Particle-in-cell simulations on nonuniform cylindrical meshes require deposition schemes that respect cylindrical metrics while controlling numerical self-fields. This work develops cylindrical-volume-weighted charge and current deposition using nodal control volumes and swept-volume factors on logically structured stretched grids. Uniform-density and controlled-transport tests demonstrate accurate charge recovery, well-controlled current-density errors, and continuity residuals substantially lower than the corresponding current-density errors. Single-particle diagnostics show that charge-transport consistency alone does not ensure self-field cancellation; face-centered electric fields give the smallest residual, whereas cell-centered and shifted cell-centered layouts produce larger residuals.

Recently proposed experiments are expected to significantly improve the measurement sensitivities of the electric dipole moments (EDMs) of muon ($d_μ$) and tau ($d_τ$). Given that theoretical predictions for $d_μ$ and $d_τ$ typically surpass those for the electron EDM, this work focuses on studying the contributions from the CP-violating (CPV) effects in the B-L supersymmetric (SUSY) standard model (B-LSSM) to $d_μ$ and $d_τ$. After considering the corrections from some two-loop diagrams, the contributions in the B-LSSM to the EDMs of charged leptons are presented analytically in general forms. The numerical results show that the traditional $μ$-term in most SUSY models makes dominant contributions to $d_μ$ and $d_τ$, while the B-LSSM specific CPV parameters also induce significant effects. It is found that across a substantial region of the B-LSSM parameter space, $d_μ$ falls well within the projected sensitivity at Phase II of the proposed experiment, and $|d_τ|$ can reach about $10^{-21}e\cdot\text{cm}$.

In this paper, we continue to study the barcode entropy of relative symplectic cohomology $SH_M(K)$ of a Liouville domain $K$ embedded in a symplectic manifold $M$. This barcode entropy measures the exponential growth rate of the number of not-too-short bars in the persistence module $SH_M(K)$. We prove that this Floer-theoretic invariant admits a nontrivial lower bound in terms of the topological entropy of the Reeb flow on $\partial K$ when the Reeb flow possesses a hyperbolic invariant set. More precisely, we show that the barcode entropy of $SH_M(K)$ is bounded below by the topological entropy of the Reeb flow restricted to a hyperbolic invariant set.

EnclaveScale is a distributed, hardware-assisted telemetry architecture providing post-extraction attestation, enabling operators to collaboratively model high-resolution generative AI power transients. Existing cryptographic techniques scale poorly for 10-Hz streaming or fail to authenticate origins, permitting malicious hosts to spoof sensor inputs. We implement and evaluate a post-extraction pipeline utilizing DCAP attestation, differential privacy noise injection, and Byzantine rejection across 32 GCP Confidential VMs, achieving 0\% post-extraction attack success rate. This edge-DP approach distils continuous GPU transients into discrete Markov-chain transition matrices, guaranteeing event-level differential privacy. To mitigate pre-ingestion vulnerabilities, we propose an SPDM-authenticated first-mile layer. While current platforms lack attested I/O, emerging hardware architectures integrate PCIe IDE and TDISP to natively prevent host-level synthesis, securing the end-to-end provenance boundary. A Global Aggregation Enclave verifies these cryptographic proofs prior to capacity-weighted aggregation. Evaluation demonstrates a steady-state throughput of $131{,}406$ samples/s per enclave, amortising attestation overhead to $0.23\,μ$s/sample. On empirical NVML-sampled H100, A100, and L4 traces, EnclaveScale achieves a dynamic orchestration margin error of $1.3$\,MW compared to $0.1$\,MW for an honest-aggregator central-DP baseline. EnclaveScale establishes a secure foundation for dynamic multi-tenant power orchestration, obfuscating sub-second anomalies locally and protecting macro-workload confidentiality via spatial dilution during global aggregation.

The structure of locally differentially private (LDP) mechanisms can be understood through the geometry of the corresponding privacy polytope. While the extreme points of the \( (ε,0)\)-LDP polytope are well characterized (Kairouz \emph{et al.}, 2014; Holohan \emph{et al.}, 2017; Pensia \emph{et al.}, 2017), comparatively little is known for the \((ε,δ)\)-LDP polytope with \(δ>0\). Recent work (Elangovan and Jog, 2024) has shown that even in the special case \(ε=0\), the \( (0,δ) \)-LDP privacy polytope exhibits fundamentally different behaviour. In this work, we provide complete characterizations of the extreme points for the low-input-alphabet regime \(k=2\) and \(k=3\) and with arbitrary output alphabet size \(m \). We also identify new extreme mechanisms for larger input alphabet sizes $k$, of the star configuration type, as introduced by Elangovan and Jog (2024).

The interest in studying the size of the intersection of multiple $q$-ary Hamming balls has grown due to the recent advances in DNA-based data storage systems. We present an exact formula for the cardinality of the intersection of $s$ Hamming balls of varying radii over a $q$-ary alphabet. It is known that the distances between the center points of the Hamming balls are not enough, in general, to determine the size of the intersection. Based on our formula, we are able to find more refined structural properties of the center points for determining the exact size of the intersection. Moreover, we also analyze the size of the intersection for sufficiently large $n$. When $s=3$, we give the necessary and sufficient conditions (for all $q\ge 2$, $q\neq 6$ and sufficiently large $n$) to obtain the maximum size of the intersection when the center points of the Hamming balls have a given minimum distance and demonstrate how to compute it using our general formula.

This paper studies the asymptotic behavior of sample canonical directions in a finite-rank spiked high-dimensional canonical correlation analysis model under a Gaussian population assumption. Under the asymptotic regime in which the dimensions of the two data blocks grow proportionally with the sample size, sample canonical directions are generally not consistent estimators of their population counterparts, even when the corresponding sample canonical correlations separate from the bulk spectrum. To quantify directional recovery, we investigate the squared alignment between a sample canonical direction and its associated population direction. For each simple population spike, we first establish a deterministic first-order limit for this squared alignment, which gives an explicit measure of the population-level directional information retained by the sample direction. We then prove a central limit theorem for its fluctuations around the deterministic limit, with an explicit asymptotic variance expressed through deterministic limits of resolvent trace functionals. To make the theoretical quantities computable from data, we further construct plug-in estimators for both the limiting mean and the asymptotic variance by inverting the deterministic outlier eigenvalue map, and prove their consistency. Numerical simulations and a real-data illustration support the theoretical results and demonstrate how the proposed estimators assess the recovery quality of sample canonical directions.

The prosperity of text-to-image (T2I) models has fostered a vibrant share-and-play ecosystem centered on Low-Rank Adaptation (LoRA) plugins, which allow users to customize and share model capabilities with ease. This democratization, however, comes with a hidden but severe security risk. Malicious users could share and distribute seemingly benign LoRA plugins that contain hidden functionalities to poison the model-sharing market, like Civitai or Liblib, severely undermining the user trust that underpins this collaborative ecosystem and threatening the safety of countless downstream applications. Despite these risks, plugin poisoning in the real-world T2I ecosystem remains underexplored. This paper introduces PoisonLoRA, the first systematic study of LoRA plugin supply-chain risks that exploits the trust and characteristics within the T2I ecosystem. We identify two primary attack instances: (1) Concept Hijacking, where a hijacked LoRA could generate images to influence public opinion and spread propaganda, and (2) Task Injection, where a LoRA is injected to produce harmful content (e.g., NSFW images) only activated by a secret key. Critically, the malicious payload persists with virus-like propagation. Such propagations weaponize the very act of creative collaboration (e.g., LoRA merging) to spread its contagion, turning every remix into a new carrier. Extensive experiments validate that PoisonLoRA is both effective and stealthy. Specifically, we achieve approximately 100% attack success rates (ASR) on both Civitai and Liblib on 6 datasets across 4 scenarios, without being detected by the platforms. The poisoned LoRA demonstrates extreme robustness, with nearly 100% ASR even transferred to different base models and remixed more than 5 times.

For a real variety with smooth point, we construct a complex of sheaves on its real points which behaves like intersection cohomology. This real geometric extension is (shifted) Verdier self-dual, and occurs as a direct summand within any resolution of singularities. The stalks of this object provide lower bounds for the Betti numbers of the real fibres in any resolution of singularities. On the real flag variety, the category of real geometric extensions along the Schubert stratification is equivalent to the corresponding category of even parity sheaves on the complex flag variety.

Three-dimensional all-inorganic halide perovskites, represented by CsPbX$_3$ (X = Cl, Br, I), have attracted broad interest in photovoltaics, photodetectors, and light-emitting devices because of their outstanding optoelectronic properties. Their practical deployment, however, remains limited by instability under thermal, chemical, optical, and electrical stress. Conventional studies have established important experimental and theoretical foundations, but they still struggle with multimodal data, coupled degradation pathways, protocol dependence, sparse statistics, and uncertainty quantification. Artificial intelligence (AI) offers a practical route to address these limitations. This review summarizes recent progress in AI-assisted studies of instability in 3D CsPbX$_3$ and organizes the discussion around four linked tasks, including stability discrimination and diagnosis, microscopic mechanism analysis, consequence and reliability modeling, and engineering stability enhancement. We further discuss the main limitations of current methods, especially in data quality, protocol consistency, benchmark design, interpretability, and transferability across domains. Finally, we outline future directions for the field, including standardized data infrastructures, interpretable cross-scale models, and tighter integration of AI with automated experiments and physics-based modeling. The aim of this review is to provide a coherent and practically useful framework for researchers seeking to use AI to understand, predict, and mitigate instability in inorganic perovskites.

The deposition and growth of a thin CoCrFeMnNi high-entropy alloy film on an Al(100) substrate were investigated by molecular dynamics simulation. Interatomic interactions were described using a calibrated set of Morse potentials combined with mixing rules for regular solutions. During a 100 ns simulation, 50,000 atoms with an incident energy of 10 eV were deposited, producing a film of about 6.1 nm thickness. The resulting film contains face-centred cubic (FCC), body-centred cubic (BCC), hexagonal close-packed (HCP), and amorphous regions. Analysis of the radial distribution function (RDF) was used to determine nearest-neighbour distances and estimate lattice parameters for the crystalline phases. The simulated phase composition and structural parameters are in good agreement with available experimental data.

Large language models fine-tuned on instruction-code pairs may memorize and subsequently leak sensitive training data. Existing differentially private (DP) code generation methods primarily protect code snippets while assuming prompts are public, which fails in realistic scenarios where prompts may also contain sensitive information. When prompts cannot be explicitly learned or used during generation, code synthesis suffers from severe utility degradation as well as reduced diversity and fidelity. To address these challenges, we propose PrivCode-Plus, the first work to explore DP code generation where both prompts and code snippets are considered sensitive in LLM fine-tuning. PrivCode-Plus introduces a two-stage DP framework with a Privacy-Free Latent Conditioning module, enabling effective DP fine-tuning and data synthesis without direct access to sensitive prompts or code. Extensive experiments show that PrivCode-Plus achieves substantially higher utility than baselines, remains competitive with the method with relaxing privacy assumptions, and provides stronger privacy guarantees.

The "Brain Imaging Data Structure" (BIDS) has become a widely adopted standard for organizing and sharing neuroimaging datasets of various modalities. However, converting raw brain imaging data into BIDS framework remains a complex and time-consuming task. BIDSme is a semi-automated tool developed to streamline this conversion process, but until recently, it lacked the portability and accessibility needed for widespread adoption. This paper presents the containerization of BIDSme using Docker and Docker Compose, improving usability, reproducibility, and integration into existing platforms like Neurodesk. It also details the design choices, iterative refinements, and validation process that led to a flexible, lightweight, and user-friendly containerized application.

This work presents a modal formulation for flexible rectangular plates, accounting for nonlinear geometric effects arising from in-plane foreshortening and centrifugal stiffening. The model is linearized with respect to elastic deformations while retaining the full dependence on spacecraft angular velocities and accelerations. System matrices depend nonlinearly on spacecraft states through squared and cross-product terms, capturing gyroscopic coupling and dynamic stiffening phenomena for arbitrary rotational maneuvers. Polynomial approximation of mode shapes enables efficient computation while preserving accuracy. Model predictions are validated against finite element simulations and literature data for transient response under prescribed hub motion.

Multiversion concurrency control (MVCC) enables scans to read data from a committed snapshot (version), reducing conflicts with write operations compared to traditional concurrency approaches. Currently, versioned records are often managed in a B$^+$-tree using version chains. However, version chains introduce overhead during scans and can still lead to conflicts between scans and writers. The multiversion B-tree (MVBT) was designed for optimal range scan performance on arbitrary versions, but has been considered impractical due to its structural complexity and, until recently, the lack of effective concurrency control. In this paper, we present the concurrent MVBT (cMVBT), a redesign of the MVBT featuring a novel concurrency control protocol that uses optimistic latches for write operations and requires no latches for range scans, while preserving all the optimality guarantees of the original MVBT. Additionally, cMVBT supports continuous garbage collection without activity spikes, seamlessly integrating free-space management. Experiments with mixed workloads derived from a standard benchmark show that the cMVBT achieves low overhead, high write throughput, and excellent range scan performance, outperforming state-of-the-art methods based on version chains.

Singlet fission converts an optically excited singlet state into a spin-entangled triplet pair state (TT$_1$)$^1$ that can, in principle, yield two free triplets for photovoltaics and/or a polarized high spin state for quantum technologies. Synthetically tunable templates suggest that the above photophysics is governed by a subtle but poorly understood interplay of molecular motifs, geometry and structural fluctuations. Here, we investigate the (TT$_1$)$^1$ state in a library of conformationally flexible pentacenic dimers, where a (TT$_1$)$^1$-specific near-IR spectral feature is readily available. Using a suite of polarization-controlled impulsive optical spectroscopies, we find that (TT$_1$)$^1$ formation is specific to planar conformations and is accompanied by large nuclear reorganization in the (TT$_1$)$^1$ photoproduct. Introducing polarization anisotropy to track the electronic character of the (TT$_1$)$^1$ species, supported by screened configuration interaction based electronic structure theory, we find that significant singlet-triplet electronic mixing is persistent throughout its evolution. This behavior is universal across diverse bridging motifs and indicates that, once the triplet pair is strongly bound, neither substantial nuclear reorganization nor structural fluctuations on longer timescales are sufficient to suppress persistent singlet-triplet electronic mixing, such that triplet-pair decorrelation is outcompeted by its decay. Our observations establish polarization-selective pump-probe and anisotropy as a direct optical probe of triplet pair decorrelation, complementary to spin-selective measurements at longer timescales.

This paper proposes a co-optimization framework that jointly optimizes SRAM architecture and transistor sizing using equivalent circuit models. The framework simplifies inactive SRAM cells into equivalent RC loads and static power models, achieving up to 61.4$\times$ simulation speedup while maintaining high fidelity (read/write delay error $<$0.22%, power error $<$1.68%). A joint search space encompassing architecture parameters and device sizing integrates seven algorithms including SA, PSO, Bayesian Optimization variants, and multi-objective evolutionary algorithms. Based on FreePDK45, ablation experiments confirm complementary gains from architecture selection and transistor sizing. Among all algorithms, MOEA/D achieves the best Figure of Merit (8.2721), yielding 6.2% improvement in SNM, 73.6% reduction in area, and 42.3% reduction in peak power. The framework is publicly available at https://github.com/W1Y1K1/OpenOpt.

Two-dimensional (2D) altermagnets (AMs) offer a compelling paradigm for advanced spintronics, yet their fully compensated macroscopic spin currents inherently limit practical device integration. In this work, using first-principles calculations and theoretical analysis, we demonstrate that the 2D material Ru2MoSe4 hosts AM ground state protected by S4zT symmetry. Using uniaxial strain modulation and stacking configuration, we show that the monolayer and the AC-stacking bilayer Ru2MoSe4 host fully spin-polarized currents, piezomagnetically induced net magnetization, and the magneto-optical Kerr effect. Our findings establish Ru2MoSe4 as a tunable platform, offering a feasible mechanism to simultaneously trigger electrical spin transport signals and amplify optical readout signatures for next-generation spintronics and valleytronics.

Scalable Vehicle-to-Everything (V2X) networks are key to support the large-scale deployment of connected and automated mobility. However, the scalability of V2X networks is currently challenged by the limitations of existing V2X communication paradigms, which prioritize the reliable and timely delivery of the transmitted information over a careful message content selection - an approach that can potentially lead to the transmission of unnecessary information and an inefficient usage of communication resources. Semantic and task-oriented V2X communications have recently been proposed to address these scalability challenges by focusing on the content of the transmitted messages, particularly on its relevance to the intended receivers. In this paper, we numerically demonstrate that semantic and task-oriented V2X communications can substantially improve the scalability of V2X networks, increasing by up to a 4.1x factor the number of supported vehicles under high-density conditions. In addition, we show that semantic and task-oriented V2X communications can also decrease the inter-reception time between consecutive messages by up to 67% and lead to a twofold increase in the probability of successfully delivering all required relevant information to the intended receivers.

We study finite expected-signature information for mixed-fBm paths with Hurst indices above $1/4$. Up to level three, the only parameter-dependent expected features are the variance transform $q_θ$ and the time-ordered transform $R_θ$. We prove the scale tradeoff $2K$ level-two scales versus $K$ selected level-two/three scales, together with separation and local inverse bounds.

Recent Byzantine Fault Tolerant (BFT) protocols achieve strong performance by combining the low-latency advantages of leader-based BFT protocols with the high-throughput benefits of DAG-based data dissemination. Despite exposing a wide spectrum of internal tunable parameters, these protocols typically rely on static and heuristic configurations, which leads to performance degradation under dynamic workloads, heterogeneous network conditions, and evolving adversarial behaviors. In this paper, we present AutoPilot, a reinforcement learning-based framework that continuously monitors runtime conditions and dynamically adjusts protocol parameters online to optimize consensus performance. To ensure robustness, AutoPilot coordinates learning in a decentralized manner, providing resilience against adversarial data pollution. We implement AutoPilot on top of Autobahn, a state-of-the-art, highspeed, robust BFT protocol, and evaluate it across diverse dynamic environments. Experimental results demonstrate that AutoPilot quickly converges to the optimal configuration under changing environments, reduces end-to-end latency by 49.8% compared to the default protocol configuration, and outperforms random configuration exploration by 73.3%.

In a recent work \href{https://doi.org/10.1103/PRXQuantum.2.010325}{PRX QUANTUM 2 (2021) 010325}, a new way of investigating quantum gravity signatures using quantum information theoretic techniques, have been proposed. The primary result of this analysis revealed that non-Gaussianity can arise only through the consideration of a quantum model for the gravity part. Compared to classical gravity, only quantum gravity can result in non-quadratic operators in the Hamiltonian which leads to the non-Gaussian behavior. In our current analysis, we have considered a more realistic scenario taking into effect leading order post-Newtonian corrections in the analysis. We have stayed with the same model of a Bose-Einstein condensate placed inside a harmonic trap potential which indeed works as the detector of the non-Gaussianity generated due to quantum gravitational effects. Bose-Einstein condensates are experimentally well studied; apart from being a single quantum system, they include Feshbach resonances, which helps tuning the strength of the electromagnetic interactions which in principle can be set to zero. This is important since it can help distinguish quantum gravity from electromagnetic interactions without affecting gravitational interactions, and any non-Gaussianity can then be solely attributed to quantum gravity. We observe that the signal to noise ratio gets slightly damped due to the post-Newtonian effects taken under consideration.

The underground cosmic-ray muon rate is known to be modulated by atmospheric temperature. It can be explained by the theories of Barrett, Gaisser, and others. However, at the Daya Bay Neutrino Experiment, the dependence on temperature is observed to be nonlinear. We found that, when deriving the temperature dependence of muon rate, existing theories only consider the impact of local temperature on muon production at the layer where muons are produced. In this work, we provide an more general solution to the cascade equations, which is complex enough to fully depict how entire temperautre profile would influence the final muon rate. Corresponding definitions of the effective temperature weight and temperature coefficient are also presented. We examine the results with numerical tool MCEq and real atmospheric temperature input. A linear modulation is recovered and verified. This work can help to explain the nonlinear effect found at Daya Bay, and provide a more refined calculation framework for temperature coefficient calculation for other experiments.

This paper proposes a generative AI (GenAI)-enabled digital twin (DT) framework for proactive and energy-aware wireless optimization in future 6G ecosystems. Most existing AI-assisted DT approaches remain fundamentally reactive, adjusting network parameters only after performance degradation occurs or restricting GenAI to isolated signal-level tasks such as channel estimation. This work adopts a proactive approach. Instead of responding to problems after they appear, the proposed framework continuously synchronizes channel states, mobility dynamics, traffic conditions, and energy information within a real-time DT environment, enabling the system to anticipate congestion, interference, and energy demand before they materialize. The result is a closed-loop proactive architecture that operates at the system level, jointly managing communication, mobility, and resource dynamics for autonomous wireless control. Evaluations on a UAV-assisted non-terrestrial network (NTN) scenario show approximately 69.2\% energy savings over reactive baselines while maintaining reliable quality-of-service (QoS) under dense and mobility-intensive conditions. Beyond this specific scenario, the framework offers a scalable foundation for broader AI-native 6G applications, including aerial platforms, autonomous systems, extended reality (XR), industrial automation, and space-air-ground-sea (SAGS) integrated infrastructures.

We introduce the braided cochain complex and the braided cohomology of braided coalgebras in linear monoidal categories, and compare the braided cohomology of braided coalgebras living in different linear monoidal categories using relative morphisms. The symmetric cohomology was introduced for groups by Staic, and was generalized to cocommutative Hopf algebras by Shiba, Sanada, and the second author. This cohomology involves degreewise actions of the symmetric groups on a cochain complex, which come from the usual symmetric monoidal structure on the category of modules. We generalize this framework by dealing with arbitrary linear monoidal categories, and by replacing symmetries with braidings defined merely on an object. We first give a convenient description of relative morphisms, and apply this result to prove that the braided cochain complex of quasi-triangular bialgebras is a braided Morita invariant under a certain condition, which is automatically satisfied in the finite-dimensional case.

We study Family-Vicsek scaling of charge, spin, and energy fluctuations in the one-dimensional Hubbard model at infinite temperature. Using a quantum generating function approach, we compute time-dependent cumulants of transferred conserved quantities and analyze how the corresponding roughness depends on subsystem size and time. We start by focusing on a single interface at half the chain and determine the transport exponents. Then we turn to fluctuations of a small finite interval and study the Family-Vicsek universality of fluctuations over an extended timescale. We find that the long-time scaling behavior is controlled by integrability. In the free limit, charge, spin, and energy all display ballistic transport. In the interacting integrable Hubbard chain, charge and spin cross over to a KPZ scaling regime, while the energy sector remains ballistic. Once integrability is broken by a next-nearest-neighbor density interaction, the long-time dynamics becomes diffusive in all sectors. In every case we also observe a short-time microscopic regime with apparently universal ballistic growth before the hydrodynamic scaling window sets in. The Family-Vicsek setup allows us to determine the growth, the saturation as well as the dynamical exponents.

In \cite{PO1} and \cite{PO2}, Pfante defined a noncommutative Chern-Simons action for 3-summable spectral triples and computed the Chern-Simons action for $\mathrm{SU}_q(2)$ and the noncommutative 3-torus. In this paper,we compute the Chern-Simons action for well-know perturbed Dirac triples.

The official C++ MPI bindings were removed from the standard in 2008, leaving a gap that numerous third-party libraries have attempted to fill. However, existing wrappers typically cover only a limited subset of MPI or target specific use cases, falling short of a general-purpose solution. A recent conceptual paper proposed general design principles for modern C++ bindings based on C++20 concepts, without committing to a concrete interface. We present the first concrete realization of these principles in a layered architecture. At the foundation, we define a core layer: refined C++20 concepts formalizing the MPI standard's notion of data buffers, automatic mapping of standard C++ constructs, non-intrusive customization points for third-party types, and concept-based wrappers for MPI procedures. The result is a low-level native C++ MPI interface that works directly with STL containers, is highly extensible, and lends itself to standardization. Built on this core, we present KaMPIng-v2 -- a C++ MPI library offering the convenience and memory-safety of KaMPIng with composable, pipe-based syntax inspired by C++ ranges for efficient, boilerplate-free MPI programming. Finally, we demonstrate the core layer's broad applicability by designing lightweight adapters for GPU and performance-portability libraries, making the HPC ecosystem a first-class citizen in MPI. Kokkos views, Thrust device vectors, and SYCL buffers can be passed directly to MPI procedures, with adapter logic remaining self-contained. All contributions are backed by a fully functional open-source reference implementation, demonstrating the practical viability of the proposed design.

Plasmon-exciton hybrids, or plexcitons, offer deeply subwavelength light-matter interactions with versatile pathways for energy redistribution. Incorporating chirality into such systems is particularly compelling, enabling spin-sensitive optical functionality that can operate on ultrafast timescales and within ultracompact volumes. Despite recent progress in chiral plexcitonic systems, how structural chirality and plasmon-exciton coupling determine chiroptical spectra and ultrafast energy flow remains elusive. Here we realize chiral plexcitons by functionalizing intrinsically chiral gold helicoid nanoparticles with molecular J-aggregates. Within a non-Hermitian framework, we trace the microscopic origin of the helicoid chiroptical response and its coupling to the excitonic transition, revealing how the helicity of light selectively addresses distinct hybrid responses. At the spatiotemporal extreme, we find that the gap-localized response not only enhances polarization-sensitive contrast but also strengthens the local hybrid interaction, leading to accelerated ultrafast relaxation. Together, these space-, time-, and polarization-resolved measurements provide a physically grounded and experimentally benchmarked picture of chiral plexcitonic coupling, identifying chirality as a practical control parameter for selectively steering nanoscale energy pathways and dynamics.

We establish a unified framework for understanding the results on the Weil quadratic form obtained by Yoshida (1992), Bombieri (2001, 2003), Connes--Consani (2023), and Connes--Consani--Moscovici (2025+) from the perspective of the screw function introduced in Suzuki (2023). An advantage of the approach via the screw function is that it provides a method to study the Weil quadratic form, which is originally defined in terms of distributions, by means of continuous functions. Based on this framework, we formulate a conjecture stating that a self-adjoint operator whose eigenvalues are the imaginary parts of the nontrivial zeros of the Riemann zeta function can be obtained as the limit, as $a \to \infty$, of self-adjoint operators arising from nonlocal realizations of the first-order differential operator on the finite interval $[-a,a]$. All these results are obtained without assuming the Riemann Hypothesis. This conjecture may be compared with the limit formula for the Riemann zeta function expressed in terms of zeta-regularized products proposed by Connes, Consani, and Moscovici, and it sheds new light on the spectral-theoretic interpretation of the nontrivial zeros of the Riemann zeta function.