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From the beginning of May 2018, the Kilauea Volcano on the island of Hawaii experienced its largest eruption in 200 yr followed by a period of unrest for months. Because hot molten lava entered the ocean from the ocean-entry point near the lower East Rift Zone, the lava–water interaction led to explosions. Some explosions were near the water surface and ejected fragments of lava, also known as lava bombs. In the early morning on 16 July 2018, one of those lava bombs, which was almost the size of a basketball, hit a sightseeing boat and injured 23 people. In this study, we analyzed the hydrophone data recorded from July to mid-September by ocean-bottom seismometers (OBSs) deployed offshore near the ocean entry point to identify and locate the hydroacoustic signals of the lava–water explosions. Acoustic signals of hydrovolcanic explosions are characterized by a short duration (less than a few seconds) and a broad frequency range (at least up to 100 Hz). To automate event detection, a short-term average versus long-term average method was applied to the complete dataset. Approximately 4300 events were detected and located near the coastline and further used to prepare a catalog. The distribution of the lava–water explosions is consistent with the pattern of the offshore lava delta formed during the 2018 eruption. Identifying such hydroacoustic signals recorded by OBSs may provide new avenues of research using various seismoacoustic events associated with volcanic eruptions.

 

Liu et al. (2022,https://doi.org/10.1029/2021GL093691) used Rayleigh waves extracted from the cross‐correlation of ambient noise recorded by two stations to monitor the seismic velocity variations associated with the 2018 Kı̄lauea eruption. However, their study ignored the fact that the tremors on the Island of Hawai'i were dominated by a source at the Kı̄lauea summit before the eruption. Close inspection of the waveforms of the station pair PAUD‐STCD shows a simple, mistakenly identified wave traveling direction in Liu et al. (2022,https://doi.org/10.1029/2021GL093691). A correct wave traveling direction agrees with the noise source model, where the dominant tremor source should be at the Kı̄lauea summit. Because of the drastic change in the tremor source after the eruption, the cross‐correlation of the tremor records may reflect predominantly changes in the source rather than in the medium properties between the two stations.

 

Computational methods for compound–protein affinity and contact (CPAC) prediction aim at facilitating rational drug discovery by simultaneous prediction of the strength and the pattern of compound–protein interactions. Although the desired outputs are highly structure-dependent, the lack of protein structures often makes structure-free methods rely on protein sequence inputs alone. The scarcity of compound–protein pairs with affinity and contact labels further limits the accuracy and the generalizability of CPAC models.

To overcome the aforementioned challenges of structure naivety and labeled-data scarcity, we introduce cross-modality and self-supervised learning, respectively, for structure-aware and task-relevant protein embedding. Specifically, protein data are available in both modalities of 1D amino-acid sequences and predicted 2D contact maps that are separately embedded with recurrent and graph neural networks, respectively, as well as jointly embedded with two cross-modality schemes. Furthermore, both protein modalities are pre-trained under various self-supervised learning strategies, by leveraging massive amount of unlabeled protein data. Our results indicate that individual protein modalities differ in their strengths of predicting affinities or contacts. Proper cross-modality protein embedding combined with self-supervised learning improves model generalizability when predicting both affinities and contacts for unseen proteins.

Data and source codes are available at https://github.com/Shen-Lab/CPAC.

Supplementary data are available at Bioinformatics online.

 

Seismic tomography of shield volcanoes can be used to better understand its structure, formation, and evolution. Previous tomographic studies on the Island of Hawai'i used body waves from earthquakes and active sources and had limited resolution in the shallow crust. In this study, we obtained the empirical Green Functions (EGFs) and empirical Green Tensors (EGTs) from cross‐correlating and stacking of multiyear seismic ambient noise recorded on the island. The EGFs/EGTs contained fundamental mode and first higher mode Rayleigh waves. The different modes were separated with a new algorithm and their group velocities were measured. Using the group arrival times, we inverted for two‐dimensional group velocity maps, which provide, for the first time, a full coverage of the Island of Hawai'i. From the group velocity maps, we inverted for a three‐dimensional shear wave velocity model, which shows strong lateral variations and yields new insights into the structure and growth of the volcanoes on the island: Kı̄lauea's East Rift Zone has prominent high velocities at all depths, whereas the current rift zones of Mauna Loa are characterized by intermediate to high velocities only at depths greater than 1 km below ground surface, which may be attributed to their relatively short history and less developed state. The flanks of the volcanoes, some cut by fault zones, displayed low velocities at over a range of depths, generally interpreted as consisting of extrusive rocks, which could be further shattered by faulting.

 

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions. Meanwhile, another relevant but very different question remains yet open: how to model and quantify the uncertainty of an optimization algorithm (aka, optimizer) itself? To close such a gap, the prerequisite is to consider the optimizers as sampled from a distribution, rather than a few prefabricated and fixed update rules. We first take the novel angle to consider the algorithmic space of optimizers, and provide definitions for the optimizer prior and likelihood, that intrinsically determine the posterior and therefore uncertainty. We then leverage the recent advance of learning to optimize (L2O) for the space parameterization, with the end-to-end training pipeline built via variational inference, referred to as uncertainty-aware L2O (UA-L2O). Our study represents the first effort to recognize and quantify the uncertainty of the optimization algorithm. The extensive numerical results show that, UA-L2O achieves superior uncertainty calibration with accurate confidence estimation and tight confidence intervals, suggesting the improved posterior estimation thanks to considering optimizer uncertainty. Intriguingly, UA-L2O even improves optimization performances for two out of three test functions, the loss function in data privacy attack, and four of five cases of the energy function in protein docking. Our codes are released at https://github. com/Shen-Lab/Bayesian-L2O. 

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions. Meanwhile, another relevant but very different question remains yet open: how to model and quantify the uncertainty of an optimization algorithm (a.k.a., optimizer) itself? To close such a gap, the prerequisite is to consider the optimizers as sampled from a distribution, rather than a few prefabricated and fixed update rules. We first take the novel angle to consider the algorithmic space of optimizers, and provide definitions for the optimizer prior and likelihood, that intrinsically determine the posterior and therefore uncertainty. We then leverage the recent advance of learning to optimize (L2O) for the space parameterization, with the end-to-end training pipeline built via variational inference, referred to as uncertainty-aware L2O (UA-L2O). Our study represents the first effort to recognize and quantify the uncertainty of the optimization algorithm. The extensive numerical results show that, UA-L2O achieves superior uncertainty calibration with accurate confidence estimation and tight confidence intervals, suggesting the improved posterior estimation thanks to considering optimizer uncertainty. Intriguingly, UA-L2O even improves optimization performances for two out of three test functions, the loss function in data privacy attack, and four of five cases of the energy function in protein docking. Our codes are released at https://github.com/Shen-Lab/Bayesian-L2O. 

Empirical Green Functions (EGFs) obtained from ambient noise cross‐correlation are important for imaging and monitoring underground structures. The EGFs on the Island of Hawai'i in different years are similar at low frequencies (0.1–0.4 Hz), but very different at high frequencies (0.4–1.0 Hz): Only the EGFs after the 2018 Kı̄lauea eruption show clear P waves. Grid search reveals a strong noise source near the Kı̄lauea summit before the eruption, which contaminated the EGFs but became silent after the eruption. Modeling of the P waves identifies the direct arrival and post‐critical reflections from two velocity discontinuities at 4.7 and 7.2 km depth beneath the island, which we interpret as the base of volcanic edifices and deposits and the boundary between basaltic dikes and gabbros, respectively. The P waves in EGFs could provide valuable high‐resolution constraints for monitoring deep magmatic changes and imaging the volcano structures.