As large language models continue to scale, fine-grained block-scaled low-precision formats such as NVFP4 are increasingly adopted for their substantial throughput and memory benefits. However, a single FP4 micro-format often mismatches heterogeneous block-level tensor statistics. To address this without changing the standard block-scaled MMA/GEMM execution path, we propose MixFP4, a mixed micro-format extension to NVFP4 that selects between two stored FP4 micro-formats (E2M1 and E1M2) per block. MixFP4 reuses NVFP4's scale hierarchy and encodes the format choice with zero additional metadata by repurposing the sign bit of the FP8 E4M3 block scale. By decoding both micro-formats into a unified internal E2M2 compute representation, MixFP4 avoids datapath duplication. Across representative LLM families, MixFP4 improves FP4 quantization robustness and accuracy over NVFP4 baselines with modest tensor-core overhead (3.1\% area, 1.5\% power).

In this work, we study the application of a hybrid mixed domain decomposition (HMDD) method for the rotor-stator coupling of a permanent magnet synchronous machine. For this, we derive a variational formulation on the electric machine inspired by hybridized discontinuous Galerkin methods using a mixed magnetostatics problem, an affine material law and boundary conditions respecting the symmetry of the motor. We are then able to locate the resulting finite element method within the HMDD framework presented in arXiv:2604.22543. This enables us naturally to transfer the well-posedness results and error estimates for the HMDD method to the finite element method considered in this work. Lastly, as a proof of concept, we consider an academic example and compare the resulting magnetic flux density and potential lines to their counterparts obtained by a well-established in-house code using iso-geometric analysis.

We develop a light-front Hamiltonian formulation of symmetric nuclear matter within the quark-meson coupling model, using Basis Light-Front Quantization to solve the in-medium nucleon eigenvalue problem. The Hamiltonian incorporates confinement in the valence sector and is truncated to include up to one dynamical gluon. Medium effects are introduced via scalar and vector mean fields, yielding a self-consistent, density-dependent effective quark mass and modified nucleon structure. The resulting energy per nucleon, pressure, and incompressibility are consistent with empirical constraints at the saturation point. At nuclear saturation density, the gluon probability in the nucleon wave function increases slightly, while the valence probability and quark momentum fraction decrease. The unpolarized quark and gluon distributions show a noticeable enhancement at large momentum fraction ($x \gtrsim 0.4$), illustrated at an evolved scale of $Q^{2} = 10 \mathrm{GeV}^{2}$.

We introduce and study a one-parameter Hermitian deformation of the Yamabe problem on compact complex manifolds. The deformation is defined by adding natural torsion terms to the Riemannian scalar curvature, and includes both the classical Yamabe equation and a scalar-curvature equation arising in locally conformally Kähler geometry as a momentum map. We analyze criteria for the existence of solutions, and discuss several examples.

Data visualization is a powerful tool for conveying statistical information, but when representing populations, it tends to hide individuals. We introduce Zoomable Empathic Visualizations (ZEVs), interactive experiences allowing users to smoothly navigate between abstract statistical visualizations and more qualitative, relatable representations focused on individuals. We present three use cases of ZEVs and report on a qualitative user study that highlights opportunities for deeper understanding and emotional engagement, while pointing to areas for improvement and further refinement. In summary, ZEVs point toward new approaches for revealing the individuals behind the data.

We construct Pulsar Polarisation Arrays (PPA), using regular pulsars monitored in MeerKAT's Thousand Pulsar Array (TPA) Programme, to search for Axion-like Dark Matter (ALDM) within Milky Way. Specifically, from a catalogue of 1237 regular pulsars, we select the 50 ones with the highest signal-to-noise ratio and set upper limits on the ALDM Chern-Simons coupling. We find no signals with statistical significance over the mass range of $[10^{-23},10^{-20}]\,{\rm eV}$ in the six-year MeerKAT's data. By combining the high-quality TPA pulsars and the accurate ionospheric subtraction of spinifex, we establish the most sensitive upper limits to the date on the ALDM Chern-Simons coupling, namely $\lesssim 10^{-14} - 3\times 10^{-13}\,{\rm GeV}^{-1}$, for the mass range of $[10^{-23},10^{-21}]\,{\rm eV}$ except at $m_a \sim 1.3 \times 10^{-22}\,$eV. This study underscores the great potential of constructing regular-pulsar PPAs for scientific tasks.

Despite the widespread use of Transport Layer Security (TLS), its security guarantees are frequently compromised by outdated versions and misconfigurations. To analyze this problem, we collected more than 50 million TLS handshakes over a two-week period at our research institution, Fondazione Bruno Kessler, and analyzed three server-selected parameters against the recommendations of four TLS guidelines. Our analysis shows that while the use of insecure or outdated options is minimal, it remains persistent. More importantly, servers are adopting the latest TLS advancements much faster than official guidelines can be updated to provide directives for them. These findings, combined with the difficulty of configuring TLS clients due to their ephemeral, ubiquitous and server-dependent nature, leave users vulnerable to non-standard or outright insecure connections. To address this, we present TLSGatekeeper, a real-time, network-based tool that transparently monitors handshakes, analyzes server parameters, and, based on organizational policy, reports non-compliant connections without requiring client-side modifications. Unlike Next-Generation Firewalls, TLSGatekeeper preserves end-to-end privacy by validating only handshakes, and offers greater flexibility in defining undesired configurations. Our evaluation shows that TLSGatekeeper sustains traffic rates of up to 100 Gbps while preventing insecure connections, with an average added processing delay of 671 ns (TLS 1.3) and 795 ns (TLS 1.2) per handshake packet, making enforcement feasible at scale.

We study a simple 1-parameter perturbation of the regular holonomic Trace System satisfied by a complex power of the root of the universal polynomial of degree k as a holomorphic function of the coefficients. We prove that these systems have many analogous properties than the Trace System studied in [4] and we prove that they are, in general, minimal extensions of a simple pole meromorphic connection on a rank $k$ trivial bundle on $\mathbb{C}^k$. We also examine the structure of these $D$-modules for the special values of the parameters. This explicites many examples of perverse sheaves associated to representations of the $π_1$ of the complement of the hyper-surface $\{σ_kΔ(σ) = 0\}$ in the affine space with coordinates $σ_1,\ldots,σ_k$, where $Δ(σ)$ is the discriminant of the universal monic polynomial of degree $k$, $P_σ(z) := z^k + \sum_{h=1}^k (-1)^h σ_h z^{k-h}$.

This paper concerns a class of non-divergence nonlinear elliptic equations driven by the cone degenerate Laplacian, which is motivated by cone calculus. We establish the existence of viscosity solutions by proving the Alexandrov-Bakelman-Pucci and Hölder estimates. Furthermore, we obtain the existence of weak solutions by proving the equivalence between weak solutions and viscosity solutions.

Purpose: T1-weighted MPRAGE remains a cornerstone of clinical anatomical imaging, yet its long acquisition times constrain routine use. Established acceleration techniques, namely Parallel Imaging (PI) and Compressed Sensing (CS), tend to introduce substantial noise and blurring when pushed to high acceleration factors. Although they rely on fundamentally different redundancies, combining them synergistically remains an open challenge. Methods: The GoLF-SPARKLING framework was extended to jointly exploit two acceleration mechanisms: GRAPPA-based PI in the central k-space region and variable-density CS in the periphery, with independent acceleration factors in each zone. To preserve smooth signal evolution throughout the inversion-recovery period and avoid modulation artifacts, the acquisition trajectory was reordered accordingly. The resulting method was evaluated prospectively in vivo at 1mm isotropic resolution and benchmarked against Wave-CAIPI and Poisson-disk sampling. Results: The proposed hybrid approach produced sharper, less noisy, and more stable whole-brain images in approximately one minute than either acceleration strategy alone. Purely PI-based reconstructions were degraded by high g-factor noise, while purely CS-based reconstructions exhibited pronounced blurring. Furthermore, this method yielded lower average volumetric errors in downstream automated brain segmentation than state-of-the-art acceleration techniques, demonstrating its clinical utility. Conclusion: By jointly leveraging PI and CS, GoLF-SPARKLING achieves high acceleration factors that enable sub-minute, high-quality anatomical MRI. This translates into greater clinical throughput and more reliable imaging in patients who are challenging to scan.

We compute the entropy of a Dirac quantum field near a static, spherically symmetric black hole in (f(Q)) gravity by combining the residue-based Robson--Villari--Biancalana method with the thin-film state-counting approach. The RVB prescription introduces a residue correction to the Hawking temperature, while the Dirac field entropy is obtained from the near-horizon WKB mode density and fermionic free energy. For an (f(Q))-deformed metric, we derive the Hamilton--Jacobi equation, radial momentum, mode number, and entropy at the residue-corrected temperature. The result shows that the Dirac-field entropy remains proportional to the horizon area after regularization, but its coefficient is modified by a cubic RVB temperature factor. An explicit expression is obtained for the quadratic model (f(Q)=Q+αQ^{2}).

The Saint-Venant shallow water equations (SWE) govern depth-integrated free-surface flows arising in dam-break inundation, flood routing, tsunami runup, and estuarine tidal dynamics. Closed-form analytical solutions exist only for highly idealized Riemann configurations, making rigorously verified numerical solvers essential. This work presents amerta, an open-source Python library that solves the one-dimensional frictionless Saint-Venant system on a uniform Cartesian grid using Monotone Upstream-centered Schemes for Conservation Laws (MUSCL) reconstruction with a minmod slope limiter, the Harten-Lax-van Leer-Contact (HLLC) approximate Riemann solver, and two-stage strong-stability-preserving Runge-Kutta (SSP-RK) time integration. Numba just-in-time (JIT) compilation accelerates the performance-critical kernels. The solver is verified end-to-end against the four canonical Riemann configurations: wet-bed dam break, dry-bed dam break, double rarefaction, and double shock. A six-component post-processing pipeline quantifies space-time topology, final-time error norms with empirical quantile decomposition, self-similarity collapse onto the analytical Riemann fan, integral-norm evolution, boundary-flux-corrected mass and energy diagnostics, and phase-plane analysis against analytical wave curves. The implementation conserves discrete mass to floating-point precision, satisfies discrete entropy admissibility identically, and reproduces all four analytical wave-curve geometries to within sub-centimetre accuracy in the depth-velocity phase plane. The complete source code, analytical-solution evaluators, post-processing scripts, and Network Common Data Format (NetCDF) archives are released under the MIT license.

No-insulation (NI) and metal-insulation (MI) high-temperature superconducting (HTS) magnets require three-dimensional (3D) models to describe the current distribution around critical current defects. In this work, we design and validate the EXTRA homogenisation method, standing for explicit turn resolution with anisotropic homogenisation method. It allows 3D magneto-thermal finite-element (FE) simulations of large-scale magnets to be performed with high accuracy at a reasonable computational cost. The method combines the anisotropic homogenisation of turn-to-turn contact layers (T2TCLs) and their neighbouring winding turns with the explicit resolution of specific T2TCLs. In particular, the inner- and outermost winding turns and adjacent contact layers are explicitly resolved to properly describe the current distribution near current leads. In addition, the method is able to simulate local $J_{\textrm{c}}$ defects for a broad range of turn-to-turn contact resistances, provided the winding turns and T2TCLs next to the defect are explicitly resolved. For efficiency, the resolved T2TCLs are modelled using the surface contact approximation. The consistency of the proposed method is first verified on a 50-turn single pancake benchmark. It is shown to reproduce AC losses and temperature distributions obtained with a turn-resolved FE reference model, for both nominal operation and during thermal runaway. The computational efficiency of the EXTRA method is demonstrated with the simulation of a stack of three 150-turn pancake coils, for which computation time is reduced by a factor of up to 13 with respect to a turn-resolved FE reference model. Finally, the results of a large-scale 3D FE simulation, currently out of reach of turn-resolved models, are provided for an insert HTS magnet with 10,000 turns. The EXTRA method is open-source and input files to reproduce all results are made available.

We investigate how galaxy evolution varies with environment in the nearby Universe by comparing an ``average'' reference volume in the Southern Galactic Pole (SGP) dataset from \citet{VanKempen2024} to the Nexus region, a dynamically assembling superstructure centred on the Abell~4038 galaxy cluster. We quantify environmental effects using the quenched fraction ($f_{\mathrm{Q}}$) and the specific star formation rate ($\mathrm{sSFR}$) for the star-forming population, measured as functions of stellar mass and group-scale halo mass from \citet{VanKempen2026}. We decouple the stellar--halo mass dependence, demonstrating that $f_{\mathrm{Q}}$ increases with stellar mass in both field and group environments, while group galaxies show an additional dependence on halo mass. The Nexus exhibits systematic differences relative to the SGP baseline, consistent with increased heterogeneity in accretion histories and pre-processing within a forming superstructure. For star-forming galaxies, the mean $\log(\mathrm{sSFR})$ declines strongly with stellar mass and shows additional environment-linked suppression in group-scale halos. Within the Nexus, splitting the sample into three projected radial zones around Abell~4038 shows that environmental regulation is not spatially uniform, driven largely by variations in the sampled halo mass function. Finally, a projected phase-space (PPS) analysis of Abell~4038 links quenching to orbital history within the cluster, though this trend is strongly mass-dependent: low-mass galaxies ($\log(M_{\mathrm{stellar}}) < 10$) show no significant change in $f_{\mathrm{Q}}$. These results demonstrate that the drivers of galaxy evolution depend jointly on stellar mass, local group halo mass, and location within the surrounding large-scale structure, motivating future large-scale, multi-wavelength cosmic web surveys.

A central challenge in quantum machine learning is the state preparation bottleneck that describes the prohibitive computational cost of loading high-dimensional classical data into a quantum state. Although amplitude encoding can represent $2^n$-dimensional data using only $n$ qubits in principle, preparing arbitrary states remains computationally expensive, typically requiring variational optimization of a parameterized quantum circuit for each individual data instance. In this work, we propose a method that avoids iterative optimization by training a classical neural network to map input data directly to the continuous parameters of a fixed quantum circuit. We demonstrate the generation of quantum image states with high fidelity on data not seen during training. Since all optimization is performed once during training, the resulting model encodes new inputs in a single inference step, providing a scalable pathway for data loading in near-term quantum algorithms. We validate our method on the MNIST and Fashion-MNIST datasets, achieving fidelities up to 0.992 on unseen images and reducing the per-data-instance runtime by more than 5000-fold.

CKKS, an emerging fully homomorphic encryption (FHE) scheme, has been promising in privacy-preserving applications by enabling SIMD fixed-point computations on ciphertexts. Despite its strong security guarantees, CKKS involves both compute-intensive operators (ComOps) with high computational cost and memory-intensive operators (MemOps) with large memory footprints, making existing ASIC-based or NMP-based acceleration approaches suffer from high hardware overhead and limited efficiency. This observation motivates the integration of the architectural advantages of both paradigms into a heterogeneous xPU (ASIC)-xMU (NMP) architecture. However, in such a design, frequent and long-latency heterogeneous communication caused by the dominant keyswitch operator remains a key performance bottleneck. In this paper, we propose $HE^2$, a communication-light xPU-xMU heterogeneous FHE accelerator with dataflow graph (DFG) optimization and architecture co-design. First, we observe that the majority of communication arises at the interface between ModUp/ModDown and neighboring MemOps. To address this, we propose a DFG-level optimization framework to fully exploit the ModUp/ModDown reduction potential of the hoisting algorithm by identifying parallel keyswitch blocks and fusing them for reduced communication frequency. Second, we design an efficient heterogeneous architecture that adopts a group-level pipelined execution to effectively hide communication latency by leveraging the inherent parallelism across decomposed groups. End-to-end evaluation results show that $HE^2$ achieves 1.66$\times$ speedup and 9.23$\times$ lower EDAP (Energy-Delay-Area Product) compared to the state-of-the-art accelerator, with communication stalls accounting for only 6.67% of the total latency.

Search relevance modeling is a core task in e-commerce search systems, assessing how well a user query matches candidate products. Rather than relying on a single holistic matching signal, relevance judgment often requires structured reasoning over query understanding, product understanding, and facet-level matching. With large language models (LLMs), this process is increasingly formulated as chain-of-thought (CoT) reasoning and optimized with reinforcement learning (RL). However, existing RL methods mainly rely on outcome-level rewards and treat the entire reasoning chain as a single optimization unit. This makes it difficult to distinguish faulty reasoning steps from correct intermediate ones, leading to misaligned credit assignment. Although process-reward methods provide denser supervision, they often treat reasoning steps independently and ignore dependency-driven error propagation, making responsibility attribution difficult and limiting the optimization of structured relevance reasoning. We propose Graph-GRPO, a graph-structured extension of GRPO for multi-component relevance reasoning. Graph-GRPO constructs a relevance reasoning dependency graph, where CoT steps are modeled as nodes and their logical dependencies as edges. It propagates outcome-level rewards over the graph to derive step-level credit signals, enabling more accurate fine-grained credit assignment. We further introduce a main-loss-driven controller that adaptively adjusts edge-wise credit-propagation coefficients. Together with CoT random masking for supervised policy initialization and graph-node-based multi-head distillation, we build a trainable and deployable framework for generative relevance modeling. Extensive offline evaluations and online A/B tests on a leading e-commerce platform demonstrate that the Graph-GRPO-based framework improves relevance classification metrics and key engagement metrics.

In the era of big data, e-commerce and Internet platforms face the challenge of processing massive amounts of data. However, due to data being scattered across different machines in distributed database, extra communication costs are incurred in gathering relevant data to complete transactions. Without a carefully designed data placement scheme, this cost can severely impact the performance of Online Transaction Processing systems. To meet industry requirements, algorithms that output a data placement scheme that achieves i) data balance and ii) low communication overhead within a fixed period of time are eagerly investigated. Although some existing methods have been studied, they do not adequately meet the aforementioned requirements. In this paper, inspired by the normalized cut of spectral clustering, we introduce a novel model for data allocation problem. The normalized cut reconciles the inherent conflict between the two objectives. Taking into account the variable characteristics of the model, we formulate the problem as a 0-1 optimization problem, and solve the relaxed problem using the Bregman proximal gradient method with guaranteed convergence. The numerical experiments reveal that the convergent solutions can be smoothly rounded to discrete solutions. Furthermore, our algorithm surpasses both simple and meta-heuristic partitioning schemes by minimizing migration costs while maintaining a superior balance.

We investigate the convergence of partial sum processes based on a strictly stationary $β$-mixing sequence of random variables. The convergence in the space of continuous function as well as in H{ö}lder spaces is considered. The conditions are close to optimality.

The agentic economy demands programmatic financial rails, positioning the x402 protocol as the de facto standard for machine-to-machine payments. However, bridging synchronous HTTP requests with asynchronous blockchain finality introduces profound state synchronization challenges. In this work, we perform the first comprehensive security analysis of the x402 ecosystem. By formalizing five Security Invariants, we reveal that current implementations fail to enforce transactional atomicity and cryptographic context binding, leading to systemic vulnerabilities. We identify a semantic gap in signature design enabling cross-resource substitution, where payment proofs are transplanted to other unauthorized contexts. Furthermore, we expose a temporal gap where concurrency race conditions allow probabilistic service duplication. In the AI inference domain, we demonstrate how dynamic pricing models are vulnerable to allowance overdrafts and infrastructure rate limits. We validate these vulnerabilities against official SDKs and live deployments. Specifically, we show that attackers can exploit the synchronization gap in dynamic authorization schemes to force merchants to subsidize compute costs, achieving a resource leakage ratio of up to 100% on production middleware. Finally, we propose architectural mitigations, advocating for request-bound signatures and pessimistic state locking to secure the financial rails of autonomous agents. All discovered issues have been disclosed to Coinbase and ThirdWeb.

User interactions on social media platforms are unevenly distributed: a small subset of users consistently captures most of the activity, while the majority remains marginal. Although this pattern is well known and often described by power-law distributions, its consistency across time, platforms, and interaction types has not been systematically assessed. In this study, we analyze user-post bipartite networks from multiple social media platforms. We consider both active contributions (posts) and passive engagement (likes and comments), and quantify distributional properties and inequality using a KL-divergence-based model comparison, an inverse coefficient of variation, and a log-transformed Gini index. Our results show that interaction inequality remains stable over time within each platform. This holds across systems with different sizes, topical focuses, and governance models. These findings indicate that inequality in online engagement is not incidental but reflects structural constraints that shape how visibility and participation are distributed in digital environments.

Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent dialogue TTS systems have begun to address this setting, but they still struggle to keep expressive coherence, controllable speaker switching, and monologue quality at the same time. We present SwanData-Speech and SwanVoice. SwanData-Speech builds monologue and dialogue corpora from in-the-wild audio, using Swan Forced Aligner for pause-aware word-level alignment and RobustMegaTTS3 for pronunciation-hard cases. Built on these data, SwanVoice is a zero-shot TTS model for 1--4 speakers, combining a 25 Hz VAE, raw-text conditioning with pause-aware symbols and pinyin substitution, and a flow-matching DiT with speaker-turn conditioning. Training starts from monologue speech, moves through mixed and real dialogue data, and then uses DiffusionNFT post-training with phone-level and speaker-similarity rewards. On SwanBench-Speech, SwanVoice obtains higher richness and hierarchy scores than all evaluated open-source baselines in both monologue and dialogue settings, while content accuracy remains the main limitation. Audio demos are available at https://swanaigc.github.io//#swanvoice.

The transition to electric heavy-duty freight is constrained by a strong interdependence between vehicle adoption and charging infrastructure deployment. While system dynamics models are well-suited to simulate these socio-technical feedback loops, their reliance on conditional logic and heuristic rules makes the underlying dynamics difficult to interpret mathematically. This limits the direct application of formal systems and control theory. This paper proposes a solution to that problem through a general hybrid system identification framework that extracts a continuous-time analytical surrogate from simulations of complex system dynamics. The approach combines the sparse identification of nonlinear dynamics algorithm with neural ordinary differential equations to form a grey-box model. The first component identifies the dominant interpretable dynamics under causal coupling constraints, while the neural residual captures unmodelled nonlinearities. The framework is trained using multiple shooting over a 40-year horizon. The resulting model reproduces the training trajectories with a normalized root-mean-square error below 4\%, while maintaining reliable predictive accuracy when evaluated on unseen initial conditions.

Semantic communication has demonstrated significant potential for image transmission, especially in bandwidth-limited and low signal-to-noise ratio scenarios. However, most existing methods are based on analog transmission, which poses challenges to the compatibility with existing digital communication systems. Existing digital semantic communication methods commonly adopt conventional quadrature amplitude modulation constellations, which mismatch the empirical distribution of semantic features produced by the semantic encoder. This paper proposes a distribution-aware learnable modulation for semantic communication framework, which bridges semantic feature representations and discrete modulation through constellation learning. Specifically, a learnable constellation module, initialized with an amplitude phase shift keying geometric prior, is developed to refine the constellation geometry as a trainable codebook, enabling modulation symbols to better align with the distribution of semantic features. To enable end-to-end optimization, a two-stage training strategy is introduced, combining differentiable soft assignment with straight-through estimator. Simulation results show that the proposed framework consistently outperforms existing digital semantic communication schemes and achieves performance comparable to advanced analog methods.

We theoretically investigate strong-coupling properties of an attractively interacting Fermi atomic gas, where the Cooper-pair formation occurs between atoms belonging to different dimensional bands. Including pairing fluctuations within the framework of the self-consistent $T$-matrix approximation (SCTMA), we examine how the BCS-type superfluid phase transition temperature $T_\mathrm{c}$ varies as one moves from the 3D-3D to the 2D-3D system, in the wide parameter region with respect to the strength of the pairing interaction. In the 2D-3D limit, we find that, while the mean-field BCS theory predicts $T_\mathrm{c}>0$ in the strong-coupling regime, $T_\mathrm{c}$ is remarkably suppressed down to zero by pairing fluctuations that are strongly enhanced by the mixed-dimensionality of the system. As the origin of this, we clarify that the lower-dimensional (2D) component dominates the superfluid instability, so that the vanishing $T_\mathrm{c}$ is the same phenomenon as that in the 2D-2D case. We also point out that this can already be seen in the mean-field level, when one examines the propagation of the Goldstone mode. On the other hand, we find that the pseudogap phenomenon, which is known as a precursor of Cooper-pair formation, exhibits a 3D character of the 2D-3D system. These results indicate that the dimensionality of a strongly interacting Fermi gas depends on what we observe.

As cellular core networks evolve toward distributed and cloud-native architectures, control-plane interactions become more intricate and bring new challenges. Among these challenges, we find that introducing specific timing between two control-plane interactions can cause network function crash, which we define as timing-induced interaction failures. Prior research primarily addresses identifying malformed inputs and specification violations, while timing-induced interaction failures remain largely unexplored. In this paper, we conduct a systematic study of timing-induced interaction failures in LTE and 5G core networks. First, we establish a taxonomy of control-plane interaction patterns and analyze the failure modes of each pattern. Then, we design and implement Kairos, a lightweight testing framework to expose timing-induced interaction failures without analyzing cellular standard documents. Evaluating Kairos on two open source and two commercial LTE and 5G core networks, we uncover 20 new vulnerabilities and reproduce 34 existing issues. Our results show that timing-induced interaction failures are prevalent in LTE and 5G core networks and should be explicitly considered in future specifications.

Let $G$ be a connected, undirected simple graph. Denote by $L(G)$ the Laplacian matrix of $G$, and let $m_{G}(λ)$ be the multiplicity of an eigenvalue $λ$ of $L(G)$. When $G$ is a tree $T$ with $n \ge 6$ vertices, Tian et al. [Discrete Mathematics, 2026] proved that if $T$ is reduced and contains no pendant $P_3$, then \[ m_{T}(1) \le \frac{n-6}{4}, \] and they gave a complete characterization of the graphs for which equality holds. In this paper, we further investigate the above problem. Still assuming that $T$ is a tree with $n \ge 7$ vertices which is reduced and has no pendant $P_3$, we prove the following results. If $m_T(1) \neq \frac{n-6}{4}$, then \[ m_{T}(1) \le \frac{n-7}{4}, \] and we give a complete characterization of the graphs for which equality holds. If, moreover, $m_T(1) \neq \frac{n-6}{4}, \frac{n-7}{4}$, then \[ m_{T}(1) \le \frac{n-8}{4}, \] and we also give a complete characterization of the extremal graphs.

In this work we establish weighted Poincar{é} inequalities for multivariate Liouville distributions, which are a generalization of the Dirichlet distribution. We also consider continuous elliptically contoured distributions, whose density levels are unions of hyperellipsoids. Our approach is based on a transport argument which allows weighted Poincar{é} inequalities to be transferred between probability measures. We apply our results to global sensitivity analysis and illustrate their practical use in a flood model case study, where the structure of dependence of the input variables is encoded by classical copulas.

Acute spectral sensitivity to boundary conditions and the formation of a generalized Brillouin zone associated with complex quasimomenta are features frequently attributed to systems with non-trivial non-Hermitian topology, showcasing the non-Hermitian skin effect. We show that, away from the thermodynamic limit, these features themselves are not uniquely tied to this phenomenon; they can similarly arise as parity-induced even-odd effects in non-Hermitian systems without skin effect. Despite an underlying generalized Brillouin zone description, wavefunctions remain delocalized. In addition, the effect can arise in skin-effect models as entirely separate distinguishable feature

Generating reliable random and pseudo-random sequences is important in many electronic and signal processing systems, such as secure communications, radar, spread-spectrum methods, and autonomous platforms. Although true and quantum random number generators provide stronger unpredictability, classical pseudo-random number generators, including Linear Feedback Shift Registers (LFSRs) and the Mersenne Twister (MT), are still widely used because they are efficient and easy to implement. This work introduces a user-friendly software platform for generating, analyzing, and evaluating the predictability of pseudo-random bit sequences. The software supports two main functions: generating sequences using classical PRNGs and hybrid combinations, and analyzing input sequences through statistical measures and data-driven methods. In particular, hybrid LFSR-MT structures are studied to examine how they affect sequence complexity and resistance to prediction. The platform also includes machine-learning and deep-learning tools to investigate when deterministic PRNGs may remain partially predictable, even when their structure becomes more complex. The results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability, which supports the use of quantum random sequences in security-critical applications. A comparative study between classical LFSR-MT sequences and quantum random sequences shows that quantum randomness offers higher unpredictability due to its non-deterministic physical origin. The potential use of quantum random sequences in jamming applications is also discussed, highlighting their improved robustness against prediction-based attacks. Overall, the proposed software provides a practical tool for analyzing, comparing, and benchmarking random sequence generators in modern electronic, sensing, and quantum-enabled communication systems.