The discovery of chaotic quantum circuits with (partially) solvable dynamics has played a key role in our understanding of non-equilibrium quantum matter and, at the same time, has helped the development of concrete platforms for quantum computation. It was shown that solvability does not prevent the generation of chaotic dynamics, however, it imposes non-trivial constraints on the generated correlations. A natural question is then whether it is possible to gain insight into the generic case despite the latter being very hard to access. To address this question here we introduce a family of 'asymptotically solvable' quantum circuits where the solvability constraints only affect correlations on length scales beyond a tuneable threshold. This means that their dynamics are only solvable for long enough times: for times shorter than the threshold they are generic. We show this by computing both their dynamical correlations on the equilibrium (infinite temperature) state and their thermalisation dynamics following quantum quenches from compatible (asymptotically solvable) non-equilibrium initial states. The class of systems we introduce is generically ergodic but contains a non-interacting point, which we use to provide exact analytical results, complementing those of numerical experiments, on the non-solvable early time regime.

Active tissues exhibit tension fluctuations that are correlated in space and time. We study a minimal overdamped surface model in which such fluctuations enter as a zero-mean, multiplicative modulation of the local surface tension. Although the deterministic elastic dynamics (tension plus bending) stabilizes the flat state for all nonzero wave numbers, we find that sufficiently persistent active fluctuations generate positive ensemble growth rates for a finite band of Fourier modes, leading to stochastic buckling with wavelength selection. A non-Markovian theory based on the Novikov--Furutsu theorem captures the instability threshold and unstable band observed in simulations.

Homomorphic encryption (HE) enables computations directly on encrypted data, offering strong cryptographic guarantees for secure and privacy-preserving data storage and query execution. However, despite its theoretical power, practical adoption of HE in database systems remains limited due to extreme cipher-text expansion, memory overhead, and the computational cost of bootstrapping, which resets noise levels for correctness. This paper presents NSHEDB, a secure query processing engine designed to address these challenges at the system architecture level. NSHEDB uses word-level leveled HE (LHE) based on the BFV scheme to minimize ciphertext expansion and avoid costly bootstrapping. It introduces novel techniques for executing equality, range, and aggregation operations using purely homomorphic computation, without transciphering between different HE schemes (e.g., CKKS/BFV/TFHE) or relying on trusted hardware. Additionally, it incorporates a noise-aware query planner to extend computation depth while preserving security guarantees. We implement and evaluate NSHEDB on real-world database workloads (TPC-H) and show that it achieves 20x-V1370x speedup and a 73x storage reduction compared to state-of-the-art HE-based systems, while upholding 128-bit security in a semi-honest model with no key release or trusted components.

We show that every connected graph $G$ has a tree decomposition indexed by a tree $T$ such that $T$ is a subgraph of $G$ and the width of the tree decomposition is bounded from above by a function of the pathwidth of $G$. This answers a question of Blanco, Cook, Hatzel, Hilaire, Illingworth, and McCarty (2024), who proved that it is not possible to have such a tree decomposition whose width is bounded by a function of the treewidth of $G$. The proof relies on Simon's Factorization Theorem for finite semigroups, a tool that has already been applied successfully in various areas of graph theory and combinatorics in recent years. Our application is particularly simple and can serve as a good introduction to this technique.

Processing-in-Memory (PIM) architectures enable computation directly within DRAM and help combat the memory wall problem. Bit-shifting is a fundamental operation that enables PIM applications such as shift-and-add multiplication, adders using carry propagation, and Galois field arithmetic used in cryptography algorithms like AES and Reed-Solomon error correction codes. Existing approaches to in-DRAM shifting require adding dedicated shifter circuits beneath the sense amplifiers to enable horizontal data movement across adjacent bitlines or vertical data layouts which store operand bits along a bitline to implement shifts as row-copy operations. In this paper, we propose a novel DRAM subarray design that enables in-DRAM bit-shifting for open-bitline architectures. In this new design, we built upon prior work that introduced a new type of cell used for row migration in asymmetric subarrays, called a "migration cell". We repurpose and extend the functionality by adding a row of migration cells at the top and bottom of each subarray which enables bidirectional bit-shifting within any given row. This new design maintains compatibility with standard DRAM operations. Unlike previous approaches to shifting, our design operates on horizontally-stored data, eliminating the need and overhead of data transposition, and our design leverages the existing cell structures, eliminating the need for additional complex logic and circuitry. We present an evaluation of our design that includes timing and energy analysis using NVMain, circuit-level validation of the in-DRAM shift operation using LTSPICE, and a VLSI layout implementation in Cadence Virtuoso.

We propose a geometric control framework on $SE(3)$ for quadrotors that enforces pointing-driven missions without completing a full attitude reference. The mission is encoded through virtual constraints defining a task manifold and an associated set of admissible velocities, and invariance is achieved by a feedback law obtained from a linear system in selected inputs. Under a transversality condition with the effective actuation distribution, the invariance-enforcing input is uniquely defined, yielding a constructive control law and, for relevant tasks, closed-form expressions. We further derive a local off-manifold stabilization extension. As a case study, we lock a body axis to a prescribed line-of-sight direction while maintaining fixed altitude.

In this work, we study the dynamics of a spinning particle in pp-waves spacetimes; in particular, plane gravitational waves and impulsive shockwaves. W pay special attention to analytical considerations; this is possible due to an appropriate choice of the spin supplementary condition, various Hamiltonian formalisms (including a non-minimal one) and constants of motion associated with conformal fields. Based on these results, we establish a relation between the motions of a spinning particle in pp-waves and electromagnetic fields suggested by a gauge-gravity duality.

Background: The E-value has become widely used for assessing robustness to unmeasured confounding in observational studies, but the original framework was developed for single time-point exposure-outcome settings. This study extends the E-value methodology to longitudinal set up with time-varying treatments and confounders, where treatment-confounder feedback occurs. Methods: A combined bias factor accounting for unmeasured confounding at multiple time points was extended, with three reporting scenarios presented: equal bias distribution across time points, confounding at a single time point, and a general case visualizing all possible confounder strength combinations. Results: In simulations with an observed risk ratio of 1.73, unmeasured confounders with 1.96-fold associations at each time point could nullify the effect under equal distribution-substantially lower than the single time-point E-value of 2.85. Re-analysis of a published insulin resistance and cardiovascular disease study yielded similar patterns, with time-varying E-values of 1.63 at each time point compared to the originally reported 2.09. Conclusions: Studies more like longitudinal set up may be more vulnerable to unmeasured confounding than single time-point E-values suggest. This extension provides accessible tools for transparent sensitivity analysis in time-varying settings while preserving the simplicity and minimal assumptions that make E-values widely applicable.

We address load-parameter estimation in cooperative aerial transport with time-varying mass and inertia, as in fluid-carrying payloads. Using an intrinsic manifold model of the multi-quadrotor-load dynamics, we combine a geometric tracking controller with an observer for parameter identification. We estimate mass from measurable kinematics and commanded forces, and handle variable inertia via an inertia surrogate that reproduces the load's rotational dynamics for control and state propagation. Instead of real-time identification of the true inertia tensor, driven by high-dimensional internal fluid motion, we leverage known tank geometry and fluid-mechanical structure to pre-compute inertia tensors and update them through a lookup table indexed by fill level and attitude. The surrogate is justified via the incompressible Navier-Stokes equations in the translating/rotating load frame: when effective forcing is gravity-dominated (i.e., translational/rotational accelerations and especially jerk are limited), the fluid approaches hydrostatic equilibrium and the free surface is well approximated by a plane orthogonal to the body-frame gravity direction.

Precise tension control in roll-to-roll (R2R) manufacturing is difficult under varying operating conditions and process uncertainty. This paper presents a curriculum-based Soft Actor-Critic (SAC) controller for multi-section R2R tension control. The policy is trained in three phases with progressively wider reference ranges, from 27 to 33 N to the full operating envelope of 20 to 40 N, so it can generalize across nominal and disturbed conditions. On a three-section R2R benchmark, the learned controller achieves accurate tracking in nominal operation and handles large disturbances, including 20 N to 40 N step changes, with a single policy and no scenario-specific retuning. These results indicate that curriculum-trained SAC is a practical alternative to model-based control when system parameters vary and process uncertainty is significant.

Tuberculosis (TB) is an airborne disease caused by the bacterium Mycobacterium tuberculosis (M. tb). Prior to the COVID-19 pandemic, TB was the leading cause of death from an infectious agent globally. However, most people exposed to M. tb do not develop active TB and go on to display symptoms. Instead, in the majority of cases, the bacteria are contained within a granuloma (an aggregation of immune cells) without being eliminated; this is called latent TB. The spatial organisation of the bacteria and immune cells is important in determining whether an individual exposed to M. tb will develop latent or active TB. In this paper, we present a multi-cell, multiscale model of TB progression to investigate the importance of the spatial organisation. This is a novel TB within-host dynamics modelling framework, having been developed using CompuCell3D (CC3D), an open-source computer software used for simulating cellular biological processes both within and between cells. We used this model to compare the generated results with those from a previously developed within-host infectious disease model. We found that, although the results of our CC3D model mostly agree qualitatively with those from the previously developed model, there are quantitative differences. Additionally, we conducted a robustness analysis of key model parameters from the CC3D model to determine their importance to the CC3D model output, using a methodology specifically designed for agent-based models. The model output appears to be robust in response to perturbations in parameters controlling chemotactic movement, but less so in response to perturbations in parameters controlling persistence of movement in cells, cell adhesion and volume constraints. This work compares our CC3D model of TB progression with another agent-based modelling approach to the same problem.

This work extends the CoLoRFulNNLO subtraction method to address soft and collinear divergences in the computation of higher-order corrections for hadronic collisions. By utilizing universal local counterterms which can be integrated analytically over the unresolved phase space, we achieve numerically stable, fully-differential predictions. Our publicly available NNLOCAL code serves as a proof-of-concept implementation, validated by calculating the NNLO cross-section for Higgs boson production in gluon-gluon fusion with no light quarks.

We study a family of (multivariate-)Gaussian Hamiltonian Monte Carlo (GHMC) operators and prove that the family of Gaussian distributions and their mixtures are invariant under such operators. Furthermore, each such operator is a contraction on the space of parameters and an explicit formulae are derived. These results then enable us to analyze the dynamics and convergences of independent and identically distributed random sequences of such operators.

We present a class of models based on a gauged $U(1)_F$ flavor symmetry that explains the hierarchical structure of fermion masses and mixings via the Froggatt-Nielsen (FN) mechanism, while also solving the strong CP problem by the Peccei-Quinn (PQ) mechanism. A global $U(1)_{\rm PQ}$ symmetry with a nonzero QCD anomaly emerges accidentally in this setup as a byproduct of the gauged $U(1)_F$ symmetry. The resulting axion is shown to be of high quality, with the axion potential safeguarded against quantum gravity corrections by the gauge symmetry. Three models, which are generalizations of the Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axion model, are presented realizing this idea. The right-handed neutrino mass scale is identified as the Froggatt-Nielsen scale in these models. We present explicit UV completions of the FN sectors of these models and show that they preserve the high quality of the axion. In these models, the axion acts as a flavon field, leading to testable predictions in flavor-changing decays of neutral mesons. The axion also serves as the dark matter of the universe with the right amount of relic abundance without causing cosmological domain wall problems. Baryon asymmetry of the universe is realized via leptogenesis which is calculable in these models and found to be of the right order of magnitude.

While prior research has focused on providers, caregivers, and adult patients, little is known about adolescents' perceptions of AI in health learning and management. Utilizing design fiction and co-design methods, we conducted seven workshops with 23 adolescents (aged 14-17) to understand how they anticipate using health AI in the context of a family celiac diagnosis. Our findings reveal that adolescents have four main envisioned roles of health AI: enhancing health understanding and help-seeking, reducing cognitive burden, supporting family health management, and providing guidance while respecting their autonomy. We also identified nuanced trust and a divided view on emotional support from health AI. These findings suggest that adolescents perceive AI's value as a tool that moves them from efficiency to meaning-one that creates time for valued activities. We discuss opportunities for future health AI systems to be designed to encourage adolescent autonomy and reflection, while also supporting meaningful, dialectical activities.

Silicon is the undisputed cornerstone of modern technology, with applications ranging from micro- and opto-electronics to quantum technologies. Recently, the exploration of its allotropes has emerged as a pivotal frontier for engineering materials with tailored optical and electronic functionalities. High-pressure experiments have revealed several metastable silicon phases, among which is Si-XIII. First observed more than 20 years ago, this phase has remained structurally unidentified, representing a significant gap in our understanding of elemental silicon allotropy. In this work, a convergent methodology is employed combining advanced theoretical modeling with experimental characterization to finally resolve the long-standing structural assignment of Si-XIII. Guided by careful experimental observations, a structural model validated through first-principles optimization and systematically tested against multiple experimental signatures is constructed. All the fingerprints of this phase are rationalized by our proposed crystal structure: interplanar spacings, Raman frequencies, thermodynamic stability, and kinetic pathways. These findings provide a crucial missing piece in the high-pressure phase diagram of silicon and demonstrate the power of integrating computational predictions with experimental validation to resolve complex structural problems in materials science.

High-impedance arc faults (HIAFs) in medium-voltage electrical distribution systems are difficult to detect due to their low fault current levels and nonlinear transient behavior. Traditional detection algorithms generally struggle with predictions under dynamic waveform scenarios. This research provides our approach of using a unique data-driven linearization (DDL) framework for early prediction of HIAFs, giving both interpretability and scalability. The proposed method translates nonlinear current waveforms into a linearized space using coordinate embeddings and polynomial transformation, enabling precise modelling of fault precursors.The total duration of the test waveform is 0.5 seconds, within which the arc fault occurs between 0.2 seconds to 0.3 seconds. Our proposed approach using DDL, trained solely on the pre-fault healthy region (0.10 seconds to 0.18 seconds) effectively captures certain invisible fault precursors, to accurately predict the onset of fault at 0.189 seconds, which is approximately 0.011 seconds (i.e., 11 milliseconds) earlier than the actual fault occurrence. In particular, the framework predicts the start of arc faults at 0.189 seconds, significantly earlier of the actual fault incidence at 0.200 seconds, demonstrating substantial early warning capability. Performance evaluation comprises eigenvalue analysis, prediction error measures, error growth rate and waveform regeneration fidelity. Such early prediction proves that the model is capable of correctly foreseeing faults which is especially helpful in preventing real-world faults and accidents. It confirms that our proposed approach reliably predicts arc faults in medium-voltage power distribution systems

We examined the long-term behavior of the superbias calibration frames for the Advanced Camera for Surveys Wide Field Channel (ACS/WFC) aboard the Hubble Space Telescope (HST). Superbias frames are used to remove detector-level bias structure from science images and are currently generated after an anneal and delivered monthly. The primary goal of this study was to determine whether the frequency of superbias generation could be reduced without compromising calibration quality, potentially aligning with the Wide Field Camera 3 UVIS (WFC3/UVIS) approach of generating only one superbias per year. We analyzed superbias frames produced from 2007 through 2024 to investigate whether these calibration products have changed significantly over time, and whether the frequency of superbias generation and delivery could be safely reduced without loss of calibration accuracy. In addition to visual inspections and pixel-level comparisons, we employed Principal Component Analysis (PCA) to evaluate whether any long-term, global structure exists beneath the apparent noise in these frames. Our findings show that the superbias structure has remained fairly stable post-Servicing Mission 4 (SM4), a 15-year period, and no significant or unexpected global trends or systematic shifts were detected. However, due to unstable hot columns and increasing readout dark observed in ACS/WFC data, it is likely that these calibrations still benefit from more frequent superbias updates than the annual cadence adopted for WFC3/UVIS.

Simulating nuanced user experiences within complex interactive search systems poses distinct challenge for traditional methodologies, which often rely on static user proxies or, more recently, on standalone large language model (LLM) agents that may lack deep, verifiable grounding. The true dynamism and personalization inherent in human-computer interaction demand a more integrated approach. This work introduces UXSim, a novel framework that integrates both approaches. It leverages grounded data from traditional simulators to inform and constrain the reasoning of an adaptive LLM agent. This synthesis enables more accurate and dynamic simulations of user behavior while also providing a pathway for the explainable validation of the underlying cognitive processes.

The development of a novel exact two-component (X2C) scheme with the inclusion of the picture-change correction for the fluctuation potential, the X2Ccorr scheme, is reported, hereby establishing a hierarchy of X2C schemes with systematic improvement for the treatments of relativistic two-electron contributions. Using benchmark X2C complete active space self-consistent-field (CASSCF) calculations for zero-field splittings in chalcogen diatomics, the contributions of two-electron spin-orbit coupling, electron spin-spin coupling, and quantum electrodynamics are carefully analyzed. The capability of the new Cholesky decomposition-based implementation for relativistic two-component CASSCF method using super-configuration-interaction algorithms is further demonstrated with calculations for the low-lying electronic states of neodymium aqua-ions with up to the second coordination shells.

Estimation of the population total of a variable can be improved by calibration on a set of auxiliary variables. It is difficult to establish that such a set of variables is sufficient, that estimation could not be improved by calibration on any further variables. We address this issue by finding an upper bound for the change of the calibration estimate of the population total of a variable when the auxiliary information is supplemented by another variable for which the population total is known. This upper bound can be interpreted as a measure of sensitivity of the estimate to unavailable auxiliary information and considered as a factor in deciding whether to seek further data sources that would be included in calibration.

Identifying which Wikipedia articles are related to science fiction, fantasy, or their hybrids is challenging because genre boundaries are porous and frequently overlap. Wikipedia nonetheless offers machine-readable structure beyond text, including categories, internal links (wikilinks), and statements if corresponding Wikidata items. However, each of these signals reflects community conventions and can be biased or incomplete. This study examines structural and semantic features of Wikipedia articles that can be used to identify content related to science fiction and fantasy (SF/F).

Effective field theories (EFTs) are widely used to study many-body systems by describing two-body interactions using zero-ranged contact potentials. However, when extended to three-body processes, these contact interactions lead to divergences due to the absence of an intrinsic length scale. In EFT, this is typically resolved by introducing a zero-ranged three-body interaction, which can be renormalized to make the low-energy physics independent of the short-distance physics. However, when the two-body potential has a finite range, such as in separable potentials, there is no need for such renormalization. In this work, we derive the integral equation for the three-body scattering amplitude for separable potentials, and solve it in the strongly-interacting regime. With our model, we retrieve the known analytic form of the scattering amplitude for inelastic scattering processes and formulate a new scaling law for elastic three-body scattering processes.

Graphs are essential for modeling complex relationships and capturing structured interactions in data. Graph Neural Networks (GNNs) are particularly effective when such relational structure is explicitly available, but many real-world datasets, most notably tabular data, lack an inherent graph representation. To address this limitation, we propose RF-GNN, a framework that constructs instance-level graphs from tabular data using proximity measures induced by random forests. These proximities capture nonlinear feature interactions and data-adaptive similarity without imposing restrictive assumptions on feature geometry. The resulting graphs enable the direct application of GNNs to tabular learning problems. Extensive experiments on 36 benchmark datasets demonstrate that RF-GNN consistently outperforms strong classical baselines and recent graph-construction methods in terms of weighted F1-score. Additional ablation studies highlight the impact of proximity design choices and graph construction settings.

Job-based smishing scams, where victims are recruited under the guise of remote job opportunities, represent a rapidly growing and understudied threat within the broader landscape of online fraud. In this paper, we present Anansi, the first scalable, end-to-end measurement pipeline designed to systematically engage with, analyze, and characterize job scams in the wild. Anansi combines large language models (LLMs), automated browser agents, and infrastructure fingerprinting tools to collect over 29,000 scam messages, interact with more than 1900 scammers, and extract behavioral, financial, and infrastructural signals at scale. We detail the operational workflows of scammers, uncover extensive reuse of message templates, domains, and cryptocurrency wallets, and identify the social engineering tactics used to defraud victims. Our analysis reveals millions of dollars in cryptocurrency losses, highlighting the use of deceptive techniques such as domain fronting and impersonation of well-known brands. Anansi demonstrates the feasibility and value of automating the engagement with scammers and the analysis of infrastructure, offering a new methodological foundation for studying large-scale fraud ecosystems.

We present a renormalization-group perspective on spontaneous stochasticity in hydrodynamic turbulence, viewed through the lens of multiscale dynamical systems. Building on previously established results for a solvable multiscale Arnold's cat model, we show that spontaneous stochasticity emerges as a universal fixed point of an RG transformation acting on Markov kernels, independent of the microscopic regularization. Classical examples - including the Feigenbaum equation, the central limit theorem, and hierarchical spin models - are reinterpreted within the same framework, placing spontaneous stochasticity alongside other universality phenomena.

Being the most massive known young stellar cluster in the Milky Way, Westerlund 1 (Wd1) constitutes an ideal benchmark for understanding the evolution of massive stars. However, the cluster age remains highly controversial (~4-10 Myr), hindering the use of Wd1 as a reference for massive star evolution. One of the main issues is high foreground extinction, which has so far prevented the detection of the main sequence. Using infrared spectroscopy we seek to detect the cluster's main sequence for the first time, to characterise the Hertzsprung-Russell diagram, and to use the cluster's turn-off to obtain a robust age estimate. We obtained multi-epoch, near-infrared VLT/KMOS spectroscopic observations of Wd1 to map its population of massive stars. The spectra of ~110 members were analysed with CMFGEN models to derive stellar parameters, populate the cluster Hertzsprung-Russell diagram, and compare it with isochrones from evolutionary models. Our observations returned 47 new spectroscopically identified cluster members, with spectral types O9-B1 III-V. The cluster turn-off indicates an age of 5.5+/-1.0 Myr at a distance of 4.23+0.23-0.21 kpc, displaying a moderate degree of coevality. We demonstrate that our estimate of the age of Wd1 is robust against reasonable changes in the distance and extinction law, and the adopted rotational velocity and metallicity of the stellar isochrones. We further find that ~65% of the OB stars with multi-epoch coverage exhibit radial-velocity variability. Infrared observations of the unevolved stellar population support a single episode of star formation with an age of ~5.5 Myr, reinforcing its potential as a benchmark for massive star evolution and providing a reference sample for future binary population studies.

We present a broadband study of the WLQ SDSS J101353.45+492758.1, which displays a nearly featureless UV-optical spectrum with only a weak Mg II line alongside an exceptionally low X-ray flux. We model its spectral energy distribution using the relativistic thin-disk model kerrbb with a power law, and the multicomponent AGN model relagn, a physically motivated extension of agnsed incorporating warm and hot Comptonizing regions. Our fits constrain the black hole mass, accretion rate, X-ray loudness, and coronal energetics. Both approaches yield consistent BH masses of M_{BH} \approx 2 \times 10^{9} M_\odot and an Eddington accretion rate of \dot m \approx 0.1. The relagn fit including a warm Comptonizing region provides a significantly improved representation of the UV-soft X-ray continuum. The warm corona, characterized by kTe \simeq 0.20 keV, Γ \simeq 3.8, and an optical depth τ \simeq 7.26, extends to \sim 34 R_g. The hot corona appears compact and energetically suppressed, leading to an intrinsically weak X ray output with log(L_{X}/L_{bol}) \simeq -4.29, among the lowest reported for WLQs. The α_{ox} \sim 2.06 indicates the source to be in high/soft AGN spectral state. The combination of a luminous, standard disk and extremely weak hot corona suggests that this quasar hosts a highly inefficient inner coronal region. This explains its X-ray faintness and extreme deficit of high-ionization emission lines. The source may represent an AGN analog in "ultrasoft" accretion state, or a system in which the ionizing continuum is suppressed by a compact or quenched corona. Our study suggests that the source is not accreting at high Eddington ratio, highlighting the physical diversity of WLQs, and supports the view that geometric and radiative effects jointly shape their extreme spectral properties.

Resolving growth mechanisms and thickness evolution of functional properties is one of the key tasks in materials discovery and optimization involving thin-film materials, traditionally requiring significant experimental budgets. Here we introduce the combination of thickness-gradient libraries and automated scanning probe microscopy as a systematic pathway to elucidate growth modes and disentangle ferroelectric and electrochemical contributions in ferroelectric thin films. As a model system, we explore the Hf0.5Zr0.5O2 (HZO) gradient thin films grown on LaxSr1-xMnO3 (LSMO) bottom electrode thin films. Automated piezoresponse force microscopy, spectroscopy, and lithography reveals that irreversible topographic deformation arises from electrochemical activity at the LSMO surface, whereas reversible phase inversion in HZO reflects ferroelectric switching. Automated topography height-map scans are used to further quantify nucleation density, particle-size evolution, and roughness correlations across the thickness-gradient, demonstrating that improved plume stabilization during growth suppresses interfacial reactions and promotes dense, fine-grained HZO conducive to ferroelectric phase formation. This combined materials-engineering and automated-SPM framework establishes a platform for high-throughput, mechanism-resolved characterization of ferroionic and ferroelectric responses in complex oxide films.

In the linear-in-means model, endogeneity arises naturally due to the reflection problem. A common solution is to use Instrumental Variables (IVs) based on higher-order network links, such as using friends-of-friends' characteristics. We first show that such instruments are unlikely to work well in many applied settings: in very sparse or very dense networks, friends-of-friends may be similar to the original links. This implies that the IVs may be weak or their first stage estimand may be undefined. For a class of random graphs, we use random graph theory and characterize regimes where such instruments perform well, and when they would not. We prove how weak-IV robust inference can be adapted to this environment, and how scaling the network can help. We provide extensive Monte Carlo simulations and revisit empirical applications, showing the prevalence of such issues in empirical practice, and how our results restore valid inference.