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  • Glitches cause small to minor bias in EMRI inference

    What the study found

    The study found that extreme mass ratio inspiral, or EMRI, parameter estimates in the Laser Interferometer Space Antenna can be biased by streams of transient noise artifacts called glitches. For moderately mitigated glitch streams, the biases were negligible to minor, while weaker mitigation allowed larger biases.

    Why the authors say this matters

    The authors conclude that EMRI inference is notably more robust to glitches than inference for some other sources, such as massive black hole binaries. They also stress that some glitch modeling and mitigation remains essential for unbiased EMRI analyses in the LISA era.

    What the researchers tested

    The researchers used simulated LISA observations with injected EMRIs and streams of shapelet-based glitches drawn from the LISA Pathfinder catalog. They estimated glitch-induced parameter biases and uncertainties with a Fisher-matrix-based analysis and checked its accuracy with Markov-chain Monte Carlo.

    What worked and what didn't

    Moderately mitigated glitch streams, containing only glitches up to moderate signal-to-noise ratios, produced biases of about 0.04σ to 0.6σ in inferred EMRI parameters. Weakly mitigated streams with higher-signal-to-noise events could produce biases nearing 1σ. The abstract reports that the Fisher-matrix approach was verified with Markov-chain Monte Carlo, but it does not give further details here.

    What to keep in mind

    The summary only describes simulated observations, so the reported results are limited to that setting. The abstract does not provide additional limitations beyond the need for at least some glitch modeling and mitigation.

    • The study examined how glitch streams affect EMRI parameter estimation in LISA.
    • Moderately mitigated glitch streams caused only negligible to minor biases, about 0.04σ to 0.6σ.
    • Weakly mitigated glitch streams with higher-signal-to-noise events could produce biases nearing 1σ.
    • The authors say EMRI inference is more robust to glitches than inference for massive black hole binaries.
    • Some glitch modeling and mitigation is still described as essential for unbiased EMRI analyses.

  • High mass ratios widen post-common-envelope separations somewhat

    What the study found

    The study found that higher companion-to-primary mass ratios can produce wider post-common-envelope separations in red giant binary interactions, but the widest separations they predicted were still smaller than the observed range. The authors also report that the inspiral becomes more stable around mass ratio q ≥ 1, and that fall-back material from the leftover bound envelope is a more likely source of circumbinary discs in their setup.

    Why the authors say this matters

    The authors are trying to explain post-red giant and post-asymptotic giant binary systems, which have longer periods and eccentric orbits than a standard common-envelope inspiral would leave. The study suggests that high mass ratio interactions and fall-back discs may help account for some of the observed features, although the abstract says their simulated separations remain too small.

    What the researchers tested

    The researchers carried out a series of three-dimensional hydrodynamical common-envelope binary interaction simulations using the smoothed particle hydrodynamics code Phantom. They modeled a 0.88 solar-mass, 90-solar-radius red giant branch star with companions spanning mass ratios q = M2/M1 from 0.68 to 1.5.

    What worked and what didn't

    Larger q values led to wider post-common-envelope separations, and the pre-common-envelope mass transfer phase lasted longer for more massive companions. Around q ≥ 1, the inspiral became significantly more stable, as predicted by analytical theory, but the abstract says this phase was not converged with respect to simulation resolution. Even with more material flowing through the L2 and L3 Lagrange points, the authors conclude that fall-back of bound envelope material is more likely than L2/L3 flow to form circumbinary discs for their parameters.

    What to keep in mind

    The abstract says the maximum predicted separation was only about 50 solar radii, which is still below the observed range for the systems they discuss. It also notes that the stability of the pre-inspiral phase was not converged with simulation resolution, so higher-resolution simulations are expected to give even more stability and a longer pre-inspiral phase. The available summary does not describe other limitations.

    • Higher companion-to-primary mass ratios produced wider post-common-envelope separations.
    • The widest predicted separation was still only about 50 solar radii, below the observed range.
    • The inspiral became more stable around mass ratio q ≥ 1.
    • The pre-common-envelope mass transfer phase lasted longer for more massive companions.
    • The authors conclude that circumbinary discs are more likely to form from fall-back of bound envelope material than from L2/L3 outflow.

  • Compact formula for conserved three-point tensor structures in 4D CFT

    What the study found

    The study derives a compact analytic formula for a complete basis of conformally invariant tensor structures for three-point functions of conserved operators in four-dimensional conformal field theory (CFT). It also shows that the same framework can be used for cases with one non-conserved operator.

    Why the authors say this matters

    The authors indicate that the formalism provides a unified way to handle these tensor structures, and they also state that the same results can be reinterpreted as three-point N=2 and N=4 superconformal tensor structures through analytic superspace. The findings also suggest a counting map to finite-dimensional SU(2n) representations solved by Littlewood-Richardson coefficients.

    What the researchers tested

    The researchers used a unified SU(m,m|2n) analytic superspace framework, where conservation conditions are automatically solved, and then reduced the result back to 4D CFT. They derived the formula from a novel constraint equivalent to applying conservation conditions at each point, with the leading terms in operator product expansion limits appearing as symmetric traceless tensors.

    What worked and what didn't

    The method produced a compact analytic formula for the complete basis of conserved three-point tensor structures in arbitrary 4D Lorentz representations. The same method was also used for situations involving one non-conserved operator, and the abstract states that all results can be directly reinterpreted in terms of N=2 and N=4 superconformal tensor structures.

    What to keep in mind

    The abstract does not describe experimental data, numerical benchmarks, or comparison with alternative formulas. It also does not provide detailed limitations beyond the scope stated: conserved three-point functions in 4D, with an extension to cases involving one non-conserved operator.

    • A compact analytic formula was derived for conserved three-point tensor structures in 4D CFT.
    • The formula covers a complete basis for arbitrary 4D Lorentz representations.
    • The construction uses SU(m,m|2n) analytic superspace, where conservation conditions are automatically solved.
    • The approach also applies to cases with one non-conserved operator.
    • The counting of tensor structures maps to finite-dimensional SU(2n) representations via Littlewood-Richardson coefficients.

  • Sociotechnical barriers hinder digital engineering transformation

    What the study found

    The study found that digital engineering transformation is often undermined by sociotechnical barriers, meaning obstacles involving people, technology, processes, culture, infrastructure, and goals. It also found that technological investments alone are insufficient.

    Why the authors say this matters

    The authors say the study fills a gap in digital engineering scholarship by giving a structured, policy-grounded account of why these efforts stall. They conclude that the barrier-to-policy mapping can help stakeholders diagnose risks, prioritize resources, and support long-term change management.

    What the researchers tested

    The researchers presented a structured synthesis based on the literature and sociotechnical systems theory. They organized barriers across dimensions of people, technology, processes, culture, infrastructure, and goals, then mapped those barriers to the U.S. Department of Defense's digital engineering policy goals.

    What worked and what didn't

    The analysis suggests that barriers such as workforce readiness, leadership support, and cultural alignment are important in digital engineering failures. It also indicates that barriers can cascade across multiple policy goals, which complicates accountability, prioritization, and long-term sustainment.

    What to keep in mind

    The abstract describes a literature-based synthesis rather than a direct empirical test of implementation outcomes. It does not provide detailed limitations beyond noting that the framework is intended as a diagnostic lens rather than prescriptive guidance.

    • Digital engineering transformation is described as a shift toward integrating digital artifacts into an authoritative source of truth.
    • The study finds that sociotechnical barriers span people, technology, processes, culture, infrastructure, and goals.
    • Workforce readiness, leadership support, and cultural alignment are highlighted as social factors linked to failure.
    • The barrier-to-policy mapping suggests obstacles can affect multiple Department of Defense policy goals at once.
    • The authors present the framework as a diagnostic tool for managers, policymakers, and engineers.

  • Neural-network surrogate matches particle-shape hydrodynamics closely

    What the study found

    The study found that a neural-operator surrogate can predict hydrodynamic responses for complex-shaped rigid particles in Stokes flow with low evaluation cost. In testing, it reached median relative errors below 1% for the deviatoric stresslet, with similar accuracy for angular velocity and chiral thrust.

    Why the authors say this matters

    The authors conclude that combining validated particle-resolved calculations with fast surrogate inference provides a practical route to coupling complex particle shapes into mesoscale solvers such as the force-coupling method. The study suggests this may support large-ensemble studies of microstructure and suspension rheology.

    What the researchers tested

    The researchers built a data-driven surrogate framework for quasi-dilute suspensions of rigid, non-spherical particles in Stokes flow. They used a regularized-Stokeslet boundary element method to compute hydrodynamic responses for spheroids and helicoidal particles, then trained a neural-operator model on the resulting datasets.

    What worked and what didn't

    For spheroids, the boundary element solver was validated against analytical benchmarks for the stresslet and Jeffery's theory for rotation. For helicoidal particles, where no analytical solution exists, accuracy was assessed by self-convergence and additional tests of linearity, frame objectivity, and chirality-dependent symmetries; the surrogate then performed well on independent test sets across random orientations and flow types. The reported errors were below 1% median relative error for the deviatoric stresslet, with the 95th percentile below 3%, and comparable accuracy for angular velocity and thrust.

    What to keep in mind

    The abstract does not describe limitations beyond the scope of the tested particle shapes, flow conditions, and quasi-dilute suspensions of rigid particles in Stokes flow. The reported performance is based on the independent test sets and quantities named in the abstract.

    • A neural-operator surrogate was trained to predict stresslet, angular velocity, and chiral thrust for complex-shaped rigid particles.
    • The boundary element solver was validated for spheroids against analytical benchmarks and Jeffery's theory.
    • For helicoidal particles, accuracy was checked with self-convergence and symmetry tests because no analytical solution was available.
    • The surrogate achieved median relative errors below 1% for the deviatoric stresslet and below 3% at the 95th percentile.
    • The authors say the approach may help couple complex particle shapes into mesoscale solvers such as the force-coupling method.

  • Derivative-free sequential Bayesian experimental design framework introduced

    What the study found

    The study introduces a gradient-free framework for Bayesian optimal experimental design, which is choosing experiments to gain the most information, in sequential settings. It combines Ensemble Kalman Inversion for design optimization with Affine-Invariant Interacting Langevin Dynamics for posterior sampling.

    Why the authors say this matters

    The authors say the framework is aimed at complex systems where gradient information is unavailable. They also state that the variational approximations make utility estimation scalable in high-dimensional spaces and in partial differential equation-constrained inverse problems.

    What the researchers tested

    The researchers proposed variational Gaussian and parametrized Laplace approximations to provide tractable upper and lower bounds on Expected Information Gain, a measure of how much an experiment is expected to reduce uncertainty. They demonstrated the framework with numerical experiments ranging from linear Gaussian models to partial differential equation-based inference tasks.

    What worked and what didn't

    According to the abstract, the framework performed robustly, accurately, and efficiently in the reported experiments. The method is described as derivative-free and ensemble-based, and the approximations are presented as a way to handle nested expectations in Bayesian optimal experimental design.

    What to keep in mind

    The abstract does not describe detailed quantitative results, comparisons, or failure cases. It also does not state specific limitations beyond the general challenge of nested expectations and unavailable gradient information.

    • A gradient-free framework for sequential Bayesian optimal experimental design is introduced.
    • The method combines Ensemble Kalman Inversion with Affine-Invariant Interacting Langevin Dynamics.
    • Variational Gaussian and parametrized Laplace approximations are used to bound Expected Information Gain.
    • The framework is presented as scalable for high-dimensional and PDE-constrained inverse problems.
    • Numerical experiments are reported for linear Gaussian models and PDE-based inference tasks.
  • Wild tomato genomes show structural variants affect recombination

    Wild tomato genomes show structural variants affect recombination

    What the study found

    The study found that new genome assemblies of two wild tomato species revealed shared and species-specific structural variants, repeat-content differences, and recombination barriers. It also found that crossover rates were higher in female meiosis than in male meiosis in the recombinant plants they analyzed.

    Why the authors say this matters

    The authors say high-quality genome assemblies are needed to track genetic introgression, which is the movement of genes from one population or species into another. They conclude that these assemblies help show how repeat content diverged in nature and during breeding, and how reproductive gender and structural variants shape recombination landscapes in tomato hybrids.

    What the researchers tested

    The researchers produced de novo genome assemblies, meaning genome sequences built from scratch, for two wild tomato species: Solanum pennellii (LA0716) and Solanum cheesmaniae (LA1039). They aligned these assemblies with multiple gold-standard assemblies, analyzed repeat content, and sequenced 709 recombinant plants from male and female backcrosses of three hybrids.

    What worked and what didn't

    The improved S. pennellii genome added 146 Mbp to the twelve chromosomes compared with the original reference. The alignments identified both shared and species-specific structural variants, and repeat analysis showed independent expansions of Tekay retrotransposons in S. pennellii and S. peruvianum. In the recombinant plants, female meiosis showed a higher crossover rate, conserved female-enhanced recombination regions were found, and recombination coldspots were linked to megabase-scale inversions and insertion-deletion polymorphisms.

    What to keep in mind

    The abstract does not describe limitations in detail. The findings are based on two wild tomato species and three hybrid backcross systems, so the scope described in the summary is specific to those materials.

    • New genome assemblies were generated for Solanum pennellii and Solanum cheesmaniae.
    • The improved S. pennellii genome added 146 Mbp to the twelve chromosomes compared with the original reference.
    • Tekay retrotransposons expanded independently in S. pennellii and S. peruvianum.
    • Female meiosis showed a higher crossover rate than male meiosis in the recombinant plants analyzed.
    • Recombination coldspots were associated with megabase-scale inversions and insertion-deletion polymorphisms.

  • Refined hydrodynamic models add lipid alignment to bilayer descriptions

    What the study found

    The study derives refined continuous models for lipid bilayers that include a scalar order parameter for lipid alignment along the surface normal. In the fully ordered case, these models reduce to the known surface (Navier–)Stokes–Helfrich models.

    Why the authors say this matters

    The authors conclude that their work provides more detailed continuous models for lipid bilayers. They also say it offers an alternative derivation of surface (Navier–)Stokes–Helfrich models.

    What the researchers tested

    The researchers started from hydrodynamic surface liquid crystal models and derived two model types: a hydrodynamic surface Landau–Helfrich model for asymmetric lipid bilayers and a surface Beris–Edwards model for symmetric lipid bilayers. They then used numerical simulations to demonstrate the impact on dynamics.

    What worked and what didn't

    The derived models incorporate both membrane viscosity and an additional description of molecular alignment, rather than treating the bilayer as only a homogeneous continuum. The abstract states that the fully ordered limit reproduces the known surface (Navier–)Stokes–Helfrich models, but it does not give detailed numerical outcomes here.

    What to keep in mind

    The abstract does not provide specific simulation results, quantitative comparisons, or limitations beyond the scope of the model derivations. It also does not describe experimental validation.

    • The paper adds a scalar order parameter for lipid alignment to bilayer models.
    • It derives a hydrodynamic surface Landau–Helfrich model for asymmetric lipid bilayers.
    • It derives a surface Beris–Edwards model for symmetric lipid bilayers.
    • The fully ordered case recovers the known surface (Navier–)Stokes–Helfrich models.
    • Numerical simulations were used to show effects on dynamics.

  • Encoding choice drives performance in hybrid quantum neural networks

    What the study found

    The study found that different design choices in quantum and hybrid convolutional neural networks had uneven effects on performance. In hybrid models, data encoding was the dominant factor, while in purely quantum models, measurement protocol and data-to-amplitude mapping mattered most.

    Why the authors say this matters

    The authors suggest these results matter because they clarify which parameterized quantum circuit choices have the largest impact on model performance. This may help guide design decisions for quantum and hybrid neural network architectures, according to the study.

    What the researchers tested

    The researchers studied parameterized quantum circuits inside quantum convolutional neural networks and hybrid quantum convolutional neural networks for satellite image classification using the EuroSAT dataset. They evaluated about 500 model configurations, comparing data encoding techniques, variational ansätze, and measurement choices; hybrid models were also benchmarked against matching classical versions without the quantum circuits.

    What worked and what didn't

    For hybrid architectures, data encoding had the strongest effect, with validation accuracy varying by more than 30% across different embeddings. Variational ansätze and measurement basis had much smaller effects in these models, with validation accuracy changes below 5%. For purely quantum models, restricted to amplitude encoding, measurement strategy changed validation accuracy by up to 30%, and the encoding mapping changed it by about 8 percentage points.

    What to keep in mind

    The abstract does not describe limitations beyond the study’s focus on EuroSAT satellite image classification. The purely quantum models were restricted to amplitude encoding, so the findings for those models apply within that setup.

    • The study compared about 500 quantum and hybrid convolutional neural network configurations.
    • In hybrid models, data encoding had the largest impact on validation accuracy.
    • In hybrid models, variational ansätze and measurement basis changed validation accuracy by less than 5%.
    • In purely quantum models, measurement strategy affected validation accuracy by up to 30%.
    • The study used the EuroSAT satellite image classification dataset and compared hybrid models with classical counterparts.

  • Thermodynamically consistent models estimate open quantum system components

    What the study found

    The study found that a data-driven model for open quantum systems can directly estimate the system Hamiltonian, which describes the system’s energy, and linear coupling to the environment while including learnable, thermodynamically consistent terms. The authors describe the model as interpretable.

    Why the authors say this matters

    The authors say this matters because characterizing Hamiltonians and other parts of open quantum dynamical systems plays a crucial role in quantum computing and other applications. The study suggests that bringing physical principles into learnable models may be useful for this problem.

    What the researchers tested

    The researchers developed a data-driven model for open quantum systems with learnable, thermodynamically consistent terms. They validated it on synthetic two-level and three-level data, as well as experimental two-level data from a quantum device at Lawrence Livermore National Laboratory.

    What worked and what didn't

    The abstract says the model was validated on both synthetic and experimental data. It also reports that the model directly estimates the Hamiltonian and linear components of coupling to the environment; it does not describe any failures or comparison results.

    What to keep in mind

    The available summary does not describe detailed performance metrics, specific limitations, or cases where the model did not work. It also does not provide enough information to judge how the approach compares with other methods.

    • The model includes learnable, thermodynamically consistent terms for open quantum systems.
    • It directly estimates the system Hamiltonian and linear environmental coupling components.
    • The authors describe the model as interpretable.
    • Validation was done on synthetic two-level and three-level data.
    • The model was also tested on experimental two-level data from a quantum device at Lawrence Livermore National Laboratory.