Conservation Voltage Reduction (CVR) relies on the effective coordination of slow-acting devices, such as OLTCs and CBs, and fast-acting devices, such as SVGs and PV inverters, typically implemented through a hierarchical multi-stage Volt-Var Control (VVC) spanning day-ahead scheduling, intra-day dispatch, and real-time control. However, existing sequential methods fail to account for the cas-cading impact of forecast errors on multi-stage decision-making. This oversight results in suboptimal day-ahead schedules for OLTCs and CBs that hinder the ef-fective coordination with fast-acting SVGs and inverters, inevitably driving a trade-off between real-time voltage security and CVR efficiency. To improve the Pareto front of this trade-off, this paper proposes a novel bi-level multi-timescale forecasting (Bi-MTF) framework for multi-stage VVC optimization. By integrating the downstream multi-stage VVC optimization into the upstream forecasting mod-els training, the decision-focused forecasting models are able to learn the trade-offs across temporal horizons. To solve the computationally challenging bi-level for-mulation, a modified sensitivity-driven integer L-shaped method is developed. It utilizes a hybrid gradient feedback mechanism that integrates numerical sensitivity analysis for discrete variables with analytical dual information for continuous fore-cast parameters to ensure tractability. Numerical results on a modified IEEE 33-bus system demonstrate that the proposed approach yields superior energy savings and operational safety compared to conventional MSE-based sequential paradigms. Specifically, as the capacity of fast-acting devices increases, the energy savings of the proposed method rise from 2.74% to 3.41%, which is far superior to the 1.50% to 1.76% achieved by conventional MSE-based sequential paradigms.

Given an $r$-uniform hypergraph $H$ and a positive integer $n$, the weak saturation number $\mathrm{wsat}(n,H)$ is the minimum number of edges in an $r$-uniform hypergraph $F$ on $n$ vertices such that the missing edges in $F$ can be added, one at a time, so that each added edge creates a copy of $H$. For the case of graphs ($r = 2$), asymptotically optimal general lower bounds for these numbers in terms of the minimum vertex degree of $H$ are known. In this work, we generalize these bounds to the case of hypergraphs and establish their asymptotic optimality. To prove this, we introduce a lower bound method based on polymatroids. This method generalizes a linear algebraic method but, unlike the original version, makes it possible to derive lower bounds with non-integer asymptotic coefficients.

Spherical centroidal Voronoi tessellations (SCVTs), currently used in numerical weather forecasting models such as the Model for Prediction Across Scales (MPAS), are a type of spherical grid that is highly flexible, allowing the construction of locally refined regions with higher resolution without requiring modifications to the numerical discretization or its implementation. However, the irregularity of SCVT grids makes the construction of robust high-order schemes challenging. In particular, in atmospheric modeling, high-order advection schemes are desirable since they reduce numerical diffusion and improve the representation of fine-scale tracer structures. Therefore, in this work, we propose a new class of high-order advection schemes on the sphere based on the $k$-exact reconstruction approach, extending their successful use on planar domains to the spherical surface. We assess the performance of the proposed method and compare it with existing advection schemes for SCVT grids used in MPAS. The evaluation includes classical advection test cases on the sphere as well as simulations with a mimetic finite-volume moist shallow-water model, in which the advection scheme is applied to the transport of moisture tracers. Grid-related robustness was investigated using locally refined spherical grids with a local focus on the Andes topography. Our results show that the proposed schemes achieve high-order accuracy in the advection tests, exhibit little sensitivity to grid distortion, and produce comparable results to existing schemes in the moist shallow-water model. Overall, grid robustness is therefore limited to the sensitivity of the discretization of the shallow-water model, irrespective of the advection scheme.

Representation of cities as organisms with metabolic processes is a useful analogy for urban design, development and sustainability. Urban metabolism can be modeled by representing urban systems as networks. The various networks included in a city's metabolism are interdependent in complex ways. Thus, understanding the interaction among these networks is essential to understanding how a healthy urban metabolism is sustained and how injuries to the metabolic system can "heal". It is particularly important to understand how disruptions to one system in an urban area affect the functioning of other systems. Using distribution-level data from a real U.S. city on the electricity distribution system and road geometry, we apply connected network modeling to two critical inter-connected urban infrastructure sectors: energy and transportation. We quantify the robustness of these interdependent networks by evaluating the connectivity disruptions that may occur due to natural or synthetic disruptive events, using both unweighted and weighted metrics.

When selecting a model to characterize an astrophysical population, it is crucial to assess whether that model fits the data and, if not, how it can be improved. To this end, posterior predictive checks (PPCs) are a widely-used statistical test of model fit when inferring gravitational-wave source populations. However, PPCs exhibit limitations when assessing single-event parameters with large measurement uncertainty, like the spin tilt angles of the binary black holes (BBHs) observable with the LIGO-Virgo-KAGRA (LVK) detectors. When single-event inference is prior-dominated, traditional PPCs fail to flag even very poor model fits. In this work, we assess the efficacy of various alternative PPCs on poorly-constrained parameters. We compare PPCs conducted on event- vs. data-level parameters (e.g. posterior samples vs. maximum likelihood points), and explore two additional event-level PPCs: partial predictive checks and split predictive checks. Independent of measurement uncertainty, we find that PPCs on maximum likelihood parameters are always more discerning of model misspecification than any event-level PPC. However, when investigating simulated GWTC-3.0-like catalogs, none of the alternative PPCs show significant improvement over those traditionally used, indicating that at that sensitivity, any limited information in the data about spin tilts is insufficient to diagnose model misspecification. Finally, we apply our suite of PPCs to the spin magnitude and tilt distributions inferred in the most recent LVK catalog, GWTC-4.0. We conclude that the Gaussian Component Spins model used therein under-predicts BBHs with large spin magnitudes and over-predicts those with perfectly anti-aligned tilts.

Anti-forensics includes a growing set of techniques designed to obstruct forensic analysis. While cybercriminals increasingly rely on these methods, they also help researchers identify and remedy weaknesses in forensic tools, advancing the overall robustness of digital forensics. Despite repeated efforts to define it, anti-forensics remains vague and inconsistent in its use. It also poses ethical challenges regarding the appropriateness of research practices and the legitimacy of the field itself. This article presents a systematic analysis of 123 publications on anti-forensics, combining qualitative and quantitative methods. We quantify the main techniques and attack vectors, examine their occurrence in different digital forensic subdomains, and identify typical research methods, motivations, and applications. This work also discusses what these findings mean for future research and proposes directions for building a more coherent and ethically grounded understanding of anti-forensics.

While dense retrieval models, which embed queries and documents into a shared low-dimensional space, have gained widespread popularity, they were shown to exhibit important theoretical limitations and considerably lag behind traditional sparse retrieval models in certain settings. Generative retrieval has emerged as an alternative approach to dense retrieval by using a language model to predict query-document relevance directly. In this paper, we demonstrate strengths and weaknesses of generative retrieval approaches using a simple synthetic dataset, called LIMIT, that was previously introduced to empirically demonstrate the theoretical limitations of embedding-based retrieval but was not used to evaluate generative retrieval. We close this research gap and show that generative retrieval achieves the best performance on this dataset without any additional training required (0.92 and 0.99 R@2 for SEAL and MINDER, respectively), compared to dense approaches (< 0.03 Recall@2) and BM25 (0.86 R@2). However, we then proceed to extend the original LIMIT dataset by adding simple hard negative samples and observe the performance degrading for all the models including the generative retrieval models (0.51 R@2) as well as BM25 (0.21 R@2). Error analysis identifies a failure in the decoding mechanism, caused by the inability to produce identifiers that are unique to relevant documents. Future generative retrieval must address these issues, either by designing identifiers that are more suitable to the decoding process or by adapting decoding and scoring algorithms to preserve relevance signals.

Industrial control systems (ICSs) often consist of many legacy devices, which were designed without security requirements in mind. With the increase in cyberattacks targeting critical infrastructure, there is a growing urgency to develop legacy-compatible security solutions tailored to the specific needs and constraints of real-time control systems. We propose a least significant bits (LSBs) coding scheme providing message authentication and integrity, which is compatible with legacy devices and never compromises availability. The scheme comes with provable security guarantees, and we provide a simple yet effective method to deal with synchronization issues due to packet dropouts. Furthermore, we quantify the control performance loss for both a fixed-point and floating-point quantization architecture when using the proposed coding scheme. We demonstrate its effectiveness in detecting cyberattacks, as well as the impact on control performance, on a hydro power turbine control system.

3GPP Release~16 specifies how a 5G system can operate as a transparent IEEE~802.1 TSN bridge, yet no existing simulation framework implements the complete bridge architecture with end-to-end QoS mapping through the SDAP layer, per-flow Data Radio Bearer selection, and IEEE~802.1AS transparent clock behaviour with measured residence time. Existing tools model either QoS mapping without time synchronisation, or time synchronisation without a data plane. This paper presents nascTime, a simulation framework built on OMNeT++~6.3, INET~4.6, and Simu5G that implements the full 3GPP 5G-TSN bridge model. The NW-TT and DS-TT are realised as modular compound modules that integrate with INET's \texttt{LayeredEthernetInterface} and streaming PHY. QoS mapping traverses the complete PCP\,$\rightarrow$\,DSCP\,$\rightarrow$\,QFI\,$\rightarrow$\,SDAP/DRB pipeline, and gPTP frames are transported through the simulated 5G radio path via L2-in-GTP-U encapsulation with per-message residence-time correction. We validate the framework with a three-endpoint factory topology under both ideal and fading channel conditions. In the ideal scenario, high-priority traffic achieves 99.9\% delivery with a mean end-to-end delay of 2.58\,ms, while the measured 5GS residence time exhibits a variance below 0.2\,$μ$s. Under a fading channel, residence-time variance increases to 48\,$μ$s, confirming that the framework captures radio-induced timing effects absent from abstract-delay simulators. nascTime is publicly available and constitutes the first full-stack 5G-TSN bridge simulation with SDAP-based QoS differentiation and measured IEEE~802.1AS transparent clock behaviour.

People working with data often move their data across multiple applications, because they rely on these apps' complementing user experiences to best complete their tasks. Since traditional copy-and-paste approaches do not accommodate diverse table representations adopted by different apps, users spend considerable effort to reconstruct data formats and visual representations, making cross-app workflows costly. For example, when transferring a spreadsheet table with conditional formatting to a markup document, users spend substantial time translating its structure into appropriate tags and manually reformat color. This paper introduces MagicCopy, an AI-powered cross-app copy-and-paste, leveraging source and target contexts and user-specified instructions in natural language to automatically extract, parse, transform, and (re)format data from one app to another. In a study with sixteen participants, users quickly learned and applied MagicCopy to move data across three pairs of tools. Participants further explored diverse applications of MagicCopy to support more streamlined crossed-application interaction in their workflows.

Nelson's stochastic mechanics may be understood as a stochastic underpinning, or reconstruction, of nonrelativistic quantum mechanics, once the diffusion scale is fixed by $\hbar$ and the admissible states are restricted by the usual single-valuedness condition on the wavefunction. In this note I briefly indicate what this route achieves and why it remains conceptually attractive. Four advantages are emphasized. First, it supplies a clear configuration-space stochastic picture of the underlying processes. Second, the Born rule is built in from the outset, with $|ψ|^2$ arising as the probability density $ρ$ of the underlying diffusion process rather than as an independent postulate. Third, it offers a markedly different perspective on measurement and nonlocality: in particular, collapse need not be treated as an extra axiom, and the nonlocality associated with entangled states is softened relative to the deterministic Bohmian guidance picture. Fourth, by tying quantumness to a diffusion scale, it naturally suggests a continuum of physical descriptions ranging from the strictly classical to the strictly quantum-mechanical regime. I conclude by proposing a natural distance scale in stochastic mechanics and examining its implications for testing possible limits of Bell correlations.

The Earth's Moon presents a uniquely advantageous environment for detecting astrophysical gravitational waves (GWs), particularly in the scientifically interesting deciHz regime. The Laser Interferometer Lunar Antennae (LILA) project plans to perform GW measurements on the lunar surface, using the Moon's unique seismic quietness to access the deciHz regime. Two mission concepts are considered: the initial LILA-Pioneer L-shaped strainmeter and the more advanced LILA-Horizon triangular interferometer. Because the detection frequency is so low, LILA requires only the Moon's precession around the Earth and Sun to triangulate (unlike Earth-based detectors). Thus, the science return of LILA is site-agnostic; however, significant constraints are imposed by practical considerations. These include the need for isolation from anthropogenic noise, protection from the lunar environment, accessibility for lunar terrain vehicles, and line-of-sight. Candidate sites are shown for both LILA-Pioneer and LILA-Horizon, demonstrating that many options exist for deployment of both tools.

Software malleability allows applications to be easily changed, configured, and adapted even after deployment. While prior work has explored configurable systems, adaptive recommender systems, and malleable GUIs, these approaches are often tailored to specific software and lack generalizability. In this work, we envision per-user malleable mobile applications, where end-users can specify requirements that are automatically implemented via LLM-based code generation. However, realizing this vision requires overcoming the key challenge of designing automated test generation that can reliably verify both the presence and correctness of user-specified functionalities. We propose ALADDIN, a user-requirement-driven GUI test generation framework that incrementally navigates the UI, triggers desired functionalities, and constructs LLM-guided oracles to validate correctness. We build a benchmark spanning six popular mobile applications with both correct and faulty user-requested functionalities, demonstrating that ALADDIN effectively validates per-user features and is practical for real-world deployment. Our work highlights the feasibility of shifting mobile app development from a product-manager-driven to an end-user-driven paradigm.

The quantum approximate optimization algorithm (QAOA) is a leading variational approach to combinatorial optimization, but its practical performance depends strongly on objective design, parameter search, and shot allocation. We present a resource-efficient QAOA framework that uses the cut value of the most probable measured bitstring as the optimization objective, combines it with Bayesian optimization, and adaptively allocates shots using dual criteria based on mode confidence and normalized cut-value variance. Numerical experiments on 3-regular MaxCut show that, for both unweighted and weighted instances, the proposed scheme achieves discrete-solution quality comparable to that of the conventional expectation-based objective while typically requiring fewer total shots to reach the same final mode accuracy. These results indicate that reorganizing QAOA around the maximum-probability bitstring provides an effective route to improving practical performance under limited measurement budgets.

Normalization is ubiquitous in economics, and a growing literature shows that ``normalizations'' can matter for interpretation, counterfactual analysis, misspecification, and inference. This paper provides a general framework for these issues, based on the formalized notion of modeling equivalence that partitions the space of unknowns into equivalence classes, and defines normalization as a WLOG selection of one representative from each class. A counterfactual parameter is normalization-free if and only if it is constant on equivalence classes; otherwise any point identification is created by the normalization rather than by the model. Applications to discrete choice, demand estimation, and network formation illustrate the insights made explicit through this criterion. We then study two further sources of fragility: an extension trilemma establishes that fidelity, invariance, and regularity cannot simultaneously hold at a boundary singularity, while a normalization can itself introduce a coordinate singularity that distorts the topological and metric structures of the parameter space, with consequences for estimation and inference.

Tree-ordered weakly sparse models have recently emerged as a robust framework for representing structures in an ``almost sparse'' way, while allowing the structure to be reconstructed through a simple first-order interpretation. A prominent example is given by twin-models, which are bounded twin-width tree-ordered weakly sparse representations of structures with bounded twin-width derived from contraction sequences. In this paper, we develop this perspective further. First, we show that twin-models can be chosen such that they preserve linear clique-width or clique-width up to a constant factor. Then, we introduce \emph{merge-models}, a natural analog of twin-models for merge-width. Merge-models represent binary relational structures by tree-ordered weakly sparse structures. The original structures can then be recovered by a fixed first-order interpretation. A merge-model can be constructed from a merge sequence. Then, its radius-$r$ merge-width will be, up to a constant factor, bounded by the radius-$r$ width of the merge sequence from which it is derived. Finally, we show that twin-models arise naturally as special cases of merge-models, and that binary structures with bounded twin-width are exactly those having a loopless merge-model with bounded radius-$r_0$ merge-width (for some sufficiently large constant $r_0$).

We find first structural background information about the reasons that for any C*-algebra $A$ and any two Hilbert $A$-modules $M \subseteq N$ with $M^\perp=\{0\}$, every bounded $A$-linear map $N \to A$ (or $N \to N)$ vanishing on $M$ might be only the zero map. The self-adjoint case is proved, whereas the general case is open with partial insights. Unfortunately, the proof of Lemma 3.3 of our first version contains the implicit assumption that the projection $P$ and the operator $S$ commute, which is not the case for non-zero non-self-adjoint operators $S$.

We establish and explore the correspondence between positive functionals and cocycles in higher unitary cohomology. We generalize the classical cocycle version of the Gelfand-Naimark-Segal construction to higher degrees and apply it to characterize vanishing of higher unitary cohomology as an extension property for positive functionals. We also prove that under mild conditions the algebraic spectral gap for the one sided Laplacian characterizes cohomological vanishing instead of reducedness of unitary cohomology

Active particles locally transduce energy into motion, leading to unusual and emergent behaviors. However, current synthetic particles lack sensing and adaptation mechanisms. Here, we demonstrate a novel regulation pathway, through the combined use of thermophoretic propulsion and nanometric building blocks. We build an active fluid composed of artificial nanomotors and study its three-dimensional (3D) dynamics. We use laser-induced photo-thermal effect to actuate nanoparticles, and probe their self-propulsion within assemblies. Despite significant thermal fluctuations at the nanoscale, our results reveal a strong dependence of the thermophoretic propulsion on the concentration of nanomotors, leading to ultrafast velocities of up to ~ 800 um/s. This unique behavior originates from a strong coupling of the local concentration of nanomotors and the temperature field, which feeds back on the thermophoretic mobility of the nanoparticles. We rationalize our results from independent modeling of all thermal effects, accounting for nonlinearities of thermophoretic self-propulsion. Our results open novel routes for the design and self-regulation of 3D active fluids by thermal processes.

The symplectic fermion is a much-studied non-unitary conformal field theory with $c=-2$ and is known to contain an infinite family of mutually commuting conserved charges. We derive expressions for the conserved charges on the cylinder and use these to construct Generalised Gibbs Ensembles (GGEs) in the particular case of the ${W}(1,2)$ triplet model. We derive exact expressions for the modular $S$-transforms in each sector of the symplectic fermion (and so of the whole GGE) and further extract the expressions in the asymptotic regime where the chemical potentials go to zero. Subsets of the conserved charges are known to reproduce the KdV and Boussinesq hierarchies. For the case in which the charge is identified with the zero mode $W_0$ of the $W_3$ algebra, we obtain asymptotic behaviour in precise agreement with the conjecture proposed in our companion paper [1]; for the KdV subset we obtain results which exactly mirror the case for a single free fermion. Finally we identify the GGE with a translation invariant and purely transmitting defect for the symplectic fermion fields, and make some comments on the relation to other $W_n$ algebras.

Motivated by the combinatorics of parking functions and their several generalizations, we study the Ehrhart theory of Pitman--Stanley polytopes. We prove a strong positivity phenomenon called \emph{magic positivity} for the Ehrhart polynomials of these polytopes, which in turn implies that their $h^*$-polynomials are real-rooted (and thus log-concave and unimodal). Our result is achieved by interpreting the coefficients of these Ehrhart polynomials in the \emph{magic basis} in terms of the number of \emph{lucky cars} in a modified parking protocol. Furthermore, we address the magic positivity problem for $\mathbf{y}$-generalized permutohedra and also discuss a \emph{magic} combinatorial interpretation for them, under the assumption that the input parameters are sufficiently large.

Astrophysical luminous objects such as the first stars have not yet formed in the Dark Ages. However, primordial black holes (PBHs) always exist throughout cosmic history since the inflation epoch. During the Dark Ages, PBHs may accrete the ambient gas and release radiation like astrophysical luminous objects, change the cosmic radiation field, the thermal status of the intergalactic medium (IGM), and the hydrogen spin temperature. The accretion rate is modulated by the relic supersonic relative streaming velocities between dark matter (DM) and baryons, imprinting Velocity Acoustic Oscillations (VAOs) features in the 21 cm power spectrum. Such VAOs features could be a promising probe for detecting the PBHs in Dark Ages. We find that even if PBHs comprise only a small fraction of DM, they can generate VAOs wiggles with a relative amplitude up to about 30% in Dark Ages. For example, for PBHs with a mass at recombination of 200 solar masses and mass fraction in the total DM f_PBH,rec around 1e-13 at the recombination era, VAOs features appear at redshift around 20; if f_PBH,rec is around 3e-10, then VAOs features could appear as early as redshift around 40. Moreover, the redshift evolution of the VAOs features exhibits clearly separated stages dominated by inhomogeneous Ly-alpha scattering, and inhomogeneous X-ray heating, respectively. It reflects the characteristics of PBHs (mass and fraction in total DM) and their interactions with the IGM. We also estimate that, the VAOs wiggles at redshift around 20 are detectable for the upcoming SKA-low AA*, while wiggles at redshift around 40 are detectable for an hypothetic lunar surface-based interferometer array in the future.

Climate models are essential for understanding large-scale climate dynamics and long-term climate change, yet they exhibit systematic biases when compared with historical observations. Existing multivariate bias correction (MBC) approaches do not explicitly handle spatiotemporal dependence. However, preserving both spatiotemporal and inter-variable consistency is essential for realistic climate dynamics and reliable regional impact assessments. To address this gap, we propose a novel MBC method called GN-VBC that uses generalized additive models (GAMs) to disentangle spatiotemporal deterministic effects from stochastic residuals. To model joint distributions and dependencies across variables and locations, we introduce nested vine copulas (NVCs), a hierarchical vine merging strategy. NVC in the context of MBC combines two dependence levels: (i) spatial dependence across locations, modeled separately for each variable, and (ii) inter-variable dependence modeled at a selected reference location, which links the spatial models into a coherent multivariate and spatial structure. An application to Switzerland shows improvements in preserving inter-variable, spatial and temporal dependence across a wide range of evaluation metrics.

We study the eigenvalue profile of concentration operators (multiplication by an indicator function followed by projection) acting on reproducing kernel Hilbert spaces. The spectral profile of such operators provides a useful notion of local degrees of freedom. We formalize this idea by estimating the number of eigenvalues that lie away from 0 and 1, commonly referred to as the plunge region. Our main motivation is to treat discrete and continuous settings simultaneously and uniformly, and to be able to argue that approximations arising from discretization schemes reflect, in a non-asymptotic sense, the spectral profile of their continuous counterparts. As a case in point, we show that Gabor multipliers computed on sufficiently fine grids obey spectral deviation estimates similar to those available for the short-time Fourier transform (STFT) with bounds that are uniform in the discretization step. Concretely, this means that the theoretical localization properties of the STFT are observable in practice.

This study investigates the use of large language models (LLMs) for story point estimation. Story points are unitless, project-specific effort estimates that help developers on the scrum team forecast which product backlog items they plan to complete in a sprint. To facilitate this process, machine learning models, especially deep neural networks, have been applied to predict the story points based on the title and description of each item. However, such machine learning models require sufficient amounts of training data (with ground truth story points annotated by human developers) from the same software project to achieve decent prediction performance. This motivated us to explore whether LLMs are capable of (RQ1) predicting story points without training data or (RQ2) with only a few training data points. Our empirical results with four LLMs on 16 software projects show that, without any training data (zero-shot prompting), LLMs can predict story points better than supervised deep learning models trained on 80% of the data. The prediction performance of LLMs can be further improved with a few training examples (few-shot prompting). In addition, a recent study explored the use of comparative judgments (between a given pair of items which one requires more effort to implement) instead of directly annotating the story points to reduce the cognitive burden on developers. Therefore, this study also explores (RQ3) whether comparative judgments are easier to predict than story points for LLMs and (RQ4) whether comparative judgments can serve as few-shot examples for LLMs to improve their predictions of story points. Empirical results suggest that it is not easier for LLMs to predict comparative judgments than to directly estimate the story points, but comparative judgments can serve as few-shot examples to improve the LLMs' prediction performance as well as the human-annotated story points.

Tensor networks provide discrete representations of quantum many-body systems, yet their precise connection to continuum field theories remains relatively poorly understood. Invoking a notion of typicality, we develop a continuum description for random ensembles of two-dimensional fermionic matchgate tensor networks with spatially fluctuating parameters. As a diagnostic of the resulting universal physics, we analyze disorder-averaged moments of fermionic two-point functions, both in flat geometry and on a hyperbolic disk, where curvature reshapes their long-distance structure. We show that disorder drives universal long-distance behavior governed by a nonlinear sigma-model of symmetry class D with a topological term, placing random matchgate networks in direct correspondence with the thermal quantum Hall problem. The resulting phase structure includes localized phases, quantum Hall criticality, and a robust thermal metal with diffusive correlations and spontaneous replica-symmetry breaking. Weak non-Gaussian deformations reduce the symmetry to discrete permutations, generate a mass for the Goldstone modes, and suppress long-range correlations. In this way, typicality offers a route from ensembles of discrete tensor networks to continuum quantum field theories.

This paper presents a scenario generation framework that creates diverse, parametrized, and safety-critical driving situations to validate the safety features of autonomous vehicles in simulation [15]. By modeling factors such as road geometry, traffic participants, environmental conditions, and perception uncertainties, the framework enables repeatable and scalable testing of safety mechanisms, including emergency braking, evasive maneuvers, and vulnerable road user protection. The framework supports both regulatory and edge case scenarios, mapped to hazards and safety goals derived from Hazard Analysis and Risk Assessment (HARA), ensuring traceability to ISO 26262 functional safety requirements and performance limitations. The output from these simulations provides quantitative safety metrics such as time-to-collision, minimum distance, braking and steering performance, and residual collision severity. These metrics enable the systematic evaluation of evasive maneuvering as a safety feature, while highlighting system limitations and edge-case vulnerabilities. Integration of scenario-based simulation with safety engineering principles offers accelerated validation cycles, improved test coverage at reduced cost, and stronger evidence for regulatory and stakeholder confidence.

Several approaches have been recently proposed for community search in bipartite graphs. These methods have shown promising results in identifying communities in real-world bipartite networks, such as social and biological networks. Given a query user $q$, community search in bipartite graphs involves identifying a group of users containing $q$, with common characteristics or functions within a given bipartite graph. These problems are particularly challenging because bipartite graphs have two distinct sets of nodes, and community search algorithms must account for this structure. However, finding communities in keyword-based bipartite spatial-social networks has yet to be investigated enough. The spatial-social networks are naturally structured as bipartite graphs. Thus, this paper proposes a new community search problem in Bipartite spatial-social networks with a novel $(ω, π)\mbox{-}keyword\mbox{-}core$, named Keyword-based Community Search in Bipartite Spatial-Social Networks ($KCS\mbox{-}BSSN$). The $KCS\mbox{-}BSSN$ returns a tightly-knit community, significant social influence, minimal travel distance, and includes a $(ω, π)\mbox{-}keyword\mbox{-}core$. To address the $KCS\mbox{-}BSSN$ problem, we have developed pruning methods that effectively filter out irrelevant users and points of interest. To improve query-answering efficiency, we have also proposed an indexing technique named the bipartite-spatial-social index. Our pruning techniques, and indexing approach, have proven effective and efficient through experiments with real and artificial data sets.

Let $n,$ $m \geq 2$ be integers, and let $R$ be a subring of $\mathbb R$ with field of fractions $F.$ In this article, we generalize the rational angle bisection problem previously proposed by the author to the following problem: which linearly independent vectors $\boldsymbol{a},$ $\boldsymbol{b} \in R^n$ form an angle with a sequence of $m$-sector vectors lying in $R^n$? When $\boldsymbol{a}$ and $\boldsymbol{b}$ are nonorthogonal, we prove that this condition is equivalent to the existence of a root in $F$ of a certain $m$-th degree polynomial over $R.$ In particular, when $R = \mathbb Z,$ the condition holds if and only if the polynomial has a root among the divisors of its constant term. When $m = 2^e$ with an integer $e \geq 1,$ we also prove that the condition is equivalent to $\cos (θ/2^{e-1}) \in F,$ where $θ$ is the angle between $\boldsymbol{a}$ and $\boldsymbol{b}.$

The pole structure of the $Λ(1405)$ has been a topic of debate for a long time. Chiral perturbation theory predicts that its experimental spectrum may be explained by a two pole structure originating in the $SU(3)$ chiral dynamics of the baryon-meson interaction. The $SU(3)$-symmetric flavor point is readily accessible in lattice QCD, in this work we study the baryon-meson states directly at this point. We construct interpolation operators that belong to the irreducible representations of $SU(3)$ that are attractive in the channel with the quantum numbers of the (singlet and two octets). The extracted energy levels can be used as input for chiral perturbation theory to find the poles associated with each representation. The relevant correlation functions are computed on $SU(3)$-symmetric ensembles with $M_π\approx 714$ MeV using the distillation technique.