In this paper, we consider the essential spectrum of submanifolds in Euclidean spaces under various geometric hypotheses. Our results involve extrinsic conditions such as finite total mean curvature, the convergence of the gradient of the extrinsic distance, and the extrinsic volume growth or the pinching curvature. In particular, we prove that the essential spectrum of a complete non-compact submanifold $M^n$ in a Euclidean space is $[0, +\infty)$ provided the second fundamental form $A$ of $M^n$ satisfies $\|A\|_{L^p} < \infty$, $p>n$.

Polar topological textures in ferroelectrics can host internal structure beyond a single integer topological charge. Here, effective-Hamiltonian molecular-dynamics simulations are used to examine whether such internal fractional topology can be reconfigured by local electric excitation in ordered 12.5% Zr-substituted barium titanate. Chemical doubling along the polar axis stabilizes a coupled nanodomain texture consisting of alternating Q = -2 antiskyrmionic and Q = +4 skyrmionic slices, in which the local topological charge fragments into six -1/3 and six +2/3 localized contributions, denoted here as topological quarks, separated by Bloch-point-like singular conversion regions. Picosecond local electric-field pulses applied to selected vortex-core columns drive reconfiguration of the internal dipolar texture of a 2.6 nm nanodomain. Under a binary pulse-mask protocol addressing the six vortex cores, all 64 masks lead, within the chosen low-temperature simulation protocol, to distinct relaxed metastable configurations. The switching calculations are performed in a cryogenic regime, and the programmed states remain stable over at least 1 ns of field-free evolution on the simulation timescale. The resulting configurations are distinguishable both by sector-resolved topological fingerprints and by their real-space polarization fields. These results provide a computational proof of concept that fractional polar topology in a ferroelectric nanodomain can be locally reconfigured by ultrafast electric excitation and used as a multistate configurational degree of freedom in an idealized low-temperature setting.

We consider a static linear panel model with both correlated and uncorrelated random coefficients, where the former can depend arbitrarily on observable regressors while the latter are independent of them. We provide sufficient conditions for identification of the distributions of the random coefficients without imposing restrictions on the time-series structure of the error terms in short panels. Our framework applies to regular and irregular designs. The distribution of the correlated coefficients follows via a deconvolution argument. In irregular designs, identification relies on a stayer-based argument exploiting near-singular realizations of the regressor matrix. We develop a two-step minimum distance sieve estimator, with tuning parameters selected by cross-validation. In an application to calorie-expenditure elasticities using data from the randomized evaluation of a conditional cash transfer program, we interpret the estimated distributions by program status as distributions of regime-specific structural calorie-expenditure elasticities. The estimated densities themselves reveal substantial heterogeneity in household-specific elasticities, with nontrivial mass concentrated near zero and a non-negligible share of negative realizations. This heterogeneity implies that responses to income or expenditure changes are not uniformly positive and vary widely across households. Taken together, these features support a framework in which households adjust along both quantity and quality margins, rather than conforming to a homogeneous Engel-curve response.

In the directed landscape, the Martin boundary coincides with the horofunction boundary. We show that functions in this boundary are precisely the eternal solutions possessing a spatial growth rate, and that the minimal Martin boundary is given by the Busemann functions. Moreover, every eternal solution can be expressed as a max-plus convex combination of countably many Busemann functions. Horofunctions are exactly those eternal solutions that admit a representation in terms of at most two Busemann functions with a common growth rate. As a consequence of instability, not all horofunctions are Busemann functions, and the Martin boundary is strictly larger than its minimal part.

Optimal transport provides an inherently geometric and highly structured framework for studying spaces of probability measures, supplying a rich theoretical toolkit for contemporary statistics, machine learning, and generative modelling. In applications, however, the measures of interest are almost never known precisely, calling for a theory of optimal transport that accounts for statistical uncertainty. We construct such a framework, lifting the classical theory to the setting of random probability measures. We introduce the $L^2$ over Wasserstein space establishing that it inherits the formal Riemannian structure of the Wasserstein space by characterising distances and geodesic geometry. The structure induces random flows with Wasserstein gradient flow sample paths, making it the natural extension of the Wasserstein space which allows for random gradient flow dynamics. We ensemble statistical convergence results of the optimal transport machinery using the empirical measure within the $L^2$ over Wasserstein framework. Moreover, in the setting of Bayesian non-parametrics, we refine Schwartz's consistency theorem to the Wasserstein topology and deduce posterior convergence of the same machinery in the $L^2$ over Wasserstein space. We demonstrate that the growing theory of random token sampling for transformer models using self-attention flow paths can be embedded into the our framework. The results provide a unified treatment of random optimal transport and its consequences for principled inference and generative modelling under the statistical uncertainty of random sampling.

Despite the knowledge that social, economical, and ecological networks are often of a small-world nature with inter-nodal distance growing even slower than logarithmically with system size, we often assume theoretical systems to be outside of this regime, to make them easier to treat analytically. Here we derive a framework to apply the powerful dynamical mean-field theory on highly heterogeneous networks that is able to account for more of the degree correlations naturally arising from network constraints, known as structural cut-offs. We apply this framework to the well-studied and understood disordered Lotka-Volterra model, and show typically reported observables such as survival rates and stability for these systems on ultra small-world networks. We find much better agreement for these variables for all ranges of exponents for simulated power-law networks as well as empirically sourced networks.

The "Gen-AI-tecture" project embeds a locally executed, discipline-specific tool into a mixed-methods focus-group design, structured around three research objectives: (a) to evaluate how generative AI tools impact students' creativity in design-thinking processes and outcomes, (b) to assess whether these tools enhance inclusivity in learning processes, and (c) to examine how they develop students' AI-handling skills with a view to boosting future employability. Findings indicate enhanced creative fluency, broadened participation across diverse learner profiles, and strengthened confidence in AI-supported design processes. The study contributes evidence-based guidance for integrating generative-AI workflows into architectural pedagogy, demonstrating how such tools can operationalise constructivist principles of learner-led meaning-making, support connectivist understandings of learning as participation in human-AI networks, and advance universal learning theories by promoting more inclusive, flexible and accessible educational practices for contemporary learners.

We study the problem of testing $H_0: ξ^\topβ=t_0$ in high-dimensional sparse linear regression with Gaussian random design and unknown design covariance. The loading vector $ξ$ is arbitrary, and the exact sparsity level $k$ is unknown but bounded by a known value $k_u$. Tests are required to control Type I error uniformly over the $k_u$-sparse null, while power is evaluated against $k$-sparse alternatives. We construct a computationally efficient mixed test that gives an upper bound on the adaptive separation distance and establish an information-theoretic lower bound calibrated to the magnitude profile of $ξ$. In the ultra-sparse regime $k_u\lesssim \sqrt n/\log p$, these bounds characterize the adaptive separation rate up to logarithmic factors for arbitrary $ξ$. In the moderately sparse regime $\sqrt n/\log p\ll k_u\lesssim n/\log p$, these bounds match for several classes of loading vectors but may differ in general. In this regime, we further prove a low-degree lower bound that matches the upper bound up to logarithmic factors. This provides evidence that improving on the rate of the mixed test, if statistically possible, may be computationally hard. For flat sparse loadings, we complement this evidence with a polynomial-time reduction from sparse CCA. Finally, we examine how information about the design covariance affects the adaptive separation rate in two settings. Under a sparse signed-spiked covariance model, the information-theoretic lower bound is attainable up to logarithmic factors by a computationally inefficient procedure, while the low-degree lower bound and sparse-CCA reduction continue to apply, providing evidence for a statistical-computational gap. When the design covariance is known and diagonal, the adaptive separation rate takes the same form as in the ultra-sparse regime.

All standard formulations of relativistic dissipative hydrodynamics, from Eckart through Israel-Stewart to the recent BDNK framework, assume that the viscous stress depends on the shear tensor $σ_{αβ}$ and the expansion scalar $θ$ but not on the vorticity $ω_{αβ}$ or the acceleration $a_α$. We derive this structure from a Lagrangian kinematic construction on Lorentzian spacetimes, extending a recent result on Riemannian manifolds. The spatial strain rate, constructed from the rate of change of spatial inner products of Lie-dragged connecting vectors, is the spatially projected Lie derivative of the projected metric $h_{αβ} = g_{αβ} + u_αu_β$. The acceleration terms drop out exactly under spatial projection, and the vorticity cancels by symmetry. We show that material frame-indifference fails for generic Killing perturbations by an amount $δ\mathfrak{h}_{αβ} = +ε(ξ_αa_β+ ξ_βa_α)$ proportional to the acceleration, and is restored only for flow-preserving isometries. We prove that the non-relativistic limit of the BDNK equations gives the deformation Laplacian universally in the viscous sector, with the BDNK parameter dependence identified by Hegade K R, Ripley, and Yunes arising entirely from the thermal (heat-flux) sector. As an application, we derive the Weinberg gravitational-wave damping formula directly from the kinematic strain rate in a perturbed FRW spacetime.

The US housing market exhibits pronounced seasonal cycles: prices and sales rise through spring, peak in summer, and decline through autumn and winter. Since 2021, this pattern has shifted earlier in the calendar year, with spring strengthening at the expense of the traditional summer peak. A leading explanation for housing market seasonality is the search-and-matching model of Ngai and Tenreyro (2014), which links these cycles to household mobility through a thick-market mechanism. In this framework, periods with higher mobility generate thicker markets and higher prices and transaction volumes. Viewed through this lens, a shift in the seasonal cycle of prices and sales raises the question of whether the timing of household moves has changed. Did residential mobility shift earlier in the calendar year after 2021? We find that it did. Using SIPP data, and corroborating evidence from Google Trends indicators, we document a post-2021 shift in mobility toward spring. We extend the model to a monthly frequency, prove the existence and uniqueness of the equilibrium, and calibrate it to the observed mobility patterns. The calibrated model reproduces the spring shift in both prices and transaction volumes, consistent with the view that a change in the timing of household mobility alone can account for the recent shift in housing market seasonality.

Grid cells in the medial entorhinal cortex and place cells in the hippocampus together support spatial navigation. The two regions are reciprocally connected, and there is a chicken-and-egg problem for how both arise and reinforce each other during development. Current computational accounts either derive one type from the other or use network dynamics to model the emergence of one type in isolation. We introduce a unified recurrent network model that instantiates Dale's Law (every neuron is either excitatory or inhibitory), and is trained to predict the next sensory observation from masked previous sensory observations and egocentric motion. To our knowledge, this is the first single-objective model in which grid and place cells co-emerge without supervision of either type, or reliance on pre-existing spatial-cell representations. The two kinds of spatial codes coexist across 1,000 different training configurations, with their balance set by the amount of sensory noise and masking. Without retraining, the network qualitatively reproduces experimentally observed grid fragmentation in hairpin mazes, grid merging after wall removal, lattice alignment across connected rooms, locally ordered 3D fields observed in freely flying bats, as well as the developmental order in which place cells precede grid cells. We interpret these results in terms of two complementary encoding pressures within a single sensory-prediction objective: (1) correcting errors or reconstructing missing components of sensory observations, and (2) prediction of the next sensory state during navigation. Our results suggest a circuit-level account of the co-emergence of grid and place cells, and experimentally testable predictions for the two kinds of spatial codes.

We study a family of Jacobi operators in which the diagonal entries are multiplied by a coupling parameter $λ\geq0$. Under suitable conditions, the operator is self-adjoint for every $λ>0$, while the formal limit at $λ=0$ is a symmetric Jacobi operator admitting a one-parameter family of self-adjoint extensions. A central ingredient of our analysis is the derivation of uniform bounds for square-summable generalized eigenvectors in the small-$λ$ regime, which combines discrete WKB methods with Airy-function asymptotics. Using these estimates, we analyze the limiting behavior $λ\to0$ in the strong resolvent sense, proving that for every sequence $λ_j\to0$ one can extract a subsequence along which the corresponding Jacobi operators converge to some self-adjoint extension of the limiting operator; conversely, every such extension can be obtained in this way. We call this behavior an essentially singular limit, by analogy with essential singularities in complex analysis. As an application, we study higher-order squeezing operators arising in quantum optics. Using the connection with Jacobi operators, we show that when the relative strength of the free-field term tends to zero, different self-adjoint extensions of the squeezing operator are selected along different sequences. In particular, this limit does not single out a physically distinguished self-adjoint extension, but instead identifies a distinguished subclass of extensions compatible with the underlying symmetry.

Compared with traditional fuels, emulsified fuels can improve fuel atomization and combustion, and nanoparticles as additives have the potential to enhance combustion and reduce emissions. Previous studies on micro-explosion mainly considered emulsion droplets, but the role of nanoparticles in emulsion droplets is still unclear. In this study, we experimentally investigate the micro-explosion of emulsion droplets with nanoparticles via high-speed photography, digital image processing, optical microscopy, and scanning electron microscopy. The results show that the presence of nanoparticles can greatly improve the strength and probability of micro-explosion, particularly for carbon nanoparticles. This is mainly because nanoparticles can agglomerate during the evaporation of emulsion droplets, facilitate the absorption of radiation energy, inhibit the diffusion of superheated vapor, and ultimately promote micro-explosion. The effects of nanoparticle mass fraction and water content are also investigated, and the results show that the increase of nanoparticles and water can facilitate micro-explosion.

Software Product Line Engineering enables systematic reuse across families of related software intensive systems. This survey synthesises key SPLE foundations, lifecycle concepts, adoption models, tooling and AI era challenges. Based on a structured review of the SPLE literature, we compare major adoption and evaluation models, including BAPO, FEF, PuLSE, SIMPLE, COPLIMO, PROMOTE-PL, and APPLIES. We further summarise the historical evolution of SPLE research from domain engineering foundations to AI assisted variability management. The survey also examines tool interoperability, UVL-based standardisation, SME adoption, migration from clone-and-own development, variability aware DevOps, empirical evidence gaps and assurance challenges for AI assisted SPLE. The paper provides a compact research agenda for software engineering and ICT researchers by consolidating open challenges and future research directions in contemporary SPLE.

This study investigates radar technology for non-invasive brain imaging and tumour detection, offering an alternative to MRI and CT scans. Using Ansys HFSS to simulate electromagnetic interactions in brain tissues, we evaluate the penetration, signal strength, and safety of Patch and Vivaldi antennas. Results show Patch antennas are optimal for tumour localization, while Vivaldi antennas suit broader scanning applications. Although promising for safer, more accessible imaging, especially in resource-limited environments, further research with diverse models and actual patient data is essential to advance this technology in non-invasive medical diagnostics.

Public-key primitives that today anchor session-key establishment - RSA, Diffie-Hellman, and elliptic-curve cryptography - reduce to integer factorization or discrete logarithm and are therefore vulnerable to Shor's algorithm on a sufficiently capable quantum computer. The harvest-now, decrypt-later (HNDL) threat model turns this future capability into a present liability: ciphertext archived today can be decrypted retrospectively once a cryptographically relevant quantum computer becomes available. We propose a session-key establishment scheme that distributes a freshly generated key as multiple, independently encrypted fragments across distinct, ephemeral Tor circuits between an onion-service proxy and an onion-service client. Reconstruction requires every fragment; each fragment travels its own per-bundle circuit established via a NEWNYM signal. The security argument rests on the standard end-to-end correlation bound for onion routing: an adversary controlling a fraction of Tor relays must independently deanonymize every fresh circuit to correlate the fragments belonging to one session, and the per-fragment probability of success decays multiplicatively in the number of fragments. We implement the design as a Flask-based prototype on AWS EC2, with both the proxy and the client deployed as Tor onion services, and measure end-to-end key-establishment latency. The implemented prototype completes a key establishment in 13-20 s on average (7-50 s including tails), of which approximately 88% is attributable to Tor-related delay - a cost we discuss in the context of the privacy-versus-responsiveness trade-off.

Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and under noiseless conditions, coherent processing of quantum data outperforms fixed-measurement schemes followed by classical processing. It remained uncertain whether this performance gap persists at a finite scale, and in the presence of noise that is unavoidable with current quantum devices. In this work, we present simulations and analysis of the performance of existing hardware on a learning problem known to exhibit asymptotic advantage, now subjected to noisy quantum data. Comparing coherent quantum processing directly against fixed-measurement schemes, our results demonstrate a clear performance separation at a scale of just 30 to 40 noisy qubits. Already at this scale, the fundamental bottleneck is no longer classical computation but data acquisition; matching the noisy coherent protocol with measure-first strategies would still require months or even years of measurements. By systematically evaluating hardware constraints such as state preparation, gate errors, readout errors, connectivity, and coherence times, we provide evidence that a demonstration of such a strong learning advantage is accessible on near-term devices.

Light scattering by two-dimensional (2D) van der Waals heterostructures (vdWHs) is immense, especially given their infinitesimal volume, thus enabling strong light-matter interactions. Surface 2D polariton waves manifest through large concentration of electromagnetic field in vertical direction, normal to their propagation. By confining vdWH materials into 2D photonic shapes, one can manipulate and compress light in lateral directions. Scattering-type scanning near-field optical microscopy is a perfect tool for direct imaging of the propagating polaritons and studying the properties of confined polaritons in nanostructures. Though, thus far the quantitative analysis, such the wavelength extraction, has been challenged for confined polaritons by incapability of mapping of the wave period on sub-wavelength scale and difficulty of identifying an adequate substrate's "background" to subtract. Here, an analytical approach is developed to reveal the local propagation constant of confined polaritons under abovementioned constraints and map it with the sub-wavelength resolution. Applied to analysis of the SiC/2D-Ag/EG (epitaxial graphene) photonic nanostructures, the technique uncovered that the polaritons are highly confined in both vertical ($\simλ$/50) and lateral directions ($\simλ$/40) by 2D metal.

Eduardo Sontag and coauthors studied Input-to-Output Stability (IOS) and the output asymptotic gain property. These notions changed control theory and recently had an impact on robust adaptive control through the Deadzone-Adapted Disturbance Suppression (DADS) control scheme. Moreover, recently the notion of IOS was extended to systems described by Partial Differential Equations (PDEs). In this work, we celebrate Eduardo Sontag by combining DADS and IOS for PDEs: we study the partial-state regulation problem for a scalar Ordinary Differential Equation (ODE) which is interconnected with a possibly infinite-dimensional system. In such a case the DADS control scheme can allow an escape from the requirements of the small-gain theorem that is mainly used for partial-state feedback. We show the design procedure of partial-state DADS controllers and we prove robust regulation even in the presence of external inputs (disturbances) without assuming knowledge of any disturbance/parameter bounds. The DADS controller is applied to three different cases of the interconnection of an ODE with an almost completely unknown: (a) heat PDE, (b) transport PDE, and (c) wave PDE with viscous damping. We show that the same DADS controller can achieve robust regulation in all three cases.

Dwarf galaxies in dense cluster environments are susceptible to tidal interactions that can alter their morphology and kinematics. Boxy isophotes are well studied in massive galaxies but remain poorly understood in dwarfs. We aim to identify and characterise boxy/peanutty dwarf galaxies in the Perseus cluster and determine the origin of their isophotal shapes. Using Euclid Early Release Observations of the Perseus cluster, we present a cumulative light fraction method for robustly measuring the isophotal shape parameter $c_4$, particularly suited to low surface brightness regimes. From ~1100 cataloged dwarfs, we select a clean sample of ~190 early-type systems with reliable $c_4$ measurement. Observed trends are interpreted through comparison with mock Euclid observations of $N$-body simulations of tidally transformed dwarfs. We identify 13 dwarfs with significantly boxy isophotes ($c_4 < -0.0175$). These galaxies lack visible thin disks, lie on the cluster red sequence, and show no preferential spatial concentration within Perseus. We find a significant anticorrelation between $c_4$ and effective radius: larger galaxies exhibit more boxy isophotes. An analogous size-shape anticorrelation is recovered in the simulations, where inner regions are dominated by box orbits associated with a triaxial peanut structure and outer regions by short-axis tube orbits. The boxy dwarfs in Perseus are tidally transformed remnants of moderately rotating progenitors, with boxy isophotes tracing inner box-orbit-dominated peanut structures. The size-shape correlation arises from viewing geometry: face-on orientations reveal the rectangular profile of the elongated triaxial structure (large and boxy), while edge-on views yield rounder, compact morphologies. Our sample represents an orientation-selected subset of tidally transformed, peanutty dwarfs in the cluster.

The dynamics of a Lamb-Oseen vortex dipole in a viscoelastic fluid are investigated, with emphasis on asymmetry, coupling strength, and transverse shear waves relevant to strongly coupled dusty plasmas. Dusty plasmas provide a natural realization of strongly coupled VE behavior, where transverse shear modes dominate in the incompressible limit. Numerical simulations are carried out using the incompressible generalized hydrodynamic model for both symmetric and asymmetric dipoles, with variations in vortex core size, circulation strength, and separation distance. In the symmetric case, dipoles exhibit sustained translational motion, with propagation speed decreasing as the initial separation distance increases, consistent with inviscid predictions. In contrast, asymmetric configurations-arising from unequal core radii or circulation strengths-lead to rotational motion due to imbalance in induced velocities, with the weaker vortex orbiting the stronger one. Viscoelasticity introduces transverse shear waves whose strength and propagation speed increase with coupling. In weakly coupled regimes, their influence is minor, while in moderately coupled regimes they modify propagation and induce deformation. In strongly coupled regimes, transverse shear waves significantly enhance vortex-vortex interaction, accelerating strain-induced deformation and leading to rapid dissipation of the weaker vortex. The evolution also satisfies the conservation theorem, where the contributions from convective, radiative, and dissipative processes dynamically compensate each other, maintaining global balance throughout the dynamics. These results provide insight into wave-vortex coupling in complex fluids, with implications for transport processes and structure formation in strongly coupled plasmas and other viscoelastic media.

Completeness proofs in categorical semantics usually proceed by building a syntactic category whose composition is given by substitution. For untyped effectful Call-by-Value languages, this runs into a basic obstacle: there is no canonical notion of simultaneous substitution of computations, since evaluation order is semantically meaningful. We address this by taking single computation substitutions, that is, binding steps, as primitive, and representing computation substitution by finite sequential lists composed by concatenation. We formalize this idea in a one-object Freyd-multicategorical setting. We introduce Freyd operads, separating a cartesian operad of values from a symmetric Ren-cartesian preoperad of computations, connected by a Freyd functor, and from any Freyd operad we construct a corresponding Freyd PROP of substitutions. We prove that this construction is representable and, in the strict one-object setting, left adjoint to restriction to codomain 1. Using the induced term model, we interpret untyped computational lambda-calculus with procedures and higher-order functions in weakly closed Freyd operads, and prove soundness, initiality, and completeness. This yields a categorical semantics tailored to untyped effectful computation and broad enough to encompass realizability-oriented models such as monadic combinatory algebras.

Unconventional magnetism has emerged as a transformative frontier in condensed matter physics. Such phases are characterized by substantial non-relativistic spin splitting (NSS) in symmetry-compensated magnets. They have been classified by the parity of their spin textures under momentum inversion, leading to the paradigms of altermagnets (even-parity) and odd-parity magnets. However, the full symmetry landscape remains largely unexplored. In this Letter, we present a systematic classification framework for unconventional magnetism based on the representation theory of the spin textures and the associated parity properties. Within this framework, we predict two previously unidentified classes beyond the established pure-parity categories: hybrid-parity magnets (HPMs) and unconstrained-parity magnets (UPMs), where the spin textures exhibit contrasting parities among their Cartesian components and the parity of the spin textures is ill-defined, respectively. We derive universal symmetry criteria that categorize HPMs into three distinct types. Importantly, by combining the spin splitting characteristics of altermagnets and odd-parity magnets, HPMs can enable the coexistence of the spin current and Edelstein effects. Taking FePO4 as an example, we perform first-principles calculations to demonstrate this coexistence. Finally, we discuss the potential applications of HPMs in spintronic devices. Our work provides a comprehensive symmetry classification of unconventional magnetism and establishes HPMs as a promising platform for multi-functional spintronics.

The two-watched literal scheme, a core component of efficient CDCL (Conflict-Driven Clause Learning) implementations for propositional logic, is extended to first-order logic. Given a set of first-order clauses and a set of ground literals, our lifted two-watched literal scheme efficiently detects all propagating and false clauses with respect to the ground literals. We present the algorithm as a system of rules and prove its soundness and completeness. Additionally, we provide an implementation of the two-watched literal scheme, which outperforms a standard dynamic programming approach for detecting propagatable literals and conflicts, especially when dealing with long clauses.

This paper presents our research software engineering (RSE) experiences over two years with libNEGF, a quantum transport code. We describe practical approaches to code quality assurance--including continuous integration, automated testing, and compiler warning correction--and performance engineering through continuous benchmarking. Our systematic application of these practices revealed critical defects: uninitialized memory reads, out-of-bounds writes, and notably, a misunderstood mathematical model in our boundary condition handling. We also document how continuous benchmarking exposed performance regressions caused by HPC system configuration changes. Our findings provide data points suggesting that a dangerous class of defects--equivalent to undefined behavior in C/C++ and processor-dependent behavior in Fortran--is as prevalent in Fortran scientific codes as elsewhere. While libNEGF is implemented in Fortran, most recommendations are applicable to scientific software regardless of implementation language, and they can be implemented selectively or in their entirety for both new and existing projects.

Automatic subjective speech quality assessment (SSQA) traditionally estimates speech quality on an utterance or system level. While this resolution was adequate for older transmission or synthesis systems that produced speech signals of mediocre quality, modern systems generate high-quality speech with degradations that may occur only locally. With suitable model architectures and regularization losses, SSQA models trained with utterance-level targets can also yield useful local predictions of speech quality. In this work, we extend such models to produce frame-level embeddings that cluster by degradation type. Specifically, we employ a partial mix-up strategy on a parallel corpus of clean and degraded utterances and apply a contrastive loss to distinguish between degradation types. Through experiments on both in- and out-of-domain data, we demonstrate that our approach improves degradation detection and enables the identification of degradation types by analyzing embedding clusters.

The unexpected localization of the repeating FRB 20200120E to a globular cluster challenges conventional FRB models based on magnetars formed via core collapse. One alternative model suggests that FRB 20200120E is a millisecond pulsar (MSP) producing giant pulses (GPs). To test this hypothesis, we compared the characteristics of FRB 20200120E bursts with the GPs of the most energetic Galactic MSP known, M28A (PSR B1821$-$24A), using observations with the Parkes (Murriyang) telescope's Ultra Wideband Low-frequency (UWL) receiver. Our analysis provides insight into the spectral structure and frequency extent of M28A's GPs, revealing broad-band spectra spanning $700-4000$ MHz (in some cases) with complex spectral peaks. We find that known M28A GP characteristics persist at this bandwidth, such as durations, luminosities, periodicity, wait-time, and energy distribution. A sub-band search for narrow-band GPs yielded no detection of genuinely narrow-band GPs. However, we do find narrow-band spectral peaks of $\sim100$ MHz bandwidth, a similar scale observed for FRB 20200120E's bursts. Compared to FRB 20200120E's bursts, M28A's GPs have $50\times$ shorter durations, $10^5\times$ lower spectral luminosities, clear periodicity (vs. no periodicity), a purely Poissonian wait-time distribution (vs. quasi-Poissonian), and generally broad-band spectra with narrow-band peaks (vs. only narrow-band bursts). Both sources show a steep energy distribution and minor dispersion measure variability. Our study finds no strong links between M28A and FRB 20200120E. However, we cannot rule out the possibility that FRB 20200120E is a rare and unique type of MSP with no Galactic analogue. Furthermore, higher-cadence monitoring of M28A, for hundreds to thousands of hours, might reveal rare but extremely luminous pulses.

Olfactory search in turbulent environments is a sensorimotor challenge solved with remarkable efficiency by many animals, yet replicating this ability in artificial systems remains difficult because detections are intermittent and wind direction fluctuates strongly, rendering standard search strategies unreliable. We introduce a wind-relative reinforcement-learning framework in which an agent navigates a turbulent plume with a single internal variable -- the elapsed time since the last odor detection -- and selects actions relative to a locally estimated wind direction filtered through an exponential memory kernel. Policies are trained and evaluated in direct numerical simulations of turbulence, capturing the multi-scale characteristics of velocity and odor fields in natural environments, both in the presence and absence of a mean wind. In a mild mean wind, the learned policy outperforms cast-and-surge regardless of the wind memory time, yet adapts its movement pattern to wind-estimation quality. In isotropic turbulence, performance peaks at an intermediate wind memory time, identifying temporal wind integration as a regime-dependent resource. Our results highlight the importance of developing and validating olfactory-navigation strategies under realistic turbulent conditions, and offer a compact design principle for minimal robotic olfactory navigation and testable predictions for biological search behavior.

In future large-scale deployments of 6G and beyond networks, collecting timely information, as measured by the Age of Information (AoI) metric, is becoming increasingly important. At the same time, the environmental impact, often characterized by the resulting Carbon Footprint (CF), depends on both the amount of consumed energy and the Carbon Intensity (CI), i.e., the amount of CO$_2$-equivalent emissions produced per unit of consumed energy. Since CI varies over time, minimizing energy is not equivalent to minimizing CF, as a status update with the same energy demand may result in a different carbon cost depending on when it is transmitted. This makes timely status updating a nontrivial scheduling problem. To address this challenge, we formulate carbon-aware status updating as a constrained Markov Decision Process (MDP) that minimizes AoI subject to CF budget, transmission duty-cycle, and channel-capacity constraints. We then propose Sustainable Age-of-Information-Based Timely Status Updating for Hardware-constrained Edge networks (SAOITHE), a Whittle-index-based scheduling solution that enables scalable real-time scheduling. Using real-world CI traces across low-, medium-, and high-CI regions, the results show that SAOITHE remains within the allocated CF budget while achieving lower AoI than baseline policies. Moreover, the gains are around 25% and 20% in low- and medium-CI regions, respectively, and up to 75% in high-CI settings, while preserving scalability.

We show that anyon chains, after stabilizing with infinite-dimensional ancilla spaces, factorize locally as tensor products of infinite-dimensional Hilbert spaces. This implies that any unitary fusion category can be realized as symmetries on a tensor product of infinite-dimensional Hilbert spaces. We then show that any two anyon chains with the same symmetry category are related by a symmetry-compatible locality-preserving unitary after stabilizing with infinite-dimensional ancilla, showing that for a fixed fusion category, there is a single stable equivalence class of symmetry realizations on the lattice via anyon chains. As a corollary of our proof, we show that the physical boundary algebras of Levin-Wen type models are bounded spread isomorphic after stabilization if and only if they have the same bulk topological order.