size, making cross-cluster KVCache transport increasingly plausible. However, smaller KVCache alone does not make heterogeneous cross-datacenter PD serving practical: real workloads remain bursty, request lengths are highly skewed, prefix caches are unevenly distributed, and inter-cluster bandwidth fluctuates. A naive design that fully externalizes prefill can therefore still suffer from congestion, unstable queueing, and poor utilization. We present Prefill-as-a-Service (PrfaaS), a cross-datacenter serving architecture that selectively offloads long-context prefill to standalone, compute-dense prefill clusters and transfers the resulting KVCache over commodity Ethernet to local PD clusters for decode. Rather than treating reduced KVCache as sufficient, PrfaaS combines model-side KV efficiency with system-side selective offloading, bandwidth-aware scheduling, and cache-aware request placement. This design removes the requirement that heterogeneous accelerators share the same low-latency RDMA fabric, enabling independent scaling of prefill and decode capacity across loosely coupled clusters. In a case study using an internal 1T-parameter hybrid model, a PrfaaS-augmented heterogeneous deployment achieves 54% higher serving throughput and 64% lower P90 TTFT than a homogeneous PD baseline, with approximately 15% throughput gain at equal cost, while consuming only modest cross-datacenter bandwidth.

We prove a new "Toeplitz exactness" theorem for strong convergence. This is a machine to upgrade strong convergence in the general setting of $C^\ast$-correspondences, and has several applications.

Odrzywolek (2026) recently introduced the Exp-Minus-Log (EML) operator eml (x, y) = exp(x) - ln(y) and proved constructively that, paired with the constant 1, it generates the entire scientific-calculator basis of elementary functions; in this sense EML is to continuous mathematics what NAND is to Boolean logic. We investigate whether such a uniform single-operator representation can accelerate either the forward simulation or the parameter identification of a six-branch RC equivalent-circuit model (6rc ECM) of a lithium-ion battery cell. We give the analytical EML rewrite of the discretized state-space recursion, derive an exact operation count, and quantify the depth penalty of the master-formula construction used for gradient-based symbolic regression. Our analysis shows that direct EML simulation is slower than the classical exponential-Euler scheme (a ~ 25x instruction overhead per RC branch), but EML-based parametrization offers a structurally complete, gradient-differentiable basis that competes favourably with non-parametric DRT deconvolution and metaheuristic optimisation when the cardinality of RC branches is unknown a priori. We conclude with a concrete recommendation: use EML only on the parametrization side of the 6rc workflow, keeping the classical recursion at runtime.

If an operator $H$ anticommutes with a chirality operator $Γ_*$ such that $Γ_*^2=1$, the null space of $H$ can be decomposed in a direct sum of two spaces having positive and negative chiralities, respectively. When both spaces are finite dimensional, one can define an index, $\mathrm{Ind}(Γ_*,H)$, as the difference of dimensions of these two spaces. The key issue is whether $\mathrm{Ind}(Γ_*,H)$ is topologically protected, i.e., whether it remains constant under smooth variations of the parameters and background fields entering $H$. For Hermitian Dirac operators, topological protection of the index is guaranteed by the Atiyah--Singer theorem. In this paper, by using the heat kernel methods, we show that $\mathrm{Ind}(Γ_*,H)$ is topologically protected also for non-hermitian operators $H$ as long as they are diagonalizable and satisfy some ellipticity conditions.

When variable selection methods are applied to bootstrapped and multiply imputed datasets, the set of selected variables typically varies across iterations. Aggregating results via the union rule can lead to overly dense models. We propose a sequential evidence aggregation procedure that models detection outcomes across perturbation iterations as Bernoulli trials and accumulates evidence for variable relevance through a likelihood-ratio process admitting an approximate Bayes-factor interpretation. The procedure provides both a variable inclusion criterion and a stopping rule that eliminates the need to fix the number of bootstrap-imputation iterations ex ante. A Monte Carlo study across 126 scenarios and an empirical illustration demonstrate the method's performance relative to existing aggregation approaches.

Sub-wavelength thick photonic crystal (PhC) slabs coupled to 2D excitonic materials, such as transition metal dichalcogenides (TMDs), are a promising platform for highly tunable, room-temperature, on-chip optoelectronic devices. Unlike conventional Fabry-Perot microcavities, these compact open cavities exhibit non-trivial electric field profiles, leading to spatially distinct regions of weak and strong coupling with excitons within the PhC unit cell. Using coupled mode theory and rigorous solutions to Maxwell's equations, we investigate how the PhC geometry can be used to control these coexisting exciton/polariton contributions and tailor the resulting optical spectra. For large filling factors, i.e., small air gaps, we show that PhC polaritons can be modeled as dark waveguide modes brightened via the periodicity of the PhC slab. Furthermore, by spatially patterning the TMD monolayer based on the local field intensity, we reveal the simultaneous presence of excitons in both the weak and strong coupling regimes. Overall, this work provides fundamental insights into the strong light-matter coupling regime in structured photonic environments, offering a pathway to design and optimize metal-free, ultra-compact polaritonic devices.

Sparse penalized quantile regression provides an effective framework for variable selection and robust estimation in high-dimensional data analysis. When ex planatory variables are organized into groups, achieving sparsity both within and between groups is essential. However, existing quantile regression methods often fail to meet this dual objective. To address this gap, we introduce the adaptive sparse group lasso penalized quantile regression, which integrates adaptive lasso and adaptive group lasso penalties. We optimize the model parameters via the alternating direction method of multipliers (ADMM) applied to the dual problem, and establish global convergence. Through extensive simulation studies and real data analyses, we demonstrate (i) the efficacy of the proposed method in achieving simultaneous within- and between-group sparsity, and (ii) the computational efficiency of our algorithm relative to existing alternatives.

The cubic-kilometer neutrino telescopes have opened neutrino astronomy as an observational discipline. The recent detection of KM3-230213A, the highest-energy neutrino ever observed at ~220 PeV, as a near-horizontal muon track underscores that the ultra-high-energy frontier is accessed through horizontal directions where the Earth's opacity above ~100 TeV confines the observable sky to a narrow band around and above the horizon. Yet extending general-purpose detector architectures into this regime requires disproportionate increases in instrumentation, cost, and logistical complexity. A compelling alternative is to deploy specialized detectors that target this natural geometry. POLARIS (Pacific Ocean Large Area Radial Instrumented Sparse array) is a sparse planar deep-water Cherenkov array optimized for neutrino-induced muon tracks from horizontal directions in the multi-TeV to PeV regime. By rotating the conventional vertical string layout into a radial planar configuration, the detector presents maximal cross-section to horizontal tracks while naturally suppressing the down-going atmospheric background. With only 1100 optical modules, the five-arm design reaches point source and diffuse flux sensitivities at PeV energies competitive with detectors deploying several times more instrumentation. As a dedicated $ν_μ$ track detector, POLARIS provides the muon-flavor channel that tau-optimized experiments such as TAMBO and Trinity do not cover, enabling full flavor composition measurements from astrophysical sources. Using the Prometheus simulation framework, this study demonstrates that targeted sparse geometries can open new discovery space at the high-energy frontier at a fraction of the cost of general-purpose arrays.

We analytically describe the dynamically separated $\mathbb{Z}_{2}$ skin channels (wavepacket evolutions) under periodic boundary condition (PBC) in non-Hermitian systems with anomalous time-reversal symmetry (ATRS), by combining the semiclassical worldline perspective with an enhanced understanding of skin effects. These channels, tied to the initial state and relevant symmetries, exhibit individually exponential-dominated time evolution in momentum space, where their amplitude maxima evolve toward the dominant momenta. In real space, their center of masses (COMs) circulate around the one-dimensional (1D) chain, tracing semiclassical worldlines. Such circulations imply quantum revivals and effective dynamical quantum phase transitions (DQPTs) regardless of any wavepackets' phase interference, with the latter showing scale-dependent behavior, a feature distinct from conventional DQPTs. This work rigorously demonstrates our previous findings on worldline windings and the winding-control mechanism, confirming that the core physics is shared with the ordinary skin effect.

The kicked rotor provides a simple yet powerful model for introducing many of the central concepts of classical and quantum chaos. Despite its apparent simplicity, it exhibits rich dynamical behavior and has found applications across a wide range of fields, including atomic and optical physics, condensed matter physics, and emerging quantum technologies. This chapter begins by exploring foundational ideas using the kicked rotor as a unifying framework. We first discuss the transition from regular to chaotic motion in the classical system, and then introduce key quantum phenomena such as dynamical localization and quantum resonances. Special attention is devoted to the emergence of characteristic time scales and their role in the quantum-classical correspondence. To make these ideas more concrete, we also provide a brief overview of experimental realizations of the kicked rotor and its variants, illustrating how theoretical concepts are implemented in practice. In the second part of the chapter, we guide the reader toward more recent and advanced developments. Topics include near-resonant dynamics, topological features of kicked systems, the emergence of quantum dynamical phases inferred from classical transport properties, and extensions to non-Hermitian physics. We conclude with a discussion of open problems and future perspectives, outlining directions in which the kicked rotor continues to offer valuable insights.

Although optimization is one of the most promising applications of quantum computers, the development of effective optimization strategies requires real-world test cases. When planning our recent wedding reception, we realized that the problem of optimally seating our guests, given constraints related to guests' relatedness, shared interests, and physical needs, could be mapped to a cost function network (CFN) form solvable with classical or quantum optimization algorithms. We compared the seating optimization performance of classical Monte Carlo CFN solvers in the Masala software suite to that of quantum annealing-based CFN optimization algorithms using one-hot, domain-wall, and approximate binary mappings, which we had developed for protein design problems. Surprisingly, the D-Wave Advantage 2 system, which performs well on similarly-structured CFN problems for protein design, struggled to return optimal seating arrangements that were easily found by classical Monte Carlo methods. We provide our seating optimization benchmark set, and code to convert seating optimization problems to CFN problems, as a plugin library for Masala, permitting this class of real-world problems to be used to benchmark performance of current and future classical CFN solvers, quantum optimization algorithms, and quantum computing hardware.

Crystalline symmetries give rise to topological invariants that can distinguish quantum phases of matter. Understanding these in strongly interacting systems is an ongoing research direction requiring non-perturbative methods. Recent developments have demonstrated that even classic models, like the Harper-Hofstadter model of free fermions on a lattice in a magnetic field, yield a host of crystalline symmetry protected topological invariants. Here we review some of these developments, focusing mainly on how to characterize, classify, and detect invariants arising from lattice translation and rotation symmetries along with charge conservation in two-dimensional systems, including integer and fractional Chern insulators.

Two-dimensional (2D) SnSe is an emerging 2D material exhibiting intriguing properties such as ferroelectricity and nonlinear optical response. Here, high-quality single-crystalline SnSe nanosheets were synthesized via NaCl-assisted chemical vapor deposition (CVD) method. The addition of NaCl was found to significantly increase the surface coverage of the nanosheets with less influence on their lateral size. The crystalline structure and composition of as-grown nanosheets were charactered by XRD, Raman spectroscopy, and XPS. Ferroelectric domains in SnSe nanosheets are directly visualized by piezoresponse force microscopy (PFM). The magnetic hysteresis loops of SnSe nanosheets are achieved at 2 K, which indicated their weak ferromagnetism. A spin glass-like behavior was observed below 115K, which is attributed to the presence of SnSe2 impurity. This work further establishes a controllable synthesis route for SnSe nanosheets, thereby paving the way for subsequent investigation of their ferroelectric properties.

Neurocognitive disorders (NCDs), such as Alzheimer's disease, are globally prevalent and require scalable screening methods for proactive management. Prior research has explored the potential of technologies like conversational AI (CAI) to administer NCD screening tests. However, challenges remain in designing CAI-based solutions that make routine NCD screening socially acceptable, engaging, and capable of encouraging early medical consultation. In this study, we conducted interviews with 36 participants, including clinicians, individuals at risk of NCDs, and their caregivers, to explore the speculative future of adopting CAI for NCD screening. Our findings reveal shared expectations, such as deploying CAI in home or community settings to reduce social stress. Nonetheless, conflicts emerged among stakeholders, for example, users' need for emotional support may conflict with clinicians' preference for CAI's professional and standardized administration. Then, we look into the user journey of NCD screening based on the current practice of manual screening and the expected CAI-supported screening. Finally, leveraging the human-centered approach, we provide actionable implications for future CAI design in NCD screening.

We analyze mesoscopic electronic transport in a Chern mosaic: a regular pattern of domains whose electronic bands carry differing local Chern numbers. An example platform where a Chern mosaic can arise is a moiré heterostructure, where variations in the local moiré parameters can produce such domains. We compute resistances at linear response for a variety of domain wall network geometries at zero temperature and magnetic field. Simple domain configurations can exhibit zero, integer, or fractional multiples of the quantum of resistance in both the longitudinal and transverse (Hall) responses. Our simple semi-classical analysis provides a useful computational method and comparative catalog for ongoing experiments in two-dimensional topological materials.

The mass flux of solar and stellar winds is a key quantity for stellar evolution and space weather, yet its physical regulation mechanism remains an unsolved problem. In particular, conventional Alfvén wave--driven models that self-consistently connect the stellar surface to the stellar wind fail to reproduce the observed scaling between stellar X-ray flux and mass-loss rate, a discrepancy that can be largely attributed to the dissipation of a substantial fraction of the wave energy by chromospheric turbulence. To address this issue, we aim to clarify the role of chromospheric turbulence in regulating the stellar wind mass flux. We perform one-dimensional wave-driven wind simulations, comparing cases with and without chromospheric turbulence suppression to assess its impact on coronal and wind properties. We find that suppressing chromospheric turbulence leads to a systematic increase in the coronal particle flux, and hence the wind mass flux, by up to an order of magnitude, particularly in regions of moderately strong magnetic field. This behavior arises from a combination of changes in the Poynting flux at the coronal base and in the asymptotic wind speed. Furthermore, the model with chromospheric turbulence suppression reproduces the observed empirical scaling between coronal magnetic field strength and mass flux without invoking additional energy input mechanisms such as interchange reconnection. These results identify the chromospheric turbulence as a key factor in regulating stellar wind mass flux and highlight the importance of incorporating its effects in models that connect the stellar surface and the stellar wind.

Recent progress in scaling large models has motivated recommender systems to increase model depth and capacity to better leverage massive behavioral data. However, recommendation inputs are high-dimensional and extremely sparse, and simply scaling dense backbones (e.g., deep MLPs) often yields diminishing returns or even performance degradation. Our analysis of industrial CTR models reveals a phenomenon of implicit connection sparsity: most learned connection weights tend towards zero, while only a small fraction remain prominent. This indicates a structural mismatch between dense connectivity and sparse recommendation data; by compelling the model to process vast low-utility connections instead of valid signals, the dense architecture itself becomes the primary bottleneck to effective pattern modeling. We propose SSR (Explicit Sparsity for Scalable Recommendation), a framework that incorporates sparsity explicitly into the architecture. SSR employs a multi-view "filter-then-fuse" mechanism, decomposing inputs into parallel views for dimension-level sparse filtering followed by dense fusion. Specifically, we realize the sparsity via two strategies: a Static Random Filter that achieves efficient structural sparsity via fixed dimension subsets, and Iterative Competitive Sparse (ICS), a differentiable dynamic mechanism that employs bio-inspired competition to adaptively retain high-response dimensions. Experiments on three public datasets and a billion-scale industrial dataset from AliExpress (a global e-commerce platform) show that SSR outperforms state-of-the-art baselines under similar budgets. Crucially, SSR exhibits superior scalability, delivering continuous performance gains where dense models saturate.

We construct a ``noncommutative'' Villadsen algebra $B$ and show that, given an extreme tracial state $ν$ on its canonical AF subalgebra, the subset of $T(B)$ consisting of those tracial states that equal $ν$ when restricted to the canonical AF subalgebra is the Poulsen simplex. In particular, if the canonical AF subalgebra has a unique trace, then $T(B)$ is the Poulsen simplex. We go on to show that in certain instances, the tracial cone of a ``classical'' AF-Villadsen algebra $D$ is isomorphic to the tracial cone of the algebra obtained from $D$ by deleting all point evaluations.

LLMs used for code generation are typically guided by engineering constraints--technology choices, dependency restrictions, and architectural patterns--expressed in verbose natural language. We investigate whether compact, structured constraint headers can reduce prompt token consumption without degrading constraint compliance. Across six experimental rounds spanning 11 models, 16 benchmark tasks, and over 830 LLM invocations, we find that compact headers reduce constraint-portion tokens by approximately 71% and full-prompt tokens by 25--30%, replicated across three independent rounds. However, we detect no statistically significant differences in constraint satisfaction rate (CSR) across three encoding forms or four propagation modes; observed effect sizes are negligible (Cliff's $δ$ < 0.01, 95% CI spanning $\pm$2.6 percentage points). This null pattern holds across two models from different capability tiers. A supplementary experiment with four non-CSS tasks provides additional cross-domain support for the encoding null result. The largest observed sources of compliance variance are constraint type ($Δ$ = 9 percentage points between normal and counter-intuitive constraints) and task domain: counter-intuitive constraints opposing model defaults fail at 10--100%, while conventional constraints achieve 99%+ compliance regardless of encoding. Model self-assessments systematically overestimate compliance relative to rule-based scoring, revealing a gap between constraint understanding and execution. Under the tested conditions, the primary benefit of compact constraint encoding is token reduction rather than compliance improvement, and engineering effort toward compliance is better directed at constraint design than prompt formatting.