We prove central limit theorems (CLTs) for topological functionals of Bernoulli bond percolation on infinite graphs beyond the Euclidean lattice $\mathbb{Z}^{d}$. For quasi-transitive graphs of subexponential growth, we show that the number $K_{r}$ of open clusters intersecting the metric ball $B_{r}$ satisfies a CLT as $r\to\infty$. For amenable Cayley graphs, we prove a general CLT for stationary percolation functionals along Folner sequences under sequential stabilization and a finite-moment assumption, provided the group admits a left-orderable finite-index subgroup. This applies in particular to groups of polynomial growth. As an application, we obtain CLTs for Betti numbers of graph-generated random simplicial complexes, including clique and neighbor complexes. The proofs combine invariant edge orderings, martingale decompositions, and stabilization estimates for single-edge perturbations.

Brightfield time-lapse imaging is widely used in cardiac tissue engineering, yet the absence of standardized, interpretable analytical frameworks limits reproducibility and cross-platform comparison. We present an open, scalable computational pipeline for quantifying spatiotemporal contractile dynamics in microscopy videos of human induced pluripotent stem cell-derived cardiac microbundles. Building on our open-source tools "MicroBundleCompute" and "MicroBundlePillarTrack," we define a suite of 16 interpretable structural, functional, and spatiotemporal metrics that capture tissue deformation, synchrony, and heterogeneity. The framework integrates full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis within a unified workflow. Applied to a dataset of 670 cardiac microbundles spanning 20 experimental conditions, the pipeline reveals continuous variation in contractile phenotypes rather than discrete condition-specific clustering, with intra-condition variability often exceeding inter-condition differences. Redundancy analysis identifies a reduced core set of 10 metrics that retain most informational content while minimizing multicollinearity. Analysis of denoised displacement fields shows that contraction is dominated by a global isotropic mode, with localized saddle-type deformation patterns present in approximately half of the samples. All software and workflows are released openly to enable reproducible, scalable analysis of dynamic tissue mechanics.

In recommender systems, it is well-established that both accuracy and diversity are crucial for generating high-quality recommendation lists. However, achieving a balance between these two typically conflicting objectives remains a significant challenge. In this work, we address this challenge by proposing four novel hybrid multi-objective algorithms inspired by the Non-dominated Neighbor Immune Algorithm (NNIA), Archived Multi-Objective Simulated Annealing (AMOSA), and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), aimed at simultaneously enhancing both accuracy and diversity through multi-objective optimization. Our approach follows a three-stage process: First, we generate an initial top-$k$ list using item-based collaborative filtering for a given user. Second, we solve a bi-objective optimization problem to identify Pareto-optimal top-$s$ recommendation lists, where $s \ll k$, using the proposed hybrid algorithms. Finally, we select an optimal personalized top-$s$ list from the Pareto-optimal solutions. We evaluate the performance of the proposed algorithms on real-world datasets and compare them with existing methods using conventional metrics in recommender systems such as accuracy, diversity, and novelty. Additionally, we assess the quality of the Pareto frontiers using metrics including the spacing metric, mean ideal distance, diversification metric, and spread of non-dominated solutions. Results demonstrate that some of our proposed algorithms significantly improve both accuracy and diversity, offering a novel contribution to multi-objective optimization in recommender systems.

We investigate two neighboring clusters in the Cygnus complex, Berkeley 86 and Berkeley 87, with a primary emphasis on the evaluation of extinction in the field of view towards and across the clusters. We also analyze their kinematic behavior in space and time to discern their possible common origin and relation to the Cyg~OB1 association. New CCD photometry in the Vilnius seven-color system, obtained down to V=19.0 mag in the fields of these two clusters, is used to classify stars in terms of spectral and luminosity classes and to determine the individual values of interstellar extinction. The probable cluster members are identified in a 5-parameter space based on Gaia DR3. The cluster ages and stellar masses are derived through the use of the HR diagrams. To obtain the 3D kinematics of the clusters and trace their orbits back in time, we combine the Gaia-based proper-motions and distances with radial velocities from the literature. The estimated cluster properties show that both clusters are almost equidistant (1.7 kpc) and nearly coeval, with average ages of 6.1$\pm$0.5 and 6.5$\pm$0.4 Myr, respectively, and age dispersion of 3 Myr. The nonuniformity of extinction is evident within each cluster, especially pronounced across the face of Berkeley 86 where the most-massive stars show substantial substructure. By extrapolating the observed mass function to a minimum stellar mass, we obtain cluster masses of 519 M(Sun) and 1551 M(Sun) for Berkeley 86 and 87, respectively. Although both clusters share very similar properties, their orbital paths show no indication that they had a common birthplace, however Berkeley 87 and its neighbor NGC 6913 are very likely to have been born in pair.

The systematics of energies and the contribution of pygmy dipole resonance (PDR) to the energy-weighted sum rule of dipole gamma transitions in medium-heavy and heavy nuclei with an excess of neutrons are considered. The modified macroscopic model of Isacker-Nagarajan-Warner was used for calculating PDR energies with the number of surface neutrons proportional to the thickness of the neutron skin according to the Pethick- Ravenhall expression (PR INW approach). Such modification of the macroscopic approach by Isacker-Nagarajan-Warner enables to take into account microscopic evidence of direct relationship between skin thickness and low-energy dipole response. The results are compared with the microscopic calculations for the chains of Ni, Sn and Pb isotopes. It was demonstrated that the dependence of the magnitudes of the energies within the PR INW approach on neutron excess is in rather good agreement with experimental data and microscopic calculations if the absolute value of the strength of the neutron-proton interaction is nearly three times as large as that obtained by Isacker-Nagarajan-Warner by the volume integral of the nucleon-nucleon interaction. While the macroscopic INW PR model can describe the main features of the PDR, above mentioned discrepancy of the strength values doesn't not provide reason enough for the conclusion that PDR is pure collective state. The analytical expressions for the PDR fraction of the energy-weighted sum rule for electric dipole transitions (E1 EWSR) are used. They are based on the "molecular" energy-weighted E1 sum rule considering the number of surface neutrons as a function of the neutron thickness (PR MSR approach). Systematics for the PDR fraction of E1 EWSR are proposed with parameters obtained by fitting the experimental data and microscopic calculations.

Maximal Extractable Value, or MEV, remains a structural threat to blockchain fairness because a block producer can often observe pending transactions and unilaterally decide their ordering or inclusion. Existing mitigations hide transaction contents or outsource ordering, but they often leave two gaps unresolved. First, commitments are not authenticated by slashable identities. Second, inclusion obligations are not backed by transferable evidence that other validators can verify. This paper presents MEV ACE, a fair ordering protocol for proposer controlled ordering MEV. MEV ACE combines three mechanisms. First, it uses registered economic identities whose authentication keys are deterministically derived from the ACE GF framework and bonded on chain. Second, it uses authenticated commit and open messages with validator receipt thresholds, which make admissibility and inclusion obligations independently auditable. Third, it uses verifiable delay based randomness to determine transaction order only after the admissible commitment set is fixed. We formalize the protocol in a Byzantine fault tolerant validator model with threshold receipts and show three properties under standard assumptions: order unpredictability after the admissible set is locked, commitment authenticity under signature unforgeability, and accountable inclusion for transactions that obtain threshold commit and open receipts. Under these conditions, and when producer and user bonds exceed the one slot gain from invalid execution or selective non opening, MEV ACE removes unilateral proposer discretion over front running, sandwich attacks, and censorship against admitted transactions. The protocol remains single slot in structure, requires no threshold decryption committee, and is compatible with post quantum signature schemes such as ML DSA 44.

This paper develops a copula-based time-series framework for modelling sovereign credit rating activity and its dependence dynamics, with extensions incorporating climate risk. We introduce a mixed-difference transformation that maps discrete annual counts of sovereign rating actions into a continuous domain, enabling flexible copula modelling. Building on a MAG(1) copula process, we extend the framework to a MAGMAR(1,1) specification combining moving-aggregate and autoregressive dependence, and establish consistency and asymptotic normality of the associated maximum likelihood estimators. The empirical analysis uses a multi-agency panel of sovereign ratings and country-level carbon intensity, aggregated to an annual measure of global rating activity. Results reveal strong nonlinear dependence and pronounced clustering of high-activity years, with the Gumbel MAGMAR(1,1) specification delivering the strongest empirical performance among the models considered, while standard Markov copulas and Poisson count models perform substantially worse. Climate covariates improve marginal models but do not materially enhance dependence dynamics, suggesting limited incremental explanatory power of the chosen aggregate climate proxy. The results highlight the value of parsimonious copula-based models for sovereign migration risk and stress testing.

In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables (IVs) to investigate causal relationships between exposures and outcomes based on observational data. However, numerous genetic studies have shown the pervasive pleiotropy of genetic variants, meaning that many, if not most, variants are associated with multiple traits, potentially violating the core assumptions of IV estimation. Uncorrelated pleiotropy occurs when genetic variants have a direct effect on the outcome that is not mediated by the exposure, while correlated pleiotropy occurs when genetic variants affect the exposure and outcome via shared heritable confounders. In this work, we propose a novel MR method, called MR-Quantile, based on weighted quantile regression (WQR) that is robust to both correlated and uncorrelated pleiotropy. We propose a procedure for selecting the optimal quantile of the ratio estimates through a likelihood-based formulation of WQR using the asymmetric Laplace distribution. Monte Carlo simulations demonstrate the empirical performance of the proposed method, especially in settings with many invalid IVs with weak pleiotropic effects. Finally, we apply our method to study the causal effect of resting heart rate on atrial fibrillation. Genetic variants associated with heart rate were identified in a genome-wide association study of 425,748 individuals from the VA Million Veteran Program, and used as instruments in a two-sample MR analysis with summary statistics from a genetic meta-analysis of 228,926 AF cases across eight studies.

The use of Large Language Models (LLMs) like ChatGPT and DeepSeek for translation and language polishing is a welcome development, reducing the longstanding publishing barrier to non-English speakers. Assessing the uptake of this facility is useful to give insights into changing nature of scientific writing. Although the prevalence of LLM-associated terms has been tracked across science in abstracts and for full text biomedical research, their science-wide prevalence in full texts is unknown. In response, this article investigates an expanded set of 80 potentially LLM-associated terms during 2021-2025 in a science-wide full text collection from the publisher MDPI (1.25 million articles), partly focusing on the 73 journals that published at least 500 articles in 2021. The results demonstrate the increasing prevalence of LLM-associated terms science-wide in full texts to 2024, with some terms declining from 2024 to 2025 and others continuing to increase. LLMs seem to avoid some terms (e.g., thus, moreover) and a few terms have stronger associations with abstracts than full texts (e.g., enhanced) or the opposite (e.g., leveraged). The term family "underscore" had the biggest increase: up to 29-fold. There are substantial differences between journals in the apparent use of LLMs for writing, from lower uptake in the life sciences to higher uptake in social sciences, electronic engineering and environmental science. Fields in which there is currently low uptake may need improved or specialist support, such as for reliably translating complex formulae, before the full benefits of automatic translation can be realised.

Hadron production in relativistic nuclear collisions is well described in the framework of the statistical hadronization model, over a broad range of collision energies. We outline this for hadrons composed of light (u, d, s) and heavy (charm and beauty) quarks, discuss recent findings relevant for understanding the phase structure of QCD and formulate some open issues.

Changing-look transitions challenge our understanding of active galactic nuclei (AGN), exhibiting dramatic changes in broad-line emission and continuum flux on timescales of months to years. We present a detailed study of the spectroscopically confirmed changing-look AGN ZTF18abuamgo. Combining photometric survey data with spectroscopy spanning three epochs over 20 years, we identify a turn-on transition from a Type 1.5 to Type 1.2 AGN and estimate the timescale of this change to be as short as four years. Spectral analysis indicates that this transformation is driven by a rapid increase in accretion rate, with the Eddington ratio rising from $0.032 \pm 0.005$ in the dim state to $0.08 \pm 0.01$ in the bright state. For the first time in a changing-look AGN, we apply the Boltzmann plot method to the visible Balmer series emission, deriving broad line region electron temperatures of $11,800 \pm 900$ K and $11,900 \pm 2,400$ K in 2022 and 2024, respectively. Applying single-epoch black hole mass estimation to the brightening H$α$ emission, we find a mass of $(5.0 \pm 0.4) \times 10^7 M_\odot$. The consistency in this estimate across all spectroscopic epochs suggest that even highly variable broad lines in CL-AGN do not bias the results derived using this method. Our results demonstrate that objects like ZTF18abuamgo provide a unique laboratory to study extreme AGN variability, probe the physical conditions in the broad line region, and assess the limitations of widely used black hole mass estimation methods.

We consider the spin-c Dirac operator on the unit circle bundle of a positive line bundle over a Fano manifold of even complex dimension. We compute the corresponding eta invariant in terms of Zhang's value of its adiabatic limit. This extends the earlier computation of the author from small to arbitrary values of the adiabatic parameter.

We use the fuzzy-sphere approach to study the Bose-Kondo impurity problem, namely a spin-$S$ impurity coupled to the $(2+1)$-dimensional $O(3)$ Wilson-Fisher CFT (Heisenberg universality class). We demonstrate that for $S=1/2,1,3/2$ the impurity flows to a distinct stable interacting conformal defect for each $S$. Using large-scale exact diagonalization and density-matrix renormalization group methods, we observe integer-spaced defect spectrum consistent with defect conformal symmetry and compute several low-lying defect primary operators as well as the RG monotonic $g$-function. Our findings show that despite sharing the same symmetry and anomaly, Bose-Kondo impurities flow to distinct stable infrared conformal fixed points, which we refer to as \emph{fortuitous universality}. We expect this fortuitous universality to persist for all $S$, extending to $S\rightarrow\infty$, with each spin-$S$ impurity flowing to its own stable infrared conformal fixed point.

Trust management in VANETs is critically important for secure communication between vehicles. In event-based trust systems, vehicles broadcast the events they witness to their surroundings and send feedback reports about other vehicles to a central authority. However, when the event status changes, vehicles that have left the witness area cannot see this change and produce erroneous feedback. This leads to unfair penalization of honest nodes. To solve this problem, the SAFE (Spatially-Aware Feedback Enhancement) approach is proposed. In SAFE, vehicles continue to record messages as long as they remain in the witness area and send updated feedback reports before leaving the area. Additionally, by keeping records between witness and decision distances, more accurate evaluation is ensured. SAFE and TCEMD were compared in single-event, multi-event, and different decision distance scenarios. The results clearly demonstrate SAFE's superiority. In single-event, feedback report count increased 2.5 times, and in multi-event, it increased over 6 times. Negative feedback rate dropped from 77 percent to below 1 percent. While TCEMD incorrectly blacklisted 34 nodes, this number remained at 1 in SAFE. Even when the decision distance was reduced to 200 m, SAFE showed high accuracy. The findings show that SAFE protects honest nodes in attack-free systems and increases network reliability.

In a mean field game of controls, players seek to minimize a cost that depends on the joint distribution of players' states and controls. We consider an ergodic problem for second-order mean field games of controls with state constraints, in which equilibria are characterized by solutions to a second-order MFGC system where the value function blows up at the boundary, the density of players vanishes at a commensurate rate, and the joint distribution of states and controls satisfies the appropriate fixed-point relation. We prove that such systems are well-posed in the case of monotone coupling and Hamiltonians with at most quadratic growth.

Artificial Intelligence (AI) chatbots are increasingly used for emotional, creative, and social support, leading to sustained and routine user interaction with these systems. As these applications evolve through frequent version updates, changes in functionality or behavior may influence how users evaluate them. However, work on how publicly expressed user feedback varies across app versions in real-world deployment contexts is limited. This study analyzes 210,840 Google Play reviews of the chatbot application Character AI, linking each review to the app version active at the time of posting. We specifically examine negative reviews to study how version-level rating trends, and linguistic patterns reflect user experiences. Our results show that user ratings fluctuate across successive versions, with certain releases associated with stronger negative evaluations. Thematic analysis indicates that dissatisfaction is concentrated around recurring issues related to technical malfunctions and errors. A subset of reviews additionally frames these concerns in terms of potential psychological or addiction-related effects. The findings highlight how aggregate user evaluations and expressed concerns vary across software iterations and provide empirical insight into how update cycles relate to user feedback patterns and underscore the importance of stability and transparent communication in evolving AI systems.

Estimation of covariance matrices is a fundamental problem in multivariate statistics. Recently, growing efforts have focused on incorporating covariate effects into these matrices, facilitating subject-specific estimation. Despite these advances, guaranteeing the positive definiteness of the resulting estimators remains a challenging problem. In this paper, we present a new varying-coefficient sequential regression framework that extends the modified Cholesky decomposition to model the positive definite covariance matrix as a function of subject-level covariates. To handle high-dimensional responses and covariates, we impose a joint sparsity structure that simultaneously promotes sparsity in both the covariate effects and the entries in the Cholesky factors that are modulated by these covariates. We approach parameter estimation with a blockwise coordinate descent algorithm, and investigate the $\ell_2$ convergence rate of the estimated parameters. The efficacy of the proposed method is demonstrated through numerical experiments and an application to a gene co-expression network study with brain cancer patients.

Advanced doping strategies enable oxide ceramic functionalities by tailoring bulk defect chemistry and space-charge-layer (SCL) behavior at interfaces. Charge transition levels (CTLs), defined as the Fermi level at which a defect changes its stable charge state, play a central role. Their alignment governs bulk defect chemistry, while their bending within SCLs induces additional charge-state transitions. Incorporating CTLs is therefore essential for a consistent description of defect equilibria and SCL formation. In this work, we propose a defect-chemistry-consistent phase-field model explicitly coupled with CTLs to investigate their role in SCL evolution. The model includes multivalent oxygen vacancies, multivalent acceptor dopants, electrons, and holes. It is applied to Fe-doped SrTiO3 over wide ranges of oxygen partial pressure and temperature, capturing both symmetric SCLs at stationary grain boundaries and asymmetric SCLs during migration. Two distinct grain boundary types, slow and fast boundaries, emerge during migration, consistent with experimental observations. Simulations reveal that CTL-governed bulk defect chemistry, together with CTL-induced charge-state transitions within SCLs, critically determine SCL characteristics. Moreover, CTL-mediated hole transport is significantly faster than acceptor dopant diffusion, modulating solute drag and grain boundary kinetics. Finally, the model predicts grain boundary properties dependent on both thermal history and boundary type, with slow and fast boundaries exhibiting distinct behaviors. This framework links defect chemistry, Fermi level, CTLs, and grain boundary kinetics, providing new insights for designing oxide ceramics with tailored properties.

High-harmonic generation in solids has emerged as a powerful probe of ultrafast electron dynamics and lattice motion, and recent theoretical work has suggested that thermally driven lattice fluctuations can act as an effective source of decoherence in the harmonic-generation process. However, a direct experimental link between high-harmonic emission and temperature-driven incoherent phonons has remained unclear. Here, we investigate the temperature dependence of high-harmonic generation in ultrapure silicon using reflection-geometry measurements over a wide temperature range. We observe that the harmonic yield increases significantly with decreasing temperature. To interpret these results, we introduce a one-dimensional atomic-chain model in which finite temperature is represented by random lattice displacements that mimic incoherent phonon fluctuations. The simulations reproduce the magnitude of temperature-dependent change of the harmonic signal and support a picture in which thermally induced lattice disorder enhances electron-hole decoherence, thereby reducing high-harmonic emission. Our results establish incoherent phonons as an important source of decoherence in solid-state high-harmonic generation.

Brown and Colbourn (1992) showed that the complex roots of the reliability polynomial of connected multigraphs are dense in the unit disk and that the closure of the real roots is $[-1,0] \cup \{1\}$. We prove the simple graph analogues of both results, confirming a recent conjecture of Brown and McMullin. The proof uses the family of graphs $C_m[K_n]$ obtained by substituting each edge of a cycle $C_m$ with a complete graph $K_n$, and relies on the asymptotic behavior of the reliability and split reliability polynomials of $K_n$.

Magnetic cataclysmic variables provide a natural laboratory for studying how accretion interacts with compact-object magnetospheres and generates stochastic variability. We present an optical variability study of the intermediate-polar candidate EP240309a, an Einstein Probe X-ray transient, using BOOTES photometry, high-cadence TESS light curves, and a SOAR/Goodman optical spectrum. Previous studies found a white-dwarf spin period of 3.97 min (Pspin ~ 238 s) and an orbital period of Porb = 3.7614(4) h. Power spectral densities from the BOOTES data are consistent with single power laws with slopes alpha ~ 1.2-1.8, with no statistically significant evidence for a bend across the sampled frequency range. Using red-noise simulations and injection-recovery tests, we place one-sided constraints on any putative break frequency, which translate, under standard dynamical identifications, into an upper limit on the magnetospheric radius of Rm <= few x 10^10 cm for MWD = 0.8 Msun. In the TESS data, we detect a linear rms-flux relation on hour timescales in three high-cadence sectors, while two other sectors do not show a robust detection, indicating epoch-dependent rms-flux behavior. The SOAR spectrum shows Balmer and He II emission lines with FWHM about 1000-1600 km s^-1; under a Keplerian interpretation, these imply characteristic radii of r about (0.9-3.4) x 10^10 cm, broadly comparable to the timing-based constraints. Overall, the data provide conservative, order-of-magnitude radius constraints consistent with accretion onto a magnetic white dwarf, but they do not establish the detailed accretion geometry or exclude stream-fed or mixed accretion scenarios.

We prove that minimizers of variational integrals $$ \mathcal E(v)=\int_Ωf(v)\quad\text{for }v\in\mathcal M(Ω)\text{ such that } \mathscr{A} v=0, $$ are partially continuous provided that the integrands $f$ are strongly $\mathscr{A}$-quasiconvex in a suitable sense. We consider linear growth problems, linear PDE operators $\mathscr{A}$ of constant rank, and variations of the form $v+φ$ with $\mathscr{A}$-free $φ\in \mathrm{C}_{\mathrm{c}}^\infty(Ω)$. Our analysis also covers the ``potentials case'' $$ \mathcal F(u)=\int_Ωf( \mathscr{B} u)\quad\text{for } u\in\mathscr D'(Ω)\text{ such that }\mathscr B u\in \mathcal M(Ω), $$ where $\mathscr{B}$ is a different linear pde operator of constant rank. Both our main results extend to $x$-dependent integrands.

Large language models (LLMs) increasingly rely on external tools to perform time-sensitive tasks and real-world actions. While tool integration expands LLM capabilities, it also introduces a new prompt-injection attack surface: tool poisoning attacks (TPAs). Attackers manipulate tool descriptions by embedding malicious instructions (explicit TPAs) or misleading claims (implicit TPAs) to influence model behavior and tool selection. Existing defenses mainly detect anomalous instructions and remain ineffective against implicit TPAs. In this paper, we present TRUSTDESC, the first framework for preventing tool poisoning by automatically generating trusted tool descriptions from implementations. TRUSTDESC derives implementation-faithful descriptions through a three-stage pipeline. SliceMin performs reachability-aware static analysis and LLM-guided debloating to extract minimal tool-relevant code slices. DescGen synthesizes descriptions from these slices while mitigating misleading or adversarial code artifacts. DynVer refines descriptions through dynamic verification by executing synthesized tasks and validating behavioral claims. We evaluate TRUSTDESC on 52 real-world tools across multiple tool ecosystems. Results show that TRUSTDESC produces accurate tool descriptions that improve task completion rates while mitigating implicit TPAs at their root, with minimal time and monetary overhead.

This paper provides approximation orders for a class of nonlinear interpolation procedures for univariate data sampled over $σ$ quasi-uniform grids. The considered interpolation is built using both essentially nonoscillatory (ENO) and subcell resolution (SR) reconstruction techniques. The main target of these nonlinear techniques is to reduce the approximation error for functions with isolated corner singularities and in turn this fact makes them useful for applications to other fields, such as shock capturing computations or image processing. We start proving the approximation capabilities of an algorithm to detect the presence of isolated singularities, and then we address the approximation order attained by the mentioned interpolation procedure. For certain nonuniform grids with a maximum spacing between nodes $h$ below a critical value $h_c$, the optimal approximation order is recovered, as it happens for uniformly smooth functions \cite{ACDD}.

Vehicular Ad Hoc Networks (VANETs) are vulnerable to intelligent attackers who exploit the homogeneous treatment of traffic events in existing trust models. These attackers accumulate reputation by reporting correctly on low-priority events and then inject false data during safety-critical situations - a strategy that current approaches cannot detect because they ignore event severity and location criticality in trust calculations. This paper addresses this gap through three contributions. First, it introduces event-aware and location-aware intelligent attack models, which have not been formally defined or simulated in prior work. Second, it proposes an asymmetric local trust mechanism where penalties scale with event and location severity while rewards follow an asymptotic model, making trust difficult to regain after misuse. Third, it adapts Dempster-Shafer Theory for global trust fusion using Yager's combination rule - assigning conflicting evidence to uncertainty rather than forcing premature decisions - combined with sequential source-reliability ordering and an asymmetric risk accentuation mechanism. Simulations using OMNeT++, Veins, and SUMO compare the proposed system (IPEK) against MDT and TCEMD under attacker densities of 15-35 percent. IPEK maintained 0 percent False Positive Rate across all scenarios, meaning no honest vehicle was wrongly revoked, while sustaining Recall above 75 percent and F1-scores exceeding 0.86. These results demonstrate that integrating context-awareness into both attack modeling and trust evaluation significantly outperforms symmetric approaches against strategic adversaries.

Young autistic adults may garner benefits through social media but also disproportionately experience privacy harms. Prior research found that these harms often stem from perceiving the affordances of social media differently than the general population, leading to unintentional risky behaviors and interactions with others. While educational interventions have been shown to increase social media privacy literacy for the general population, research has yet to focus on effective educational interventions for autistic young adults. We address this gap by developing and deploying Privacy Rules for Inclusive Social Media (PRISM), a classroom-based educational intervention tailored to the unique risks and neurodevelopmental differences of this population. Twenty-nine autistic students with substantial (level 2) support needs participated in a 14-week social media privacy literacy class. During these classes, participants often communicated their existing rule-based "all or nothing" approaches to privacy management (such as completely disengaging from social media to avoid privacy issues). Our course focused on empowering them by providing more nuanced guidance on safe privacy practices through the use of scenario-based formats and contextual, rule-based scenarios. Using pre- and post-knowledge assessments for each of our 6 course topics, our intervention led to a statistically significant increase in their making safer social media privacy decisions. We conclude with recommendations for how privacy educators and technology designers can leverage neuro-affirming educational interventions to increase privacy literacy for autistic social media users.

This article is dedicated to the study of the normal functor in the category of smooth real vector bundles. Particularly, we focus on a symmetry phenomena which occurs after iterating two times the normal functor on a commutative square of smooth immersions. To do so, a theory of pullback and quotient is developed for double vector bundles but also for some classes of morphisms. These two operations turn out to be the key ingredients in order to study the naturality of the normal functor. The expected symmetry is then obtained thanks to the universal behavior and the mutual compatibility of these operations.

We prove quantitative homogenization results for high contrast parabolic equations with random coefficients depending on both space and time. In particular, we prove that under a sufficient decorrelation assumption the homogenization length scale is bounded by $\exp(C\log^2(1+Λ/λ)) + C\sqrtλ$. The proof is based on a parabolic coarse-graining framework which generalizes the results of Armstrong and Kuusi in the elliptic setting.

Short-period white dwarf+main-sequence binaries are Post-Common-Envelope Binaries (PCEB) that have survived a common envelope phase. Such systems, if detached and eclipsing, enable precise measurements of the constituent stars, providing a unique opportunity to probe the effects of the common envelope phase on the system. We report the discovery of one such nearby (57 pc) system, TIC-460388167, using a combination of multi-band photometric light curves and spectroscopic radial velocities. In addition to eclipses, the system exhibits a continuously variable light curve that we model as a combination of ellipsoidal variations and star spots. We determine a period $P$=0.63596258$\pm$0.00000012 d and inclination $i$=89.0$\pm$0.4 deg. The best-fitting model specifies a white dwarf with T$_1$=7607$\pm$127 K and radius R$_1$=0.0131$\pm$0.0003 $R_\odot$, which is eclipsed by a T$_2$=3151 $\pm$ 59 K, R$_2$=0.327$\pm$0.006 $R_\odot$ M dwarf. The white dwarf mass is 0.61$\pm$0.04 M$_\odot$. We present the first velocity resolved profile for a PCEB secondary and show that the rotation of the M-dwarf is synchronous with the orbital period, as expected. We compare the constituent stars to other PCEB systems and find TIC-460388167A is one of the coolest known white dwarfs in such systems. TIC-460388167 is among the longest period eclipsing PCEB systems known.

Rerandomization is an experimental design technique that repeatedly randomizes treatment assignments until covariates are balanced between treatment groups. Rerandomization in the design stage of an experiment can lead to many asymptotic benefits in the analysis stage, such as increased precision, increased statistical power for hypothesis testing, reduced sensitivity to model specification, and mitigation of p-hacking. However, the standard implementation of rerandomization via rejection sampling faces a severe computational bottleneck in high-dimensional settings, where the probability of finding an acceptable randomization vanishes. Although alternatives based on Metropolis-Hastings and constrained optimization techniques have been proposed, these alternatives rely on discrete procedures that lack information from the gradient of the covariate balance metric, limiting their efficiency in high-dimensional spaces. We propose Langevin-Gradient Rerandomization (LGR), a new sampling method that mitigates this dimensionality challenge by navigating a continuous relaxation of the treatment assignment space using Stochastic Gradient Langevin Dynamics. We discuss the trade-offs of this approach, specifically that LGR samples from a non-uniform distribution over the set of balanced randomizations. We demonstrate how to retain valid inference under this design using randomization tests and empirically show that LGR generates acceptable randomizations orders of magnitude faster than current rerandomization methods in high dimensions.