This paper develops a robust fixed time optimization framework for constrained problems that guarantees exact constraint satisfaction and convergence to KKT points within fixed time , independent of initial conditions. The approach treats the Lagrange multipliers as control inputs, composed of an equivalent control and a switching control, with the system states representing the decision variables. An equivalent control steers the gradient flow to a local KKT point asymptotically for nonconvex objectives and to unique global optimum in fixed time for convex objectives. Constraint enforcement is achieved by embedding the equality constraints directly as a sliding manifold, with a fixed time switching control ensuring rapid and reliable feasibility. The framework further accounts for the matched disturbances, providing robustness guarantees that are theoretically characterized and illustrated using spherical constraints. Numerical studies on a 3-bus AC optimal power flow problem and distributed consensus=based parameter estimation problem demonstrate the effectiveness, scalability and robustness of proposed approach.

In this paper we introduce the novel Deontic Simplicial Logic (DSL), a deontic logic for group obligations based on simplicial complexes. We provide the first deontic interpretation of simplicial models in which vertices represent individual commitments and higher-dimensional simplices represent joint obligations of groups of agents. We further extend DSL to the Dynamic Deontic Simplicial Logic (DDSL), resulting in the first dynamic logic based on simplicial complexes. DDSL models agents' choices among mutually exclusive commitments and captures the effects of individual and joint actions via update operations on simplicial models. We prove soundness and completeness for both the static and dynamic deontic simplicial logics. We motivate our results with multiple examples, both in the static and dynamic settings.

In an era dominated by big data and machine learning, establishing valuable data collaboration has never been more critical. However, such collaborations must operate under regulatory and legal constraints. Two-party Privacy-Preserving Record Linkage (PPRL) emerges to assess the potential collaboration value and also ensure the privacy and security of the involved data. Nevertheless, the substantial computational and communication overheads associated with PPRL hinder its practical adoption in data markets with numerous potential collaborators. Therefore, we present the Screening-then-Linkage framework, which incorporates a lightweight Screening phase prior to the resource-intensive PPRL phase, i.e., PPRS, to mitigate the scalability issue of PPRL. We propose a circuit-PSI-based system, named Appraisal to realize a secure, effective, and efficient PPRS. To reconcile the approximate matching and/or schema-aware setting required in PPRS with the limitations of the circuit-PSI supporting only symmetric functions, we propose a more communication-efficient secure permutation, i.e., Oblivious Attribute/Feature Alignment protocol tailored for PPRS. This protocol supports a broader range of comparison functions and significantly improves efficiency, i.e., reducing communication costs by a factor of 14 compared to the conventional protocol. Our rigorous analysis and comprehensive empirical evaluations demonstrate the security, effectiveness, and efficiency of Appraisal. Appraisal can accommodate up to $850\times$ more records than the SOTA PPRS system, SFour, within the same constraints. Moreover, it is $165 \times$ faster than SOTA PPRL, indicating the Screening-then-Linkage framework substantially decreases the computation time required to identify the most valuable collaborators from a large pool of candidates.

We address the problem of observation noise misspecification in Bayesian filtering of dynamical systems via recent advances in generalised Bayesian inference. Mis-match in tail decay between the true data generating process and an assumed observation model, often showing via frequent outliers, can strongly impact Bayesian updates and analysis in Kalman filtering. Existing approaches often employ detect-and-delete-schemes or covariance inflation to avoid assimilation of influential instances of mis-specification. In challenging settings where the analysis updates are barely sufficient to counteract the induced forecast uncertainty, these strategies may destabilize or struggle to provide reliable uncertainty quantification. We consider a novel Kalman filter adjusting information processing in the analysis step by employing diffusion score matching for inference to obtain robustness while maintaining well-quantified uncertainties. We provide theoretical properties of the diffusion score matching Kalman filter in linear Gaussian state space systems covering conjugacy and closed form parameter update in the analysis step, robustness, covariance stability, and tuning as well as high-dimensional consistency. We derive ensemble approximations via stochastic and deterministic coupling as well as implementing localization to obtain EnKF, ESRF and LETKF varieties. We evaluate the methods in appropriate simulation studies on target-tracking, the chaotic Lorenz 63 system and the Lorenz 96 system in 40 dimensions. Our insights highlight a critical trade-off between robustness and stability in Bayesian filtering. Methods employing generalized Bayesian inference can navigate this balance and improve data assimilation in challenging environments combining non-linear dynamics and potentially non-Gaussian observation noise.

Gaussian Splatting (GS) has emerged as an efficient representation for high-quality 3D reconstruction and novel view synthesis. However, its large model size poses challenges for storage and transmission. While several GS compression solutions have been proposed, their perceptual impact remains poorly understood due to the lack of dedicated evaluation datasets. To address this gap, this paper introduces GScomp-QA, a subjective quality assessment dataset for evaluating synthesis quality from compressed GS models. The dataset comprises 331 video stimuli from 13 real-world scenes, covering 9 state-of-the-art GS compression solutions. By using videos synthesized from uncompressed models as reference, GScomp-QA isolates compression-induced distortions from synthesis artifacts. A subjective study with 20 participants was conducted, providing reliable perceptual scores. Based on these data, GS compression solutions are evaluated through perceptual rate-distortion analysis. In addition, 18 objective quality metrics are evaluated, showing that they do not fully capture GS-specific distortions. GScomp-QA will be publicly available and provide a benchmark for evaluating GS compression solutions and supporting the development of quality metrics tailored to GS compression.

In neutrino oscillation experiments, exclusive measurements of neutrino-nucleus interactions play a critical role, by providing the theoretical and experimental input needed for a reliable estimation of the neutrino energy. In this paper, we derive the general form of the azimuthal angle distribution for quasi-elastic scattering, focusing on a dependency that has been routinely overlooked. We demonstrate that the outgoing nucleon exhibits a preference for emission outside the lepton scattering plane, with an asymmetric azimuthal distribution. In the context of neutrino-nucleus scattering, we argue that this asymmetry is caused by parity violation in the weak interaction. Furthermore, we show that in cross section calculations the asymmetry is sensitive to nuclear modeling choices and to the shell structure of the initial nucleus, thus providing a novel source of information for energy reconstruction in neutrino experiments. We study the experimental feasibility of observing this effect by applying a realistic momentum detection threshold and an intranuclear cascade. We estimate that the asymmetry is observable with $\mathcal{O}$($10^4$) events at the 99% confidence level for neutrino interactions on $^{12}$C, suggesting that the effect is within reach of the current generation of neutrino detectors.

Unmanned aerial vehicle (UAV) swarms are increasingly deployed in vast low-altitude applications, owing to their capabilities in distributed sensing, flexible communication, and autonomous coordination. Nevertheless, the open and highly dynamic operating environment of UAV swarms introduces serious security risks, including GPS spoofing, insider threats, and multi-hop intrusion. These threats are aggravated by limited on-board resources, frequently changing network topology, and the presence of intelligent adversaries. To tackle these issues, this paper proposes a cloud-edge-end collaborative defense framework for UAV swarms. Based on this framework, three complementary mechanisms are developed. First, a cooperative perception scheme is designed to resist GPS spoofing via interactive attack-defense game modeling. Second, a behavior-driven authentication method with trust evaluation is developed to mitigate insider threats. Third, a multi-agent attack forensics framework is devised to intelligently trace the propagation paths of multi-hop attacks in UAV networks. Experimental results validate the effectiveness of the proposed approaches. Finally, several open research directions are outlined.

Hybridization of highly itinerant Dirac electrons with localized flat-band states is predicted to yield emergent phenomena such as exotic heavy-fermion behaviour. Epitaxial graphene on two-dimensional adsorbate structures on SiC(0001), which host flat bands, offers a promising platform to explore these effects. However, direct experimental evidence of interlayer hybridization in such systems has so far been lacking. Here, we address this gap using time- and angle-resolved photoemission spectroscopy (trARPES) where interlayer hybridization manifests in three key observations: (1) accelerated Dirac-carrier relaxation arising from additional electronic and phononic decay channels provided by the flat-band subsystem, (2) transient charging of the Dirac cone enabled by direct optical excitation from the flat bands, and (3) ultrafast back-transfer of charge into the flat bands on timescales governed by the interlayer coupling strength. We further demonstrate that the degree of hybridization can be tuned via the atomic number of the atoms intercalated at the graphene-SiC interface, establishing a controllable platform for investigating exotic correlated ground states.

We present EUWA310, a seismic velocity model obtained by full seismic waveform inversion (FWI) model of the upper mantle beneath Europe and Western Asia. Inferred via 310 quasi-Newton updates from more than 260$\,$000 three-component seismograms at 18 s minimum period. It increases the number of iterations and the amount of data by an order of magnitude, compared to previous FWI studies in the region. The data include recordings from two dense regional arrays: IberArray on the Iberian Peninsula and in Northern Africa, and AlpArray in the wider Alpine region. The construction of EUWA310 became computationally tractable through the use of dynamic mini-batch optimization and the adoption of wavefield-adaptive spectral-element meshes from the global to regional scales. We also demonstrate that EUWA310 may be used to reduce uncertainties of moment tensor estimates by inverting waveforms with decreasing minimum period until an optimal minimum period of 22 s, below which no additional improvements is achieved. This limit is connected to the termination of the FWI at 18 s period, which did not result from limited computational resources, but from failure to explain data at even shorter periods. Since other continental-scale arrays -- such as USArray or ChinaArray -- are comparable in density and coverage, this observation suggests that we may have reached the regime where data coverage instead of compute power is the main bottleneck in regional FWI.

We study a random walk driven by a particle system from a generic class, and establish a law of large numbers for the walk for almost all densities of the environment. To do so, we exploit the finite-ranged approximations of the environment from arXiv:2409.02096 in a new way, whereby the monotonicity (in the density) of the walker's displacement is leveraged to show the existence of an actual speed. This bypasses the constructions in arXiv:1906.03167 and generalises its Theorem 1.1, which applied to specific environments. We illustrate this with a family of particle systems where the particles have underlying drifts, namely mixtures of APCRWs (Asymmetric Poisson Cloud of Random Walks). In particular, when all particles have the same drift, we prove the LLN under any choice of parameters save one critical density of the environment. To our knowledge, this is the first time that such a conservative and slow-mixing environment with drift is treated outside of the non-nestling case (in which the walker is already assumed to travel strictly faster/slower than the drift arXiv:2205.00282).

This study utilizes Density Functional Theory (DFT) to investigate the thermodynamic stability, elastic anisotropy, and electronic properties of C14 and C15 Laves phases within the Cr--Hf--Nb system. Both formation enthalpies and comprehensive elastic property analyses confirm the energetic and mechanical stability of the C14 (HfNb$_2$, HfCr$_2$, NbCr$_2$) and C15 (HfCr$_2$, NbCr$_2$) phases. Furthermore, the evaluation of elastic anisotropy reveals a descending order of HfCr$_2$ > NbCr$_2$ > HfNb$_2$ for the C14 phase, contrasting with NbCr$_2$ > HfNb$_2$ > HfCr$_2$ for the C15 phase. Finally, electronic structure and COHP analyses indicate that strong anti-bonding behavior near the Fermi level within the XM$_2$ M--M bonds acts as a primary destabilization mechanism for both of these Laves phases.

The no-cloning theorem prohibits the creation of identical copies of quantum information, imposing fundamental constraints on quantum technologies. A recently proposed protocol, encrypted cloning, introduced by Yamaguchi and Kempf, showed that perfect qubit clones can be produced if they are simultaneously encrypted with a single-use key. They also observed a connection between this scheme and quantum secret sharing (QSS). However, it remained an open question whether encrypted cloning could be generalised to arbitrary dimensions, and the broader relationship between the two schemes had not been formally established. In this work, we address both questions by framing encrypted clones as Absolutely Maximally Entangled (AME) states. In parallel with recent work by Ceará that utilises Zadoff-Chu sequences, we independently develop a complementary framework for arbitrary dimensions based on Weyl-Heisenberg displacement operators, both tracing back to the original qubit construction by Yamaguchi and Kempf. We analytically compute the encrypted state and prove that an encrypted qudit system comprising two signal-noise qudit pairs is equivalent to a five-party AME state in any dimension, provided the input state is uniform. We then formalise the connection to QSS by proving that a threshold QSS scheme can achieve the fundamental objectives of encrypted cloning, establishing QSS as the natural general framework within which encrypted cloning can be contextualised.

We present new spectroscopic observations of the inner circumgalactic medium (CGM) of NGC 891 taken with the Mid-Infrared Imager/Medium Resolution Spectroscopy instrument onboard JWST, in four positions: two near the bulge and two at galactocentric radii (r) of ~ 1.5, 4.7 kpc. Each pair of pointings has one position along the minor axis (h) at ~ 0.5 kpc and one at ~ 1 kpc away from the mid-plane. We analyse both 1D spectra and 3D cubes using the dust emission model PAHFIT to extract properties of typical mid-IR features. These spectra reveal that the earlier reported mid-IR emission out to 4 kpc is dominated by the emission of polycyclic aromatic hydrocarbons (PAHs), and not hot dust continuum, providing direct evidence of the survival of PAHs in the inner CGM of NGC 891. Comparing PAH band ratios with other environments (Orion, M51), it is obvious that the 11.2 $μ$m PAH feature -- and not the usual 7.7 $μ$m -- dominates in NGC 891, which seems to imply the presence of more neutral, large PAHs in the CGM. Overall, PAH-to-continuum ratios show little variations with scale-height and radius in NGC 891, which suggests little PAH processing. However, we do see a decrease in the PAH feature strengths with the [Ne III]/[Ne II] ratio, pointing at elevated dust processing with increased radiation field hardness. We also confirm a tight correlation between H2 and PAH features, suggesting that both tracers must be co-spatial and, hence, implying that PAH emission predominantly arises from cool dense parts of cloudlets entrained in galactic outflows. Finally, we report the clear detection of a previously unidentified PAH feature at 16.72 $μ$m.

Dipolar interactions are a major source of decoherence in dense ensembles of color centers in diamond. Current protocols demand using bias magnetic fields detrimental in many scenarios. We present ZENITH (Zero-field Ensemble Neutralization via Interleaved Trilevel Handling), a pulsed sequence to cancel dipolar interactions among V-degenerate spin-1 systems. We reveal excellent coherence survival, compatibility with existing sensing sequences, and improved DC detection, advancing a general framework to control interacting degenerate multilevel systems, of broad interest in quantum technologies.

The continuous spontaneous localization (CSL) model is one of the most promising approaches to address the wave function collapse problem in the measurement process of standard quantum mechanics. In this work, the effect of the CSL model on a Bragg atom interferometer was investigated. A Bragg interferometer achieving high fringe contrast of 99$\%$ has been demonstrated, maintaining this performance level at interrogation time up to $T=60~\mathrm{ms}$. The primary factors responsible for fringe contrast loss in the atom interferometer were systematically analyzed and corrected. This improvement established a new upper limit of $λ_{\rm CSL}=1.27\times10^{-5}~\mathrm{s}^{-1}$ at $r_C=10^{-5}~\mathrm{m}$ for the CSL collapse rate, representing approximately 4 times enhancement over previous atom-interferometric constraints.

The class of intersection bigraphs of unit intervals of the real line whose ends may be open or closed is called a class of mixed unit interval bigraphs. This class of bigraphs is a strict superclass of the class of unit interval bigraphs. In a previous paper [6] we have provided four infinite families of forbidden induced subgraphs including two separate forbidden induced subgraphs of mixed unit interval bigraphs. In that paper, we also posed a conjecture concerning characterization of mixed unit interval bigraphs and verified parts of it. In the present paper we shall give a complete characterization of mixed unit interval bigraphs.

There has been a proliferation of media reports about so-called AI psychosis in the last year. Not surprisingly, this has prompted growing academic work on the ways in which AI chatbots such as ChatGPT, Claude, and Replika might aggravate or even induce psychosis, typically understood in terms of users acquiring or maintaining delusional beliefs. Our paper consists of two parts. First, we provide a number of reasons to be sceptical about understanding 'AI psychosis' as a novel psychiatric category. We argue that many of the purportedly new phenomena are better understood through Stompe et al.'s (2003) metaphor of 'old wine in new bottles' and highlight conceptual, nosological, clinical, and social risks associated with the uncritical adoption of this terminology. Second, we develop a positive phenomenological account of what may nevertheless be at stake in sustained human-AI interaction. Rather than focusing primarily on whether AI systems induce, amplify, or sediment delusional beliefs, we examine how conversational AI may participate in transforming a person's lived experience of reality itself. We claim that the sycophantic and pseudo-intersubjective nature of AI could lead to what we call "existential drift", whereby individuals may continue to feel rooted in a shared reality through their interactions with AI, while actually becoming entrenched in increasingly private and subjective worlds.

We introduce a nonabelian surface holonomy that is constructed from a one-form gauge potential that takes values in a loop algebra of the $U(N)$ gauge group. The surface holonomy parallel transports a nonabelian string. Although it is not manifest in our formulation, we will see that our nonabelian surface holonomy is invariant under reparametrizations of the surface.

Phylogenetic networks generalize phylogenetic trees by representing reticulate evolution. Tree-based networks and their support trees have been extensively studied, but not all networks are tree-based. To measure how far such networks are from being tree-based, Suzuki and Hayamizu (2025) formulated the problem of finding the support network with minimum level of a given rooted almost-binary phylogenetic network. They conjectured that this problem is NP-hard and provided exponential-time algorithms. In this paper, we prove this conjecture by showing that, for every fixed integer $k \geq 1$, it is NP-complete to decide whether the minimum level is at most $k$.

Large language models (LLMs) have shown strong potential for automated test generation, yet most approaches to generating Java unit tests still rely on mocking frameworks to handle dependencies. Mockless test generation could exercise more real low-level code, but it faces challenges such as invalid test code generation due to hallucination, strict language constraints, and inadequate dependency awareness. We identify two causes behind these hallucinations: not knowing, where the LLM lacks sufficient context, and not following, where the LLM fails to comply with constraints even when they are provided. We present MocklessTester, a mockless unit test generation approach built around two strategies: context-enriched generation and constraint-enforced fixing. To mitigate not knowing, context-enriched generation mines real usage patterns from existing code to generate tests. To mitigate not following, constraint-enforced fixing performs two-stage repair under symbol-, protocol-, and iteration-level constraints, using a ClassIndex, a Markov typestate model, and experience memory. We evaluate MocklessTester against the state-of-the-art baseline on Defects4J and Deps4J. Results show that MocklessTester improves line coverage by 19.99% and 22.69% and branch coverage by 24.90% and 15.78% on the two benchmarks, respectively, and improves mutation score by 13.67% and 0.17%. Beyond the class under test, MocklessTester also exercises more real dependency code, covering 378 and 55 additional lines in dependency classes, respectively. The improvement in test quality comes with higher total token and time costs than the baseline. Nevertheless, the cost per method remains practical, averaging 108.97 seconds and 26.59k tokens on Defects4J, and 69.85 seconds and 25.46k tokens on Deps4J. Ablation results confirm that all major components contribute positively to the final performance.

Physical reservoir computing offers an energy-efficient route to sequential cognitive inference by outsourcing nonlinear temporal mixing to hardware substrates with rich intrinsic dynamics, with free-space light-scattering systems particularly attractive for their parallelism and reconfigurability-yet practical design principles linking hardware control variables to computational performance have remained unestablished. Here, we establish such principles by systematically mapping three physical control axes of a reconfigurable optoelectronic light-scattering reservoir-reservoir dynamics, input-reservoir coupling, and reservoir interconnectivity-and identifying a quantitative optimum along each axis. Within this design landscape, we observe a memory-capacity peak that coincides with near-zero maximal Lyapunov exponent and is quantitatively reproduced in numerical simulation, extending edge-of-chaos confirmations previously reported in ion-gating and spin-wave reservoirs into the photonic substrate. The two remaining axes exhibit a density-magnitude trade-off in input coupling and an intermediate optimum in reservoir interconnectivity. Operating at the resulting three-axis optimum, the reservoir achieves stable Mackey-Glass chaotic time-series prediction in free-running mode and 84.5% blind classification accuracy on the 10-class Speech Commands spoken-digit benchmark; the principles, stated in substrate-specific units yet rooted in substrate-independent concepts of criticality and balanced coupling, provide a transferable framework for reconfigurable optical reservoir hardware.

The growing demand for efficient communication in distributed training and inference has sparked significant interest in reconfigurable photonic interconnects across both academia and industry. Mixture-of-Experts (MoE) models, with their highly skewed communication patterns, present a natural opportunity for such circuit-switched fabrics. However, existing approaches largely optimize communication in isolation, overlooking the interaction between communication and the expert computation that follows. In this paper, we revisit circuit scheduling for all-to-all communication in MoE execution. We show that the dispatch--compute--combine structure fundamentally challenges classical scheduling techniques such as Birkhoff--von Neumann (BvN) decomposition. First, MoE communication matrices are rarely doubly stochastic, introducing significant scheduling bubbles in BvN-based schedules. Second, while decomposition enables communication--compute overlap, the excessive number of matchings produced by BvN fragments execution into small batches, leading to severe compute inefficiencies due to fixed execution overheads. Motivated by these observations, we explore a simple greedy max-weight decomposition strategy that bounds the number of matchings while preserving large batch sizes per matching. Despite its simplicity, the approach significantly improves overlap efficiency, reduces compute overheads, and approaches the performance of an ideal congestion-free all-to-all.

Metabolic syndrome is a complex clinical condition characterized by the simultaneous presence of multiple metabolic risk factors and represents a major public health concern. The syndrome develops silently and may remain undiagnosed for long periods, highlighting the importance of investigating early metabolic alterations before overt disease onset. Longitudinal monitoring of predominantly healthy individuals may help identify metabolic risk early. The paper proposes a Bayesian statistical model to estimate the probability of metabolic syndrome among blood donors during pre-donation screening, incorporating information collected at previous visits. Using longitudinal data from one of the main blood donor associations in Italy, AVIS Milan, we analyze repeated clinical and lifestyle measurements from a predominantly healthy population of donors. In particular, we fit a Bayesian multivariate model that jointly represents the logarithm of the five diagnostic components of metabolic syndrome. The model accounts for within-donor dependence across repeated visits and provides probabilistic estimates of individual risk. Our framework aims to provide clinicians at AVIS Milan with an interpretable traffic-light warning system (low, intermediate, high risk) during pre-donation screening to facilitate the identification of individuals at risk of metabolic syndrome at future visits and to support targeted preventive interventions during routine donor assessment, ultimately contributing to a long-term reduction in healthcare costs for the Italian national healthcare system.

Continuum-buried defect states in semiconductors are generally expected to be optically inactive due to their strong coupling to continuum bands. Here, we show that such defects can instead host radiative electronic bound states in the continuum (BICs), using the silicon G-center as a prototypical example. Hybrid-functional first-principles calculations with a Hubbard $U$ correction reveal that a localized defect state, initially buried below the valence band maximum (VBM) in the ground state, undergoes exchange-driven energy-level reordering under optical excitation and shifts above the VBM. This exchange-induced transition suppresses nonradiative decay and enables robust radiative emission. By computing temperature-dependent nonradiative lifetimes and comparing them with experimental photoluminescence (PL) lifetimes, we quantitatively reproduce the observed temperature dependence of the emission. These results uncover a stabilization mechanism for continuum-embedded defect states and establish electronic BICs as a general paradigm for designing defect-based optical systems, including quantum emitters and qubits.

We obtain positive lower bounds on the Hausdorff dimension of sets of real numbers given by expressions of the form $\sum_{n=1}^\infty \frac{1}{a_n b_n}$, where $b_n$ satisfies some growth condition and $a_n$ lies in some set, possibly depending on $n$. As a consequence of our results, some of the irrational numbers arising from Erdős' celebrated construction from 1976 are not Liouville numbers.

This article presents a closed-loop, uncertainty-aware framework for defending a protected zone against coordinated incursions by swarms of small uncrewed aircraft systems (UAS). The interaction structure of the attackers is modeled as time-varying, while defenders operate under imperfect sensing. The proposed criticality-driven defender-to-attacker assignment strategy integrates three components: a probabilistic graph-based representation of the attacking swarm inferred from uncertain observations; a risk-aware attacker criticality model combining time-to-breach urgency with uncertainty; an online defender allocation mechanism that assigns and selectively reassigns defenders while limiting switching-induced instability through robust execution constraints. Analytical guarantees are established within a filtration-based first-hitting-time framework. In particular, finite-time triggering of the first capture event following detection is proven, and explicit mixed linear-geometric upper bounds are derived for the expected neutralization time. Monte Carlo simulations demonstrate the effectiveness of the proposed framework, achieving 85.6% neutralization efficiency under probabilistic sensing and 99.9% under deterministic sensing. Systematic ablation and sensitivity studies further quantify how detection thresholds and coordination parameters influence reliability and time-to-first-capture.

Spin transport across interfaces is critical for spintronic devices, yet remains difficult to probe on ultrafast timescales. We use terahertz emission spectroscopy on Co|Pt heterostructures whose interface roughness is tuned through the thickness of an underlying Au buffer layer, while leaving other growth parameters unchanged. From the measured THz electric field, we extract the interface spin-current transparency ts after correcting for the changes in sample impedance and optical absorption of the stack. Surprisingly, we find that ts decreases by only approximately 30% as the interface root-mean-square roughness and the lateral grain size both increase by a factor of three, with no measurable change in the THz spectrum. These results demonstrate that interfacial spin transport is relatively robust against morphological variations on ultrafast timescales, establishing terahertz emission spectroscopy as a reliable probe of spin dynamics across imperfect interfaces.

Wi-Fi sensing has emerged as a versatile tool for tasks such as localization, gesture recognition, and vital-sign monitoring, enabling applications from smart environments to personalized healthcare. However, sensing accuracy often significantly degrades when pretrained models are deployed across different commodity receivers. We present the first systematic comparison of Channel State Information (CSI) across diverse Commercial Off-The-Shelf Wi-Fi sensing platforms. Using a unified experimental setup delivering precisely precoded signals simultaneously to multiple receivers, we isolate receiver-specific variability. We find that dominant cross-device differences arise from Automatic Gain Control and consistent subcarrier nonlinearities. We propose a simple gain-alignment preprocessing step, recovering most of the lost accuracy (up to 75%) in cross-device Human Activity Recognition model deployments. Without preprocessing, model accuracy sharply drops-effectively breaking practical deployments. Additional analyses reveal measurable inherent differences in receiver faithfulness, sensitivity and noise. While these receiver-induced differences do not significantly affect robust sensing tasks such as Human Activity Recognition, they become relevant in scenarios demanding high precision (e.g., single-shot time of flight). Our findings demonstrate that cross-device variability in CSI is real but manageable, and we provide tools and guidelines for robust, hardware-agnostic Wi-Fi sensing.

This study explores the dynamics of Bose-Einstein condensates (BECs) with helicoidal spin-orbit coupling (SOC) and Rabi coupling, confined under a two-dimensional harmonic potential. The relationship between helicoidal SOC, non-linear interactions along with Rabi coupling and their impact on the stability and non linear trapped modes of the condensate is analyzed using a coupled Gross-Pitaevski (GP) framework. A linearized GP equation is used to investigate modulation instability (MI), demonstrating the impact of strong coupling effects and anisotropic confinement on the instability dynamics. It has been shown that the modulation instability in the condensate is predominantly governed by the competition between the attractive and repulsive mean-field interactions. Additionally, stability regimes are altered by the harmonic confinement, enhancing their susceptibility to SOC-induced asymmetry and intra- and intercomponent interactions. These results shed light on the possibility for unique quantum phases and emergent characteristics of helicoidal SOC-driven condensates.

Classical Laguerre spectral approximations are highly effective on the half-line when the target function is smooth in the usual polynomial scale. However, their accuracy deteriorates for nonsmooth functions. Such behavior appears naturally in fractional models, weakly singular integral equations, and semi-infinite-domain approximations with limited regularity near the origin. The main contribution of this work is the construction and analysis of a fractional Laguerre approximation framework tailored to nonsmooth functions on the half-line. We establish projection and interpolation error estimates in nonuniformly weighted Sobolev space. These estimates clarify how the fractional parameter adapts the approximation space to the regularity of nonsmooth functions and improves the resulting convergence behavior. We further introduce a generalized fractional Laguerre family with an additional algebraic parameter, which gives greater flexibility in controlling both the approximation space and the underlying weight. Numerical experiments confirm the theoretical estimates and demonstrate the advantage of the proposed functions over standard Laguerre-type approximations.