Offering terahertz of bandwidths and femtosecond timescales, ultrafast optics is enabling both the study of fundamental quantum optical phenomena and the advancement of quantum-enhanced applications. However, unlocking the full potential of ultrafast quantum optics requires accessing the temporal characteristics of ultrashort quantum pulses across ultrabroad bandwidths. This is particularly important in the near-infrared and visible range of the optical spectrum, which, unlike the terahertz and long-wave infrared, has remained beyond the reach of current techniques. Here, we break this barrier by translating frequency-resolved optical gating (FROG), a widely used technique for ultrafast classical pulse characterization, to the quantum regime. We show how such a quantum FROG can measure complex temporal modes and sub-optical-cycle quadrature covariances in the near-infrared, enabling complete characterization of microscopic Gaussian states. We experimentally use the quantum-FROG to report the measurement of quadrature correlations, complex temporal modes, and squeezing levels of multimode ultrafast squeezed vacuum states generated on a nanophotonic chip. We access multimode squeezing levels of a femtosecond quantum pulse approaching 7 dB and demonstrate FROG-based measurement bandwidths exceeding 100 THz. Quantum FROG enables measurement of previously inaccessible quantum features of ultrashort pulses at the sub-optical-cycle regime and highlights a practical path to accessing terahertz of bandwidths in quantum optics for applications in computing, sensing, and imaging.

Model-assisted regression estimation is fundamental in survey sampling for incorporating auxiliary information. However, when the auxiliary dimension grows with the sample size, the standard Generalized regression (GREG) estimator can exhibit non-negligible bias under informative sampling, even when the working model is correctly specified. This failure stems from the double use of sampled outcomes simultaneously for fitting the regression and for forming the residual correction. We propose a sample-split REGression (SREG) estimator based on K-fold cross-fitting that eliminates this bias by pairing each unit's residual with an out-of-fold prediction. The resulting estimator is first-order equivalent to the oracle difference estimator under a weak prediction-norm consistency requirement, without requiring root-n consistent estimation of regression coefficients. We establish asymptotic normality and prove consistency of a variance estimator based on cross-fitted residuals. The key conditional fluctuation assumption is verified for simple random, stratified, and rejective sampling. Simulations demonstrate that SREG effectively removes high-dimensional bias while maintaining competitive efficiency.

We analyze fermionic entanglement and correlation measures in the ground and the low temperature thermal state of the water molecule as a function of the internuclear distance in the context of the full configuration interaction approach. The aim is to obtain a general entanglement based characterization of the electronic eigenstates. We consider first the spin-up - spin-down partition and the associated Schmidt decomposition, examining the total up-down entanglement of the electronic wave function. We then consider the one- and two-body entanglement derived from the one- and two-body reduced density matrices (DMs), which measure both the deviation of the state from a Slater Determinant (SD) as well as the up-down correlation at the two-body level. All blocks of these DMs are examined. We also introduce and analyze new measures like the up-down two-body mutual information and two types of two-body negativities, the latter measuring the "inner" entanglement of the reduced two-body DMs, i.e., their deviation from a convex mixture of SDs. Finally, the dissociation limit is also analyzed, considering both the exact ground state (GS) as well as the thermal state in the zero temperature limit, representing the projector onto the "GS band" of almost degenerate lowest lying eigenstates.

The quantum dynamics of quasiperiodic systems display a rich variety of physical behaviors due to the combination of rotational symmetry that is mathematically forbidden in periodic systems, and long-range order despite the lack of translation symmetry. New experimental probes into these dynamics with a quantum simulator, consisting of ultracold atoms in an optical lattice potential, will yield new insights into the physics of quasiperiodic systems. This potential is imbued with the flexibility, tunability, and purity of the individual laser beams that constitute it, allowing for exquisite control over a rich system. Programmable dynamic control over the lattice beam phases opens up an even richer space of achievable systems via Floquet engineering. We thus describe an experimental scheme for creating a programmable, dynamic, two-dimensional (2D) quasiperiodic optical lattice with heavily suppressed phase noise. We observe suppression of phase noise for frequency components up to 5 kHz, and report phase noise suppression of over 70 dB over the DC-60 Hz frequency band. We further demonstrate a phase modulation bandwidth of 350 kHz. This scheme allows for full translational and phasonic control of the lattice, including changes to the rotational symmetry of the potential, at speeds exceeding the lattice recoil velocity, which paves a path towards direct observation and control of quantum dynamics in quasicrystals.

A long-standing vision of computing is the personal AI system: one that understands us well enough to address our underlying needs. Today's AI focuses on what users do, ignoring why they might be doing such things in the first place. As a result, AI systems default to optimizing or repeating existing behaviors (e.g., user has ChatGPT complete their homework) even when they run counter to users' needs (e.g., gaining subject expertise). Instead we require systems that can make connections across observations, synthesizing them into insights about the motivations underlying these behaviors (e.g., user's ongoing commitments make it difficult to prioritize learning despite expressed desire to do so). We introduce an architecture for building user understanding through behavior latticing, connecting seemingly disparate behaviors, synthesizing them into insights, and repeating this process over long spans of interaction data. Doing so affords new capabilities, including being able to infer users' needs rather than just their tasks and connecting subtle patterns to produce conclusions that users themselves may not have previously realized. In an evaluation, we validate that behavior latticing produces accurate insights about the user with significantly greater interpretive depth compared to state-of-the-art approaches. To demonstrate the new interactive capabilities that behavior lattices afford, we instantiate a personal AI agent steered by user insights, finding that our agent is significantly better at addressing users' needs while still providing immediate utility.

Self-attention in Transformers generates dynamic operands that force conventional Compute-in-Memory (CIM) accelerators into costly non-volatile memory (NVM) reprogramming cycles, degrading throughput and stressing device endurance. Existing solutions either reduce but retain NVM writes through matrix decomposition or sparsity, or move attention computation to digital CMOS at the expense of NVM density. We present TrilinearCIM, a Double-Gate FeFET (DG-FeFET)-based architecture that uses back-gate modulation to realize a three-operand multiply-accumulate primitive for in-memory attention computation without dynamic ferroelectric reprogramming. Evaluated on BERT-base (GLUE) and ViT-base (ImageNet and CIFAR), TrilinearCIM outperforms conventional CIM on seven of nine GLUE tasks while achieving up to 46.6\% energy reduction and 20.4\% latency improvement over conventional FeFET CIM at 37.3\% area overhead. To our knowledge, this is the first architecture to perform complete Transformer attention computation exclusively in NVM cores without runtime reprogramming.

We show that the Green biset functor $R_{\mathbb{C}}$ of complex characters over $\mathbb{Z}$, is not separable, i.e. it is not projective as a bimodule over itself. Also, we show that $RB_G$, the Burnside biset functor shifted by a finite group $G$, over a commutative ring $R$, is separable if and only if $|G|$ is invertible in $R$. Finally, to address the question of the relation between functors and their evaluations, we show that the Burnside $R$-algebra $RB(G)$ is separable if and only if $|G|$ is invertible in $R$.

So far, only a single theory of multiple spin-2 fields is known that features genuine multi-field interactions while remaining free of Boulware-Deser-type ghost instabilities. In this paper we show that this is the most general ghost-free multi spin-2 interaction type possible. We start with the general class of multivielbein interactions containing antisymmetrised products of vielbeins, considered earlier by Hinterbichler and Rosen. We formulate a necessary condition for these theories to be ghost-free. For two vielbeins the theory parameters remain unrestricted, reproducing the ghost-free bimetric theory. But for more than two vielbeins with genuine multi-field interactions, we show that the couplings are restricted precisely to yield the known ghost-free multivielbein theory, thus establishing its uniqueness. We also show that more general interactions, constructed using the ghost-free bimetric and multivielbein potentials as building blocks, satisfy the necessary ghost-free conditions provided the associated interaction graphs have a tree structure.

Software developers frequently receive vulnerability reports that require them to reproduce the vulnerability in a reliable manner by generating a proof-of-concept (PoC) input that triggers it. Given the source code for a software project and a specific code location for a potential vulnerability, automatically generating a PoC for the given vulnerability has been a challenging research problem. Symbolic execution and fuzzing techniques require expert guidance and manual steps and face scalability challenges for PoC generation. Although recent advances in LLMs have increased the level of automation and scalability, the success rate of PoC generation with LLMs remains quite low. In this paper, we present a novel approach called Program Analysis Guided proof of concept generation agENT (PAGENT) that is scalable and significantly improves the success rate of automated PoC generation compared to prior results. PAGENT integrates lightweight and rule-based static analysis phases for providing static analysis guidance and sanitizer-based profiling and coverage information for providing dynamic analysis guidance with a PoC generation agent. Our experiments demonstrate that the resulting hybrid approach significantly outperforms the prior top-performing agentic approach by 132% for the PoC generation task.

We present the Boston Open-Shell Transition Metal Complex (BOS-TMC) dataset, a set of density functional theory (DFT) properties for 159k experimentally characterized mononuclear transition metal complexes (TMCs) in multiple spin states with a range of formal charges derived from the Cambridge Structural Database (CSD). To curate this set, we carried out an iterative procedure to confidently assign overall TMC charge. From this information, we then obtained properties in up to three spin states, i.e., low-, intermediate-, and high-spin for 3d metals and low- and intermediate-spin for 4d and 5d metals, depending on compatibility with the metal electron configuration, for a total of 343.8k TMC/spin combinations. At odds with prior sets, we preserved experimental heavy-atom coordinates in these structures during optimization. We report all properties using PBE0/def2-TZVP single-point energies on these structures. We introduce a scheme for computing metal-spin-dependent atomization energies, which we report for each TMC. Alongside electronic energies, we report up to seven additional properties including: HOMO, LUMO, HOMO-LUMO gap, atomic partial charges, dipole moments, atomization energies, and spin-splitting energies for a total of over 2.9M TMC-associated properties. For a representative subset of over 10k complexes chosen based on size, we evaluate the sensitivity of computed properties to exchange-correlation (xc) functional choice from a set of twelve xcs spanning rungs of "Jacob's ladder", highlighting hotspots of TMC space that have the greatest uncertainty. In comparison to prior transition-metal datasets, BOS-TMC is both larger and more diverse in terms of charge and spin configurations and, as a result, more diverse in its range of properties. This dataset is expected to provide a high-fidelity foundation for machine-learning model development, DFT benchmarking, and exploration.

The Sen action for self-dual fields has been generalised by Hull to include two metrics which allows it to be defined on generic manifolds. In this paper we consider Kaluza-Klein compactifications of this action. The existence of two metrics presents novel challenges as there are two Kaluza-Klein towers of fields. We show that to find a consistent truncation one must include zero-modes associated to each of the two towers. Although this naively leads to a doubling of the massless degrees of freedom we show that on-shell this is not the case. We also discuss a deformation of the Sen action to include an additional form-field but which does not lead to new degrees of freedom on-shell but which arises naturally upon compactification.

Dynamic control of visible light is crucial for technologies such as holographic displays and adaptive optics. Passive metasurfaces can shape wavefronts at the subwavelength scale and active metasurfaces promise to extend this functionality into the temporal domain. However, existing metasurfaces for dynamic phase manipulation typically cannot deliver phase modulation across a broad range without causing variations in the scattering amplitude. Here, we use an inverse-design pipeline to numerically demonstrate a hybrid-2D excitonic metasurface platform offering independent amplitude and phase control in the visible regime. Harnessing the gate-tunable excitonic response of monolayer WS2 retrieved from experiments, we design a pi-phase modulator with a uniform amplitude profile. Adding a second tunable monolayer, we achieve independent control of the amplitude and phase over the full 0-2pi phase range, which we leverage for a reconfigurable beam-steering metadevice. Our results demonstrate how hybrid-2D excitonic metasurfaces enable electrically tunable wavefront shaping in the visible regime.

We complete our study of the relativistic spatial distributions of the energy-momentum tensor inside polarized nucleons within the quantum phase-space formalism. In the present work, we focus on the components of the energy-momentum tensor involving at least one transverse index. We also explore the multipole structure of the transverse distributions in a moving nucleon. In the infinite-momentum frame, we show that the formalism reproduces the standard light-front distributions, including those with a ``bad'' component, and explains the origin of their structure.

Galaxy environment plays a crucial role in quenching star formation in dwarf galaxies. In Milky Way (MW)-like environments, dwarf satellite quenching is primarily driven by ram pressure stripping (RPS), the direct removal of satellite gas by the host halo gas. Using a suite of 20-pc resolution hydrodynamical wind tunnel simulations, we constrain the satellite mass scale at which the stripping of a dwarf galaxy's interstellar medium (ISM) becomes inefficient in MW-like halos. The simulations include radiative cooling in a multiphase satellite ISM, star formation, and stellar feedback, and vary both satellite masses ($M_{\star}=10^{6.2}, 10^{6.8}, 10^{7.2}\ M_{\odot}$) and host halo gas densities along a first-infall and post-pericentric orbit. We find that the degree of ISM stripping in our dwarf galaxies is consistent with the analytical prediction by McCarthy et al. (2008). Star formation is rapidly quenched when RPS is effective, but can be mildly enhanced or temporarily quenched and subsequently reignited when RPS is incomplete. ISM stripping is efficient for satellites with $M_{\star} \lesssim 10^{7}\ M_{\odot}$ (or $M_{200} \lesssim 10^{10}\ M_{\odot}$) but highly inefficient above this scale. This transitional mass ($M_{\star} \approx 10^{7}\ M_{\odot}$) is 0.5-1 dex lower than that found in observations and cosmological simulations, suggesting that additional mechanisms are needed to quench more massive satellites, such as tidal stripping of the satellite dark matter or RPS from a clumpy gaseous halo.

In this paper, we consider the data-driven discovery of stable dynamical models with a single equilibrium. The proposed approach uses a basis-function parameterization of the differential equations and the associated Lyapunov function. This modeling approach enables the discovery of both the dynamical model and a Lyapunov function in an interpretable form. The Lyapunov conditions for stability are enforced as constraints on the training data. The resulting learning task is a mixed-integer quadratically constrained optimization problem that can be solved to optimality using current state-of-the-art global optimization solvers. Application to two case studies shows that the proposed approach can discover the true model of the system and the associated Lyapunov function. Moreover, in the presence of noise, the model learned with the proposed approach achieves higher predictive accuracy than models learned with baselines that do not consider Lyapunov-related constraints.

We revisit Brockett's attention in the context of bilinear gradient flow of an ensemble, and explore an alternative formalism that aims to reduce shear by minimizing the conditioning number of the dynamics; equivalently, we minimize the range of the eigenvalues of the dynamics. Remarkably, the evolution is isospectral, and this property is inherited by the coupled nonlinear dynamics of the control problem from a Lax isospectral flow.

Deformations of skyrmions arising from the presence of more than one magnetic sublattice lead to their massive dynamics in ferrimagnets as compared to the massless dynamics in 2D ferromagnets. This results in the gyroscopic motion of skyrmions, which manifests as skyrmion cyclotron resonance that can be excited by microwaves or spin currents. We investigate analytically and numerically the motion and resonant oscillations of individual skyrmions and skyrmion lattices in the presence of dissipation in a two-sublattice transition-metal -- rare-earth (TM/RE) system. The focus is on the dependence of the skyrmion dynamics on the RE concentration. Parameters of the CoGd ferrimagnet are utilized in the numerical work. The massive dynamics of skyrmions in ferrimagnets, as well as the spectrum of their excitations, undergo significant changes near the angular momentum compensation point, which should not be difficult to detect in experiments.

Exclusion and exogeneity are core assumptions in instrumental variable (IV) analyses, but their empirical validity is often debated. This paper develops new sensitivity analyses for these assumptions. Our results accommodate arbitrary heterogeneity in treatment effects and do not impose any monotonicity requirements on the first stage. Specifically, we derive identified sets for the marginal distributions of potential outcomes and their functionals, like average treatment effects, under a broad class of nonparametric relaxations of the exclusion and exogeneity assumptions. These identified sets are characterized as solutions to linear programs and have desirable theoretical properties. We explain how to estimate these solutions using computationally tractable methods even when the linear program is infinite-dimensional. We illustrate these methods with an empirical application to peer effects in movie viewership, using weather as a potentially imperfect instrument.

The world is full of systems of distributed agents, collaborating and competing in complex ways: firms and workers specialise within economies, neurons adapt their tuning across brain circuits, and species compete and coexist within ecosystems. In that context, individual research fields built theories explaining how comparative advantage drives trade specialisation, how balanced neural representations emerge from sensory coding, and how biodiversity sustains ecological productivity. Here we propose that many of these well-understood findings across fields can be captured in one simple joint cross-disciplinary model, which we call the Distributed Production System. It captures how agent heterogeneity, resource constraints, communication topology, and task structure jointly determine the productivity, efficiency, and robustness of distributed systems across biology, economics, neuroscience, and computing. This model reveals that a small set of underlying laws generates the complex dynamics observed across fields. These can be summarised in our Principle of Maximum Heterogeneity: any distributed production system optimising for performance will converge on an increasingly heterogeneous configuration; environmental demands place an upper bound on the degree of heterogeneity required; and the communication topology determines the spatial scale over which heterogeneity spreads, with this principle applying recursively across all layers of nested production systems. Beyond explaining existing systems, these principles act as a blueprint for constructing ideal ones. We demonstrate this by suggesting specific redesigns for compute systems executing large-scale AI. In total, The Principle of Maximum Heterogeneity reveals a unique convergence of complex phenomena across fields onto simple underlying design principles with important predictive value for future distributed production systems.

Let \(Ω\subset\mathbb{C}^n\) be a hyperconvex domain and let \(χ:\mathbb{R}^-\to\mathbb{R}^+\) be a decreasing function. This note studies the local weighted energy class \(\mathcal{E}_{χ,\mathrm{loc}}(Ω)\) introduced in \cite{HHQ13}. We establish two main results on local membership in this class. First, we prove a new local boundedness property for the weighted Monge--Ampère energy: if \(u\in\mathrm{PSH}^-(Ω)\) admits suitable local majorants in \(\mathcal{E}_{χ,\mathrm{loc}}\) near the boundary of every relatively compact hyperconvex subdomain \(D\SubsetΩ\), then the weighted energy \(\int_K χ(u)(dd^c u)^n\) remains locally finite for every compact set \(K\subset D\). This gives the first explicit local control of the energy functional and is new even in the unweighted setting. Second, we obtain a substantial improvement concerning the local control of the Monge--Ampère measure. We show that if, in addition to the boundary condition, \((dd^c u)^n\) is locally dominated by \((dd^c w)^n\) for some \(w\in\mathcal{E}_{χ,\mathrm{loc}}(D)\) inside \(D\), then \(u\in\mathcal{E}_{χ,\mathrm{loc}}(D)\). This domination condition is strictly weaker than the previous requirement of local finiteness of the weighted energy, thereby significantly enlarging the class of admissible functions. Our results extend and refine the local theory developed in \cite{Q24,Q25} and provide a more flexible framework for plurisubharmonic functions with possible singularities on compact subsets.

The European Space Agency's Ariel mission will conduct a survey of the atmospheric properties of exoplanets around bright stars. The mission is nominally divided into three Tiers. The Tier 1 survey will consist of low-precision observations of ~1000 planets, with a subset of these included in the higher-precision Tier 2 survey expected to be necessary for atmospheric characterization. Tier 3 will be repeated observations of a small number of benchmark planets. Though previous studies have assessed the ability of Ariel to uncover population-level trends, they have generally presupposed a given Tier. Here we interrogate this assumption and assess the information content of Ariel transmission spectra as a function of Tier for three benchmark planets: a hot-Saturn, warm-Neptune, and temperate sub-Neptune. We simulate a grid of Ariel transit spectra at different Tiers for each target and use retrievals to assess which chemical species are detectable. We find that for giant planets like a hot-Saturn or warm-Neptune, Tier 1-quality observations are sufficient for <1.5dex constraints on H2O and CO2, irrespective of the presence of clouds -- meaning important chemical insights are already obtainable in the Tier 1 survey. Moving to Tiers 2 and 3 result in an incremental increase in precision as well as other molecules becoming detectable in certain scenarios (e.g., H2S, CO). Tier 1 observations are also sufficient to constrain CH4 in a cloud-free, temperate sub-Neptune, whereas observations with at least Tier 2 precision are necessary if the atmosphere is cloudy. The number of transits necessary to reach this precision, however, may be prohibitive for the inclusion of temperate sub-Neptunes in even the Tier 1 survey.

We apply a general relativistic framework to static and rotating black hole solutions in Scalar-Tensor-Vector Gravity or modified gravity (MOG). Our results yield exact analytic, closed-form relations expressing the mass $M$, the MOG coupling parameter $α$, and the distance $D$ of a Schwarzschild-MOG black hole in terms of a minimal set of directly measurable elements of the accretion disk: the total frequency shift, the telescope aperture angle, and the redshift rapidity. The resulting expressions are derived for particles close to the midline and line of sight, where the redshift rapidity is treated as a relativistic invariant encoding the evolution of the frequency shift with respect to the emitter's proper time in MOG spacetime. We further extend the formalism to the rotating Kerr-MOG geometry and obtain corresponding relations that determine the rotation parameter $a$ jointly with $M$, $α$, and $D$ on the midline. In the rotating background, we introduced the redshift acceleration (general-relativistic version of jerk) to disentangle the spacetime parameters. Crucially, the explicit appearance of $α$ in these formulas enables direct empirical estimation of this parameter, thereby providing a means to test for departures from standard general relativity. The previous results obtained in the standard Schwarzschild/Kerr backgrounds are recovered in the limit $α\to 0$. The derived expressions are concise and suitable for incorporation into black hole parameter-estimation pipelines.

Due to the inhomogeneous polarisation across the beam profile, cylindrical vector beams interact with optically active media in a complex manner. Here, we analyse evolution of polarisation of cylindrical vector beams propagating in an isotropic optically-active medium. After identifying polarisation normal modes of three-dimensional electromagnetic fields, we predict periodic inter-conversion between azimuthally- and radially-polarised modes of the beams accompanied by rotation of the transverse optical spin and pulsing field during the propagation. Theory and simulations are validated by experimental observations. The observed effects maybe important for imaging in biological chiral media, enhanced chiral sensing and enantioselective spectroscopy, nonlinear optics in chiral media, and generally enhanced spin-orbit coupling and nanoscale vector field engineering.

As humanity pivots toward long-duration interplanetary travel, the psychological constraints of Isolated and Confined Environments (ICE) emerge as a primary mission risk. This paper presents COSMIC (COmpanion System for Mission Interaction and Communication) representing the inaugural investigation into the deployment of a high-fidelity, emotionally intelligent AI companion in an analog astronaut setting. By integrating a Large Language Model (LLM) architecture with a diffusion-based digital avatar interface, COSMIC transcends traditional task-oriented automation to provide longitudinal affective support. We detail a modular system architecture designed for temporal continuity through short- and long-term memory systems and outline a robust naturalistic observational framework for evaluating psychological resilience at the LunAres Research Station. This work constitutes the first formal submission in the field to evaluate the efficacy of state-of-the-art generative AI and synthesized visual empathy in mitigating the effects of extreme isolation.

The central few parsecs of the Milky Way host a complex X-ray-emitting environment in which several extended plasma components are blended along the line of sight, complicating attempts to measure the intrinsic properties of individual components. In particular, the supernova remnant (SNR) Sgr A East is strongly confused with the stellar wind-fed plasma associated with Sagittarius A* and the surrounding nuclear environment. Here we apply Poissonian Generalized Morphological Component Analysis (pGMCA) to deep, stacked Chandra ACIS-I observations of the Galactic Center to disentangle these overlapping X-ray components. By comparing the separated X-ray components with multiwavelength data, we identify the location of the reflected shock in Sgr A East and construct spatially resolved maps of Fe and S/Ar/Ca emission. The Fe emission is centrally concentrated, consistent with the properties of mixed-morphology supernova remnants. Separating the SNR emission from the shocked wind plasma around Sgr A* allows us to recover uncontaminated SNR properties and improve the robustness of the derived parameters. Spectral modeling of the isolated Sgr A East component reveals a lower ionization age and a higher electron density than previously reported, indicating strong interaction with dense surrounding material.

We present a study of the multiphase gas structure and kinematics of the circumnuclear disk (CND) of NGC 5506, a nearby edge-on Seyfert galaxy, at a spatial resolution of $\sim20$ pc. Observations of [C I](1-0), CO(3-2), and HCO$^{+}$(4-3) obtained with the Atacama Large Millimeter/submillimeter Array reveal the CND dominated by rotational motion on scales of several hundred parsecs. No significant differences in geometrical thickness or velocity structure are found between [C I](1-0) and CO(3-2) across the CND, whereas HCO$^{+}$(4-3) emission is more concentrated toward the disk plane. The ratio of velocity dispersion to rotational velocity, a proxy for disk scale height-to-radius ratio, is high ($\gtrsim0.9$) in the central region ($\lesssim30$ pc) for both [C I](1-0) and CO(3-2), indicating geometrically thick structures in both tracers. Regions where the [C I](1-0)/CO(3-2) ratio exceeds the CND average are spatially correlated with the [O III]$λ$5007 bicone observed with the Hubble Space Telescope, suggesting that CO is preferentially dissociated by the AGN-driven biconical ionized outflow. The observed CND scale height and velocity dispersions traced by [C I](1-0) and CO(3-2) are consistent with a model in which supernova-driven turbulence provides the vertical support for the CND.

Controlled Environment Agriculture (CEA) demands precise, adaptive climate management across distributed infrastructure. This paper presents IOGRUCloud, a scalable three-tier IoT platform that integrates AI-driven control with edge computing for automated greenhouse climate regulation. The system architecture separates field-level sensing and actuation (L1), facility-level coordination (L2), and cloud-level optimization (L3-L4), enabling progressive autonomy from rule-based to fully autonomous operation. A Vapor Pressure Deficit (VPD) cascading control loop governs temperature and humidity with GRU-enhanced PID tuning, reducing manual calibration effort by 73%. Deployed across 14 production greenhouses totaling 47,000 m2, the platform demonstrates 23% reduction in energy consumption and 31% improvement in climate stability versus baseline. The system handles 2.3M daily sensor events with 99.7% uptime. We release the architecture specification and deployment results to support reproducibility in smart agriculture research.

Gamma-ray bursts (GRBs) are among the most luminous and rapidly evolving transients in the Universe. While space-based instruments have extended GRB observations up to energies of $\sim$100 GeV, the detection of very-high-energy (VHE; $E>100$ GeV) emission from ground-based telescopes, especially during prompt or/and the early afterglow phase, remains challenging. These difficulties arise from the rapid temporal decay of GRB afterglows, strong attenuation by the extragalactic background light (EBL), observational latency, and the typical poor sky localization provided by MeV-detectors such as Fermi/GBM. In this work, we investigate the prospects for detecting TeV ($\sim$100 GeV--1 TeV) emission from poorly localized GRBs by adopting optimized follow-up strategies based on rapid tiling of large localization regions. We simulate a realistic population of GRBs informed by more than fifteen years of Fermi/GBM and Swift/XRT detections and recent progresses in the afterglow emission modeling. Using these simulations, we evaluate the detectability of GRB early afterglows by the next-generation Imaging Atmospheric Cherenkov Telescopes, equipped with larger field-of-view (FoV), as a function of latency, exposure time, and observational strategy. Our strategy can significantly enhance the detection rate; for instruments such as ASTRI and LACT, it increases by up to a factor of two compared to strategies limited to well-localized (Swift-like) events. For CTAO, our proposed approach provides up to four VHE detections per year.

It can be difficult for practitioners to interpret the quality of differentially private (DP) statistics due to the added noise. One method to help analysts understand the amount of error introduced by DP is to return a Randomization Interval (RI), along with the statistic. A RI is a type of confidence interval that bounds the error introduced by DP. For queries where the noise distribution depends on the input, such as the median, prior work degrades the quality of the median itself to obtain a high-quality RI. In this work, we propose PostRI, a solution to compute a RI after the median has been estimated. PostRI enables a median estimation with 14%-850% higher utility than related work, while maintaining a narrow RI.

When multiple investigators analyze a common dataset, the data reuse induces dependence across testing procedures, affecting the distribution of errors. Existing techniques of managing dependent tests require either cross-study coordination or post-hoc correction. These methods do not apply to the current practice of uncoordinated groups of researchers independently evaluating hypotheses on a shared dataset. We investigate the use of subsampling techniques implemented at the level of individual investigators to remedy dependence with minimal coordination. To this end, we establish the asymptotic joint normality of test statistics for the class of asymptotically linear test statistics, decomposing the covariance matrix as the product of a data overlap term and a test statistic association term. This decomposition shows that controlling data overlap is sufficient to control dependence, which we formalize through the notion of Expected Variance Ratio. This enables the closed form derivation of the variance of the joint rejection region under the global null as a function of pairwise correlations of test statistics. We adopt mean-variance portfolio theory to measure risk, defining the Expected Variance Ratio (EVR) as the ratio of the expected variance of the Type I error count to the independent baseline. We show that data splitting is asymptotically optimal among rules that ensure exact independence. We then use concentration inequalities to establish that subsampling techniques implementable by individual investigators can ensure an EVR close to $1$. Finally, we show that such subsampling techniques are able to simultaneously perform a number of tests while ensuring sufficient power and that the bounded EVR is $O\left(\frac{1}{r^2}\right)$ compared to data splitting's $O\left(\frac{1}{r}\right)$, where $r$ is the per-statistic fraction of data required.