We propose a robust nonlinear model predictive control (MPC) scheme for trajectory-tracking control of autonomous vehicles at the limits of handling on non-planar road surfaces. We derive the dynamics from first principles and selectively omit terms with negligible dynamic influence to maintain real-time capability. The resulting MPC with a three-dimensional (3D) dynamic single-track model integrates relevant dynamic effects directly into the prediction model and leverages them to improve prediction accuracy and therefore control performance. Even if the influence of terrain-induced vertical loads on the total acceleration potential is modeled, tire-road interactions are subject to uncertainty and disturbance. The uncertainty-aware constraint tightening scheme introduces a margin to constraint bounds to keep the vehicle controllable and stable in this environment. To validate our proposed approach, we perform high-fidelity dynamic double-track vehicle dynamics simulations on a model of a real circuit. We find that our algorithm can improve trajectory-tracking accuracy while maintaining low computation times.

We study a family of local depth-based corrections to maxmin landmark selection for lazy witness persistence. Starting from maxmin seeds, we partition the cloud into nearest-seed cells and replace or move each seed toward a deep representative of its cell. The principal implemented variant, \emph{support-weighted partial recentering}, scales the amount of movement by cell support. The contributions are both mathematical and algorithmic. On the mathematical side, we prove local geometric guarantees for these corrections: a convex-core robustness lemma derived from halfspace depth, a $2r$ cover bound for subset recentering, and projected cover bounds for the implemented partial-recentering rules. On the algorithmic side, we identify a practically effective variant through a layered empirical study consisting of planar synthetic benchmarks, a parameter-sensitivity study, and an MPEG-7 silhouette benchmark, together with a modest three-dimensional torus extension. The main planar experiments show that support-weighted partial recentering gives a consistent geometric improvement over maxmin while preserving the thresholded $H_1$ summary used in the study. The three-dimensional experiment shows the same geometric tendency but only mixed topological behavior. The paper should therefore be read as a controlled study of a local depth-based alternative to maxmin, rather than as a global witness-approximation theorem or a claim of uniform empirical superiority.

Spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid are mapping the spatial distribution of millions of galaxies, with Emission Line Galaxies (ELGs) serving as the dominant tracer in the redshift range $0.8<z<1.6$. Standard approaches for extracting cosmological information from galaxy clustering, however, typically discard highly constraining measurements from the nonlinear regime. We apply SHAMe-SF - a modification of Subhalo Abundance Matching tailored for star-forming galaxy samples - to analyse the three-dimensional clustering of DESI ELGs from the One-Percent data release, extending their cosmological analysis deep into the nonlinear regime. We validate our pipeline using two mock ELG samples drawn from the state-of-the-art cosmological hydrodynamical simulation MillenniumTNG, demonstrating that our model yields unbiased constraints on $σ_8$ and $Ω_{\rm m}h^2$ down to scales of $0.3~h^{-1}$Mpc on both samples. We find that including scales below $0.8~h^{-1}$Mpc is critical for mitigating projection effects and obtaining unbiased constraints on $σ_8$. Applied to the DESI One-Percent measurements, our analysis yields $\sim6$% constraints on $σ_8 = 0.81^{+0.05}_{-0.06}$ and $Ω_{\rm m}h^2=0.146^{+0.009}_{-0.009}$. Remarkably, the accuracy of these constraints is similar to that obtained from the combined full-shape analysis of all DESI DR1 tracers, yet using only 1% of the survey volume. A naive extrapolation of our results from the One-Percent to the full survey area suggests that the complete survey could deliver roughly an order-of-magnitude improvement in precision - a prospect that, while subject to significant practical challenges, illustrates the cosmological potential encoded in the nonlinear regime.

As deductive verifiers mature, their potential user base is growing from the initial core developers to other users. To convince external users of the suitability of verifiers, these tools must run reliably out of the box, give meaningful error messages and display correct results. Yet deductive verifiers are large and complex software systems and their own full verification is often out of reach. We therefore need complementary means to provide such guarantees. This paper advocates the use of fuzzing as a practical way to improve the quality and robustness of deductive verifiers. We outline how fuzz testing can be applied to deductive verifiers, and demonstrate the idea with the prototype tool AValAnCHE, which is integrated with the VerCors verifier. We report on our experiments in which AValAnCHE uncovered several issues in VerCors and demonstrate that the approach also works for other deductive verifiers

Solar eruptive events such as flares and coronal mass ejection (CME)-driven shocks can release solar energetic particles (SEPs) into the heliosphere. The heliospheric current sheet (HCS) is a large-scale structure that separates regions of opposite magnetic polarity, and its influence on SEP propagation remains poorly understood. We classify SEE events into two groups: same-side events, where both the solar source and spacecraft are in the same magnetic sector, and opposite-side events. The magnetic polarities of the solar source region and the spacecraft location are determined comprehensively based on Potential Field Source Surface (PFSS), magnetic field measurements, the pitch angle distribution of strahl electrons, and the first-order anisotropy of energetic electrons. The spacecraft magnetic polarities determined by footpoint positions at the source surface and in-situ observations are consistent for most events, providing a useful methodological reference for future studies. We identify 60 same-side events and 9 opposite-side events. Our results show that opposite-side events tend to be more isotropic, and that both the solar source and the spacecraft are closer to the HCS than in same-side events. This suggests that particle transport across the HCS is inefficient unless the source or the observer is close to the HCS. These preliminary statistical findings advance our understanding of the role of the HCS in shaping SEP transport.

Superconducting films emerge from the complex interplay of multiple growth parameters, making their optimization challenging. In iron-based superconductors, compressive strain is known to enhance the transition temperature (Tc) of FeSe films, yet reported Tc values vary widely even on identical substrates, indicating factors beyond strain are critical. Here, we develop a high-throughput off-center pulsed laser deposition strategy that transforms plume inhomogeneity into combinatorial FeSe film libraries with continuous gradients in lattice parameter, composition, and disorder. We discover that the maximum Tc does not coincide with the plume center but can shift off-center, revealing a competition between favorable c-axis expansion, stoichiometry, and defect scattering. Systematic characterization of 80 thick films (>50 nm), combined with interpretable machine learning, shows that besides the strong correlate of c-axis lattice parameter to Tc, the stoichiometry and disorder scattering impose critical constraints on the achievable transition temperature, defining a narrow optimization window rather than a simple monotonic relationship. This framework yields Tconset=17.1 K in thick FeSe films and establishes a general framework combining combinatorial synthesis with machine learning to uncover constrained optimization landscapes in complex functional materials.

Let $π:X\to Δ$ be a one-parameter degeneration whose central fiber $X_0$ is a complex threefold with finitely many ordinary double points $Σ=\{p_1,\dots,p_r\}\subset X_0$. Associated with this degeneration is the corrected finite-node perverse extension, together with its mixed-Hodge-module refinement and a finite-node schober datum whose perverse-sheaf shadow is identified with the corrected perverse sheaf $\mathcal P$. The purpose of the present paper is to extract from these finite-node geometric, extension-theoretic, mixed-Hodge, and categorical inputs the intrinsic algebraic state data carried by the degeneration. More precisely, we isolate the finite localized quotient $Q_Σ:=\bigoplus_{k=1}^r i_{k*}\Q_{\{p_k\}}$, the nodewise coupling space $E_Σ:=\Ext^1_{\Perv(X_0;\Q)}(Q_Σ,IC_{X_0})$, its canonical nodewise decomposition $E_Σ\cong\bigoplus_{k=1}^r \Q e_k$, and the coefficient vector $c_Σ=(c_1,\dots,c_r)\in\Q^r$ defined by $[\mathcal P]_{\mathrm{perv}}=\sum_{k=1}^r c_k e_k$. We then prove that these state variables are compatible with both the mixed-Hodge-module lift and the schober realization of $\mathcal P$, so that the same finite-node architecture appears simultaneously in perverse, mixed-Hodge, and categorical form. The resulting package $(V_Σ,E_Σ,c_Σ)$ is the intrinsic algebraic state data attached to the finite-node conifold degeneration. It provides the first algebraic layer in the passage from finite-node geometry to later incidence, quiver, stability, BPS-spectral, and wall-crossing structures.

In this paper, we calculate the decay widths and branching fractions for the decays $Z \to J/ψ+Υ(nS)$ ($n=1,2,3$) at future super $Z$ factory and at the CEPC/FCC-ee, including both the relativistic and QCD corrections within the framework of nonrelativistic QCD. Both the relativistic and QCD corrections are found to be large and negative. Compared to the leading-order results, the decay widths are significantly reduced by the higher-order corrections. Therefore, it is essential to take these corrections into account for a reliable estimation. For a high-luminosity electron positron collider running around the $Z$-pole, sizable event rates could be produced from these rare decay channels due to the $Z$-boson resonance effect.

Pre-trained machine learning models (PTMs) are commonly provided via Model Hubs (e.g., Hugging Face) in standard formats like Pickles to facilitate accessibility and reuse. However, this ML supply chain setting is susceptible to malicious attacks that are capable of executing arbitrary code on trusted user environments, e.g., during model loading. To detect malicious PTMs, state-of-the-art detectors (e.g., PickleScan) rely on rules, heuristics, or static analysis, but ignore runtime model behaviors. Consequently, they either miss malicious models due to under-approximation (blacklisting) or miscategorize benign models due to over-approximation (static analysis or whitelisting). To address this challenge, we propose a novel technique (DynaHug) which detects malicious PTMs by learning the behavior of benign PTMs using dynamic analysis and machine learning (ML). DynaHug trains an ML classifier (one-class SVM (OCSVM)) on the runtime behaviours of task-specific benign models. We evaluate DynaHug using over 25,000 benign and malicious PTMs from different sources including Hugging Face and MalHug. We also compare DynaHug to several state-of-the-art detectors including static, dynamic and LLM-based detectors. Results show that DynaHug is up to 44% more effective than existing baselines in terms of F1-score. Our ablation study demonstrates that our design decisions (dynamic analysis, OCSVM, clustering) contribute positively to DynaHug's effectiveness.

We prove a local-global principle for primitive representations of binary quadratic forms by quaternary quadratic forms. Our method is a variant of Linnik's ergodic method showing density for certain homogenous toral sets. The central ingredient is a measure classification result of Einsiedler and Lindenstrauss for actions of rank two diagonalizable groups on quotients of products of $\mathrm{SL}_2$. This rigidity result together with an application of the Siegel mass formula reduces the density problem to a counting problem on a certain affine variety. We solve that counting problem using the determinant method of Bombieri-Pila and Heath-Brown.

SDSSJ110546.07+145202.4 stands out as a unique radio changing-look Narrow-line Seyfert 1 (NLS1) galaxy that has brightened dramatically and shows an exceptionally long duration of its "on" phase. We present the first high-frequency radio observations, the first simultaneous radio spectral energy distributions (SEDs), the first optical--UV--X-ray SEDs, and the first X-ray monitoring and spectroscopy of this recently discovered event. Importantly for understanding the nature of the outburst, we show that the X-ray spectrum is soft with a photon index Gamma_X=2.5; line-of-sight absorption and extinction are low or absent; the radio SED is peaked at low frequencies ~2 GHz; and the radio outburst emission is very long-lived (t > 8 yr) and roughly constant. The softness of the X-ray spectrum, low supermassive black hole (SMBH) mass, and high Eddington ratio all corroborate the optical NLS1 classification. We discuss multiple outburst scenarios, including lensing, absorption, a binary SMBH merger, a long-duration giant-star tidal disruption, a newly ignited active galactic nucleus (AGN), and an accretion-rate change. While most of them can be either excluded or are deemed too rare and lack positive evidence so far, most or all types of these transients are expected to be detected in ongoing VLA and upcoming SKA surveys. SDSSJ110546.07+145202.4 itself is well explained by an accretion rate change that triggered the powerful radio jet emission. The low redshift and SMBH mass of this system offer a unique perspective of the physical processes of radio-jet ignition that are expected to operate in the early Universe around growing SMBHs.

In many chemical reactions, short-lived radical intermediates play a crucial role, while detecting such short-lived species in-situ remains challenging. The optically readable electronic spin of nitrogen-vacancy (NV) centers in diamond is a nanoscale sensor for such radical species: its longitudinal spin relaxation time (T$_{1}$) reacts to magnetic fluctuations from the unpaired electrons of radical species in its local environment. In this setting, we demonstrate the successful in-situ detection of the nitroxide radical 2,2,6,6-Tetramethylpiperidinyloxyl (TEMPO) using NV center-based T$_1$ relaxometry after depositing nanodiamonds onto the inner wall of a glass cuvette. A significant concentration-dependent shortening of the relaxation time was observed, from $197\:μs \pm 21\:μs$ without radical to $66\:μs \pm 30\:μs$ at a concentration of 1 M TEMPO. The detection is sensitive in the nanomolar (nM) range and the determined signal-to-noise ratio is between 1.6 and 3.

We present an update on the ongoing computation of the isospin-breaking effects in the Pion Decay Constant from the BMW Collaboration. The calculation is carried out with N$_f$=2+1+1 staggered quarks with a near-physical pion mass and QED$_{\text{L}}$. We give an update on the isosymmetric value and the current determination used to compute the gradient-flow scale $w_{0}$, then we present some preliminary results of the valence-valence contribution to the axial-pseudoscalar correlator for different volumes and lattice spacings. We also discuss the next steps and plans.

For online health communities, community trust is paramount. Yet, advances in Large Language Models (LLMs) generating advice may erode this trust, especially if users cannot identify whether LLMs have been used. We investigate the feasibility of community-based detection of health advice authorship and how self-moderation of LLMs could help enhance advice utilization. In an online experiment, we evaluate people's ability to distinguish AI-generated from human-written advice across two health conditions, considering lived experience with a condition, AI-recognition training, and user attitudes towards transparency and trust around AI use. Our results indicate the need for transparency coupled with trust. We find little evidence of people's ability to discern advice authorship. However, we find a consistent effect of the health condition. Our qualitative findings identify unreliable signals, resulting in flawed heuristic evaluations of the advice. Our findings point to opportunities to improve the self-moderation of LLM-based AI and aid community-based AI moderation.

Wireless links deployed in orchards often exhibit significant variability in the strength of the received signal that is not adequately captured by classical distance-based propagation models. In row-structured olive groves, signal attenuation differs markedly between along-row and cross-row propagation directions, leading to discrepancies when using omnidirectional propagation assumptions such as those adopted in the Free Space Path Loss (FSPL) model or ITU-R vegetation loss formulations. This paper proposes a topology-based propagation model that explicitly accounts for orchard layout and the relative positions of radio devices within the plantation structure. Experimental validation was conducted using LoRa technology operating at 868 MHz, and the results were compared with established models from the literature and with the proposed two-dimensional model. The proposed approach achieves a closer fit to measured RSSI data than conventional models, providing a more reliable basis for link budgeting and network planning in structured agricultural environments.

We introduce and empirically validate Landscape Span Compression (LSC), a device-agnostic metric for quantifying how hardware noise distorts the variational energy landscape of the Quantum Approximate Optimization Algorithm (QAOA). Intuitively, LSC measures how much noise flattens the energy landscape, approaching 1 as the landscape collapses toward a barren plateau. We report an experience study of applying QAOA with LSC-based noise characterization on IBM's ibm_fez for three constrained QUBO portfolio instances, distilling practical lessons for parameter transfer, calibration-model fidelity, and error mitigation. Running p=1 QAOA on ibm_fez (Heron r2, 156 qubits) with up to 57,344 shots per grid point across three constrained binary optimization instances encoded as QUBO problems, we find: (i) hardware noise uniformly compresses the landscape span by 24-30% without displacing the global minimum, supporting classical-to-hardware parameter transfer; (ii) feasibility fractions at the optimal parameters remain 1.5-1.7 times above random sampling despite noise-induced degradation; (iii) the IBM calibration-based noise model achieves Pearson r=0.959 structural agreement with hardware but explains only approximately 42% of approximation-ratio degradation, with crosstalk and coherent errors as the leading unexplained contributors; (iv) a consistent noise cost of approximately 0.03 approximation-ratio units is observed across all instances; and (v) Zero-Noise Extrapolation yields mixed energy improvements of +7%/+9%/-4% per instance with 3-5 times uncertainty inflation. We compare LSC against four existing metrics and argue it is the most robust discriminator of noise severity for constrained QAOA on near-term devices.

Knowledge work demands sustained self-regulation, prioritization, and reflection-yet existing planning tools only partially support these needs. Digital to-do list applications feature task persistence but lack goal representation. Paper-based planning frameworks offer effective planning strategies but cannot adapt to individual users. Conversational AI systems enable flexible reflection but lack persistence and accountability. Moreover, none of these tools address a fundamental challenge: users' expressed demands often diverge from their underlying needs. This paper introduces seneca, a conceptual framework for a personalized, AI-assisted planner that integrates the complementary strengths of these three approaches. seneca combines a conversational agent that scaffolds reflection and asks clarifying questions, a persistent database that tracks goals and behavioral patterns, and a processor that synchronizes information between them. We describe this architecture and outline a phased evaluation strategy combining automated testing with simulated users and longitudinal human studies measuring goal attainment, planning realism, and goal-value alignment.

The widespread, addictive consumption of short-form videos, which allegedly causes "brain rot," has become an urgent public concern. This study proposes that self-related cues serve as an intrinsic, self-reflective strategy that enhances self-control over media overuse. We developed an app that de-immerses users by periodically displaying different self-related cues (live camera, selfie, name in text, and black screen) and tested their effects in a laboratory experiment (N=84). Overall, findings show that self-related cues effectively disrupt mindless viewing, enabling users to voluntarily stop short-form video consumption. Interestingly, the black screen, intended as a control, elicited the greatest intention to use the app: Participants noted in the follow-up interview that they preferred the subtler reflection on a black screen over the explicit image from a live camera. The findings offer practical design guidelines for implementing self-awareness interventions in mobile contexts, including which modalities work best and how real-time contextual anchoring enhances effectiveness.

Mixed reality systems support shared anchors and co-located interaction, yet they lack a socially legible protocol for entering another person's mixed reality in public settings. We frame this as a protocol problem: co-located MR sharing requires a staged sequence -- Discover, Consent, Confirm, Allow, Spatial Colocation, Sync Objects, Permission Management -- each demanding user understanding and agreement. Using AirDrop and Apple Vision Pro SharePlay as a baseline, we show that MR encounter complexity far exceeds file transfer, yet must feel equally effortless. We present TouchPort, an embodied sharing protocol that collapses this multi-stage sequence into a single gesture: a handshake and pull that simultaneously signals intent, negotiates consent, and initiates a temporary shared encounter layer between otherwise separate mixed realities. Through three implied scenarios, we demonstrate the protocol's expressive range in the transition from isolated to spontaneously shared realities. We discuss how embodied gestures can address the consent problem in ubiquitous MR and examine the ethical tensions of encounter protocols for MR futures.

With the growing use of eye tracking on VR and mobile platforms, gaze data is increasing. While scanpath comparison is important to gaze behavior analysis, existing methods lack privacy-preserving capabilities for real-world use. We present a garbled-circuit (GC)-based approach enabling secure storage and privacy-preserving scanpath comparison under the semi-honest model. It supports two configurations: (1) a two-party setting where the data owner and processor jointly compute similarity scores without revealing their inputs, and (2) a server-assisted setting where encrypted scanpaths are stored and processed while the data owner remains offline. All decryption and comparison operations are executed inside the GC. Experiments on three eye-tracking datasets evaluate fidelity, runtime, and communication, and show secure results for MultiMatch, ScanMatch, and SubsMatch closely match plaintext outcomes, with manageable runtime and communication overhead. Tests under various network conditions indicate that the design remains feasible for real-world privacy-preserving scanpath analysis and can be extended to other GC-based behavioral algorithms.

Precise and flexible control of structured light fields is essential for applications ranging from optical trapping and quantum simulation to microscopy and materials processing. Acousto-optical deflectors (AODs) are widely used in these settings due to their high speed, large damage threshold, and ability to generate steerable optical tweezers. Multi-tone driving offers a powerful alternative to slow sequential scanning, enabling the projection of complex patterns with high accuracy as rapid acoustic modulation averages out inter-spot interference. In two dimensions, however, intermodulation between tones in orthogonal AODs can reintroduce coherent artifacts. We present a fast, feedback-free AOD projection scheme based on an incommensurately staggered frequency lattice that intrinsically suppresses such artifacts. For separable two-dimensional target patterns, our method removes the need for scanning entirely, enabling substantially faster and highly accurate projections. We further extend the approach to non-separable images using a minimal scanning strategy that maintains rather high projection speeds. These results demonstrate that appropriately engineered multi-tone AOD driving offers an efficient and robust route to high-speed, high-fidelity generation of arbitrary intensity patterns.

General relativity is a background-independent theory of a dynamical classical spacetime geometry. Quantum theory is formulated in a classical spacetime, as an intrinsically probabilistic, contextual theory of non-classical, interfering probabilities, with a fixed Born rule for computing those probabilities. We argue that the quantum nature of spacetime, which includes a non-commutative dual companion to the (observed) classical spacetime, is the reason behind an intrinsically probabilistic and contextual nature of quantum theory, with the fixed Born rule. In quantum gravity, we claim, quantum theory is gravitized into a background-independent structure with dynamical and contextual quantum probabilities. This proposal implies intrinsic triple and higher-order interference in the context of massive quantum probes, which sheds light on string theory and the observed vacuum energy as well as the masses of elementary particles.

Thanks to electron-electron ($e$-$e$) collisions conserving momentum, metallic electron fluids are viscous. Yet, this viscosity is rarely detectable in bulk transport. Here, we report on the canonical realization of the Gurzhi effect in an elemental three-dimensional metal: cadmium. Using focused ion beam microstructuring to tune the effective thickness, we detected a low-temperature size-dependent resistivity upturn in a finite window sandwiched between ballistic and diffusive regimes. Within this window, the electrical conductivity displays a simultaneous quadratic dependence on both sample size and temperature -- fingerprint of a hydrodynamic flow. This leads us to quantify the amplitude and the temperature dependence of kinematic and dynamic viscosity of the electron fluid. In cadmium, in contrast with graphene and $^3$He, the rate of momentum-conserving $e$-$e$ collisions is not set by the main Fermi energy, but by Lilliputian energy scales and inter-valley bottlenecks.

In this paper, we determine the graphs with maximum value of the sum number from $k$-clique spectral radius to $(2r-1)$-clique spectral radius among all $2K_{r}$-free graphs on $n$ vertices for $ r\le k$ and large $n$. We also determine the graphs with maximum $3$-clique spectral radius among all $2K_{3}$-free graphs on $n$ vertices. Our results are spectral versions of some results on generalized Turán numbers.

Purpose: Quasi-random Sobol-based sampling schemes exhibit deterministic structural artifacts when aggressively undersampled, particularly at low encoding densities required for accelerated 2D SPI/CSI. To address these limitations, two advanced undersampling strategies are investigated to mitigate deterministic behavior, improving image quality for time-constrained applications such as hyperpolarized MRI. Methods: An optimized Sobol sequence-derived point distribution with Heaviside-type density gradient center oversampling served as the initial sampling pattern. Undersampling was performed using two point-reduction algorithms: radius-adaptive stochastic undersampling (RAST), which applies a geometric, radius-dependent minimum-distance criterion, and Bayesian Information Gain Optimization (BINGO), that removes points based on their information gain to the reconstructed image. Phantom experiments were conducted on a 3 T clinical MRI system using up to 16-fold undersampling. Image quality was quantified using a performance score derived from RMSE, SSIM, and HFEN. Results: Both RAST and BINGO outperformed deterministic undersampling across all metrics. RAST achieved highest and most robust performance, with improvements up to 238% in the averaged metric score, while BINGO yielded improvements of 133% across matrix resolutions. Conclusion: The proposed strategies effectively reduce the number of encoding points in low-discrepancy 2D SPI point distributions while maintaining image quality under strong acceleration. RAST provides superior metric performance, whereas BINGO offers broad applicability, including suitability for non-linear encoding fields. These approaches support rapid acquisition workflows required for real-time and hyperpolarized applications.

Maintaining consistency between code and documentation is a crucial yet frequently overlooked aspect of software development. Even minor mismatches can confuse API users, introduce new bugs, and increase overall maintenance effort. This creates demand for automated solutions that can assist developers in identifying code-documentation inconsistencies. However, since automatic reports still require human confirmation, false positives carry serious consequences: wasting developer time and discouraging practical adoption. We introduce CASCADE (Consistency Analysis for Source Code And Documentation through Execution), a novel tool for detecting inconsistencies with a strong emphasis on reducing false positives. CASCADE leverages Large Language Models (LLMs) to generate unit tests directly from natural-language documentation. Since these tests are derived from the documentation, any failure during execution indicates a potential mismatch between the documented and actual behavior of the code. To minimize false positives, CASCADE also generates code from the documentation to cross-check the generated tests. By design, an inconsistency is reported only when two conditions are met: the existing code fails a test, while the code generated from the documentation passes the same test. We evaluated CASCADE on a novel dataset of 71 inconsistent and 814 consistent code-documentation pairs drawn from open-source Java projects. Further, we applied CASCADE to additional Java, C#, and Rust repositories, where we uncovered 13 previously unknown inconsistencies, of which 10 have subsequently been fixed, demonstrating both CASCADE's precision and its applicability to real-world codebases.

Scientific tools dictate the boundaries of human knowledge, serving as the foundation for perceptions and explorations. In the era of Big Science, science are increasingly dependent on advanced analytical technologies and experimental platforms. Over the past decades, national and supranational entities have invested massive financial resources, collaborative networks, and collective intelligence to construct Big Science Facilities (BSFs) aimed at generating cutting edge knowledge. However, empirical evaluations of these machines actual performance in driving scientific innovation remain scarce. To address this gap, we collected 310,086 publications from 88 global BSFs and constructed a matched control dataset of approximately 3 million publications sharing the same last authors. Our analysis reveals that the utilization of BSFs has expanded significantly since 1950s. Crucially, publications supported by these facilities exhibit higher recombinant novelty and interdisciplinary integration. Furthermore, this improvement is most pronounced in non physical sciences domains traditionally peripheral to BSFs core focus indicating the emergence of a powerful intra facility knowledge spillover effect. By enriching the Facilitymetrics framework, our findings provide empirical evidence that BSFs act as vital engines for scientific discovery, offering policymakers essential metrics to justify infrastructural investments, while prompting the science of science community to reassess the profound impact of scientific tools on knowledge production

Cosmic distances can be measured using two complementary probes: Type Ia supernovae (SN Ia), serving as standard candles, and baryon acoustic oscillations (BAO), serving as standard rulers. The luminosity distance derived from supernovae and the angular diameter distance obtained from BAO must be mutually consistent if these data are to be combined for cosmological inference. Hence, the existence of potential discrepancies, whether arising from systematics in either dataset or from violation of the cosmic duality relation (in an unconventional cosmology), remains an important issue to address. Testing consistency under a particular cosmological model can be limiting, as the model may not be sensitive to every kind of inconsistency possible in the data. Thus, in this work we use a model-independent Crossing Statistics framework to test the consistency, using DESI DR2 BAO, and the Pantheon+ and Union3 SN Ia datasets. We find adding up to two additional degrees of freedom, using Crossing Statistics on the LambdaCDM distance-redshift relation, to be statistically justified. In these cases, the two probes remain mutually consistent at the 1-2 sigma level. Having established this statistical consistency, we combine the datasets to reconstruct the expansion history of the Universe and the inferred evolution of dark energy. The reconstructions obtained using different crossing variables show compatible behaviour where the data constraints are strongest, particularly at low redshift. Overall, the results are suggestive of a dark energy component that is evolving at low redshift, compatible with results from other reconstruction methods.

This paper presents a framework to bridge the gap between subjective stakeholder context and formal system architecture. This is achieved using Soft Systems Methodology (SSM) and Systems Modelling Language version 2 (SysML v2). The methodology utilises the precision of Kernel Modelling Language (KerML) and the alignment of SysML v2 with ISO 42010 to define a reference architecture for the mapping of SSM outputs to SysML v2 concepts such as stakeholders and concerns. Application of the framework is demonstrated through the use of a case study, highlighting the traceable path from stakeholder context to system architecture. The structured mapping and increased semantic precision of SysML v2 are anticipated to reduce the risk of misinterpretation compared to less formal approaches, though empirical validation across diverse stakeholder contexts remains as future work. The primary identified trade-off is the increased barrier to entry associated with SysML v2's textual notation.

We study the focusing semilinear heat equation with an additional defocusing Hénon-type nonlinearity, the coupling of which is measured by a constant $c >0$. For $c \in (0,c^*)$, the model admits a closed-form self-similar blowup solution in every space dimension $d \geq 1$. Restricting ourselves to the three-dimensional case, we study the stability of this solution under small non-radial perturbations. By working in intersection Sobolev spaces with additional angular regularity, we prove finite co-dimension stability for all admissible values of $c$. Furthermore, we analyze the spectrum of the underlying linearized operator and we prove stable blowup for the cubic-quintic case and $c$ sufficiently close to $c^*$. Finally, we discuss the situation for small values of $c$ and use a modified version of the classical GGMT criterion to give an upper bound on the number of unstable eigenvalues.