We introduce a variational method for simulating the dynamics of interacting open quantum spin systems. The method is based on the spin phase-space representation and variationally targets the Husimi-$Q$ function with an ansatz based on a multi-dimensional mixture of spin-coherent states. Crucially, the mixture coefficients are allowed to take negative values, enabling the faithful capture of quantum correlations beyond semiclassical descriptions. The resulting equations of motion are derived from the Dirac-Frenkel variational principle and can be evaluated efficiently without resorting to Monte Carlo sampling by exploiting the analytical structure of the ansatz. As a first application, we demonstrate that this approach accurately captures both the full quantum dynamics and the non-equilibrium steady states of the transverse-field quantum Ising model, in excellent agreement with exact diagonalization. Furthermore, we show that the method scales efficiently to large two-dimensional lattices, a regime that remains challenging for other techniques.

We present a Bayesian approach to estimate the parameters of mathematical models of cardiac electrophysiology with quantified uncertainty. Such models capture the dynamics of the electrical signal that coordinates the muscle cell contraction in the heart wall and can support cardiac arrhythmia treatment. We consider an illustrative case motivated by a cardiac arrhythmia, namely, by intra-atrial reentrant tachycardia. We estimate a low-dimensional geometrical parameter that describes the boundary of an electrically non-conducting region in the heart tissue from synthetic electrical measurements outside of the tissue. Instead of relying on a deterministic fit for this region, we estimate a posterior distribution on the geometrical parameter using Bayesian inference that captures the uncertainty due to measurement errors. We propose a likelihood based on a set of quantities that characterize the data for improved accuracy. To efficiently approximate the posterior distribution, we propose a compressed likelihood function and an adapted Metropolis-Hastings (MH) algorithm. We obtain an algorithm that strongly decreases the number of samples by using an adaptive proposal strategy. Our algorithm also gives attention to the impact of discretization errors on inference outcomes, as these introduce artificial discontinuities in the posterior if not properly addressed. We account for discretization errors in the likelihood and in the accept-reject step of our adapted MH algorithm to improve the robustness of our estimates and to further increase the sampling efficiency. All of these elements combined give us a method that efficiently estimates the non-conducting parameters with uncertainty. We perform several experiments with different amounts of measurement noise and illustrate how this translates into the posterior distributions.

Affine normal directions provide intrinsic affine-invariant descent directions derived from the geometry of level sets. Their practical use, however, has long been hindered by the need to evaluate third-order derivatives and invert tangent Hessians, which becomes computationally prohibitive in high dimensions. In this paper, we show that affine normal computation admits an exact reduction to second-order structure: the classical third-order contraction term is precisely the gradient of the log-determinant of the tangent Hessian. This identity replaces explicit third-order tensor contraction by a matrix-free formulation based on tangent linear solves, Hessian-vector products, and log-determinant gradient evaluation. Building on this reduction, we develop exact and stochastic matrix-free procedures for affine normal evaluation. For sparse polynomial objectives, the algebraic closure of derivatives further yields efficient sparse kernels for gradients, Hessian-vector products, and directional third-order contractions, leading to scalable implementations whose cost is governed by the sparsity structure of the polynomial representation. We establish end-to-end complexity bounds showing near-linear scaling with respect to the relevant sparsity scale under fixed stochastic and Krylov budgets. Numerical experiments confirm that the proposed MF-LogDet formulation reproduces the original autodifferentiation-based affine normal direction to near machine precision, delivers substantial runtime improvements in moderate and high dimensions, and exhibits empirical near-linear scaling in both dimension and sparsity. These results provide a practical computational route for affine normal evaluation and reveal a new connection between affine differential geometry, log-determinant curvature, and large-scale structured optimization.

In this paper we study Moser-Trudinger type inequalities for some nonlocal energy functionals in presence of a logarithmic convolution potential, when the domain is a ball of $\mathbb{R}^N$ with $N \geq 2$. In particular, we perform a blow-up analysis to prove existence of extremal functions in the borderline case of critical growth. Using this, we extend the results in \cite{CiWeYu} to higher dimension and sharpen \cite{CC}.

We study scattering resonances of finite one-dimensional systems of high-contrast resonators beyond the subwavelength regime. Introducing a novel tridiagonal frequency-dependent capacitance matrix, we derive quantitative asymptotic expansions of the hybridized Fabry-Pérot resonant frequencies in terms of the material contrast parameter. The leading-order shifts are governed by the eigenvalues of this matrix, while the corresponding eigenmodes are approximated, to leading order, by trigonometric functions on selected spacings between resonators. Our results extend the use of discrete approximations as a powerful tool for characterizing the resonant properties of a system of high-contrast resonators at arbitrarily high frequencies.

We investigate entanglement in and out of equilibrium in a one-dimensional Bose gas in its low-energy Bogoliubov regime. In this Gaussian setting, the state is fully characterized by its covariance matrix, which allows us to detect and quantify entanglement using a covariance-based framework and associated entanglement monotones. For thermal states, we determine the optimal entanglement witness arising from the covariance matrix criterion and show that it has a remarkably simple mode-resolved structure: it is diagonal in the normal-mode basis and admits a simple analytic form that can be expressed as a product of only two normal-mode uncertainties. We then study out-of-equilibrium dynamics induced by unitary compression and show that entanglement can be generated even from initially separable thermal states. When the evolution is fully adiabatic, the optimal witness retains the same two-mode structure as in the thermal case. Departing from this regime, i.e., performing increasingly rapid compression, the optimal witness becomes genuinely more intricate. Our methods and results provide a unified and physically intuitive picture of how entanglement emerges and evolves in 1D quantum Bose gases, and identify an optimal witness structure relevant more broadly to the analysis of entanglement in quadratic bosonic models and its role in thermodynamic cycles.

This paper develops a data-driven safe control framework for nonlinear discrete-time systems with parametric uncertainty and additive disturbances. The proposed approach constructs a data-consistent closed-loop representation that enables controller synthesis and safety certification directly from data. Unlike existing methods that treat unmodeled nonlinearities as global worst-case uncertainties using Lipschitz bounds, the proposed approach embeds nonlinear terms directly into the invariance conditions via a geometry-aware difference-of-convex formulation. This enables facet- and direction-specific convexification, avoiding both nonlinearity cancellation and the excessive conservatism induced by uniform global bounds. We further propose a vertex-dependent controller construction that enforces convexity and contractivity conditions locally on the active facets associated with each vertex, thereby enlarging the class of certifiable invariant sets. For systems subject to additive disturbances, disturbance effects are embedded directly into the verification conditions through optimized, geometry-dependent bounds, rather than via uniform margin inflation, yielding less conservative robust safety guarantees. As a result, the proposed methods can certify substantially larger safe sets, naturally accommodate joint state and input constraints, and provide data-driven safety guarantees. The simulation results show a significant improvement in both nonlinearity tolerance and the size of the certified safe set.

The $Λ_c p$ momentum correlation functions are investigated using $Λ_c N$ interactions derived within the covariant chiral effective field theory. Our analysis reveals that the interaction is weakly attractive in the spin-singlet ${}^1S_0$ channel. In contrast, the ${}^3S_1$ channel exhibits a pronounced sensitivity to coupled-channel effects, i.e., the inclusion of $S$--$D$ mixing results in a repulsive $Λ_c p$ interaction; its absence leads to a weakly attractive one. Consequently, the spin-averaged correlation function -- dominated by the triplet state weight -- exhibits repulsive behavior when the $S$-- $D$ mixing is present. Furthermore, the source size dependence of the correlation functions is examined, demonstrating that the resulting variations remain experimentally resolvable within the precision of current femtoscopic measurements. A systematic comparison with non-relativistic chiral effective field theory and phenomenological models yields distinct discrepancies in the femtoscopic correlation functions. These findings underscore the capacity of femtoscopy to discriminate between different theoretical descriptions of the $Λ_c N$ interaction and provide useful references for upcoming experimental data.

This paper develops a closed-form spectral decomposition framework for the Gramian matrices of discrete-time linear dynamical systems. The main results provide explicit decompositions of the discrete-time controllability Gramian and its inverse in terms of the eigenvalues of the dynamics matrix, yielding a mode-resolved representation of these matrices. In contrast to the more common use of aggregate Gramian characteristics, such as eigenvalues, singular values, determinants, and trace-based metrics, the proposed approach describes the internal structure of the Gramian itself through contributions associated with individual modes and their pairwise combinations. The framework is extended further to the solution of the discrete-time Lyapunov difference equation, placing the obtained formulas in a broader context relevant to the analysis and computation of time-varying and nonlinear systems. In addition, the decomposition is generalized to systems whose dynamics matrix has multiple eigenvalues, enabling a closed-form estimation of the effects of resonant interactions between eigenmodes. The proposed results provide a structural tool for the analysis of controllability, observability and stability in discrete-time systems and complement existing Gramian-based methods used in model reduction, estimation, actuator and sensor selection, and energy-aware control. Beyond their theoretical interest, the derived decompositions may support the development of improved computational procedures and more informative performance criteria for a range of discrete-time control problems.

Most defects in mobile applications are visually observable on the device screen. To track these defects, users, testers, and developers must manually submit bug reports, especially in the absence of crashes. However, these reports are frequently ambiguous or inaccurate, often omitting essential components such as the Observed Behavior (OB), Expected Behavior (EB), or Steps to Reproduce (S2Rs). Low-quality reports hinder developers' ability to understand and reproduce defects, delaying resolution and leading to incorrect or unresolvable fixes. In this paper, we posit that providing specific app-related information (e.g., GUI interactions or specific screens where bugs appear) to LLMs as key points of context can assist in automatically generating clear, detailed, and accurate OB, EB, and S2Rs. We built and evaluated a novel approach, BugScribe, that generates bug reports in this way. To support the evaluation, we introduce a unified quality framework that defines correctness and completeness dimensions for OB, EB, and S2Rs. Using 48 bug reports from 26 Android apps, we show that BugScribe produces higher-quality and more accurate components than the original reports and outperforms recent LLM-based baselines. We envision that BugScribe can serve as a practical assistant for testers and developers by enhancing incomplete bug reports with reliable and accurate OB, EB, and S2Rs, thereby streamlining bug resolution and improving mobile app quality.

As Large Language Models (LLMs) for code increasingly utilize massive, often non-permissively licensed datasets, evaluating data contamination through Membership Inference Attacks (MIAs) has become critical. We propose SERSEM (Selective Entropy-Weighted Scoring for Membership Inference), a novel white-box attack framework that suppresses uninformative syntactical boilerplate to amplify specific memorization signals. SERSEM utilizes a dual-signal methodology: first, a continuous character-level weight mask is derived through static Abstract Syntax Tree (AST) analysis, spellchecking-based multilingual logic detection, and offline linting. Second, these heuristic weights are used to pool internal transformer activations and calibrate token-level Z-scores from the output logits. Evaluated on a 25,000-sample balanced dataset, SERSEM achieves a global AUC-ROC of 0.7913 on the StarCoder2-3B model and 0.7867 on the StarCoder2-7B model, consistently outperforming the implemented probability-based baselines Loss, Min-K% Prob, and PAC. Our findings demonstrate that focusing on human-centric coding anomalies provides a significantly more robust indicator of verbatim memorization than sequence-level probability averages.

We propose a novel superconducting quantum circuit that should be robust against both relaxation and dephasing over a wide and experimentally accessible parameter range. The circuit consists of a parallel arrangement of a large inductance, a small capacitor, and a well-transmitting Josephson weak link. Protection against relaxation arises from the hybridization between the fermionic degree of freedom associated with Andreev levels in the weak link and the bosonic electromagnetic mode of the LC circuit, hence its name: FerBo. Furthermore, as in the fluxonium qubit, delocalization of the wavefunctions in phase space provides resilience against dephasing.

In this work, we revisit the discrete-time Schrödinger Bridge (SB) and Density Steering (DS) problems for Gaussian mixture model (GMM) boundary distributions. Building on the existing literature, we construct a set of feasible Markovian policies that transport the initial distribution to the final distribution, and are expressed as mixtures of elementary component-to-component optimal policies. We then study the policy optimization within this feasible set in the context of discrete-time SBs and density-steering problems, respectively. We show that for minimum-effort density-steering problems, the proposed policy achieves the same control cost as existing approaches in the literature. For discrete-time SB problems, the proposed policy yields a cost smaller than or equal to that in the literature, resulting in a less conservative approximation. Finally, we study the continuous-time limit of our proposed discrete-time approach and show that it agrees with recently proposed approximations to the continuous-time SB for GMM boundary distributions. We illustrate this new result through two numerical examples.

A collection $B$ of patterns is called inversion monotone if $\mathrm{av}_n^k(B)$, the number of $B$-avoiding permutations of length $n$ with $k$ inversions, is weakly increasing in $n$ for any fixed $k$. In 2012, Claesson, Jelínek and Steingrímsson posed the inversion monotonicity conjecture, which states that the pattern $1324$ is inversion monotone and implies a new upper bound for its Stanley--Wilf limit. We prove that the collections $\{1324, 231\}$ and $\{1324, 2314, 3214, 4213\}$ are inversion monotone via explicit injections. The latter follows from a general procedure for constructing inversion-monotone sets. Our results constitute the first known nontrivial examples of inversion-monotone sets. A key feature of the inversion monotonicity conjecture is that $1324$ has a limit sequence: $\mathrm{av}_n^k(1324)$ is constant in $n$ when $n$ is large. We characterize the sets of patterns that have limit sequences, and determine the limit sequences of all pairs $\{1324, p\}$, where $p$ is a pattern of length four. Connections to various families of integer partitions arise. Finally, we expand on work by Linusson and Verkama (2025) on almost decomposable permutations to determine a broad family of sets containing $1324$ that are inversion monotone under the assumption $n \geq \frac{k+7}{2}$. The method yields an enumeration of $\mathrm{av}_n^k(1324, 1342)$ when $n \geq \frac{k+7}{2}$.

We study non-equilibrium quantum dynamics by performing principal component analysis on the data sets of wavefunction snapshots. We show that a specific transformation of the data sets maximizes the information content in the largest principal component and further enables its connection to certain observables. This connection enables us to explain the dynamical features revealed by such a dimensionality-reduction scheme. We demonstrate this using quantum dynamics of the Heisenberg spin chain, starting from different initial states, and further extend the approach to extract higher-order correlations. Our framework should also be applicable to other unsupervised machine-learning methods based on dimensionality-reduction schemes and is highly relevant to experiments with quantum simulators, including those in higher dimensions.

An old and well-known open problem in the critical point theory asks whether, for some $p \neq 2$ and some bounded domain $Ω$, there exists a critical value of the $p$-Dirichlet energy $\|\nabla u\|_p^p$ over an $L^p(Ω)$-sphere in $W_0^{1,p}(Ω)$ lying outside of a Ljusternik--Schnirelman type sequence of critical values, the latter will be called LS eigenvalues of the $p$-Laplacian. In this work, we provide a positive answer by showing the existence of a non-LS eigenvalue when $p>2$ is sufficiently close to $2$ and $Ω$ is just a planar rectangle close to the square. The arguments pursue the observation that a simple eigenvalue of the Laplacian can be a meeting point for several branches of eigenvalues of the $p$-Laplacian as $p$ varies. Since LS eigenvalues are continuous with respect to $p$ and exhaust the whole spectrum when $p=2$, we deduce that at least one of the branches must contain non-LS eigenvalues.

We show that most of the results proven in the localized regime of the pinning model with independent disorder (notably, $\mathcal{C}^\infty$ regularity of the free energy, size of the largest gap among pinned sites and Central Limit Theorem for the contact fraction) can be generalized to translation ergodic correlated disorder under the hypothesis that disorder is Gaussian. Most of the results, in particular $\mathcal{C}^\infty$ regularity and the Central Limit Theorem, are proven assuming only summability of the covariances. For some of the remaining main results we introduce the extra assumption that the covariance operator is invertible. The two key ingredients for the proof are the Birkhoff-sum approach introduced in~\cite{GZ25concentration} for independent disorder, but particularly adapted to handle correlated disorder, and decorrelation tools like the general and powerful Nelson's Gaussian hyper-contractivity and other tools that we develop and that are more specific to the one dimensional structure of the model we consider.

Ultrafast microwave power pulsation is demonstrated as an effective strategy to enhance CO2 conversion in atmospheric-pressure plasma reactors. While initial experiments at several hundred watts in a compact coaxial plasma torch showed improved performance, the present study investigates the scalability of this approach to kilowatt-range microwave power. Conversion and energy efficiency were examined in two reactor configurations: a Surfaguide-based system (KIT) and a cavity-based plasma torch (IPP), and benchmarked against the compact coaxial torch. Both kilowatt-scale setups share similar microwave coupling schemes, power levels, reactor tubes, and gas injection geometries, but differ in afterglow treatment. The torch at IPP employs rapid nozzle-based quenching, whereas the Surfaguide-based reactor relies on slower cooling along an extended quartz tube. Stable plasma operation was achieved at pulsation peak powers of ~4 kW and pulse durations from sub-microseconds to microseconds, with stability limited to inter-pulse times of ~10 us (cavity-based torch) and ~12 us (Surfaguide-based reactor). In contrast to the coaxial torch, no plasma reignition regime was observed in either kilowatt-scale reactor, resulting in weaker plasma temperature modulation. Notably, the period-averaged gas temperature in the Surfaguide-based reactor exceeded that under continuous-wave operation. Under these conditions, relative enhancements of <40% in CO2 conversion and <20% in energy efficiency were measured compared with continuous-wave operation. These improvements were largely suppressed in the torch at IPP, presumably due to rapid afterglow quenching. Finally, analysis of the instantaneous reflected microwave power provided qualitative insights into electron density dynamics during the power-OFF and power-ON phases.

We study the emergence of periodic oscillations through a Hopf bifurcation in a scalar diffusion equation on the half line coupled to a dynamic boundary condition. Our results quantify the effect of delay through the buffering in the diffusive field on boundary kinetics, drawing a parallel to the emergence of oscillations in delay equations. Technically, the Hopf bifurcation occurs in the presence of essential spectrum induced by the diffusive field, preventing a simple approach via center-manifold reduction. The results are motivated by observations in biological systems where dynamic boundary conditions arise when modeling surface dynamics coupled to bulk diffusion.

We show that cosmological wavefunctions in $ϕ^n$ theories naturally generalize flat-space $\mathrm{Tr}(ϕ^3)$ scattering amplitudes: via a simple map from tube variables to Mandelstam invariants, each wavefunction coefficient $ψ_{\mathcal{G}}$ becomes an on-shell amplitude-like object $\mathcal{A}_G$ associated with a generating graph $G$. At tree level these objects coincide with the Cachazo-He-Yuan construction based on Cayley functions that generalizes Parke-Taylor factors. We uncover new graph-based hidden zeros that extend and unify all known cosmological zeros. Based on this zero structure, we uncover a factorization principle dual to unitarity. Instead of factorization across poles, $A\to A_L\times A_R$, a zero at $p_{a\in G_L}\!\cdot\! p_{b\in G_R}=0$ factorizes the generating graph, $G\to G_L\times G_R$, and is equivalent to the shuffle decomposition $\mathcal{A}_G=\mathcal{A}_{G_L}\unicode{x29E2}\mathcal{A}_{G_R}$. Near-zero factorization is a simple consequence of this new structure. Using dual factorization, we show that locality together with the full set of hidden zeros uniquely fixes tree-level cosmological wavefunctions without assuming unitarity. We show that these zeros are equivalent to special enhanced large-$z$ behavior under Britto-Cachazo-Feng-Witten (BCFW) shifts, extending the zeros--BCFW correspondence beyond flat-space amplitudes. We also find evidence for further extensions of the zero structure and loop-level uniqueness. Our results show that cosmology provides a natural arena for on-shell methods and even reveals new structure in flat-space amplitudes.

File-level defect prediction models traditionally rely on product and process metrics. While process metrics effectively complement product metrics, they often overlook commit size the number of files changed per commit despite its strong association with software quality. Network centrality measures on dependency graphs have also proven to be valuable product level indicators. Motivated by this, we first redefine process metrics as commit size aware process metric vectors, transforming conventional scalar measures into 100 dimensional profiles that capture the distribution of changes across commit size strata. We then model change history as a hyper co change graph, where hyperedges naturally encode commit-size semantics. Vector centralities computed on these hypergraphs quantify size-aware node importance for source files. Experiments on nine long-lived Apache projects using five popular classifiers show that replacing scalar process metrics with the proposed commit size aware vectors, alongside product metrics, consistently improves predictive performance. These findings establish that commit size aware process metrics and hypergraph based vector centralities capture higher-order change semantics, leading to more discriminative, better calibrated, and statistically superior defect prediction models.

Code obfuscation is widely adopted in modern software development to protect intellectual property and hinder reverse engineering, but it also provides attackers with a powerful means to conceal malicious logic inside otherwise legitimate JavaScript code. In a software supply chain where a single compromised package can affect thousands of applications, this raises a critical question: how robust are the Static Application Security Testing (SAST) tools that CI/CD pipelines rely on as automated security gatekeepers? This paper answers that question by empirically quantifying the impact of JavaScript obfuscation on state-of-practice SAST. We define a realistic supply-chain threat model in which an adversary injects vulnerable code and iteratively obfuscates it until the pipeline reports a clean scan. To measure the resulting degradation, we introduce the Vulnerability Detection Loss (VDL) metric and conduct a two-phase study. First, we analyze 16 vulnerable-by-design Node.js web applications from the OWASP directory; second, we extend the analysis to 260 in-the-wild JavaScript/Node.js projects from GitHub. Across both datasets, we apply eight semantics-preserving obfuscation techniques and their combinations and evaluate two representative SAST tools, Njsscan and Bearer. Even a single obfuscation technique typically suppresses most baseline findings, including high-severity issues, while stacking techniques yield near-total evasion, with VDL often approaching 100%. Our results show that current JavaScript SAST is fundamentally not robust against commonplace obfuscations and that "clean" reports on obfuscated code may offer only a false sense of security. Finally, we discuss practical mitigation guidelines and directions for obfuscation-aware analysis.

Software-Defined Networking (SDN) is increasingly adopted to secure Internet-of-Things (IoT) networks due to its centralized control and programmable forwarding. However, SDN-IoT defense is inherently a closed-loop control problem in which mitigation actions impact controller workload, queue dynamics, rule-installation delay, and future traffic observations. Aggressive mitigation may destabilize the control plane, degrade Quality of Service (QoS), and amplify systemic risk. Existing learning-based approaches prioritize detection accuracy while neglecting controller coupling and short-horizon Reinforcement Learning (RL) optimization without structured, auditable policy evolution. This paper introduces a self-reflective two-timescale SDN-IoT defense solution separating fast mitigation from slow policy governance. At the fast timescale, per-switch Proximal Policy Optimization (PPO) agents perform controller-aware mitigation under safety constraints and action masking. At the slow timescale, a multi-agent Large Language Model (LLM) governance engine generates machine-parsable updates to the global policy constitution Pi, which encodes admissible actions, safety thresholds, and reward priorities. Updates (Delta Pi) are validated through stress testing and deployed only with non-regression and safety guarantees, ensuring an auditable evolution without retraining RL agents. Evaluation under heterogeneous IoT traffic and adversarial stress shows improvements of 9.1% Macro-F1 over PPO and 15.4% over static baselines. Worst-case degradation drops by 36.8%, controller backlog peaks by 42.7%, and RTT p95 inflation remains below 5.8% under high-intensity attacks. Policy evolution converges within five cycles, reducing catastrophic overload from 11.6% to 2.3%.

An $i$-independent set is a vertex set whose pairwise distance is at least $i+1$. A proper (square) $k$-coloring of a graph $G$ is a partition of its vertex set into $k$ independent ($2$-independent) sets. A packing $(1^{j}, 2^k)$-coloring of a graph $G$ is a partition of $V(G)$ into $j$ independent sets and $k$ $2$-independent sets. It can be viewed as intermediate colorings between proper and square coloring. Wegner conjectured in 1977 that every planar graph with maximum degree at most four is square $9$-colorable. Bousquet, Deschamps, de Meyer, and Pierron proved an upper bound of $12$, which is the current best result toward the conjecture of Wegner. In this paper, we prove two analogue results that every planar graph with maximum degree at most four is packing $(1,2^{10})$-colorable and packing $(1^2,2^7)$-colorable.

We use a functional approach based on the background-field formalism and the expansion by regions to integrate out the heavy Higgs field (associated with the mass eigenstate H) in a singlet extension of the Standard Model (SM) at the one-loop level. In this way, we obtain an effective Lagrangian to $\mathcal{O}(1/M_H^2)$ in the limit of large Higgs mass ($M_H \gg M_h\approx125$GeV) providing a consistent treatment of effects from Higgs mixing and the renormalization of the underlying model. In particular, we address how the choice of the scaling behaviour of the model parameters in the large-$M_H$ limit determines whether the effective Lagrangian can be accommodated in the SM Effective Field Theory (SMEFT) or involves non-SMEFT operators within the more general Higgs Effective Field Theory (HEFT) framework. For our calculation, we choose a limit that ensures decoupling of beyond-SM effects at $\mathcal{O}(1/M_H^0)$ by demanding that the Higgs mixing angle $α$ is of $\mathcal{O}(M_h/M_H)$ and putting minimal constraints on the other input parameters, which represents a phenomenologically viable scenario. The considered model is restricted to massless fermions, and the emerging Effective Field Theory (EFT) involves only bosonic EFT operators in addition to the SM Lagrangian. We explicitly show that any attempt of a bottom-up (diagrammatic) matching with only bosonic SMEFT operators at $\mathcal{O}(1/M_H^2)$ necessarily fails. We validate our results for the effective Lagrangian at next-to-leading order in the coupling expansion by verifying that the difference between EFT and full-theory predictions vanishes faster than $1/M_H^2$ for several electroweak precision observables in the large-$M_H$ limit. Moreover, both full-theory and EFT predictions asymptotically approach the corresponding SM results for very large values of $M_H$ as required by decoupling.

Scapulohumeral rhythm (SHR), the coordinated motion between the scapula and humerus during arm elevation, is frequently altered in rotator cuff pathologies, yet the mechanical principles underlying coordination redistribution remain difficult to explain from experimental data alone. This study presents a predictive optimal control framework for investigating scapulohumeral coordination, combining a quaternion-based shoulder model with EMG-informed muscle parameter calibration. The quaternion formulation eliminated kinematic singularities and associated non-physiological activation artifacts observed in the Euler-angle model, while maintaining comparable tracking accuracy. EMG-informed calibration reduced discrepancies between predicted and measured muscle excitations by up to 60% on independent validation tasks. In predictive simulations where only thoracohumeral elevation was prescribed, scapular and clavicular kinematics emerged from musculoskeletal mechanics and minimization of muscular effort, producing SHR values consistent with established experimental ranges. Simulated rotator cuff deficiency resulted in increased reliance on glenohumeral rotation. The proposed framework may serve as a tool for understanding impaired coordination patterns across a broad range of shoulder pathologies, with potential to inform personalized rehabilitation strategies and the design of assistive and prosthetic devices.

Generative image codecs aim to optimize perceptual quality, producing realistic and detailed reconstructions. However, they often overlook a key property of human vision: our tendency to focus on particular aspects of a visual scene (e.g., salient objects) while giving less importance to other regions. An ideal perceptual codec should be able to exploit this property by allocating more representational capacity to perceptually important areas. To this end, we propose a region-adaptive diffusion-based image codec that supports non-uniform bit allocation within an image. We design a novel spatially varying diffusion model capable of denoising varying amounts of noise per pixel according to arbitrary importance maps. We further identify that these maps can serve as effective priors on the latent representation, and integrate them into our entropy model, improving rate-distortion performance. Built on these contributions, our spatially-adaptive diffusion-based codec outperforms state-of-the-art ROI-controllable baselines in both full-image and ROI-masked perceptual quality.

Employing Bayesian inference to calibrate constitutive model parameters has grown substantially in recent years. Among the available techniques, Markov Chain Monte Carlo (MCMC) sampling remains one of the most widely used approaches for estimating the posterior distribution. Nevertheless, the selection of a specific MCMC algorithm is often driven by practical considerations, such as software availability or prior user experience. To support sampler selection, we present a comparison of three prominent samplers in the context of two distinct physical systems: a thermal conduction system and a viscous flow system. Calibration data are obtained through tailor-made experimental setups. We use the Kullback-Leibler (KL) divergence, which quantifies the statistical distance between the sampled posterior and the reference ('true') posterior, as a measure of convergence to compare the performance of the following MCMC sampling methods: the Metropolis-Hastings (MH) sampler, the Affine Invariant Stretch Move (AISM) sampler, and the No-U-Turn Sampler (NUTS). We study how this metric correlates to heuristic indicators such as the Gelman-Rubin diagnostic and the effective sample size. In addition, we assess the samplers' computational effort in terms of required number of model evaluations. Based on the results, we find that the heuristic convergence and performance indicators provide a good qualitative measure for KL-divergence for both systems. Regarding computational effort, the NUTS is net beneficial for the viscous flow system, as the high effective sample size outweighs the additional effort required for gradient-based proposal generation. For the thermal conduction system, which involves more expensive model evaluations, the NUTS is not advantageous. Thus, the computational efficiency of gradient evaluations is an important argument in sampler selection.

While diffusion models are best known for their performance in generative tasks, they have also been successfully applied to many other tasks, including audio source separation. However, current generative approaches to music source separation often underperform on standard objective metrics. In this paper, we address this issue by introducing a novel generative vocal separation model based on the Elucidated Diffusion Model (EDM) framework. Our model processes complex short-time Fourier transform spectrograms and employs an improved U-Net architecture based on music-informed design choices. Our approach matches discriminative baselines on objective metrics and achieves perceptual quality comparable to state-of-the-art systems, as assessed by proxy subjective metrics. We hope these results encourage broader exploration of generative methods for music source separation

The design of triboelectric nanogenerators (TENGs) for efficient energy harvesting requires predictive models that capture the interplay between surface roughness, real contact area, and electrostatic behaviour across diverse tribolayer materials and roughness levels. To address this demand, this paper presents a multiphysics finite element framework that couples mechanical contact analysis with electrostatic simulations, considering exact surface roughness representations rather than idealised statistical approximations. Compared with optical interference microscopy measurements, the framework predicts the real contact area ratio more accurately than analytical models. The proposed approach captures the electrostatic behaviour by scaling the TENG surface charge density with the real contact area ratio between the rough tribolayers, computed for a given mechanical load. This method improves agreement with experiments for open-circuit voltage and capacitance relative to approximate analytical models. To represent the TENG circuit, a time-dependent ordinary differential equation is integrated, enabling evaluation of electrical responses under varying load conditions and elucidating the roles of surface roughness, mechanical load, contact-separation frequency, and resistive load. The framework provides a robust, scalable tool for performance optimisation across dielectric materials, mechanical behaviours, and operating conditions and is readily extendable to other surface-dependent energy-harvesting devices.