We present a general nonparametric approach for testing whether a statistical parameter defined through conditional distributions is constant across the conditioning variables. Such hypotheses arise naturally in problems such as assessing treatment effect heterogeneity, conditional associational effects, and conditional mean dependence. Our framework studies function-valued parameters obtained by evaluating a smooth statistical functional on conditional probability distributions. We establish an explicit connection between our test and procedures based on studying the norm of the function-valued parameter. Unlike many existing norm-based tests, which exhibit poor asymptotic behavior under the null, the proposed test statistic admits a tractable limiting null distribution. We illustrate the applicability of the proposed test through several examples, assess its operating characteristics in simulation studies, and apply it to data from a breast cancer trial to identify predictive biomarkers for response to adjuvant chemotherapy.

Pairwise Compatibility Graphs (PCGs) form a tree-metric graph class that originated in phylogeny and has since attracted sustained interest in graph theory. Several natural generalizations have been proposed in order to overcome the expressive limitations of classical PCGs, including $k$-interval-PCGs, $k$-OR-PCGs, and $k$-AND-PCGs. In this paper, we introduce $(k,t)$-threshold-PCGs, a threshold-based framework that unifies these generalized notions: adjacency is determined by whether at least $t$ among $k$ underlying PCG predicates accept the vertex pair. We investigate the expressive power of this model from both constructive and asymptotic viewpoints. On the positive side, we show that every graph on $n$ vertices is a $(n,t)$-threshold-PCG for every $1 \le t \le n$. On the negative side, we prove that for every fixed pair $(k,t)$, the class of $(k,t)$-threshold-PCGs is asymptotically rare among all graphs. As a consequence, we obtain sharp separations from previously studied models, including a strict expressive gap relative to $k$-interval-PCGs. We also study explicit obstruction families through incidence graphs and derive additional structural consequences for the conjunction case, including the strictness of the $k$-AND-PCG hierarchy and the failure of closure under complement.

The determination of quartic force fields for use in vibrational second-order perturbation (VPT2) calculations, currently available in numerous electronic structure packages, becomes very expensive as the size of the molecule increases, especially if high-level coupled cluster theory is used. Machine-learned potentials (MLPs) for large molecules and clusters offer a viable alternative to obtain the quartic force field (QFF). Here, we report Fortran and Python software to determine the QFF and perform VPT2 calculations of energies from MLPs. We describe this software briefly and then apply it to \ce{H2O} and protonated oxalate as test cases. The Fortran software is applied to 21-atom aspirin, using a fast MLP reported by us. Despite the fact that there are 32,509 unique cubic force constants for aspirin, the computer time to calculate them using this MLP is trivial, i.e., around one minute. These results are the first quantum anharmonic ones for such a large molecule. The present protocol offers an efficient way to study quantum anharmonic effects for vibrational energies in large molecules. Currently, these are obtained overwhelmingly from classical molecular dynamics simulations, which cannot describe strong anharmonicity.

Central exclusive diffractive production in proton-proton collisions at hadron colliders is characterised by hadronic activity at or close to midrapidity, and by the two forward scattered protons, or their remnants. In such events, no particles are produced between the midrapidity system and the forward beam particles. These events can hence be identified with appropriately placed detectors for measuring the forward scattered protons, or their remnants, and a detector system covering the midrapidity range. At the energies of the LHC, central diffractive production in proton-proton collisions is dominated by pomeron-pomeron fusion. The description of the pomeron within the Regge approach is summarized, and the feasibility of identifying pseudoscalar mesons $η,η'$ in pomeron-pomeron fusion is studied for determining the spin structure of the pomeron.

Compressible multiphase and multicomponent solvers require accurate interface representation without spurious pressure oscillations. At material interfaces, pressure and velocity are continuous while density and the equation of state exhibit abrupt discontinuities. Standard approaches reconstruct primitive or characteristic variables to capture these properties, but do not clarify the failure mechanisms of conservative reconstruction or fully leverage the wave-decoupling advantages of characteristic decomposition. This work derives the complete eigenstructure of the Allaire five-equation model for two variable sets. In the fully conservative~(FC) formulation, $\mathbf{U} = [α_1ρ_1,\,α_2ρ_2,\,ρu,\,ρv,\,ρE,\,α_1]^T$, eigenvectors contain a thermodynamic jump term~$Ψ$ that enforces $dp=0$ and $du=0$ at material contacts by compensating for compressibility mismatches. In the semi-conservative~(SC) formulation, $\mathbf{V} = [α_1ρ_1,\,α_2ρ_2,\,ρu,\,ρv,\,p,\,α_1]^T$, the volume-fraction eigenvector carries a structural zero in the pressure slot, enforcing equilibrium without thermodynamic correction. Explicit left and right eigenvectors are derived for one- and two-dimensional stiffened-gas flows. Both formulations satisfy Abgrall's equilibrium condition when reconstruction is performed in characteristic space; reconstruction in physical space yields $\mathcal{O}(1)$ pressure and velocity errors at interfaces regardless of the variable set. The eigenvector structure further reveals that the shear wave is decoupled from all thermodynamic and interface fields in both formulations, extending this result from single-species to compressible multiphase flows including gas-liquid configurations. One- and two-dimensional gas-gas and gas-liquid test cases confirm oscillation-free, accurate results.

We show how to directly and efficiently approximate arbitrary one-qubit unitaries, bypassing the Euler decomposition and the magnitude approximation problem, at the cost of one ancillary qubit. Our technique also applies to approximating unitaries with multi-qubit gate sets such as Clifford and CS, or Clifford and CCZ, as well as to approximating orthogonal matrices using multi-qubit gate sets such as Real Clifford and CCZ. The key tools are repeat-until-success circuits, lattice-based exact synthesis algorithms, integer point enumeration in convex sets, and relative norm equations.

More than half of the Top 500 supercomputers employ GPUs as accelerators. On GPU-accelerated platforms, developers face a key diagnostic gap: profilers show source lines where stalls occur, but not why they occur. Furthermore, the same kernel may have different stalls and underlying causes on different GPUs. This paper presents LEO, a root-cause analyzer for NVIDIA, AMD, and Intel GPUs that performs backward slicing from stalled instructions, considering dependencies arising from registers as well as vendor-specific synchronization mechanisms. LEO attributes GPU stalls to source instructions with the goal of explaining root causes of these inefficiencies. Across 21 workloads on three GPU platforms, LEO-guided optimizations deliver geometric-mean speedups of 1.73$\times$--1.82$\times$. Our case studies show that (1) the same kernel may require different optimizations for different GPU architectures, and (2) LEO's structured diagnostics improve code optimization with large language models relative to code-only and raw-stall-count baselines.

We extend classical evolutionary game dynamics based on the momentary action choices of agents by accounting for two elements: forward-looking behavior and exploration cost. We focus on pairwise comparison protocols that cover major evolutionary game dynamics, such as replicator and logit models. In the proposed mathematical framework, agents update their actions by paying a cost so that a utility or its relative difference is maximized. We show that forward-looking behavior can be modeled as a coupling between the evolutionary game dynamic and static Hamilton-Jacobi-Bellman equation: a mean field game. The exploration cost and its constraint are naturally related to these equations as a function of the optimal Lagrangian multiplier serving as a relaxation parameter, and it is incorporated into the game as a constraint. We show that under certain conditions, our evolutionary game dynamic admits a unique solution. Finally, we computationally investigate one- and two-dimensional problems.

Due to attractive inter-particle forces, cohesive particles suspended in turbulence undergo a complex process of aggregation, breakup, and restructuring. Despite a growing body of knowledge on the ``flocculation'' of cohesive granular materials suspended in homogeneous isotropic turbulence, little focus has so far been placed on wall-bounded flows where turbulence and shear are inhomogeneous. This study presents a first investigation of a fully developed wall-bounded flow of resolved cohesive particles. Five direct numerical simulations of turbulent channel flows laden with finite-sized particles at successively increasing cohesive strength are performed. A population balance equation (PBE) framework is used to analyze aggregate dynamics. When integrated over the full domain, the PBE is closed by aggregation and breakup alone. However, this balance is found to not hold locally in the wall-normal direction, where regions of net aggregate production and depletion are identified. This imbalance is shown to be compensated by the size-dependent wall-normal transport of aggregates, revealing a mean circulation: larger aggregates are preferentially produced in the channel center and migrate toward the wall where they break, while smaller aggregates are transported away from the wall, grow, and reenter the cycle.

We present error estimates for the BMZ (Bubble Mesh Zoom) residual-free bubble method applied to a convection-diffusion equation in the convection-dominated regime. The method incorporates both element bubbles and residual-free bubbles supported on patches of two adjacent elements. We focus on the case of a parallel flow in a square domain and derive error estimates in an energy norm that remain valid as diffusion becomes small. The theoretical findings are corroborated by numerical experiments, which also exhibit a competitive performance of the method.

We report a comprehensive determination of the broadband dielectric permittivity tensor of van der Waals (vdW) muscovite also referred to as mica, establishing it as a low-index low-loss platform for ultrathin nanophotonics. Resolving its anisotropic vibrational response and extracting accurate tensor components across broadband ultraviolet (UV) to near-infrared (NIR) spectral region, we show that vdW muscovite exhibits consistently low refractive indices negligible extinction and weak in-plane anisotropy allowing its effective treatment as a uniaxial dielectric in thin-film limits. Leveraging these properties, we design muscovite based vdW heterostructures pairing it MoS2, engineering few-layer distributed Bragg reflectors (DBR) and dichroic beam splitters (DBS) with high efficiency robust optical performance in a broad NIR spectral region achieved with sub-micron thicknesses. Our findings spotlight the high significance of low-index extinctionless vdW crystals, positioning muscovite as a highly perspective atomically flat building block for next-generation, broadband, all-vdW nanophotonic components.

As microelectronics flourish and outsourcing of the design and manufacturing stages of integrated circuits (ICs) and printed circuit boards (PCBs) becomes the norm, microelectronics stakeholders must also confront a new wave of security challenges, including the threats posed by hardware Trojans, counterfeit electronics, and reverse engineering attacks. Traditional detection and prevention methods like testing and side-channel analysis have limitations in reliability and scalability. Automated reverse engineering by deep learning (DL) models is a foolproof approach to hardware assurance, but faces challenges due to limited data. By pooling data from different stakeholders (competitors in industry, governments, etc.), DL models can be more effectively trained but privacy of intellectual property (IP) is a significant concern. Federated Learning (FL) has been proposed as a potential alternative allowing for the collaborative training of a DL model without sharing raw data. While FL has been widely used in healthcare, IoT, and finance, its application in hardware assurance remains underexplored. This study investigates, for the first time, FL-based DL for hardware assurance, demonstrating that FL outperforms single-client centralized learning in segmentation tasks for reverse engineering. Our results show that increasing the number of clients improves FL performance by collaboratively training the model with more data. However, and more importantly, a major pitfall of FL is also exposed -- it remains vulnerable to gradient inversion attacks. We show that SEM images used in FL can be recovered by attackers, which would therefore expose the sensitive and proprietary IPs that FL was supposed to protect. We highlight these privacy risks and also suggest future research directions to improve security and effectiveness in hardware assurance.

We develop a family of $H(\mathrm{div})$-conforming hybridizable discontinuous Galerkin methods for the steady Stokes equations based on BDM and RT velocity spaces with either discontinuous or continuous hybrid traces. In contrast to our earlier pressure-robust HDG method for tangential boundary control, the present analysis does not require the pressure to belong to $H^1$; instead, the consistency argument only assumes low pressure regularity. The discrete velocities are exactly divergence-free, which yields pressure robustness. For the BDM variants we derive optimal energy-norm estimates and optimal $L^2$-velocity convergence, while for the RT variants we obtain optimal velocity convergence and weaker pressure estimates. We also analyze the hybridized linear system and prove a uniform spectral equivalence for the pressure Schur complement relevant to iterative solvers. As an application, we revisit the Stokes tangential boundary control problem and derive error estimates for the control, state, and adjoint variables using the BDM discontinuous-trace scheme. Two- and three-dimensional numerical experiments confirm the predicted convergence rates, the exact divergence-free property, and the robustness of the method with respect to the viscosity parameter.

In many statistical applications, particularly in clinical studies, hypotheses may carry different levels of importance, motivating the use of weighted multiple testing procedures (wMTPs) to control the familywise error rate (FWER). Among these approaches, two weighted Holm procedures are commonly used: the weighted Holm procedure (WHP), which is based on ordered weighted $p$-values, and the weighted alternative Holm procedure (WAP), which relies on ordered raw $p$-values. This paper provides a systematic comparison of these two procedures, along with practical recommendations for their use. We first examine their corresponding closed testing procedures (CTPs) and show that WHP is uniformly more powerful than WAP. We further investigate their structural properties, demonstrating that WAP, while consonant, lacks monotonicity. To facilitate communication with non-statisticians, we introduce graphical representations of both procedures using a common initial graph and distinct updating strategies. In addition, we derive adjusted $p$-values and adjusted weighted $p$-values for both methods. Finally, we establish an optimality result: WHP cannot be improved by enlarging any of its critical values without violating FWER control, whereas WAP is optimal only under specific conditions. Simulation studies support these theoretical findings and highlight the superior FWER control and average power of WHP.

Automated third-party library analysis tools help developers by addressing key dependency management challenges, such as automating version updates, detecting vulnerabilities, and detecting breaking updates. Dependency reachability analysis aims at improving the precision of dependency management, by reducing the space of dependency issues to the ones that actually matter. Most tools for dependency reachability analysis are static and fundamentally limited by the absence of execution. In this paper, we propose FIKA, a pipeline for providing guarantees of executability for third-party library call sites. FIKA generates code that is executed, and whose execution trace provides guarantees that a third-party library call site is actually reachable. We apply our approach to a dataset of eight Java projects to empirically evaluate the effectiveness of FIKA. On average, 54% of these call sites are covered by the existing test suites, and therefore, have evidence for their executability. FIKA further improves this coverage by 20% and is able to demonstrate executability for 2363 dependency methods. In six out of eight projects, FIKA provides strong guarantees that more than 75% of call sites are executable. We further demonstrate that FIKA is capable of improving the results provided by Semgrep, a state-of-the-art static vulnerability reachability analysis tool. We show that FIKA can help prioritize the vulnerability updates with stronger guarantees of executability in cases where Semgrep yields inconclusive reachability results.

Early modular fault tolerant quantum computers remain constrained by costly inter-module communication and limited magic state factory service. Understanding such bottlenecks and investigating compiler optimizations most close the gap between algorithm requirements and hardware capabilities is a concrete and practically urgent systems problem. We study the modular architectures based on Bivariate Bicycle codes and identify the dominant bottleneck: inter-module communication induced by non-Clifford operations. We build a compilation pipeline to fill the missing parts of prior works and propose compiler optimizations: synthesizing arbitrary-angle rotations at the factory (syn@fac), transvection based Clifford deferral, and Clifford insertion for critical path duration reduction. We extend the evaluation scope of the prior work to 40+ benchmark categories drawn from PennyLane and MQTBench, including quantum algorithms and Hamiltonian simulations with varying sizes. Under the present instruction cost, syn@fac reduces estimated circuit failure probability by a factor of 9.0 on average across non-Clifford benchmarks. The robustness persists across sweeps of instruction cost ratios, LPU count, and factory count. Besides, transvection reduces Clifford deferral compile time by 77.04\%, while Clifford insertion reduces end-to-end circuit duration by 11.54\% on average on MQTBench, with smaller gains on Hamiltonian simulations. We hope this work inspires the studies on compiler optimizations for early modular FTQC systems.

We present the first high-resolution near-infrared spectroscopic analysis of the solar analog HD115617 (61 Virginis), complemented by optical spectroscopy, asteroseismology, and spectral energy distribution modeling. Using ESPRESSO and IGRINS spectra with a newly calibrated NIR line list, we derived atmospheric parameters that revealed notable differences between spectral regions: the optical analysis yielded Teff = 5500 +- 140 K, log g = 4.40 +- 0.16, and solar metallicity, whereas the NIR yielded Teff = 5750 +- 140 K. We tested this 250 K discrepancy using the independent line depth ratio (LDR) method for both spectra. When applied to the optical lines, LDR confirmed the cooler scale (5553 +- 73 K); for the NIR lines, it provided an intermediate temperature (5636 +- 15 K). Asteroseismic scaling with TESS data yielded a radius of 0.98 +- 0.09 R_sun, consistent with SED fitting and confirming the star's main-sequence solar-like status. However, the age estimates diverged between methods, with optical and NIR analyses yielding ages of 10.97 and 8.04 Gyr, respectively. Critically, a condensation temperature analysis revealed no significant trend, confirming the star's bulk solar-like composition and showing no chemical signature of planetary formation processes. Kinematic diagnostics place HD115617 in the thin Galactic disk, with a birth radius of approximately 5.7-8.0 kpc. Although the spectral differences may be linked to the star's multi-planet system or debris disk, our analysis highlights the critical challenge of distinguishing such effects from methodological systematics in multi-wavelength studies. Consequently, we propose a systematic, homogeneous optical-NIR survey of solar-type stars to resolve this ambiguity, which could ultimately inform novel indirect methods for characterizing stellar environments.

Motivated by the exact weight perfect matching problem and recent parameterized algorithms for finding an $\ell$-th smallest perfect matching, we study structural properties of edge-weight symmetries in graphs. Recent work by El Maalouly et al. (ESA 2025) showed that excluding all perfect matchings whose weight is at most the $(\ell - 1)$-th smallest possible value in the graph requires fixing at most $2(\ell-1)$ edges in non-bipartite graphs and at most $\ell-1$ edges in bipartite graphs. A natural open question is whether fixing a single edge is always sufficient to shift the extreme (minimum or maximum) weight of a perfect matching when the global minimum and maximum weights differ. To address this, we define and analyze a hierarchy of progressively weaker edge-weight properties: node-induced weights, even walk and cycle symmetries, perfect matching equality, and the edge min-max property. We derive a basic hierarchy among these conditions and show that they become equivalent in bipartite graphs. For general graphs, we provide tight structural characterizations, based on block and tight cut decompositions, under which even cycle symmetry and perfect matching equality force node-induced weights. Finally, we resolve the motivating open question in the negative by constructing a matching-covered non-bipartite graph that satisfies the edge min-max property (every edge is contained in a minimum-weight perfect matching and a maximum-weight one) but violates perfect matching equality (all perfect matchings have the same weight). This counterexample shows that a single edge is not always sufficient to eliminate all minimum-weight or maximum-weight perfect matchings, thereby proving the tightness of the $2(\ell-1)$ bound for $\ell=2$. We also discuss extensions of this framework to $b$-factors and arborescences.

We investigate gas injection into water-saturated porous channels with Gaussian and parabolic axisymmetric centrelines, as idealized models of underground gas storage in dome-shaped anticlines. Exploiting the slenderness of each channel, we derive an evolution equation for the gas/liquid interface using a composite asymptotic approximation that accommodates large channel slopes and has a simplified small-slope form describing spreading in weakly curved channels. In the high gas-mobility limit, in contrast with flat planar channels, buoyancy influences the dynamics through different mechanisms in each geometry. For gas injected steadily into a Gaussian channel, buoyancy can continually affect the flow due to the attenuation of the gas velocity caused by axisymmetry. In parabolic channels, the increasing channel slope ensures that buoyancy eventually influences the flow, at a timescale depending on injection rate and fluid properties. Asymptotic analysis of the parabolic channel flow reveals five temporal regimes, each with multiple spatial regions and a distinct spreading rate, reflecting the evolving spatiotemporal competition between injection and buoyancy. Initially, a thin film of gas spreads along the upper boundary; the channel slope and elongation of the film then generate a hydrostatic pressure gradient, which strengthens until buoyancy arrests the upper contact line and thickens the film. Beneath the film, liquid then drains until the interface flattens under buoyancy. Analytical solutions of reduced-order models capture interface evolution and contact-line motion through each regime and are validated against full numerical simulations. These results have implications for subsurface hydrogen and CO$_2$ storage, where a horizontal interface that advances vertically enhances both safety and storage efficiency.

In this paper we show that any Noetherian $F$-finite scheme has a dualizing complex $ω^{\bullet}_{X}$ with the property that for all finite type maps $f \colon X \to Y$ between $F$-finite Noetherian schemes there is a canonical isomorphism $ω^{\bullet}_{X} \xrightarrow{\cong} f^!ω^{\bullet}_{Y}$ in $D^b_{coh}(X)$. This, in particular, applies to the Frobenius morphism $F \colon X \to X$ so that we obtain a canonical isomorphism $ω^{\bullet}_{X} \xrightarrow{\cong} F^!ω^{\bullet}_{X}$. To prove this, we rely on a result of Gabber that every Noetherian $F$-finite ring is a quotient of a regular ring, from which it follows that every $F$-finite Noetherian scheme has a (potentially non-canonical) dualizing complex. To make this canonical, we identify the dualizing complex of any $F$-finite Noetherian scheme as a unit of an alternate symmetric monoidal structure on $D^b_{coh}(X)$ we call the $!$-tensor product. We also sketch an alternate approach to finding this canonical dualizing complex following the more classical approach to Grothendieck duality.

We develop new aspects of the homological algebra theory for persistence modules, in both the one-parameter and multi-parameter settings. For a poset $P$ and an order preserving map $φ:P\times P\to P$, we introduce a novel tensor product of persistence modules indexed by $P$, $\otimes_φ$. We prove that each $\otimes_φ$ has a right adjoint, $\mathbf{Hom}^φ$, the internal hom of persistence modules that also depends on $φ$. We prove that every $\otimes_φ$ yields a Künneth short exact sequence of chain complexes of persistence modules. Dually, the $\mathbf{Hom}^φ$ also has an associated Künneth short exact sequence in cohomology. As special cases both of these short exact sequences yield Universal Coefficient Theorems. We show how to apply these to chain complexes of persistence modules arising from filtered CW complexes. For the special case of $P=\mathbb{R}_+$, the $p$-quasinorms for each $p\in (0,\infty]$ yield a distinct $\otimes_{\ell^p_c}$ and its adjoint $\mathbf{Hom}^{\ell^p_c}$. We compute their derived functors, $\mathbf{Tor}^{\ell^p_c}$ and $\mathbf{Ext}_{\ell^p_c}$ explicitly for interval modules. We show that the Universal Coefficient Theorem developed can be used to compute persistent Borel-Moore homology of a filtration of non-compact spaces. Finally, we show that for every $p\in [1,\infty]$ the associated Künneth short exact sequence can be used to significantly speed up and approximate persistent homology computations in a product metric space $(X\times Y,d^p)$ with the distance $d^p((x,y),(x',y'))=||d_X(x,x'),d_Y(y,y')||_p$.

multisphere is an open-source Python package for generating multi-sphere representations of complex particles for use in DEM simulations. It reconstructs triangulated surface meshes and voxelized volumes as sets of intersecting spheres and provides tools for evaluation, visualization, and export.

We analyze the quantum dynamics of the fractional-time Jaynes-Cummings model using a recent unitary framework for the fractional-time Schrödinger equation. We examine how the fractional derivative order $α$ influences non-classical features under different initial conditions. For an initial Fock state, fractional evolution introduces transient dynamics and heightened sensitivity to coupling strength. Through an inverse problem approach, we interpret these effects as arising from an effective time-dependent coupling with a strong initial pulse. For an initial coherent state, the fractional order tunes the system between dynamical regimes, with a transition at $α= 0.50 $ where standard collapse-and-revival is replaced by stable, periodic evolution. This regime enhances non-classical field properties, including stronger sub-Poissonian statistics, periodic quadrature squeezing, and the formation of Schrödinger cat states.

This set of notes complements the lectures and recitation sessions discussed in the following graduate schools: HUGS at Jefferson Lab (years 2018, 2019, 2021), the International School and Workshop on Probing Hadron Structure at the Electron-Ion Collider at ICTS (2024), Frontiers in Nuclear and Hadronic Physics at GGI (2025), and the International Workshop and School on Hadron Structure and Strong Interactions at Nanjing University (2025).

Network meta-analysis of diagnostic test accuracy (NMA-DTA) is a relatively new field, involving combining evidence across studies to evaluate and compare the accuracy of different tests for a given condition. However, the methods proposed to date cannot always capture complex aspects of the data. In fact, many commonly used diagnostic tests are continuous biomarkers, whose accuracy is evaluated at multiple thresholds within a study. Using current NMA-DTA methods we are feasibly able to include in our analysis only a few thresholds per study, discarding this way a big amount of data which could have provided us with useful information. We introduce an approach that can efficiently encompass all available data. This is a hierarchical model that incorporates multinomial likelihoods for studies reporting results across multiple thresholds and a parametric structure for the relationship between the probability of testing positive and threshold within each disease class. This approach enables us to obtain accuracy estimates of tests across the whole range of observed thresholds, while it retains all the useful properties of standard NMA-DTA methods. We explore different variations of this model based on different covariance structures, the inclusion of study-level random effects, and the addition of a further hierarchical structure on the test-level variance components. This framework is applied to data from two systematic reviews, allowing the inclusion of a larger number of tests (compared to alternative approaches) and estimation of sensitivity and specificity at different thresholds with increased precision.

We study a finite-horizon covariance steering problem for discrete-time Markov jump linear systems (MJLS) with both state- and control-dependent multiplicative noise. The objective is to minimize a quadratic running cost while steering the system from given mode-conditioned initial means and covariances to a prescribed terminal mean and covariance. We first show that, without loss of generality, feasible controls may be represented by mode-dependent linear feedback together with feedforward and independent random components, and we highlight that, in contrast to the case without multiplicative noise, a purely affine state-feedback law does not in general suffice. To this end, we introduce a lifted-state formulation that embeds the mean and covariance information into a unified second-moment description, and we prove that the resulting lifted problem is equivalent to the original covariance steering problem formulation. This leads to a lossless relaxation in moment variables and an SDP reformulation for the unconstrained case. We further study chance-constrained covariance steering with ball and half-space constraints on the state and control, derive tractable sufficient convex surrogates, and establish an iterative reference-update scheme to reduce conservatism. Numerical experiments on a finance application illustrate our results.

A Cyber-physical underwater vehicle is equipped with bio-inspired flapping fins positioned on the sides of the vehicle's main body. The proposed control surfaces are inspired by fish pectoral fins, generating forces and moments that can potentially be harnessed for maneuvering, hovering and station keeping. The streamwise and cross-stream forces produced by the fins are characterized for a range of reduced frequencies and Strouhal numbers. The streamwise forces are shown to be predominantly a function of the fin's projected frontal area, while the lateral forces also depend on the Strouhal number. When operated simultaneously, different flapping synchronizations can be employed for specific goals; a symmetric motion suppresses the lateral forces, while an anti-symmetric motion decreases the peaks of the streamwise force produced. The Cyber-physical vehicle demonstrates how the pair of fins can successfully maneuver the vehicle in the lateral direction.

Higher-dimensional quantum systems, such as qudits, offer architectural and algorithmic advantages over qubits, but their increased spectral crowding and limited controllability render high-fidelity quantum gates particularly challenging. We propose a hybrid optimization framework that integrates optimal control theory methods with contextual deep reinforcement learning to achieve robust controlled-phase gates on two qutrits. Optimal control is first used to design high-fidelity control pulses for a nominal system model. Reinforcement learning is then employed as a calibration stage that learns small residual corrections to these pulses in the presence of static model mismatch, thereby preserving good gate performance under realistic parameter uncertainties. By learning structured, low-dimensional residual corrections conditioned on device-specific parameter variations, reinforcement learning enhances the transfer robustness of nominally optimal but parameter-sensitive control solutions across ensembles of devices. Crucially, the reinforcement learning step in our framework does not compete with the optimal control step but provides the adaptability required for realistic hardware, systematically reducing the sensitivity to parameter fluctuations. Our results establish reinforcement learning as a practical and scalable ingredient for robust calibration of quantum gates in high-dimensional systems.

Little Red Dots (LRDs) have emerged as one of the central puzzles of the JWST era. Their spectra increasingly require dense gas close to the source, yet the physical origin of that cocoon-like structure remains unclear. We examine whether late pulsational mass loss from supermassive stars (SMS)leads to dense gas cocoons. We analyze five accreting GENEC models at different metallicities with characteristic masses of order $10^5\,M_\odot$, following them through post-accretion evolution with radial pulsation calculations and general relativistic (GR) stability diagnostics. Mass loss during the final stages of evolution occurs not as a steady wind, but through discrete strange-mode ejection episodes. In the $Z=10^{-2}\,Z_\odot$ model, which provides the clearest LRD analogue, four late episodes last $41$--$282$ yr and eject $10$--$348\,M_\odot$ each, for a total loss of $(4.8-10)\times10^2\,M_\odot$; the final episode alone contributes $\simeq 73\%$ of that budget. Since the last episode dominates the mass-loss, it is the only event sufficiently massive enough to leave behind a compact, optically thick shell extending out to 0.4 pc that reproduces the LRD dense gas cocoon. The final ejecta are H/He dominated but chemically distinctive, with a robust nitrogen-rich composition, $\log(\mathrm{N/O})\simeq0.13$ and $\log(\mathrm{C/O})\simeq-0.23$. The SMS reaches GR instability at an age of $\sim 1$ Myr and collapses in $\sim10^4$ s, retaining $\sim 99\%$ all of its mass. Across the full metallicity range from Pop III to $10^{-2}\,Z_\odot$, this shell-ejection channel persists. Pulsational mass-loss from SMSs therefore provides a physically motivated origin for the compact cocoon-like structure implied by LRDs, while remaining the natural progenitors of the massive black hole seeds invoked in direct collapse scenario.

Between 2005 and 2019, U.S. business applications rose 40 percent while conversion to employer firms fell by nearly half. We study whether boundary redrawing helps explain this pattern. Structured routine-cognitive work can be governed through deliverables and thinner buyer and supplier interfaces. When such work remains place-bound, outsourcing creates demand for domestic specialist suppliers. Across 722 commuting zones, a one percentage-point higher baseline routine employment share raises applications by 27.8 per 100,000 residents. Realized entry concentrates in micro-establishments, with no startup quality gains. Contract and industry evidence point to local supplier entry, not routine-manual displacement.