Search for: All records

Abstract Infrasound may be used to detect the approach of hazardous volcanic mudflows, known as lahars, tens of minutes before their flow fronts arrive. We have analyzed signals from more than 20 secondary lahars caused by precipitation events at Fuego Volcano during Guatemala’s rainy season in May through October of 2022. We are able to quantify the capabilities of infrasound monitoring through comparison with seismic data, time lapse camera imagery, and high-resolution video of a well-recorded event on August 17. We determine that infrasound sensors, deployed adjacent to the lahar path and in small-aperture (10 s of meters) arrays, are particularly sensitive to remote detection of lahars, including small-sized events, at distances of at least 5 km. At Fuego Volcano these detections could be used to provide timely alerts of up to 30 min before lahars arrive at a downstream monitoring site, such as in the frequently impacted Ceniza drainage. We propose that continuous infrasound monitoring, from locations adjacent to a drainage, may complement seismic monitoring and serve as a valuable tool to help identify approaching hazards. On the other hand, infrasound arrays located a kilometer or more from the lahar path can be effectively used to track a lahar’s progression. 

We study a model-free federated linear quadratic regulator (LQR) problem where M agents with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to minimize an average quadratic cost while keeping their data private. To exploit the similarity of the agents' dynamics, we propose to use federated learning (FL) to allow the agents to periodically communicate with a central server to train policies by leveraging a larger dataset from all the agents. With this setup, we seek to understand the following questions: (i) Is the learned common policy stabilizing for all agents? (ii) How close is the learned common policy to each agent's own optimal policy? (iii) Can each agent learn its own optimal policy faster by leveraging data from all agents? To answer these questions, we propose a federated and model-free algorithm named FedLQR. Our analysis overcomes numerous technical challenges, such as heterogeneity in the agents' dynamics, multiple local updates, and stability concerns. We show that FedLQR produces a common policy that, at each iteration, is stabilizing for all agents. We provide bounds on the distance between the common policy and each agent's local optimal policy. Furthermore, we prove that when learning each agent's optimal policy, FedLQR achieves a sample complexity reduction proportional to the number of agents M in a low-heterogeneity regime, compared to the single-agent setting. 

A powerful concept behind much of the recent progress in machine learning is the extraction of common features across data from heterogeneous sources or tasks. Intuitively, using all of one's data to learn a common representation function benefits both computational effort and statistical generalization by leaving a smaller number of parameters to fine-tune on a given task. Toward theoretically grounding these merits, we propose a general setting of recovering linear operators M from noisy vector measurements y=Mx+w, where the covariates x may be both non-i.i.d. and non-isotropic. We demonstrate that existing isotropy-agnostic meta-learning approaches incur biases on the representation update, which causes the scaling of the noise terms to lose favorable dependence on the number of source tasks. This in turn can cause the sample complexity of representation learning to be bottlenecked by the single-task data size. We introduce an adaptation, 𝙳𝚎-𝚋𝚒𝚊𝚜 & 𝙵𝚎𝚊𝚝𝚞𝚛𝚎-𝚆𝚑𝚒𝚝𝚎𝚗 (𝙳𝙵𝚆), of the popular alternating minimization-descent (AMD) scheme proposed in Collins et al., (2021), and establish linear convergence to the optimal representation with noise level scaling down with the total source data size. This leads to generalization bounds on the same order as an oracle empirical risk minimizer. We verify the vital importance of 𝙳𝙵𝚆 on various numerical simulations. In particular, we show that vanilla alternating-minimization descent fails catastrophically even for iid, but mildly non-isotropic data. Our analysis unifies and generalizes prior work, and provides a flexible framework for a wider range of applications, such as in controls and dynamical systems. 

We study the problem of learning a linear system model from the observations of M clients. The catch: Each client is observing data from a different dynamical system. This work addresses the question of how multiple clients collaboratively learn dynamical models in the presence of heterogeneity. We pose this problem as a federated learning problem and characterize the tension between achievable performance and system heterogeneity. Furthermore, our federated sample complexity result provides a constant factor improvement over the single agent setting. Finally, we describe a meta federated learning algorithm, FedSysID, that leverages existing federated algorithms at the client level. 

We address the problem of learning linear system models from observing multiple trajectories from different system dynamics. This framework encompasses a collaborative scenario where several systems seeking to estimate their dynamics are partitioned into clusters according to their system similarity. Thus, the systems within the same cluster can benefit from the observations made by the others. Considering this framework, we present an algorithm where each system alternately estimates its cluster identity and performs an estimation of its dynamics. This is then aggregated to update the model of each cluster. We show that under mild assumptions, our algorithm correctly estimates the cluster identities and achieves an approximate sample complexity that scales inversely with the number of systems in the cluster, thus facilitating a more efficient and personalized system identification process. 

We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting with environments that share the same state and action space but differ in their reward functions and state transition kernels. Assuming agents can communicate via a central server, we ask: Does exchanging information expedite the process of evaluating a common policy? To answer this question, we provide the first comprehensive finite-time analysis of a federated temporal difference (TD) learning algorithm with linear function approximation, while accounting for Markovian sampling, heterogeneity in the agents' environments, and multiple local updates to save communication. Our analysis crucially relies on several novel ingredients: (i) deriving perturbation bounds on TD fixed points as a function of the heterogeneity in the agents' underlying Markov decision processes (MDPs); (ii) introducing a virtual MDP to closely approximate the dynamics of the federated TD algorithm; and (iii) using the virtual MDP to make explicit connections to federated optimization. Putting these pieces together, we rigorously prove that in a low-heterogeneity regime, exchanging model estimates leads to linear convergence speedups in the number of agents. 

We explore the capabilities of volcano opto‐acoustics, a promising technique for measuring explosion and infrasound resonance phenomena at open‐vent volcanoes. Joint visual and infrasound study at Yasur Volcano (Vanuatu) demonstrate that even consumer‐grade cameras are capable of recording infrasound with high fidelity. Passage of infrasonic waves, ranging from as low as 5 Pa to hundreds of Pa, from both explosions and persistent tremor, pressurizes and depressurizes ambient plumes inducing visible vaporization and condensation respectively. Optical tracking of these pressure wavefields can be used to identify spectral characteristics, which vary within Yasur's two deep craters and are distinct for explosion and tremor sources. Wavefield maps can illuminate the propagation of blasts as well as the dynamics of persistent infrasonic tremor associated with standing waves in the craters. We propose that opto‐acoustic monitoring is useful for extraction of near‐vent infrasound signal and for tracking volcanic unrest from a remote distance.

 

Hydrogen-rich Type II supernovae (SNe II) are the most frequently observed class of core-collapse SNe (CCSNe). However, most studies that analyse large samples of SNe II lack events with absolute peak magnitudes brighter than −18.5 mag at rest-frame optical wavelengths. Thanks to modern surveys, the detected number of such luminous SNe II (LSNe II) is growing. There exist several mechanisms that could produce luminous SNe II. The most popular propose either the presence of a central engine (a magnetar gradually spinning down or a black hole accreting fallback material) or the interaction of supernova ejecta with circumstellar material (CSM) that turns kinetic energy into radiation energy. In this work, we study the light curves and spectral series of a small sample of six LSNe II that show peculiarities in their H α profile, to attempt to understand the underlying powering mechanism. We favour an interaction scenario with CSM that is not dense enough to be optically thick to electron scattering on large scales – thus, no narrow emission lines are observed. This conclusion is based on the observed light curve (higher luminosity, fast decline, blue colours) and spectral features (lack of persistent narrow lines, broad H α emission, lack of H α absorption, weak, or non-existent metal lines) together with comparison to other luminous events available in the literature. We add to the growing evidence that transients powered by ejecta–CSM interaction do not necessarily display persistent narrow emission lines.

 

Scaffolding learning in science museum exhibits can be a challenging endeavor. Learning in these settings is self-directed, sporadic, and lacking in structure (Falk, Dierking & Semmel, 2013). Museum educators and exhibit designers struggle to provide the appropriate types and amounts of scaffolding, where too little scaffolding can result in suboptimal learning outcomes while too much scaffolding can result in an “over-formalization” of the exhibit (Yoon et al., 2013). This study examines the use of signage in scaffolding students’ engagement with a science exhibit about light. Twelve students were asked to engage in four activities within the exhibit. Videos of student behavior were recorded and thematically coded. Findings indicate that textual scaffolds, as they were implemented in this exhibit, may have missed opportunities to promote meaningful engagement with exhibit activities. Implications for exhibit design practice and research are discussed. 

We present extensive ultraviolet (UV) and optical photometric and optical spectroscopic follow-up of supernova (SN) 2021gno by the ‘Precision Observations of Infant Supernova Explosions’ (POISE) project, starting less than 2 d after the explosion. Given its intermediate luminosity, fast photometric evolution, and quick transition to the nebular phase with spectra dominated by [Ca ii] lines, SN 2021gno belongs to the small family of Calcium-rich transients. Moreover, it shows double-peaked light curves, a phenomenon shared with only four other Calcium-rich events. The projected distance from the centre of the host galaxy is not as large as other objects in this family. The initial optical light-curve peaks coincide with a very quick decline of the UV flux, indicating a fast initial cooling phase. Through hydrodynamical modelling of the bolometric light curve and line velocity evolution, we found that the observations are compatible with the explosion of a highly stripped massive star with an ejecta mass of $0.8\, M_\odot$ and a 56Ni mass of 0.024 M⊙. The initial cooling phase (first light-curve peak) is explained by the presence of an extended circumstellar material comprising ∼$10^{-2}\, {\rm M}_{\odot }$ with an extension of $1100\, R_{\odot }$. We discuss if hydrogen features are present in both maximum-light and nebular spectra, and their implications in terms of the proposed progenitor scenarios for Calcium-rich transients.