Search Results (1,098)

The rapid electrification of transportation and proliferation of distributed energy resources (DERs) are transforming distribution grids into highly dynamic, data-intensive, and cyber-physical systems. While reinforcement learning (RL), multi-agent coordination, and edge computing offer powerful tools for adaptive control, their deployment in safety-critical utility environments raises concerns regarding stability, certification compatibility, cyber-resilience, and regulatory acceptance. This paper presents an architecture-centric framework for edge-intelligent and cyber-resilient coordination of electric vehicles (EVs) and DERs that reconciles adaptive learning with deterministic safety guarantees. The proposed hierarchical edge–cloud architecture integrates multi-agent system (MAS) coordination, constraint-invariant reinforcement learning, and embedded cybersecurity mechanisms within a structured control hierarchy. Learning-enabled edge agents operate exclusively within standards-compliant safety envelopes enforced through supervisory constraint projection, control barrier functions, and Lyapunov-consistent stability safeguards. Protection-critical functions remain deterministic and isolated from adaptive layers, preserving compatibility with IEEE 1547 and existing utility protection schemes. The framework further incorporates anomaly triggered policy freezing, fail-safe fallback modes, and communication-aware resilience mechanisms to prevent unsafe transient behavior in non-stationary, distributed environments. Unlike simulation-only learning approaches, the architecture embeds progressive validation through software-in-the-loop (SIL), hardware-in-the-loop (HIL), and power hardware-in-the-loop (PHIL) testing to empirically verify transient stability, constraint compliance, and cyber-resilience under realistic timing and disturbance conditions. Beyond technical performance, the paper situates edge intelligence within standards evolution, governance structures, workforce transformation, techno-economic assessment, and equitable deployment pathways. By framing adaptive control as a bounded, auditable augmentation layer rather than a disruptive replacement for certified infrastructure, the proposed architecture provides a pragmatic roadmap for evolutionary modernization of distribution systems. Full article

In this paper, we introduce two analytic deformations of probability measures that unify and extend two classical deformations from free probability theory, namely the $\mathbb{T}=(s,t)$-deformation $U^{\mathbb{T}}$ and the $T_a$-deformation, where $a,t\in \mathbb{R}$ and $s>0$. The corresponding operators, denoted by ${\mathbb{Y}}_{(a,s,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,s,t)}^{\prime \prime }$, are defined via a functional equation involving the Cauchy–Stieltjes transform (CST). This framework recovers the classical cases as particular instances, specifically ${\mathbb{Y}}_{(0,s,t)}^{\prime }={\mathbb{Y}}_{(0,s,t)}^{\prime \prime }=U^{\mathbb{T}}$ and ${\mathbb{Y}}_{(a,1,1)}^{\prime }={\mathbb{Y}}_{(a,1,1)}^{\prime \prime }=T_a$. We analyze the analytic and structural properties of the operators ${\mathbb{Y}}_{(a,s,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,s,t)}^{\prime \prime }$ within the concept of Cauchy–Stieltjes kernel (CSK) families, with particular emphasis on their action on variance functions (VFs). In particular, we derive explicit formulas for the VFs associated with measures deformed by ${\mathbb{Y}}_{(a,s,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,s,t)}^{\prime \prime }$. As an application, we establish an invariance property showing that the class of free Meixner family (FMF) is stable under both deformations. Furthermore, by restricting the parameters to ${\mathbb{Y}}_{(a,1,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,1,t)}^{\prime \prime }$, we obtain two new characterizations of the semicircle law. These results highlight the role of symmetry in the analytic deformation and in the stability properties of fundamental distributions in free probability. Full article

Baroclinic instability is a fundamental mechanism of midlatitude atmospheric variability, and the Eady model remains one of its most useful idealized representations. In this work, we revisit the Eady configuration from the viewpoint of solution-space geometry rather than the classical normal-mode/growth-rate analysis. Starting from the reduced Eady vertical-structure equation, we show that the ratio of two independent solutions satisfies a Schwarzian-type relation that is invariant under homographic transformations, which naturally leads to an SL(2R) projective symmetry of the solution family. On this basis, we introduce a complex amplitude representation and reformulate coherence in terms of phase–amplitude synchronization constrained by projective invariants. Using Riccati-type constructions along geodesic parametrizations, the reduced dynamics are connected to a Stoler-type transform. Numerical exploration of the reduced model shows a systematic dependence on the control parameter $\omega$: small $\omega$ is associated with simple oscillatory or burst-like behavior, intermediate $\omega$ with period-doubling-like behavior, and large $\omega$ with strongly modulated dynamics and more intricate reconstructed attractors. These results should be interpreted as properties of the reduced symmetry-based model, and they suggest that projective invariants may provide a useful framework for classifying organization regimes in Eady-type disturbances, complementary to classical growth-rate analyses. Full article

The derivations presented in this paper suggest an intimate relationship between geometry and the electroweak sector at the Planck scale. A Lorentz-invariant maximally symmetric stochastically perturbed spacetime transformed to spherical coordinates reveals an emergent Schwarzschild metric, entirely a statistical structure of stochastic spacetime. Similarly, the transition from a maximally symmetric universe with a complex $SU\left(2\right)$ scalar doublet $\varphi$, comprising four independent real scalar fields with a zero vacuum expectation value (VEV), to spherical coordinates at the Planck scale reveals the spontaneously broken electroweak (EW) sector. Working in the unitarity gauge, the resulting EW potential can be simultaneously mapped in space at the Planck scale and across the EW sector. In space, the resulting EW potential includes a deep well within the Schwarzschild sphere and a shallow well just outside corresponding to an accretion disk. The same potential mapped in the EW space provides an entire family of possible sombrero hat potentials with fourth-order coupling specific to a point in space. At the minimum points of the potential in space, inside the Schwarzschild sphere and at the accretion disk, the $\lambda$ corresponding to the Standard Model (SM) fourth-order coupling is instead derived as $\frac{\lambda }{5}$. The factor of $\frac{1}{5}$ is a simple consequence of the conservation of the EW VEV and the fact that the SM formulation of the EW potential does not account for situations where the perturbations in $\varphi$ dominate. A more general formulation of the EW potential restores the SM quartic coupling and preserves $\lambda$ in space. An emergent Higgs field inside the Schwarzschild black hole is found to directly relate to the stochastic spacetime fields normalized by the Schwarzschild radius. The corresponding Higgs vacuum has both a ground and excited state and the possibility of both positive and negative vacuum entropy. Finally, the scalar-field VEV degeneracy in EW space of the metastable Higgs vacuum appears instead differentiated in space with possible probability, tunneling, and entropy implications. Full article

High-power jamming may drive the radio-frequency (RF) front end of a satellite receiver into a nonlinear regime, thereby invalidating the linear superposition assumption underlying conventional excision and blanking methods. We formulate dual-receiver direct-sequence spread-spectrum (DSSS) anti-jamming as a nonlinear source-separation problem in complex baseband using stacked I/Q observations. We then propose a time-domain separator that jointly estimates the desired DSSS signal and the jammer on a designated reference receiver. The separator combines a multi-scale convolutional front end with a Transformer encoder and is pretrained on synthetic nonlinear mixtures that include multi-tone or burst jamming as well as typical satellite impairments, including Doppler/carrier-frequency offset (CFO), phase noise, multipath, and additive white Gaussian noise (AWGN). Robustness under high-jammer-to-signal-ratio (JSR) conditions is improved through high-JSR oversampling and JSR-aware loss reweighting. After Stage I supervised pretraining on labeled synthetic mixtures, an optional Stage II mixture-only adaptation step further refines the separator using nonlinear reconstruction consistency and lightweight communication-motivated priors. Across 1000 test mixtures with JSRs from −5 to 15 dB, SNRs from 15 to 25 dB, and cubic coefficients $a\in \left[\mathrm{0,0.5}\right]$, the proposed method improves the desired-signal scale-invariant signal-to-noise ratio (SI-SNR) from −4.79 dB for the mixture baseline to 13.32 dB after supervised pretraining and to 17.73 dB after mixture-only blind fine-tuning. Over the same test set, the failure rate (SI-SNR < 0 dB) decreases from 60.7% to 2.3%. Full article

The geometric signatures of macroscopic interfaces in the two-dimensional critical Ising model strictly adhere to Schramm–Loewner Evolution (SLE) theory. In this study, we propose a physics-driven generative approach using Super-Resolution Generative Adversarial Networks (SRGANs) to approximate the inverse coarse-graining operation to generate larger configurations. From the perspective of Geometric Deep Learning (GDL), we leverage the geometric priors of Convolutional Neural Networks (CNNs)—specifically their translational and rotational symmetries—to effectively encode the universal physical laws of the Ising Hamiltonian. This inductive bias allows the model to be trained on small scales yet be generalized to large-scale systems ($2048 \times 2048$) while preserving physical conservation. To accommodate spin discreteness, we employ an L1-based loss function to maintain domain wall sharpness. SLE analysis and long-range correlation functions confirm that the model reproduces critical dynamics and conformal invariance, successfully serving as a physics-preserving inverse coarse-graining transformation framework. Full article

This article proposes a single-deformation scheme for probability measures that simultaneously encompasses two classical deformations from free probability: the $V_a$ deformation ($a\in \mathbb{R}$) and the $T_c$ deformation ($c\in \mathbb{R}$). The associated operator, written as ${\mathcal{W}}_{(a,c)}$, is introduced via a functional relation involving the Cauchy–Stieltjes transform and is constructed so as to recover the initial deformations as special cases, namely ${\mathcal{W}}_{(0,c)}=T_c$ and ${\mathcal{W}}_{(a,0)}=V_a$. Working within the concept of Cauchy–Stieltjes kernel families, we analyze the action of ${\mathcal{W}}_{(a,c)}$ on variance functions and establish an explicit expression for the variance function induced by this deformation. This approach leads to a structural invariance property demonstrating that the free Meixner class is preserved under the action of ${\mathcal{W}}_{(a,c)}$. In addition, the operator provides a new perspective on the semicircle distribution, yielding a characterization that reflects the symmetric nature of the deformation and its compatibility with fundamental distributions in free probability. Full article

In visual object recognition problems, low light exposure and low-quality images present significant challenges in navigation, surveillance, and image retrieval applications, where reliable feature detection is critical. Although recent deep learning–based image enhancement methods improve visual quality in the pixel domain, these improvements often do not translate to downstream machine vision performance, as important local gradient structures required for stable key point detection are frequently suppressed. In this work, we propose IllumiSIFT, a task-driven dark image enhancement framework that focuses on preserving Scale-Invariant Feature Transform (SIFT) key points by directly learning the Difference-of-Gaussian (DoG) pyramid from low-light image inputs. Unlike conventional pixel-level recovery approaches, the proposed method employs a cascaded residual learning architecture to predict Gaussian-blurred representations at multiple scales, enabling the generation of enhanced DoG images that are inherently aligned with the SIFT detection process. Extensive experiments conducted on the CDVS, Oxford Buildings, and Paris datasets demonstrate that the proposed approach consistently outperforms state-of-the-art enhancement methods in downstream SIFT matching performance under severe low-light conditions. These results confirm that gradient-domain, task-aligned enhancement provides a more effective and practical solution for recognition-centric low-light imaging applications. Full article

This paper develops Tone as Ontology, a structural account of being grounded in the invariants of generative systems. We articulate the ontological significance of tone, distinguishing this foundational work from a companion paper that explores its methodological application and formalization. We redefine “tone” as the structural profile of constraints that allows entities to maintain coherence under transformation. The tonal ontology formalizes three invariants—Resonance, Responsibility, and Closure—as conditions of persistence that bridge operational and metaphysical ontology. Concretely, we specify Resonance (relational continuity via recursive feedback), Responsibility (traceable accountability that conserves integrity across transformations), and Closure (recursive self-consistency enabling bounded openness). In contrast to informational or substance-based views, tonal being is understood as the conservation of structure through change. The resulting framework unites physical coherence, informational integrity, and ontological continuity into a generative ontology of integrity, suggesting that to exist is to maintain one’s tone. This paper addresses fundamental questions in meta-ontology, demonstrates how tone generates classical ontological frameworks, and advances a conceptual reorientation for understanding existence as resonant persistence. It outlines testable implications across philosophy of mind, AI ethics, and social/environmental theory. Overall, tonal ontology is presented as a post-informational, structurally grounded account of being. Full article

In this paper, we clarify several issues concerning the abstract geometrical formulation of thermodynamics on non-compact symmetric spaces $\mathrm{U}/\mathrm{H}$ that are the mathematical model of hidden layers in the new paradigm of Cartan Neural Networks. We introduce a clear-cut distinction between the generalized thermodynamics associated with Integrable Dynamical Systems and the challenging proposal of Gibbs probability distributions on $\mathrm{U}/\mathrm{H}$ provided by generalized thermodynamics à la Souriau. Our main result is the proof that $\mathrm{U}/\mathrm{H}$.s supporting such Gibbs distributions are only the Kähler ones. Furthermore, for the latter, we solve the problem of determining the space of temperatures, namely, of Lie algebra elements for which the partition function converges. The space of generalized temperatures is the orbit under the adjoint action of $\mathrm{U}$ of a positivity domain in the Cartan subalgebra $C_c\subset \mathbb{H}$ of the maximal compact subalgebra $\mathbb{H}\subset \mathbb{U}$. We illustrate how our explicit constructions for the Poincaré and Siegel planes might be extended to the whole class of Calabi–Vesentini manifolds utilizing Paint Group symmetry. Furthermore, we claim that Rao’s, Chentsov’s, and Amari’s Information Geometry and the thermodynamical geometry of Ruppeiner and Lychagin are the very same thing. In particular, we provide an explicit study of thermodynamical geometry for the Poincaré plane. The key feature of the Gibbs probability distributions in this setup is their covariance under the entire group of symmetries U. The partition function is invariant against $\mathrm{U}$ transformations, and the set of its arguments, namely the generalized temperatures, can always be reduced to a minimal set whose cardinality is equal to the rank of the compact denominator group $\mathrm{H}\subset \mathrm{U}$. Full article

In size, weight, and power (SWaP)-constrained optical systems, such as spaceborne LiDAR, high-precision centroid localization often relies on focal-plane measurements without dedicated wavefront sensors. Under such conditions, the nonlinear coupling between optical aberrations and sensor noise introduces systematic bias that is difficult to mitigate using conventional centroiding methods. To address this issue, we propose a physics-conditioned feature correction framework based on an aberration-conditioned attention mechanism. A hybrid CNN–Transformer architecture is employed to predict and compensate for systematic centroid bias. Specifically, convolutional layers encode the degraded spot morphology, while a multi-head attention mechanism leverages Seidel aberration coefficients to adaptively modulate spatial features for precise regression. Given the unavailability of absolute ground-truth coordinates in empirical scenarios, a physics-consistent simulation framework based on scalar diffraction theory is constructed to generate synthetic data for supervised learning. Simulation results indicate that the proposed method objectively reduces anisotropic systematic bias, achieving a localization root-mean-square error (RMSE) of 0.011 to 0.021 pixels, and maintains stable sub-pixel accuracy even under a 10% empirical prior perturbation. To evaluate generalization performance and engineering reliability, a wedge-based double-spot platform is developed to verify physical consistency via geometric invariance. Experimental results demonstrate a measured spacing standard deviation (SD) of 0.015 to 0.039 pixels. This validates the framework’s transferability from theoretical simulation to controlled physical measurements, providing an algorithmic foundation for precision optical metrology in hardware-constrained environments. Full article

In this paper, we propose an analytic deformation acting on probability measures, designed to encompass and extend two fundamental operators in free probability: the $(a,b)$- and the $T_c$-deformations. This unified operator, indicated by ${\mathcal{X}}_{(a,b,c)}$, is introduced through a functional relation for the Cauchy–Stieltjes transform. We have ${\mathcal{X}}_{(a,b,0)}={\tilde{U}}^{(a,b)}$ and ${\mathcal{X}}_{(1,1,c)}=T_c$. We examine the structural properties of this transformation within the setting of Cauchy–Stieltjes kernel (CSK) families, with special emphasis on the behavior of the associated variance functions (VFs). An explicit formula for the VF corresponding to measure deformed by ${\mathcal{X}}_{(a,b,c)}$ is established. This result allows us to demonstrate a key invariance property: the free Meixner class of probability measures remains stable under the ${\mathcal{X}}_{(a,b,c)}$-transformation. Furthermore, a novel characterization of the semicircle law is obtained through the action of ${\mathcal{X}}_{(a,1,c)}$, highlighting the role of symmetry in the deformation and preservation of free-probabilistic distributions. Full article

We formulate a structural stability criterion for dimensionless physical constants within standard perturbative field frameworks. The analysis introduces a response-ratio functional $\mathit{\Gamma }=\kappa /\tau$, defined from second-order sensitivity and first-order deformation measures associated with admissible variations in a field configuration. Stability is characterized by proportional stationarity of $\mathit{\Gamma }$, expressed as a first-order operator condition along transformation flows. The framework characterizes, within a declared variational model, when invariance of fixed constants can be represented as a stationarity condition. Under compactness and convexity assumptions typical of variational systems, stationary response ratios arise as isolated solutions of the associated operator equation; more general settings permit continuous spectra. Explicit functional definitions are provided within a conventional analytic setting, and the criterion is illustrated in representative classical field models. The results position proportional stationarity as a model-relative structural consistency condition for perturbative stability; isolation is conditional on compactness and non-degeneracy hypotheses, and continuous families may occur outside that regime. Limitations and possible extensions, including discretized spacetime formulations, are discussed. Full article

The accurate description of nonadiabatic quantum molecular dynamics represents one of the most significant challenges in modern computational chemistry, serving as a gateway to understanding complex phenomena ranging from photochemistry and electron transfer to surface scattering and biological exciton transport. A key difficulty lies in bridging high-level electronic structure theory for ground and excited states with accurate quantum dynamics theory. Although on-the-fly semiclassical approaches are increasingly viable, most quantum dynamics simulations still rely on pre-constructed potential energy surfaces, or in the nonadiabatic context, diabatic potential energy matrices (DPEMs). This perspective paper addresses the theoretical foundations, construction methodologies, and emerging frontiers of DPEMs. We examine the mathematical framework of the adiabatic-to-diabatic transformation, addressing the inherent topological challenges imposed by the geometric phase and the curl condition. We further analyze the transformative impact of machine learning, detailing how machine learning algorithms, such as permutation invariant polynomial neural networks and deep learning architectures, are reshaping the construction of global, high-dimensional DPEMs. Finally, we explore the disruptive potential of quantum computing, discussing how quantum algorithms are automating the direct simulation of nonadiabatic dynamics. In emerging quantum-centric workflows, DPEMs will continue to provide the critical bridge which enables the mapping of realistic, time-dependent molecular Hamiltonians onto quantum hardware. Full article

The integration of heterogeneous geospatial data, specifically low-cost unmanned aerial vehicle (UAV) imagery and mobile light detection and ranging (LiDAR) system point clouds, presents a significant challenge due to the significant radiometric and structural discrepancies between the two modalities. This study proposes a novel air-to-ground semantic feature matching framework to achieve precise geometric registration between these data sources by effectively incorporating semantic-constraint deep learning-based matching. The methodology transformed the cross-sensor alignment challenge into a robust two-dimensional image matching problem. This was achieved by first using YOLOv11 for semantic segmentation of common road markings in both the UAV orthoimage and the converted LiDAR intensity image to generate highly consistent feature references. Subsequently, the SuperPoint detector and a graph neural network matcher, SuperGlue, were applied to these semantic images to establish reliable geomatics information correspondence points. Experimental results confirmed that this semantic-guided strategy consistently outperformed traditional feature-based matching (i.e., scale-invariant feature transform + fast library for approximate nearest neighbors), particularly by converting the noisy LiDAR intensity image into a stabilized semantic representation. The explicit application of semantic constraints further proved effective in eliminating false matches between geometrically similar but semantically distinct objects. The final object-specific analysis demonstrated that features with clear, complex geometric structures (e.g., pedestrian crossings and directional arrows) provide the most robust matching control. In summary, the proposed framework successfully leverages semantic context to overcome cross-sensor heterogeneity, offering an automated and precise solution for the geometric alignment of mobile LiDAR data. Full article