We present a scalable quantum simulation framework for real-time dynamics of the multi-flavor Gross-Neveu model in 1+1 dimensions. Using superconducting quantum processors at utility scale, we develop a hardware-efficient Trotterization whose per-step circuit depth scales with fermion flavor number rather than total system size, enabling simulations beyond 100 qubits. A central contribution of this work is the Localized Diagonal Operator Approximation (LDOA), which systematically reduces the overhead associated with quartic interactions. We formulate diagonal unitary synthesis as a structured least-squares problem in phase space and obtain analytic solutions via the Moore-Penrose pseudoinverse. This formulation provides a principled and quantitatively controlled approximation: in the small Trotter-step regime, the unitary error is directly linked to the phase reconstruction error and vanishes asymptotically as the Trotter step size decreases. This establishes a clear mathematical foundation for the LDOA while significantly reducing two-qubit gate counts and circuit depth, and is broadly applicable to diagonal quantum operators with long-range structure, making it particularly well suited for quantum hardware with limited qubit connectivity. Using these techniques, we run large-scale simulations on IBM superconducting processors and study real-time observables, including density-density correlators. We benchmark against exact diagonalization and tensor network-based methods, finding strong agreement across system sizes. These results show that combining hardware-aware circuit design with rigorous approximations enables practical near-term simulation of interacting fermionic field theories and provides a scalable pathway toward more complex quantum field theory simulations.

It is shown that a standard one-dimensional coined discrete-time quantum walk can generate operationally admissible post-quantum correlations in a coin-position Bell scenario, without any modification of its unitary nearest-neighbor dynamics. Post-quantum features enter exclusively through an extended operational preparation of the coin, described by a complementarity-violating Hermitian trace-one operator, while physical consistency is enforced solely at the level of observable statistics via admissibility and no-signaling. The extended preparation admits an experimental emulation through a two-component quasiprobability reconstruction over physical coin states, at the price of an increased sampling overhead. The walk-generated coin-position entanglement can support CHSH values exceeding Tsirelson's bound, even though the walk dynamics remains fully standard. We also show that physically natural coarse-grained position measurements can render such post-quantum correlations operationally inaccessible, strongly suppressing observable Bell violations. The purpose here is to contrast the separation between the existence of post-quantum behavior and its accessibility under realistic measurement constraints.

For over a decade, processor design has focused on implementing sophisticated policies for various components of the out-of-order pipeline, including cache replacement and prefetching. The prevailing design philosophy has been to build processors with a single, static selection of policies across these different mechanisms. This paper investigates a fundamental question: do different workloads, or even different execution phases within the same workload, benefit from different policy combinations? We present a comprehensive analysis exploring whether a hypothetical processor capable of dynamically selecting from multiple policies could significantly outperform traditional static-policy processors. Using ChampSim-based simulation across 49 benchmarks segmented into 490 execution phases of 20M instructions each, we evaluate performance across multiple policy combinations for cache replacement and prefetching. Our findings reveal that significant performance headroom exists: the best static policy achieves optimal performance for only 19.18\% of execution phases and incurs a mean IPC loss of 1.54\% compared to an oracle. Moreover, 85 phases (17.35\%), spanning 14 of the 49 applications, exhibit more than 2.5\% IPC loss relative to the oracle. Furthermore, we demonstrate that a processor capable of dynamically switching between two carefully chosen policies can achieve a 13.6$\times$ reduction in mean IPC loss (from 1.54\% to 0.11\%) and match oracle performance 52.65\% of the time. These results suggest that dynamic policy selection represents a promising avenue for unlocking single-thread performance improvements that have become increasingly difficult to achieve.

We compare different Poisson solvers within the context of an electrostatic Vlasov-Poisson system. These schemes are implemented as part of the IPPL (Independent Parallel Particle Layer) library (Frey et al., 2024), which provides performance portable and dimension independent building blocks for scientific simulations requiring particle-mesh methods, with Eulerian (mesh-based) and Lagrangian (particle-based) approaches. The simulation used to compare the performance and portability of the schemes is Landau damping, part of a set of mini-applications implemented to benchmark and showcase the capabilities of the IPPL library (Muralikrishnan et al., 2024). We use grid-sizes of $512^3$ and $1024^3$ with 8 particles per cell, running with different algorithms in the solve phase of the Particle-in-Cell (PIC) loop: a Fast Fourier Transform (FFT) pseudo-spectral solver, a matrix-free finite difference Preconditioned Conjugate Gradient (PCG) solver, and a matrix-free Finite Element (FEM) solver. We also compare these PIC schemes to the novel Particle-in-Fourier (PIF) scheme, which performs interpolations using non-uniform FFTs thereby avoiding a grid in the real space. We obtain results on different computing architectures, such as AMD GPUs (LUMI at CSC), and Nvidia GPUs (Alps at CSCS and JUWELS Booster at Jülich Supercomputing Center), showcasing portability. In terms of absolute time the FFT solver is advantageous, but is limited in its applicability. All other field solvers in the PIC scheme are an order-of-magnitude more expensive in terms of time, but scale similarly to the FFT case in the electrostatic PIC context. The PIF scheme serves as a high fidelity alternative to standard PIC, and while it is costlier than the FFT-based PIC scheme, it shows excellent scalability on all the architectures.

LLM serving is increasingly multi-tenant: the same deployment must handle latency-critical interactive requests and more relaxed background workloads under a fixed GPU budget. This creates a tiered-SLO setting where maximizing overall goodput (requests that satisfy both TTFT and TPOT targets) is challenging because workload mix, request lengths, and load intensity vary over time. Existing systems mainly optimize request-level controls (e.g., queuing and batching) while keeping execution configuration largely static, which limits adaptation under multi-tier contention. We present Nitsum, a distributed LLM serving system that treats tensor parallelism (TP) as a first-class runtime control surface rather than a static deployment choice. Nitsum jointly optimizes TP level, prefill/decode GPU split, and request scheduling. To make frequent TP adaptation practical, Nitsum introduces TP-aware weight reuse and fast KV migration. Experiments on real traces and targeted microbenchmarks show that Nitsum improves SLO-compliant goodput over SoTA by up to 5.3 times.

We investigate the role of magnetic sublevels in Autler-Townes spectra of Rydberg atoms driven by radio-frequency (RF) fields with arbitrary polarization. While conventional treatments predict two symmetric sidebands from independent mJ transitions, experiments have reported additional unexplained spectral features. We show that these arise from elliptical RF polarization, which coherently couples multiple magnetic sublevels and requires a full multi-level treatment. We develop and diagonalize a Hamiltonian including all coupled mJ sublevels, predicting polarization-dependent degeneracies that produce two, three, or four resolved peaks. Using long-wavelength transitions and an anechoic environment we realize homogeneous RF fields that for the first time enable complete resolution of the mJ-dependent dressed states. We observe excellent agreement with theory as the RF ellipticity is varied. These results demonstrate that RF polarization fundamentally modifies Autler-Townes spectra and provide a consistent framework for interpreting magnetic-sublevel structure, with implications for Rydberg-based RF electrometry and polarimetry.

The prediction of aircraft icing is conventionally performed using multishot simulation frameworks that fail to predict the progressive roughening of the ice surface. To understand roughness formation, we investigate droplet impingement on clean and laser-scanned rough ice shapes using a high-fidelity computational framework based on wall-modeled large-eddy simulations and Lagrangian particle tracking. This methodology is validated against experimental data for a NACA 23012 airfoil and a NACA 64A008 swept tail, accurately predicting collection efficiency and supercooled large droplet splashing. The framework is subsequently applied to laser-scanned rime ice geometries to quantify the impact of surface roughness on local impingement distributions. The results reveal that physical roughness induces a highly nonuniform collection efficiency, with droplet impingement intensely concentrated on upstream-faces of roughness elements, creating sheltered shadow zones immediately downstream. While the spanwise-averaged collection efficiency remains remarkably similar to that of an equivalent smooth body, idealized smooth surfaces completely suppress these localized impingement peaks. Ice accretion simulations demonstrate that this localized impingement creates a self-reinforcing feedback loop, actively amplifying existing roughness features over time. These findings provide a direct physical explanation for the formation of characteristic rime ice structures and highlight the critical role of local surface topology in the accretion process.

Taking symmetric extensions can be considered as a generalisation of forcing, which produces a richer multiverse of models with and without the axiom of choice. We can study the structure of this multiverse using modal logic. In particular, we define the concept of of choice-switches, and show any independent system of choice-switches is not itself independent from any standard example of an independent system of buttons.

High-dimensional functional data are becoming increasingly common in fields such as environmental monitoring and neuroimaging. This paper studies high-dimensional functional linear regression models that relate a scalar response to ultra-high-dimensional functional predictors, where each predictor is treated as a random element in an infinite-dimensional functional space. To address the dual challenges of high-dimensionality and model interpretability, we propose MoFI-FLR, a novel two-step estimation framework rooted in reproducing kernel Hilbert space (RKHS) theory. The first step employs a functional elastic-net penalty to screen out irrelevant covariates, while the second step decomposes each selected predictor's functional coefficient into an interpretable finite-dimensional simple component and an infinite-dimensional complementary complement. By penalizing only the complementary component, our method automatically distinguishes simple effects, which consist only of the simple component, from complex effects, which also include complementary deviations. Under mild regularity conditions, we establish non-asymptotic theoretical guarantees, demonstrating that MoFI-FLR consistently recovers the active covariates and accurately identifies their true functional forms. We develop a computationally efficient algorithm to implement the proposed method and evaluate its performance through comprehensive simulation studies and an application to Psychomotor Vigilance Task EEG data.

In this paper, we investigate the spectrum of the unit-graph of the ring of $3 \times 3$ matrices over a finite field $\mathbb{F}_q$, which is equivalently the Cayley digraph $ \mathrm{Cay}\!\left((\mathrm{Mat}_3(\mathbb{F}_q),+), \mathrm{GL}_3(\mathbb{F}_q)\right)$. This unit-graph has a vertex set $\mathrm{Mat}_3(\mathbb{F}_q)$ with a directed edge from $A$ to $B$ whenever $B - A \in \mathrm{GL}_3(\mathbb{F}_q)$. Then, two vertices are adjacent precisely when their difference is invertible. With relevant character theory, we consequently demonstrate that the adjacency spectrum of $ \mathrm{Cay}\!\left((\mathrm{Mat}_3(\mathbb{F}_q),+), \mathrm{GL}_3(\mathbb{F}_q)\right) $ consists of four distinct eigenvalues together with their multiplicities. Using the Spectral Gap Theorem for Cayley digraphs, we show that if two subsets of vertices in $\mathrm{Mat}_3(\mathbb{F}_q)$ are sufficiently large, then there are matrices in the two subsets whose difference lies in $\mathrm{GL}_3(\mathbb{F}_q)$. In particular, any sufficiently large subset of $\mathrm{Mat}_3(\mathbb{F}_q)$ contains two distinct matrices whose difference has nonzero determinant. This spectral gap implies that large vertex sets cannot avoid each other and must be connected by at least one edge.

We present the first direct computation of spatially averaged dynamical quantities in the local Universe, employing the Cosmicflows-4++ reconstruction and a covariant scalar averaging formalism. We extract the domain-averaged density, expansion rate, spatial curvature, and kinematical backreaction over cosmologically relevant domains around our Galaxy, extending up to a comoving radius of $300~\mathrm{Mpc}/h$. The resulting domain-averaged present-day energy budget features nontrivial variations with scale that reflect a nested structure within the cosmic neighborhood, including a large-scale void shell encompassing the local cosmic web. Remarkably, we find significant contributions to this energy budget from the average spatial curvature at the $\mathcal{O}(10\%)$ level on all probed scales. By contrast, the kinematical backreaction remains much smaller throughout the surveyed volume, reaching at most a $\mathcal{O}(1\%)$ contribution on the smallest scales considered, i.e., $30~\mathrm{Mpc}/h$. Convergence to the global $Λ$CDM background is not observed within this range of scales.

Silicon detectors typically require an insensitive area around their periphery to accommodate guard rings, which help maintain the electric field uniformity around edge pixels and isolate the high leakage current from the physical edges of the detector. Minimization of this insensitive region is desirable for applications in high-energy physics, X-ray experiments, and medical imaging. Existing active edge technology offers a solution for reduction or total elimination of the insensitive region, via a continuation of the highly doped backside up the sidewalls of the device. However, current methods for realizing this technology are complex and expensive. We propose a new technique that simplifies the fabrication of highly doped edges using side ion implantation and microwave annealing. Tests demonstrating the feasibility of this proposed process were performed on a set of sensors, and current versus bias voltage measurements probing the edge effects were performed before and after the edge implantation and annealing. To aid in interpretation of the results, TCAD simulations of the test devices were performed. Significant improvement in the edge leakage current is observed, indicating the promise of this simplified process for fabrication of active edge sensors.

This work focuses on the development and demonstration of tunable superconducting on-chip resonator, leveraging the intrinsic current-dependent non-linear kinetic inductance of superconducting aluminium, and investigating the effect of oxygen content. Thin films are deposited using standard metal evaporation. We present results from a comprehensive study based on a series of evaporated Al thin films. This research aims to inform and constrain optimisation strategies for the design of mm-wave on-chip spectrometers, particularly regarding yield, resolution, and efficiency. By systematically varying film stoichiometry, we use a series of DC measurements to extract fundamental film properties such as resistivity, critical current and critical temperature. Furthermore, we employ low-loss DC-coupled microwave resonators to characterise both their microwave properties and the non-linear kinetic inductance, comparing these findings to a determined non-linear kinetic model. Finally, we discuss the possibility of usage in a parametric amplifier.

In this paper, we develop hybridized discontinuous Galerkin (HDG) methods for poroelastic wave equations. We first rewrite the governing equations to a first-order symmetric hyperbolic system in order to use dual mixed formulations for discretization. Subsequently, we combine two HDG approaches in the discretization of the system, the $\text{HDG}+$ method for the linear elasticity equations and the $\text{LDG-H}$ method for the diffusion equations, with adjustments for the poroelastic wave equations. In our proposed HDG methods, the numerical approximation of the stress tensor is strongly symmetric and the convergence of the errors are robust for nearly incompressible materials. Upon performing static condensation, the system retains numerical trace variables solely for the solid displacement and the fluid pressure. We provide comprehensive error analyses for both the semidiscrete formulation and the Crank--Nicolson time-stepping scheme. Finally, extensive numerical examples illustrate optimal convergence results and simulate different poroelastic wave propagation scenarios relevant in the literature.

We compute the Brauer group of some of the known Enriques manifolds. We then build special Brauer-Severi varieties on these manifolds and study the pull-back map from the Brauer group of an Enriques manifold to that of its hyper-Kähler universal cover, from both a geometric and an algebraic perspective.

Three decades ago, humanity entered the Exoplanet Era, with the discovery of the first planets orbiting other stars. Today, more than 6000 exoplanets are known - a tally recently bolstered by NASA's TESS spacecraft. Whilst TESS is an exceptional planet finding machine, dedicated follow-up observations from the ground are required to confirm the existence of the planets it discovers. To achieve this, we constructed the southern hemisphere's only dedicated exoplanet detection and characterisation facility, MINERVA-Australis, at the University of Southern Queensland's Mt Kent Observatory. Funded in 2015, MINERVA-Australis saw first light in 2018, in time for the launch of TESS. MINERVA-Australis has since been scouring the skies, working to confirm and characterise the incredible harvest of planets detected by TESS. To date, the facility has contributed to the discovery of 40 new exoplanets, and continued the legacy of radial velocity data from the Anglo-Australian Planet Search program.

We show that chiral order in two-dimensional nonreciprocal flocking mixtures is generically unstable. Combining large-scale agent-based simulations with a coarse-grained continuum description, we demonstrate that rotating chiral states emerging from antisymmetric couplings are destroyed by the proliferation of topological defects. The resulting dynamics is spatiotemporally chaotic and characterized by a finite correlation length that diverges as nonreciprocity vanishes. On length scales below this cutoff, density and orientational order fluctuations remain scale-free, but the associated scaling exhibits nonuniversal exponents. We attribute this atypical behavior to the coupling between density and order, which causes topological defects to act as persistent sources of nonlinear fluctuations.

Models of many-body localization (MBL) exhibit slow numerical drifts towards delocalization with increasing system size, for which no satisfactory theory exists. Numerics indicates that these drifts are driven by the proliferation of many-body resonances at intermediate disorder strengths. We develop a statistical method to predict the distribution of resonance oscillation frequencies which captures how the formation of resonances at larger frequency scales subsequently affects the formation of resonances at lower frequencies. Working within the statistical Jacobi approximation (SJA), we derive a flow equation for a power-law exponent $θ(w)$ characterizing the density of resonances at frequency scale $w$. A localized phase is described by a line of fixed points with $θ(w)>0$, while an instability of the flow signals resonance proliferation and the onset of thermalization. The predicted $θ(w)$ matches numerics on the Anderson model on random regular graphs and the Lévy-Rosenzweig-Porter random matrix ensemble, both of which host resonance-driven delocalization transitions. We further connect the flow to eigenstate properties such as the participation ratio and to dynamical observables such as the return probability. The predicted $θ(w)$ also matches what is numerically measured in real-space models of MBL at intermediate disorder strengths, representing a significant step towards explaining the finite-size drifts observed in MBL.

We report on a gravitational wave search for compact binary coalescences involving at least one component with mass between $0.2 M_\odot$ to $1 M_\odot$, and ratio of component masses between 0.1 and 1. The analysis uses data collected by the LIGO detectors between May 24 2023 15:00 UTC and January 16 2024 16:00 UTC. No statistically significant sub-solar mass candidates were identified by the participating search algorithms. We report the detection sensitivity of the current searches to the target sub-solar mass black hole population, while also reporting the sensitivity of the search to low-mass neutron star binaries for the first time. With the absence of detections, we place upper limits on the merger rate of sub-solar mass black holes, ranging from $110$ $\mathrm{Gpc^{-3}yr^{-1}}$ to $10000$ $\mathrm{Gpc^{-3}yr^{-1}}$ at 90% confidence. We use the merger rate limits to constrain two illustrative dark matter scenarios that can form sub-solar mass compact objects: primordial black holes, and dark black holes forming in a dissipative dark matter model. For late-forming primordial black hole binaries, our search excludes the fraction of dark matter in primordial black holes to be 1 for masses above $0.9 M_\odot$. In the early-formation scenario, we limit this fraction to be $\leq$ 7% at $1 M_\odot$, and $\leq$ 40% at $0.35 M_\odot$. For the dissipative model, the excluded region in the parameter space of dark matter fraction in dark black holes and their minimum possible mass extends down to (1.2 to 1.3) $\times10^{-5}$ when the minimum mass is $1 M_\odot$. For binary neutron stars that include sub-solar mass components, we estimate the sensitive space-time hypervolume to be $10^{-3}$ $\mathrm{Gpc^3yr}$, and report the upper limit on their merger rate for a simple, fixed population as ~86 $\mathrm{Gpc^{-3}yr^{-1}}$.

In mixed-ability collaboration, eye contact is often treated as a default cue for attention and turn-taking. As these signals are primarily visual, they are not reliably accessible to people with visual impairments. While prior work emphasized technical solutions, mechanism-level explanations of their experiences with sighted partners remain scarce. We interviewed 17 people with visual impairments about everyday interactions across work, education, and social settings. Using a critical-realist lens, we link events to plausible causal mechanisms and identify three recurring mechanisms: First, when gaze cannot allocate the floor, addressability hinges on explicit naming. Second, unclear speech entry cues and ongoing access work split attention and build fatigue, sometimes leading to withdrawal. Third, eye-contact norms can skew judgments of participation, prompting active management of visibility. We translate these mechanisms into five design challenges that reframe accessible eye contact as supporting configurable interaction contracts rather than merely making gaze visible.

We show that in a minimal setup of two waveguide-coupled quantum emitters, separated by long distances and subject to an external field, time-delayed feedback can be a resource for sensing field gradients. While the field gradient induces a detuning between the emitters; the large interatomic separations render the system dynamics non-Markovian. We show that the quantum Fisher information (QFI) for estimating the detuning parameter, and thereby the field gradient, is enhanced in the presence of non-Markovian delay. Such an enhancement can be attributed to the formation of atom-photon quasi-bound states that enable the field to interact with the emitters for longer times, thereby gaining more information about their relative detunings. Additionally, in the presence of delay, the interaction between the emitters is mediated via multiple spectral modes of the field, further enhancing the sensing capabilities of the system. Our results establish non-Markovian time-delayed feedback and multimode reservoirs as a resource for distributed quantum sensing with waveguide-coupled quantum emitters.

We analyze the spectrum of collective modes in a superconductor in which pairing is mediated by long-range nematic fluctuations. Previous experimental and theoretical studies have found that the superconducting gap in such a system is highly anisotropic and, at any finite $T<T_c$, vanishes on four arcs of the Fermi surface, even when the pairing symmetry is $s$ wave ($s^{+-}$ between hole and electron pockets). We derive the expression for the pair susceptibility $χ(\mathbf{q},Ω)$ at finite momentum $\mathbf{q}$ and frequency $Ω$ deep in the superconducting phase. We analyze the spectral function, $\operatorname{Im}χ(\mathbf{q},Ω)$, and its pole structure in the transverse (phase) and longitudinal (amplitude) channels, and compare the results with those of a conventional $s$-wave superconductor. We find that the analytic structure of the pair susceptibility in both channels is qualitatively distinct from that in a BCS superconductor. This gives rise to a highly unconventional dispersion of phase and amplitude collective modes.

In a free Fermi gas at temperature $T$ much higher than the Fermi temperature one expects that the fluctuations of the number of particles in a given region has Poissonian/classical statistics. On the other hand at low temperature the Pauli exclusion principle leads to non trivial counting statistics. It is of great interest from a theoretical and experimental point of view to characterize the crossover between these two limits. Here we focus on the hole probability $P(R,T)$, i.e. the probability that a region of size $R$ is devoid of particles, in dimension $d$, and on the case of a spherical region of large radius $R$. We show that at low temperature it takes the scaling form $P(R,T)\sim \exp\big[-(k_F R)^{d+1}Φ_d(u=2R\,T/k_F)\big],$ where $k_F$ is the Fermi momentum. By mapping the problem to an effective Coulomb gas, we compute exactly the scaling function $Φ_d(u)$ in any dimension. Remarkably, it exhibits a transition of order $\tfrac{3}{2}(d+1)$ at the universal critical value $u_c=2/π$, signaling a sharp change in the mechanism of rare fluctuations, associated with the emergence of a macroscopic gap in the optimal density of the associated Coulomb gas. Our analytical predictions are supported by precise numerical evaluations of the corresponding Fredholm determinants.

In this paper, we study boundary-value problems describing the exit distribution of finite-velocity random motions from prescribed domains. For the standard telegraph process, with and without drift, we derive the Dirichlet problems governing the exit point and mean exit time from a closed interval. We then extend the analysis to a planar finite-velocity model with orthogonal directions, for which we obtain the associated Laplace and Poisson-type equations for the exit distribution and mean exit time. In the special case of an infinite strip, explicit solutions are obtained. In all cases, we show that our equations and results converge, in the hydrodynamic limit, to the corresponding ones for Brownian motion.

Despite recommendations to deploy real-time Open Radio Access Network (O-RAN) applications (dApps) in containerized environments, existing approaches predominantly rely on bare-metal servers. Moreover, current dApp deployments offer limited visibility into the resource usage patterns of both intelligent and non-intelligent dApps, hindering informed deployment decisions. This work addresses these gaps by implementing and evaluating representative dApps across multiple deployment scenarios (bare-metal and containers) to characterize the trade-offs in latency, scalability, and resource utilization. Additionally, we identify key performance bottlenecks and demonstrate how offloading dApps to emerging hardware accelerators, such as smart Network Interface Cards (NICs), can alleviate these limitations and improve real-time responsiveness in O-RAN systems.

We extract a target-side Cu transverse-momentum profile from fixed-target $p$--Cu Drell--Yan data by holding a momentum-space proton reference fixed and training only an asymmetric Cu kernel in the small-$q_T$ region. In the supported window, $0.15 \le x_{Cu} \le 0.46$ and $7.5 \le Q_M \le 15.75$ GeV, the nuclear modification is not a universal width increase. It appears as $Q$-dependent redistribution: an $\mathcal{O}(1~{\rm GeV})$ shoulder and compensating probability flow between shoulder and resolved-tail regions, beyond one-parameter broadening.

Proper implementation of technical and administrative controls reinforces an organization's cybersecurity posture and business resilience, reduces risks, and enhances governance, ultimately elevating business maturity. The dynamics of the technological landscape and emerging threats negatively affect the most diverse companies, regardless of their size. This, associated with a global gap in the cybersecurity workforce, imposes enormous challenges and the need for a profound change in how companies respond to threats. Generative Artificial Intelligence from large language models has become an influential tool across various companies, emerging as a viable option to help address those challenges while partially addressing the shortage of skilled labor. Although large language models can help in this scenario, there may be risks, such as generating unreliable or 'hallucinated' content, which could lead people and companies to make bad decisions. Our study proposes integrating human experts into the validation process as a crucial step toward ensuring the proper implementation of technical and administrative controls. Furthermore, we sought to identify how large language models perform in assessing cybersecurity risk scenarios compared to human experts, highlighting the importance of integrating humans and machines in the cybersecurity risk assessment process. Using a questionnaire with risk scenarios, we analyzed responses from 50 human experts. We compared their responses with those of five popular large language models to determine whether it is possible to use only large language models for cybersecurity risk assessment. The results reveal that the large language models consistently underestimated cybersecurity risks compared to human experts, reinforcing the need for human oversight and suggesting that LLMs should be used as complementary tools rather than standalone assessors.

Band theory provides the foundation for understanding electronic structure in crystalline materials, but its reliance on exact translational symmetry limits its applicability to systems with defects, disorder, incommensurate modulations, or large unit cells. Here, we introduce a band unfolding framework that directly generalizes traditional band theory to systems where exact periodicity is absent, and which remains well-defined for both aperiodic and finite systems. To do so, we employ a pseudospectral approach to identify approximate joint eigenvectors of a system's Hamiltonian and translation operators, thereby yielding an unfolded band structure whose features are directly connected to the manifestation of approximate extended states simultaneously localized in energy and crystalline momentum. To reveal bulk-only spectral phenomena in finite systems, we further show that this pseudospectral framework naturally accommodates additional operators that suppress contributions from boundary-localized states, enabling the systematic isolation of intrinsic bulk behavior. We benchmark the scheme on several representative systems in one and two dimensions, including a Fibonacci chain, where our approach is able to both reveal a dispersive envelope while preserving the underlying hierarchy of spectral gaps. Looking forward, this pseudospectral approach may yield a broad framework for predicting momentum-resolved material responses in aperiodic, disordered, and finite systems where conventional band-theoretic methods are not applicable.

We study a local trace estimate for the microlocal angular square function \[ G_R f := \left(\sum_Θ|f_Θ|^2\right)^{1/2} \] associated with a parabolic decomposition of the frequency annulus of radius $R$ in $\mathbb{R}^3$. The measure under consideration is \[ μ_Q=χ\, H^2\lfloor S_Q, \] where $χ\in L^\infty(S_Q)$ is a measurable nonnegative density compactly supported in the patch, and \[ S_Q=\{(u_1,u_2,Q(u_1,u_2)):u\in U\}, \qquad Q(u_1,u_2)=\frac12(λ_1u_1^2+λ_2u_2^2), \qquad λ_1λ_2 >0. \] Writing $ρ=R^{-1/2}$, we prove \[ \| G_R f\|_{L^2(\mathrm dμ_Q)} \lesssim R^{1/8}\|f\|_{L^2(\mathbb R^3)}. \] Under local positivity of the density near the tangency point, the factor $R^{1/8}$ is attained by a tangent wave packet test and hence cannot be improved within this elliptic quadratic model, at this parabolic scale and for this angular square function. In particular, it measures the failure of a trace bound uniform in $R$ within this class. Its source is the extreme tangential interaction between a tube of radius $ρ$ and $S_Q$: the relevant surface measure is $\simρ^{3/2}$, whereas an $L^2$-normalized wave packet has quadratic size $\simρ^{-2}$. Thus the optimal quadratic cost is $ρ^{-1/2}$, producing the norm factor $ρ^{-1/4}=R^{1/8}$.

Two important decisions in the management of an ambulance fleet are ambulance selection decisions and ambulance reassignment decisions. Ambulance selection decisions determine what to do when an emergency call arrives (such as choosing what ambulance to dispatch to the emergency or putting the emergency in a queue of emergencies waiting for an ambulance to be dispatched). Ambulance reassignment decisions determine where to send an ambulance next when it has finished service for an emergency. Making good ambulance selection decisions and ambulance reassignment decisions is challenging because a decision made at a point in time affects the ability of the emergency medical service to respond to future emergencies (that are typically not known when the decision is made). We propose a new preparedness metric that quantifies the ability of the emergency medical service to respond to future emergencies. The preparedness metric can be used to make ambulance selection decisions and ambulance reassignment decisions by solving a tractable optimization problem each time that a decision has to be made. We compare the performance of the resulting method with 9 methods that have been proposed in the literature, based on data from a real emergency medical service for a large city.