The binary executable format is the standard method for distributing and executing software. Yet, it is also as opaque a representation of software as can be. If the binary format were augmented with metadata that provides security-relevant information, such as which data is intended by the compiler to be executable instructions, or how memory regions are expected to be bounded, that would dramatically improve the safety and maintainability of software. In this paper, we propose a binary format that is a middle ground between a stripped black-box binary and open source. We provide a tool that generates metadata capturing the compiler's intent and inserts it into the binary. This metadata enables lifting to a correct and recompilable higher-level representation and makes analysis and instrumentation more reliable. Our evaluation shows that adding metadata does not affect runtime behavior or performance. Compared to DWARF, our metadata is roughly 17% of its size. We validate correctness by compiling a comprehensive set of real-world C and C++ binaries and demonstrating that they can be lifted, instrumented, and recompiled without altering their behavior.

Bastos, Geloso, and Bologna Pavlik (2026) argue that the US embargo explains less than one tenth of the difference in per capita income between Cuba and a counterfactual scenario in which the country did not follow socialist economic policies. We show that their results are driven by the use of an elasticity of income to trade openness that is neither representative nor a reasonable upper bound of the values found in the literature and by their choice to attribute the effect of the interaction between the embargo and other determinants of growth solely to those other determinants. We show that, once these problems are corrected, the embargo can account for a substantial fraction, and in some cases all, of Cuba's post 1959 economic underperformance.

Chemical sensing in real-world environments requires resolving rapidly fluctuating and spatially heterogeneous concentration fields. However, these dynamics are strongly distorted by widely used, low-cost metal-oxide (MOx) gas sensors, whose thermal and surface-kinetic response acts as a low-pass filter on the underlying concentration signal. Quantifying and compensating for these effects remains challenging, largely due to the lack of benchmark datasets that simultaneously capture the spatiotemporal structure of turbulent odour fields and the time-resolved response of point sensors. Here, we present a dataset combining planar laser-induced fluorescence (PLIF) measurements of an acetone tracer plume with synchronised recordings from a custom, kilohertz-rate microelectromechanical (MEMS) MOx electronic nose deployed in a laboratory wind tunnel. The PLIF system provides quantitative, two-dimensional concentration fields at high spatial and temporal resolution, while the co-located e-nose records film resistance, heater currents, and environmental parameters with aligned timestamps. The dataset enables quantitative assessment of sensor dynamics, development and benchmarking of reconstruction and deconvolution algorithms, and data-driven modelling of plume structure. All recordings, metadata, calibration files, and example analysis scripts are released in open, platform-independent formats. Together, these provide a valuable reference for researchers working in odour-guided robotics, environmental monitoring, computational fluid dynamics, and neuromorphic sensing, supporting the design and evaluation of high-speed odour-sensing systems.

We introduce coherent-state propagation, a computational framework for simulating bosonic systems. We focus on bosonic circuits composed of displaced linear optics augmented by Kerr nonlinearities, a universal model of bosonic quantum computation that is also physically motivated by driven Bose-Hubbard dynamics. The method works in the Schrödinger picture representing the evolving state as a sparse superposition of coherent states. We develop approximation strategies that keep the simulation cost tractable in physically relevant regimes, notably when the number of Kerr gates is small or the Kerr nonlinearities are weak, and prove rigorous guarantees for both observable estimation and sampling. In particular, bosonic circuits with logarithmically many Kerr gates admit quasi-polynomial-time classical simulation at exponentially small error in trace distance. We further identify a weak-nonlinearity regime in which the runtime is polynomial for arbitrarily small constant precision. We complement these results with numerical benchmarks on the Bose-Hubbard model with all-to-all connectivity. The method reproduces Fock-basis and matrix-product-state reference data, suggesting that it offers a useful route to the classical simulation of bosonic systems.

Although electrolyte density measurements have been reported for over a century, employing them to obtain accurate partial molar volume (PMV) profiles as a function of salt concentration has remained elusive. Obtaining such curves requires precise density measurements combined with a proper treatment of the associated virial expansion. In this work, we obtain PMV profiles for aqueous LiCl solutions. The resulting data enable the development of highly accurate force fields for Li$^+$ and Cl$^-$ ions, revealing a clear progression from isolated ions to ion pairs and ultimately to higher-order chain and ring structures. Because ion clustering emerges from complex, nonlocal interactions, it cannot be easily mapped onto specific virial terms. Instead, a direct structural and volumetric interpretation can be achieved by partitioning molecular dynamic (MD) simulation snapshots into three-dimensional polyhedral regions associated with individual salt ions and water molecules. The corresponding ionic and water volumes from this treatment quantitatively reproduce the experimental PMV curve. The results demonstrate that the PMV for salt increases (while that of water decreases) up to 6.7 M. Above this concentration, the direction reverses as three- and four-body interactions become prominent. Complementary multivariate curve resolution (MCR) Raman spectroscopy and density functional theory (DFT) calculations elucidate the molecular-level details of water electrostriction, which also persists up to 6.7 M. Significantly, the PMV data can be correlated with key thermodynamic properties, including the osmotic coefficient and the eutectic point. The procedures established here provide a general framework for modeling electrolyte solutions and enable the development of a new generation of accurate force fields for aqueous ions.

Achieving high-fidelity control in the presence of strong non-Markovian noise is critical for the optimization of emergent solid-state quantum devices. We present a highly efficient optimization framework that combines automatic differentiation with the non-Markovian uniTEMPO algorithm, enabling direct gradient-based optimization of complex objective functions. We apply this method to semiconductor quantum dots, optimizing multi-pulse excitation schemes: specifically Swing-UP of a Quantum EmmiteR (SUPER) and Floquet-engineered Two-Photon Excitation (FTPE) for single- and bi-exciton generation. Our approach yields high preparation fidelities within experimentally accessible parameter regimes. By integrating adiabatic rapid passage (ARP), we systematically enhance both SUPER and FTPE, demonstrating that these optimized protocols consistently outperform standard resonant pi-pulses and two-photon excitation. Notably, this performance gap widens at elevated temperatures, establishing the superior thermal robustness of our optimized multi-pulse strategies for real-world quantum hardware.

We develop a molecular dynamics framework to compute the mode-resolved phonon spectral density from classical correlations of an annihilation-like phonon variable. For harmonic oscillators, classical molecular dynamics exactly reproduces the corresponding quantum Kubo-transformed correlator, providing the basis for extension to anharmonic systems. Using PbTe as a benchmark and Cs$_3$Bi$_2$I$_6$Cl$_3$ as a strongly anharmonic test case, we show that the method captures both quasiparticle and non-Lorentzian spectra beyond perturbative quasiparticle theory, while yielding thermal conductivity in good agreement with experiment. This framework provides a direct route from classical molecular dynamics to quantum-mechanical Wigner heat transport in solids.

This paper develops a principled foundation for goal-oriented semantic communication for logical decision-making. Consider a setting where autonomous agents engage in collaborative perception. In such settings, the volume of sensory data and limited bandwidth often make transmission of raw observations infeasible, requiring intelligent selection of task-relevant information. Because these scenarios are safety-critical, the selection and decision processes must also be transparent and verifiable. To address this, we propose an explainable semantic communication framework grounded in a First-Order Logic (FOL) hierarchical representation of the world. We define semantic information, entropy, conditional entropy, and mutual information by assigning an inductive logical probability measure over semantic structures in the language. Based on these definitions, we formulate a goal-oriented semantic communication objective through semantic rate-distortion theory and, equivalently, through the semantic information bottleneck principle. In this framework, task rules are represented as goal-oriented states, defined as a layer over the world states to capture decision-relevant abstractions. The resulting principle selects evidence that is most informative about these states, aiming to transmit only those FOL clauses most critical for decision-making while preserving logical verifiability. We demonstrate the effectiveness of the approach in a deduction-based safe path-following task within an FOL-based urban environment simulator with multiple dynamic agents.

Accurate modeling of non-linear gravitational dynamics is essential for constraining extensions to the standard cosmological model using large-scale structure observations. While high-resolution $N$-body simulations provide the required fidelity, they are computationally prohibitive for the large ensembles needed to analyze Modified Gravity (MG) scenarios. We present MG-NECOLA, a field-level emulator based on a convolutional neural network that upgrades fast, approximate MG-PICOLA simulations to near--$N$-body accuracy at a fraction of the computational cost. Trained on a suite of QUIJOTE_MG simulations for $f(R)$ gravity, MG-NECOLA achieves nearly sub-percent accuracy ($\lesssim 1\%$) in both the matter power spectrum and bispectrum up to $k \simeq 1~h\,\mathrm{Mpc}^{-1}$. Crucially, although being trained on a fixed cosmology, the network generalizes robustly to cosmologies outside its training manifold keeping the error below $5\%$. It successfully recovers the General Relativity limit ($Λ$CDM) without introducing spurious MG signals and accurately captures the power suppression induced by massive neutrinos ($M_ν\leq 0.4$ eV), despite being trained on cosmologies with massless neutrinos. The pipeline delivers a speed-up factor of $\sim 1500\times$ relative to full $N$-body runs, generating a high-fidelity realization in O$(10^3)$ CPU seconds compared to O$(10^6)$ for the baseline. This accuracy-efficiency trade-off establishes MG-NECOLA as a powerful tool for generating the massive mock catalogs required for next-generation galaxy surveys.

Using experimental information on branching ratios as well as direct and mixing-induced CP asymmetries, we perform a data-driven analysis of charmless non-leptonic $B \to PP$ decays, where $P$ is any of the light pseudoscalar mesons. Implementing flavour-$SU(3)$ breaking at the level of transition form factors, decay constants and phase space factors, we find a good fit to the current experimental data. Our best-fit point materializes in QCD-factorization amplitudes whose central values resemble many features of the dynamical predictions obtained within the QCD factorization framework. Moreover, we do not find any strong indications that the size of annihilation amplitudes is numerically enhanced beyond the naïve $Λ_{\textrm{QCD}}/m_b$ scaling. Subsequently, we address a number of phenomenological applications, among which are various flavour puzzles that have been persisting in non-leptonic $B$ decays for quite some time.

Classical work of Thurston and Gabai shows that finitely many taut sutured manifold hierarchies determine the Thurston norm of a compact oriented irreducible $3$-manifold with toroidal boundary. We give an explicit procedure to extract this information from such hierarchies. This is achieved via the maw dual graph construction, which can be incorporated into a general method for computing the Thurston norm of a manifold. As an application, we compute the Thurston norm of the exterior of all alternating and some nonalternating pretzel links with three components. Using these computations, we give a negative answer to a question of Baker--Taylor. Moreover, we show that if a nonseparating surface $S$ in a Haken manifold $M$ with toroidal boundary is disjoint from a boundary torus, then the class $[S] \in H_2(M,\partial M)$ does not lie in the interior of a top-dimensional cone of the Thurston norm. In particular, if two components $\ell_i$ and $\ell_j$ of a nonsplit link have zero linking number, then neither represents a class in an open top-dimensional cone of the Thurston norm ball of the link exterior.

Open Radio Access Network (O-RAN) enables network control through multi-vendor xApps operating both within and across layers, subnets, and domains, whose concurrent execution can trigger conflicts that are latent during the development phase. Existing conflict management approaches rely heavily on joint-execution data, which is often unavailable in practice. To address this limitation, we formalize a novel problem termed conflict reasoning, which involves identifying conflict-inducing conditions given only marginal datasets from each individual xApp. We propose ZODIAC, a three-stage framework for zero-shot conflict condition inference that comprises uncertainty-aware surrogate model training, trajectory-level diffusion training, and compositional guided denoising for efficient, physics-constrained, and reliable condition search. We derive a theoretical lower confidence bound showing that the compositional reasoning in ZODIAC serves as a principled surrogate for true conflict severity, with the epistemic penalty directly controlling the approximation gap. We evaluate ZODIAC on both the lightweight Mobile-Env platform across all three O-RAN Alliance conflict types (direct, indirect, and implicit) and a realistic NS-O-RAN-Flexric simulator. ZODIAC consistently outperforms baseline condition search methods, achieving over 20% higher True Positive Rate at Top-20, substantially stronger Spearman rank correlation, greater scenario diversity, and competitive computational efficiency. Ablation studies confirm the necessity of each guidance component, with epistemic uncertainty penalties proving essential for filtering spurious conflicts. To the best of our knowledge, ZODIAC is the first framework in O-RAN that enables conflict reasoning from marginal offline data without requiring any joint-execution traces.

Extending moiré graphene beyond twisted bilayers, helical trilayer graphene has shown topological bands and correlated states with reshaped moiré periodicity. Here we develop a theoretical framework for helical multilayer graphene to investigate its supermoiré relaxation and low-energy electronic structure. Using real-space lattice calculations, we find that relaxation reconstructs the system into locally periodic single-moiré domains, which provide the basis for a continuum description. Within each reconstructed domain, downfolding the first-shell model yields effective Hamiltonians near the Dirac points that reveal how the low-energy spectrum decomposes into folded Dirac sectors. We further evaluate the valley Chern numbers encoded in these effective Hamiltonians, obtaining domain-dependent and gate-tunable topological responses consistent with the lattice calculations. Our results establish a domain-resolved organizing principle for thicker helical graphene stacks, in which folded Dirac sectors partition the low-energy spectrum, while local stacking families determine the corresponding band character and topological response.

We evaluate whether dissipation of turbulence injected into hot cluster atmospheres by jets and bubbles can offset radiative cooling flows. No trends are found between atmospheric velocity dispersion, $σ_v$, and either the ratio of kinetic to thermal energy or jet power over nearly four decades of jet power. Apparently, jets disperse their energy gently at roughly constant energy per gram of gas. Assuming the velocity dispersions at the centers of Perseus, Virgo, and Hydra A reflect jetted turbulence, up to roughly half the bubble enthalpy could be dissipated by turbulent motion. A model is presented that balances radiation losses and turbulent power injected by radio bubbles rising at their terminal speeds. The model is anchored by XRISM measurements of $σ_v$ and is governed by the ratio of the bubble's terminal speed to the atmospheric sound speed. Bubbles must rise close to the sound speed and impart energy with a broad range of injection scales to heat the entire cooling volume. The level of turbulence in the powerful Hydra A system may offset cooling over some of the cooling volume. However, turbulent dissipation would struggle and probably fail to balance cooling in Perseus and Virgo, except perhaps in their inner regions. Several factors including, low velocity dispersions, small injection scales, short duty cycles, anisotropic turbulence injection, and long turbulent diffusion timescales present severe challenges for jetted turbulence heating models. A larger sample of spatially resolved cluster atmospheres is needed to reach a definitive conclusion.

We introduce and analyse a mathematical model describing the dynamics of particles generated by charge-exchange interactions. The model extends the well-established exchange-driven growth model, previously studied in several works, by allowing for particle densities defined on the entire integer lattice. Despite the many similarities between the two models, substantial differences arise both in their qualitative behaviour and in their mathematical analysis. Under suitable assumptions on the kernel in the collision operator, we establish global well-posedness in the class of nonnegative densities with finite first moment. Moreover, under a detailed balance condition, we investigate the structure of equilibria and analyse their stability by means of entropy methods.

A notable difference between the ordinary and Hadamard products is that the Hadamard product of two singular positive semidefinite matrices can be nonsingular, and one of the factors can even be indefinite. We present an eigenvalue lower bound for a Hadamard product that depends on the rank, effective condition number, and diagonal entries of one factor, and the smallest eigenvalues of certain principal submatrices of the other factor. We give numerical examples and discuss its applications in array signal processing and matrix time series analysis.

Fractional PDEs involving the fractional Laplacian on bounded domains are challenging because of hypersingular nonlocal kernels, exterior Dirichlet constraints, reduced boundary regularity, and the high computational cost in high dimensions. To address these issues, we first adopt a spatially varying radius with directional distance-to-boundary information, which yields a geometry-adaptive three-part decomposition of the fractional Laplacian: singular near-field, regular interior far-field, and analytical exterior far-field contributions. Then we employ Gauss-Jacobi quadrature for the singular radial integral, Gauss quadrature for the regular interior radial integral, and Monte Carlo sampling for the angular variables. A feature-enhanced physics-informed neural network trial space is finally used to tackle the low-regularity behavior near the boundary. Through the above steps, we obtain a quadrature-enhanced Monte Carlo fractional physics-informed neural network (QE-MC-fPINN) method. Numerical experiments on fractional Poisson equations and time-dependent fractional PDEs show that, on the tested benchmarks, the proposed method outperforms two representative MC-fPINN discretizations in accuracy and convergence, especially for solutions with strong boundary singularities.

It is a long-standing open question to determine whether the Sierpiński carpet attains its conformal dimension or not. While this problem remains unresolved, we prove that Cartesian products $\mathbb{S}^k$, where $\mathbb{S}$ is the Sierpiński carpet and $k \geq 2$, do not attain their conformal dimension. Our approach is based on the Sobolev spaces and energy measures on $\mathbb{S}$ -- constructed by Shimizu, Kigami, and Murugan and Shimizu -- together with a certain singularity result of energy measures from the theory of analysis on fractals. This work formulates a general non-attainment result of conformal dimension for product metric spaces $X^k$ for $k \geq 2$ in terms of self-similarity and energy measures of the factor $X$. It applies, in particular, to the cases where $X$ is the Sierpiński carpet, the Sierpiński gasket, the Menger sponge, and the Laakso diamond.

Wireless agentic systems enable agents to autonomously perceive, reason, and act. However, existing works neglect the tight coupling between sensing and control in closed-loop integrated sensing and communication (ISAC) systems. In this paper, we propose an active inference (AIF)-driven wireless agentic system for closed-loop ISAC, which jointly optimizes control and sensing resource allocation via backward--forward message passing on a factor graph. The AIF agent maintains a generative model as a digital twin by integrating a localization model for uncertainty-aware state inference and a localization channel knowledge map (CKM) for approximating observation quality during planning. Simulation results demonstrate that the AIF-enabled agent adaptively allocates sensing resources based on spatially varying channel conditions, achieving superior balance among tracking accuracy, control effort, and sensing resource consumption over baseline strategies.

In this paper an algorithm for transient simulation of switching converters using prediction and correction to calculate duty ratio is proposed. It provides large signal simulation on the level of averaged currents and voltages in the circuit. Calculation of duty ratio using inductor current and capacitor voltage prediction and correction do not require their priori knowledge. Number of circuit solving per switching period is fixed and equal to two. Using this algorithm various of constant frequency regulated switching converters can be simulated. Due to predetermined circuit values convergence problems are avoided. This algorithm results in very fast and accurate large signal simulation.

We consider a reaction-diffusion equation in a one-dimensional space, where the diffusion coefficient changes sign from positive to negative and back to positive. The reaction term is bistable, with its interior zero located in the region where the diffusivity is negative. The model does not admit continuous wavefronts, i.e., continuous traveling waves that connect the steady states $0$ and $1$. We prove the existence of a family of shock wavefronts, that is, wavefronts with profiles exhibiting a jump discontinuity. We investigate the properties of these profiles and their propagation speeds. Finally, we apply the results to a recently proposed model describing the movement of a population composed of both isolated and grouped individuals.

Color centers in diamond are a promising platform for quantum computing applications because of their optical and spin properties. However, diamond presents some technological challenges that limit its use in complex or large photonic circuits. To mitigate these limitations, it is technically effective to separate the smallest possible diamond photonic structures or chiplet containing the color center(s) from the rest of the circuit, which is fabricated on another material platform, and then heterogeneously integrate them. Considering efficient excitation and photon collection from waveguide-coupled color centers, we design a cross waveguide as the primary component of our chiplet to access the color centers, channeling excitation and emitted photons into different waveguides, and connecting the structure to the other components of the photonic circuit. The chiplet containing the cross waveguide and supporting structures requires careful optimization of each subcomponent. The receptor's design is also critical for optimal signal transmission. In this paper, we develop a simple but efficient methodology to optimize the main components constituting both the chiplet and the receptor for their synergistic operation. The designed structure has an excitation-to-emission conversion of more than 5.4%, crosstalk of less than -40 dB, a working bandwidth of 160 nm, fabrication feasibility and tolerance within the limits of modern nanofabrication, and a mechanical solid structure with a footprint of less than 2000 $μ$m

In this paper, we theoretically and numerically study the minimizers for the Cahn-Hilliard energy with the Flory-Huggins potential under the strong anchoring condition, i.e., the Dirichlet boundary condition. We reveal bifurcation phenomena mediated by the boundary condition, the transition layer thickness, and the temperature of the system. Numerical simulations are also presented to approximate the minimizers of this energy by solving a gradient-flow equation, namely the Allen-Cahn equation constrained with strong anchoring conditions and random initial data. The effects of varying the transition layer thickness and temperature are presented to confirm the theoretical analysis.

In open-ended domains, teams must reconcile diverse viewpoints to produce strong deliverables. Answer aggregation approaches commonly used in closed domains are ill-suited to this setting, as they tend to suppress minority perspectives rather than resolve underlying disagreements. We present TeamFusion, a multi-agent system designed to support teamwork in open-ended domains by: 1. Instantiating a proxy agent for each team member conditioned on their expressed preferences; 2. Conducting a structured discussion to surface agreements and disagreements; and 3. Synthesizing more consensus-oriented deliverables that feed into new iterations of discussion and refinement. We evaluate TeamFusion on two teamwork tasks where team members can assess how well their individual views are represented in team decisions and how consensually strong the final deliverables are, finding that it outperforms direct aggregation baselines across metrics, tasks, and team configurations.

Among the possible types of magnetic dipole interactions in molecular systems, couplings between nuclear motion and the nuclear spin have probably received the least attention in molecular spectroscopy. Although very small in comparison to effects related to electron spin, this type of hyperfine interaction plays an important role in the NMR spectroscopy of molecular systems. While measurement and prediction of spin-rotation tensors are a common place, vibrationally induced effects still lack a comprehensive description. In this article we develop a generic, theoretical framework that is well embedded in modern electronic structure theory and inspired by the Breit-Pauli Hamiltonian for electronic interactions, distinguishing between nuclear spin-orbit and spin-other-orbit contributions. We show that the interaction of nuclear spins with pseudorotational excitations of highly symmetric molecules may lead to experimentally accessible hyperfine splittings in NMR spectra, triggered by infrared light.

In this paper, we study the right invariant metric $d_{H^{\infty}}$ on the analytic automorphism group $\rm{Aut}(\mathbb{D})$ of the unit open disk $\mathbb{D}$ induced by maximal modulus, that is, $d_{H^{\infty}}(φ, ψ)=\sup_{z\in\mathbb{D}}|φ(z)-ψ(z)|$ for any $φ, ψ\in \rm{Aut}(\mathbb{D})$. We give the explicit formula of the right invariant metric $d_{H^{\infty}}$ and characterize the almost regular Finsler geometric structure of $(\rm{Aut}(\mathbb{D}), d_{H^{\infty}})$.

We show that the class of twisted fractionally Calabi-Yau algebras of finite global dimension coincides with the stable endomorphism algebras of $d$-cluster tilting modules over $d$-representation-finite algebras. This is an application of our main result stating that an algebra $A$ of finite global dimension is twisted fractionally Calabi-Yau if and only if there exists $i$ such that the replicated algebra $A^{(i)}$ is a higher Auslander algebra if and only if there exist infinitely many $i$ such that $A^{(i)}$ is a higher Auslander algebra. This gives a new connection between the study of higher Auslander-Reiten theory and twisted fractionally Calabi-Yau algebras, and provides a new construction of large classes of higher Auslander algebras and higher representation-finite algebras. We give several applications such as an explicit characterisation of twisted $\frac{n}{2}$-Calabi-Yau algebras, and a triangle equivalence between the bounded derived category of a twisted fractionally Calabi-Yau algebra of finite global dimension and the $\mathbb{Z}$-graded stable module category of an associated higher preprojective algebra.

We study charge transport through a trivial-topological-trivial junction described by the continuous Qi-Wu-Zhang model, which realizes a two-dimensional Chern-insulating phase. The central region is tuned into the topological regime, while the adjoining leads remain trivial, and an electrostatic barrier of tunable height and width is applied exclusively to the topological slab. By matching wave functions across the interfaces, we obtain the angle- and energy-resolved transmission probability and demonstrate the occurrence of Klein tunneling despite the presence of a bulk spectral gap. Within the continuum Dirac description, this perfect transmission originates from the inversion of the Dirac mass across the junction, which reflects the band inversion of the central layer relative to the trivial leads. In the Qi-Wu-Zhang model considered here, this mass inversion coincides with the transition between trivial and Chern-insulating phases and is accompanied by finite Berry curvature that governs the nonlinear transport response. The resulting transmission function is then incorporated into a Landauer-Büttiker framework to analyze both linear and nonlinear transport. Closed-form expressions for the linear and nonlinear conductances are derived at zero and finite temperatures. In addition, we investigate the role of dephasing, showing how partial loss of coherence suppresses Fabry-Pérot oscillations while leaving the overall transport trends intact. Finally, we map out the interplay between barrier height, slab thickness, and topological mass parameter, identifying optimal regimes that yield enhanced rectification in the nonlinear response.

Electricity price forecasting supports decision-making in energy markets and asset operation. Probabilistic forecasts are increasingly adopted to explicitly quantify uncertainty, typically issued as quantile predictions or ensembles of the full predictive distribution. However, how improvements in statistical forecast quality translate into economic value remains unclear. Battery storage arbitrage in day-ahead markets is a popular application-based benchmark for this purpose. We analyze quantile-based trading strategies (QBTS) and identify two critical flaws: they do not incentivize honest probabilistic forecasting and they ignore the intertemporal dependence structure of electricity prices. We therefore frame battery optimization as a stochastic program based on fully probabilistic forecasts and examine decision quality measurement for risk-neutral and risk-averse settings under different uncertainty models. Our discussion touches both sides of the coin: How reliable is the economic evaluation of forecasting models though (simplified) application studies - and how do improvements in statistical forecast quality for stochastic programs relate to the decision-quality and economic performance? We provide theoretical justification and empirical evidence from a case study on the German electricity market. Our results highlight the pitfalls of ranking forecasting models through battery trading strategies. We conclude with implications for evaluation practice and directions for future research in application-based forecast assessment.

In this paper, we propose a novel machine learning method based on an adaptive tensor neural network subspace for solving quasiperiodic elliptic problems. To this end, we first provide a theoretical analysis of the associated quasiperiodic and periodic function spaces and establish regularity estimates for the quasiperiodic elliptic problems. In particular, under the Diophantine condition, we derive a suitable condition on the source term to guarantee the regularity of the solution, which provides a theoretical basis for the design of numerical schemes. An efficient numerical method is then designed by combining the projection method with tensor neural networks. Leveraging the special structure of tensor neural networks, high-dimensional integration can be performed directly and with high accuracy, without relying on Monte Carlo methods. Finally, several numerical experiments are presented to demonstrate the accuracy and efficiency of the proposed method.