We formulate a general prescription for spurion analysis in particle-physics models whose selection rules are described by commutative non-invertible fusion algebras. The construction applies to fusion algebras containing non-invertible basis elements that need not be self-conjugate, thereby allowing us to systematically track coupling constants in arbitrary particle scattering processes at tree and loop orders, but without assuming faithful realization of the fusion algebra, or no other quantum numbers for dynamical particles. This unifies and streamlines the previous analysis of near-group fusion algebras and of the $\mathbb{Z}_M/\mathbb{Z}_2$ fusion algebras, and supports the broader viewpoint that the non-invertible selection rules often admit auxiliary descriptions using lifted Abelian groups with a structured set of explicit breaking terms.

We investigate the relationship between product offerings, information dissemination, and consumer decision-making in a monopolistic screening environment in which consumers lack information about their valuation of quality-differentiated products. An intermediary, who is driven by the objective of maximizing consumer surplus but is also biased towards high-quality products, provides recommendations after the monopolist announces the menu of product choices. We characterize the monopolist's profit-maximizing finite-item menu. Our results show that as intermediaries place greater emphasis on consumer surplus over product quality, sellers are prompted to strategically expand their product range. Intriguingly, this augmented product variety decreases economic efficiency compared to scenarios where direct seller-to-consumer information provision is the norm. The role of information intermediaries proves pivotal in shaping consumer welfare, market profitability, and overarching economic efficiency. Our insights underscore the complexities introduced by these intermediaries that policymakers and market designers must consider when designing policies centered on consumer learning and market information transparency.

In this paper, we derive explicit closed-form solutions for the value function and the associated optimal stopping boundaries in an optimal annuitization problem under a mortality shock. We consider an individual whose retirement wealth is invested in a financial fund following the dynamics of a geometric Brownian motion and has the option at any time to irreversibly convert their wealth into a life annuity. The individual faces a sudden, permanent health deterioration occurring at a random, exponentially distributed time, and the annuitization decision is modelled as an optimal stopping problem across two health states. Our analytical expressions characterise both the value function and the optimal timing of annuitization. The results provide clear economic intuition: the optimal strategy is governed by the critical interplay between the relative attractiveness of the annuity (money's worth), the financial returns from the investment fund, and bequest motives across different health states. A numerical analysis compares the optimal annuitization strategy of an individual facing a health shock against a benchmark case with constant mortality, highlighting how the likelihood and severity of a health shock significantly alter optimal annuitization behaviour.

We analyze Fock-state lattices (FSLs) from an algebraic viewpoint. Starting from a Lie algebra, we associate a FSL constructed from the action of its generators: diagonal (Cartan) generators define the lattice sites, while off-diagonal (root) generators determine the lattice bonds. This construction reveals that identifying an underlying algebraic structure provides direct physical insight into FSLs, including their dimensionality, connectivity, symmetry constraints, and possible transport and revival phenomena. By examining several common Lie algebras, we identify not only their associated FSLs but also the corresponding Lie phase spaces, thereby establishing a systematic connection between FSL dynamics and phase-space geometry. In many cases, both the phase space and the FSL exhibit nontrivial curvature, opening possibilities for exploring quantum dynamics in curved synthetic spaces. We further address whether every integrable Hamiltonian admits an underlying Lie algebra that reproduces the same FSL structure. We show that this is not generally the case, particularly for Hamiltonians that are nonlinear in the generators, and that for systems combining different types of degrees of freedom the appropriate underlying structure may instead be a Lie superalgebra.

Economic institutions often influence market outcomes not by directly controlling sellers' menus, but by shaping the market composition sellers face. We study this problem in the canonical monopoly screening model. An upstream actor chooses the distribution of buyer valuations, after which a monopolist offers the optimal quality-price menu. We characterize the optimal market composition and the efficient frontier of consumer surplus and profit. If the upstream actor places at least as much weight on profits as on consumer surplus, the optimal market collapses to the top type. If the weight on consumer surplus is larger than the weight on profits, the optimal market exhibits no exclusion, no interior bunching, and a positive mass at the highest valuation. Under a mild curvature condition, the optimum is unique. As the weight on consumer surplus rises, the optimal market becomes more heterogeneous and less concentrated at the top: the interior expands while the top segment shrinks. Consumer surplus rises, profit falls, and total surplus declines.

Multiple access techniques are vital for 5G and beyond. While Orthogonal Frequency Division Multiple Access (OFDMA) is standard, its high peak-to-average power ratio (PAPR) reduces energy efficiency in uplink transmissions. This paper presents Periodic OFDMA (P-OFDMA), a novel multiple access scheme with reduced PAPR and computational complexity. By assigning subcarriers in a periodic pattern across the entire frequency band, P-OFDMA enhances frequency diversity and simplifies allocation. We also introduce two precoded variants: P-OFDMA-DCT and P-OFDMA-DFT. Comprehensive simulations comparing P-OFDMA with OFDMA and SC-FDMA show that P-OFDMA-DFT consistently achieves the lowest PAPR. Furthermore, the standard P-OFDMA scheme outperforms SC-FDMA in PAPR for low subcarrier-per-user scenarios and achieves better bit error rate (BER) performance under high delay-spread conditions. Notably, P-OFDMA and its variants reduce transmitter-side processing by up to an eightfold factor compared to SC-FDMA, greatly benefiting low-complexity uplink devices. Although receiver complexity increases, the overall system processing load decreases, yielding improved energy efficiency. Thus, P-OFDMA offers a robust, energy-efficient uplink solution for future wireless networks.

Tensor network techniques are becoming increasingly popular tools to solve partial differential equations within the so-called quantics representation. Their popularity stems from the fact that their spatial resolution depends only logarithmically on the number of grid points, making them very tempting approaches in situations where two or more characteristic length scales are vastly different. A first generation of technique used ``out-of-the-box'' algorithms of the tensor network toolkit (e.g. the celebrated Density Matrix Product State (DMRG) algorithm) to solve these problems. These techniques were designed for situations (e.g. quantum magnetism) where the different degrees of freedom (e.g. spins) play equivalent roles. In the quantics representation, however, the different degrees of freedom correspond to the physics at different scales and therefore play inequivalent role. Here we show that by tailoring the tensor network algorithms to this particular case, in the spirit of the multigrid approach, we obtain faster and more robust convergence of the algorithms. We showcase the approach on linear (Poisson equation) and eigenvalue (Schrödinger equation) problems in two, three and four dimensions. Our simulations involve up to $2^{80}$ grid points and would represent, we argue, a very strong challenge for conventional approaches.

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) significantly improve wireless performance by allowing tunable interconnections among elements, but their design in multiple-input multiple-output (MIMO) systems has so far relied on complex iterative algorithms or suboptimal approximations. This work introduces a simple yet powerful approach: instead of directly maximizing the achievable rate, we maximize the absolute value of the determinant of the equivalent MIMO channel. We derive a closed-form symmetric unitary scattering matrix whose rank is exactly twice the channel's degrees of freedom ($2r$). Remarkably, this low-rank solution achieves the same determinant value as the optimal unitary BD-RIS. Using log-majorization theory, we prove that the rate loss relative to the optimal unitary BD-RIS vanishes at high signal-to-noise ratio (SNR) or when the number of BD-RIS elements becomes large. Moreover, the proposed solution can be perfectly implemented using a $q$-stem BD-RIS architecture with only $q=2r-1$ stems, requiring a minimum number of reconfigurable circuits. The resulting Max-Det solution is orders of magnitude faster to compute than existing iterative methods while achieving near-optimal rates in practical scenarios. This makes high-performance BD-RIS deployment feasible even with large surfaces and limited computational resources.

This paper presents an improved Matlab routine, FO_LE, for the numerical computation of Lyapunov exponents of fractional-order systems modeled by Caputo's derivative. It is conceived as an enhanced version of the former FO_Lyapunov and FO_NC_Lyapunov codes for commensurate and non-commensurate orders, respectively. The proposed approach replaces the Gram-Schmidt orthogonalization procedure with QR-based reorthonormalization and uses the new quadratic LIL predictor-corrector scheme for the integration of the extended variational system. Compared with the former implementations, the present routine benefits from the higher order of the fractional integrator LIL and applies to both commensurate and non-commensurate models. Like the previous code, FO_LE retains the full memory structure of the underlying Caputo model. The Matlab code for the LIL solver and for the computation of Lyapunov exponents with FO_LE are provided, while a fast implementation of LIL for commensurate and non-commensurate orders, LIL_nc, is available on MathWorks File Exchange. A benchmark problem with exact solution is used to compare the LIL-based solver with ABM-type methods, whereas the Rabinovich-Fabrikant system illustrates the computation of Lyapunov exponents in different dynamical regimes. The results indicate that the proposed implementation is a compact, robust, and efficient tool for the numerical study of stability and chaos in fractional-order systems.

Integrating pretrained speech encoders with large language models (LLMs) is promising for ASR, but performance and data efficiency depend on the speech-language interface. A common choice is a learned projector that maps encoder features into the LLM embedding space, whereas an alternative is to expose discrete phoneme sequences to the LLM. Using the same encoder and LLM backbones, we compare phoneme-based and vanilla projector-based interfaces in high-resource English and low-resource Tatar. We also propose a BPE-phoneme interface that groups frequent local phoneme patterns while preserving explicit word-boundary cues for phoneme-to-grapheme generation. On LibriSpeech, the phoneme-based interface is competitive with the vanilla projector, and the BPE-phoneme interface yields further gains. On Tatar, the phoneme-based interface substantially outperforms the vanilla projector. We further find that phoneme supervision yields a phoneme-informed hybrid interface that is stronger than the vanilla projector.

High-temperature deformation in superalloys is governed by the cooperative glide-climb motion of dislocations. Superlattice stacking faults (SFs) in the gamma prime phase are predominantly interpreted as nucleating via conservative Shockley partial glide. Here, we demonstrate that non-conservative climb of a/3<111> Frank partials constitutes a general and kinetically viable pathway for both superlattice intrinsic (SISFs) and extrinsic stacking faults (SESFs) formation in the L12 structure of CoNi-based superalloys during compression at 850 Celsius. High-resolution transmission electron microscopy reveals that Frank partials form at gamma/gamma prime interface can climb into the gamma prime phase, generating SISFs via positive climb and SESFs via negative climb. Importantly, the negative climb-assisted nucleation of SESFs is experimentally confirmed for the first time, and the observed positive climb-assisted SISF configuration differs fundamentally from previously reported mechanisms. We show that these Frank partials originate from the reaction between a leading 30 degree Shockley partial and a 60 degree mixed dislocation on conjugate {111} planes, producing energetically stable configurations that promote subsequent climb. Energetic and kinetic analyses demonstrate that solute segregation induced reduction of SF energy provides a dominant contribution to Frank partial climb, enabling sustained climb and consequent SF expansion. Quantitative comparisons further indicate that, at elevated temperatures, solute drag-controlled Shockley glide can achieve mobilities comparable to vacancy diffusion-controlled Frank climb. These findings establish climb-assisted SF formation as a unified deformation mechanism in gamma prime phase, and that both SISF and SESF expansion can proceed through Frank partial climb.

We show that the perfect Euler brick (perfect cuboid) problem is equivalent to the following elementary question: do there exist coprime integers $a, b, m, n$ such that the two expressions $(2(a^2-b^2)mn)^2 + ((a^2+b^2)(m^2-n^2))^2$ and $(4abmn)^2 + ((a^2+b^2)(m^2-n^2))^2$ are simultaneously perfect squares? Despite their near-identical structure (differing only in the first summand), no solution has ever been found. We reduce this quartic pair to a one-parameter family of genus-3 hyperelliptic curves $C_A\colon w^2 = λ^8 + Aλ^4 + 1$ and develop obstructions on the distinguished elliptic quotient $E_A$: the Kummer character $χ_f$ is non-trivial on the 4-torsion, and 2-descent arguments exclude several families of square classes. Computationally, we verify that no solution exists for parameters up to $10^3$. These results do not yet exclude perfect Euler bricks unconditionally; the remaining gap and possible approaches (including a genus-5 covering obstruction and connections to $\mathbb{Q}(\sqrt{2})$) are discussed.

In this work, we aim at efficiently solving a parametrized family of optimal transport problems by using model order reduction methods. We propose a reduced-order model by adding to the primal (respectively dual) version of the high-fidelity model the additional constraint to live in a non negative sub cone (resp. in subspaces) of small dimension. The reduced-order model then reads as a linear program with a small number of degrees of freedom and constraints. We identify explicit conditions under which this reduced-order model has at least one solution. We propose two a posteriori error estimations that bounds the error between the optimal values of the high-fidelity problem and the reduced-order model. As one of these estimations requires the computation of non linear terms (with respect to the reduction of dimension), we use an Empirical Interpolation Method (EIM) (see e.g. \cite{maday2007general} or \cite{barrault2004empirical}) to numerically efficiently compute this estimation. We apply the whole methodology on a simple 1D example and on a problem of color transfer between images, and compare its performances to Sinkhorn algorithm.

Infrastructure as a Service (IaaS) in cloud environments provides compute, storage, networking, and other fundamental resources that allow consumers to deploy and run arbitrary software, including operating systems and applications. To support multi-tenant environments, IaaS leverages virtualization, but conventional overlay network architectures have become a direct cause of scalability limitations. In particular, current IaaS virtual networks face challenges in high availability and load balancing. To address these issues, we present EYWA, a virtual network architecture that scales to support very large data centers with high availability, efficient load balancing, and large layer-2 semantics. EYWA overcomes scalability limitations by: (1) accommodating a large number of tenants (about 2^24 = 16,777,216) through logically isolated virtual LANs with unique IP ranges, (2) providing per-tenant public network services without throughput bottlenecks or single points of failure in network address translation (SNAT/DNAT), and (3) enabling a single large IP subnet per tenant with extended layer-2 semantics. EYWA combines existing techniques into a distributed scale-out control and data plane. Its only component is an agent running on each hypervisor host, which collectively act as a distributed controller. As a result, EYWA can be deployed in today's multi-tenant cloud environments. We have implemented a proof-of-concept (PoC) of EYWA and evaluated its effectiveness through measurements and experiments in our lab.

Single-cell transcriptomic data approximates the abundance of proteins at a high resolution, but its noisiness necessitates transformation by a pipeline of methods before analysis and inference. In the absence of robust validation of these pipelines and methods, it remains unclear how best to process any particular dataset. To compensate for this, popular visualisation methods, e.g., t-SNE and UMAP, are commonly used to produce descriptions of datasets. Such visualisations are incomplete and provide subjective descriptions of samples rather than statistically meaningful statements about technical noise or biology. In this paper, we introduce the Zero-Inflated Negative-Binomial with Geometric Tail (ZINBGT), a mixture-model-based strategy for producing interpretable visualisations of each gene's expression across cells, along with diagnostic summaries that use Wasserstein distance to highlight outlier genes. These diagnostics are used to reveal an outlier gene within a T. brucei sample. This method is applied to a human immune-cell dataset, highlighting the relationship between sparsity, mean, and spread across genes, as well as revealing an issue with the use of zero-inflated negative-binomial distributions to model single-cell RNA data. An investigation of simulated datasets intended to replicate the immune-cell data revealed discrepancies with the ground truth, establishing purposes for which these simulated datasets are unsuitable. Finally, we list a number of different domains to which this method can be applied.

Recovering concurrency structure directly from source code is difficult because shared-resource identity and protection relations are often obscured by aliasing, ownership, and API-specific idioms. We therefore study a specification-driven, model-first verification architecture for LLM-assisted concurrent program construction. Instead of verifying arbitrary source code, a large language model first synthesizes a verification-oriented concurrency artifact from a natural-language requirement or system specification. The first formalism, the Concurrency Intermediate Representation (Cir), is a statement-level, alias-free model in which shared resources are globally named, protection relations are explicit, and each statement carries a stable identifier. The second formalism, the Concurrency Verification Net (Cvn), is a weighted place/transition Petri net with a finite global store and three-valued guards for data-dependent branching. A validated Cir artifact is translated mechanically to Cvn, explored exhaustively, and any counterexample is mapped back to statement identifiers to guide targeted repair. To reduce the risk of bug-free but behavior-dropping repairs, acceptance additionally applies a lightweight goal-reachability check over designated critical outcomes. We formalize both representations, prove translation-correspondence results for deadlock and signal-loss analysis, define a two-layer checking architecture with 61 static rules and 5 analysis predicates, and evaluate the pipeline on 9 representative bounded-concurrency patterns. The results show that the method supports iterative bug detection and repair on Cir artifacts and that goal reachability helps filter semantically incomplete repairs. The trust boundary of the present work is the generated Cir artifact rather than arbitrary source code.

With the release of ChatGPT in 2022, generative AI has significantly lowered the cost of polishing and rewriting text. Due to its widespread usage, conference organizers instated specific requirements researchers need to adhere to when using GenAI. When asked to rewrite text, GenAI can introduce stylistic changes, often concentrated to a handful of ``marker words`` commonly associated with AI usage. Prior large-scale studies in preprints and biomedical science report post-2022 discontinuities of those marker words and broad linguistic features. This paper investigates whether similar patterns appear in top-tier cybersecurity conference papers (NDSS, USENIX Security, IEEE S\&P, and ACM CCS) over the period 2000-2025. Using text extracted from paper PDFs, we compute lexical and syntactic metrics and track curated marker-word usage. Our findings reveal a gradual long-run drift toward higher lexical complexity and a pronounced post-2022 increase in marker-word usage across all venues showing an emerging trend towards more complex language in cybersecurity papers possibly hindering accessibility.

This work introduces an ultralow-power voltage-to-spike encoder that achieves near-linear voltage-to-firing-rate conversion by pairing a linearized bulk-driven transconductor with a DPI-based LIF neuron. A tail-less bulk-driven differential pair improves large-signal linearity, while a translinear linearization network suppresses the dominant sinh nonlinearity and stabilizes the bias-tunable V-to-I gain. The resulting current feeds a DPI front-end that linearizes current-to-spike conversion. Fabricated in TSMC 0.18-um CMOS and operating at VDD = 0.5 V with 2-27 nA reference current, the encoder achieves a deviation of less than 5.6 percent from linearity over 0.1-0.4 V input, consumes 22-180 nW, and occupies 0.0074 mm^2.

We investigate the emergence of semiclassical dynamics in the quantum Rabi model using a recently developed limiting procedure that formally establishes correspondence with the semiclassical Rabi Hamiltonian [E. K. Twyeffort Irish and A. D. Armour, Phys. Rev. Lett. 129, 183603 (2022)]. While the limit itself is defined at the Hamiltonian level, how it is reached depends on the choice of quantum states. Defining a set of quantitative measures that capture the differences between quantum and semiclassical dynamics, we examine convergence to the semiclassical limit when the field is prepared in a displaced number state. These states, which interpolate to Fock states for zero displacement, are more general than the set of coherent states usually employed when considering the emergence of semiclassical behavior. Numerical computations of these measures consistently demonstrate the progressive emergence of semiclassical behavior as the joint limit of vanishing coupling and infinite displacement is approached. Complementing the numerical results, analytical approximations are developed that reproduce the behavior in the vicinity of the semiclassical limit with a high degree of fidelity and allow scaling relations to be derived. Although any initial displaced number state will eventually converge to the corresponding semiclassical dynamics as the limit is taken, the rate of convergence depends on the Fock number $n$ of the state. States with larger values of $n$, which behave less classically than coherent states, converge more slowly to the limit.

Over the past decade, holographic nanodiamond-polymer composite gratings have been developed and optimized as high-efficiency diffractive elements for very cold neutrons (VCN), for use as mirrors and beam splitters in a triple-Laue (LLL) interferometer. We report their optical characterization and, crucially, their neutron-optical performance, including diffraction efficiency and angular selectivity under VCN conditions. We further demonstrate their integration into a VCN interferometer. The layout of the interferometer and its first implementation at the beamline are described, highlighting practical considerations for long-term operation. We discuss avenues for performance improvement, in particular grating fabrication refinements. These results establish nanodiamond-polymer composite gratings as viable components for VCN interferometry and pave a way toward precision neutron phase measurements in the very cold regime.

We consider Brillouin scattering on short-lived phonon modes, such that the relative Brillouin shift between propagating and scattered waves is smaller than the relative width of phonon modes. In this case one phonon mode facilitates scattering between many pairs of optical modes. We show that in this limit two phonon modes are sufficient for cascade Brillouin scattering (one forward propagating wave and one counter propagating wave), and that the cascade behavior is qualitatively different from the cascade in conventional Brillouin systems with distinct phonon modes for each optical mode pair. In particular, our results show that there is a pump threshold above which many optical modes become excited simultaneously, as opposed to a cascade gradually building up. The resulting cascade scattering can be exploited for frequency comb generation with uniform amplitudes and without the need for anomalous dispersion in the medium.

Spin defects in solids, such as the nitrogen-vacancy (NV) center in diamond, have emerged as a key tool for detecting nuclear spins at the nanoscale. While active nuclear spin control via radio-frequency (RF) irradiation is often unnecessary for standard spin-noise detection, it becomes essential for advanced protocols like multidimensional nanoscale NMR. In this work, we investigate nuclear spin control using correlation spectroscopy techniques. We demonstrate, both theoretically and experimentally, that the resulting nuclear spin dynamics depend critically on the initial RF phase and its orientation relative to the NV crystalline axis. Depending on these parameters, identical nuclear rotations can yield full, partial, or even vanishing contrast in the NV readout. These findings highlight a previously underappreciated aspect of spin manipulation in the spin-noise regime: the link between the phase and direction of the applied RF field and its direct impact on correlation-based experiments. Consequently, imperfect calibration of these parameters can lead to ambiguous signal contrasts and misinterpretation of the underlying nuclear spin dynamics. Our results provide deeper insight into nanoscale spin control and pave the way toward reliable multidimensional spin resonance experiments.

The sustainability impacts of ICT systems are difficult to assess and govern due to structural complexity, fragmented measurement practices, and unclear responsibilities across system layers. We argue that these challenges cannot be addressed solely by metrics and motivate the need for a shared Green ICT reference framework that integrates sustainability across multiple perspectives and domains, lifecycle phases, and governance contexts. We present an initial framework developed within the Informatics Europe Green ICT Working Group as a first step towards a comprehensive reference framework.

In this paper we consider the $β$-plane equation with a smooth external force which is a quasi-periodic traveling wave of large amplitude $O(λ^{α- 1})$, $1 < α< 2$, and with large speed of propagation of size $O(λ)$. In a previous paper, the second and the third author proved the existence of quasi-periodic traveling wave solutions of large amplitude of order $O(λ^θ)$, for some $θ> 0$. The purpose of this paper is to analyze the long time dynamics for smooth initial data close to these traveling wave solutions. In particular, we shall prove that, for initial data sufficiently close to a fixed traveling wave solution (in the $H^s$ topology), the corresponding solution remains close to the traveling wave solution for arbitrary long time (independent of the size of the traveling wave solution). As a consequence, we prove that there are open sets of large initial for which one has almost global existence, namely such that the corresponding solution remains of the same size of the initial datum for arbitrary long time (independent of the size of the initial data). The proof combines several ingredients: an analysis of the linearized PDE at any traveling wave solution via normal form methods, a sharp analysis of the transformed nonlinear problem under the change of coordinates that diagonalizes the linearized equation and energy estimates.

In this paper, we prove that for any weak Del Pezzo surface $S$ of degree at least $4$, the tangent bundle $T_S$ is almost nef. For the proof, we use total dual VMRTs induced by conic bundle structures.

A recyclable and cuttable wireless power transfer (WPT) sheet is proposed, enabled by H-tree wiring and water-soluble channels filled with liquid metal (LM). Conventional 2D WPT systems lose their functionality when physically damaged or modified. The H-tree wiring pattern maintains the operation of the remaining coils even after the outer region of the sheet is cut away. The LM can be recovered by dissolving 3D-printed polyvinyl alcohol (PVA) channels in water. The sheet dimensions were experimentally optimized, and a Q-factor over 55 was achieved at 6.78 MHz. The sheet maintained its bending stiffness and electrical resistance during 100 bending cycles. After four dissolution-refabrication cycles, 98 percent of the LM was recovered with stable electrical properties. The WPT sheet can be integrated into everyday objects and enables long-term, continuous operation of surrounding electronic devices, contributing to IoT applications and ambient computing.

Automated decision systems produce operational data across multiple infrastructure layers, yet no single logging format captures the complete governance-relevant record of how a decision was reached. Regulatory frameworks prescribe what must be recorded without specifying a data model for how to record it -- a gap this paper terms the Fragmented Trace Problem. Following a design science methodology, the paper presents the Decision Event Schema (DES), a JSON Schema specification that bridges four infrastructure layers -- ML inference, rule/policy evaluation, cross-system coupling, and governance metadata -- within a single per-decision event structure. The schema employs degradation-aware field design: each of six top-level field groups maps to a governance evidence property and the degradation type it must resist. DES defines ten required root-level fields and introduces a tiered evidence strategy (lightweight, sampled, full) that enables organizations to match evidence completeness to decision risk and throughput. A mechanism feasibility analysis demonstrates compatibility with the highest-throughput integrity mechanisms at production-scale decision rates. Evaluation against 25+ existing formats confirms that DES is the only specification covering all four layers simultaneously. The schema offers practitioners a reference adoptable directly or adaptable through namespace extensions, and regulators a mapping from requirements to minimum evidence tiers.

This paper presents a generalized circuit framework for constructing Shih-type fractionalizations of unitary operators of dyadic order, i.e., operators $U$ satisfying $U^{2^n}=I$. Building upon the architecture of the quantum fractional Fourier transform (QFrFT), we show that fractionalization can be implemented coherently as a weighted superposition of integer powers, $\sum_k c_k(α)U^k$, where the coefficients are generated through an ancilla-domain quantum Fourier transform and a diagonal phase modulation. Under the assumption that controlled implementations of the required powers of $U$ are available, the resulting circuit yields a parameterized family of operators that interpolates the integer powers of $U$ and satisfies the additive property of fractional transforms. As concrete applications, we derive explicit quantum circuit realizations of the quantum fractional Hartley transform (QFrHT) and of the fractional cosine-transform families associated with Types~I and~IV. These constructions demonstrate the versatility of the proposed dyadic-order fractionalization framework for structured operators arising in quantum signal processing.

Phase change materials provide a powerful platform for dynamically modulating optical responses in nanophotonic systems. While plasmonic metasurfaces have been widely employed to enhance photocatalytic efficiency and promote particular light-driven reactions, active and dynamical control over reaction pathways within a single device remains challenging. Here, we report a phase-induced tunable metasurface that tailors photoexcited electron populations through mode hybridization, enabling selective control over the reactivity of light-driven chemical processes. By exploiting thermally induced refractive-index switching in a Sb2S3 cavity, the plasmonic resonance strength of Au nanodisks is actively tuned via cavity-plasmon hybridization. This reconfiguration modulates the product yield of methylene blue degradation by a factor of 2.4, suppressing to 0.45 in the crystalline phase and enhancing to 1.09 in the amorphous phase. Importantly, this reconfigurable platform enables dynamic control of the reaction yield using a single metasurface architecture under identical illumination conditions. Our approach establishes a dynamically programmable light-driven reaction platform capable of precisely manipulating reaction reactivity, offering new opportunities for selective photocatalysis in complex multibranch reaction systems.

Photocatalytic nitrogen reduction under ambient conditions represents a promising pathway toward sustainable ammonia production. However, the fundamental mechanisms, particularly the role of photogenerated charge carriers and their interactions with surface defects and adsorbates, remain elusive. Here, we employ density functional theory with Hubbard U corrections and hybrid functionals to demonstrate that the synergistic interactions between photogenerated electron polarons and point defects are essential for enabling nitrogen reduction on TiO$_2$(110). We reveal that water adsorption promotes polaron migration from subsurface to surface sites, while subsequent water dissociation stabilizes polarons near oxygen vacancies through proton coupled electron polaron transfer (PCEpT). This surface localization of polarons is critical for effective N$_2$ adsorption and activation. Our findings are consistent with previous experimental reports utilizing EPR that confirm the presence of reduced Ti species and STM, which shows the presence of water dimers on the surface. Moreover, the simultaneous interaction between polarons and reaction intermediates facilitates polaron transfer, thereby driving the completion of the nitrogen reduction reaction. Our findings elucidate the pivotal role of surface polarons in photocatalytic nitrogen fixation and provide mechanistic insights applicable to a broad range of oxide surfaces and interfaces capable of hosting small polarons, offering new design principles for efficient photocatalysts operating under ambient conditions.