Accurate modeling of tsunamis (such as those generated by landslides) requires capturing both wave dispersion in the deep ocean and wave breaking near the shore. The shallow water equations are often preferred for working with tsunamis, but neglect dispersion and may be inaccurate in scenarios where dispersive effects are significant. In this work, we develop an approach that seeks to incorporate the best aspects of both hyperbolic and dispersive models by combining either of two hyperbolic reformulations of the Serre-Green-Naghdi equations away from the shore with the non-dispersive shallow water equations near the shore. The model is discretized and implemented within the GeoClaw software, and incorporates adaptive mesh refinement as well as shared-memory parallelism. We validate it through comparison with benchmarks and real tsunami data. The results and performance compare favorably with the existing dispersive water wave solvers, including a speedup of about 2x relative to GeoClaw's existing dispersive solver for a large-scale tsunami simulation.

We discuss diffusion-model paths interpolating between a target distribution known only through p patterns and a reference distribution that can be sampled. These interpolating paths can be constructed symmetrically or else in forward direction (often referred to as a "noising") from the target patterns to the reference distribution or in backward direction (as a "denoising") from the reference distribution to the patterns. For backward paths with identical diffusion noise, we consider the path convergence in number of patterns p towards the path for infinitely many patterns. In a one-dimensional test case, we show that this convergence is on a scale 1/sqrt(p), but with infinite mean square deviation. We demonstrate that the path convergence allows for extrapolation towards the p=infinity path which samples the target distribution. We provide a proof-of-concept extrapolation algorithm and propose the convergence and extrapolation of paths as a possible strategy for density estimation and generalization. We illustrate all our algorithms through pseudo-codes and provide Python implementations.

We investigate the ground-state phase diagram of repulsively interacting bosons on a two-leg ladder threaded by a uniform artificial magnetic flux, using the cluster Gutzwiller mean-field method. In the strong-rung-coupling regime, self-consistent calculations are performed on a $2\times4$ cluster. By analyzing the superfluid order parameter, leg-resolved currents, chiral current, the current ratio on adjacent legs, and the density imbalance between the two legs, we distinguish Mott-insulating from superfluid regimes and characterize the observed states as Meissner-like, vortex-like (superfluid or Mott insulating), or biased-ladder. In regions overlapping with previous DMRG studies, our results qualitatively agree with the established phase structure, demonstrating that the cluster Gutzwiller approach balances computational efficiency and physical accuracy. We then construct the first grand-canonical $t$--$μ$ phase diagrams for this system, revealing how the magnetic flux modifies the shape, tilt, and extent of the Mott lobes. We further explore previously inaccessible regimes, including higher fillings $ρ\gtrsim1$ and the intermediate interaction window $U/t\in[7.69,9.09]$. Special attention is paid to $φ=π$, where the effective triangular-ladder mapping becomes singular. Owing to the equivalence of $φ=π$ and $-π$ modulo $2π$, a combined symmetry forbids net chiral currents, leading to a nonchiral Mott-insulating state, in contrast to the chiral-superfluid tendency expected away from $φ=π$. Our results offer a computationally efficient route for mapping the global phase structure of bosonic flux ladders and provide guidance for future ultracold-atom experiments in artificial gauge fields.

The $η$ and $η^{\prime}$ mesons are nearly unique in the particle universe since they are nearly Goldstone bosons, and their decay dynamics are strongly constrained. While earlier experiments collected samples of order $\sim 10^{9}$ $η$, the proposed REDTOP (Rare Eta Decays To Observe Physics Beyond the Standard Model) facility targets $\mathcal{O}(10^{14})$ $η$ and $\mathcal{O}(10^{12})$ $η'$, enabling broad searches for physics beyond the Standard Model. In this work, we present studies evaluating REDTOP sensitivity to processes that couple the Standard Model to New Physics through four portals: the Vector (dark photon), the Scalar (Higgs-mixing), the Axion-like, and the Heavy Lepton. In parallel, the proposed statistics allow precise tests of $CP$ and $T$ invariance and lepton universality and improve determinations of the $η/η'$ transition form factors, which are crucial inputs to the hadronic light-by-light contribution to the muon anomalous magnetic moment $(g-2)_μ$.

Positron annihilation lifetime spectroscopy (PALS) spectra are commonly analyzed by fitting multi-exponential lifetime models convoluted with the detector resolution function. In practice, this inverse problem is sensitive to initial parameter choices, parameter bounds, source corrections, and correlations between lifetime and intensity parameters. This paper presents fitPALSpectra, an open-source Python workflow for configurable PALS spectrum simulation, fitting, visualization, and reporting. The implementation uses an analytically integrated exponential--Gaussian response model, configurable source and sample components, constrained optimization, optional least-squares refinement, and machine-readable output of fit results, correlation matrices, and fitted curves. Validation on fully synthetic spectra with known ground-truth parameters shows accurate recovery of the simulated lifetimes, intensities, detector full width at half maximum, prompt shift, and background.

The shutdown of star formation - quenching - marks a pivotal transition in the lives of massive galaxies, which dominate the present-day stellar mass density. This review synthesizes our current understanding of the mechanisms that trigger and maintain quiescence. We discuss the nuances of how quiescent systems are identified across cosmic time and summarize the evolving physical properties of the growing massive population, including their stellar populations, chemical enrichment histories, and gas and dust reservoirs, highlighting several key results: (1) Quiescent galaxies can be identified with empirical color selections, but evolving specific star formation rate thresholds offer a more robust physical distinction from star-forming systems. (2) The earliest massive quiescent stellar populations show rapid formation histories and high metallicities, with enhanced $α$-elemental abundances often distinct from local analogs. (3) Nascent studies of gas and dust in quiescent galaxies reveal diverse multiphase reservoirs and outflows, pointing to fast ejective and slow regulatory modes of galaxy quenching. (4) In situ processes establish galaxy central density, while assembly continues via (minor) mergers post-quenching, reshaping all massive galaxies and disrupting rotation in most cases. We distill observations into two broad modes by which massive galaxies form and quench: one involves a rapid, early shutdown driven by supermassive black hole outflows on short timescales; the other proceeds gradually through gas exhaustion, virial heating, or preventative feedback, each leaving distinct observational signatures. Together, these pathways offer a testable framework for modeling the formation and evolution of massive galaxies, which will be informed by future studies of their stars, gas, dust, and dynamics.

One of the most important neutrino interactions is the Inverse Beta Decay (IBD). However, the IBD events typically carry no directional information in water Cherenkov detectors as the positron directions are mostly isotropic at low energies, such as those in supernova studies. As Gadolinium is being added to Super-Kamiokande, the improved neutron capture efficiency not only allows better background rejection, but the neutron capture information could potentially provide additional information that allows better event reconstruction. Due to neutron diffusion in water, event-by-event reconstruction is difficult. However, if the final neutron capture position is correlated with the initial neutrino momentum, it may be possible that neutrino directionality could be reconstructed statistically, with or without using the positron information. In this work, we use Geant4 to simulate neutron propagation in water. We show that in a wide range of neutrino energies from about 10 MeV to several hundred MeV, neutron capture information could statistically enhance the neutrino directionality, compared to positron-only inference, even with neutron diffusion considered. However, practical application of this technique depends crucially on detection effects, especially the vertex reconstruction resolutions. Our work therefore motivates developments of better reconstruction algorithms and techniques, as well as detector upgrades.

In earlier work I showed that a 35B-class Mixture-of-Experts model can be loaded and executed on a consumer laptop with 8 GB of GPU memory. That result solved a placement problem and immediately exposed a different one: even correctly placed, the large model needed roughly four seconds to answer, because it was still being invoked at every query. This paper documents what happened when I stopped invoking it. During an offline phase, the large model reads source documents and writes verified answer entries into a structured knowledge store; at runtime, only a lightweight router, a deterministic renderer, and a 1B-class model are active. On the same 8 GB laptop, end-to-end response time fell from approximately 4,465 ms to 518 ms, effective end-to-end throughput rose from 15.7 to 131 tokens per second, and the small model's streaming decode rate held at 226-237 tokens per second with a time-to-first-token of 29-62 ms. The bottleneck is structural: three different large models (Qwen, Gemma, and GLM class) all showed the same multi-second runtime cost, and all three produced usable knowledge stores offline. On a 563-entry store built from seventeen real documents, keyword routing collapsed to 1.5% top-1 accuracy while BM25-based routing reached 92.8% (99.4% top-3), and a confidence gate raised effective top-1 to 98.0% by escalating 12.3% of queries. Exact-match fidelity of the small model ranged from 9/9 to 0/9 across envelope formats carrying identical content. A 16-case verification gate blocked all ten corrupted entries while admitting all six supported ones.

As part of the international research programme TEAMx (multi-scale transport and exchange processes in the atmosphere over mountains - programme and experiment) a one-year long measurement campaign, the TEAMx Observational Campaign (TOC), was conducted between 2024 and 2025 in a north-south transect through the Alps. Building on the dense operational measurement network in the Alps, the TOC was designed to collect long-term atmospheric observations over the highly complex Alpine terrain. During two six-week long Extended Observational Periods, more than 40 research institutions came together to instrument about 30 sites in the four target areas of the TEAMx domain and study different transport processes, from gravity waves to orographic convection, thermally driven flows, and turbulent exchange. In addition to a suite of ground-based in-situ and remote-sensing instruments, observational activities included airborne measurements with up to three research aircraft and multiple UAS. This paper gives an overview of the science goals and the TOC design, together with preliminary analyses that highlight the potential of the collected dataset.

Optimization-based feedrate planning offers the potential to significantly increase machining productivity, but its industrial adoption has been limited by high computational cost and extensive tuning effort. This paper proposes a lexicographic feedrate optimization principle that adaptively balances finishing time and motion smoothness in a tuning-free manner. To further improve computational efficiency, the optimization scheme is extended by a sparsity-exploiting formulation combined with a sequential windowing strategy, enabling real-time capable execution. In addition, a unified toolpath parameterization scheme is incorporated to synchronously handle tool position and orientation within the optimization framework. For a five-axis freeform test contour, the proposed method takes 14 s on an Intel i5-3470 CPU to optimize feedrate profiles for long toolpaths with 100,000 constraint checkpoints, and 52 s on a high-performance AMD 9950X CPU to handle one million checkpoints. Compared to an industrial CNC kernel, the resulting finishing time is reduced by more than 15 %.

The interdiffusion of immiscible elements is generally considered both thermodynamically unfavorable and kinetically hindered. At the nanoscale, however, the mixing behavior of multielements materials often diverges from bulk equilibrium, yet a quantitative, atomically resolved description of this transformation has remained challenging. Using ex situ four dimensional atomic resolution electron tomography combined with in situ scanning transmission electron microscopy, here we reveal the atomic scale miscible transition driven by interdiffusion in immiscible PdIr nanoparticles at temperatures far below the melting point. The pathway involves surface reconstruction atom hopping at 200oC and surface flattening at 300oC, followed by a critical transition at 400oC where Ir interfacial diffusion and discrete Ir intermediates drive miscible intermixing. Upon reaching the nanoscale melting point 900oC, collective inward Ir diffusion yields the thermodynamically stable IrPd configuration. Our findings provide quantitative atomic scale insights into how metastable nanostructures evolve through distinct intermediates, offering a design framework for advanced multielement materials.

Our work revolves around Fictitious Play, one of the first iterative methods that is known to converge to a Nash equilibrium in zero-sum games. In recent years, there has been a revived interest, due to applications in various machine learning problems, which has motivated a line of work on its convergence properties and on proposing new variants of the initial algorithm. Our paper is along this direction and introduces one new variant, which we refer to as Almost Greedy Fictitious Play. The proposed algorithm greedily attempts to find the optimal stepsize at each iteration but its search space is constrained and includes almost all the line between the cumulative mixed strategy and the current best response. Our main result is that the method achieves an instance dependent convergence rate of $\mathcal{O}(1/T)$ with respect to the duality gap. This matches the rate of Continuous Fictitious Play, and offers an alternative to discretization. We complement our theoretical findings with experiments that demonstrate the effectiveness of the method.

Irradiation of a material with ions can cause various defects that can lead to structural phase transitions and the modification of the material's properties. Here we study the irradiation of the epitaxyally grown thin films of the high-temperature superconductor $\mathrm{YBa_2Cu_3O_7}$ with $30\,\mathrm{keV}$ He$^{+}$ ions which leads to the expansion of the crystal lattice, decrease of the critical temperature $T_c$ and eventually transition to an insulator. Fabrication of such insulating regions with a focused He-Ion beam with a spot size of $\sim 10\,\mathrm{nm}$ is a powerful technique for fabrication of superconducting nano-devices. Using low-temperature resistivity measurements, diffraction with a nanofocused X-ray beam and atomic force microscopy, we investigated how the structure and the electric transport properties of $\mathrm{YBa_2Cu_3O_7}$ depend on the irradiation dose in a range $10$--$100\,\mathrm{ions/nm^2}$ and on the lateral size of the irradiated area in a range $30$--$5000\,\mathrm{nm}$.

In this paper, we prove that output-sensitive sparse polynomial GCD computation over finite fields is NP-hard under BPP many-one reduction. More precisely, for two sparse univariate polynomials $f,g$ with finite field coefficients, there exists no randomized algorithm to compute $\mathrm{gcd}(f,g)$, which is polynomial-time in the sizes of $f,g,\gcd(f,g)$ under the standard complexity assumption $\mathrm{NP}\nsubseteq\mathrm{BPP}$. This settles the open problem posed as Challenge 5 in The Sparsity Challenges in the finite field setting. Furthermore, we show that the Roots of Unity Detection problem over finite fields is NP-hard; that is, determining whether the GCD of a sparse univariate polynomial and $x^n - 1$ has nonzero degree is NP-hard.

We study a class of continuous stochastic flows driven by a space-time white noise and characterize their dual flows by explicit stochastic differential equations. A key ingredient of the proof is the convergence of solutions under coefficient approximations. As an application, we derive the dual flows in two illustrative examples, the squared Bessel flow and the Jacobi flow. We also introduce a new model of polynomially self-repelling (PSR) flow and show that it enjoys a self-duality property.

The transition to next-generation mobile communication networks, particularly 6G, demands advanced technologies to meet the requirements for ultra-reliable, low-latency communication, massive connectivity, and intelligent applications. Reconfigurable antennas (RAs) play a crucial role in achieving these objectives by enabling dynamic adjustments to the radio frequency (RF) characteristics of antennas, such as gain, radiation pattern, impedance, and polarization. Unlike traditional fixed-position antennas, RAs can alter both their radiation patterns and positions, offering flexibility in response to varying communication environments. This paper presents a comprehensive survey and tutorial on RAs, with a focus on fluid antennas (FAs), movable antennas (MAs), pinching antennas (PAs), and reconfigurable holographic antennas (RHAs), examining their potential in next-generation mobile networks. We explore the channel modelling and estimation, performance analysis, resource allocation strategies, and their synergy with other emerging wireless technologies for each type of RA. Finally, we provide a comparative analysis of different RAs and discuss the open challenges and future research directions, offering insights and guidance for future investigations in the exciting research area.

We measure the dark matter halo mass function, with backsplash halos removed, from a wide range of cosmological-box and zoom-in simulations. These include the MultiDark Planck boxes, along with a suite of zoom-in simulations of Group, Milky Way, and LMC-mass halos. The Milky Way simulations include both CDM and non-CDM initial conditions. Using these measurements, we calibrate the parameters of flexible fitting functions for the halo mass function and the window function, along with parameterized models for various systematics, including finite box size effects, halo isolation criteria, halo detection efficiency, and contamination by artificial halos (objects forming from particle noise in the initial conditions). We show that this model shows remarkable consistency with N-body simulations over a broad range of redshifts, and ten orders of magnitude in halo mass ($10^6\mathrm{M}_\odot$ to $10^{16}\mathrm{M}_\odot$). Our model typically maintains a high precision of 12% and captures complex behaviors, including small-scale cut-offs, oscillations, and enhancements. In specific mass intervals for certain power spectra, we see larger deviations of 40-50%. Furthermore, when integrated with a simple model for environmental dependence, this fitting function provides a robust description of how environmental density influences the halo mass function. This precision model captures a wide variety of dark matter paradigms (including thermal relics, axions, and models with dark-sector interactions), is accurate for halo masses down to $10^7\mathrm{M}_\odot$, and is a critical ingredient for model-independent dark-matter inference from forthcoming data.

Energy consumption is an increasing concern in IoT-Edge-Cloud infrastructures, where containerized application orchestration must balance performance with sustainability. This paper investigates how the Kubernetes CODECO framework integrates cross-layer energy-awareness into scheduling decisions for containerized applications across the IoT-Edge-Cloud continuum. CODECO monitors energy at both the computational level, via Kepler, and at a network (IP) level, and uses these metrics to define greenness heuristics that guide pod placement decisions through its ILP-based scheduler. The approach is experimentally evaluated on a real-world far Edge testbed composed of ARM-based embedded devices, comparing CODECO against vanilla Kubernetes across multiple scenarios. The results show that CODECO consistently reduces the energy consumption of the cluster, with savings of up to 11.01 mJ in computational energy and 4.14 mJ in network transmission energy consumption at peak load, for a wide set of scenarios which combine different types of injected fault conditions, including CPU stress, asymmetric network delay, and bandwidth contention. A composite greenness score combining both energy dimensions provides a stable and consistent ranking of scheduling strategies across all conditions, demonstrating its suitability as a unified energy indicator for cluster-level orchestration decisions across the IoT-Edge-Cloud continuum.

We study a mean-field limit for a $N$-particle system in which each particle follows a diffusion and interacts with other particles through a weight on each directed edge. Each weight evolves according to its own nonlinear SDE driven by a Brownian motion, with coefficients involving the states of the two endpoint particles of the edge. The initial vertex and edge variables are assumed to have a dissociated Aldous--Hoover form. We construct the limiting nonlinear SDE by averaging the interaction over an independent neighbor and an edge input, prove its well-posedness, and show that the dissociated vertex-edge structure is propagated by the dynamics. This propagation property is an analogue of propagation of chaos in the case where the weight of each edge may remain correlated with the states of the two endpoint particles. Under either a bounded-observable assumption or a sub-Gaussian edge-input condition, the finite system converges to this limit through quantitative coupling estimates for a typical particle and a typical edge. We also prove the convergence of the empirical measure of particle's state pairs and their interaction weights.

In this paper we construct the first example of a non-locally trivial $\mathrm{W}^*$-bundle whose fibres are all isomorphic to some fixed $\mathrm{II}_1$ factor. This is achieved by introducing a notion of uniformly having spectral gap for $\mathrm{W}^*$-bundles. For bundles with fixed factorial fibres, the negation of having this uniform spectral gap property provides an obstruction for being locally trivial. This results in seemingly elementary examples of $\mathrm{W}^*$-bundles whose fibres are all isomorphic to some fixed factor but that are not locally trivial, even over spaces with covering dimension equal to zero.

Let $S$ and $T$ be disjoint sets with $|S|=i$ and $|T|=r-1$ for $2\le i\le r-1$, and let $B_i^{(r)}$ be the $r$-graph on $S\cup T$ whose edges are the $r$-subsets containing $S$ or $T$. We study the deficit $q_{r,i}:=1-π(B_i^{(r)})$ in its Turán density. Balogh, Bohman, Bollobás, and Zhao previously obtained bounds for these deficits with logarithmic gaps near both ends of the sequence $B_i^{(r)}$, namely, when $i=O(1)$ or $i=r-O(1)$. We close these gaps by showing that, as $r\to\infty$, for every fixed integer $a\ge1$, $q_{r,a+1}=Θ_a(r^{-a})$, and for every fixed integer $b\ge2$, $q_{r,r-b}=Θ_b(r^{-b}\log r)$.

How do we detect topological phases from a single ground state? Entanglement entropy and spectrum have long been the standard tools -- but the reduced density matrix (RDM) itself contains far more information. We show that the RDM of a 2D topologically ordered system, expressed at the entanglement cut, realizes a 1D mixed-state phase. For the $\mathbb{Z}_2$ toric code phase, it is a 1D $\mathbb{Z}_2$ strong-to-weak spontaneous symmetry breaking (SW-SSB) phase, where deconfinement of anyons manifests as the short-range correlation of both $\mathbb{Z}_2$ charge and $\mathbb{Z}_2$ domain-wall in the RDM. The bulk $e$-$m$ duality translates into a Kramers--Wannier self-duality of the SW-SSB phase. Extending the framework to gapped $\mathbb{Z}_2$ spin liquids, the global spin-rotation symmetry manifests as an additional weak symmetry for the 1D RDM. Spin-$\frac{1}{2}$ spinons result in a cusp on the disorder parameter of spin-rotation at $θ=π$, providing a direct, ground-state signature of symmetry fractionalization. We verify this prediction analytically using the matrix product density operator formalism and numerically for the kagome-lattice resonating valence bond state. The proposed observable requires only a single ground-state wavefunction, making it amenable to quantum simulation platforms.

This paper develops a fully discrete cumulative distribution transform (CDT) for atomic probability measures on the real line. The transform is defined through monotone quantile maps and admits explicit linear-time algorithms for both forward transformation and inverse reconstruction based solely on cumulative mass matching. Unlike the classical continuous setting, deterministic transport between atomic measures cannot generally split masses, so exact reconstruction may fail at finite resolution. We establish a precise cumulative-mass compatibility criterion for exact finite-resolution recovery and prove weak convergence of reconstructed measures under reference refinement. Several structural properties of the discrete CDT are derived, including translation, composition, and scaling laws, and the framework is extended to a discrete signed cumulative distribution transform with thresholded stabilization near zero crossings. By avoiding continuous interpolation, the proposed framework provides a simple fixed-reference transport representation for discrete data. Numerical examples illustrate translation linearization, compatibility-controlled reconstruction, refinement consistency, and stabilization of the signed transform.

In this paper, we show that deciding whether a sparse polynomial does not divide another sparse polynomial exactly over finite fields is NP-hard under BPP many-one reductions. Equivalently, the sparse polynomial divisibility test over finite fields is CoNP-hard. This resolves the long-standing open problem concerning the computational complexity of the divisibility test for sparse polynomials in the setting of finite fields.

For a given Coxeter group, we study distributive lattices called middle orders refining the weak order and refined by the Bruhat order. In type $A$, we construct such lattices indexed by binary trees using a direct bijection between permutations and lower sets of a certain partition of the root poset into rectangles. When the binary tree is a left-comb tree, we recover the middle order defined by Bouvel, Ferrari, and Tenner (2025). We study combinatorial properties of these lattices, and show they are the only distributive lattices between the weak and Bruhat orders in type $A$. For general Coxeter groups, we study middle orders on parabolic quotients and use these to generalize our construction in type $A$ to other Weyl groups, obtaining so-called ``minuscule middle orders''. We show that they are a subset of sorting orders defined by Armstrong (2009), and we give conjectural descriptions of all middle orders that are not minuscule.

This paper examines whether blockchain-registered AI agents demonstrate operational readiness beyond identity registration. Using a dataset of ERC-8004 agents on Ethereum, we construct an agent-level feature table covering identity status, metadata, service declarations, reputation feedback, transfers, and cross-chain registration. We develop an operational readiness framework based on observable evidence layers and complement it with network analysis of owner-agent, feedback-client, wallet-transfer, and combined evidence relationships. The results show that early ERC-8004 adoption is registration-heavy but operationally shallow. While the identity layer is visible at scale, metadata availability, service exposure, reputation formation, and cross-chain evidence remain limited. Ownership and feedback activity are also highly concentrated, suggesting that early participation is shaped by a small number of high-activity wallets and clients. The network analysis further shows that richer operational evidence clusters around a small subset of agents rather than being broadly distributed across the ecosystem. The findings suggest that ERC-8004 provides an important identity layer for decentralized AI agents, but the transition from agent identity to agent economy remains incomplete.

We report the discovery of a previously unrecognized electrostatic hose instability in electron-beam-sustained plasmas, driven by the coupling between the electron beam centroid and the plasma generated via the beam-impact ionization. Unlike the conventional hose instability of relativistic beams propagating in underdense plasmas, this instability requires only ionization-capable electron beams readily produced by common emission processes and sheath acceleration, indicating broad relevance across various discharges. A linear theory is developed to predict the hosing frequency and growth rate, and particle-in-cell/Monte Carlo simulations confirm both the onset of instability and the theoretical predictions.

We propose ZOMA, a unified Zeroth-Order decentralized accelerated MinimAx framework for multi-agent nonconvex Polyak--Łojasiewicz minimax optimization. The proposed framework only requires evaluating the function value and, as such, is tailored to gradient-free environments, where exact gradient information is either unavailable or computationally prohibitive to obtain. A central contribution of our \textbf{ZOMA} framework is a multi-level unification, along the following directions: (i) \emph{estimator} - our framework adopts a hybrid zeroth-order estimator, which accommodates, among others, both coordinate-wise and randomized uniform smoothing estimators; (ii) \emph{bias correction} - our framework subsumes a wide range of bias-correction strategies, including gradient tracking (GT), exact diffusion (ED), and EXTRA and (iii) \emph{acceleration} - our framework facilitates a broad class of acceleration techniques, including zeroth-order versions of STORM, PAGE, and L2S. The general nature of \textbf{ZOMA} leads to many novel decentralized zeroth-order minimax methods and allows us to establish unified convergence guarantees, matching the performance of state-of-the-art centralized zeroth-order minimax methods, while providing benefits, such as linear speed-up in the number of users. The unified framework also provides a systematic way to assess algorithmic suitability by specializing the convergence rates to specific problem structures and method designs. We validate the performance of the proposed algorithms via numerical simulations.

A privacy-compliant indoor localization approach utilizing a 3-D near-field (NF) passive radar imaging technique is presented. This technique leverages ubiquitously radiated electromagnetic fields for imaging, with passive tags introduced to enhance the strength of scattering fields, thereby enabling precise localization at the imaging level. The method also supports localization in non-ideal imaging scenarios, such as for limited bandwidth or in highly-reflective environments. Based on their geometrical properties the simple and low-cost passive tags enable intuitive differentiation between individuals or objects. Associated privacy protection mechanisms are discussed, where the frequency-varying properties of the passive tags provide additional flexibility and potential applications under privacy and ethical considerations. Several forms of passive tags are presented, where both simulation and experimental results validate the effectiveness of the proposed passive tag designs.

Spin-orbit torque (SOT) enables the electrical manipulation of the magnetization with high speed and low energy consumption for magnetic random-access memory (MRAM) applications. Previous studies of short-pulse SOT switching have mainly focused on the nanosecond regime, whereas reports employing picosecond pulses remain scarce and have largely relied on field-assisted switching using bulky, high-power laser systems, limiting prospects for chip-level integration. Here, we introduce an all-electrical on-chip nanoplasma pulse generator capable of producing pulses as short as 6.4 ps, enabling ultrafast picosecond field-free SOT switching in magnetic trilayers. We show that reducing the pulse width lowers the writing energy by 2-3 orders of magnitude, with ultrafast Joule heating assistance playing an essential role in the enhanced efficiency of the picosecond regime. Our demonstration of ultrafast, all-electrical, and field-free SOT switching establishes the nanoplasma pulse generator as an on-chip platform for ultrafast spintronic studies, with promise for high-speed, energy-efficient, and scalable SOT-MRAM technologies.