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Acoustic compressional and shear wave velocities (VP, VS) of anhydrous (AHRG) and hydrous rhyolitic glasses (HRG) containing 3.28 wt% (HRG-3) and 5.90 wt% (HRG-6) total water concentra- tion (H2Ot) have been measured using Brillouin light scattering (BLS) spectroscopy up to 3 GPa in a diamond-anvil cell at ambient temperature. In addition, Fourier-transform infrared (FTIR) spectroscopy was used to measure the speciation of H2O in the glasses up to 3 GPa. At ambient pressure, HRG-3 contains 1.58 (6) wt% hydroxyl groups (OH–) and 1.70 (7) wt% molecular water (H2Om) while HRG-6 contains 1.67 (10) wt% OH– and 4.23 (17) wt% H2Om where the numbers in parentheses are ±1σ. With increasing pressure, very little H2Om, if any, converts to OH– within uncertainties in hydrous rhyolitic glasses such that HRG-6 contains much more H2Om than HRG-3 at all experimental pressures. We observe a nonlinear relationship between high-pressure sound velocities and H2Ot, which is attributed to the distinct effects of each water species on acoustic velocities and elastic moduli of hydrous glasses. Near ambient pressure, depolymerization due to OH– reduces VS and G more than VP and KS. VP and KS in both anhydrous and hydrous glasses decrease with increasing pressure up to ~1–2 GPa before increasing with pressure. Above ~1–2 GPa, VP and KS in both hydrous glasses converge with those in AHRG. In particular, VP in HRG-6 crosses over and becomes higher than VP in AHRG. HRG-6 displays lower VS and G than HRG-3 near ambient pressure, but VS and G in these glasses converge above ~2 GPa. Our results show that hydrous rhyolitic glasses with ~2–4 wt% H2Om can be as incompressible as their anhydrous counterpart above ~1.5 GPa. The nonlinear effects of hydration on high-pressure acoustic velocities and elastic moduli of rhyolitic glasses observed here may provide some insight into the behavior of hydrous silicate melts in felsic magma chambers at depth. 

Following rapid decompression in the conduit of a volcano, magma breaks into ash- to block-sized fragments, powering explosive sub-Plinian and Plinian eruptions that may generate destructive pyroclastic falls and flows. It is thus crucial to assess how magma breaks up into fragments. This task is difficult, however, because of the subterranean nature of the entire process and because the original size of pristine fragments is modified by secondary fragmentation and expansion. New textural observations of sub-Plinian and Plinian pumice lapilli reveal that some primary products of magma fragmentation survive by sintering together within seconds of magma break-up. Their size distributions reflect the energetics of fragmentation, consistent with products of rapid decompression experiments. Pumice aggregates thus offer a unique window into the previously inaccessible primary fragmentation process and could be used to determine the potential energy of fragmentation. 

Silicic volcanic activity has long been framed as either violently explosive or gently effusive. However, recent ob- servations demonstrate that explosive and effusive behavior can occur simultaneously. Here, we propose that rhyolitic magma feeding subaerial eruptions generally fragments during ascent through the upper crust and that effusive eruptions result from conduit blockage and sintering of the pyroclastic products of deeper cryptic frag- mentation. Our proposal is supported by (i) rhyolitic lavas are volatile depleted; (ii) textural evidence supports a pyroclastic origin for effusive products; (iii) numerical models show that small ash particles !10−5 m can diffusive- ly degas, stick, and sinter to low porosity, in the time available between fragmentation and the surface; and (iv) inferred ascent rates from both explosive and apparently effusive eruptions can overlap. Our model reconciles previously paradoxical observations and offers a new framework in which to evaluate physical, numerical, and geochemical models of Earth’s most violent volcanic eruptions. 

The di!usion of water through silicate melts is a key process in volcanic systems. Di!usion controls the growth of the bub- bles that drive volcanic eruptions and determines the evolution of the spatial distribution of dissolved water during and after magma mingling, crystal growth, fracturing and fragmentation, and welding of pyroclasts. Accurate models for water di!u- sion are therefore essential for forward modelling of eruptive behaviour, and for inverse modelling to reconstruct eruptive and post-eruptive history from the spatial distribution of water in eruptive products. Existing models do not include the kinetics of the homogeneous species reaction that interconverts molecular (H2Om) and hydroxyl (OH) water; reaction kinetics are impor- tant because final species distribution depends on cooling history. Here we develop a flexible 1D numerical model for di!usion and speciation of water in silicate melts. We validate the model against FTIR transects of the spatial distribution of molecular, hydroxyl, and total water across di!usion-couple experiments of haplogranite composition, run at 800–1200 C and 5 kbar. We adopt a stepwise approach to analysing and modelling the data. First, we use the analytical Sauer-Freise method to deter- mine the e!ective di!usivity of total water DH2Ot as a function of dissolved water concentration CH2Ot and temperature T for each experiment and find that the dependence of DH2 Ot on CH2 Ot is linear for CH2 Ot K 1:8 wt.% and exponential for CH2 Ot J 1:8 wt.%. Second, we develop a 1D numerical forward model, using the method of lines, to determine a piece-wise function for DH2 Ot !CH2 Ot ; T " that is globally-minimized against the entire experimental dataset. Third, we extend this numerical model to account for speciation of water and determine globally-minimized functions for di!usivity of molecular water DH2 Om !CH2 Ot ; T " and the equilibrium constant K for the speciation reaction. Our approach includes three key novelties: (1) functions for dif- fusivities of H2Ot and H2Om, and the speciation reaction, are minimized simultaneously against a large experimental dataset, covering a wide range of water concentration (0:25 CH2 Ot 7 wt.%) and temperature (800  C T 1200  C), such that the resulting functions are both mutually-consistent and broadly applicable; (2) the minimization allows rigorous and robust analysis of uncertainties such that the accuracy of the functions is quantified; (3) the model can be straightforwardly used to determine functions for di!usivity and speciation for other melt compositions pending suitable di!usion-couple experiments. The modelling approach is suitable for both forward and inverse modelling of di!usion processes in silicate melts; the model is available as a MATLAB script from the electronic supplementary material. 

One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions. Recent advances in inducing point methods have sped up GP marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remaining computational bottlenecks. In this paper we address these shortcomings by using the Lanczos algorithm to rapidly approximate the predictive covariance matrix. Our approach, which we refer to as LOVE (LanczOs Variance Estimates), substantially improves time and space complexity. In our experiments, LOVE computes covariances up to 2,000 times faster and draws samples 18,000 times faster than existing methods, all without sacrificing accuracy. 

Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kernel Interpolation (SKI) exploits these techniques by deriving approximate kernels with very fast MVMs. Unfortunately, such strategies suffer badly from the curse of dimensionality. We develop a new technique for MVM based learning that exploits product kernel structure. We demonstrate that this technique is broadly applicable, resulting in linear rather than exponential runtime with dimension for SKI, as well as state-of-the-art asymptotic complexity for multi-task GPs. 

We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation. As the name suggests, it relies only on simple linear interpolation of deep convolutional features from pre-trained convnets. We show that despite its simplicity, DFI can perform high-level semantic transformations like "make older/younger", "make bespectacled", "add smile", among others, surprisingly well - sometimes even matching or outperforming the state-of-the-art. This is particularly unexpected as DFI requires no specialized network architecture or even any deep network to be trained for these tasks. DFI therefore can be used as a new baseline to evaluate more complex algorithms and provides a practical answer to the question of which image transformation tasks are still challenging in the rise of deep learning. 

Abstract We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19, during the LIGO–Virgo–KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered ∼14% of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz, where we assume the gravitational-wave emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy 1 × 10⁻⁴M_⊙c²and luminosity 2.6 × 10⁻⁴M_⊙c²s⁻¹for a source emitting at 82 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as 1.08, at frequencies above 1200 Hz, surpassing past results. 

Abstract Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of general relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO–Virgo–KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering single-harmonic and dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is 6.4 × 10⁻²⁷for the young energetic pulsar J0537−6910, while the lowest constraint on the ellipticity is 8.8 × 10⁻⁹for the bright nearby millisecond pulsar J0437−4715. Additionally, for a subset of 16 targets, we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of nonstandard polarizations as predicted by the Brans–Dicke theory.