What proportion of treated units actually benefited from an experimental intervention? What is the median or the largest individual treatment effect? This paper develops methods for answering such questions about the distribution of individual causal effects in randomized experiments. Existing approaches require the analyst to select a rank-based test statistic before observing the data. A poor choice can substantially reduce power, while searching over multiple test statistics and adjusting for multiplicity using Bonferroni correction also incurs power loss. We propose inference procedures that adaptively combine multiple rank-based statistics while maintaining finite-sample validity. For stratified experiments, we further develop weighting schemes that effectively aggregate evidence across strata of heterogeneous sizes. The resulting combined test achieves power comparable to, or exceeding, that of the best individual test, without requiring prior knowledge of the optimal statistic. When applied to a randomized experiment evaluating a teacher training program, the combined test suggests that roughly half of treated teachers benefited, whereas a single rank-based test may indicate only a small minority. Thus, the choice of test determined whether the program appears broadly successful or narrowly effective.

Approximate stationarity conditions provide necessary optimality conditions without requiring additional assumptions by demanding that a perturbed stationarity system possesses solutions as the involved perturbations tend to zero. Together with associated approximate constraint qualifications, which are typically rather mild, they raised much interest in the optimization community during the last decade. In parallel, directional stationarity conditions became quite popular as they sharpen standard stationarity conditions by incorporating data associated with underlying critical directions. The purpose of this paper is twofold. First, we melt the aforementioned concepts of approximate and directional stationarity to formulate and study so-called approximate directional stationarity. For the underlying model problem, an optimization problem with nonsmooth geometric constraints is chosen, which covers diverse practically relevant applications. The role of approximate directional stationarity as a necessary optimality condition is investigated in much detail, complementing results from the literature. Second, we formulate a qualification condition which, based on an approximately directionally stationary point, can be exploited to infer its directional stationarity. The latter condition depends on one particular sequence verifying approximate directional stationarity and merely requires to check a simple condition of Mangasarian--Fromovitz type stated in terms of the directional tools of limiting variational analysis. This contrasts standard approximate constraint qualifications that typically demand a certain stable behavior of all sequences validating approximate stationarity. Throughout, various approaches to verify directional stationarity of local minimizers are established, and illustrative examples are presented to make the theoretical results more accessible.

We derive the exact asymptotic rates of the small eigenvalues of the Laplacian on one-parameter degenerations of compact Kähler manifolds equipped with induced background metrics. This generalizes a recent result of Dai and Yoshikawa to higher dimensions. To achieve this, we combine Li's uniform Skoda inequality with the method of auxiliary Monge-Ampère equations, introduced by Guo--Phong--Song--Sturm--Tong and adapted by Guedj--Tô. As an application, we establish estimates for degenerations of compact Kähler manifolds with reducible singular fibers.

Driven nonlinear quantum oscillators are a central platform for quantum technologies, yet their dissipative dynamics are typically described using Lindblad or Caldeira-Leggett master equations derived under assumptions that exclude nonlinearities and driving. Here, we derive a generalized Caldeira-Leggett master equation for driven nonlinear oscillators by retaining the full nonlinear and time-dependent system dynamics in the construction of the dissipator. For position- and momentum-dependent system-bath coupling, the dissipator itself becomes dynamically dressed, generating nonlinear and drive-dependent dissipative channels beyond conventional fixed-dissipator approaches. This produces nonlinear damping together with dissipation-induced corrections to the effective drive. The resulting dissipative dynamics suppress large-amplitude excitations and reduce phase-space fluctuations. For a driven Kerr oscillator, this leads to the suppression of bistability, asymmetric resonance responses, and strongly modified fluctuation distributions. More broadly, our results establish a microscopic framework in which nonlinear dynamics and driving directly reshape the dissipative sector of driven open quantum systems.

Missing data are pervasive in modern functional datasets, where trajectories are often sparsely or irregularly observed. Although Functional Principal Component Analysis (FPCA) is widely used to reconstruct incomplete curves, existing FPCA-based approaches typically employ single imputation, leading to overly optimistic inferences in downstream analyses. To address these challenges, we develop a novel Bayesian multiple imputation framework for functional data (BAMIFun). For single-level functional data, we impose a Bayesian low-rank model that incorporates penalized spline representations to enforce smoothness of eigenfunctions and derive an efficient Gibbs sampler algorithm for posterior computation. In addition, we demonstrate and validate how to properly account for the estimation uncertainties in downstream analysis. Furthermore, we extend the framework to multiway functional data using a low-rank Functional Tensor Singular Value Decomposition (FTSVD) model, enabling Bayesian multiple imputation in settings not supported by existing methods. Simulation studies show that, compared to existing methods, BAMIFun achieves accurate imputation while providing substantially improved coverage and more reliable downstream inference. Case studies using a physical activity dataset and an infant gut microbiome dataset further demonstrate the practical advantages of our proposed methods under severe missingness. Code for our algorithms is available at https://github.com/ZirenJiang/BAMIFun.

The problem of recognizing (k, l)-tight graphs is a fundamental problem that has close connections to well studied problems like graph rigidity. The problem is better understood for planar graphs as compared to general graphs. For example, deterministic NC-algorithms for the problem are known for planar graphs, but no such algorithm is known for general graphs. A common approach to reduce a graph problem to the planar case is to use planarizing gadgets. Our main contribution is to show that, unconditionally, planarizing gadgets for the problem of recognizing (k, l)-tight graphs do not exist.

Safe operation of connected vehicle platoons under stochastic disturbances and time-delayed dynamics requires accurate quantification of rare but dangerous events, such as inter-vehicle collisions. We propose a rigorous framework for quantifying the risk of inter-vehicle collisions in connected vehicle platoons subject to time-delayed stochastic dynamics. We adopt the \emph{entropic value-at-risk} (EVaR) as a conservative metric to capture \emph{risk due to extreme events}, highlighting its advantages over conventional Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). By expressing the inter-vehicle distance covariance in terms of the Laplacian eigenvalues of the communication network, we derive \emph{network-and time-delay-induced bounds} on both the minimum inherent risk and the worst-case risk. Specifically, the algebraic connectivity dictates the maximum EVaR, while the largest Laplacian eigenvalue determines the minimum risk inherently induced by the network structure. Numerical simulations illustrate how network topology and time delay shape collision risk, offering actionable insights for the safe design of vehicle platoons operating under stochastic disturbances.

Oscillatory activity in auditory cortex is thought to play a central role in auditory and speech processing by synchronizing neural rhythms to external acoustic features of the speech stream. To support this function, cortical oscillators must flexibly phase-lock to inputs spanning a wide range of timescales, including rhythms substantially slower than their intrinsic frequency. Here we identify a general dynamical mechanism by which intrinsic inhibitory currents operating on multiple timescales enable such flexible phase-locking. Using tools from dynamical systems theory, we show that interactions between slow and superslow inhibitory processes generate prolonged post-input recovery delays through delayed Hopf phenomena, thereby substantially expanding the frequency range over which entrainment can occur. We demonstrate this mechanisms in a biophysically grounded cortical theta oscillator model for speech segmentation. Specifically, we show that both a theta-timescale (4-8 Hz) inhibitory current $I_m$ and a slower delta-timescale (1-4 Hz) inhibitory potassium current $I_{\rm K_{SS}}$ are crucial for entrainment flexibility. Their interaction creates a three-timescale structure that gives rise to pronounced delay phenomena associated with a delayed Hopf bifurcation (DHB). Interestingly, the superslow $I_{\rm K_{SS}}$ and the associated DHB play little role in the unforced oscillatory dynamics, but are recruited to support phase locking under external forcing. Moreover, the intermediate-timescale current $I_m$, rather than being redundant, further expands the phase-locking range by prolonging delayed recovery along the superslow manifold. Together, these results suggest that coordination among intrinsic inhibitory currents operating on multiple timescales may represent a key mechanism supporting flexible phase locking to rhythmic inputs in the brain.

To be useful, quantum computers will be required to successfully correct errors occurring at the hardware level. Bosonic codes provide a hardware-efficient option for error correction, but fault-tolerance further requires that the available gate interactions be compatible with the code. A promising bosonic code is the Gottesman-Kitaev-Preskill (GKP) code, for which a linear beamsplitter-like coupling between two bosonic modes is fault-tolerant, making this a key primitive for building larger systems. Here, using two motional modes of a trapped ion, we demonstrate the generation of entangled states of GKP qubits by interfering two qunaught states, which have a grid structure but carry no logical information, on a beamsplitter. We generate all four Bell states with an average fidelity of 69%, and subsequently demonstrate an extension of the entangled state lifetime through the use of quantum error correction. These results complete the set of Gaussian operations required for quantum computing with GKP codes and enable explorations of multi-mode bosonic encodings as well as fundamental tests of information channels.

Multivariate linear regression is a fundamental statistical task, but classical estimators such as ordinary least squares are highly sensitive to outliers. These may occur as casewise outliers that affect entire observations, or as outlying cells, that are individual contaminated entries in the predictor and/or response matrix. Moreover, modern datasets frequently contain missing values and are high-dimensional. To address these challenges we propose the cellwise multivariate regression (cellMR) estimator, a robust regression method that simultaneously accommodates casewise and cellwise outliers, missing data, and high dimensionality. The approach builds on a cellwise robust covariance estimator and uses ridge regularization for numerical stability. We further introduce cellBoot, a novel bootstrap-based inference procedure tailored to the cellMR framework. Relying on indirect inference, cellBoot provides asymptotically valid confidence intervals that are robust to casewise and cellwise contamination. We derive influence functions of the regression estimator and prove the asymptotic validity of the cellBoot confidence intervals. Simulations and a real genomics application illustrate the strong finite-sample performance of the proposed methods.

Geometric medians on product manifolds are sensitive to the relative scaling of factor metrics because the median objective couples the factors rather than separating them. We study this scale-selection problem and first prove that naive joint minimization over location and scale is degenerate: the scale is driven to the boundary and the problem collapses to a marginal median, effectively discarding one factor. Thus relative scale is not identifiable from the raw median loss alone. We develop three alternatives to mitigate this issue. The first treats scale as indexing a sensitivity path and establishes uniform consistency, a functional central limit theorem, and a derivative-based sensitivity measure. The second constructs a robust scale-calibrated median using marginal radial median scales, yielding unit invariance, consistency, a two-step central limit theorem, and bounded influence. The third introduces a bounded balance equation for direct scale estimation, with monotonicity, uniqueness, joint asymptotic normality, and bounded influence. Simulations illustrate boundary collapse, sensitivity, unit invariance, and balanced estimation in Euclidean and Bures-Wasserstein settings.

I present the Spitzer Spectroscopic Data Fusion, a collection of merged spectroscopic redshift catalogs covering fourteen of the most widely studied extragalactic survey fields. Building on the Spitzer Data Fusion multi-wavelength photometric database, the collection merges several publicly available spectroscopic redshift catalogs within each field using a 1 arcsec matching radius, delivers a single best redshift per source together with provenance and overlap flags, and is available on Zenodo at https://zenodo.org/doi/10.5281/zenodo.6368347 The dataset is regularly updated as new spectroscopic surveys are published. It is intended as a community calibration resource for photometric redshift training, SED fitting, and multi-wavelength cross-identification studies.

In the heavy-quark and large-energy limits, symmetry relations reduce the number of independent form factors governing heavy-to-light $B$-meson decays. Exploiting these relations, the form factors can be parametrized while systematically incorporating symmetry-breaking corrections from perturbative QCD. Using vertex renormalization together with light-cone distribution amplitudes, we compute the vertex and hard-spectator contributions for the $B \to K_0^*(1430)$ transition. We then analyze the impact of these form factors on physical observables, including the branching ratio and lepton polarization asymmetries $(P_L, P_N)$, in $B \to K_0^*(1430)μ^+μ^-$. Our results indicate that perturbative corrections induce modest shifts of $\sim 3\%$ in both the branching ratio and the normal lepton polarization asymmetry. Consequently, any significant deviation observed experimentally from these predictions would provide a clear signal of potential New Physics effects.

Nash equilibrium serves as a fundamental mathematical tool in economics and game theory. However, it classically assumes knowledge of player utilities, whereas economics generally regards preferences as more fundamental. To leverage equilibrium analysis in strategic scenarios, one must first elicit numerical utilities consistent with player preferences, a delicate and time-consuming process. In this work, we forgo precise utilities and generalize the Nash equilibrium to a setting where we only assume a player is capable of providing an ordinal ranking of their actions within the context of other players' joint actions. The key technical challenge is to rethink the definition of a best-response. While the classical definition identifies actions maximizing expected payoff, we naturally look towards social choice theory for how to aggregate preferences to identify the most preferred actions. We define this generalized notion of a context-ordinal Nash equilibrium, establish its existence under mild conditions on aggregation methods, introduce notions of regularization, approximation, and regret, explore complexity for simple settings, and develop learning rules for computing such equilibria. In doing so, we provide a generalization of Nash equilibrium and demonstrate its direct applicability to elicited preferences in human experiments.

The [$^{12}$C II] emission at 158 $μ$m is a key cooling line of the interstellar medium and traces gas kinematics in spectrally resolved observations. Its spectral profile is often modified by optical depth effects. The intrinsic line shape can be reconstructed by comparison with emission from the less abundant $^{13}$C isotope. Due to the additional neutron spin, [$^{13}$C II] emission splits into three hyperfine structure (hfs) transitions. Laboratory measurements have provided the centroid frequency and the strongest component ($F=2-1$); the two weaker components ($F=1-0$ and $F=1-1$) have been inferred only from quantum-mechanical calculations. The magnetic-dipole hfs constants, from which the transition frequencies follow, have not been measured experimentally. The high spectral resolution of observations with the upgraded German Receiver for Astronomy at Terahertz Frequencies (upGREAT) on board SOFIA enabled simultaneous detection of all three hfs transitions. From these astronomical data we determine, for the first time, the magnetic-dipole hfs constants $A_{1/2}^{\rm hf} = 810.71(11)$ MHz and $A_{3/2}^{\rm hf} = 162.18(5)$ MHz of the [$^{13}$C II] $2s^2\,2p\,{}^2P^\circ$ ground term. Combined with the laboratory centroid frequency, this yields the rest frequencies of all three hfs lines. Using [$^{12}$C II] as a reference, we also improve the precision of the [$^{13}$C II] centroid frequency. This work shows that spectrally resolved astronomical observations can constrain fundamental atomic properties, with hfs precision rivaling laboratory measurements. The approach extends to other atomic and molecular transitions where laboratory data are difficult to obtain.

We propose semantic smoothing, a smoothing method for language models that uses embeddings to share statistical observations across semantically similar contexts. The starting point is a decomposition of log-perplexity that motivates smoothing as a collection of distribution-estimation problems under Kullback-Leibler (KL) loss. We then show that, under a Lipschitz-logit model for embedding-based language generation, proximity of context embeddings implies proximity of the corresponding next-word distributions in KL divergence. Combining these observations, we formulate semantic smoothing as distribution estimation in KL loss with KL-proximity side information. For $n$ samples on a $d$-symbol alphabet with a side-information distribution at KL distance $Δ$, we give an interpolation estimator with worst-case KL risk $O(\min\{Δ,d/n\})$, and prove a matching-order lower bound for uniform side information. We extend the estimator to multiple and empirically estimated synonymous distributions. Experiments on synthetic Markov data and WikiText-103 bigram models using Word2Vec, GloVe, and GPT-2 embeddings show that semantic smoothing consistently reduces test perplexity when applied to add-constant and Kneser-Ney estimates.

We consider quantum gravity corrections to the maximum entropic force that arise from several gravitational uncertainty principles. These include the Generalized Uncertainty Principle (GUP), the Extended Uncertainty Principle (EUP), the Generalized Extended Uncertainty Principle (GEUP), and the Linear-Quadratic GUP (LQGUP). We find that the modified entropic force depends on the dimensionless parameters of the uncertainty principles and, thus, on the underlying quantum gravity theory. Furthermore, the entropic force, which is quantum gravity corrected in the framework of the extended uncertainty principles, also depends on the number of Planck areas that made the ``EUP area".

We review the basic theory of bands and band schemes introduced by Baker-Jin-Lorscheid, which is an algebraic framework for tropicalization, analytification, and $\mathbb{F}_1$-geometry. For an affine scheme $X$ over a non-Archimedean valued field $k$, one can associate to every affine embedding $ι$ of $X$ a naturally defined affine band scheme $Y_ι$ whose rational points over the tropical band $\mathbb{T}$ recover the tropicalization $Trop(X,ι)$. We prove that $X$ is the limit of the $Y_ι$ in the category of band schemes, thereby obtaining a scheme-theoretic enhancement of Payne's limit theorem. By taking $\mathbb{T}$-rational points, this recovers Payne's theorem for affine tropicalizations from the perspective of band scheme theory and the same method provides an analogous result in the real tropical setting.

This paper provides an in-depth analysis on how different aspects of the dynamic operating envelope (DOE) formulation impact the computation and allocation of network capacity. We show that the envelopes are significantly affected by the power flow model (non-linear or linear), binding network constraint (thermal or voltage) and by the calculation case (import or export envelope). We also propose a novel DOE algorithm (LACE) that presents transparent and scalable computation that is useful for larger networks or to act in tandem with other optimization engines. We run numerical simulations with different test feeders, including a realistic low-voltage feeder with real-world data from Belgium. This paper provides crucial insights and tools to distribution system operators (DSOs), stakeholders and academics alike to make sure DOE calculation achieves desirable and efficient outcome.

A promising approach to achieving scalable fault-tolerant quantum computation is the use of quantum error correction (QEC) codes augmented with magic states i.e. resource states produced via distillation, cultivation, or $R_z$ synthesis and teleported into the circuit as needed. Because magic-state production dominates the space-time volume of fault-tolerant programs, system architects must decide how many production units to allocate. Current approaches rely on deterministic analysis that either provisions for worst-case peak demand (wasting valuable qubit resources on factories that are never simultaneously utilized) or assumes average demand, which increases execution time. In this work, we build a simulation framework that couples circuit scheduling with different stochastic magic state production models, and use it to quantify the impact of non-determinism on circuit execution. We show that non-determinism has a dual effect that deterministic models cannot capture: it inflates total execution time (the price), while deflating peak per-cycle resource demand (the payoff). For distillation-based architectures, this demand smoothing shifts the space-time-optimal provisioning point: fewer factories are needed to minimize space-time volume than deterministic analysis predicts. Across benchmarks, stochastic-aware provisioning reduces space-time volume by up to 27% compared to the deterministic optimum for distillation, while requiring up to 30% fewer factories. We characterize these effects across each preparation mechanism, map the resulting design-space tradeoffs, and demonstrate that static resource estimation systematically mis-characterizes the cost of fault-tolerant execution. Our results establish that stochastic-aware analysis is necessary for right-sizing the factory allocations and should replace deterministic heuristics as the standard methodology for FTQC resource planning.

Turbulence is a phenomena that is {\it locally} and statistically characterized by measurements, but it is caused by {\it nonlocal} energy cascades associated with the environment. The presence of turbulence coincides with fluctuations in the refractive index, which impact optical sensing, imaging, and signaling applications. Here, we study the machine learning models that predict near-surface optical turbulence strength $C_n^2$, derived from anemometer-based surface flux measurements through Monin-Obukhov similarity theory, using ERA5 reanalysis data as model inputs. We evaluate the model's ability to perform temporal extrapolation by training on one year of co-located $C_n^2$ observations and ERA5 data, and applying the model to ERA5 data from other years at the same site to reconstruct a multi-year time series. We compare the predictions across Southern California and New York. In spite of varying weather and terrain, the ML models show consistent performance and seasonal behavior across training years. All models show greater correlation, faster convergence, and lower prediction errors in the summer. However, some ERA5 features drive predictions in New York but not California and vice versa, and such feature dependence depends on the season. Seasonal error and feature trends suggest that turbulence is affected by atmospheric composition or other seasonal environmental considerations that are not currently monitored by ERA5. We find, regardless of terrain, the primary feature of importance to turbulence prediction is solar radiation, which underlines the central role of radiative energy transfer in driving atmospheric turbulence. We point toward physics-informed ML translation and feature selection as tools for improving the generalizability of data-driven models.

In the black hole star (BH*) model, the characteristic "V"-shaped SED of LRDs is produced by an accreting BH embedded in a dense neutral-gas envelope with a near-unity covering factor. This envelope reprocesses radiation and emits as a ~5,000K blackbody, producing the optical continuum. Meanwhile, the UV is powered by a low-mass, dust-free, metal-poor host. The BH* scenario is promising, but it has yet to undergo detailed testing; conducting a self-consistent UV-to-optical spectral-fitting analysis of LRDs would provide a robust assessment of the model. In this work, we test the BH* scenario by fitting the full JWST/NIRSpec PRISM spectrum of The Cliff ($z_{spec}=3.55$), an LRD that played a pivotal role in the development of this model. A Bagpipes fit that allows stellar, nebular, AGN, and blackbody components naturally yields a BH*-like solution for The Cliff, even with broad priors. Our method allows us to characterize its host, despite remaining unresolved in JWST imaging. From the continuum, we infer the host to be low-mass (log $M_\star/M_\odot$~7.7), star-forming, metal-poor, affected by non-negligible dust attenuation ($A_V$~0.5 mag) acting on both stellar and nebular components. Larger $M_\star$ (up to log $M_\star/M_\odot$~8.1) and attenuations (up to $A_V$~1 mag) are obtained depending on the assumed dust attenuation law. Modest AGN UV leakage is consistently allowed by the code, but remains weak and not robustly constrained, with both AGN+host and host-dominated UV scenarios yielding equivalent fits. The star formation history of the host is relatively smooth, with the galaxy already assembling log $M_\star/M_\odot$~7 about 200 Myr before $z_{spec}=3.55$. The BH-to-$M_\star$ ratio exceeds the values expected from BH-host scaling relations, especially at recent times. This tension may indicate either inaccurate estimates of the BH properties or non-coeval BH-host evolution.

Let $U\subseteq H^0(\mathcal{O}_{\mathbb{P}^1\times \mathbb{P}^1}(a,b))$ be a four-dimensional vector space and consider the rational map $ϕ_U:\,\mathbb{P}^1\times \mathbb{P}^1 \dashrightarrow \mathbb{P}^3$ defined by its basis of bihomogeneous polynomials. The tensor product surface $X_U\subseteq \mathbb{P}^3$ is the closed image of $ϕ_U$, and a fundamental problem in this setting is to determine its implicit equation. As these surfaces are ubiquitous within the field of geometric modeling and design, knowledge of their implicit equations is particularly advantageous, allowing for more effective and efficient computations. In this article, we expand upon work of Duarte-Schenck and work of the present author to solve this implicitization problem when the bigraded ideal $I_U$ admits a singly graded syzygy.

We define a quantum computational model over infinite words, called Measure-Many Quantum Büchi Automata (MMQBA), which extends Measure-many Quantum Finite automata (MMQFA) to the infinite word setting with Büchi acceptance condition. In MMQBA, the quantum state evolves through unitary transformations followed by repeated projective measurements. An infinite word is accearaq2ppted with respect to a cutpoint p is in (0, 1] if (i) the run visits accepting states infinitely often, (ii) the limiting cumulative acceptance probability is at least p, and (iii) the limiting cumulative rejection lprobability is strictly less than p. We formalize the semantics of MMQBA, establish a language-theoretic characterization showing that MMQBA languages are precisely of the form lim(L(M, p)) for MMQFA M , and develop a decomposition of the non-halting subspace. We prove that MMQBA is closed under union but not under intersection or complementation. On the algorithmic side, we show that the emptiness problem is semi-decidable, while universality, inclusion, equivalence, and membership remain undecidable.

This paper deals with the kernel density estimator based on the so-called sinc (or Fourier integral) kernel $K(x)=(πx)^{-1}\sin x$. We study in detail both asymptotic and finite sample properties of this estimator. It is shown that, contrary to widespread opinion, the sinc estimator is superior to other estimators in many respects: it is more accurate for quite moderate values of the sample size, has better asymptotics in non-smooth case (the density to be estimated has only first derivative), is more convenient for the bandwidth selection, etc.

Acoustic droplet vaporization denotes the phase-change of micron- and sub-micron-sized droplets upon the application of high-amplitude ultrasound. The asymmetric collapse of the incepted vapor bubbles within the droplets can give rise to high-speed liquid microjets. Here, we describe acoustically-driven and bubble-pair jetting arising within the vaporizing droplet, observed experimentally with ultra-high-speed imaging at the microscale. The existence of complex pressure fields due to the continued acoustic wave-droplet interaction and the nucleation of multiple bubbles within the droplet leads to rich dynamics, with the jets presenting behavioral self-similarity to millimetric bubbles under comparable conditions. Evaporative instabilities that develop during bubble growth impede jet formation during bubble collapse. Furthermore, the ability of the jets to pierce the droplet interface and penetrate into the surrounding fluid is discussed. These powerful microjets could be harnessed to induce cell permeabilization for targeted drug delivery and treatment of cancerous tissue.

Extreme sudden changes in the flow of accreting gas onto SMBHs manifest themselves via large-amplitude continuum variability and changes to broad Balmer emission profiles, driving changing-look AGN. X-ray flux monitoring with SRG/eROSITA revealed that in the Seyfert AGN HE 1237-2252 the soft X-ray flux dipped abruptly, by a factor of 17 within 18 months. We initiated a follow-up campaign that caught the luminosity recovery after the dip, and enabled us to study how the various accretion components responded during this flux recovery. Our campaign included multiband photometry, X-ray spectroscopy, and optical spectroscopy. We tracked as the accretion rate relative to Eddington increased by a factor of 7 in 3 years. Based on broad Hbeta variability, HE 1237-2252 was subtype 1.0-1.2 in 2002, transitioned to subtype 1.8 by the time of the luminosity dip, and then transitioned back to subtype 1.0 within 3 months as luminosity recovered. Both transitions saw broad Hbeta integrated line flux change by factors of 4-6. The broad Balmer profile is decomposed into a broad Gaussian consistent with virialized gas at 27+/-3 lt-dy, plus a double-peaked profile, consistent with a diskline structure at more than roughly 5 lt-dy. The diskline component's relative contribution to the total profile increases as continuum flux rises. The lack of obscuration in the X-ray spectra, as well as the IR continuum dip, point to an intrinsic pause in the accretion rate as opposed to variable line-of-sight obscuration. Candidates for the underlying mechanisms include propagating cold and warm fronts in the accretion disk. The increased prominence of the diskline BLR component's emission could be due to evolution in the physical extent of the X-ray corona, and in the fraction of >13.6 eV photons intercepted by the diskline, as the accretion rate increases.

While context-aware personalization has been widely explored in modern tourism Recommender Systems (RS), the delivery of real-time notifications remains a significant design challenge due to issues of intrusiveness and user fatigue. This paper presents a proof-of-concept for a tourism recommendation framework that utilizes a virtual pet as a social mediator for delivering context-aware alerts. The system integrates real-time environmental data - including air quality, noise levels, and weather forecasts - and proximity-based notifications with a Multi-Agent Microservice that generates personalized recommendations based on the user's personality traits and preferences. A within-subjects pilot study (n=11) was conducted to evaluate the feasibility and user acceptance of this pet-mediated approach. Participants interacted with two versions of the system - a baseline without contextual alerts and a version featuring pet-mediated notifications - over a four-week period (two weeks per version) in real-world scenarios. Quantitative and qualitative data were collected to assess engagement, perceived naturalness, notification utility, and acceptance. Preliminary results suggest that the virtual pet effectively can "soften" the perceived intrusiveness of system alerts, making safety-critical information feel more welcome and natural. Furthermore, the character-mediated justifications significantly improved the clarity of the notifications, effectively supporting users in their real-time travel decisions. These findings provide a foundation for using virtual pet companions to enhance the transparency and acceptance of context-aware communication in tourism RS.

We extend the multilayer model of \etal{Kubo} for superconductor-insulator-superconductor (SIS) structures in two ways: first, by generalizing it to arbitrary sequences of layers of arbitrary type, i.e. superconducting, normal conducting, and insulating; and second, by accounting for all contributions, including ohmic losses and dielectric effects. We examine the maximum applicable field for $(\text{SI})^n\text{S}$ structures. We find that the optimum configuration corresponds to the $n=1$ case. However, the thickness of the superconducting coating layers can be reduced to below their penetration depth with minor performance penalty. We discuss the ability to model transitions in SS bilayers by introducing a set of virtual layers that represent the transition region through interpolated parameters. We find degradation of the maximum applicable field with thicker transition layers, and a larger effective penetration depth of the electromagnetic fields. Furthermore, the surface impedance of the multilayer structure is calculated using the Leontovich boundary condition, yielding a formulation suitable for integration into finite element simulations. Additionally, the Poynting theorem is used to determine the loss contributions of individual layers.

Software failures remain a major challenge in modern software development, and identifying the code elements responsible for failures is a time-consuming debugging task. While extensive research has focused on fault localization in the system under test (SUT), failures can also originate from faulty system test scripts. This problem, known as Test Code Fault Localization (TCFL), has received significantly less attention despite its importance in continuous integration (CI) environments where large test suites are executed frequently. TCFL is particularly challenging because it typically operates under black-box conditions, relies on limited diagnostic signals such as error messages and partial logs, and involves large system-level test scripts that expand the fault localization search space. In this paper, we propose SPARK, a framework that integrates accumulated debugging knowledge from continuous integration (CI) environments into Large Language Model (LLM)-based TCFL. Given a newly observed failing test case, SPARK retrieves similar fault-labeled test cases from a debugging knowledge corpus and selectively annotates suspicious lines of the failing test based on their similarity to previously observed fault patterns. These annotations guide the LLM's reasoning while maintaining scalability and avoiding the prompt-length explosion common to naive retrieval-augmented approaches. We evaluate SPARK on three industrial datasets containing real-world faulty Python test cases from different software products. The results show that SPARK consistently improves fault localization effectiveness compared to the existing LLM-based TCFL baseline while maintaining comparable inference cost and token usage. In particular, the approach advances the state of the art by identifying more correct faulty locations in complex test cases containing multiple faults.